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var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type>.accordion-header .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type>.accordion-header .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type>.accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush>.accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush>.accordion-item:first-child{border-top:0}.accordion-flush>.accordion-item:last-child{border-bottom:0}.accordion-flush>.accordion-item>.accordion-header .accordion-button,.accordion-flush>.accordion-item>.accordion-header .accordion-button.collapsed{border-radius:0}.accordion-flush>.accordion-item>.accordion-collapse{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x:0;--bs-breadcrumb-padding-y:0;--bs-breadcrumb-margin-bottom:1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color:var(--bs-secondary-color);--bs-breadcrumb-item-padding-x:0.5rem;--bs-breadcrumb-item-active-color:var(--bs-secondary-color);display:flex;flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/")}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:var(--bs-body-bg);--bs-pagination-border-width:var(--bs-border-width);--bs-pagination-border-color:var(--bs-border-color);--bs-pagination-border-radius:var(--bs-border-radius);--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:var(--bs-tertiary-bg);--bs-pagination-hover-border-color:var(--bs-border-color);--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:var(--bs-secondary-bg);--bs-pagination-focus-box-shadow:0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:var(--bs-secondary-color);--bs-pagination-disabled-bg:var(--bs-secondary-bg);--bs-pagination-disabled-border-color:var(--bs-border-color);display:flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media (prefers-reduced-motion:reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.active>.page-link,.page-link.active{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.disabled>.page-link,.page-link.disabled{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x:1.5rem;--bs-pagination-padding-y:0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius:var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x:0.5rem;--bs-pagination-padding-y:0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius:var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x:0.65em;--bs-badge-padding-y:0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight:700;--bs-badge-color:#fff;--bs-badge-border-radius:var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg:transparent;--bs-alert-padding-x:1rem;--bs-alert-padding-y:1rem;--bs-alert-margin-bottom:1rem;--bs-alert-color:inherit;--bs-alert-border-color:transparent;--bs-alert-border:var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius:var(--bs-border-radius);--bs-alert-link-color:inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-primary{--bs-alert-color:var(--bs-primary-text-emphasis);--bs-alert-bg:var(--bs-primary-bg-subtle);--bs-alert-border-color:var(--bs-primary-border-subtle);--bs-alert-link-color:var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color:var(--bs-secondary-text-emphasis);--bs-alert-bg:var(--bs-secondary-bg-subtle);--bs-alert-border-color:var(--bs-secondary-border-subtle);--bs-alert-link-color:var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color:var(--bs-success-text-emphasis);--bs-alert-bg:var(--bs-success-bg-subtle);--bs-alert-border-color:var(--bs-success-border-subtle);--bs-alert-link-color:var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color:var(--bs-info-text-emphasis);--bs-alert-bg:var(--bs-info-bg-subtle);--bs-alert-border-color:var(--bs-info-border-subtle);--bs-alert-link-color:var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color:var(--bs-warning-text-emphasis);--bs-alert-bg:var(--bs-warning-bg-subtle);--bs-alert-border-color:var(--bs-warning-border-subtle);--bs-alert-link-color:var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color:var(--bs-danger-text-emphasis);--bs-alert-bg:var(--bs-danger-bg-subtle);--bs-alert-border-color:var(--bs-danger-border-subtle);--bs-alert-link-color:var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color:var(--bs-light-text-emphasis);--bs-alert-bg:var(--bs-light-bg-subtle);--bs-alert-border-color:var(--bs-light-border-subtle);--bs-alert-link-color:var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color:var(--bs-dark-text-emphasis);--bs-alert-bg:var(--bs-dark-bg-subtle);--bs-alert-border-color:var(--bs-dark-border-subtle);--bs-alert-link-color:var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height:1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg:var(--bs-secondary-bg);--bs-progress-border-radius:var(--bs-border-radius);--bs-progress-box-shadow:var(--bs-box-shadow-inset);--bs-progress-bar-color:#fff;--bs-progress-bar-bg:#0d6efd;--bs-progress-bar-transition:width 0.6s ease;display:flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;flex-direction:column;justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion:reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion:reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color:var(--bs-body-color);--bs-list-group-bg:var(--bs-body-bg);--bs-list-group-border-color:var(--bs-border-color);--bs-list-group-border-width:var(--bs-border-width);--bs-list-group-border-radius:var(--bs-border-radius);--bs-list-group-item-padding-x:1rem;--bs-list-group-item-padding-y:0.5rem;--bs-list-group-action-color:var(--bs-secondary-color);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-tertiary-bg);--bs-list-group-action-active-color:var(--bs-body-color);--bs-list-group-action-active-bg:var(--bs-secondary-bg);--bs-list-group-disabled-color:var(--bs-secondary-color);--bs-list-group-disabled-bg:var(--bs-body-bg);--bs-list-group-active-color:#fff;--bs-list-group-active-bg:#0d6efd;--bs-list-group-active-border-color:#0d6efd;display:flex;flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:focus,.list-group-item-action:hover{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width:576px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:768px){.list-group-horizontal-md{flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:992px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:1400px){.list-group-horizontal-xxl{flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-primary{--bs-list-group-color:var(--bs-primary-text-emphasis);--bs-list-group-bg:var(--bs-primary-bg-subtle);--bs-list-group-border-color:var(--bs-primary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-primary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-primary-border-subtle);--bs-list-group-active-color:var(--bs-primary-bg-subtle);--bs-list-group-active-bg:var(--bs-primary-text-emphasis);--bs-list-group-active-border-color:var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color:var(--bs-secondary-text-emphasis);--bs-list-group-bg:var(--bs-secondary-bg-subtle);--bs-list-group-border-color:var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-secondary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-secondary-border-subtle);--bs-list-group-active-color:var(--bs-secondary-bg-subtle);--bs-list-group-active-bg:var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color:var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color:var(--bs-success-text-emphasis);--bs-list-group-bg:var(--bs-success-bg-subtle);--bs-list-group-border-color:var(--bs-success-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-success-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-success-border-subtle);--bs-list-group-active-color:var(--bs-success-bg-subtle);--bs-list-group-active-bg:var(--bs-success-text-emphasis);--bs-list-group-active-border-color:var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color:var(--bs-info-text-emphasis);--bs-list-group-bg:var(--bs-info-bg-subtle);--bs-list-group-border-color:var(--bs-info-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-info-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-info-border-subtle);--bs-list-group-active-color:var(--bs-info-bg-subtle);--bs-list-group-active-bg:var(--bs-info-text-emphasis);--bs-list-group-active-border-color:var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color:var(--bs-warning-text-emphasis);--bs-list-group-bg:var(--bs-warning-bg-subtle);--bs-list-group-border-color:var(--bs-warning-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-warning-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-warning-border-subtle);--bs-list-group-active-color:var(--bs-warning-bg-subtle);--bs-list-group-active-bg:var(--bs-warning-text-emphasis);--bs-list-group-active-border-color:var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color:var(--bs-danger-text-emphasis);--bs-list-group-bg:var(--bs-danger-bg-subtle);--bs-list-group-border-color:var(--bs-danger-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-danger-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-danger-border-subtle);--bs-list-group-active-color:var(--bs-danger-bg-subtle);--bs-list-group-active-bg:var(--bs-danger-text-emphasis);--bs-list-group-active-border-color:var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color:var(--bs-light-text-emphasis);--bs-list-group-bg:var(--bs-light-bg-subtle);--bs-list-group-border-color:var(--bs-light-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-light-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-light-border-subtle);--bs-list-group-active-color:var(--bs-light-bg-subtle);--bs-list-group-active-bg:var(--bs-light-text-emphasis);--bs-list-group-active-border-color:var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color:var(--bs-dark-text-emphasis);--bs-list-group-bg:var(--bs-dark-bg-subtle);--bs-list-group-border-color:var(--bs-dark-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-dark-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-dark-border-subtle);--bs-list-group-active-color:var(--bs-dark-bg-subtle);--bs-list-group-active-bg:var(--bs-dark-text-emphasis);--bs-list-group-active-border-color:var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color:#000;--bs-btn-close-bg:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity:0.5;--bs-btn-close-hover-opacity:0.75;--bs-btn-close-focus-shadow:0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity:1;--bs-btn-close-disabled-opacity:0.25;--bs-btn-close-white-filter:invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close.disabled,.btn-close:disabled{pointer-events:none;-webkit-user-select:none;-moz-user-select:none;user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex:1090;--bs-toast-padding-x:0.75rem;--bs-toast-padding-y:0.5rem;--bs-toast-spacing:1.5rem;--bs-toast-max-width:350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg:rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width:var(--bs-border-width);--bs-toast-border-color:var(--bs-border-color-translucent);--bs-toast-border-radius:var(--bs-border-radius);--bs-toast-box-shadow:var(--bs-box-shadow);--bs-toast-header-color:var(--bs-secondary-color);--bs-toast-header-bg:rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color:var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex:1090;position:absolute;z-index:var(--bs-toast-zindex);width:-webkit-max-content;width:-moz-max-content;width:max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex:1055;--bs-modal-width:500px;--bs-modal-padding:1rem;--bs-modal-margin:0.5rem;--bs-modal-color: ;--bs-modal-bg:var(--bs-body-bg);--bs-modal-border-color:var(--bs-border-color-translucent);--bs-modal-border-width:var(--bs-border-width);--bs-modal-border-radius:var(--bs-border-radius-lg);--bs-modal-box-shadow:var(--bs-box-shadow-sm);--bs-modal-inner-border-radius:calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x:1rem;--bs-modal-header-padding-y:1rem;--bs-modal-header-padding:1rem 1rem;--bs-modal-header-border-color:var(--bs-border-color);--bs-modal-header-border-width:var(--bs-border-width);--bs-modal-title-line-height:1.5;--bs-modal-footer-gap:0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color:var(--bs-border-color);--bs-modal-footer-border-width:var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0,-50px)}@media (prefers-reduced-motion:reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 2)}.modal-content{position:relative;display:flex;flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex:1050;--bs-backdrop-bg:#000;--bs-backdrop-opacity:0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;flex-shrink:0;align-items:center;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header 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(min-width:576px){.modal{--bs-modal-margin:1.75rem;--bs-modal-box-shadow:var(--bs-box-shadow)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width:300px}}@media (min-width:992px){.modal-lg,.modal-xl{--bs-modal-width:800px}}@media (min-width:1200px){.modal-xl{--bs-modal-width:1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-footer,.modal-fullscreen .modal-header{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media (max-width:575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-footer,.modal-fullscreen-sm-down .modal-header{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media (max-width:767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-footer,.modal-fullscreen-md-down .modal-header{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media (max-width:991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-footer,.modal-fullscreen-lg-down .modal-header{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media (max-width:1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-footer,.modal-fullscreen-xl-down .modal-header{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media (max-width:1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-footer,.modal-fullscreen-xxl-down .modal-header{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex:1080;--bs-tooltip-max-width:200px;--bs-tooltip-padding-x:0.5rem;--bs-tooltip-padding-y:0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color:var(--bs-body-bg);--bs-tooltip-bg:var(--bs-emphasis-color);--bs-tooltip-border-radius:var(--bs-border-radius);--bs-tooltip-opacity:0.9;--bs-tooltip-arrow-width:0.8rem;--bs-tooltip-arrow-height:0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:var(--bs-font-sans-serif);font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:transparent;border-style:solid}.bs-tooltip-auto[data-popper-placement^=top] 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var(--bs-tooltip-arrow-height))}.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before,.bs-tooltip-bottom .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow,.bs-tooltip-start .tooltip-arrow{right:calc(-1 * var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before,.bs-tooltip-start .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width) * .5) 0 calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) 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0 0 .25rem rgba(13,110,253,.25);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:var(--bs-secondary-color);--bs-pagination-disabled-bg:var(--bs-secondary-bg);--bs-pagination-disabled-border-color:var(--bs-border-color);display:flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media 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(min-width:768px){.float-md-start{float:left!important}.float-md-end{float:right!important}.float-md-none{float:none!important}.object-fit-md-contain{object-fit:contain!important}.object-fit-md-cover{object-fit:cover!important}.object-fit-md-fill{object-fit:fill!important}.object-fit-md-scale{object-fit:scale-down!important}.object-fit-md-none{object-fit:none!important}.d-md-inline{display:inline!important}.d-md-inline-block{display:inline-block!important}.d-md-block{display:block!important}.d-md-grid{display:grid!important}.d-md-inline-grid{display:inline-grid!important}.d-md-table{display:table!important}.d-md-table-row{display:table-row!important}.d-md-table-cell{display:table-cell!important}.d-md-flex{display:flex!important}.d-md-inline-flex{display:inline-flex!important}.d-md-none{display:none!important}.flex-md-fill{flex:1 1 auto!important}.flex-md-row{flex-direction:row!important}.flex-md-column{flex-direction:column!important}.flex-md-row-reverse{flex-direction:row-reverse!important}.flex-md-column-reverse{flex-direction:column-reverse!important}.flex-md-grow-0{flex-grow:0!important}.flex-md-grow-1{flex-grow:1!important}.flex-md-shrink-0{flex-shrink:0!important}.flex-md-shrink-1{flex-shrink:1!important}.flex-md-wrap{flex-wrap:wrap!important}.flex-md-nowrap{flex-wrap:nowrap!important}.flex-md-wrap-reverse{flex-wrap:wrap-reverse!important}.justify-content-md-start{justify-content:flex-start!important}.justify-content-md-end{justify-content:flex-end!important}.justify-content-md-center{justify-content:center!important}.justify-content-md-between{justify-content:space-between!important}.justify-content-md-around{justify-content:space-around!important}.justify-content-md-evenly{justify-content:space-evenly!important}.align-items-md-start{align-items:flex-start!important}.align-items-md-end{align-items:flex-end!important}.align-items-md-center{align-items:center!important}.align-items-md-baseline{align-items:baseline!important}.align-items-md-stretch{align-items:stretch!important}.align-content-md-start{align-content:flex-start!important}.align-content-md-end{align-content:flex-end!important}.align-content-md-center{align-content:center!important}.align-content-md-between{align-content:space-between!important}.align-content-md-around{align-content:space-around!important}.align-content-md-stretch{align-content:stretch!important}.align-self-md-auto{align-self:auto!important}.align-self-md-start{align-self:flex-start!important}.align-self-md-end{align-self:flex-end!important}.align-self-md-center{align-self:center!important}.align-self-md-baseline{align-self:baseline!important}.align-self-md-stretch{align-self:stretch!important}.order-md-first{order:-1!important}.order-md-0{order:0!important}.order-md-1{order:1!important}.order-md-2{order:2!important}.order-md-3{order:3!important}.order-md-4{order:4!important}.order-md-5{order:5!important}.order-md-last{order:6!important}.m-md-0{margin:0!important}.m-md-1{margin:0.25rem!important}.m-md-2{margin:0.5rem!important}.m-md-3{margin:1rem!important}.m-md-4{margin:1.5rem!important}.m-md-5{margin:3rem!important}.m-md-auto{margin:auto!important}.mx-md-0{margin-right:0!important;margin-left:0!important}.mx-md-1{margin-right:0.25rem!important;margin-left:0.25rem!important}.mx-md-2{margin-right:0.5rem!important;margin-left:0.5rem!important}.mx-md-3{margin-right:1rem!important;margin-left:1rem!important}.mx-md-4{margin-right:1.5rem!important;margin-left:1.5rem!important}.mx-md-5{margin-right:3rem!important;margin-left:3rem!important}.mx-md-auto{margin-right:auto!important;margin-left:auto!important}.my-md-0{margin-top:0!important;margin-bottom:0!important}.my-md-1{margin-top:0.25rem!important;margin-bottom:0.25rem!important}.my-md-2{margin-top:0.5rem!important;margin-bottom:0.5rem!important}.my-md-3{margin-top:1rem!important;margin-bottom:1rem!important}.my-md-4{margin-top:1.5rem!important;margin-bottom:1.5rem!important}.my-md-5{margin-top:3rem!important;margin-bottom:3rem!important}.my-md-auto{margin-top:auto!important;margin-bottom:auto!important}.mt-md-0{margin-top:0!important}.mt-md-1{margin-top:0.25rem!important}.mt-md-2{margin-top:0.5rem!important}.mt-md-3{margin-top:1rem!important}.mt-md-4{margin-top:1.5rem!important}.mt-md-5{margin-top:3rem!important}.mt-md-auto{margin-top:auto!important}.me-md-0{margin-right:0!important}.me-md-1{margin-right:0.25rem!important}.me-md-2{margin-right:0.5rem!important}.me-md-3{margin-right:1rem!important}.me-md-4{margin-right:1.5rem!important}.me-md-5{margin-right:3rem!important}.me-md-auto{margin-right:auto!important}.mb-md-0{margin-bottom:0!important}.mb-md-1{margin-bottom:0.25rem!important}.mb-md-2{margin-bottom:0.5rem!important}.mb-md-3{margin-bottom:1rem!important}.mb-md-4{margin-bottom:1.5rem!important}.mb-md-5{margin-bottom:3rem!important}.mb-md-auto{margin-bottom:auto!important}.ms-md-0{margin-left:0!important}.ms-md-1{margin-left:0.25rem!important}.ms-md-2{margin-left:0.5rem!important}.ms-md-3{margin-left:1rem!important}.ms-md-4{margin-left:1.5rem!important}.ms-md-5{margin-left:3rem!important}.ms-md-auto{margin-left:auto!important}.p-md-0{padding:0!important}.p-md-1{padding:0.25rem!important}.p-md-2{padding:0.5rem!important}.p-md-3{padding:1rem!important}.p-md-4{padding:1.5rem!important}.p-md-5{padding:3rem!important}.px-md-0{padding-right:0!important;padding-left:0!important}.px-md-1{padding-right:0.25rem!important;padding-left:0.25rem!important}.px-md-2{padding-right:0.5rem!important;padding-left:0.5rem!important}.px-md-3{padding-right:1rem!important;padding-left:1rem!important}.px-md-4{padding-right:1.5rem!important;padding-left:1.5rem!important}.px-md-5{padding-right:3rem!important;padding-left:3rem!important}.py-md-0{padding-top:0!important;padding-bottom:0!important}.py-md-1{padding-top:0.25rem!important;padding-bottom:0.25rem!important}.py-md-2{padding-top:0.5rem!important;padding-bottom:0.5rem!important}.py-md-3{padding-top:1rem!important;padding-bottom:1rem!important}.py-md-4{padding-top:1.5rem!important;padding-bottom:1.5rem!important}.py-md-5{padding-top:3rem!important;padding-bottom:3rem!important}.pt-md-0{padding-top:0!important}.pt-md-1{padding-top:0.25rem!important}.pt-md-2{padding-top:0.5rem!important}.pt-md-3{padding-top:1rem!important}.pt-md-4{padding-top:1.5rem!important}.pt-md-5{padding-top:3rem!important}.pe-md-0{padding-right:0!important}.pe-md-1{padding-right:0.25rem!important}.pe-md-2{padding-right:0.5rem!important}.pe-md-3{padding-right:1rem!important}.pe-md-4{padding-right:1.5rem!important}.pe-md-5{padding-right:3rem!important}.pb-md-0{padding-bottom:0!important}.pb-md-1{padding-bottom:0.25rem!important}.pb-md-2{padding-bottom:0.5rem!important}.pb-md-3{padding-bottom:1rem!important}.pb-md-4{padding-bottom:1.5rem!important}.pb-md-5{padding-bottom:3rem!important}.ps-md-0{padding-left:0!important}.ps-md-1{padding-left:0.25rem!important}.ps-md-2{padding-left:0.5rem!important}.ps-md-3{padding-left:1rem!important}.ps-md-4{padding-left:1.5rem!important}.ps-md-5{padding-left:3rem!important}.gap-md-0{gap:0!important}.gap-md-1{gap:0.25rem!important}.gap-md-2{gap:0.5rem!important}.gap-md-3{gap:1rem!important}.gap-md-4{gap:1.5rem!important}.gap-md-5{gap:3rem!important}.row-gap-md-0{row-gap:0!important}.row-gap-md-1{row-gap:0.25rem!important}.row-gap-md-2{row-gap:0.5rem!important}.row-gap-md-3{row-gap:1rem!important}.row-gap-md-4{row-gap:1.5rem!important}.row-gap-md-5{row-gap:3rem!important}.column-gap-md-0{column-gap:0!important}.column-gap-md-1{column-gap:0.25rem!important}.column-gap-md-2{column-gap:0.5rem!important}.column-gap-md-3{column-gap:1rem!important}.column-gap-md-4{column-gap:1.5rem!important}.column-gap-md-5{column-gap:3rem!important}.text-md-start{text-align:left!important}.text-md-end{text-align:right!important}.text-md-center{text-align:center!important}}@media (min-width:992px){.float-lg-start{float:left!important}.float-lg-end{float:right!important}.float-lg-none{float:none!important}.object-fit-lg-contain{object-fit:contain!important}.object-fit-lg-cover{object-fit:cover!important}.object-fit-lg-fill{object-fit:fill!important}.object-fit-lg-scale{object-fit:scale-down!important}.object-fit-lg-none{object-fit:none!important}.d-lg-inline{display:inline!important}.d-lg-inline-block{display:inline-block!important}.d-lg-block{display:block!important}.d-lg-grid{display:grid!important}.d-lg-inline-grid{display:inline-grid!important}.d-lg-table{display:table!important}.d-lg-table-row{display:table-row!important}.d-lg-table-cell{display:table-cell!important}.d-lg-flex{display:flex!important}.d-lg-inline-flex{display:inline-flex!important}.d-lg-none{display:none!important}.flex-lg-fill{flex:1 1 auto!important}.flex-lg-row{flex-direction:row!important}.flex-lg-column{flex-direction:column!important}.flex-lg-row-reverse{flex-direction:row-reverse!important}.flex-lg-column-reverse{flex-direction:column-reverse!important}.flex-lg-grow-0{flex-grow:0!important}.flex-lg-grow-1{flex-grow:1!important}.flex-lg-shrink-0{flex-shrink:0!important}.flex-lg-shrink-1{flex-shrink:1!important}.flex-lg-wrap{flex-wrap:wrap!important}.flex-lg-nowrap{flex-wrap:nowrap!important}.flex-lg-wrap-reverse{flex-wrap:wrap-reverse!important}.justify-content-lg-start{justify-content:flex-start!important}.justify-content-lg-end{justify-content:flex-end!important}.justify-content-lg-center{justify-content:center!important}.justify-content-lg-between{justify-content:space-between!important}.justify-content-lg-around{justify-content:space-around!important}.justify-content-lg-evenly{justify-content:space-evenly!important}.align-items-lg-start{align-items:flex-start!important}.align-items-lg-end{align-items:flex-end!important}.align-items-lg-center{align-items:center!important}.align-items-lg-baseline{align-items:baseline!important}.align-items-lg-stretch{align-items:stretch!important}.align-content-lg-start{align-content:flex-start!important}.align-content-lg-end{align-content:flex-end!important}.align-content-lg-center{align-content:center!important}.align-content-lg-between{align-content:space-between!important}.align-content-lg-around{align-content:space-around!important}.align-content-lg-stretch{align-content:stretch!important}.align-self-lg-auto{align-self:auto!important}.align-self-lg-start{align-self:flex-start!important}.align-self-lg-end{align-self:flex-end!important}.align-self-lg-center{align-self:center!important}.align-self-lg-baseline{align-self:baseline!important}.align-self-lg-stretch{align-self:stretch!important}.order-lg-first{order:-1!important}.order-lg-0{order:0!important}.order-lg-1{order:1!important}.order-lg-2{order:2!important}.order-lg-3{order:3!important}.order-lg-4{order:4!important}.order-lg-5{order:5!important}.order-lg-last{order:6!important}.m-lg-0{margin:0!important}.m-lg-1{margin:0.25rem!important}.m-lg-2{margin:0.5rem!important}.m-lg-3{margin:1rem!important}.m-lg-4{margin:1.5rem!important}.m-lg-5{margin:3rem!important}.m-lg-auto{margin:auto!important}.mx-lg-0{margin-right:0!important;margin-left:0!important}.mx-lg-1{margin-right:0.25rem!important;margin-left:0.25rem!important}.mx-lg-2{margin-right:0.5rem!important;margin-left:0.5rem!important}.mx-lg-3{margin-right:1rem!important;margin-left:1rem!important}.mx-lg-4{margin-right:1.5rem!important;margin-left:1.5rem!important}.mx-lg-5{margin-right:3rem!important;margin-left:3rem!important}.mx-lg-auto{margin-right:auto!important;margin-left:auto!important}.my-lg-0{margin-top:0!important;margin-bottom:0!important}.my-lg-1{margin-top:0.25rem!important;margin-bottom:0.25rem!important}.my-lg-2{margin-top:0.5rem!important;margin-bottom:0.5rem!important}.my-lg-3{margin-top:1rem!important;margin-bottom:1rem!important}.my-lg-4{margin-top:1.5rem!important;margin-bottom:1.5rem!important}.my-lg-5{margin-top:3rem!important;margin-bottom:3rem!important}.my-lg-auto{margin-top:auto!important;margin-bottom:auto!important}.mt-lg-0{margin-top:0!important}.mt-lg-1{margin-top:0.25rem!important}.mt-lg-2{margin-top:0.5rem!important}.mt-lg-3{margin-top:1rem!important}.mt-lg-4{margin-top:1.5rem!important}.mt-lg-5{margin-top:3rem!important}.mt-lg-auto{margin-top:auto!important}.me-lg-0{margin-right:0!important}.me-lg-1{margin-right:0.25rem!important}.me-lg-2{margin-right:0.5rem!important}.me-lg-3{margin-right:1rem!important}.me-lg-4{margin-right:1.5rem!important}.me-lg-5{margin-right:3rem!important}.me-lg-auto{margin-right:auto!important}.mb-lg-0{margin-bottom:0!important}.mb-lg-1{margin-bottom:0.25rem!important}.mb-lg-2{margin-bottom:0.5rem!important}.mb-lg-3{margin-bottom:1rem!important}.mb-lg-4{margin-bottom:1.5rem!important}.mb-lg-5{margin-bottom:3rem!important}.mb-lg-auto{margin-bottom:auto!important}.ms-lg-0{margin-left:0!important}.ms-lg-1{margin-left:0.25rem!important}.ms-lg-2{margin-left:0.5rem!important}.ms-lg-3{margin-left:1rem!important}.ms-lg-4{margin-left:1.5rem!important}.ms-lg-5{margin-left:3rem!important}.ms-lg-auto{margin-left:auto!important}.p-lg-0{padding:0!important}.p-lg-1{padding:0.25rem!important}.p-lg-2{padding:0.5rem!important}.p-lg-3{padding:1rem!important}.p-lg-4{padding:1.5rem!important}.p-lg-5{padding:3rem!important}.px-lg-0{padding-right:0!important;padding-left:0!important}.px-lg-1{padding-right:0.25rem!important;padding-left:0.25rem!important}.px-lg-2{padding-right:0.5rem!important;padding-left:0.5rem!important}.px-lg-3{padding-right:1rem!important;padding-left:1rem!important}.px-lg-4{padding-right:1.5rem!important;padding-left:1.5rem!important}.px-lg-5{padding-right:3rem!important;padding-left:3rem!important}.py-lg-0{padding-top:0!important;padding-bottom:0!important}.py-lg-1{padding-top:0.25rem!important;padding-bottom:0.25rem!important}.py-lg-2{padding-top:0.5rem!important;padding-bottom:0.5rem!important}.py-lg-3{padding-top:1rem!important;padding-bottom:1rem!important}.py-lg-4{padding-top:1.5rem!important;padding-bottom:1.5rem!important}.py-lg-5{padding-top:3rem!important;padding-bottom:3rem!important}.pt-lg-0{padding-top:0!important}.pt-lg-1{padding-top:0.25rem!important}.pt-lg-2{padding-top:0.5rem!important}.pt-lg-3{padding-top:1rem!important}.pt-lg-4{padding-top:1.5rem!important}.pt-lg-5{padding-top:3rem!important}.pe-lg-0{padding-right:0!important}.pe-lg-1{padding-right:0.25rem!important}.pe-lg-2{padding-right:0.5rem!important}.pe-lg-3{padding-right:1rem!important}.pe-lg-4{padding-right:1.5rem!important}.pe-lg-5{padding-right:3rem!important}.pb-lg-0{padding-bottom:0!important}.pb-lg-1{padding-bottom:0.25rem!important}.pb-lg-2{padding-bottom:0.5rem!important}.pb-lg-3{padding-bottom:1rem!important}.pb-lg-4{padding-bottom:1.5rem!important}.pb-lg-5{padding-bottom:3rem!important}.ps-lg-0{padding-left:0!important}.ps-lg-1{padding-left:0.25rem!important}.ps-lg-2{padding-left:0.5rem!important}.ps-lg-3{padding-left:1rem!important}.ps-lg-4{padding-left:1.5rem!important}.ps-lg-5{padding-left:3rem!important}.gap-lg-0{gap:0!important}.gap-lg-1{gap:0.25rem!important}.gap-lg-2{gap:0.5rem!important}.gap-lg-3{gap:1rem!important}.gap-lg-4{gap:1.5rem!important}.gap-lg-5{gap:3rem!important}.row-gap-lg-0{row-gap:0!important}.row-gap-lg-1{row-gap:0.25rem!important}.row-gap-lg-2{row-gap:0.5rem!important}.row-gap-lg-3{row-gap:1rem!important}.row-gap-lg-4{row-gap:1.5rem!important}.row-gap-lg-5{row-gap:3rem!important}.column-gap-lg-0{column-gap:0!important}.column-gap-lg-1{column-gap:0.25rem!important}.column-gap-lg-2{column-gap:0.5rem!important}.column-gap-lg-3{column-gap:1rem!important}.column-gap-lg-4{column-gap:1.5rem!important}.column-gap-lg-5{column-gap:3rem!important}.text-lg-start{text-align:left!important}.text-lg-end{text-align:right!important}.text-lg-center{text-align:center!important}}@media (min-width:1200px){.float-xl-start{float:left!important}.float-xl-end{float:right!important}.float-xl-none{float:none!important}.object-fit-xl-contain{object-fit:contain!important}.object-fit-xl-cover{object-fit:cover!important}.object-fit-xl-fill{object-fit:fill!important}.object-fit-xl-scale{object-fit:scale-down!important}.object-fit-xl-none{object-fit:none!important}.d-xl-inline{display:inline!important}.d-xl-inline-block{display:inline-block!important}.d-xl-block{display:block!important}.d-xl-grid{display:grid!important}.d-xl-inline-grid{display:inline-grid!important}.d-xl-table{display:table!important}.d-xl-table-row{display:table-row!important}.d-xl-table-cell{display:table-cell!important}.d-xl-flex{display:flex!important}.d-xl-inline-flex{display:inline-flex!important}.d-xl-none{display:none!important}.flex-xl-fill{flex:1 1 auto!important}.flex-xl-row{flex-direction:row!important}.flex-xl-column{flex-direction:column!important}.flex-xl-row-reverse{flex-direction:row-reverse!important}.flex-xl-column-reverse{flex-direction:column-reverse!important}.flex-xl-grow-0{flex-grow:0!important}.flex-xl-grow-1{flex-grow:1!important}.flex-xl-shrink-0{flex-shrink:0!important}.flex-xl-shrink-1{flex-shrink:1!important}.flex-xl-wrap{flex-wrap:wrap!important}.flex-xl-nowrap{flex-wrap:nowrap!important}.flex-xl-wrap-reverse{flex-wrap:wrap-reverse!important}.justify-content-xl-start{justify-content:flex-start!important}.justify-content-xl-end{justify-content:flex-end!important}.justify-content-xl-center{justify-content:center!important}.justify-content-xl-between{justify-content:space-between!important}.justify-content-xl-around{justify-content:space-around!important}.justify-content-xl-evenly{justify-content:space-evenly!important}.align-items-xl-start{align-items:flex-start!important}.align-items-xl-end{align-items:flex-end!important}.align-items-xl-center{align-items:center!important}.align-items-xl-baseline{align-items:baseline!important}.align-items-xl-stretch{align-items:stretch!important}.align-content-xl-start{align-content:flex-start!important}.align-content-xl-end{align-content:flex-end!important}.align-content-xl-center{align-content:center!important}.align-content-xl-between{align-content:space-between!important}.align-content-xl-around{align-content:space-around!important}.align-content-xl-stretch{align-content:stretch!important}.align-self-xl-auto{align-self:auto!important}.align-self-xl-start{align-self:flex-start!important}.align-self-xl-end{align-self:flex-end!important}.align-self-xl-center{align-self:center!important}.align-self-xl-baseline{align-self:baseline!important}.align-self-xl-stretch{align-self:stretch!important}.order-xl-first{order:-1!important}.order-xl-0{order:0!important}.order-xl-1{order:1!important}.order-xl-2{order:2!important}.order-xl-3{order:3!important}.order-xl-4{order:4!important}.order-xl-5{order:5!important}.order-xl-last{order:6!important}.m-xl-0{margin:0!important}.m-xl-1{margin:0.25rem!important}.m-xl-2{margin:0.5rem!important}.m-xl-3{margin:1rem!important}.m-xl-4{margin:1.5rem!important}.m-xl-5{margin:3rem!important}.m-xl-auto{margin:auto!important}.mx-xl-0{margin-right:0!important;margin-left:0!important}.mx-xl-1{margin-right:0.25rem!important;margin-left:0.25rem!important}.mx-xl-2{margin-right:0.5rem!important;margin-left:0.5rem!important}.mx-xl-3{margin-right:1rem!important;margin-left:1rem!important}.mx-xl-4{margin-right:1.5rem!important;margin-left:1.5rem!important}.mx-xl-5{margin-right:3rem!important;margin-left:3rem!important}.mx-xl-auto{margin-right:auto!important;margin-left:auto!important}.my-xl-0{margin-top:0!important;margin-bottom:0!important}.my-xl-1{margin-top:0.25rem!important;margin-bottom:0.25rem!important}.my-xl-2{margin-top:0.5rem!important;margin-bottom:0.5rem!important}.my-xl-3{margin-top:1rem!important;margin-bottom:1rem!important}.my-xl-4{margin-top:1.5rem!important;margin-bottom:1.5rem!important}.my-xl-5{margin-top:3rem!important;margin-bottom:3rem!important}.my-xl-auto{margin-top:auto!important;margin-bottom:auto!important}.mt-xl-0{margin-top:0!important}.mt-xl-1{margin-top:0.25rem!important}.mt-xl-2{margin-top:0.5rem!important}.mt-xl-3{margin-top:1rem!important}.mt-xl-4{margin-top:1.5rem!important}.mt-xl-5{margin-top:3rem!important}.mt-xl-auto{margin-top:auto!important}.me-xl-0{margin-right:0!important}.me-xl-1{margin-right:0.25rem!important}.me-xl-2{margin-right:0.5rem!important}.me-xl-3{margin-right:1rem!important}.me-xl-4{margin-right:1.5rem!important}.me-xl-5{margin-right:3rem!important}.me-xl-auto{margin-right:auto!important}.mb-xl-0{margin-bottom:0!important}.mb-xl-1{margin-bottom:0.25rem!important}.mb-xl-2{margin-bottom:0.5rem!important}.mb-xl-3{margin-bottom:1rem!important}.mb-xl-4{margin-bottom:1.5rem!important}.mb-xl-5{margin-bottom:3rem!important}.mb-xl-auto{margin-bottom:auto!important}.ms-xl-0{margin-left:0!important}.ms-xl-1{margin-left:0.25rem!important}.ms-xl-2{margin-left:0.5rem!important}.ms-xl-3{margin-left:1rem!important}.ms-xl-4{margin-left:1.5rem!important}.ms-xl-5{margin-left:3rem!important}.ms-xl-auto{margin-left:auto!important}.p-xl-0{padding:0!important}.p-xl-1{padding:0.25rem!important}.p-xl-2{padding:0.5rem!important}.p-xl-3{padding:1rem!important}.p-xl-4{padding:1.5rem!important}.p-xl-5{padding:3rem!important}.px-xl-0{padding-right:0!important;padding-left:0!important}.px-xl-1{padding-right:0.25rem!important;padding-left:0.25rem!important}.px-xl-2{padding-right:0.5rem!important;padding-left:0.5rem!important}.px-xl-3{padding-right:1rem!important;padding-left:1rem!important}.px-xl-4{padding-right:1.5rem!important;padding-left:1.5rem!important}.px-xl-5{padding-right:3rem!important;padding-left:3rem!important}.py-xl-0{padding-top:0!important;padding-bottom:0!important}.py-xl-1{padding-top:0.25rem!important;padding-bottom:0.25rem!important}.py-xl-2{padding-top:0.5rem!important;padding-bottom:0.5rem!important}.py-xl-3{padding-top:1rem!important;padding-bottom:1rem!important}.py-xl-4{padding-top:1.5rem!important;padding-bottom:1.5rem!important}.py-xl-5{padding-top:3rem!important;padding-bottom:3rem!important}.pt-xl-0{padding-top:0!important}.pt-xl-1{padding-top:0.25rem!important}.pt-xl-2{padding-top:0.5rem!important}.pt-xl-3{padding-top:1rem!important}.pt-xl-4{padding-top:1.5rem!important}.pt-xl-5{padding-top:3rem!important}.pe-xl-0{padding-right:0!important}.pe-xl-1{padding-right:0.25rem!important}.pe-xl-2{padding-right:0.5rem!important}.pe-xl-3{padding-right:1rem!important}.pe-xl-4{padding-right:1.5rem!important}.pe-xl-5{padding-right:3rem!important}.pb-xl-0{padding-bottom:0!important}.pb-xl-1{padding-bottom:0.25rem!important}.pb-xl-2{padding-bottom:0.5rem!important}.pb-xl-3{padding-bottom:1rem!important}.pb-xl-4{padding-bottom:1.5rem!important}.pb-xl-5{padding-bottom:3rem!important}.ps-xl-0{padding-left:0!important}.ps-xl-1{padding-left:0.25rem!important}.ps-xl-2{padding-left:0.5rem!important}.ps-xl-3{padding-left:1rem!important}.ps-xl-4{padding-left:1.5rem!important}.ps-xl-5{padding-left:3rem!important}.gap-xl-0{gap:0!important}.gap-xl-1{gap:0.25rem!important}.gap-xl-2{gap:0.5rem!important}.gap-xl-3{gap:1rem!important}.gap-xl-4{gap:1.5rem!important}.gap-xl-5{gap:3rem!important}.row-gap-xl-0{row-gap:0!important}.row-gap-xl-1{row-gap:0.25rem!important}.row-gap-xl-2{row-gap:0.5rem!important}.row-gap-xl-3{row-gap:1rem!important}.row-gap-xl-4{row-gap:1.5rem!important}.row-gap-xl-5{row-gap:3rem!important}.column-gap-xl-0{column-gap:0!important}.column-gap-xl-1{column-gap:0.25rem!important}.column-gap-xl-2{column-gap:0.5rem!important}.column-gap-xl-3{column-gap:1rem!important}.column-gap-xl-4{column-gap:1.5rem!important}.column-gap-xl-5{column-gap:3rem!important}.text-xl-start{text-align:left!important}.text-xl-end{text-align:right!important}.text-xl-center{text-align:center!important}}@media (min-width:1400px){.float-xxl-start{float:left!important}.float-xxl-end{float:right!important}.float-xxl-none{float:none!important}.object-fit-xxl-contain{object-fit:contain!important}.object-fit-xxl-cover{object-fit:cover!important}.object-fit-xxl-fill{object-fit:fill!important}.object-fit-xxl-scale{object-fit:scale-down!important}.object-fit-xxl-none{object-fit:none!important}.d-xxl-inline{display:inline!important}.d-xxl-inline-block{display:inline-block!important}.d-xxl-block{display:block!important}.d-xxl-grid{display:grid!important}.d-xxl-inline-grid{display:inline-grid!important}.d-xxl-table{display:table!important}.d-xxl-table-row{display:table-row!important}.d-xxl-table-cell{display:table-cell!important}.d-xxl-flex{display:flex!important}.d-xxl-inline-flex{display:inline-flex!important}.d-xxl-none{display:none!important}.flex-xxl-fill{flex:1 1 auto!important}.flex-xxl-row{flex-direction:row!important}.flex-xxl-column{flex-direction:column!important}.flex-xxl-row-reverse{flex-direction:row-reverse!important}.flex-xxl-column-reverse{flex-direction:column-reverse!important}.flex-xxl-grow-0{flex-grow:0!important}.flex-xxl-grow-1{flex-grow:1!important}.flex-xxl-shrink-0{flex-shrink:0!important}.flex-xxl-shrink-1{flex-shrink:1!important}.flex-xxl-wrap{flex-wrap:wrap!important}.flex-xxl-nowrap{flex-wrap:nowrap!important}.flex-xxl-wrap-reverse{flex-wrap:wrap-reverse!important}.justify-content-xxl-start{justify-content:flex-start!important}.justify-content-xxl-end{justify-content:flex-end!important}.justify-content-xxl-center{justify-content:center!important}.justify-content-xxl-between{justify-content:space-between!important}.justify-content-xxl-around{justify-content:space-around!important}.justify-content-xxl-evenly{justify-content:space-evenly!important}.align-items-xxl-start{align-items:flex-start!important}.align-items-xxl-end{align-items:flex-end!important}.align-items-xxl-center{align-items:center!important}.align-items-xxl-baseline{align-items:baseline!important}.align-items-xxl-stretch{align-items:stretch!important}.align-content-xxl-start{align-content:flex-start!important}.align-content-xxl-end{align-content:flex-end!important}.align-content-xxl-center{align-content:center!important}.align-content-xxl-between{align-content:space-between!important}.align-content-xxl-around{align-content:space-around!important}.align-content-xxl-stretch{align-content:stretch!important}.align-self-xxl-auto{align-self:auto!important}.align-self-xxl-start{align-self:flex-start!important}.align-self-xxl-end{align-self:flex-end!important}.align-self-xxl-center{align-self:center!important}.align-self-xxl-baseline{align-self:baseline!important}.align-self-xxl-stretch{align-self:stretch!important}.order-xxl-first{order:-1!important}.order-xxl-0{order:0!important}.order-xxl-1{order:1!important}.order-xxl-2{order:2!important}.order-xxl-3{order:3!important}.order-xxl-4{order:4!important}.order-xxl-5{order:5!important}.order-xxl-last{order:6!important}.m-xxl-0{margin:0!important}.m-xxl-1{margin:0.25rem!important}.m-xxl-2{margin:0.5rem!important}.m-xxl-3{margin:1rem!important}.m-xxl-4{margin:1.5rem!important}.m-xxl-5{margin:3rem!important}.m-xxl-auto{margin:auto!important}.mx-xxl-0{margin-right:0!important;margin-left:0!important}.mx-xxl-1{margin-right:0.25rem!important;margin-left:0.25rem!important}.mx-xxl-2{margin-right:0.5rem!important;margin-left:0.5rem!important}.mx-xxl-3{margin-right:1rem!important;margin-left:1rem!important}.mx-xxl-4{margin-right:1.5rem!important;margin-left:1.5rem!important}.mx-xxl-5{margin-right:3rem!important;margin-left:3rem!important}.mx-xxl-auto{margin-right:auto!important;margin-left:auto!important}.my-xxl-0{margin-top:0!important;margin-bottom:0!important}.my-xxl-1{margin-top:0.25rem!important;margin-bottom:0.25rem!important}.my-xxl-2{margin-top:0.5rem!important;margin-bottom:0.5rem!important}.my-xxl-3{margin-top:1rem!important;margin-bottom:1rem!important}.my-xxl-4{margin-top:1.5rem!important;margin-bottom:1.5rem!important}.my-xxl-5{margin-top:3rem!important;margin-bottom:3rem!important}.my-xxl-auto{margin-top:auto!important;margin-bottom:auto!important}.mt-xxl-0{margin-top:0!important}.mt-xxl-1{margin-top:0.25rem!important}.mt-xxl-2{margin-top:0.5rem!important}.mt-xxl-3{margin-top:1rem!important}.mt-xxl-4{margin-top:1.5rem!important}.mt-xxl-5{margin-top:3rem!important}.mt-xxl-auto{margin-top:auto!important}.me-xxl-0{margin-right:0!important}.me-xxl-1{margin-right:0.25rem!important}.me-xxl-2{margin-right:0.5rem!important}.me-xxl-3{margin-right:1rem!important}.me-xxl-4{margin-right:1.5rem!important}.me-xxl-5{margin-right:3rem!important}.me-xxl-auto{margin-right:auto!important}.mb-xxl-0{margin-bottom:0!important}.mb-xxl-1{margin-bottom:0.25rem!important}.mb-xxl-2{margin-bottom:0.5rem!important}.mb-xxl-3{margin-bottom:1rem!important}.mb-xxl-4{margin-bottom:1.5rem!important}.mb-xxl-5{margin-bottom:3rem!important}.mb-xxl-auto{margin-bottom:auto!important}.ms-xxl-0{margin-left:0!important}.ms-xxl-1{margin-left:0.25rem!important}.ms-xxl-2{margin-left:0.5rem!important}.ms-xxl-3{margin-left:1rem!important}.ms-xxl-4{margin-left:1.5rem!important}.ms-xxl-5{margin-left:3rem!important}.ms-xxl-auto{margin-left:auto!important}.p-xxl-0{padding:0!important}.p-xxl-1{padding:0.25rem!important}.p-xxl-2{padding:0.5rem!important}.p-xxl-3{padding:1rem!important}.p-xxl-4{padding:1.5rem!important}.p-xxl-5{padding:3rem!important}.px-xxl-0{padding-right:0!important;padding-left:0!important}.px-xxl-1{padding-right:0.25rem!important;padding-left:0.25rem!important}.px-xxl-2{padding-right:0.5rem!important;padding-left:0.5rem!important}.px-xxl-3{padding-right:1rem!important;padding-left:1rem!important}.px-xxl-4{padding-right:1.5rem!important;padding-left:1.5rem!important}.px-xxl-5{padding-right:3rem!important;padding-left:3rem!important}.py-xxl-0{padding-top:0!important;padding-bottom:0!important}.py-xxl-1{padding-top:0.25rem!important;padding-bottom:0.25rem!important}.py-xxl-2{padding-top:0.5rem!important;padding-bottom:0.5rem!important}.py-xxl-3{padding-top:1rem!important;padding-bottom:1rem!important}.py-xxl-4{padding-top:1.5rem!important;padding-bottom:1.5rem!important}.py-xxl-5{padding-top:3rem!important;padding-bottom:3rem!important}.pt-xxl-0{padding-top:0!important}.pt-xxl-1{padding-top:0.25rem!important}.pt-xxl-2{padding-top:0.5rem!important}.pt-xxl-3{padding-top:1rem!important}.pt-xxl-4{padding-top:1.5rem!important}.pt-xxl-5{padding-top:3rem!important}.pe-xxl-0{padding-right:0!important}.pe-xxl-1{padding-right:0.25rem!important}.pe-xxl-2{padding-right:0.5rem!important}.pe-xxl-3{padding-right:1rem!important}.pe-xxl-4{padding-right:1.5rem!important}.pe-xxl-5{padding-right:3rem!important}.pb-xxl-0{padding-bottom:0!important}.pb-xxl-1{padding-bottom:0.25rem!important}.pb-xxl-2{padding-bottom:0.5rem!important}.pb-xxl-3{padding-bottom:1rem!important}.pb-xxl-4{padding-bottom:1.5rem!important}.pb-xxl-5{padding-bottom:3rem!important}.ps-xxl-0{padding-left:0!important}.ps-xxl-1{padding-left:0.25rem!important}.ps-xxl-2{padding-left:0.5rem!important}.ps-xxl-3{padding-left:1rem!important}.ps-xxl-4{padding-left:1.5rem!important}.ps-xxl-5{padding-left:3rem!important}.gap-xxl-0{gap:0!important}.gap-xxl-1{gap:0.25rem!important}.gap-xxl-2{gap:0.5rem!important}.gap-xxl-3{gap:1rem!important}.gap-xxl-4{gap:1.5rem!important}.gap-xxl-5{gap:3rem!important}.row-gap-xxl-0{row-gap:0!important}.row-gap-xxl-1{row-gap:0.25rem!important}.row-gap-xxl-2{row-gap:0.5rem!important}.row-gap-xxl-3{row-gap:1rem!important}.row-gap-xxl-4{row-gap:1.5rem!important}.row-gap-xxl-5{row-gap:3rem!important}.column-gap-xxl-0{column-gap:0!important}.column-gap-xxl-1{column-gap:0.25rem!important}.column-gap-xxl-2{column-gap:0.5rem!important}.column-gap-xxl-3{column-gap:1rem!important}.column-gap-xxl-4{column-gap:1.5rem!important}.column-gap-xxl-5{column-gap:3rem!important}.text-xxl-start{text-align:left!important}.text-xxl-end{text-align:right!important}.text-xxl-center{text-align:center!important}}@media (min-width:1200px){.fs-1{font-size:2.5rem!important}.fs-2{font-size:2rem!important}.fs-3{font-size:1.75rem!important}.fs-4{font-size:1.5rem!important}}@media print{.d-print-inline{display:inline!important}.d-print-inline-block{display:inline-block!important}.d-print-block{display:block!important}.d-print-grid{display:grid!important}.d-print-inline-grid{display:inline-grid!important}.d-print-table{display:table!important}.d-print-table-row{display:table-row!important}.d-print-table-cell{display:table-cell!important}.d-print-flex{display:flex!important}.d-print-inline-flex{display:inline-flex!important}.d-print-none{display:none!important}} \ No newline at end of file diff --git a/css/default.min.css b/css/default.min.css new file mode 100644 index 000000000..a75ea9116 --- /dev/null +++ b/css/default.min.css @@ -0,0 +1,9 @@ +/*! + Theme: Default + Description: Original highlight.js style + Author: (c) Ivan Sagalaev + Maintainer: @highlightjs/core-team + Website: https://highlightjs.org/ + License: see project LICENSE + Touched: 2021 +*/pre code.hljs{display:block;overflow-x:auto;padding:1em}code.hljs{padding:3px 5px}.hljs{background:#f3f3f3;color:#444}.hljs-comment{color:#697070}.hljs-punctuation,.hljs-tag{color:#444a}.hljs-tag .hljs-attr,.hljs-tag .hljs-name{color:#444}.hljs-attribute,.hljs-doctag,.hljs-keyword,.hljs-meta .hljs-keyword,.hljs-name,.hljs-selector-tag{font-weight:700}.hljs-deletion,.hljs-number,.hljs-quote,.hljs-selector-class,.hljs-selector-id,.hljs-string,.hljs-template-tag,.hljs-type{color:#800}.hljs-section,.hljs-title{color:#800;font-weight:700}.hljs-link,.hljs-operator,.hljs-regexp,.hljs-selector-attr,.hljs-selector-pseudo,.hljs-symbol,.hljs-template-variable,.hljs-variable{color:#ab5656}.hljs-literal{color:#695}.hljs-addition,.hljs-built_in,.hljs-bullet,.hljs-code{color:#397300}.hljs-meta{color:#1f7199}.hljs-meta .hljs-string{color:#38a}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:700} \ No newline at end of file diff --git a/css/fonts/bootstrap-icons.woff.css b/css/fonts/bootstrap-icons.woff.css new file mode 100644 index 000000000..51204d27d Binary files /dev/null and b/css/fonts/bootstrap-icons.woff.css differ diff --git a/css/fonts/bootstrap-icons.woff2.css b/css/fonts/bootstrap-icons.woff2.css new file mode 100644 index 000000000..92c483021 Binary files /dev/null and b/css/fonts/bootstrap-icons.woff2.css differ diff --git a/css/theme.css b/css/theme.css new file mode 100644 index 000000000..346980092 --- /dev/null +++ b/css/theme.css @@ -0,0 +1,183 @@ + +.highlight { + padding-top: 10px; + margin-bottom: 10px; +} + +p code{ + padding : 5px; +} + +.highlighttable .copybtn , .highlighttable .filename , .highlight .copybtn , .highlight .filename,.highlighttable tr , .highlight pre{ + padding-left: 10px; + padding-right: 10px; +} +.highlighttable tr , .highlight pre{ + padding-top:10px; + padding-bottom: 10px; +} + + +img{ + width : 100%; +} + +.img-structure{ + width: auto; +} + +details > details, +details > div { + padding-left: 16px; +} + +blockquote{ + position:relative; + border-left:3px solid #c0c0c0; + padding-left:10px; +} +blockquote:before{ + font-family:bootstrap-icons; + content:'\F6B0'; + color:rgb(139, 139, 139); +} +blockquote p{ + position:relative; +} +blockquote cite{ + display: block; + font-size:0.8rem; + color:#F8C8B2; + text-align:right; +} + +a[target="_blank"]:not(.btn) +{ + text-decoration:underline; +} + +.accordion{ + position : relative; + padding: 0.5rem 0; + margin-bottom : 0.5rem; +} + +svg{ + width: 100%; +} + +/* +.accordion-button:before{ + font-family:bootstrap-icons; + padding-right: 0.25em; + font-size: 15px; +} +.note .accordion-button:before{ + content: '\F272'; +} +*/ +mark{ + font-weight: bold; +} +.note .accordion-item +{ + --bs-accordion-active-color: var(--bs-primary-text-emphasis); + --bs-accordion-active-bg : var(--bs-primary-bg-subtle); + --bs-accordion-btn-focus-box-shadow : 0 0 0 0; +} + +.abstract,.question .accordion-item +{ + --bs-accordion-active-bg : var(--bs-secondary-bg-subtle); + --bs-accordion-active-color: var(--bs-secondary-text-emphasis); + --bs-accordion-btn-focus-box-shadow : 0 0 0 0; +} + +.info .accordion-item +{ + --bs-accordion-active-bg : var(--bs-info-bg-subtle); + --bs-accordion-active-color: var(--bs-info-text-emphasis); + --bs-accordion-btn-focus-box-shadow : 0 0 0 0; +} +.success .accordion-item +{ + --bs-accordion-active-bg : var(--bs-success-bg-subtle); + --bs-accordion-active-color: var(--bs-success-text-emphasis); + --bs-accordion-btn-focus-box-shadow : 0 0 0 0; +} +.warning,.tip,.bug .accordion-item +{ + --bs-accordion-active-bg : var(--bs-warning-bg-subtle); + --bs-accordion-active-color: var(--bs-warning-text-emphasis); + --bs-accordion-btn-focus-box-shadow : 0 0 0 0; +} +.failure,.danger .accordion-item +{ + --bs-accordion-active-bg : var(--bs-danger-bg-subtle); + --bs-accordion-active-color: var(--bs-danger-text-emphasis); + --bs-accordion-btn-focus-box-shadow : 0 0 0 0; +} +.example .accordion-item +{ + --bs-accordion-active-bg : var(--bs-dark-bg-subtle); + --bs-accordion-active-color: var(--bs-dark-text-emphasis); + --bs-accordion-btn-focus-box-shadow : 0 0 0 0; +} +.quote .accordion-item +{ + --bs-accordion-active-bg : var(--bs-light-bg-subtle); + --bs-accordion-active-color: var(--bs-light-text-emphasis); + --bs-accordion-btn-focus-box-shadow : 0 0 0 0; +} + +@media print{ + .print-visible{ + display: none; + } + .print-hide{ + display: none; + } + .print-block{ + display: none; + } + .print-main{ + width: 100%; + } +} + +a{ + text-decoration: none; +} + + +@media (prefers-color-scheme: dark) { + a{ + color:385898; + } + .highlighttable , .highlight{ + background: #404040; + } + code , .highlighttable tr , .highlight pre{ + color : #e1e1e1; + background : #2f2f2f !important; + } + .highlight{ + border: solid 0.5px #404040; + } + } + + @media (prefers-color-scheme: light) { + a{ + color:0066CC; + } + .highlighttable , .highlight{ + background: #e6e6e6; + } + code , .highlighttable tr , .highlight pre{ + color : #171717; + background : #f2f2f2 !important; + } + .highlight{ + border: solid 0.5px #e6e6e6; + } + } \ No newline at end of file diff --git a/css/vs.min.css b/css/vs.min.css new file mode 100644 index 000000000..956102268 --- /dev/null +++ b/css/vs.min.css @@ -0,0 +1,33 @@ +.highlight .hll { background-color: #ffffcc } +.highlight .c { color: #008000 } /* Comment */ +.highlight .err { border: 1px solid #FF0000 } /* Error */ +.highlight .k { color: #0000ff } /* Keyword */ +.highlight .cm { color: #008000 } /* Comment.Multiline */ +.highlight .cp { color: #0000ff } /* Comment.Preproc */ +.highlight .c1 { color: #008000 } /* Comment.Single */ +.highlight .cs { color: #008000 } /* Comment.Special */ +.highlight .ge { font-style: italic } /* Generic.Emph */ +.highlight .gh { font-weight: bold } /* Generic.Heading */ +.highlight .gp { font-weight: bold } /* Generic.Prompt */ +.highlight .gs { font-weight: bold } /* Generic.Strong */ +.highlight .gu { font-weight: bold } /* Generic.Subheading */ +.highlight .kc { color: #0000ff } /* Keyword.Constant */ +.highlight .kd { color: #0000ff } /* Keyword.Declaration */ +.highlight .kn { color: #0000ff } /* Keyword.Namespace */ +.highlight .kp { color: #0000ff } /* Keyword.Pseudo */ +.highlight .kr { color: #0000ff } /* Keyword.Reserved */ +.highlight .kt { color: #2b91af } /* Keyword.Type */ +.highlight .s { color: #a31515 } /* Literal.String */ +.highlight .nc { color: #2b91af } /* Name.Class */ +.highlight .ow { color: #0000ff } /* Operator.Word */ +.highlight .sb { color: #a31515 } /* Literal.String.Backtick */ +.highlight .sc { color: #a31515 } /* Literal.String.Char */ +.highlight .sd { color: #a31515 } /* Literal.String.Doc */ +.highlight .s2 { color: #a31515 } /* Literal.String.Double */ +.highlight .se { color: #a31515 } /* Literal.String.Escape */ +.highlight .sh { color: #a31515 } /* Literal.String.Heredoc */ +.highlight .si { color: #a31515 } /* 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+ + + + + + + + + + + + \ No newline at end of file diff --git a/end-to-end-example.lisp b/end-to-end-example.lisp new file mode 100644 index 000000000..18627f477 --- /dev/null +++ b/end-to-end-example.lisp @@ -0,0 +1,342 @@ +;;;; Note: This file is not intended to be loaded directly from Lisp, +;;;; but to be executed expression by expression. +;;;; If you are using Emacs/Lem, you can simply press `C-C C-c` while your cursor is hovering over an expression +(ql:quickload :caten) + +(defpackage :end-to-end + (:use :cl :caten/air :caten/aasm :caten/apis :caten/avm :caten/codegen/expr-cache) + ;; Import some low-level APIs + (:import-from + :caten/codegen/scheduler + #:graph-schedule) + (:import-from + :caten/codegen/rewriting-rules + #:apply-rewriting-rules + #:schedule-item-write-define-global) + (:import-from + :caten/codegen/shape-inference + #:run-type-infer) + (:import-from + :caten/codegen/blueprint + #:lower-schedule-item + #:print-blueprint) + (:import-from + :caten/codegen/renderer + #:CStyle-Renderer + #:%render-kernel + #:%compile-kernel) + (:import-from + :caten/codegen/jit + #:schedule-graph->avm-graph)) +(in-package :end-to-end) + +;;; 1. High-Level API + +;;; In this example, we will show an end-to-end example of how the overall process of code generation works, starting from Caten's +;;; High-Level Interface (caten/apis) + +;;; Let's first define a class (similar to Torch's module) for the function sin(cos(x)) +;;; It will be the same class used in getting-started.lisp +(defclass SinCos (Func) nil + (:documentation "The func SinCos computes sin(cos(x))")) + +(defmethod forward ((op SinCos) &rest tensors) + ;; Defines the forward pass computation: sin(cos(x)) + (st "A[~] -> A[~]" (tensors))) + +(defmethod backward ((op SinCos) &optional prev-grad) + ;; Defines the backward pass (gradient computation), ignored in this example + (declare (ignore prev-grad)) + nil) + +(defmethod lower ((op SinCos) &rest inputs) + ;; Lowers the high-level operation into intermediate representation + (let ((x (car inputs))) + (with-context + (a (%sin (%add x (%fconst (/ pi 2))))) + (b (%sin a))))) + +;;; You can create the next lazy tensor using the forward method. +;;; Let's define a function doing this as a utility named !sincos. +(defun !sincos (tensor) + (forward (make-instance 'SinCos) tensor)) + +;;; First of all, let's initialize the module we just defined !sincos +(defparameter mytensor (make-tensor '(1) :initial-element 1.0)) + +(defparameter sincos (!sincos mytensor)) + +;;; If we print, we will see what the contents of sincos are. +(print sincos) +;;; It might surprise you that there is nothing, the reason is that Caten is lazy, we have to compile the expression into +;;; an abstract syntax tree. In order to compile, we can use the caten function as follows: + +;;; 2. Intermediate Representation +(defparameter graph (caten sincos)) + +;;; This will generate an AVM graph as seen in the REPL output. +;;; The graph generated is an intermediate representation. +(print graph) + +;;; We defined SinCos lower as: +;;; (a (%sin (%add x (%fconst (/ pi 2))))) +;;; (b (%sin a)) +;;; and in the intermediate representation we can map the operations to: +;;; +;;; -> will prepare the space to load the value of 1.0 +;;; -> the tensor value is loaded into the allocated space +;;; allocates space for the defined const / pi 2 +;;; -> loads the constant into a buffer +;;; -> performs the addition (%add x (%fconst (/ pi 2)) +;;; -> applies the sin function to the previous operation (%sin (%add x (%fconst (/ pi 2)))) +;;; -> applies sin again, now to a (b (%sin a) +;;; -> synchronizes the graph, let's call it a sink node, the output node b + +;;; This is it as far as the Intermediate Representation goes. + +;;; 3. Code Generation + +;;; The goal of run-type-infer is to propagate the type and shape information through the graph +;;; Executes vm/forward over the graph to propagate the information forward. +;;; It simulates running the graph forward and backward to infer the types of the nodes. +(print (run-type-infer graph)) + +;;; The goal is to transform and rename parts of the graph to improve readability, consistency or prepare it for code generation and optimizations. +;;; It takes the raw AVM graph, rewrites certain references, applies stable naming conventions and returns a more uniform and readable graph representation. +(print (apply-rewriting-rules graph)) + +;;; The resulting graph after run-type-infer and apply-rewriting-rules is the following: +;;; :_read_views=(NIL NIL) :_output_type=NIL> +;;; :_read_views=(NIL)> +;;; > +;;; :_read_views=(NIL)> +;;; :_read_views=(NIL NIL)> +;;; :_read_views=(NIL)> +;;; :_read_views=(NIL)> +;;; :_read_views=(NIL)> + +;;; Compared to the initial compiled graph, variables are renamed to val_X. It makes the graph more human +;;; readable and easier to debug. It also ensures type inference information (_type_relay, INFERRED-TYPE) +;;; is attached to the nodes of the graph and clear. +;;; It simplifies the IR for debugging and inspection. To make it easier in the code generation step. + +;;; graph-schedule generates a fast graph from the avm-graph, which is a wrapper around the graph we had earlier. +;;; 1. First of all, we wrap the graph around an AVM graph. +;;; 2. Then the graph-schedule function is called over it. +;;; graph-schedule does: +;;; 1. Creates a schedule context and calls graph-breadth-first-schedule +;;; 1.1 It first traverses the graph in a BFS (breadth-first) fashion +;;; 1.2 Groups nodes together into larger "fused" kernels (optimizations) +;;; 1.3 Distributes certain operations like load that makes resulting kernels simpler and more uniform. +;;; 2. Then we try to merge groups of nodes into bigger, more optimal kernels. +;;; 2.1 If two parts of a graph share operations, and they can be combined without violating constraints, +;;; they are merged +;;; 2.2 Extra rewriting is applied so data shapes, broadcasting and reduction dimensions align properly. +;;; 3. Finally, the fast graph is generated. A fast graph is a more optimized data structure for the final scheduler graph, +;;; it assumes a Directed Acyclic Graph structure and uses hash-table-based lookup for nodes to speed up further operations. +;;; 3.1 The final FastGraph is a cleaner, more optimized representation of the computation. +;;; 3.2 It contains fewer, more meaningful nodes. For instance, SIN operations are combined into a single kernel. +;;; It's also easier and more efficient to traverse and manipulate, which makes it suitable for code generation. + +(defparameter scheduled-graph (graph-schedule (avm-graph graph))) + +(print scheduled-graph) +;;; FastGraph[seen=NIL, outputs=(val_7)] { +;;; { Allocate } : [ val_0 <- (1) where lowered-p=nil ] +;;; { Allocate } : [ val_2 <- NIL where lowered-p=nil ] +;;; { KERNEL } : [ val_6 <- val_2, val_0 where lowered-p=nil :name=FUSED_SIN_SIN_ADD_LOAD_LOAD1669] +;;; { VMOP } : [ val_7 <- val_6 where lowered-p=nil :name=FUSED_BACKWARD1665] +;;; } + +;;; In the FastGraph, the operations we had previously such as add, sin, have been replaced by a fused kernel +;;; We had a sequence LOAD -> ADD -> SIN -> SIN that is converted into { KERNEL } : [ val_6 <- val_2, val_0 ... :name=FUSED_SIN_SIN_ADD_LOAD_LOAD1669] +;;; The identifiers we had in the graph were converted to val_X: TID1421 (original) -> val_0 (rewritten) + +;;; Now we can extract each graph node from the FastGraph, this is where it gets really interesting, and where the actual code generation happens + +;;; First we extract the nodes from the FastGraph we had previously created +;;; graph-nodes is a method from FastGraph, it returns a list of unique nodes from FastGraph. +(defparameter list-of-graph-nodes (graph-nodes scheduled-graph)) + +(print list-of-graph-nodes) +;;; Processing (PRINT LIST-OF-GRAPH-NODES) +;;; ({ Allocate } : [ val_0 <- (1) where lowered-p=nil ] +;;; { KERNEL } : [ val_6 <- val_2, val_0 where lowered-p=nil :name=FUSED_SIN_SIN_ADD_LOAD_LOAD964] +;;; { Allocate } : [ val_2 <- NIL where lowered-p=nil ] +;;; { VMOP } : [ val_7 <- val_6 where lowered-p=nil :name=FUSED_BACKWARD960]) + +;;; Here we test whether an item of the FastGraph is jitable or not. Jitable items are the ones lowered. +(dolist (item list-of-graph-nodes) + (print item) + (format t "~A~%" (getattr item :jitable))) + +;;; Jitable items (which at this point we can call them schedule-items) are items in the computation graph that can be just-in-time compiled. + +;;; JIT compilation is the process of translating intermediate representations into lower-level optimized machine code (in this case blueprint code and later C kernels). + +;;; A schedule-item that is jitable meets the criteria for being compiled into a kernel or blueprint suitable code. + +;;; In this case, the only schedule-item that has jitable set to true is the fused kernel! +;;; { KERNEL } : [ val_6 <- val_2, val_0 where lowered-p=nil :name=FUSED_SIN_SIN_ADD_LOAD_LOAD964] T + +;;; Now, we can take the schedule-item and lower it + +;;; Retrieve the second node in the list of graph nodes +(defparameter item1 (nth 1 list-of-graph-nodes)) + +;;; Print the node to see what it is before lowering. +;;; At this stage, `item1` is a scheduled graph node—likely a kernel node +;;; that can be lowered into a blueprint. +(print item1) + +;;; What does lower-schedule-item do? +;;; --------------------------------- +;;; `lower-schedule-item` takes a Schedule-Item node and: +;;; 1. Checks if it is jitable (i.e., suitable for JIT compilation). +;;; 2. If yes, it performs a process called 'lowering': +;;; - Extracts the computation graph within this schedule-item. +;;; - Determines iteration spaces and common loop structures for the operations inside it. +;;; - Converts the fused operations into a low-level blueprint representation that shows all loops, +;;; array indices, arithmetic operations, and potential reductions. +;;; - This blueprint is then suitable for final code generation (e.g., translating into C code). +;;; In other words, `lower-schedule-item` turns a high-level fused kernel node into a fully described, +;;; low-level intermediate form (the blueprint) that can be compiled into efficient machine code or C code. + +;;; Here we lower `item1` using the `lower-schedule-item` function. The `base-graph` is given by `(avm-graph graph)`, +;;; and `scheduled-graph` is the optimized graph after scheduling. This will mutate `item1` so that it +;;; now has a `:blueprint` attribute containing the lowered form. +(defparameter lowered-item1 (lower-schedule-item item1 (avm-graph graph) scheduled-graph)) + +;;; After this, `lowered-item1` (which should be the same as `item1` but now lowered) +;;; will hold the blueprint. Printing it might show a node with `:expr` indicating +;;; a fully lowered representation of the computation (e.g., `sin(sin(x + pi/2))`). +(print lowered-item1) +;;; Processing (PRINT LOWERED-ITEM1) +;;; ( :_type_relay=#>) + +;;; The :expr attribute describes the blueprint code represented as a low-level computation + +;;; print-blueprint allows us to see the blueprinted code before the code-gen step +(print (print-blueprint (getattr item1 :blueprint) nil)) +;;; Blueprint code generated by print-blueprint +;;; { +;;; val_6[0] = sin(sin((1.0+1.5707964))); +;;; } + +;;; Now after all these intermediate steps, it's time to generate the actual kernel! +;;; The function below makes sure parameters and other necessary variables are properly initialized and declared. +(schedule-item-write-define-global item1) + +;;; Create a renderer instance +(defparameter renderer (make-instance 'CStyle-Renderer)) + +(print renderer) + +;;; render-kernel is responsible for taking the previously generated C code for all jitable items, compiling it into a shared library or native code, and then making the resulting functions callable from Lisp. +(let ((c-kernel (%render-kernel renderer item1))) + (format t "Generated C code:~%~a~%" c-kernel) + (setf (getattr item1 :rendered-object) c-kernel)) + +;;; We can visualize the generated kernel for our high-level function we defined previously, called sincos! +;;; Generated C code: +;;; void fused_sin_sin_add_load_load964(float* val_6); +;;; void fused_sin_sin_add_load_load964(float* val_6) { +;;; val_6[0] = sin(sin((1.0+1.5707964))); +;;; } + +;;; %compile-kernel transforms the high-level Lisp IR (intermediate representation) into optimized native code, loads that code, and provides a way for Lisp to call it directly—completing the JIT compilation pipeline + +;;; We make a copy of the original graph without modifications +(defparameter original-graph (copy-graph (avm-graph graph))) + +;;; Replace the compiled kernels in the graph +(setf (avm-graph graph) (schedule-graph->avm-graph (avm-graph graph) scheduled-graph)) + +;;; The original graph contains the high-level operations still. +(print original-graph) +;;; Notice how the original graph has the high-level nodes still! +;;; Graph[seen=NIL, outputs=NIL] { +;;; :_read_views=(NIL NIL) :_output_type=NIL> +;;; :_read_views=(NIL) :declare-type=(T)> +;;; > +;;; :_read_views=(NIL) :declare-type=(T)> +;;; :_read_views=(NIL NIL)> +;;; :_read_views=(NIL) :declare-type=(T)> +;;; :_read_views=(NIL)> +;;; :_read_views=(NIL)> +;;; } + +;;; The overwritten graph has the JIT kernel we generated previously. +;;; The schedule-graph->avm-graph function integrates the compiled kernels. + +(print graph) +;;; Processing (PRINT GRAPH) +;;; #S(AVM +;;; :GRAPH +;;; Graph[seen=NIL, outputs=NIL] { +;;; +;;; +;;; :dtypes=(FLOAT32)> +;;; +;;; +;;; } +;;; :NAME :MAIN476 +;;; :FW-OUTPUTS (|val_7|) +;;; :BW-OUTPUTS NIL +;;; :ID2TENSOR # +;;; :TAPE-LENGTH 8 +;;; :PC 8 +;;; :VARIABLES # +;;; :DUMPED NIL) + +(avm-reset graph) ;; Ensure the graph is reset + +;;; Finally, we can run the lower-level graph with the replaced nodes! + +;;; Run the computation using %run +(print (%run graph (cons 'val_0 1.0))) +;;; Expected Output: +;;; The result of sin(sin(1.0 + π/2)) is printed, computed using the optimized, compiled kernel. + +;;; 4. Execution Explanation + +;;; The `%run` method orchestrates the execution of the compiled kernel within the AVM context. Here's how it works: + +;;; (defmethod %run ((avm caten/avm:AVM) &rest params) +;;; (let ((*device* (ctx:getenv :AVM)) +;;; (params (loop for (key . val) in params collect (cons key (if (tensor-p val) (tensor-buffer val) val))))) +;;; (vm/set-params avm params) +;;; (vm/forward avm) +;;; (avm/sync-tensors avm) +;;; (flet ((ap (x) (gethash x (avm-variables avm)))) +;;; (apply #'values (map 'list #'ap (avm-fw-outputs avm)))))) +;;; +;;; 5. Final Summary + +;;; In this end-to-end tutorial, we've explored the complete pipeline of defining, compiling, and executing a computation graph using Caten's AVM. Here's what we've covered: + +;;; 1. **High-Level Definition:** +;;; - Defined a `SinCos` function that computes `sin(cos(x))` using a high-level class structure similar to modules in deep learning frameworks. + +;;; 2. **Graph Construction:** +;;; - Created a tensor and instantiated the `SinCos` function, resulting in a lazy tensor representation. +;;; - Generated an intermediate representation (IR) graph using the `caten` function, which details each computation step. + +;;; 3. **Type Inference and Rewriting:** +;;; - Ran type inference to propagate type and shape information through the graph. +;;; - Applied rewriting rules to rename variables and prepare the graph for optimization and code generation. + +;;; 4. **Graph Scheduling and Optimization:** +;;; - Scheduled the graph to identify and fuse operations into optimized kernels. +;;; - Generated a FastGraph that consolidates operations like `ADD` and `SIN` into a single `JIT_KERNEL` node for efficiency. + +;;; 5. **Kernel Lowering and Compilation:** +;;; - Lowered the scheduled kernel into a blueprint, a low-level representation suitable for code generation. +;;; - Rendered the blueprint into C code and compiled it into a native kernel using `%render-kernel` and `%compile-kernel`. +;;; - Integrated the compiled kernel back into the AVM graph using `schedule-graph->avm-graph`, replacing high-level nodes with the optimized kernel. + +;;; 6. **Execution with AVM:** +;;; - Reset the AVM to ensure a clean execution state. +;;; - Executed the computation graph using the `%run` method, which set input parameters, ran the forward pass, synchronized tensors, and retrieved the results. +;;; - Leveraged the compiled kernel for efficient computation, resulting in the final output of `sin(sin(1.0 + π/2))`. + diff --git a/favicon.png b/favicon.png new file mode 100644 index 000000000..9b3180e9c Binary files /dev/null and b/favicon.png differ diff --git a/favicon.svg b/favicon.svg new file mode 100644 index 000000000..9927b1a8d --- /dev/null +++ b/favicon.svg @@ -0,0 +1,44 @@ + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/getting-started.lisp b/getting-started.lisp new file mode 100644 index 000000000..f977449dc --- /dev/null +++ b/getting-started.lisp @@ -0,0 +1,271 @@ +;;;; Note: This file is not intended to be loaded directly from Lisp, +;;;; but to be executed expression by expression. +;;;; If you are using Emacs/Lem, you can simply press `C-C C-c` while your cursor is hovering over an expression +(ql:quickload :caten) + +(defpackage :getting-started + (:use :cl :caten/air :caten/aasm :caten/apis :caten/avm)) + +(in-package :getting-started) +;;; [Introduction] +;;; Welcome to Caten. +;;; Caten is a tensor library written in Common Lisp. +;;; Our ultimate goal is to generate a fast deep learning model inference runtime across a wide range of devices with the minimal cost. +;;; +;;; We need contributors. This document was created to help new contributors to quickly understand the design of Caten. +;;; To learn how Caten works, you first need to understand the following three main systems: + +;;; - 1. caten/apis | High-Level Graph Interface +;;; - 2. caten/air | Low-Level Graph Interface +;;; - 3. caten/codegen | AIR Graph => Kernel Generator +;;; **All other systems are built on top of these packages.** +(defun present (&rest tensors) + "Present compiles and executes the given tensor, then prints the result." + (format t "~{~& =>~% ~A~}" (multiple-value-list (apply #'proceed tensors)))) +;;; ~~~[1. High Level Interface (caten/apis)]~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +;;; The main role of `caten/apis` is to provide matrix operation APIs **with the same interface as Numpy/PyTorch**. +;;; Like Petalisp/tinygrad, Caten uses lazy evaluation. + +;; For example, creating a 3x3 matrix initialized with 1.0 using make-tensor doesn't trigger any computation, that's what we call lazy evaluation. +(defparameter *tensor1* (make-tensor `(3 3) :initial-element 1.0)) + +(print *tensor1*) ;; Getting nothing! The tensor isn't evaluated. This is called lazy evaluation. + +;; If you want to see the result, you have to compile the kernel using the function `proceed`. +(print (proceed *tensor1*)) + + +;; Let's define another tensor for the next experiments + +(defparameter *tensor2* (make-tensor `(3 3) :initial-element 1.0)) + +;; To execute a previously compiled graph without recompiling, create an `AVM` using the `caten` function, then execute it with forward. +;; The graph created consists of a matrix multiplication, passing two tensors +;; The caten function creates the low-level graph, that is a compiled (low level) version of the matmul as an AST +(print (caten (!matmul *tensor1* *tensor2*))) +;; Proceed computes the lower level AST +(print (proceed (!matmul *tensor1* *tensor2* ))) + +;; Of course, Caten is designed so that all graphs can be compiled with dynamic shapes. There's no need to recompile every time the batch_size changes. + +;;; The goal of `caten/apis` is to prevent bugs by wrapping the low-level interface commands (described later) in a high-level API. +;;; You can use various optimizations by lowering the AST in `caten/apis` into `caten/air`! +;;; => In the next section, we will learn about `caten/air`. + +;;; ~~~[Low Level Interface (caten/air)]~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +;;; In general, a deep learning compiler is a program that lowers transformations and operations on a DAG (Directed Acyclic Graph). +;;; Caten implements a general-purpose DAG processing library called `caten/air`. +;;; Any data structure with a DAG structure in Caten's source code should be defined as a `caten/air:Graph`. + +(print (make-node :BinaryOps :ADD (list 'a) (list 'b 'c) :reduction t)) +(print (make-graph (make-node :BinaryOps :ADD (list 'a) (list 'b 'c)))) + +;;; Any functions starting with `%` represents low level operations for the air node creation. +;;; By wrapping such graph constructions with the with-context macro, Caten automatically organizes them into an executable graph. +;;; - caten/avm:%realize to run the graph in AVM +;;; - caten/aasm:->dot to open the graph in your browser + + +;; Example: +;; The graph defines a low level graph that performs addition, two constants are defined and added together +;; .dot requires graphdotviz! +(let ((graph + (with-context + (x (%fconst 1.0)) + (y (%fconst 2.0)) + (out (%add x y))))) + (print graph) + (print (%realize graph)) + ;;(->dot graph) + ) + +;;; - Nodes can be defined using defnode. +;;; - Pattern Matcher can be defined using `defsimplifier` +;;; - `caten/aasm` provides a set of instruction used in AVM +;;; ~~~[A Bridge between AIR and APIs]~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +;;; In this section, we will explain the mechanism of lowering the high-level interface(APIs) to the low-level interface(AIR Graph). + +;;; To lower `caten/apis` to a Graph, implement the lower method in the computation graph `Func` of `Caten/apis` and describe the lowered computation graph there. +;;; Let’s try defining a `Func` to compute `Sin(Cos(x))` as an example. +(defclass SinCos (Func) nil + (:documentation "The func SinCos computes sin(cos(x))")) + +;; Forward creates a lazy tensor for the next computation. +;; You can skip this process by using the `st` macro. +(defmethod forward ((op SinCos) &rest tensors) (st "A[~] -> A[~]" (tensors))) +;; Backward is optional (skipped this time) +(defmethod backward ((op SinCos) &optional prev-grad) (declare (ignore prev-grad)) nil) +;; Lower describes the lowered expression of `SinCos` +(defmethod lower ((op SinCos) &rest inputs) + (let ((x (car inputs))) + (with-context + (a (%sin (%add x (%fconst (/ pi 2))))) + (b (%sin a))))) + +;; You can create the next lazy tensor using forward method. +;; Let's define a function doing this as a utility named !sincos. +(defun !sincos (tensor) + (forward (make-instance 'SinCos) tensor)) + +;; Calling !SinCos (Make sure that :op = SINCOS) +(print (!sincos (make-tensor `(3 3)))) +;; Running SinCos +(present + (!sincos (make-tensor `(3 3) :initial-element 1.0))) ;; = 0.51439524 +;;; ~~~~[caten/codegen]~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +(defpackage :codegen-example + (:use :cl :caten/air :caten/aasm :caten/apis :caten/avm :caten/codegen/expr-cache) + ;; Import some low-level apis + (:import-from + :caten/codegen/scheduler + #:graph-schedule) + (:import-from + :caten/codegen/rewriting-rules + #:apply-rewriting-rules + #:schedule-item-write-define-global) + (:import-from + :caten/codegen/shape-inference + #:run-type-infer) + (:import-from + :caten/codegen/blueprint + #:lower-schedule-item + #:print-blueprint) + (:import-from + :caten/codegen/renderer + #:CStyle-Renderer + #:%render-kernel + #:%compile-kernel) + (:import-from + :caten/codegen/jit + #:schedule-graph->avm-graph)) +(in-package :codegen-example) + +;; Defining Some utils ... +(defun run-shape-inference (avm) + (run-type-infer avm) + (apply-rewriting-rules avm)) + +(defparameter *width* 120) + +(defun saying (number title object) + (dotimes (i *width*) (princ "=")) + (fresh-line) + (format t "~a. ~a~%~a~%" number title object) + object) + +(defun try-codegen! (graph outputs) + "Util for visualzing the process of compiling the `graph`." + (optimize-aasm graph) + (let ((vm (make-avm graph :main nil outputs nil))) + (fresh-line) + (saying 1 "Compiling the following initial computation graph:" graph) + (saying 2 "Created AVM (Abstract VM) with the computation graph:" vm) + (run-shape-inference vm) + (let ((schedule-graph (graph-schedule (avm-graph vm))) + (*expr-cache* (make-expr-cache)) + (renderer (make-instance 'CStyle-Renderer))) + (saying 3 "Generated schedule-graph with the computation graph" schedule-graph) + (dolist (item (graph-nodes schedule-graph)) + ;; If schedule-item was labelled as jitable, you can lower this + (when (getattr item :jitable) + (lower-schedule-item item (avm-graph vm) schedule-graph) + (saying 4 "Lowered schedule item to a blueprint suitable for code generation:" (print-blueprint (getattr item :blueprint) nil)) + (schedule-item-write-define-global item) + (let ((c-kernel (%render-kernel renderer item))) + (saying 5 "Generated C code from the blueprint:" c-kernel) + (setf (getattr item :rendered-object) c-kernel)))) + ;; Invoking gcc ... + (%compile-kernel renderer (graph-nodes schedule-graph) nil) + ;; Overwrite the base graph with compiled graph + (setf (avm-graph vm) (schedule-graph->avm-graph (avm-graph vm) schedule-graph)) + (avm-reset vm)))) + +;; Example 1. Lowering two matries addition into the C kernel. +(let* ((graph + ;; This is a graph to compile: Z(3 3) = X(3 3) + Y(3 3) + (with-context + (x (%make-tensor `(3 3) :dtype :float32 :from 'x)) + (y (%make-tensor `(3 3) :dtype :float32 :from 'y)) + (c (%add x y :id 'z)))) + ;; Simplifying the input graph + (_ (optimize-aasm graph)) + ;; Wrap the graph as an instance of AVM to manage allocations + (vm (make-avm graph :axpy-demo nil (list 'z) nil))) + (declare (ignore _)) + (fresh-line) + (saying 1 "Compiling the following initial + computation graph:" graph) + (saying 2 "Created AVM (Abstract VM) with the computation graph:" vm) + ;; caten/codegen requires the shape of all computation nodes to be known! + (run-shape-inference vm) + ;; Ready for running the scheduler. `graph-schedule` to partition the input graph. + ;; Pass the wrapped avm graph into the `graph-schedule` which divides the graph into smaller units called `schedule-items` + (let ((schedule-graph (graph-schedule (avm-graph vm))) + (*expr-cache* (make-expr-cache)) + (renderer (make-instance 'CStyle-Renderer))) + (saying 3 "Generated schedule-graph with the computation graph" schedule-graph) + ;; After the `schedule-items` are created, the items are lowered into blueprint suitable code for code-generation + (dolist (item (graph-nodes schedule-graph)) + ;; If schedule-item was labelled as jitable, you can lower this + (when (getattr item :jitable) + ;;Each item from the AST is lowered using the lower-schedule-item function + (lower-schedule-item item (avm-graph vm) schedule-graph) + (saying 4 "Lowered schedule item to a blueprint suitable for code generation:" (print-blueprint (getattr item :blueprint) nil)) + (schedule-item-write-define-global item) + ;; With the blueprint code, a C kernel is generated + (let ((c-kernel (%render-kernel renderer item))) + (saying 5 "Generated C code from the blueprint:" c-kernel) + (setf (getattr item :rendered-object) c-kernel)))) + ;; Invoking gcc ... + (%compile-kernel renderer (graph-nodes schedule-graph) nil) + ;; Overwrite the base graph with compiled graph + (setf (avm-graph vm) (schedule-graph->avm-graph (avm-graph vm) schedule-graph)) + (avm-reset vm) + ;; Try axpy! + ;; Finally, the C code is executed + (saying + 6 "Running the computation X(3x3) + Y(3x3), the result is:" + (%run vm (cons 'x (linspace `(3 3) 1 0)) (cons 'y (linspace `(3 3) 1 0)))))) +;; Example 2. Lowering two squared matrix multiplying into the C kernel. +(defun %make-squared-gemm (N out) + "a @ b.T" + (optimize-aasm + (with-context + (a (%make-tensor `(,n ,n))) + (b (%make-tensor `(,n ,n))) + (a1 (%view a `(,n ,n ,n) `(0 0 0) `(,n ,n ,n) `(1 1 1) `(nil t nil) (%stride `(,n 1 ,n) :row))) + (b1 (%view b `(,n ,n ,n) `(0 0 0) `(,n ,n ,n) `(1 1 1) `(t nil nil) (%stride `(1 ,n ,n) :row))) + (o (%mul a1 b1)) + (c (%load (%make-tensor `(,n ,n 1)) 0.0)) + (c (%view c `(,n ,n 1) `(0 0 0) `(,n ,n ,n) `(1 1 1) `(nil nil t) (%stride `(,n ,n 1) :row))) + (c (%add c o :reduction t)) + (c (%reshape c `(,n ,n) :id out))))) +;; Try matmul +(try-codegen! + (%make-squared-gemm 128 'out) + (list 'out)) + +#+(or nil) +(try-codegen! + (make-graph + ;; your code follows ... + ) + (list ... )) +;; ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +;;; Caten provides APIs that use the aforementioned JIT by default. +;;; - Set JIT=1 to enable it. +;;; - By setting JIT_DEBUG>=2, you can access a debugger similar to the previous one. +(defun present-beautiful (tensor &key (auto-scheduler 0)) + (ctx:with-contextvar (:JIT 1 :JIT_DEBUG 3 :AUTO_SCHEDULER auto-scheduler) + (with-no-grad (caten tensor)))) + +;; auto-scheduler is WIP as of this writing! +(present-beautiful + (!matmul (make-tensor `(128 512)) (make-tensor `(512 1024))) + :auto-scheduler 1) + +(present-beautiful + (!matmul (make-tensor `(a b)) (make-tensor `(b c)))) + +(present-beautiful + (forward (caten/nn:Embedding 10 20) (make-tensor `(b 20)))) diff --git a/index.html b/index.html new file mode 100644 index 000000000..adb215065 --- /dev/null +++ b/index.html @@ -0,0 +1,474 @@ + + + + + + + + + + + + + + + + + + + + + + Home | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
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e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=Gt(e,{elementContext:"reference"}),a=Gt(e,{altBoundary:!0}),l=te(r,n),c=te(a,s,o),h=ee(l),d=ee(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},ne={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=lt.reduce((function(t,i){return t[i]=function(t,e,i){var n=gt(t),s=[ot,it].indexOf(n)>=0?-1:1,o="function"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return r=r||0,a=(a||0)*s,[ot,st].indexOf(n)>=0?{x:a,y:r}:{x:r,y:a}}(i,e.rects,o),t}),{}),a=r[e.placement],l=a.x,c=a.y;null!=e.modifiersData.popperOffsets&&(e.modifiersData.popperOffsets.x+=l,e.modifiersData.popperOffsets.y+=c),e.modifiersData[n]=r}},se={name:"popperOffsets",enabled:!0,phase:"read",fn:function(t){var e=t.state,i=t.name;e.modifiersData[i]=Qt({reference:e.rects.reference,element:e.rects.popper,strategy:"absolute",placement:e.placement})},data:{}},oe={name:"preventOverflow",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0!==r&&r,l=i.boundary,c=i.rootBoundary,h=i.altBoundary,d=i.padding,u=i.tether,f=void 0===u||u,p=i.tetherOffset,m=void 0===p?0:p,g=Gt(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=gt(e.placement),b=Yt(e.placement),v=!b,y=Ct(_),w="x"===y?"y":"x",E=e.modifiersData.popperOffsets,A=e.rects.reference,T=e.rects.popper,O="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,C={x:0,y:0};if(E){if(o||a){var k="y"===y?it:ot,L="y"===y?nt:st,x="y"===y?"height":"width",D=E[y],S=E[y]+g[k],I=E[y]-g[L],N=f?-T[x]/2:0,j="start"===b?A[x]:T[x],M="start"===b?-T[x]:-A[x],P=e.elements.arrow,H=f&&P?bt(P):{width:0,height:0},R=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},B=R[k],W=R[L],q=Dt(0,A[x],H[x]),z=v?A[x]/2-N-q-B-O:j-q-B-O,$=v?-A[x]/2+N+q+W+O:M+q+W+O,U=e.elements.arrow&&Ot(e.elements.arrow),F=U?"y"===y?U.clientTop||0:U.clientLeft||0:0,V=e.modifiersData.offset?e.modifiersData.offset[e.placement][y]:0,K=E[y]+z-V-F,X=E[y]+$-V;if(o){var Y=Dt(f?Lt(S,K):S,D,f?kt(I,X):I);E[y]=Y,C[y]=Y-D}if(a){var Q="x"===y?it:ot,G="x"===y?nt:st,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=Dt(f?Lt(J,K):J,Z,f?kt(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function re(t,e,i){void 0===i&&(i=!1);var n,s,o=Et(e),r=_t(t),a=ft(e),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(a||!a&&!i)&&(("body"!==ht(e)||Ft(o))&&(l=(n=e)!==dt(n)&&ft(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:$t(n)),ft(e)?((c=_t(e)).x+=e.clientLeft,c.y+=e.clientTop):o&&(c.x=Ut(o))),{x:r.left+l.scrollLeft-c.x,y:r.top+l.scrollTop-c.y,width:r.width,height:r.height}}var ae={placement:"bottom",modifiers:[],strategy:"absolute"};function le(){for(var t=arguments.length,e=new Array(t),i=0;i"applyStyles"===t.name&&!1===t.enabled);this._popper=ue(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}"ontouchstart"in document.documentElement&&!t.closest(".navbar-nav")&&[].concat(...document.body.children).forEach(t=>P.on(t,"mouseover",u)),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.toggle("show"),this._element.classList.toggle("show"),P.trigger(this._element,"shown.bs.dropdown",e)}}hide(){if(h(this._element)||!this._menu.classList.contains("show"))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_addEventListeners(){P.on(this._element,"click.bs.dropdown",t=>{t.preventDefault(),this.toggle()})}_completeHide(t){P.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach(t=>P.off(t,"mouseover",u)),this._popper&&this._popper.destroy(),this._menu.classList.remove("show"),this._element.classList.remove("show"),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),P.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},l("dropdown",t,this.constructor.DefaultType),"object"==typeof t.reference&&!r(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError("dropdown".toUpperCase()+': Option "reference" provided type "object" without a required "getBoundingClientRect" method.');return t}_getMenuElement(){return t.next(this._element,".dropdown-menu")[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ve;if(t.classList.contains("dropstart"))return ye;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ge:me:e?be:_e}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:e,target:i}){const n=t.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(c);n.length&&y(n,i,"ArrowDown"===e,!n.includes(i)).focus()}static dropdownInterface(t,e){const i=Ae.getOrCreateInstance(t,e);if("string"==typeof e){if(void 0===i[e])throw new TypeError(`No method named "${e}"`);i[e]()}}static jQueryInterface(t){return this.each((function(){Ae.dropdownInterface(this,t)}))}static clearMenus(e){if(e&&(2===e.button||"keyup"===e.type&&"Tab"!==e.key))return;const i=t.find('[data-bs-toggle="dropdown"]');for(let t=0,n=i.length;tthis.matches('[data-bs-toggle="dropdown"]')?this:t.prev(this,'[data-bs-toggle="dropdown"]')[0];return"Escape"===e.key?(n().focus(),void Ae.clearMenus()):"ArrowUp"===e.key||"ArrowDown"===e.key?(i||n().click(),void Ae.getInstance(n())._selectMenuItem(e)):void(i&&"Space"!==e.key||Ae.clearMenus())}}P.on(document,"keydown.bs.dropdown.data-api",'[data-bs-toggle="dropdown"]',Ae.dataApiKeydownHandler),P.on(document,"keydown.bs.dropdown.data-api",".dropdown-menu",Ae.dataApiKeydownHandler),P.on(document,"click.bs.dropdown.data-api",Ae.clearMenus),P.on(document,"keyup.bs.dropdown.data-api",Ae.clearMenus),P.on(document,"click.bs.dropdown.data-api",'[data-bs-toggle="dropdown"]',(function(t){t.preventDefault(),Ae.dropdownInterface(this)})),_(Ae);class Te{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",e=>e+t),this._setElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","paddingRight",e=>e+t),this._setElementAttributes(".sticky-top","marginRight",e=>e-t)}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=i(Number.parseFloat(s))+"px"})}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","paddingRight"),this._resetElementAttributes(".sticky-top","marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)})}_applyManipulationCallback(e,i){r(e)?i(e):t.find(e,this._element).forEach(i)}isOverflowing(){return this.getWidth()>0}}const Oe={isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},Ce={isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"};class ke{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&f(this._getElement()),this._getElement().classList.add("show"),this._emulateAnimation(()=>{b(t)})):b(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove("show"),this._emulateAnimation(()=>{this.dispose(),b(t)})):b(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className="modal-backdrop",this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...Oe,..."object"==typeof t?t:{}}).rootElement=a(t.rootElement),l("backdrop",t,Ce),t}_append(){this._isAppended||(this._config.rootElement.appendChild(this._getElement()),P.on(this._getElement(),"mousedown.bs.backdrop",()=>{b(this._config.clickCallback)}),this._isAppended=!0)}dispose(){this._isAppended&&(P.off(this._element,"mousedown.bs.backdrop"),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){v(t,this._getElement(),this._config.isAnimated)}}const Le={backdrop:!0,keyboard:!0,focus:!0},xe={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"};class De extends B{constructor(e,i){super(e),this._config=this._getConfig(i),this._dialog=t.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new Te}static get Default(){return Le}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||P.trigger(this._element,"show.bs.modal",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add("modal-open"),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),P.on(this._element,"click.dismiss.bs.modal",'[data-bs-dismiss="modal"]',t=>this.hide(t)),P.on(this._dialog,"mousedown.dismiss.bs.modal",()=>{P.one(this._element,"mouseup.dismiss.bs.modal",t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)})}),this._showBackdrop(()=>this._showElement(t)))}hide(t){if(t&&["A","AREA"].includes(t.target.tagName)&&t.preventDefault(),!this._isShown||this._isTransitioning)return;if(P.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const e=this._isAnimated();e&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),P.off(document,"focusin.bs.modal"),this._element.classList.remove("show"),P.off(this._element,"click.dismiss.bs.modal"),P.off(this._dialog,"mousedown.dismiss.bs.modal"),this._queueCallback(()=>this._hideModal(),this._element,e)}dispose(){[window,this._dialog].forEach(t=>P.off(t,".bs.modal")),this._backdrop.dispose(),super.dispose(),P.off(document,"focusin.bs.modal")}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new ke({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_getConfig(t){return t={...Le,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},l("modal",t,xe),t}_showElement(e){const i=this._isAnimated(),n=t.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.appendChild(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,n&&(n.scrollTop=0),i&&f(this._element),this._element.classList.add("show"),this._config.focus&&this._enforceFocus(),this._queueCallback(()=>{this._config.focus&&this._element.focus(),this._isTransitioning=!1,P.trigger(this._element,"shown.bs.modal",{relatedTarget:e})},this._dialog,i)}_enforceFocus(){P.off(document,"focusin.bs.modal"),P.on(document,"focusin.bs.modal",t=>{document===t.target||this._element===t.target||this._element.contains(t.target)||this._element.focus()})}_setEscapeEvent(){this._isShown?P.on(this._element,"keydown.dismiss.bs.modal",t=>{this._config.keyboard&&"Escape"===t.key?(t.preventDefault(),this.hide()):this._config.keyboard||"Escape"!==t.key||this._triggerBackdropTransition()}):P.off(this._element,"keydown.dismiss.bs.modal")}_setResizeEvent(){this._isShown?P.on(window,"resize.bs.modal",()=>this._adjustDialog()):P.off(window,"resize.bs.modal")}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide(()=>{document.body.classList.remove("modal-open"),this._resetAdjustments(),this._scrollBar.reset(),P.trigger(this._element,"hidden.bs.modal")})}_showBackdrop(t){P.on(this._element,"click.dismiss.bs.modal",t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())}),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(P.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains("modal-static")||(n||(i.overflowY="hidden"),t.add("modal-static"),this._queueCallback(()=>{t.remove("modal-static"),n||this._queueCallback(()=>{i.overflowY=""},this._dialog)},this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!g()||i&&!t&&g())&&(this._element.style.paddingLeft=e+"px"),(i&&!t&&!g()||!i&&t&&g())&&(this._element.style.paddingRight=e+"px")}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=De.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}P.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=s(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),P.one(e,"show.bs.modal",t=>{t.defaultPrevented||P.one(e,"hidden.bs.modal",()=>{c(this)&&this.focus()})}),De.getOrCreateInstance(e).toggle(this)})),_(De);const Se={backdrop:!0,keyboard:!0,scroll:!1},Ie={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"};class Ne extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._addEventListeners()}static get NAME(){return"offcanvas"}static get Default(){return Se}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||P.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||((new Te).hide(),this._enforceFocusOnElement(this._element)),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add("show"),this._queueCallback(()=>{P.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})},this._element,!0))}hide(){this._isShown&&(P.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(P.off(document,"focusin.bs.offcanvas"),this._element.blur(),this._isShown=!1,this._element.classList.remove("show"),this._backdrop.hide(),this._queueCallback(()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new Te).reset(),P.trigger(this._element,"hidden.bs.offcanvas")},this._element,!0)))}dispose(){this._backdrop.dispose(),super.dispose(),P.off(document,"focusin.bs.offcanvas")}_getConfig(t){return t={...Se,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},l("offcanvas",t,Ie),t}_initializeBackDrop(){return new ke({isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_enforceFocusOnElement(t){P.off(document,"focusin.bs.offcanvas"),P.on(document,"focusin.bs.offcanvas",e=>{document===e.target||t===e.target||t.contains(e.target)||t.focus()}),t.focus()}_addEventListeners(){P.on(this._element,"click.dismiss.bs.offcanvas",'[data-bs-dismiss="offcanvas"]',()=>this.hide()),P.on(this._element,"keydown.dismiss.bs.offcanvas",t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()})}static jQueryInterface(t){return this.each((function(){const e=Ne.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}P.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(e){const i=s(this);if(["A","AREA"].includes(this.tagName)&&e.preventDefault(),h(this))return;P.one(i,"hidden.bs.offcanvas",()=>{c(this)&&this.focus()});const n=t.findOne(".offcanvas.show");n&&n!==i&&Ne.getInstance(n).hide(),Ne.getOrCreateInstance(i).toggle(this)})),P.on(window,"load.bs.offcanvas.data-api",()=>t.find(".offcanvas.show").forEach(t=>Ne.getOrCreateInstance(t).show())),_(Ne);const je=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Me=/^(?:(?:https?|mailto|ftp|tel|file):|[^#&/:?]*(?:[#/?]|$))/i,Pe=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,He=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!je.has(i)||Boolean(Me.test(t.nodeValue)||Pe.test(t.nodeValue));const n=e.filter(t=>t instanceof RegExp);for(let t=0,e=n.length;t{He(t,a)||i.removeAttribute(t.nodeName)})}return n.body.innerHTML}const Be=new RegExp("(^|\\s)bs-tooltip\\S+","g"),We=new Set(["sanitize","allowList","sanitizeFn"]),qe={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},ze={AUTO:"auto",TOP:"top",RIGHT:g()?"left":"right",BOTTOM:"bottom",LEFT:g()?"right":"left"},$e={animation:!0,template:'',trigger:"hover 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Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=P.trigger(this._element,this.constructor.Event.SHOW),i=d(this._element),n=null===i?this._element.ownerDocument.documentElement.contains(this._element):i.contains(this._element);if(t.defaultPrevented||!n)return;const s=this.getTipElement(),o=e(this.constructor.NAME);s.setAttribute("id",o),this._element.setAttribute("aria-describedby",o),this.setContent(),this._config.animation&&s.classList.add("fade");const r="function"==typeof 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e=this.getTipElement();this.setElementContent(t.findOne(".tooltip-inner",e),this.getTitle()),e.classList.remove("fade","show")}setElementContent(t,e){if(null!==t)return r(e)?(e=a(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.appendChild(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Re(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){let t=this._element.getAttribute("data-bs-original-title");return t||(t="function"==typeof this._config.title?this._config.title.call(this._element):this._config.title),t}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){const i=this.constructor.DATA_KEY;return(e=e||R.get(t.delegateTarget,i))||(e=new this.constructor(t.delegateTarget,this._getDelegateConfig()),R.set(t.delegateTarget,i,e)),e}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add("bs-tooltip-"+this.updateAttachment(t))}_getAttachment(t){return ze[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" 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this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?"focus":"hover"]=!0),e.getTipElement().classList.contains("show")||"show"===e._hoverState?e._hoverState="show":(clearTimeout(e._timeout),e._hoverState="show",e._config.delay&&e._config.delay.show?e._timeout=setTimeout(()=>{"show"===e._hoverState&&e.show()},e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?"focus":"hover"]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState="out",e._config.delay&&e._config.delay.hide?e._timeout=setTimeout(()=>{"out"===e._hoverState&&e.hide()},e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach(t=>{We.has(t)&&delete e[t]}),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:a(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),l("tooltip",t,this.constructor.DefaultType),t.sanitize&&(t.template=Re(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};if(this._config)for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=t.getAttribute("class").match(Be);null!==e&&e.length>0&&e.map(t=>t.trim()).forEach(e=>t.classList.remove(e))}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}static jQueryInterface(t){return this.each((function(){const e=Fe.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}_(Fe);const Ve=new RegExp("(^|\\s)bs-popover\\S+","g"),Ke={...Fe.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:''},Xe={...Fe.DefaultType,content:"(string|element|function)"},Ye={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class Qe extends Fe{static get Default(){return Ke}static get NAME(){return"popover"}static get Event(){return Ye}static get DefaultType(){return Xe}isWithContent(){return this.getTitle()||this._getContent()}getTipElement(){return this.tip||(this.tip=super.getTipElement(),this.getTitle()||t.findOne(".popover-header",this.tip).remove(),this._getContent()||t.findOne(".popover-body",this.tip).remove()),this.tip}setContent(){const e=this.getTipElement();this.setElementContent(t.findOne(".popover-header",e),this.getTitle());let i=this._getContent();"function"==typeof i&&(i=i.call(this._element)),this.setElementContent(t.findOne(".popover-body",e),i),e.classList.remove("fade","show")}_addAttachmentClass(t){this.getTipElement().classList.add("bs-popover-"+this.updateAttachment(t))}_getContent(){return this._element.getAttribute("data-bs-content")||this._config.content}_cleanTipClass(){const t=this.getTipElement(),e=t.getAttribute("class").match(Ve);null!==e&&e.length>0&&e.map(t=>t.trim()).forEach(e=>t.classList.remove(e))}static jQueryInterface(t){return this.each((function(){const e=Qe.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}_(Qe);const Ge={offset:10,method:"auto",target:""},Ze={offset:"number",method:"string",target:"(string|element)"};class Je extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._selector=`${this._config.target} .nav-link, ${this._config.target} .list-group-item, ${this._config.target} .dropdown-item`,this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,P.on(this._scrollElement,"scroll.bs.scrollspy",()=>this._process()),this.refresh(),this._process()}static get Default(){return Ge}static get NAME(){return"scrollspy"}refresh(){const e=this._scrollElement===this._scrollElement.window?"offset":"position",i="auto"===this._config.method?e:this._config.method,s="position"===i?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),t.find(this._selector).map(e=>{const o=n(e),r=o?t.findOne(o):null;if(r){const t=r.getBoundingClientRect();if(t.width||t.height)return[U[i](r).top+s,o]}return null}).filter(t=>t).sort((t,e)=>t[0]-e[0]).forEach(t=>{this._offsets.push(t[0]),this._targets.push(t[1])})}dispose(){P.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){if("string"!=typeof(t={...Ge,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target&&r(t.target)){let{id:i}=t.target;i||(i=e("scrollspy"),t.target.id=i),t.target="#"+i}return l("scrollspy",t,Ze),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return 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i="UL"===n.nodeName||"OL"===n.nodeName?":scope > li > .active":".active";e=t.find(i,n),e=e[e.length-1]}const o=e?P.trigger(e,"hide.bs.tab",{relatedTarget:this._element}):null;if(P.trigger(this._element,"show.bs.tab",{relatedTarget:e}).defaultPrevented||null!==o&&o.defaultPrevented)return;this._activate(this._element,n);const r=()=>{P.trigger(e,"hidden.bs.tab",{relatedTarget:this._element}),P.trigger(this._element,"shown.bs.tab",{relatedTarget:e})};i?this._activate(i,i.parentNode,r):r()}_activate(e,i,n){const s=(!i||"UL"!==i.nodeName&&"OL"!==i.nodeName?t.children(i,".active"):t.find(":scope > li > .active",i))[0],o=n&&s&&s.classList.contains("fade"),r=()=>this._transitionComplete(e,s,n);s&&o?(s.classList.remove("show"),this._queueCallback(r,e,!0)):r()}_transitionComplete(e,i,n){if(i){i.classList.remove("active");const e=t.findOne(":scope > .dropdown-menu 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ei={animation:"boolean",autohide:"boolean",delay:"number"},ii={animation:!0,autohide:!0,delay:5e3};class ni extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return ei}static get Default(){return ii}static get NAME(){return"toast"}show(){P.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove("hide"),f(this._element),this._element.classList.add("showing"),this._queueCallback(()=>{this._element.classList.remove("showing"),this._element.classList.add("show"),P.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()},this._element,this._config.animation))}hide(){this._element.classList.contains("show")&&(P.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.remove("show"),this._queueCallback(()=>{this._element.classList.add("hide"),P.trigger(this._element,"hidden.bs.toast")},this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains("show")&&this._element.classList.remove("show"),super.dispose()}_getConfig(t){return t={...ii,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},l("toast",t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout(()=>{this.hide()},this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){P.on(this._element,"click.dismiss.bs.toast",'[data-bs-dismiss="toast"]',()=>this.hide()),P.on(this._element,"mouseover.bs.toast",t=>this._onInteraction(t,!0)),P.on(this._element,"mouseout.bs.toast",t=>this._onInteraction(t,!1)),P.on(this._element,"focusin.bs.toast",t=>this._onInteraction(t,!0)),P.on(this._element,"focusout.bs.toast",t=>this._onInteraction(t,!1))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return 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+

UnaryOps

+

:CAST

+

The node :CAST casts the first read tensor into :dtype, writing the result into the first write. +When optimizing cast in-place, the 0th read is consumed.

+

superclasses = UNARYOPS, JITABLE,

+

:NOT

+

The node :NOT computes the logical-not of the given tensor if the input is a boolean, otherwise (integer) computes a bitwise-not.

+
out = not(x) (if boolean)
+out = lognot(x) (if integer)
+
+

When optimizing not in-place, the 0th read is consumed.

+

superclasses = UNARYOPS, JITABLE,

+

:SQRT

+

The node :SQRT computes square-root of the first read tensor, writing the result to the first write. +

out = sqrt(x);
+

+

When optimizing sqrt in-place, the 0th read is consumed.

+

superclasses = UNARYOPS, JITABLE,

+

:LOG2

+

The node :LOG2 computes log2 of the first read tensor, writing the result to the first write. +

out = log2(x);
+

+

When optimizing log2 in-place, the 0th read is consumed.

+

superclasses = UNARYOPS, JITABLE,

+

:EXP2

+

The node :EXP2 computes exp2 of the first read tensor, writing the result to the first write. +

out = exp2(x);
+

+

When optimizing exp2 in-place, the 0th read is consumed.

+

superclasses = UNARYOPS, JITABLE,

+

:SIN

+

The node :SIN computes sine of the first read tensor, writing the result to the first write. +

out = sin(x);
+

+

When optimizing sin in-place, the 0th read is consumed.

+

superclasses = UNARYOPS, JITABLE,

+

:RECIP

+

The node :RECIP computes the reciprocal of the first read tensor, writing the result to the first write. +

out = (1/x);
+

+

When optimizing recip in-place, the 0th read is consumed.

+

superclasses = UNARYOPS, JITABLE,

+

:NEG

+

The node :NEG flips the sign of the first read tensor, writing the result to the first write. +

out = (-x);
+

+

When optimizing neg in-place, the 0th read is consumed.

+

superclasses = UNARYOPS, JITABLE,

+

BinaryOps

+

:GCD

+

Computes the greatest common divisor of two integer tensors in read, writing the result to the first write.

+

(Note: This computation should only applied to scalar tensors, and used for only computing the dynamic shaped tensor indexing.)

+
out <- gcd(x, y)
+
+

When optimizing gcd in-place, the 0th read is consumed.

+

superclasses = BINARYOPS,

+

:MAX

+

Computes the maximum value of two tensors in read, writing the result to the first write. +

out <- max(x, y)
+
+When optimizing max in-place, the 0th read is consumed.

+

superclasses = BINARYOPS, JITABLE,

+

:MOVE

+

Moves the second read into the first read, setting the result to first write. +

out <- move(x, y)
+where move(x, y) is x = y
+

+

When optimizing move in-place, the 0th read is consumed.

+

superclasses = BINARYOPS, JITABLE,

+

:XOR

+

The node :XOR computes the bit-wise xor of two tensors in read if they are integer, otherwise (boolean) computes the logical-xor. +

out <- x ^ y (if boolean)
+out <- x ^ y (if integer)
+
+When optimizing xor in-place, the 0th read is consumed.

+

superclasses = BINARYOPS, JITABLE,

+

:OR

+

The node :OR computes the bit-wise or of two tensors in read if they are integer, otherwise (boolean) computes the logical-or. +

out <- x || y (if boolean)
+out <- x | y (if integer)
+
+When optimizing or in-place, the 0th read is consumed.

+

superclasses = BINARYOPS, JITABLE,

+

:AND

+

The node :AND computes the bit-wise and of two tensors in read if they are integer, otherwise (boolean) computes the logical-and. +

out <- x && y (if boolean)
+out <- x & y (if integer)
+
+When optimizing and in-place, the 0th read is consumed.

+

superclasses = BINARYOPS, JITABLE,

+

:IDIV

+

The node :IDIV divides the first tensor in read by the second tensor in read, writing the result to the first write. +Unlike other BinaryOps, :IDIV assumes two tensors to be an integer typed tensor. +

out <- x / y
+
+When optimizing idiv in-place, the 0th read is consumed.

+

superclasses = BINARYOPS, JITABLE,

+

:MOD

+

The node :MOD finds the reminder of the first tensor in read divided by the second tensor in read. +

out <- x % y
+
+When optimizing mod in-place, the 0th read is consumed.

+

superclasses = BINARYOPS, JITABLE,

+

:MUL

+

The node :MUL multiplies the two tensors in read and writes the result to the first write. +

out <- x + y
+
+When optimizing mul in-place, the 0th read is consumed.

+

superclasses = BINARYOPS, JITABLE,

+

:ADD

+

The node :ADD adds the two tensors in read and writes the result to the first write. +

out <- x + y
+
+When optimizing add in-place, the 0th read is consumed.

+

superclasses = BINARYOPS, JITABLE,

+

TernaryOps

+

:WMMA

+

The node :WMMA is generated during optimization (simplifiers.lisp) by AJIT and represents a fused computation of :ADD and :MUL. WMMA is not generated during VM execution.

+

WMMA is used to optimize the gemm computation: +

WMMA(c, a, b) is the equivalent to:
+c += a * b      (if reduction = t)
+out = c + a * b (if reduction = nil)
+
+When optimizing wmma in-place, the 0th read is consumed.

+

superclasses = TERNARYOPS, JITABLE,

+

:WHERE

+

If the result of the first read (boolean) is true, the second read is selected, and if false, the third read is selected and written to the first write. When optimizing in-place, note that the value of the second read is used as a placeholder since choosing the first read would result in a data type mismatch with write. +

in_dtype = dtype_of(y);
+out[in_dtype] = x[boolean] ? y[in_dtype] : z[in_dtype];
+

+

When optimizing where in-place, the 1th read is consumed.

+

superclasses = TERNARYOPS, JITABLE,

+

:<

+

Compares the second and third tensors in read with <, writing the result to the first write. The first read tensor is an placeholder for the first write tensor and is always boolean. +

x = y < z;
+out = x;
+

+

When optimizing < in-place, the 0th read is consumed.

+

superclasses = TERNARYOPS, JITABLE,

+

:!=

+

Compares the second and third tensors in read with not-equal, writing the result to the first write. The first read tensor is an placeholder for the first write tensor and is always boolean. +

x = y != z;
+out = x;
+

+

When optimizing != in-place, the 0th read is consumed.

+

superclasses = TERNARYOPS, JITABLE,

+

Buffer

+

:VIEW

+

Creates a view object of the tensor in a first read. +View object can modify the multi-dimensional offset of tensors, strides, shapes, and strides without copying. +

out = view(x, *shape-new, *upfrom, *below, *by, *stride-new)
+
+upfrom and below describes the multi-dimensional offset of the tensor. Caten applies an operation to out in the range of [upfrom, below). by indicates the step of stride. the out tensor is reinitialized with shape-new and stride-new. +View has an attributebroadcast[list]`, this indicates the stride of thecorresponding axis is recognised as 0 if set to T.

+
    +
  • nrank[(unsigned-byte 32)] the rank of viewed tensor.
    • +
  • broadcast[list] broadcasting order.
    • +
  • permute[list] is an optional parameter and does nothing in VM, but requires to apply Polyhedral Compiler. If the view was created in caten/apis:!permute, set the argument to this attribute.
    • +
+

When optimizing view in-place, the 0th read is consumed.

+

superclasses = BUFFEROPS, JITABLE,

+

:STORE

+

Just like a :MOVE, moves the second tensor in read into the first tensor in read, writing the result to the first write. +(Note: :STORE can be removed in the future refactoring)

+

When optimizing store in-place, the 0th read is consumed.

+

superclasses = BUFFEROPS, BINARYOPS, JITABLE,

+

:LOAD

+

Fills the first tensor in read with value, writing the result into the first write. The first read can be either of tensor or scalar. +

x[...] = value;
+out = x;
+

+
    +
  • value[symbol or number] initial value.
    • +
+

When optimizing load in-place, the 0th read is consumed.

+

superclasses = BUFFEROPS, JITABLE,

+

:ALLOCATE

+

Allocates a new matrix of scalar value in the VM. +

out = allocate(*shape, *stride)
+

+

:Allocate is defined as described above. The first through nrank-th read scalar tensors represent the size of the Tensor, and from the nrank-th read to the last, they represent the stride of the allocated Tensor. (they can be sliced using subseq)

+
    +
  • dtype[dtype-t] dtype to allocate.
    • +
  • nrank[(unsigned-byte 32)] a rank of tensor. If set to 0, allocates a scalar.
    • +
  • from[symbol or buffer or null] If specified, instead of allocating, an already allocated Buffer is used. If a symbol is specified, a buffer is already defined in the variable table of AVM. If buffer is specified, use the buffer directly.
    • +
  • pool[null or Buffer] A place to store the result of the previous allocation. Allocation will be performed only after this slot is set to nil, or size are different due to dynamic shape.
    • +
+

When optimizing allocate in-place, the 0th read is consumed.

+

superclasses = BUFFEROPS, JITABLE,

+

Indexing

+

:INDEX-COMPONENTS

+

The node :INDEX-COMPONENTS Indicates which element-wise computation of the Tensor is being performed. Typically, it should return the argument used when performing Aref on the Tensor with the corresponding strides.

+

out <- index_components(x, *strides)
+
+is compiled as: +
for i=0..N
+  for j=0..M
+    out[i, j] = stride[0] * i + j;
+

+

When optimizing index-components in-place, the 0th read is consumed.

+

superclasses = INDEXING, JITABLE,

+

JIT Specific Ops

+

:JIT_KERNEL

+

The node :JIT_KERNEL is an instruction that calls a jit-compiled kernel from the VM. +When optimizing jit_kernel in-place, the 0th read is consumed.

+

:EXPR

+

The node :EXPR is a data structure used by the JIT compiler, representing a node that fuses multiple composable nodes. +- Expr[caten/codegen/expr:EXPR] a tree structure comprised of caten/codegen/expr:EXPR +- reduction[boolean]

+

When optimizing expr in-place, the 0th read is consumed.

+

superclasses = JITABLE,

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
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+ + + + + + \ No newline at end of file diff --git a/packages/caten.air/index.html b/packages/caten.air/index.html new file mode 100644 index 000000000..8a29f4240 --- /dev/null +++ b/packages/caten.air/index.html @@ -0,0 +1,884 @@ + + + + + + + + + + + + + + + + + + + + + + caten/air | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
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+ In this article +
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caten/air

+ + + + +
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aIR(=AbstractIR) is a Caten's internal IR and is used to represent computations expressed as either Let-Binding Based IR or a DAG.

+

Each nodes are represented using a Node structure, and their list (or tree structure) is maintained by the Graph CLOS class. The compilation is performed by efficiently manipulating and optimizing the graph using a pattern matcher.

+

Note that All nodes must be declared using the defnode macro before compilation. This allows node-related errors to be detected and reported during compilation.

+

Node

+

[struct] Node

+

A node is a computation with input sources (reads) and outputs (writes). +

 Read1 Read2 Read3 Read4
+   \    |    |    /
+      [Operation]
+          |
+   writes1 writes2 ...
+

+

Before creating a node using make-node, the node must be defined using defnode. Attributes are defined by defnode.

+
    +
  • node-class[keyword] indicates the classification of the node (e.g.: :BinaryOps, :UnaryOps). This information is used for debugging purposes.
    • +
  • node-type[keyword] indicates the type of the node (e.g.: :Add, :Sub, :Mul, :Div).
    • +
  • node-id[symbol] a unique symbol that exists only once in the namespaces. (usually generated by gensym)
    • +
  • node-writes[list] a list of symbols that represent the output buffers.
    • +
  • node-reads[list] a list of symbols or numbers that represent the input buffers. (usually connected with the (node-writes))
    • +
  • node-attr[Attribute] additional information about the node. (e.g.: reduction, dtype, type inference etc...)
    • +
  • out-nth[fixnum] node->id will point to the out-nth element of the writes list.
    • +
+

[macro] defnode

+

Defines a new node.

+
(defnode (class type) (&rest direct-superclasses)
+  description
+  &key (placeholder 0) (verify 'identity) (slots))
+
+
    +
  • class[keyword] an identifier of the node class.
    • +
  • type[keyword] an identifier of the node type.
    • +
  • direct-superclasses[list of keyword] a list of superclasses.
    • +
  • description[string] a description of the node.
    • +
  • placeholder[(unsigned-byte 32) or -1] when mutating the node in-place, the compiler consumes the placeholder-th read buffer.
    • +
  • verify[function] a function to verify the arguments.
    • +
  • slots[list of (symbol &rest slot-options)] a list of slot definitions.
    • +
+

[function] make-node

+
(make-node class type writes reads &rest attrs)
+
+

Create a node with the given class, type, writes, reads, and attrs. class and type should be defined by defnode, otherwise the function will produce an error.

+
    +
  • class[keyword] indicates the classification of the node (e.g.: :BinaryOps, :UnaryOps). This information is used for debugging purposes.
    • +
  • type[keyword] indicates the type of the node (e.g.: :Add, :Sub, :Mul, :Div).
    • +
  • writes[list] a list of symbols that represent the output buffers.
    • +
  • reads[list] a list of symbols or numbers that represent the input buffers. (usually connected with the (node-writes))
    • +
  • attrs[keyword and value pairs] initializers for the Attribute class.
    • +
+

[function] copy-node

+

(copy-node node)
+
+Create a deepcopy of the given node.

+

[function] getattrs

+

(getattrs node)
+
+Return the list of attributes of the node.

+

[function] getattr

+

(getattr node id &key (allow-undefined nil))
+
+Return the value of the attribute id of the node. If the attribute is not defined, it will produce an error. If allow-undefined is t, it returns nil without error.

+

This function is setfable.

+

[function] remattr

+
(remattr node id &key (allow-undefined nil))
+
+

Equivalent to:

+
(setf (getattr node id :allow-undefined allow-undefined) nil)
+
+

Examples

+
lisp
CATEN-USER> (make-node :BinaryOps :ADD (list 'x) (list 'y 'z) :reduction t)
+
+
+Result +
Result
<Node[BINARYOPS] ADD(NID1744) : X* <- (Y, Z) where :reduction=T>
+
+
+ +

Graph

+

[class] Graph

+

A Graph is a data structure used to handle a collection of nodes.

+

Graph has a list of nodes (nodes), node inputs (seen), and node outputs (outputs).

+

Unlike FastGraph, Graph does not assume that the nodes form a DAG. Additionally, it guarantees that the nodes will not be sorted during each process of the Pattern Matcher. Graph structures, such as Let-Binding, are represented using Graph.

+

Note: Using Graph may lead to performance degradation if the graph is a DAG. Please use FastGraph instead.

+

[class] FastGraph

+

FastGraph is a subclass of Graph that implements faster node searches based on the assumption that the nodes form a DAG. (It is approximately 20 times faster than Graph, in the larger scale.) +Since FastGraph stores nodes as a hash-table, there are some constraints on node operations. If necessary, converting between (->fast-graph graph) and (->graph graph) can be done frequently with minimal impact on performance.

+

[generic] copy-graph

+

(copy-graph graph)
+
+Creates a copy of the given graph.

+

[function] make-graph

+

(make-graph &rest nodes)
+
+Creates a new Graph object with the given nodes.

+

[generic] graph-nodes

+

NIL

+

[generic] id->value

+

(id->value graph id)
+
+Returns a node whose node-writes includes the given id.

+

[generic] id->users

+

(id->users graph id)
+
+Returns a list of nodes whose node-reads includes the given id.

+

[generic] remnode

+

(remnode graph id)
+
+Removes all node where node-id = id from the given graph.

+

[generic] insert-nodes

+

(insert-nodes graph nodes)
+
+Inserts the given nodes (list) into the graph.

+

[function] ->fast-graph

+
(->fast-graph graph)
+
+

Creates a FastGraph object from the given graph.

+

[generic] ->graph

+

(->graph graph)
+
+Converts the given graph to a fast graph.

+

[generic] verify-graph

+

(verify-graph graph &key no-purge)
+
+Verify the consistency of the graphs and simplify them by doing following things: +- Checks if all variables are immutable +- All variables appeared in read, are also defined in writes. +- Purge all isolated graph +- Sort nodes by time. +- Set no-purge=T to ignore purge isolated graph step. This has no effect on FastGraph.

+

Simplifier

+

[macro] defsimplifier

+

(defsimplifier (name &key (speed 3)) &rest rules)
+
+Defines a new simplifier named name. The defined function has a following form: +
(name graph &key (no-verify nil) (return-changed-p nil))
+
+The graph is a graph to simplify. The no-verify is a flag to skip the verification process. The return-changed-p is a flag to return the result of the simplification process, or a boolean indicating that the graph was changed during simplification process.. The speed is an optimization level. The rules are a list of simplification rules. Each rule has a form:

+
(Node_Name (Read_Args) Attrs)
+
+

(TODO: Documentation)

+

(See also: ./source/aasm/constant-folding.lisp)

+

Visualizer

+

[function] ->dot

+
(->dot graph &key (pathname "/tmp/graph.dot") (open t) (title "node"))
+
+

Visualizes the graph using graphviz(requirement). Set open=t to open the resulting image in the default browser. A tmp file is created at the pathname location. The graph is saved as a .png and .html file. The title is used in the html file.

+

[function] pprint-graph

+
(pprint-graph graph &key (screen-width 140) (stream t))
+
+

The function pprint-graph prints the graph in a tree-like structure. screen-width controls the width of the output, if the graph is too wide and cannot fit in the screen, it will be split into multiple pages.

+
lisp
CATEN-USER> (pprint-graph (avm-graph (caten (!gelu (make-tensor `(3 3) :id 'x)))) :stream nil)
+
+
+Result +
Result
============================================================================================================================================
+=== [P: 0] =================================================================================================================================
+[P=0, ID=0]:
+   :PAUSE/BACKWARD {0}
+   └ :MUL {N1}
+     ├ :MUL {N2}
+     │ └ Allocate[:float32] (3 3)
+     │ ├ :MOVE {N3}
+     │   ├ Allocate[:float32] (3 3)
+     │   └ :VIEW {N4}
+     │     ├ load(0.5)
+     │       └ Allocate[:float32] NIL
+     └ :ADD {N5}
+       ├ :MOVE {N6}
+       │ ├ Allocate[:float32] (3 3)
+       │ └ :VIEW {N7}
+       │   ├ load(1.0)
+       │     └ Allocate[:float32] NIL
+       └ :ADD {N8}
+         └ :VIEW {N9}
+         │ ├ load(-1.0)
+         │   └ Allocate[:float32] NIL
+         ├ :MUL {N10}
+           ├ :MOVE {N11}
+           │ ├ Allocate[:float32] (3 3)
+           │ └ :VIEW {N12}
+           │   ├ load(2.0)
+           │     └ Allocate[:float32] NIL
+           └ :RECIP {N13}
+             └ :ADD {N14}
+               ├ :MOVE {N15}
+               │ ├ Allocate[:float32] (3 3)
+               │ └ :VIEW {N16}
+               │   ├ load(1.0)
+               │     └ Allocate[:float32] NIL
+               └ :EXP2 {N17}
+                 └ :MUL {N18}
+                   └ load(1.442695)
+                   │ └ Allocate[:float32] NIL
+                   ├ :MUL {N19}
+                     └ :VIEW {N20}
+                     │ ├ load(0.6931472)
+                     │   └ Allocate[:float32] NIL
+                     ├ :MUL {N21}
+                       └ :VIEW {N22}
+                       │ ├ load(-1.442695)
+                       │   └ Allocate[:float32] NIL
+                       ├ :MUL {N23}
+                         ├ :MOVE {N24}
+                         │ ├ Allocate[:float32] (3 3)
+                         │ └ :VIEW {N25}
+                         │   ├ load(2.0)
+                         └ :MUL {N26}
+                           ├ :MOVE {N27}
+                           │ ├ Allocate[:float32] (3 3)
+                           │ └ :VIEW {N28}
+                           │   ├ load(0.7978846)
+                           │     └ Allocate[:float32] NIL
+                           └ :ADD {N29}
+                             ├ Allocate[:float32] (3 3)
+                             └ :MUL {N30}
+                               └ :MUL {N31}
+                               │ └ Allocate[:float32] (3 3)
+                               │ ├ :MUL {N32}
+                               │   ├ Allocate[:float32] (3 3)
+                               │     Allocate[:float32] (3 3)
+                               ├ :MOVE {N33}
+                                 ├ Allocate[:float32] (3 3)
+                                 └ :VIEW {N34}
+                                   ├ load(0.044715)
+                                     └ Allocate[:float32] NIL
+============================================================================================================================================
+
+
+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.apis.aot/index.html b/packages/caten.apis.aot/index.html new file mode 100644 index 000000000..c69b7efb5 --- /dev/null +++ b/packages/caten.apis.aot/index.html @@ -0,0 +1,519 @@ + + + + + + + + + + + + + + + + + + + + + + AOT | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+
    + +
  • AOT + +
    • + +
+
+
+ + +
+ +

AOT

+ + + + +
+
+ +
+ + + + + + +
+
+ + + + | + + + + + +
+
+ + + + + + + + + +
    +
  • +
    Share via
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  • + + +  Edit this article + + +
    • +
    • +
+ +
+
+
+ +
+ + +

AOT

+

TODO

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.apis.differentiable_ops/index.html b/packages/caten.apis.differentiable_ops/index.html new file mode 100644 index 000000000..397a58754 --- /dev/null +++ b/packages/caten.apis.differentiable_ops/index.html @@ -0,0 +1,1949 @@ + + + + + + + + + + + + + + + + + + + + + + Func | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Func

+ + + + +
+
+ +
+ + + + + + +
+
+ + + + | + + + + + +
+
+ + + + + + + + + +
    +
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    Share via
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    • +
    • +
  • + + +  Edit this article + + +
    • +
    • +
+ +
+
+
+ +
+ + +

Func

+

Func Class

+

[class] Func

+

A CLOS class that represents a computation.

+
[Func] -> <Lower> -> [FastGraph] -> <Simplifier> -> [Completed]
+
+

Func is a syntax-sugar for generating lowered instructions defined in the caten/aasm package.

+

To properly lower the Func, You need to implement the following three methods:

+
    +
  • lower: Lower the Func into a list of caten/air:node. This should return caten/air:graph.
    • +
  • forward: Create the type for the Tensor after computation. Be mindful of its lazy evaluation nature; do not perform the actual computation. ShapeTracker might help you. (use the st macro)
    • +
  • backward: Create the graph for backward of op given prev-grad. Return: (values input_1.grad input_2.grad ...).
    • +
+

[generic] lower

+
(lower op &rest nodes)
+
+

Lowers the Func into a list of caten/air:node. This should return caten/air:graph. +- op[Func] Func to lower. +- nodes[list] list of previous nodes (each position corresponds to the position of the variables in the Func).

+

[generic] forward

+
(forward op &rest tensors)
+
+

Create the type for the Tensor after computation. Be mindful of its lazy evaluation nature; do not perform the actual computation. Use the st macro to create a new tensor.

+
    +
  • op[Func] Func to forward.
    • +
  • tensors[list] list of input tensors.
    • +
+

[generic] backward

+
(backward op &optional prev-grad)
+
+

Create the graph for backward of op given prev-grad. Return: (values input_1.grad input_2.grad ...). +save-for-backward is determined automatically, so you do not have to consider about in-place operation.

+
    +
  • op[Func] Func to backward.
    • +
  • prev-grad[Tensor] previous gradient tensor.
    • +
+

Differentiable Ops (built_in)

+

[function] !identity

+
(!identity tensor)
+
+

Equivalent to #'identity, but it is used to create a lazy computation node.

+
lisp
CATEN-USER> (proceed (!identity (make-tensor `(3 3) :initial-element 1.0)))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC98724
+   ((1.0 1.0 1.0)
+    (1.0 1.0 1.0)
+    (1.0 1.0 1.0))
+  :op #<PROCEEDNODE {10029E7253}>
+  :requires-grad NIL
+  :variables (STC98663)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !view

+
(!view base &rest subscripts)
+
+

Create a view node from the base tensor and subscripts.

+

We refer to VIEW as a node creating tensor whose buffers are shared with the base tensor, but shapes, strides, dtypes, offsets, or dtypes are different.

+

Subscripts has the following notation:

+
    +
  • t keep using the base view.
    • +
  • fixnum refers to the specified element. e.g.: A[3]
    • +
  • (a b) slices in the range of [a, b)
    • +
  • (a b c) slices in the range of [a, b) with step c. c can be negative. In that case, b must be larger than a. For example: (10 0 -1) to reverse the elements in the axis.
    • +
  • (:~ n) to broadcast the axis, with the size of n
    • +
+

It is supported to compose mutliple views; the viewed tensors can be created from the viewed tensors.

+
lisp
CATEN-USER> (proceed (!contiguous (!view (ax+b `(10 10) 1 0) `(3 6) `(3 6))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC99611
+   ((33.0 34.0 35.0)
+    (43.0 44.0 45.0)
+    (53.0 54.0 55.0))
+  :op #<PROCEEDNODE {1002BE4D73}>
+  :requires-grad NIL
+  :variables (STC98928)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !permute

+
(!permute tensor &rest order)
+
+

Returns a tensor that is a permutation of the original tensor. The new tensor has the same data as the original tensor but with the dimensions permuted according to the order specified. order can be passed as a list or separated arguments. That is, both of (!permute x 0 1) or (!permute x (list 0 1)) are valid.

+
lisp
CATEN-USER> (proceed (!contiguous (!permute (ax+b `(10 10) 1 0) `(1 0))))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC99803
+   ((0.0  10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0)
+    (1.0  11.0 21.0 31.0 41.0 51.0 61.0 71.0 81.0 91.0)
+    (2.0  12.0 22.0 32.0 42.0 52.0 62.0 72.0 82.0 92.0)
+    (3.0  13.0 23.0 33.0 43.0 53.0 63.0 73.0 83.0 93.0)
+    (4.0  14.0 24.0 34.0 44.0 54.0 64.0 74.0 84.0 94.0)
+    (5.0  15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0)
+    (6.0  16.0 26.0 36.0 46.0 56.0 66.0 76.0 86.0 96.0)
+    (7.0  17.0 27.0 37.0 47.0 57.0 67.0 77.0 87.0 97.0)
+    (8.0  18.0 28.0 38.0 48.0 58.0 68.0 78.0 88.0 98.0)
+    (9.0  19.0 29.0 39.0 49.0 59.0 69.0 79.0 89.0 99.0))
+  :op #<PROCEEDNODE {1002C657C3}>
+  :requires-grad NIL
+  :variables (TID99619)
+  :tracker #<TRACKER :order={row(1 0)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !t

+
(!t tensor)
+
+

Transposes the last two axes of the tensor

+
lisp
CATEN-USER> (proceed (!contiguous (!t (ax+b `(10 10) 1 0))))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC99995
+   ((0.0  10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0)
+    (1.0  11.0 21.0 31.0 41.0 51.0 61.0 71.0 81.0 91.0)
+    (2.0  12.0 22.0 32.0 42.0 52.0 62.0 72.0 82.0 92.0)
+    (3.0  13.0 23.0 33.0 43.0 53.0 63.0 73.0 83.0 93.0)
+    (4.0  14.0 24.0 34.0 44.0 54.0 64.0 74.0 84.0 94.0)
+    (5.0  15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0)
+    (6.0  16.0 26.0 36.0 46.0 56.0 66.0 76.0 86.0 96.0)
+    (7.0  17.0 27.0 37.0 47.0 57.0 67.0 77.0 87.0 97.0)
+    (8.0  18.0 28.0 38.0 48.0 58.0 68.0 78.0 88.0 98.0)
+    (9.0  19.0 29.0 39.0 49.0 59.0 69.0 79.0 89.0 99.0))
+  :op #<PROCEEDNODE {1003BAE1F3}>
+  :requires-grad NIL
+  :variables (TID99811)
+  :tracker #<TRACKER :order={row(1 0)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !transpose

+
(!transpose tensor &optional (dim0 1) (dim1 0))
+
+

Transposes the dim0 and dim1.

+
lisp
CATEN-USER> (proceed (!contiguous (!transpose (ax+b `(10 10) 1 0) 1 0)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC100187
+   ((0.0  10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0)
+    (1.0  11.0 21.0 31.0 41.0 51.0 61.0 71.0 81.0 91.0)
+    (2.0  12.0 22.0 32.0 42.0 52.0 62.0 72.0 82.0 92.0)
+    (3.0  13.0 23.0 33.0 43.0 53.0 63.0 73.0 83.0 93.0)
+    (4.0  14.0 24.0 34.0 44.0 54.0 64.0 74.0 84.0 94.0)
+    (5.0  15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0)
+    (6.0  16.0 26.0 36.0 46.0 56.0 66.0 76.0 86.0 96.0)
+    (7.0  17.0 27.0 37.0 47.0 57.0 67.0 77.0 87.0 97.0)
+    (8.0  18.0 28.0 38.0 48.0 58.0 68.0 78.0 88.0 98.0)
+    (9.0  19.0 29.0 39.0 49.0 59.0 69.0 79.0 89.0 99.0))
+  :op #<PROCEEDNODE {1003C1EB93}>
+  :requires-grad NIL
+  :variables (TID100003)
+  :tracker #<TRACKER :order={row(1 0)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !contiguous

+
(!contiguous x &key (force nil))
+
+

If the tensor is viewed, creates a copy of tensor with contiguous memory. Otherwise, returns the original tensor. If force is set to T, it always creates a copy.

+
lisp
CATEN-USER> (proceed (!contiguous (ax+b `(10 10) 1 0)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC100287
+   ((0.0  1.0  2.0  3.0  4.0  5.0  6.0  7.0  8.0  9.0)
+    (10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0)
+    (20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0)
+    (30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0)
+    (40.0 41.0 42.0 43.0 44.0 45.0 46.0 47.0 48.0 49.0)
+    (50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0)
+    (60.0 61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0)
+    (70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0)
+    (80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0)
+    (90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0))
+  :op #<PROCEEDNODE {1003C5ABB3}>
+  :requires-grad NIL
+  :variables (STC100194)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !copy

+

(!copy x)
+
+Creates a copy of the tensor. In Caten, the in-place operations are automatically determined, so in general, you do not have to consider using it.

+
lisp
CATEN-USER> (proceed (!copy (ax+b `(10 10) 1 0)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC100418
+   ((0.0  1.0  2.0  3.0  4.0  5.0  6.0  7.0  8.0  9.0)
+    (10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0)
+    (20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0)
+    (30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0)
+    (40.0 41.0 42.0 43.0 44.0 45.0 46.0 47.0 48.0 49.0)
+    (50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0)
+    (60.0 61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0)
+    (70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0)
+    (80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0)
+    (90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0))
+  :op #<PROCEEDNODE {1003C8A663}>
+  :requires-grad NIL
+  :variables (STC100299)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !reshape

+
(!reshape x &rest shape)
+
+

Returns a same tensor but shape is changed. shape can be passed as a list or separated arguments. That is, both of (!reshape x '(1 2 3)) or (!reshape x 1 2 3) are valid.

+

Shape is a list of integers, symbols, or tensors.

+

If x is a viewed tensor, it creates a copy of the tensor with contiguous memory (but later JIT will try to eliminate this).

+
lisp
CATEN-USER> (proceed (!reshape (ax+b `(10 10) 1 0) `(5 20)))
+
+
+Result +
Result
{Tensor[float32] :shape (5 20) :id STC100616
+   ((0.0  1.0  2.0  3.0  4.0  ~ 15.0 16.0 17.0 18.0 19.0)
+    (20.0 21.0 22.0 23.0 24.0 ~ 35.0 36.0 37.0 38.0 39.0)
+    (40.0 41.0 42.0 43.0 44.0 ~ 55.0 56.0 57.0 58.0 59.0)
+    (60.0 61.0 62.0 63.0 64.0 ~ 75.0 76.0 77.0 78.0 79.0)
+    (80.0 81.0 82.0 83.0 84.0 ~ 95.0 96.0 97.0 98.0 99.0))
+  :op #<PROCEEDNODE {1003CFB9C3}>
+  :requires-grad NIL
+  :variables (TID100426)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(5 20) :contiguous-p=T>}
+
+
+ +

[function] !flatten

+
(!flatten x &key (axis 1))
+
+

Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).

+
lisp
CATEN-USER> (proceed (!flatten (make-tensor `(3 3 3 3)) :axis 1))
+
+
+Result +
Result
{Tensor[float32] :shape (3 27) :id STC100922
+   ((0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0))
+  :op #<PROCEEDNODE {1003D9D1C3}>
+  :requires-grad NIL
+  :variables (TID100657)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 27) :contiguous-p=T>}
+
+
+ +

[function] !uprank

+
(!uprank x n)
+
+

Returns a tensor with one is inserted at the beginning of the shape of x for n times.

+
lisp
CATEN-USER> (proceed (!uprank (ax+b `(10 10) 1 0) 2))
+
+
+Result +
Result
{Tensor[float32] :shape (1 1 10 10) :id STC101186
+   ((((0.0  1.0  2.0  3.0  4.0  5.0  6.0  7.0  8.0  9.0)
+      (10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0)
+      (20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0)
+      (30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0)
+      (40.0 41.0 42.0 43.0 44.0 45.0 46.0 47.0 48.0 49.0)
+      (50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0)
+      (60.0 61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0)
+      (70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0)
+      (80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0)
+      (90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0))))
+  :op #<PROCEEDNODE {1003E27FB3}>
+  :requires-grad NIL
+  :variables (TID100930)
+  :tracker #<TRACKER :order={row(0 1 2 3)} :shape=(1 1 10 10) :contiguous-p=T>}
+
+
+ +

[function] !repeat

+
(!repeat x &rest repeats)
+
+

Returns a tensor with the shape of x broadcasted by repeats.

+
lisp
CATEN-USER> (proceed (!repeat (ax+b `(1 10) 1 0) 10 1))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC103674
+  <<Error during rendering: Invalid index 10 for (SIMPLE-ARRAY SINGLE-FLOAT (10)), should be a non...>>
+  :op #<PROCEEDNODE {10048E3C23}>
+  :requires-grad NIL
+  :variables (VID101556)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10(0 10 1) 10(0 10 1)) :contiguous-p=NIL>}
+
+
+ +

[function] !expand

+
(!expand x &rest shape)
+
+

Returns a tensor that is expanded to the shape that is specified. Expand can also increase the number of dimensions that a tensor has.

+
lisp
CATEN-USER> (proceed (!expand (ax+b `(1 10) 1 0) `(10 10)))
+
+
+Result +
Result
{Tensor[float32] :shape (1 10) :id STC104313
+   ((0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0))
+  :op #<PROCEEDNODE {1004A3A683}>
+  :requires-grad NIL
+  :variables (VID103682)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(~1 10) :contiguous-p=T>}
+
+
+ +

[function] !move

+
(!move a b &key (reduce nil))
+
+

Moves the element of b into a, returning a. If reduce is T, it will reduce the result. (Broadcast)

+
lisp
CATEN-USER> (proceed (!move (ax+b `(10 10) 0 0) (ax+b `(10 10) 0 2)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC104482
+   ((2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0))
+  :op #<PROCEEDNODE {1004BDC473}>
+  :requires-grad NIL
+  :variables (STC104328)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !assign

+

(!assign a b)
+
+Equivalent to doing (!move a b :reduce t). Useful when you want to the value of lazy ops to an pre-allocated buffer, like KV-Cache.

+
lisp
CATEN-USER> (proceed (!assign (ax+b `(10 10) 0 0) (ax+b `(10 10) 0 2)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC104651
+   ((2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)
+    (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0))
+  :op #<PROCEEDNODE {1004C332B3}>
+  :requires-grad NIL
+  :variables (STC104497)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !add

+
(!add a b &key (reduce nil))
+;; or
+(!+ &rest tensors)
+
+

Adds a and b. If reduce is T, it will reduce the result. (Broadcast)

+
lisp
CATEN-USER> (proceed (!add (ax+b `(10 10) 1 0) (ax+b `(10 10) 1 0)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC104821
+   ((0.0   2.0   4.0   6.0   8.0   10.0  12.0  14.0  16.0  18.0)
+    (20.0  22.0  24.0  26.0  28.0  30.0  32.0  34.0  36.0  38.0)
+    (40.0  42.0  44.0  46.0  48.0  50.0  52.0  54.0  56.0  58.0)
+    (60.0  62.0  64.0  66.0  68.0  70.0  72.0  74.0  76.0  78.0)
+    (80.0  82.0  84.0  86.0  88.0  90.0  92.0  94.0  96.0  98.0)
+    (100.0 102.0 104.0 106.0 108.0 110.0 112.0 114.0 116.0 118.0)
+    (120.0 122.0 124.0 126.0 128.0 130.0 132.0 134.0 136.0 138.0)
+    (140.0 142.0 144.0 146.0 148.0 150.0 152.0 154.0 156.0 158.0)
+    (160.0 162.0 164.0 166.0 168.0 170.0 172.0 174.0 176.0 178.0)
+    (180.0 182.0 184.0 186.0 188.0 190.0 192.0 194.0 196.0 198.0))
+  :op #<PROCEEDNODE {1004C78DE3}>
+  :requires-grad NIL
+  :variables (STC104666)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !sub

+
(!sub a b &key (reduce nil))
+;; or
+(!- &rest tensors)
+
+

Subtracts b from a. If reduce is T, it will reduce the result. (Broadcast)

+
lisp
CATEN-USER> (proceed (!sub (ax+b `(10 10) 1 0) (ax+b `(10 10) 1 0)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC104997
+   ((0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0))
+  :op #<PROCEEDNODE {1004CC21E3}>
+  :requires-grad NIL
+  :variables (STC104837)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !mul

+
(!mul a b &key (reduce nil))
+;; or
+(!* &rest tensors)
+
+

Multiplies a and b. If reduce is T, it will reduce the result. (Broadcast)

+
lisp
CATEN-USER> (proceed (!mul (ax+b `(10 10) 1 0) (ax+b `(10 10) 1 0)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC105173
+   ((0.0    1.0    4.0    9.0    16.0   25.0   36.0   49.0   64.0   81.0)
+    (100.0  121.0  144.0  169.0  196.0  225.0  256.0  289.0  324.0  361.0)
+    (400.0  441.0  484.0  529.0  576.0  625.0  676.0  729.0  784.0  841.0)
+    (900.0  961.0  1024.0 1089.0 1156.0 1225.0 1296.0 1369.0 1444.0 1521.0)
+    (1600.0 1681.0 1764.0 1849.0 1936.0 2025.0 2116.0 2209.0 2304.0 2401.0)
+    (2500.0 2601.0 2704.0 2809.0 2916.0 3025.0 3136.0 3249.0 3364.0 3481.0)
+    (3600.0 3721.0 3844.0 3969.0 4096.0 4225.0 4356.0 4489.0 4624.0 4761.0)
+    (4900.0 5041.0 5184.0 5329.0 5476.0 5625.0 5776.0 5929.0 6084.0 6241.0)
+    (6400.0 6561.0 6724.0 6889.0 7056.0 7225.0 7396.0 7569.0 7744.0 7921.0)
+    (8100.0 8281.0 8464.0 8649.0 8836.0 9025.0 9216.0 9409.0 9604.0 9801.0))
+  :op #<PROCEEDNODE {1004D19C83}>
+  :requires-grad NIL
+  :variables (STC105012)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !div

+
(!div a b &key (reduce nil))
+;; or
+(!/ &rest tensors)
+
+

Divides a by b. If reduce is T, it will reduce the result. (Broadcast)

+
lisp
CATEN-USER> (proceed (!div (ax+b `(10 10) 1 1) (ax+b `(10 10) 1 1)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC105364
+   ((1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0)
+    (1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0)
+    (1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0)
+    (1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0)
+    (0.99999994 1.0        1.0        1.0        1.0        1.0        0.99999994 1.0        1.0        1.0)
+    (1.0        1.0        1.0        1.0        0.99999994 1.0        1.0        1.0        1.0        1.0)
+    (0.99999994 1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0)
+    (1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0        1.0)
+    (1.0        0.99999994 0.99999994 1.0        1.0        1.0        1.0        1.0        1.0        1.0)
+    (1.0        1.0        1.0        0.99999994 1.0        1.0        0.99999994 1.0        1.0        1.0))
+  :op #<PROCEEDNODE {1004DB1473}>
+  :requires-grad NIL
+  :variables (STC105189)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !mod

+

(!mod a b &key (reduce nil))
+
+Computes the remainder of the division of a by b. If reduce is T, it will reduce the result. (Broadcast)

+
lisp
CATEN-USER> (proceed (!mod (ax+b `(10 10) 1 1 :dtype :int32) (ax+b `(10 10) 1 1 :dtype :int32)))
+
+
+Result +
Result
{Tensor[int32] :shape (10 10) :id STC105538
+   ((0 0 0 0 0 0 0 0 0 0)
+    (0 0 0 0 0 0 0 0 0 0)
+    (0 0 0 0 0 0 0 0 0 0)
+    (0 0 0 0 0 0 0 0 0 0)
+    (0 0 0 0 0 0 0 0 0 0)
+    (0 0 0 0 0 0 0 0 0 0)
+    (0 0 0 0 0 0 0 0 0 0)
+    (0 0 0 0 0 0 0 0 0 0)
+    (0 0 0 0 0 0 0 0 0 0)
+    (0 0 0 0 0 0 0 0 0 0))
+  :op #<PROCEEDNODE {10050434B3}>
+  :requires-grad NIL
+  :variables (STC105379)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !idiv

+
(!idiv a b &key (reduce nil))
+
+

Assuming both of a and b are the integer, divides a by b and returns the integer part. If reduce is T, it will reduce the result. (Broadcast)

+
lisp
CATEN-USER> (proceed (!idiv (ax+b `(10 10) 3 1 :dtype :uint32) (ax+b `(10 10) 0 2 :dtype :uint32)))
+
+
+Result +
Result
{Tensor[uint32] :shape (10 10) :id STC105703
+   ((0   2   3   5   6   8   9   11  12  14)
+    (15  17  18  20  21  23  24  26  27  29)
+    (30  32  33  35  36  38  39  41  42  44)
+    (45  47  48  50  51  53  54  56  57  59)
+    (60  62  63  65  66  68  69  71  72  74)
+    (75  77  78  80  81  83  84  86  87  89)
+    (90  92  93  95  96  98  99  101 102 104)
+    (105 107 108 110 111 113 114 116 117 119)
+    (120 122 123 125 126 128 129 131 132 134)
+    (135 137 138 140 141 143 144 146 147 149))
+  :op #<PROCEEDNODE {10050B9543}>
+  :requires-grad NIL
+  :variables (STC105553)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !maximum

+

(!maximum a b &key (reduce nil))
+
+Returns the maximum of a and b. If reduce is T, it will reduce the result. (Broadcast)

+
lisp
CATEN-USER> (proceed (!maximum (rand `(3 3)) (randn `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC130379
+   ((0.35161772 0.27699816 0.13698694)
+    (0.25436726 0.8601034  0.39287245)
+    (0.4397496  0.71887904 0.36415708))
+  :op #<PROCEEDNODE {1001F8E243}>
+  :requires-grad NIL
+  :variables (STC129657)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !minimum

+

(!minimum a b &key (reduce nil))
+
+Returns the minimum of a and b. If reduce is T, it will reduce the result. (Broadcast)

+
lisp
CATEN-USER> (proceed (!minimum (rand `(3 3)) (randn `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC131132
+   ((-0.45117822  0.61168617   -1.7840805)
+    (0.6194901    -0.007118067 -0.24680464)
+    (0.17250395   -1.4365431   0.30330306))
+  :op #<PROCEEDNODE {100222BC13}>
+  :requires-grad NIL
+  :variables (STC130395)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !gcd

+

(!gcd a b &key (reduce nil))
+
+Returns the greatest common divisor of a and b. If reduce is T, it will reduce the result. (Broadcast)

+

a, b are expected to be integer scalars. (dedicated to the view computation)

+
lisp
CATEN-USER> (ctx:with-contextvar (:jit 0) (proceed (!gcd (iconst 8) (iconst 4))))
+
+
+Result +
Result
{Tensor[int64] :shape NIL :id STC131154
+  4
+  :op #<PROCEEDNODE {10023C0073}>
+  :requires-grad NIL
+  :variables (STC131135)
+  :tracker #<TRACKER :order={rowNIL} :shape=() :contiguous-p=T>}
+
+
+ +

[function] !lcm

+

(!lcm a b)
+
+Returns the least common multiple of a and b.

+

a, b are expected to be integer scalars.

+
lisp
CATEN-USER> (ctx:with-contextvar (:jit 0) (proceed (!lcm (iconst 8) (iconst 4))))
+
+
+Result +
Result
{Tensor[int64] :shape NIL :id STC131217
+  8
+  :op #<PROCEEDNODE {100243F613}>
+  :requires-grad NIL
+  :variables (STC131160)
+  :tracker #<TRACKER :order={rowNIL} :shape=() :contiguous-p=T>}
+
+
+ +

[function] !exp

+
(!exp x)
+
+

Computes (exp x).

+
lisp
CATEN-USER> (proceed (!exp (ax+b `(10 10) 0.01 0.0)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC131340
+   ((1.0       1.0100502 1.0202013 1.0304545 1.0408108 1.0512711 1.0618366 1.0725082 1.0832871 1.0941743)
+    (1.105171  1.116278  1.1274968 1.1388284 1.1502738 1.1618342 1.1735109 1.1853049 1.1972173 1.2092496)
+    (1.2214028 1.2336781 1.2460767 1.2586    1.2712492 1.2840254 1.2969301 1.3099644 1.3231298 1.3364275)
+    (1.3498588 1.3634251 1.3771278 1.3909681 1.4049476 1.4190675 1.4333293 1.4477346 1.4622846 1.4769808)
+    (1.4918246 1.5068177 1.5219616 1.5372574 1.5527072 1.5683122 1.5840739 1.5999942 1.6160744 1.6323161)
+    (1.6487212 1.6652912 1.6820276 1.6989322 1.7160068 1.733253  1.7506725 1.768267  1.7860384 1.8039883)
+    (1.8221188 1.8404315 1.8589281 1.8776106 1.8964808 1.9155407 1.9347923 1.9542372 1.9738777 1.9937155)
+    (2.0137527 2.033991  2.054433  2.0750806 2.0959353 2.1169999 2.138276  2.1597662 2.181472  2.2033963)
+    (2.2255409 2.2479079 2.2704997 2.2933185 2.316367  2.3396468 2.3631606 2.386911  2.4108996 2.4351294)
+    (2.4596028 2.4843223 2.5092902 2.5345092 2.5599813 2.5857096 2.6116965 2.6379445 2.664456  2.691234))
+  :op #<PROCEEDNODE {10024A1273}>
+  :requires-grad NIL
+  :variables (STC131225)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !log

+
(!log x)
+
+

Computes (log x).

+
lisp
CATEN-USER> (proceed (!log (ax+b `(10 10) 0.01 0.001)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC131452
+   ((-6.9077554   -4.50986     -3.8632329   -3.4737678   -3.1941833   -2.97593     -2.7968814   -2.6450753   -2.5133061   -2.396896)
+    (-2.2926347   -2.198225    -2.1119647   -2.032558    -1.9589953   -1.8904755   -1.8263509   -1.7660917   -1.7092583   -1.6554819)
+    (-1.6044505   -1.555897    -1.5095925   -1.4653376   -1.4229584   -1.3823024   -1.343235    -1.3056365   -1.2694006   -1.2344321)
+    (-1.2006451   -1.1679624   -1.1363143   -1.105637    -1.0758728   -1.046969    -1.0188774   -0.99155325  -0.964956    -0.93904775)
+    (-0.9137939   -0.8891622   -0.8651225   -0.84164727  -0.81871045  -0.796288    -0.7743574   -0.7528972   -0.73188806  -0.7113112)
+    (-0.6911492   -0.6713857   -0.6520053   -0.63299334  -0.6143361   -0.59602046  -0.5780344   -0.5603661   -0.5430046   -0.52593935)
+    (-0.5091604   -0.49265832  -0.47642422  -0.46044946  -0.44472587  -0.42924568  -0.41400146  -0.39898625  -0.38419297  -0.36961547)
+    (-0.35524744  -0.3410829   -0.3271162   -0.3133419   -0.29975465  -0.28634965  -0.27312195  -0.26006696  -0.24718018  -0.23445737)
+    (-0.22189441  -0.20948724  -0.19723219  -0.18512551  -0.17316367  -0.1613432   -0.14966084  -0.1381133   -0.12669767  -0.11541088)
+    (-0.10425006  -0.093212426 -0.082295306 -0.07149601  -0.060812157 -0.050241243 -0.039780907 -0.029428856 -0.019182874 -0.009040808))
+  :op #<PROCEEDNODE {10024D9613}>
+  :requires-grad NIL
+  :variables (STC131348)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !sqrt

+

(!sqrt x)
+
+Computes (sqrt x).

+
lisp
CATEN-USER> (proceed (!sqrt (ax+b `(10 10) 0.01 0.0)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC131733
+   ((0.0        0.1        0.14142135 0.17320508 0.2        0.2236068  0.24494897 0.26457512 0.2828427  0.29999998)
+    (0.31622776 0.33166248 0.34641016 0.3605551  0.37416574 0.38729832 0.4        0.41231057 0.42426407 0.4358899)
+    (0.4472136  0.45825756 0.4690416  0.47958314 0.48989794 0.5        0.50990194 0.51961523 0.52915025 0.53851646)
+    (0.5477225  0.55677646 0.5656854  0.5744563  0.5830952  0.591608   0.59999996 0.60827625 0.61644137 0.6244998)
+    (0.6324555  0.64031243 0.64807403 0.65574384 0.66332495 0.67082036 0.67823297 0.6855655  0.6928203  0.7)
+    (0.70710677 0.71414286 0.7211102  0.72801095 0.7348469  0.7416199  0.7483315  0.7549834  0.7615773  0.76811457)
+    (0.77459663 0.781025   0.7874008  0.7937254  0.8        0.8062258  0.8124038  0.81853527 0.82462114 0.83066237)
+    (0.83666    0.84261495 0.84852815 0.85440034 0.86023253 0.8660254  0.8717798  0.8774964  0.8831761  0.8888194)
+    (0.8944272  0.9        0.9055385  0.91104335 0.9165151  0.92195445 0.92736185 0.9327379  0.9380832  0.9433981)
+    (0.94868326 0.9539392  0.9591663  0.96436507 0.96953595 0.9746794  0.9797959  0.98488575 0.98994946 0.9949874))
+  :op #<PROCEEDNODE {100257D633}>
+  :requires-grad NIL
+  :variables (STC131460)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !neg

+

(!neg tensor)
+
+!neg computes the negative value of the tensor.

+
lisp
CATEN-USER> (proceed (!neg (ax+b `(10 10) 0.01 0.0)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC131837
+   ((-0.0         -0.01        -0.02        -0.03        -0.04        -0.049999997 -0.06        -0.07        -0.08        -0.089999996)
+    (-0.099999994 -0.11        -0.12        -0.13        -0.14        -0.14999999  -0.16        -0.17        -0.17999999  -0.19)
+    (-0.19999999  -0.21        -0.22        -0.22999999  -0.24        -0.25        -0.26        -0.26999998  -0.28        -0.29)
+    (-0.29999998  -0.31        -0.32        -0.32999998  -0.34        -0.35        -0.35999998  -0.37        -0.38        -0.39)
+    (-0.39999998  -0.41        -0.42        -0.42999998  -0.44        -0.45        -0.45999998  -0.47        -0.48        -0.48999998)
+    (-0.5         -0.51        -0.52        -0.53        -0.53999996  -0.55        -0.56        -0.57        -0.58        -0.59)
+    (-0.59999996  -0.61        -0.62        -0.63        -0.64        -0.65        -0.65999997  -0.66999996  -0.68        -0.69)
+    (-0.7         -0.71        -0.71999997  -0.72999996  -0.74        -0.75        -0.76        -0.77        -0.78        -0.78999996)
+    (-0.79999995  -0.81        -0.82        -0.83        -0.84        -0.84999996  -0.85999995  -0.87        -0.88        -0.89)
+    (-0.9         -0.90999997  -0.91999996  -0.93        -0.94        -0.95        -0.96        -0.96999997  -0.97999996  -0.98999995))
+  :op #<PROCEEDNODE {10025BC6C3}>
+  :requires-grad NIL
+  :variables (STC131741)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !recip

+

(!recip tensor)
+
+!recip computes the reciprocal of the tensor.

+
lisp
CATEN-USER> (proceed (!recip (ax+b `(10 10) 0.01 0.1)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC131949
+   ((10.0       9.090909   8.333333   7.692308   7.142857   6.6666665  6.25       5.882353   5.5555553  5.263158)
+    (5.0000005  4.7619047  4.5454545  4.3478265  4.1666665  4.0        3.846154   3.7037036  3.5714285  3.448276)
+    (3.3333335  3.2258065  3.125      3.0303032  2.9411764  2.857143   2.777778   2.702703   2.631579   2.5641026)
+    (2.5000002  2.4390244  2.3809524  2.3255816  2.2727273  2.2222223  2.1739132  2.1276596  2.0833335  2.0408163)
+    (2.0000002  1.9607843  1.923077   1.8867925  1.8518518  1.8181818  1.7857143  1.754386   1.724138   1.6949153)
+    (1.6666666  1.6393442  1.6129032  1.5873016  1.5625     1.5384614  1.5151515  1.4925373  1.4705882  1.4492754)
+    (1.4285715  1.4084506  1.3888888  1.369863   1.3513514  1.3333334  1.3157895  1.2987013  1.2820512  1.2658228)
+    (1.25       1.2345679  1.2195122  1.2048193  1.1904762  1.1764705  1.1627907  1.1494253  1.1363636  1.1235955)
+    (1.1111112  1.098901   1.0869565  1.0752689  1.0638298  1.0526316  1.0416667  1.0309278  1.0204082  1.010101)
+    (1.0        0.990099   0.98039216 0.97087383 0.9615385  0.952381   0.9433963  0.9345795  0.92592597 0.91743124))
+  :op #<PROCEEDNODE {1002601A93}>
+  :requires-grad NIL
+  :variables (STC131845)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !square

+

(!square x)
+
+Computes the x*x

+
lisp
CATEN-USER> (proceed (!square (ax+b `(10 10) 0.01 0.1)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC132058
+   ((0.010000001 0.0121      0.014400001 0.0169      0.0196      0.0225      0.0256      0.028900001 0.0324      0.0361)
+    (0.039999995 0.044100005 0.0484      0.052899994 0.057600003 0.0625      0.0676      0.072900005 0.0784      0.08409999)
+    (0.08999999  0.0961      0.1024      0.10889999  0.115600005 0.122499995 0.12959999  0.13689998  0.1444      0.15209998)
+    (0.15999998  0.1681      0.17639999  0.18489999  0.1936      0.20249999  0.21159998  0.2209      0.2304      0.24009998)
+    (0.24999997  0.26009998  0.2704      0.28089997  0.29160002  0.3025      0.3136      0.3249      0.33639997  0.34809998)
+    (0.36        0.37210003  0.3844      0.3969      0.4096      0.42250004  0.43560004  0.4489      0.46240002  0.4761)
+    (0.48999998  0.5041      0.5184      0.53290004  0.54760003  0.5625      0.5776      0.5929      0.60840005  0.6241)
+    (0.64000005  0.6561      0.6724      0.6889      0.7056001   0.7225      0.7396      0.7569      0.7744      0.79209995)
+    (0.80999994  0.8281      0.8464      0.8649      0.8836      0.9025      0.9216      0.9409      0.96040004  0.98010004)
+    (1.0         1.0201      1.0403999   1.0609      1.0816      1.1024998   1.1235999   1.1448998   1.1663998   1.1880999))
+  :op #<PROCEEDNODE {1002646193}>
+  :requires-grad NIL
+  :variables (STC131957)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !rsqrt

+

(!rsqrt x)
+
+Computes the reciprocal of sqrt x.

+
lisp
CATEN-USER> (proceed (!rsqrt (ax+b `(10 10) 0.01 0.1)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC132353
+   ((3.1622777  3.0151134  2.8867514  2.7735012  2.6726124  2.5819888  2.5        2.4253561  2.3570225  2.2941573)
+    (2.236068   2.1821787  2.1320071  2.0851443  2.0412414  2.0        1.9611614  1.924501   1.8898224  1.8569535)
+    (1.825742   1.7960529  1.7677671  1.7407765  1.7149858  1.6903085  1.6666667  1.6439899  1.6222143  1.6012815)
+    (1.5811388  1.5617375  1.5430336  1.5249858  1.5075567  1.490712   1.4744196  1.4586499  1.4433757  1.4285715)
+    (1.4142135  1.40028    1.3867506  1.3736057  1.3608276  1.3483996  1.3363062  1.3245324  1.3130643  1.3018892)
+    (1.2909944  1.2803688  1.2700013  1.2598816  1.25       1.2403474  1.2309148  1.2216945  1.2126781  1.2038586)
+    (1.1952286  1.1867816  1.1785113  1.1704115  1.1624764  1.1547005  1.1470786  1.1396058  1.132277   1.1250879)
+    (1.118034   1.1111112  1.1043153  1.0976427  1.0910894  1.0846523  1.0783278  1.0721126  1.0660036  1.0599979)
+    (1.0540926  1.0482849  1.0425721  1.0369517  1.0314213  1.0259783  1.0206207  1.0153462  1.0101525  1.0050378)
+    (1.0        0.99503714 0.99014753 0.98532933 0.9805807  0.9759001  0.97128594 0.96673656 0.9622505  0.95782626))
+  :op #<PROCEEDNODE {1002702F63}>
+  :requires-grad NIL
+  :variables (STC132067)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !signum

+
(!signum x)
+
+

Returns the sign of the tensor. If the tensor is positive, it returns 1. If the tensor is negative, it returns -1. If the tensor is zero, it returns 0. Note that this function is not differentiable.

+
lisp
CATEN-USER> (proceed (!signum (ax+b `(10 10) 0.02 -0.1)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC135515
+   ((-1.0 -1.0 -1.0 -1.0 -1.0 -1.0 1.0  1.0  1.0  1.0)
+    (1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0)
+    (1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0)
+    (1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0)
+    (1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0)
+    (1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0)
+    (1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0)
+    (1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0)
+    (1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0)
+    (1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0))
+  :op #<PROCEEDNODE {1003CF9663}>
+  :requires-grad NIL
+  :variables (STC132953)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !gid

+

(!gid tensor rank &key (dtype *default-float*))
+
+Finds the rank th index components of the tensor.

+

For example (!gid x 1) for (3 3) tensor is a `(0 1 2). (As a tip) by combining !gid with !where, you can implement pseudo a random accessing of the tensor. For example:

+
lisp
CATEN-USER> (proceed (!gid (make-tensor `(3 3)) -1))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC136141
+   ((0.0 1.0 2.0)
+    (0.0 1.0 2.0)
+    (0.0 1.0 2.0))
+  :op #<PROCEEDNODE {1003E54E13}>
+  :requires-grad NIL
+  :variables (VID135525)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(~3 3) :contiguous-p=T>}
+
+
+ +

[function] !normalize-axis

+

(!normalize-axis ndim axis)
+
+Creates a tensor graph which normalizes the axis. If the axis is negative, it will be normalized to the positive axis.

+
lisp
CATEN-USER> (proceed (!normalize-axis 3 -1))
+
+
+Result +
Result
{Tensor[int64] :shape NIL :id STC136198
+  2
+  :op #<PROCEEDNODE {1003F05553}>
+  :requires-grad NIL
+  :variables (STC136148)
+  :tracker #<TRACKER :order={rowNIL} :shape=() :contiguous-p=T>}
+
+
+ +

[function] !abs

+
(!abs x)
+
+

Returns the absolute value of the tensor.

+
lisp
CATEN-USER> (proceed (!abs (ax+b `(10 10) 0.02 -0.1)))
+
+
+Result +
Result
{Tensor[float32] :shape (10 10) :id STC139369
+   ((0.1         0.08        0.060000002 0.040000003 0.020000003 7.450581e-9 0.019999996 0.04        0.059999995 0.07999999)
+    (0.09999999  0.12        0.13999999  0.16        0.18        0.19999999  0.22        0.24000001  0.26        0.28)
+    (0.29999998  0.32        0.34        0.35999998  0.38        0.4         0.42        0.43999997  0.46        0.48)
+    (0.49999997  0.52        0.53999996  0.55999994  0.58        0.59999996  0.61999995  0.64        0.65999997  0.67999995)
+    (0.6999999   0.71999997  0.73999995  0.75999993  0.78        0.79999995  0.81999993  0.84        0.85999995  0.87999994)
+    (0.9         0.91999996  0.93999994  0.9599999   0.9799999   1.0         1.02        1.04        1.06        1.0799999)
+    (1.0999999   1.12        1.14        1.16        1.18        1.1999999   1.2199999   1.2399999   1.26        1.28)
+    (1.3         1.3199999   1.3399999   1.3599999   1.38        1.4         1.42        1.4399999   1.4599999   1.4799999)
+    (1.4999999   1.52        1.54        1.56        1.5799999   1.5999999   1.6199999   1.64        1.66        1.68)
+    (1.6999999   1.7199999   1.7399999   1.76        1.78        1.8         1.8199999   1.8399999   1.8599999   1.8799999))
+  :op #<PROCEEDNODE {1004B9EC23}>
+  :requires-grad NIL
+  :variables (STC136799)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(10 10) :contiguous-p=T>}
+
+
+ +

[function] !>

+
(!> x y)
+
+

Compares x and y element-wise and returns the result as a boolean tensor.

+
lisp
CATEN-USER> (proceed (!where (!> (rand `(3 3)) (randn `(3 3))) (iconst 1) (iconst 0)))
+
+
+Result +
Result
{Tensor[int64] :shape (3 3) :id STC140821
+   ((1 0 0)
+    (0 0 0)
+    (1 1 0))
+  :op #<PROCEEDNODE {10050C0A03}>
+  :requires-grad NIL
+  :variables (STC139582)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !<

+
(!< x y)
+
+

Compares x and y element-wise and returns the result as a boolean tensor.

+
lisp
CATEN-USER> (proceed (!where (!< (rand `(3 3)) (randn `(3 3))) (iconst 1) (iconst 0)))
+
+
+Result +
Result
{Tensor[int64] :shape (3 3) :id STC142265
+   ((0 0 0)
+    (0 0 0)
+    (0 0 1))
+  :op #<PROCEEDNODE {1005B17883}>
+  :requires-grad NIL
+  :variables (STC141034)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !>=

+
(!>= x y)
+
+

Compares x and y element-wise and returns the result as a boolean tensor.

+
lisp
CATEN-USER> (proceed (!where (!>= (rand `(3 3)) (randn `(3 3))) (iconst 1) (iconst 0)))
+
+
+Result +
Result
{Tensor[int64] :shape (3 3) :id STC143723
+   ((1 1 1)
+    (1 1 0)
+    (1 0 0))
+  :op #<PROCEEDNODE {1005F777A3}>
+  :requires-grad NIL
+  :variables (STC142478)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !<=

+
(!<= x y)
+
+

Compares x and y element-wise and returns the result as a boolean tensor.

+
lisp
CATEN-USER> (proceed (!where (!<= (rand `(3 3)) (randn `(3 3))) (iconst 1) (iconst 0)))
+
+
+Result +
Result
{Tensor[int64] :shape (3 3) :id STC145173
+   ((1 1 0)
+    (0 0 1)
+    (1 0 1))
+  :op #<PROCEEDNODE {1006A2E083}>
+  :requires-grad NIL
+  :variables (STC143936)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !eq

+
(!EQ x y)
+
+

Compares x and y element-wise and returns the result as a boolean tensor.

+
lisp
CATEN-USER> (proceed (!where (!eq (rand `(3 3)) (randn `(3 3))) (iconst 1) (iconst 0)))
+
+
+Result +
Result
{Tensor[int64] :shape (3 3) :id STC146619
+   ((0 0 0)
+    (0 0 0)
+    (0 0 0))
+  :op #<PROCEEDNODE {1006E36A83}>
+  :requires-grad NIL
+  :variables (STC145386)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !neq

+
(!NEQ x y)
+
+

Compares x and y element-wise and returns the result as a boolean tensor.

+
lisp
CATEN-USER> (proceed (!where (!neq (rand `(3 3)) (randn `(3 3))) (iconst 1) (iconst 0)))
+
+
+Result +
Result
{Tensor[int64] :shape (3 3) :id STC148063
+   ((1 1 1)
+    (1 1 1)
+    (1 1 1))
+  :op #<PROCEEDNODE {10018D4EF3}>
+  :requires-grad NIL
+  :variables (STC146832)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !and

+
(!AND x y)
+
+

Computes the logical/bitwise and of the tensor.

+
lisp
CATEN-USER> (proceed (!and (iconst 5) (iconst 3)))
+
+
+Result +
Result
{Tensor[int64] :shape NIL :id STC148081
+  1
+  :op #<PROCEEDNODE {10018FC5B3}>
+  :requires-grad NIL
+  :variables (STC148066)
+  :tracker #<TRACKER :order={rowNIL} :shape=() :contiguous-p=T>}
+
+
+ +

[function] !xor

+
(!XOR x y)
+
+

Computes the logical/bitwise xor of the tensor.

+
lisp
CATEN-USER> (proceed (!xor (iconst 5) (iconst 3)))
+
+
+Result +
Result
{Tensor[int64] :shape NIL :id STC148099
+  6
+  :op #<PROCEEDNODE {1001913843}>
+  :requires-grad NIL
+  :variables (STC148084)
+  :tracker #<TRACKER :order={rowNIL} :shape=() :contiguous-p=T>}
+
+
+ +

[function] !or

+
(!OR x y)
+
+

Computes the logical/bitwise or of the tensor.

+
lisp
CATEN-USER> (proceed (!or (iconst 5) (iconst 3)))
+
+
+Result +
Result
{Tensor[int64] :shape NIL :id STC148117
+  7
+  :op #<PROCEEDNODE {1001922AD3}>
+  :requires-grad NIL
+  :variables (STC148102)
+  :tracker #<TRACKER :order={rowNIL} :shape=() :contiguous-p=T>}
+
+
+ +

[function] !where

+
(!where condition x y)
+
+

Selects elements from x or y based on the condition. If the condition is true, it selects the element from x, otherwise from y.

+
lisp
CATEN-USER> (proceed (!where (!eq (rand `(3 3)) (randn `(3 3))) (iconst 1) (iconst 0)))
+
+
+Result +
Result
{Tensor[int64] :shape (3 3) :id STC149563
+   ((0 0 0)
+    (0 0 0)
+    (0 0 0))
+  :op #<PROCEEDNODE {100201B003}>
+  :requires-grad NIL
+  :variables (STC148330)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !const

+

(!const tensor value)
+
+Creates a constant tensor with the specified value from the tensor.

+
lisp
CATEN-USER> (proceed (!const (make-tensor `(3 3)) 1.0))
+
+
+Result +
Result
{Tensor[float32] :shape NIL :id STC149578
+  1.0
+  :op #<PROCEEDNODE {1002030793}>
+  :requires-grad NIL
+  :variables (SID149568)
+  :tracker #<TRACKER :order={rowNIL} :shape=() :contiguous-p=T>}
+
+
+ +

[generic] !index-components

+
(!index-components object)
+
+

Returns the index components of the tensor. object can be either of tensor or list.

+
lisp
CATEN-USER> (proceed (!index-components (make-tensor `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC149661
+   ((0.0 1.0 2.0)
+    (3.0 4.0 5.0)
+    (6.0 7.0 8.0))
+  :op #<PROCEEDNODE {1002041CD3}>
+  :requires-grad NIL
+  :variables (STC149583)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +
lisp
CATEN-USER> (proceed (!index-components `(1 3)))
+
+
+Result +
Result
{Tensor[float32] :shape (1 3) :id STC149744
+   ((0.0 1.0 2.0))
+  :op #<PROCEEDNODE {100205B203}>
+  :requires-grad NIL
+  :variables (STC149666)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(1 3) :contiguous-p=T>}
+
+
+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.apis.facet_apis/index.html b/packages/caten.apis.facet_apis/index.html new file mode 100644 index 000000000..6037c3fb9 --- /dev/null +++ b/packages/caten.apis.facet_apis/index.html @@ -0,0 +1,632 @@ + + + + + + + + + + + + + + + + + + + + + + Facet APIs | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Facet APIs

+ + + + +
+
+ +
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+
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+
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+ +
+
+
+ +
+ + +

Facet APIs

+

We are going to see how to access the Tensor as a lisp object.

+

[generic] change-facet

+
(change-facet obj direction)
+
+

change-facet converts obj to the data type specified by direction. +During the conversion process, it attempts to synchronize the Buffer (i.e., no copying is performed).

+

In default, :direction could be one of :tensor, :simple-array, :array.

+

Users can extend this method if needed.

+
lisp
CATEN-USER> (change-facet 1 :tensor)
+
+
+Result +
Result
{Tensor[int64] :shape NIL :id STC315298
+  1
+  :op #<PROCEEDNODE {10086B6643}>
+  :requires-grad NIL
+  :variables (SID315288)
+  :tracker #<TRACKER :order={rowNIL} :shape=() :contiguous-p=T>}
+
+
+ +
lisp
CATEN-USER> (change-facet '((1.0 2.0 3.0) (4.0 5.0 6.0)) :tensor)
+
+
+Result +
Result
{Tensor[float32] :shape (2 3) :id TID315299
+   ((1.0 2.0 3.0)
+    (4.0 5.0 6.0))
+  :op #<ALLOCATE {10086B6C03}>
+  :requires-grad NIL
+  :variables NIL
+  :tracker #<TRACKER :order={row(0 1)} :shape=(2 3) :contiguous-p=T>}
+
+
+ +
lisp
CATEN-USER> (change-facet #(1 2 3) :tensor)
+
+
+Result +
Result
{Tensor[int64] :shape (3) :id TID315303
+   (1 2 3)
+  :op #<ALLOCATE {10086B6D03}>
+  :requires-grad NIL
+  :variables NIL
+  :tracker #<TRACKER :order={row(0)} :shape=(3) :contiguous-p=T>}
+
+
+ +
lisp
CATEN-USER> (change-facet (rand `(2 2)) :simple-array)
+
+
+Result +
Result
#(0.044882257 0.29727465 0.3416528 0.28447717)
+
+
+ +
lisp
CATEN-USER> (change-facet (rand `(2 2)) :array)
+
+
+Result +
Result
#2A((0.20724466 0.4652091) (0.09929559 0.52421707))
+
+
+ +

[macro] with-facet

+
(with-facet (bind (object &key (direction :array))) &body body)
+
+

Binds the result of (change-facet object direction) to the bind.

+

[macro] with-facets

+
(with-facets ((&rest input-forms) &body body))
+
+

Expands to a series of with-facet forms.

+

If you want to access an individual element of Tensor, it is wiser to convert it into an Array. The following code snippet initializes the diagonal of a Tensor to 0.0 without creating a copy:

+
lisp
CATEN-USER> (let ((x (rand `(3 3))))
+  (with-facet (a (x :direction :array))
+    (setf (aref a 0 0) 0.0
+          (aref a 1 1) 0.0
+          (aref a 2 2) 0.0))
+  (print x))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id TID315317
+   ((0.0         0.96047294  0.079053536)
+    (0.29605937  0.0         0.07566601)
+    (0.6344492   0.97433025  0.0))
+  :op #<ALLOCATE {1008802AC3}>
+  :requires-grad NIL
+  :variables NIL
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.apis.initializers/index.html b/packages/caten.apis.initializers/index.html new file mode 100644 index 000000000..fb0daa4a9 --- /dev/null +++ b/packages/caten.apis.initializers/index.html @@ -0,0 +1,829 @@ + + + + + + + + + + + + + + + + + + + + + + Initializers | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Initializers

+ + + + +
+
+ +
+ + + + + + +
+
+ + + + | + + + + + +
+
+ + + + + + + + + +
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+ +
+
+
+ +
+ + +

Initializers

+

[function] set-manual-seed

+

(set-manual-seed &key (seed 0))
+
+Sets the seed for random operations.

+

[macro] with-manual-seed

+

(with-manual-seed (seed) &body body)
+
+Sets the seed for random operations within the scope of the body.

+

[function] ax+b

+
(ax+b shape a b &key (out nil) (dtype *default-float*) (order *default-order*))
+
+

Generates an array sampled from this formula: x_i = a * index_components(i) + b.

+

There is a linspace function for the same purpose, but it is not lazy.

+
Lazy
CATEN-USER> (proceed (ax+b `(3 3) 2 1))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC203205
+   ((1.0  3.0  5.0)
+    (7.0  9.0  11.0)
+    (13.0 15.0 17.0))
+  :op #<PROCEEDNODE {10054EC033}>
+  :requires-grad NIL
+  :variables (STC203115)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +
Static
CATEN-USER> (linspace `(3 3) 1 0)
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id TID203206
+   ((0.0 1.0 2.0)
+    (3.0 4.0 5.0)
+    (6.0 7.0 8.0))
+  :op #<ALLOCATE {1005B710C3}>
+  :requires-grad NIL
+  :variables NIL
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !rand

+
(!rand shape &key (dtype *default-float*) (order *default-order*) (out nil))
+
+

Creates a tensor whose elements are randomly sampled from a uniform distribution over the interval [0, 1).

+

There is a rand function for the same purpose, but it is not lazy.

+
Lazy
CATEN-USER> (proceed (!rand `(3 3)))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC210745
+   ((0.5561414  0.5751883  0.832701)
+    (0.7853549  0.37660313 0.84738994)
+    (0.55430925 0.9354172  0.9739074))
+  :op #<PROCEEDNODE {10076FD1C3}>
+  :requires-grad NIL
+  :variables (STC203277)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +
Static
CATEN-USER> (rand `(3 3))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id TID210746
+   ((0.53696483  0.6626895   0.9899716)
+    (0.33544847  0.3335809   0.6769852)
+    (0.041966714 0.015882287 0.753615))
+  :op #<ALLOCATE {10076FDAB3}>
+  :requires-grad NIL
+  :variables NIL
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !normal

+
(!normal shape &key (mean 0.0) (std 1.0) (dtype *default-float*) (order *default-order*) (out nil))
+
+

Creates a tensor whose elements are randomly sampled from a normal distribution with the given mean and std.

+

There is a normal function for the same purpose, but it is not lazy.

+
Lazy
CATEN-USER> (proceed (!normal `(3 3) :mean 0.0 :std 2.0))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC226682
+   ((0.22197889  -4.817684   0.18407516)
+    (-1.9114828  -0.43231335 -0.90131146)
+    (-2.5488627  -0.80741    -1.066406))
+  :op #<PROCEEDNODE {10068BF753}>
+  :requires-grad NIL
+  :variables (STC210758)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +
Static
CATEN-USER> (normal `(3 3) :mean 0.0 :std 1.0)
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id TID226683
+   ((0.20325683 1.9010925  0.11178739)
+    (0.06818088 0.10441013 0.98638976)
+    (0.6173678  -1.0524902 -0.3097188))
+  :op #<ALLOCATE {10069284B3}>
+  :requires-grad NIL
+  :variables NIL
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !randn

+
(!randn shape &key (dtype *default-float*) (order *default-order*) (out nil))
+
+

Creates a tensor whose elements are randomly sampled from a normal distribution with a mean of 0 and a standard deviation of 1.

+

There is a randn function for the same purpose, but it is not lazy.

+
Lazy
CATEN-USER> (proceed (!randn `(3 3)))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC241391
+   ((-1.0663555   -0.2564156   0.08828225)
+    (2.0705404    -0.094690986 1.1788496)
+    (-1.4148906   1.0167512    -0.6521854))
+  :op #<PROCEEDNODE {1004677753}>
+  :requires-grad NIL
+  :variables (STC226693)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +
Static
CATEN-USER> (randn `(3 3))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id TID241392
+   ((0.8697504   -2.7880201  1.4020109)
+    (-0.47702768 0.6844121   -0.68407243)
+    (1.3354832   -0.36871713 -0.22123063))
+  :op #<ALLOCATE {1004838483}>
+  :requires-grad NIL
+  :variables NIL
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !uniform

+
(!uniform shape &key (low 0.0) (high 1.0) (dtype *default-float*) (order *default-order*) (out nil))
+
+

Creates a tensor whose elements are randomly sampled from a uniform distribution over the interval [low, high)

+

There is a uniform function for the same purpose, but it is not lazy.

+
Lazy
CATEN-USER> (proceed (!uniform `(3 3) :low 1.0 :high 2.0))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC250162
+   ((1.590658  1.0057554 1.2562647)
+    (1.1458193 1.6777649 1.8310666)
+    (1.8900917 1.3517946 1.3587387))
+  :op #<PROCEEDNODE {100706D1C3}>
+  :requires-grad NIL
+  :variables (STC241404)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +
Static
CATEN-USER> (uniform `(3 3) :low 1.0 :high 2.0)
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id TID250163
+   ((1.8804288 1.6129477 1.8870113)
+    (1.0641054 1.7846086 1.8476219)
+    (1.3157417 1.4555242 1.7016529))
+  :op #<ALLOCATE {1003FB3183}>
+  :requires-grad NIL
+  :variables NIL
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !randint

+
(!randint shape &key (low 0) (high 1) (dtype *default-int*) (order *default-order*) (out nil))
+
+

Creates a tensor whose elements are randomly sampled from a uniform distribution over the interval [low, high).

+

There is a randint function for the same purpose, but it is not lazy.

+
Lazy
CATEN-USER> (ctx:with-contextvar (:jit 1) (proceed (!randint `(3 3) :low 1 :high 10)))
+
+
+Result +
Result
{Tensor[int64] :shape (3 3) :id STC287365
+   ((6 4 4)
+    (9 3 6)
+    (9 6 9))
+  :op #<PROCEEDNODE {1005E05EC3}>
+  :requires-grad NIL
+  :variables (STC257801)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +
Static
CATEN-USER> (ctx:with-contextvar (:jit 1) (randint `(3 3) :low 1 :high 10 :dtype :int32))
+
+
+Result +
Result
{Tensor[int32] :shape (3 3) :id TID287366
+   ((3 6 4)
+    (8 9 4)
+    (7 8 6))
+  :op #<ALLOCATE {10084669A3}>
+  :requires-grad NIL
+  :variables NIL
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !full

+
(!full shape fill-value &key (dtype *default-float*) (order *default-order*))
+
+

Initializes a tensor filled with fill-value.

+
lisp
CATEN-USER> (proceed (!full `(3 3) (inf)))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC315261
+   ((#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY)
+    (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY)
+    (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY))
+  :op #<PROCEEDNODE {100849EA03}>
+  :requires-grad NIL
+  :variables (TID315199)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] xavier-uniform

+

No description provided

+

[function] xavier-gaussian

+

No description provided

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.apis.models/index.html b/packages/caten.apis.models/index.html new file mode 100644 index 000000000..3ea5a0cf4 --- /dev/null +++ b/packages/caten.apis.models/index.html @@ -0,0 +1,587 @@ + + + + + + + + + + + + + + + + + + + + + + Model | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Model

+ + + + +
+
+ +
+ + + + + + +
+
+ + + + | + + + + + +
+
+ + + + + + + + + +
    +
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    Share via
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    • +
    • +
+ +
+
+
+ +
+ + +

Models

+

TODO

+

[macro] defcall

+
(defcall (model-bind model) (&rest inputs) body0
+
+

A macro to write defmethod call in a more concise way.

+

Example

+
(defcall (model Transformer) (Tokens[Batch Seq-Len] Start-Pos[])
+  (with-slots ((wte wte) (wpe wpe) (h h) (ln-f ln-f) (lm-head lm-head)) model
+    (let* ((token-emb (forward wte tokens))
+       (pos-emb   (forward wpe (!cast (!add start-pos (!index-components `(1 ,seq-len))) (dtype-of tokens))))
+       (hi (!add token-emb pos-emb))
+       (mask (!triu (!full `(1 1 ,seq-len ,(!+ start-pos (iconst seq-len))) (-inf)) :diagonal (!+ (iconst 1) start-pos)))
+       (_ (dolist (hn h) (setf hi (forward hn hi mask start-pos))))
+       (logits (forward lm-head (forward ln-f hi))))
+      (declare (ignore _))
+      ;; (!argmax (!view logits t -1 t))
+      (!argmax logits))))
+
+

[macro] defsequence

+
(defsequence (name (&rest args) &optional docstring &rest nodes))
+
+

Defines a model which the definition is given as a sequence of nodes. Note that models defined by this macro only accept one input.

+

Example

+
(defsequence MLP (in-features hidden-dim out-features &key (activation #'!relu))
+         (Linear in-features hidden-dim)
+         (asnode activation)
+         (Linear hidden-dim hidden-dim)
+         (asnode activation)
+         (Linear hidden-dim out-features))
+
+

[function] asnode

+
(asnode function &rest rest-args)
+
+

Wraps the function as a callable node by (forward ...). function is a function which takes one argument and returns one value.

+

rest-args is a place to pass additional arguments like: (asnode #'!leaky-relu :neg-slope 1e-2)

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.apis.module/index.html b/packages/caten.apis.module/index.html new file mode 100644 index 000000000..e376032be --- /dev/null +++ b/packages/caten.apis.module/index.html @@ -0,0 +1,1195 @@ + + + + + + + + + + + + + + + + + + + + + + Module | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Module

+ + + + +
+
+ +
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+ + +

Module

+

[macro] defmodule

+
(defmodule (name ((&rest constructor-args) &rest attrs) &key (where nil) (direct-superclasses nil))
+  (&rest slots)
+  &key (documentation "") (impl nil) (forward nil) (backward nil))
+
+

Define a module named name.

+

In Caten, Module is a CLOS class that represents a set of Funcs and is defined as a subclass of Func itself. It is used to represent computational nodes that can be expressed through compositions of Funcs. Consequently, as a subclass of Func, Module utilizes the following three methods for manipulation:

+

[method] impl

+

(impl (op Module) &rest tensors)

+
    +
  • tensors[list] a list of the input tensor.
    • +
+

In the impl method, please describe the process for constructing the computational graph of the Module using a combination of Func and Module. +The computational graph must begin with the inputs. +If there are multiple outputs, bind them with cl:values. +If you need to record Tensors for the backward process, now is the time to do so.

+

[method] forward

+

(forward (op Module) &rest tensors)

+
    +
  • tensors[List] a list of the input tensor.
    • +
+

In the forward method, describe the operation to create a Tensor after computation. +Be mindful of its lazy evaluation nature; do not perform the actual computation at this stage. +The st macro in ShapeTracker is quite useful for creating the Tensor after the operation. If necessary, you may also include checks for additional attributes or slots here. +If you specify ShapeTracker in :where, the defmodule macro will automatically generate the forward. +Therefore, you must describe either :where or :forward.

+

[method] backward (optional)

+

(backward (op Module) prev-grad) -> (values input_1.grad input_2.grad ...)

+
    +
  • prev-grad[Tensor]
    • +
+

In the backward method, describe the gradient computation for the Module using a combination of Func and Module. +The arguments are fixed as (op prev-grad), where op = module instance, prev-grad is a tensor. +If you need the value of the Tensor at the input stage for the gradient calculation, temporarily store it using the module-sv4bws accessor while calling the impl method. +The compiler does not depend on module-sv4bws, so you are free to choose how to store the Tensor. +In Caten, since save-for-backward is automatically determined, there is no need to be concerned about in-place operations.

+

Note that backward is optional. If it is not provided, AD will be applied based on the computational graph from impl.

+

[method] lower

+

The lower method is automatically written by the defmodule, so there is no need to consider it when describing the module. +However, it is necessary to understand how it is lowered for when defining simplifiers for the Module.

+

lower produces the following node: +(make-node :Graph (intern (symbol-name (symb 'graph/ name)) "KEYWORD") outputs inputs &rest attrs)

+

Nodes whose class is :Graph are completely eliminated during lower by impl.

+

Syntax

+

forward, backward, impl are described in one of the following format.

+
forward := ((op &rest args) &body body)
+forward := (lambda (&rest args) &body body)
+forward := fname
+
+

Effects

+
    +
  • it defines a class named name.
    • +
  • it defines a function named name. it works as a constructor.
    • +
+

Arguments

+
    +
  • name[symbol] the name of module
    • +
  • constructor-args[list] arguments for the constructor
    • +
  • attrs[list] define attrs for the lowered graph based on the constructor-args variables using the following format: (:key1 value1 :key1 value2 ...).
    • +
  • slots[list] slots for the defined class.
    • +
  • where[nil or string] ShapeTracker
    • +
  • documentation[string] documentation
    • +
+

Notes

+
    +
  • The methods are called in the order of forward->impl->backward during compilation
    • +
  • impl is performed recursively, so modules must not be co-dependent within the impl method. (e.g.: do not define a module A that depends on B that depends on A ...)
    • +
+

Modules (built_in)

+

[function] !sum

+
(!sum x &key (axis t) (keepdims nil))
+
+

Compute the sum of the tensor.

+
lisp
CATEN-USER> (proceed (!sum (ax+b `(10 10) 1 0)))
+
+
+Result +
Result
{Tensor[float32] :shape (1 1) :id STC151141
+   ((4950.0))
+  :op #<PROCEEDNODE {1002328023}>
+  :requires-grad NIL
+  :variables (VID149847)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(~1 ~1) :contiguous-p=T>}
+
+
+ +

[function] !mean

+
(!mean x &key (axis t) (keepdims nil))
+
+

Compute the mean of the tensor.

+
lisp
CATEN-USER> (proceed (!mean (ax+b `(10 10) 1 0)))
+
+
+Result +
Result
{Tensor[float32] :shape (1 1) :id STC152868
+   ((49.5))
+  :op #<PROCEEDNODE {1002746163}>
+  :requires-grad NIL
+  :variables (VID151244)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(~1 ~1) :contiguous-p=T>}
+
+
+ +

[function] !max

+
(!max x &key (axis t) (keepdims nil))
+
+

Compute the maximum of the tensor.

+
lisp
CATEN-USER> (proceed (!max (ax+b `(10 10) 1 0)))
+
+
+Result +
Result
{Tensor[float32] :shape (1 1) :id STC154939
+   ((99.0))
+  :op #<PROCEEDNODE {1003BAD2D3}>
+  :requires-grad NIL
+  :variables (VID152971)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(~1 ~1) :contiguous-p=T>}
+
+
+ +

[function] !min

+
(!min x &key (axis t) (keepdims nil))
+
+

Compute the minimum of the tensor.

+
lisp
CATEN-USER> (proceed (!min (ax+b `(10 10) 1 0)))
+
+
+Result +
Result
{Tensor[float32] :shape (1 1) :id STC157028
+   ((0.0))
+  :op #<PROCEEDNODE {10045C5DF3}>
+  :requires-grad NIL
+  :variables (VID155042)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(~1 ~1) :contiguous-p=T>}
+
+
+ +

[function] !matmul

+
(!matmul a b)
+
+

Performs matrix multiplication between two tensors a and b.

+
lisp
CATEN-USER> (proceed (!matmul (rand `(32 64)) (rand `(64 128))))
+
+
+Result +
Result
{Tensor[float32] :shape (32 128) :id STC161494
+   ((15.361223  15.828152  14.417058  15.882298  15.45585   ~ 13.062644  16.183292  12.734098  16.734056  15.52915)
+    (14.835705  14.845199  16.404129  16.800152  16.33881   ~ 14.190987  15.975574  14.354835  15.514871  15.666682)
+    (15.371529  15.849551  15.536994  15.782414  16.02211   ~ 13.076694  15.905483  13.951732  14.709311  16.353771)
+    (17.939054  17.170252  19.312342  18.902992  19.101515  ~ 14.990455  17.7107    13.865579  17.309923  18.022947)
+    (15.27028   14.820869  15.772669  15.838371  15.730969  ~ 13.963487  16.55142   13.431679  14.782211  15.346079)
+                                                        ...
+    (14.130519  14.243071  16.591045  15.483922  15.168219  ~ 12.99691   14.225608  13.279083  14.397368  14.895684)
+    (14.720093  15.086392  16.987844  16.13062   15.741239  ~ 14.447361  16.014956  13.712358  14.148518  15.63789)
+    (18.153896  17.43257   17.920296  18.44725   17.450092  ~ 15.145452  18.734285  15.93504   17.834435  17.902695)
+    (14.291782  15.027209  17.237173  16.649723  16.29012   ~ 13.699114  16.332775  15.594537  15.32779   16.408815)
+    (13.0013075 12.744859  14.356513  13.985053  14.217204  ~ 11.638837  14.574662  12.32615   11.624662  14.840029))
+  :op #<PROCEEDNODE {10050583F3}>
+  :requires-grad NIL
+  :variables (STC157041)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(32 128) :contiguous-p=T>}
+
+
+ +

[function] !sinh

+
(!sinh x)
+
+
lisp
CATEN-USER> (proceed (!sinh (randn `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC162259
+   ((-0.07758549 1.7127867   0.5037463)
+    (-0.431553   -0.93981755 -2.0931177)
+    (0.3535945   0.108312786 -0.27719477))
+  :op #<PROCEEDNODE {1005AF0E13}>
+  :requires-grad NIL
+  :variables (STC161501)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !cosh

+
(!cosh x)
+
+
lisp
CATEN-USER> (proceed (!cosh (randn `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC163018
+   ((2.2316532 1.5844023 1.1880606)
+    (4.6957664 1.0567473 1.0100629)
+    (2.2520223 1.0415792 1.0012665))
+  :op #<PROCEEDNODE {1005F97A63}>
+  :requires-grad NIL
+  :variables (STC162266)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !tanh

+
(!tanh x)
+
+
lisp
CATEN-USER> (proceed (!tanh (randn `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC165777
+   ((0.7690325   -0.59365016 0.81341624)
+    (-0.17309564 0.36871922  -0.34405994)
+    (0.64914274  -0.7870514  -0.19824558))
+  :op #<PROCEEDNODE {1006C4B1C3}>
+  :requires-grad NIL
+  :variables (STC163025)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !cos

+
(!cos x)
+
+
lisp
CATEN-USER> (proceed (!cos (randn `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC166463
+   ((0.99518865   0.95479304   0.7415326)
+    (0.5100029    -0.06835051  0.84506404)
+    (0.99555033   -0.061528657 0.97121775))
+  :op #<PROCEEDNODE {1006E4ED53}>
+  :requires-grad NIL
+  :variables (STC165784)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !tan

+
(!tan x)
+
+
lisp
CATEN-USER> (proceed (!tan (randn `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC167188
+   ((-1.348232     -0.47360304   -0.19166948)
+    (-0.45079947   0.096330844   -0.0046434165)
+    (-1.4844729    -0.74011093   -1.0703554))
+  :op #<PROCEEDNODE {1007187653}>
+  :requires-grad NIL
+  :variables (STC166470)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !log2

+
(!log2 x)
+
+
lisp
CATEN-USER> (proceed (!log2 (ax+b `(3 3) 1 0.1)))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC167945
+   ((-3.321928  0.13750356 1.0703893)
+    (1.6322682  2.0356238  2.3504972)
+    (2.6088092  2.827819   3.017922))
+  :op #<PROCEEDNODE {100746C593}>
+  :requires-grad NIL
+  :variables (STC167196)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !exp2

+
(!exp2 x)
+
+
lisp
CATEN-USER> (proceed (!exp2 (ax+b `(3 3) 1 0.1)))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC168685
+   ((1.0717734 2.143547  4.2870936)
+    (8.574187  17.148375 34.29675)
+    (68.593475 137.18695 274.37408))
+  :op #<PROCEEDNODE {1007606D43}>
+  :requires-grad NIL
+  :variables (STC167953)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !expt

+

(!expt base power)
+
+Computes the power of base with power.

+
lisp
CATEN-USER> (proceed (!expt (ax+b `(3 3) 1 0.1) 2))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC170701
+   ((0.010000001 1.21        4.4099994)
+    (9.61        16.81       26.009998)
+    (37.21       50.41       65.61001))
+  :op #<PROCEEDNODE {100846E013}>
+  :requires-grad NIL
+  :variables (STC168988)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !truncate

+
(!truncate x)
+
+
lisp
CATEN-USER> (proceed (!truncate (randn `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC170894
+   ((0.0  0.0  1.0)
+    (0.0  0.0  0.0)
+    (1.0  -1.0 0.0))
+  :op #<PROCEEDNODE {100867B4F3}>
+  :requires-grad NIL
+  :variables (STC170708)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !ceiling

+
(!ceiling x)
+
+
lisp
CATEN-USER> (proceed (!ceiling (randn `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC171819
+   ((1.0  -1.0 1.0)
+    (1.0  0.0  0.0)
+    (0.0  0.0  1.0))
+  :op #<PROCEEDNODE {1008A14863}>
+  :requires-grad NIL
+  :variables (STC170901)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !floor

+
(!floor x)
+
+
lisp
CATEN-USER> (proceed (!floor (randn `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC172740
+   ((0.0  0.0  1.0)
+    (0.0  -1.0 -1.0)
+    (-1.0 0.0  -2.0))
+  :op #<PROCEEDNODE {1008DEEFE3}>
+  :requires-grad NIL
+  :variables (STC171826)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !triu

+
(!triu x &key (diagonal 0))
+
+

Returns the upper triangular part of the tensor (>= 2D) or batch of matrices input.

+
lisp
CATEN-USER> (proceed (!triu (rand `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC174099
+   ((0.498627    0.5731704   0.24880171)
+    (0.0         0.044707708 0.7780621)
+    (0.0         0.0         0.45015103))
+  :op #<PROCEEDNODE {1003C42673}>
+  :requires-grad NIL
+  :variables (STC172747)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !tril

+
(!tril x &key (diagonal 0))
+
+

Returns the lower triangular part of the tensor (>= 2D) or batch of matrices input.

+
lisp
CATEN-USER> (proceed (!tril (rand `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC175466
+   ((0.5187954   0.0         0.0)
+    (0.6216609   0.9618312   0.0)
+    (0.020816622 0.5980801   0.5765489))
+  :op #<PROCEEDNODE {10045A9463}>
+  :requires-grad NIL
+  :variables (STC174106)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !argmax

+
(!argmax x &key (axis -1) (keepdims nil))
+
+

Returns the indices of the maximum values along an axis.

+
lisp
CATEN-USER> (proceed (!argmax (rand `(3 3))))
+
+
+Result +
Result
{Tensor[int64] :shape (3 1) :id STC182222
+   ((2)
+    (2)
+    (2))
+  :op #<PROCEEDNODE {1005F67493}>
+  :requires-grad NIL
+  :variables (VID175568)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 ~1) :contiguous-p=T>}
+
+
+ +

[function] !argmin

+
(!argmin x &key (axis -1) (keepdims nil))
+
+

Returns the indices of the minimum values along an axis.

+
lisp
CATEN-USER> (proceed (!argmin (rand `(3 3))))
+
+
+Result +
Result
{Tensor[int64] :shape (3 1) :id STC189167
+   ((0)
+    (1)
+    (2))
+  :op #<PROCEEDNODE {10074D8023}>
+  :requires-grad NIL
+  :variables (VID182324)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 ~1) :contiguous-p=T>}
+
+
+ +

[function] !clip

+
(!clip x min max)
+
+

!clip limits the given input within an interval. The interval is specified by the inputs 'min' and 'max'. min/max is either a number, a symbol, or a tensor.

+

The implementation follows the ONNX specification. https://github.com/onnx/onnx/blob/main/docs/Changelog.md#clip-13

+
lisp
CATEN-USER> (proceed (!clip (randn `(3 3)) -0.3 0.3))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC191761
+   ((0.3         -0.3        0.3)
+    (0.3         -0.3        0.3)
+    (-0.22691669 -0.3        0.3))
+  :op #<PROCEEDNODE {1008602533}>
+  :requires-grad NIL
+  :variables (STC189364)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] !erf

+

(!erf x)
+
+Computes the error function of x.

+
lisp
CATEN-USER> (proceed (!erf (randn `(3 3))))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC203108
+   ((0.984875    0.81764907  -0.99786603)
+    (-0.8090681  0.9998888   -0.67663276)
+    (0.99501437  0.98214996  0.49436408))
+  :op #<PROCEEDNODE {1005492BB3}>
+  :requires-grad NIL
+  :variables (STC191768)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.apis.shapetracker/index.html b/packages/caten.apis.shapetracker/index.html new file mode 100644 index 000000000..c201cc8b6 --- /dev/null +++ b/packages/caten.apis.shapetracker/index.html @@ -0,0 +1,519 @@ + + + + + + + + + + + + + + + + + + + + + + ShapeTracker | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

ShapeTracker

+ + + + +
+
+ +
+ + + + + + +
+
+ + + + | + + + + + +
+
+ + + + + + + + + +
    +
  • +
    Share via
    +
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  • + + +  Edit this article + + +
    • +
    • +
+ +
+
+
+ +
+ + +

ShapeTracker

+

TODO

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.apis.state-dict/index.html b/packages/caten.apis.state-dict/index.html new file mode 100644 index 000000000..5b6c0004e --- /dev/null +++ b/packages/caten.apis.state-dict/index.html @@ -0,0 +1,637 @@ + + + + + + + + + + + + + + + + + + + + + + StateDict | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

StateDict

+ + + + +
+
+ +
+ + + + + + +
+
+ + + + | + + + + + +
+
+ + + + + + + + + +
    +
  • +
    Share via
    +
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    • +
    • +
    • +
  • + + +  Edit this article + + +
    • +
    • +
+ +
+
+
+ +
+ + +

State-Dict

+

[struct] State-Dict

+

A structure representing the state dictionary of a neural network model.

+
    +
  • Entry[Hash-Table]: A hash table containing the parameters of the model, where:
    • +
  • Key[string]: A string representing the parameter name, following a naming convention that reflects the model's architecture.
    • +
  • Value[Tensor]: A tensor containing the parameter values associated with the key.
    • +
+

This hash table stores all the parameters of the model, enabling easy saving, loading, and manipulation of model weights.

+

[generic] ->state-dict

+
(->state-dict module parents)
+
+

Generates a list of cons cells containing the keys and values for creating a State-Dict from any given Module/Class.

+

Return: A list of cons cells in the form (coms (list module_names ...) . tensor), representing parameters and their corresponding tensors. module_names are the list of symbols representing the path to the parameter slot from the root module.

+

TL;DR. this function traverses all slots of the Module/Class and recognizes the following objects as parameters:

+
    +
  • Tensor
    • +
  • Module/Class
    • +
  • List of Tensors
    • +
  • List of Modules/Classes
    • +
+

By default, the keys are created according to the following rules:

+
    +
  • Tensor
    • +
  • If a slot contains a Tensor, create a cons cell with the key as (append parent (list slot_name)) and the value as the Tensor.
    • +
  • Module
    • +
  • If a slot contains a Module, recursively apply (->state-dict slot_value parent) to that Module with setting parent = (append parent (list slot_name)) as the new Parent.
    • +
  • List of Tensors/Modules
    • +
  • If a slot contains a list of Tensors or Modules, apply the above rules to each element.
    • +
  • Keys are created by appending the slot name and the index (e.g., slot_name 0, slot_name 1, ...) to the Parent.
    • +
+

To recognize other objects as parameters, please extend this method by adding methods for the desired classes.

+

[function] get-state-dict

+
(get-state-dict module &key (key-mapper #'pytorch-style-dict-key))
+
+

Constructs a State-Dict by recursively exploring all paramters of the given Module/Class.

+
    +
  • Module[Module/Class] The module from which to extract parameters.
    • +
  • Key-Mapper[Function] A function that takes a list of names (the first element of the cons cell) and returns a string to be used as the key in the StateDict. Defaults to pytorch-style-dict-key. which must be #'(lambda (x) ...)
    • +
+

Returns: State-Dict

+

[function] load-state-dict

+
(load-state-dict module state-dict &key (silent nil) (key-mapper #'pytorch-style-dict-key))
+
+

Loads the parameters from the given state-dict into the module, returning the given module. +- silent[boolean] If set to t, suppresses warnings about unused keys, dtype mismatches, shape mismatches, and uninitialized tensors. +- key-mapper[function] A function used to map the keys in the state-dict to the keys in the module. Defaults to pytorch-style-dict-key. (see: get-state-dict)

+

Example: Transformer

+
lisp
CATEN-USER> (progn (ql:quickload :caten/llm) (get-state-dict (caten/llm:Transformer 32 2 2 1e-5 32)))
+
+
+Result +
Result
#<STATE-DICT 
+  {
+    wte.weight             -> (32 32)
+    wpe.weight             -> (1024 32)
+    h.0.attn.c_attn.weight -> (96 32)
+    h.0.attn.c_attn.bias   -> (96)
+    h.0.attn.c_proj.weight -> (32 32)
+    h.0.attn.c_proj.bias   -> (32)
+    h.0.mlp.c_fc.weight    -> (128 32)
+    h.0.mlp.c_fc.bias      -> (128)
+    h.0.mlp.c_proj.weight  -> (32 128)
+    h.0.mlp.c_proj.bias    -> (32)
+    h.0.ln_1.affine        -> (32)
+    h.0.ln_1.bias          -> (32)
+    h.0.ln_2.affine        -> (32)
+    h.0.ln_2.bias          -> (32)
+    h.1.attn.c_attn.weight -> (96 32)
+    h.1.attn.c_attn.bias   -> (96)
+    h.1.attn.c_proj.weight -> (32 32)
+    h.1.attn.c_proj.bias   -> (32)
+    h.1.mlp.c_fc.weight    -> (128 32)
+    h.1.mlp.c_fc.bias      -> (128)
+    h.1.mlp.c_proj.weight  -> (32 128)
+    h.1.mlp.c_proj.bias    -> (32)
+    h.1.ln_1.affine        -> (32)
+    h.1.ln_1.bias          -> (32)
+    h.1.ln_2.affine        -> (32)
+    h.1.ln_2.bias          -> (32)
+    ln_f.affine            -> (32)
+    ln_f.bias              -> (32)
+    lm_head.weight         -> (32 32)
+  } {1003ECC7D3}>
+
+
+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.apis.tensor/index.html b/packages/caten.apis.tensor/index.html new file mode 100644 index 000000000..de2bfad46 --- /dev/null +++ b/packages/caten.apis.tensor/index.html @@ -0,0 +1,995 @@ + + + + + + + + + + + + + + + + + + + + + + Tensor | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Tensor

+ + + + +
+
+ +
+ + + + + + +
+
+ + + + | + + + + + +
+
+ + + + + + + + + +
    +
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    Share via
    +
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  • + + +  Edit this article + + +
    • +
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+ +
+
+
+ +
+ + +

Tensor

+

[struct] Tensor

+

A struct tensor is a multi-dimensional, and strided matrix (and nrank=0 to scalar value) containing elements of the single dtype. +Also the tensor has following slots:

+
    +
  • shape[list] A list of number, symbol, or Tensor.
    • +
  • buffer[Buffer] A buffer of the tensor. Realized arrays are stored here.
    • +
  • dtype[keyword] A dtype of the tensor.
    • +
  • order[order] A memory layout of the tensor, selected from either of :row or :column.
    • +
  • id[symbol] A unique identifier of the tensor. (usually created by gensym)
    • +
  • op[Func] Func object that represents the operation of the tensor.
    • +
  • views[list] A list of ViewRange objects, determining the bound of loops.
    • +
  • requires-grad[boolean] A flag to determine whether the tensor requires a gradient.
    • +
  • grad[Tensor] A gradient and realized tensor of the tensor.
    • +
  • grad-id[symbol] A unique identifier of the gradient tensor.
    • +
  • variables[list] A list of Tensor objects that are used in the operation of the tensor. Tensors listed here are involved in the compilation.
    • +
+

[function] make-tensor

+

(make-tensor shape &key (dtype *default-float*) (order *default-order*) (id (gensym "TID")) (requires-grad nil) (initial-element nil) (views nil) (from nil))
+
+Create a new lazy tensor.

+
    +
  • shape[list] A list of number, symbol, or Tensor.
    • +
  • dtype[keyword] A dtype of the tensor.
    • +
  • order[order] A memory layout of the tensor, selected from either of :row or :column.
    • +
  • id[symbol] A unique identifier of the tensor. (usually created by gensym)
    • +
  • requires-grad[boolean] A flag to determine whether the tensor requires a gradient.
    • +
  • initial-element[null|number|symbol|ScalarTensor] An initial value of the tensor.
    • +
  • views[list] A list of ViewRange objects, determining the bound of loops.
    • +
  • from[null|Buffer|Symbol] A buffer used to initialize the tensor. If symbol is given, the buffer is taken from the variable table.
    • +
+

[function] make-scalar

+

(make-scalar value &key (dtype *default-float*) (order *default-order*) (id (gensym "SID")) (requires-grad nil))
+
+Create a new scalar tensor. ScalarTensor in Caten is a tensor with a rank of 0.

+
    +
  • value[number|symbol] A value of the scalar tensor.
    • +
  • dtype[keyword] A dtype of the tensor.
    • +
  • order[order] A memory layout of the tensor, selected from either of :row or :column.
    • +
  • id[symbol] A unique identifier of the tensor. (usually created by gensym)
    • +
  • requires-grad[boolean] A flag to determine whether the tensor requires a gradient.
    • +
+

Tips: You can use fconst, uconst, and iconst to create a scalar tensor with a default dtype. (arguments are the same as make-scalar.)

+

[function] grad

+

(grad tensor)
+
+Returns a gradient of the tensor

+

[function] shape

+

(shape tensor)
+
+Returns a copy of the shape of the tensor

+

[function] ndim

+

(ndim tensor)
+
+Returns a rank of the tensor

+

[function] dtype-of

+

(dtype-of tensor)
+
+Returns a dtype of the tensor

+

[function] order

+

(order tensor)
+
+Returns a memory-layout of the tensor.

+

[function] caten

+
(caten tensors &key (jit (= 1 (ctx:getenv :JIT))) (name :main) (simplifiers *external-simplifiers*))
+
+

Compiles the given tensors, returning an AVM struct.

+
    +
  • tensor[Tensor|List] toplevel tensors.
    • +
  • jit[boolean] If set to 0, caten only applies the graph-level compilation. If set to 1, caten calls %jit to generate the fast kernel supported by caten/codegen. This parameter should be specified using the (ctx:with-contextvar) macro.
    • +
  • name[keyword] the name of compiled avm.
    • +
  • rewriters[list] a list of graph rewriters called each time the compiler will lower the module.
    • +
  • simplifiers[list] a list of external simplifiers used in the graph-level compilation. (defined by defsimplifier) Pass the function name.
    • +
+

[function] tensor-graph

+
(tensor-graph tensor)
+
+

Lowers the given tensors into an aasm graph, only the constant folding is applied.

+

For some convenience, this function returns the first tensor if the input is not a tensor.

+

[function] tensor-lowered-graph

+

(tensor-lowered-graph &rest tensors)
+
+Creates a lowered graph from the given tensors. (= an input grpah to JIT=1)
+
+
+
+### [method] forward
+
+(forward AVM &rest params) +
Compute the forward pass of the compiled computational graph (AVM). The params are additional parameters that are used to initialize the AVM variable table, passed in the following format:
+
+
+- `(symbol . number)` Loaded as a `*default-int*` scalar tensor, used to determine the shape of dynamic shaped tensors.
+- `(symbol . buffer)` or `(symbol . tensor)` Used to assign the initial elements of the tensor specified by the name in `form` in `(make-tensor ... :from x)`
+
+Here's an example.
+
+
+#### Examples
+
+
+```lisp title="Example of forward with dynamic shaped"
+
+(let ((model (caten (!randn `(a b)))))
+  (forward model `(a . 10) `(b . 10)))
+

+
+Result +
Result
{Tensor[float32] :shape (A B) :id STC98223
+   ((-0.61595285  -0.031014156 2.2868037    -0.8684415   -0.56618977  -0.7328551   -0.2807455   2.3028476    0.79900587   1.8557153)
+    (-1.7908674   -0.46563956  -0.20578465  -0.35169426  -1.4918212   0.11561119   0.30114642   1.7040857    -2.916635    -1.5369263)
+    (0.9987553    0.45711854   1.0495216    -1.0601267   -0.54998857  0.22259901   0.15503313   -0.9351286   2.9082751    -0.12879859)
+    (0.49433577   0.56157786   0.4193139    0.72080606   0.19486764   0.35958967   0.46794757   -0.8435187   0.09039084   -0.88607216)
+    (0.239293     -0.3790358   0.439364     -0.10819176  -0.07170133  -2.9237595   -0.10269515  -0.3924638   -1.0511456   -0.93730205)
+    (-0.2857653   0.6431336    0.47663587   1.4265004    0.37391514   -0.8274696   -0.39017528  0.31971508   0.06125845   0.13821441)
+    (0.9454282    -1.7564898   0.09252599   0.7165159    0.775417     -0.00204291  0.99140173   0.1647516    0.77457863   0.6881668)
+    (0.5833384    -1.8209114   0.37789294   0.030298902  -0.2962195   2.3168423    0.72732586   -0.20335685  0.8696461    0.38297242)
+    (1.1594744    -0.035024546 1.1469852    0.55789834   2.0441046    -1.1500646   -0.091196336 0.8264797    0.7891973    0.112303734)
+    (-1.1058131   0.29724032   0.5923294    -0.1579682   0.18783261   0.33747807   -0.026869688 -0.4813399   0.6008693    -0.69397247))
+  :op #<PROCEEDNODE {1002457753}>
+  :requires-grad NIL
+  :variables (STC76067)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(A B) :contiguous-p=T>}
+
+
+ +

[method] backward

+
(forward AVM &optional prev-dout)
+
+

Compute the backward pass of the compiled computational graph (AVM). Note that the prev-dout is ignored. Forward pass must be computed first. Gradients are automatically reset to zero before the forward pass.

+

[function] proceed

+
(proceed &rest tensors)
+
+

Compiles the given tensors, returning an evaluated tensors.

+

[macro] with-no-grad

+
(with-no-grad &body body)
+
+

Under the scope of with-no-grad, the gradient computation is disabled. This parameter should be set during inference, and the compiler will generate a more efficient code.

+

[variable] inference-mode

+

When *inference-mode* is set to T, it explicitly indicates that the code is being executed in inference mode. Additionally, it has the following effects:

+
    +
  • +

    Static Random Generation (e.g., rand) with :requires-grad=T does nothing (it is expected to load a parameter from the state dictionary).

    +
    • +
  • +

    The behavior of certain NN operations, such as BatchNorm and Dropout, changes.

    +
    • +
+

[macro] with-inference-mode

+

(with-inference-mode (() &body body))
+
+Sets *inference-mode*=T and *no-grad*=T within the scope of the body.

+

Examples

+

Tensor Creation

+
lisp
CATEN-USER> (make-tensor `(30 30))
+
+
+Result +
Result
{Tensor[float32] :shape (30 30) :id TID98224
+  :buffer nil
+  :op #<ALLOCATE {1002577EF3}>
+  :requires-grad NIL
+  :variables NIL
+  :tracker #<TRACKER :order={row(0 1)} :shape=(30 30) :contiguous-p=T>}
+
+
+ +

Realize

+
lisp
CATEN-USER> (proceed (make-tensor `(30 30)))
+
+
+Result +
Result
{Tensor[float32] :shape (30 30) :id STC98288
+   ((0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)
+                     ...
+    (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)
+    (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0))
+  :op #<PROCEEDNODE {1002714CF3}>
+  :requires-grad NIL
+  :variables (TID98228)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(30 30) :contiguous-p=T>}
+
+
+ +

Creating a computational graph (Lazy)

+
lisp
CATEN-USER> (!add (ax+b `(3 3) 0 1) (ax+b `(3 3) 0 1))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC98303
+  :buffer nil
+  :op #<ADD {100274C533}>
+  :requires-grad NIL
+  :variables (STC98295 STC98302)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

Evaluating a computational graph.

+
lisp
CATEN-USER> (proceed (!add (ax+b `(3 3) 0 1) (ax+b `(3 3) 0 1)))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC98469
+   ((2.0 2.0 2.0)
+    (2.0 2.0 2.0)
+    (2.0 2.0 2.0))
+  :op #<PROCEEDNODE {10029021C3}>
+  :requires-grad NIL
+  :variables (STC98318)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

Floating Features

+

[function] inf

+

(inf &key (dtype *default-float*))
+
+Returns a positive infinity of the dtype for the current Common Lisp implementation.

+

This feature is supported by float-features

+
lisp
CATEN-USER> (inf)
+
+
+Result +
Result
#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY
+
+
+ +
lisp
CATEN-USER> (proceed (!full `(3 3) (inf)))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC98532
+   ((#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY)
+    (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY)
+    (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY))
+  :op #<PROCEEDNODE {100291A323}>
+  :requires-grad NIL
+  :variables (TID98470)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] -inf

+
(-inf &key (dtype *default-float*))
+
+

Returns a negative infinity of the dtype for the current Common Lisp implementation.

+

This feature is supported by float-features

+
lisp
CATEN-USER> (-inf)
+
+
+Result +
Result
#.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY
+
+
+ +
lisp
CATEN-USER> (proceed (!full `(3 3) (-inf)))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC98595
+   ((#.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY)
+    (#.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY)
+    (#.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY))
+  :op #<PROCEEDNODE {100292A463}>
+  :requires-grad NIL
+  :variables (TID98533)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] nan

+
(nan &key (dtype *default-float*))
+
+

Returns a NaN of the dtype for the current Common Lisp implementation.

+

This feature is supported by float-features

+
lisp
CATEN-USER> (nan)
+
+
+Result +
Result
#<SINGLE-FLOAT quiet NaN>
+
+
+ +
lisp
CATEN-USER> (proceed (!full `(3 3) (nan)))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC98658
+   ((#<SINGLE-FLOAT quiet NaN> #<SINGLE-FLOAT quiet NaN> #<SINGLE-FLOAT quiet NaN>)
+    (#<SINGLE-FLOAT quiet NaN> #<SINGLE-FLOAT quiet NaN> #<SINGLE-FLOAT quiet NaN>)
+    (#<SINGLE-FLOAT quiet NaN> #<SINGLE-FLOAT quiet NaN> #<SINGLE-FLOAT quiet NaN>))
+  :op #<PROCEEDNODE {1002942583}>
+  :requires-grad NIL
+  :variables (TID98596)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

[function] float-type-of

+
(float-type-of x)
+
+

Returns :INF if the number is a negative infinity, :-INF if the number is a negative infinity, :nan if the number is NaN, or T otherwise.

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.apis/index.html b/packages/caten.apis/index.html new file mode 100644 index 000000000..a71fb83d2 --- /dev/null +++ b/packages/caten.apis/index.html @@ -0,0 +1,576 @@ + + + + + + + + + + + + + + + + + + + + + + Overview | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Overview

+ + + + +
+
+ +
+ + + + + + +
+
+ + + + | + + + + + +
+
+ + + + + + + + + +
    +
  • +
    Share via
    +
    • +
    • +
    • +
    • +
  • +
    +
    • +
    • +
    • +
  • + + +  Edit this article + + +
    • +
    • +
+ +
+
+
+ +
+ + +

Overview

+

Welcome to Caten/APIs

+

WIP

+

proceed is a function that evaluates a computational graph.

+
lisp
CATEN-USER> (proceed (make-tensor `(3 3) :initial-element 1.0))
+
+
+Result +
Result
{Tensor[float32] :shape (3 3) :id STC24811
+   ((1.0 1.0 1.0)
+    (1.0 1.0 1.0)
+    (1.0 1.0 1.0))
+  :op #<PROCEEDNODE {1005F23D43}>
+  :requires-grad NIL
+  :variables (TID24749)
+  :tracker #<TRACKER :order={row(0 1)} :shape=(3 3) :contiguous-p=T>}
+
+
+ +

ctx:with-contextvar is a macro that sets the context variable. JIT=1 to use JIT, JIT_DEBUG=1 to see the generated code. DOT=1 to debug the pattern matcher on your browser. (need graphviz)

+
lisp
CATEN-USER> (ctx:with-contextvar (:jit 1 :jit_debug 1 :dot 0)
+  (caten (forward (Embedding 10 10) (make-tensor `(b c)))))
+
+
+Result +
Result
#S(AVM
+   :GRAPH 
+Graph[seen=NIL, outputs=(val_35)] {
+    <ALLOCATE : val_2 <- (shape=(10, 10), stride=(10, 1), from=<(10 10) Realized Array>) where :nrank=2 :dtype=FLOAT32 :_type_relay=NIL :_read_views=NIL :_output_type=NIL>
+    <Node[BUFFER] ALLOCATE(NID61992) : val_9* <- () where :nrank=0 :dtype=INT64>
+    <Node[BUFFER] LOAD(NID61994) : val_10* <- (val_9) where :value=C>
+    <Node[BUFFER] ALLOCATE(NID61964) : val_15* <- () where :nrank=0 :dtype=UINT64>
+    <Node[BUFFER] LOAD(NID61966) : val_16* <- (val_15) where :value=C>
+    <Node[BUFFER] ALLOCATE(NID61960) : val_17* <- () where :nrank=0 :dtype=UINT64>
+    <Node[BUFFER] LOAD(NID61962) : val_18* <- (val_17) where :value=B>
+    <ALLOCATE : val_22 <- (shape=(val_18, val_16), stride=(val_10, 1)) where :nrank=2 :dtype=FLOAT32 :_type_relay=NIL :_read_views=NIL :_output_type=NIL>
+    <Node[BUFFER] ALLOCATE(NID62004) : val_12* <- () where :nrank=0 :dtype=INT64>
+    <Node[BUFFER] LOAD(NID62006) : val_13* <- (val_12) where :value=10>
+    <Node[BINARYOPS] MUL(NID61996) : val_14* <- (val_13, val_10)>
+    <ALLOCATE : val_34 <- (shape=(val_18, val_16, 1, 10), stride=(val_14, 10, 10, 1)) where :nrank=4 :dtype=FLOAT32 :_type_relay=NIL :_read_views=NIL :_output_type=NIL>
+    <Node[JIT] JIT_KERNEL(NID76062) : val_34* <- (val_34, C, B, val_2, val_22) where :output-buffer-n=1 :kernel-info=<CLANG[FUSED_SUMNODE_EMBEDDING68875]> :dtypes=(FLOAT32
+                                                                                      INT64
+                                                                                      INT64
+                                                                                      FLOAT32
+                                                                                      FLOAT32)>
+    <Node[SPECIAL/VM] PAUSE/BACKWARD(NID68774) : val_35* <- (val_34)>
+}
+
+   :NAME MAIN60122
+   :FW-OUTPUTS (val_35)
+   :BW-OUTPUTS NIL
+   :ID2TENSOR #<HASH-TABLE :TEST EQL :COUNT 1 {1005F4EDE3}>
+   :TAPE-LENGTH 14
+   :PC 0
+   :VARIABLES #<HASH-TABLE :TEST EQL :COUNT 0 {10073C05F3}>
+   :PARAMS-TO-OPTIMIZE NIL
+   :DUMPED NIL)
+
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+ + + + + + \ No newline at end of file diff --git a/packages/caten.apps.gpt2/index.html b/packages/caten.apps.gpt2/index.html new file mode 100644 index 000000000..cd50580d1 --- /dev/null +++ b/packages/caten.apps.gpt2/index.html @@ -0,0 +1,512 @@ + + + + + + + + + + + + + + + + + + + + + + caten/apps.gpt2 | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
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caten/apps.gpt2

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caten/apps.gpt2

+

Implements GPT2 text generation from the pre-trained model.

+

[function] make-gpt2

+
(make-gpt2 model-type &key max-seq-len)
+
+

Creates a compiled GPT2 model from the pre-trained gguf file. The model-type must be one of :gpt2, :gpt2-medium, :gpt2-large, or :gpt2-xl. The max-seq-len is the maximum sequence length for the model. Since GPT2 is a heavy model, you should consider JIT=1 as a prerequisite.

+

The pretrained model is downloaded from the following HuggingFace repository:

+
(defun url (model-type) (format nil "https://huggingface.co/hikettei/gpt2-gguf/resolve/main/~(~a~)-f32.gguf?download=true" model-type))
+
+

[function] gpt2-inference

+
(gpt2-inference model input)
+
+

Takes a compiled GPT2 model and a string input, and generates a text output.

+ + + +
+
+ + + + + + + + + + + +
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+ + + + + +
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+ + + + + + \ No newline at end of file diff --git a/packages/caten.apps/index.html b/packages/caten.apps/index.html new file mode 100644 index 000000000..a57431855 --- /dev/null +++ b/packages/caten.apps/index.html @@ -0,0 +1,500 @@ + + + + + + + + + + + + + + + + + + + + + + Overview | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
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Overview

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Overview

+

Do you want to integrate Caten's features into your Common Lisp application?

+

With caten/apps, you can effortlessly experiment with inference using ready-made implementations of various deep learning models.

+

caten/apps is a suite of packages that implement pipelines to smoothly perform the following tasks:

+
    +
  • +

    Automatically download pretrained weights

    +
    • +
  • +

    Automatically optimize and compile models for inference

    +
    • +
+

For instance, text generation inference provided by caten/apps.gpt2 can be accomplished in just two lines:

+
(defparameter *model* (make-gpt2 :gpt2))
+(gpt2-generate *model* "What is the answer to life, and the world?")
+
+

If you're considering contributing to Caten, we warmly welcome PRs that add new models to caten/apps. If you've implemented a model using Caten, please consider adding it to caten/apps! (Feel free to create a PR!)

+ + + +
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+ + + + + + \ No newline at end of file diff --git a/packages/caten.avm/index.html b/packages/caten.avm/index.html new file mode 100644 index 000000000..63a92cec6 --- /dev/null +++ b/packages/caten.avm/index.html @@ -0,0 +1,493 @@ + + + + + + + + + + + + + + + + + + + + + + caten/avm | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
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caten/avm

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TODO

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+ + + + + + \ No newline at end of file diff --git a/packages/caten.codegen/index.html b/packages/caten.codegen/index.html new file mode 100644 index 000000000..877bdd37c --- /dev/null +++ b/packages/caten.codegen/index.html @@ -0,0 +1,903 @@ + + + + + + + + + + + + + + + + + + + + + + caten/codegen | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
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caten/codegen

+

Hackable Kernel Generator

+

The caten/codegen is a system designed to JIT-compile AASM Graphs into other languages, such as C or Metal, to achieve faster computation.

+

This package has the following two objectives:

+
    +
  1. +

    Device Independence: It imposes no constraints on the execution device, allowing users to extend it with only 200–300 lines of code.

    +
    2. +
  2. +

    Performance: It ensures computations run fairly fast on each target device.

    +
    3. +
+

Caten's code generation is divided into two stages based on the intended purpose:

+
    +
  1. +

    caten/codegen Generates unoptimized code (e.g., Scheduler, Lowerer, op fusion, etc.).

    +
    2. +
  2. +

    caten/polyhedral (Optional) Performs advanced kernel loop transformations and optimizations on the scheduled code, such as Auto Scheduler.

    +
    3. +
+

This document focuses on the first stage, caten/codegen, and provides related information and some examples.

+

How to enable JIT

+

To enable JIT, you need to set the JIT contextvar before running the function caten. The following code snippet demonstrates how to enable JIT on repl: +

;; Option1: Set globally
+(setf (ctx:getenv :JIT) 1)
+;; Option2: Set locally
+(ctx:with-contextvar (:JIT 1)
+  ;; ...
+)
+
+After setting the contextvar, you can run the function caten to JIT-compile the AASM Graphs, e.g.: +
(caten (!rand `(3 3)))
+

+

How to debug JIT

+

You can set the JIT_DEBUG contextvar to debug the JIT compilation process. The debugging level ranges from 0 to 4, as shown below: +

JIT_DEBUG | Effect
+----------|-------
+0         | None
+1         | Nothing for now
+2         | Displays blueprint and scop
+3         | Displays schedule-graph
+4        | Displays memory-planner and the compiled code
+

+

How to learn JIT

+

You can start by reading ./caten/docs/getting-started.lisp to understand the basic concepts of caten/codegen through a running example.

+

Additionally, the following sections provide documentation and explanations for each JIT mechanism, along with simple executable examples.

+

Shape Inference

+

The JIT compilation starts by inferring the shape of each points in the graph. caten/codegen/shape-inference is responsible for this task.

+

[function] run-type-infer

+

(run-type-infer avm)
+
+Run the shape inference to the given AVM, returning Type-Reporter

+

[struct] Inferred-Type

+

A structure Inferred-Type contains the shape/stride/view information of the node at each points. Also, it containts the iteration space information that is used to render the kernel.

+

If the shape inference is successfully done and properly deployed to the target graph by the rewriting-rule, the function (read-type-relay node) will return the Inferred-Type structure.

+
    +
  • relay-reads returns the list of the buffer corresponding to the node-reads. (If node-reads a number, nil is set)
    • +
  • relay-writes returns the list of the buffer corresponding to the node-writes.
    • +
  • relay-read-iters returns the list of the iteration space corresponding to the node-reads.
    • +
  • relay-write-iters returns the list of the iteration space corresponding to the node-writes.
    • +
+

[struct] Iteration-Space

+

Iteration-Space is a structure that contains the shape/stride/view information of the each buffer. It is used to render the kernel. +iteration-space-shape to get the shape, iteration-space-strides to get the stride, iteration-space-views to get the offsets and increments, and iteration-space-procedure to get how the iteraton-space is collapsed or permuted. Each elements for shape/stride are Expr.

+

iteration-space-expr-aref to render the aref.

+

(iteration-space-expr-aref iteration-space buffer gids)
+
+gids corresponds for the loop idx in the kernel.

+

Rewriting Rules

+

TODO: apply-rewriting-rules doc

+

Optimization for gemm

+

TODO: WMMA Rewriting Docs

+

Scheduler

+

The caten/codegen/scheduler is responsible for assigining the execution order of the computation graph in the aasm. +Occasionally they merges or rewrites a view in order to schedule multiple nodes into the same schedule if possible.

+

The function graph-schedule is an entry point, and takes a shape-inferred aasm graph as input, performs scheduling, and returns a schedule graph (called Schedule-Graph) whose each node is composed of Schedule-Item.

+

One Schedule-Item corresponds to one kernel in GPU, graph-schedule must ensure that each item is merged only within the bounds that can be lowered into a single kernel.## Schedule-Item

+

:SCHEDULE-ITEM

+

Schedule-Item is an intermidate object to represent a one kernel in GPU.

+
f = cache_name or name
+write_ids = f(*[storage_id_dst], *[dynamic_shape], *[inputs])
+                      ^ can be modified by the memory-planner
+
+

It has a unique name, and cache-name. If cache-name was assigned, the compiler will fail to compile this schedule-item and reuse the kernel named cache-name instead.

+

In order to lowering the computation graph as the foreign language, items must be consisted of JITAble operations (except for special irs and :allocate). If it qualifies, jitable is set to T.

+

Otherwise, the scheduled items are relocated to the compiled avm directly. Specifially, if the item was :ALLOCATE, :allocated-p is set to T.

+
    +
  • blueprint[list] is a lowered schedule-item
    • +
  • polyhedral[list] is a Polyhedral IR obtained by lowering blueprint
    • +
  • auto-schedule-p[list] is set to T if it is worth to run auto-scheduler. (If there is a symbolic incremental, loop is not an affine and cannot run isl scheduler)
    • +
  • items[list] are the scheduled items
    • +
  • items-to-cache[list] are the copies of items but having the unique read/write. It is used to determine the equivalence of the two schedule-items.
    • +
  • rank[fixnum] is the highest rank of the iteration space.
    • +
  • storage-id-src[list] is the list of the storage-id of the source buffer (optimized by running memory-planner)
    • +
  • storage-id-dst[list] is the list of the storage-id of the destination buffer (optimized by running memory-planner)
    • +
  • namespace[list] a list of symbols appeared in the items. It is used to map a new blueprint from the cached blueprint.
    • +
+

When optimizing schedule-item in-place, the 0th read is consumed.

+

[function] graph-schedule

+

(graph-schedule graph)
+
+Creates a schedule-graph(FastGraph) from the given graph.

+

Example (Scheduler + Shape Inference + Rewriting Rules)

+

This code snippet demonstrates how to create a schedule-graph from AASM Graph. AASM Graph is obtained by running caten with JIT=0.

+
lisp
CATEN-USER> (ctx:with-contextvar (:JIT 0) (pprint-graph (avm-graph (caten (!relu (!matmul (make-tensor `(3 3)) (make-tensor `(3 3))))))))
+
+
+Result +
Result
============================================================================================================================================
+=== [P: 0] =================================================================================================================================
+[P=0, ID=0]:
+   :PAUSE/BACKWARD {0}
+   └ :MAX {N1}
+     └ :VIEW {N2}
+     │ ├ load(0.0)
+     │   └ Allocate[:float32] NIL
+     ├ :VIEW {N3}
+       ├ :MOVE {N4}
+         ├ Allocate[:float32] (3 3 1)
+         └ :VIEW {N5}
+           ├ :ADD {N6}
+             ├ :VIEW {N7}
+             │ ├ load(0.0)
+             │   └ Allocate[:float32] (3 3 1)
+             └ :MUL {N8}
+               └ :VIEW {N9}
+               │ ├ Allocate[:float32] (3 3)
+               ├ :MOVE {N10}
+                 ├ Allocate[:float32] (3 3 3)
+                 └ :VIEW {N11}
+                   ├ Allocate[:float32] (3 3)
+============================================================================================================================================
+
+
+ +
Example
(let* ((graph (ctx:with-contextvar (:JIT 0) (avm-graph (caten (!relu (!matmul (make-tensor `(3 3)) (make-tensor `(3 3))))))))
+       (vm (make-avm graph :example nil (graph-outputs graph) nil)))
+  (run-type-infer vm) ;; Running the shape inference
+  (apply-rewriting-rules vm) ;; Running the rewriting rules
+  ;; graph-schedule to finally run the scheduler
+  (pprint-graph (graph-schedule (avm-graph vm))))
+
+
+Result +
Result
============================================================================================================================================
+=== [P: 0] =================================================================================================================================
+[P=0, ID=0]:
+   :PAUSE/BACKWARD {N1}
+   └ [KERNEL] FUSED_RELU_SUMNODE_MATMUL18987
+     ├ Allocate[:float32] (3 3 3)
+     ├ Allocate[:float32] (3 3)
+     ├ Allocate[:float32] (3 3)
+     ├ Allocate[:float32] (3 3 1)
+     ├ Allocate[:float32] (3 3 1)
+     └ Allocate[:float32] NIL
+============================================================================================================================================
+
+
+ +

Lowerer (Blueprint)

+

The package caten/codegen/blueprint is responsible for lowering the schedule-item into a blueprint. A blueprint is an IR that represents a computation graph with explicit loop bounds. +The lower-schedule-item method infers loop boundaries based on Schedule-item and performs lowering into a format that includes :FOR/:ENDFOR nodes.### [function] LOWER-SCHEDULE-ITEM

+
(lower-schedule-item node base-graph scheduled-graph)
+
+

Lowers the Schedule-Item into blueprint.

+
    +
  • +

    node is a schedule-item to lower which belongs to scheduled-graph.

    +
    • +
  • +

    base-graph is the graph you passed to the graph-schedule.

    +
    • +
  • +

    scheduled-graph is the scheduled graph obtained by graph-schedule.

    +
    • +
+

Example (Scheduler + Lowerer)

+

This code snippet demonstrates how to lower the schedule-graph into a blueprint created in the previous section.

+
Example
(let* ((graph (ctx:with-contextvar (:JIT 0) (avm-graph (caten (!relu (!matmul (make-tensor `(3 3)) (make-tensor `(3 3))))))))
+       (vm (make-avm graph :example nil (graph-outputs graph) nil)))
+  (run-type-infer vm) ;; Running the shape inference
+  (apply-rewriting-rules vm) ;; Running the rewriting rules
+  ;; graph-schedule to finally run the scheduler
+  (let ((schedule-graph (graph-schedule (avm-graph vm))))
+    (caten/codegen/expr-cache::with-expr-cache () ;; need this to forcibly keep the loop affine.
+      (let ((bp (lower-schedule-item (nth 1 (graph-nodes schedule-graph)) graph schedule-graph)))
+        (print-blueprint bp nil)))))
+
+
+Result +
Result
{
+}
+
+
+ +

Renderer

+

TODO (Docs for Renderer/rendre-expr/extensible polyhedral compiler/etc)

+

Here is a list of nodes used to render the kernel.

+

Render IRs

+

:DEFINE-GLOBAL

+

The node :DEFINE-GLOBAL declares a global variable in the kernel. (it corresponds to the argument of the kernel.)

+

When optimizing define-global in-place, the 0th read is consumed.

+

:AREF

+

:AREF corresponds to the following code: +

ID[*space]
+

+
    +
  • storage-id[symbol] An index to the reference pointer optimized by the memory-planner.
    • +
  • buffer[Buffer] The buffer to be accessed.
    • +
  • space[Iteration-Space] The iteration space :AREF belongs to.
    • +
+

When optimizing aref in-place, the 0th read is consumed.

+

:ENDIF

+
} // endif
+
+

When optimizing endif in-place, the 0th read is consumed.

+

:IF

+

if(condition)
+
+When optimizing if in-place, the 0th read is consumed.

+

:ENDFOR

+

} // idx
+
+When optimizing endfor in-place, the 0th read is consumed.

+

:FOR

+
for(int idx=upfrom, below, by)
+
+
    +
  • scope[keyword] If :global, the loop is parallelizable. One of :global, :local.
    • +
+

When optimizing for in-place, the 0th read is consumed.

+

EXPR

+

TODO

+

Memory Planner

+

TODO: Docs (MIP Solver)

+

Scop Analyzer

+

TODO: Docs for scop

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.external.gguf/index.html b/packages/caten.external.gguf/index.html new file mode 100644 index 000000000..3a2f21235 --- /dev/null +++ b/packages/caten.external.gguf/index.html @@ -0,0 +1,594 @@ + + + + + + + + + + + + + + + + + + + + + + caten/gguf | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
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caten/gguf

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+ + +

GGUF

+

Looking to work with quantized models? The caten/gguf package enables you to read quantized model weights directly from GGUF files, construct a StateDict, and create tokenizers for caten/llm. It's a convenient tool for integrating quantized models into your projects.

+

Please note that the quantization functionality currently supports only a limited number of bit depths. We appreciate your understanding and are continuously working to expand this feature.

+

(As of this writing, fp32/fp16 only, Int8 Quant will be added soon!)

+

[class] GGUF

+

A class that represents the GGUF file format.

+
    +
  • +

    (gguf-version gguf) returns a fixnum indicating the version of the GGUF file.

    +
    • +
  • +

    (gguf-tensor-count gguf) returns the number of tensors in the GGUF file.

    +
    • +
  • +

    (gguf-metadata-kv-count gguf) returns the number of metadata key-value pairs in the GGUF file.

    +
    • +
  • +

    (gguf-metadata gguf) returns a list of metadata key-value pairs.

    +
    • +
  • +

    (gguf-tensor-info gguf) returns a list of tensor information.

    +
    • +
  • +

    (gguf-metadata-get gguf key) to get the corresponding value of the key where key is a string.

    +
    • +
+

[function] make-gguf

+

Creates GGUF from the given stream.

+

The definition is described in the following link:

+ +

In short, this function accepts the following format: +

[Magic Number (4 Byte)] | [GGUF Version (4 Byte)] | [Tensor_Count (8 Byte)] | [Metadata_KV_Count (8 Byte)] | [Rest_data]
+

+

[function] load-gguf

+

(load-gguf pathname)
+
+Creates a gguf file from the given pathname.

+

[function] load-gguf-url

+

(load-gguf-url url filename &optional output-directory)
+
+Creates a gguf file from the given URL. The downloaded file will be saved in the output-directory named as filename. If the file already exists, it will not download the file again.

+

[function] gguf->state-dict

+

(gguf->state-dict gguf)
+
+Creates a caten/state-dict from the given gguf file's tensor-info.

+

[function] gguf->bpe-tokenizer

+
(gguf->bpe-tokenizer gguf &key (metadata-tokens "tokenizer.ggml.tokens") (metadata-merges "tokenizer.ggml.merges"))
+
+

Creates a BPE tokenizer (which is caten/llm:Tokenizer) from the given gguf file's metadata.

+

[struct] Metadata

+

A structure to represent metadata in GGUF file.

+

(metadata-key metadata) to access the key typed as string. (metadata-value-type metadata) to access the type of value typed as a keyword. (metadata-value metadata) to access the value typed as a number, boolean, or simple-array.

+

[struct] Tensor-Info

+

Tensor-Info stores the information of a tensor in the gguf file. +(tensor-info-name tensor-info) returns the name of the tensor which is a string, (tensor-info-n-dimension tensor-info) returns the rank of the tensor. (tensor-info-dimensions tensor-info) returns the shape of the tensor. (tensor-info-ggml-type tensor-info) returns the data type of the tensor which is a keyword. (tensor-info-relative-offset tensor-info) returns the offset of the tensor in the gguf file. (tensor-info-absolute-offset tensor-info) returns the absolute offset of the tensor in the stream, (inconviniently buffers are stored with this offset but caten will precompute them). (tensor-info-buffer tensor-info) returns the parsed buffer of the tensor.

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.external.llm/index.html b/packages/caten.external.llm/index.html new file mode 100644 index 000000000..24f51319e --- /dev/null +++ b/packages/caten.external.llm/index.html @@ -0,0 +1,563 @@ + + + + + + + + + + + + + + + + + + + + + + caten/llm | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

caten/llm

+ + + + +
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+
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+ + +

Overview

+

TODO: Docs

+

Tokenizers

+

TODO: Docs

+

[class] Tokenizer

+

A tokenizer is a class that can encode and decode strings into and from. +- (encode tokenizer string) -> (list fixnum ...) encodes a string into a tokenized form. +- (decode tokenizer (list fixnum ...)) -> string decodes a tokenized form into a string.

+

[class] BPETokenizer

+

An implementation of Byte Pair Encoding tokenizer. Initialized by the make-bpe-tokenizer function.

+

[generic] encode

+

NIL

+

[generic] decode

+

NIL

+

Models

+

(TODO: Complete docs)

+

Definitions are here: https://github.com/hikettei/Caten/blob/main/external/llm/layers.lisp

+

Currently, the following models are implemented and tested:

+
    +
  • +

    Attention

    +
    • +
  • +

    Scaled-Dot-Product-Attention

    +
    • +
  • +

    FeedForward

    +
    • +
  • +

    TransformerBlock

    +
    • +
  • +

    Transformer

    +
    • +
+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.external.onnx/index.html b/packages/caten.external.onnx/index.html new file mode 100644 index 000000000..df95f30fe --- /dev/null +++ b/packages/caten.external.onnx/index.html @@ -0,0 +1,507 @@ + + + + + + + + + + + + + + + + + + + + + + caten/oonx | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
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+ + + + + +
+
+ In this article +
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+ +

caten/oonx

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+ + +

ONNX

+

Looking to compile Caten models directly from ONNX files? The caten/onnx package has you covered. It supports dynamic shapes and weight loading, allowing for flexible and efficient model integration.

+

Currently, caten/onnx supports a limited set of operations. If you encounter any unsupported operations, we warmly welcome your contributions to expand its functionality. Please consider submitting a PR to add them! We'd like to support all opsets which is impossible!

+

Requirement: cl-onnx requires cl-protobufs, and cl-protobufs may require protocol-compiler to be installed on your system. (Follow the instruction in cl-protobufs first)

+
# Example
+$ sudo apt-get install protobuf-compiler
+
+

[function] from-onnx

+
(from-onnx path &key opset)
+
+

Constructs a caten ir from an onnx model file at path. opset specifies the opset version to use. If not specified, uses the opset version in the file.

+

Inputs or dynamic shapes for caten is created as a keyword (so pass the input tensor like (:input . , (rand (list 3 3))) ...

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
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+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.nn.activations/index.html b/packages/caten.nn.activations/index.html new file mode 100644 index 000000000..85b60d5c1 --- /dev/null +++ b/packages/caten.nn.activations/index.html @@ -0,0 +1,722 @@ + + + + + + + + + + + + + + + + + + + + + + Activation | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Activation

+ + + + +
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+ +
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+
+ +
+ + +

Activations

+

[function] !sigmoid

+

(!sigmoid x)
+
+Applies the Sigmoid function element-wise to the input tensor.

+

[function] !hard-sigmoid

+

(!hard-sigmoid x &key (alpha 0.2) (beta 0.5))
+
+Applies the HardSigmoid function element-wise to the input tensor.

+

[function] !relu

+

(!relu x)
+
+Applies the ReLU function element-wise to the input tensor.

+

[function] !leaky-relu

+

(!leaky-relu x &key (neg-slope 1e-3))
+
+Applies the LeakyReLU function element-wise to the input tensor.

+

[function] !log-softmax

+

(!log-softmax x &key (axis -1))
+
+Applies the LogSoftmax function element-wise to the input tensor.

+

[function] !elu

+

(!elu x &key (alpha 1.0))
+
+Applies the ELU function element-wise to the input tensor.

+

[function] !relu6

+

(!relu6 x)
+
+Applies the ReLU6 function element-wise to the input tensor.

+

[function] !softmax

+

(!softmax x &key (axis -1))
+
+Applies the Softmax function to the input tensor.

+

[function] !softplus

+

(!softplus x &key (beta 1.0))
+
+Applies the Softplus function element-wise to the input tensor.

+

[function] !softsign

+

(!softsign x)
+
+Applies the Softsign function element-wise to the input tensor.

+

[function] !softshrink

+

(!softshrink x &key (lmd 0.5))
+
+Applies the SoftShrink function element-wise to the input tensor.

+

[function] !celu

+

(!celu x &key (alpha 1.0))
+
+Applies the CeLU function element-wise to the input tensor.

+

[function] !silu

+

(!silu x)
+
+Applies the SiLU function element-wise to the input tensor.

+

[function] !logsigmoid

+

(!logsigmoid x)
+
+Applies the LogSigmoid function element-wise to the input tensor.

+

[function] !gelu

+

(!gelu x &key (approx :tanh))
+
+Applies the GeLU activation to the input tensor. There are two ways to approximate the GeLU function. :tanh to use tanh. :sigmoid to use sigmoid.

+

[function] !selu

+

(!selu x)
+
+Applies the SeLU function element-wise to the input tensor.

+

[function] !mish

+

(!mish x)
+
+Applies the Mish function element-wise to the input tensor.

+

[function] !hardswish

+

(!hardswish x)
+
+Applies the HardSwish function element-wise to the input tensor.

+

[function] !hardtanh

+

(!hardtanh x &key (min_val -1.0) (max_val 1.0))
+
+Applies the HardTanh function element-wise to the input tensor.

+

[function] !softmin

+

(!softmin x)
+
+Applies the Softmin function element-wise to the input tensor.

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.nn.convolutions/index.html b/packages/caten.nn.convolutions/index.html new file mode 100644 index 000000000..d01dc37d7 --- /dev/null +++ b/packages/caten.nn.convolutions/index.html @@ -0,0 +1,543 @@ + + + + + + + + + + + + + + + + + + + + + + Convolution | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Convolution

+ + + + +
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+
+ +
+ + +

Convolutions

+

[class] ConvND

+
(ConvND in-channels out-channels kernel-size &key (groups 1) (stride 1) (dilation 1) (padding 0) (bias t))
+
+

Applies a convolution over a tensor with a given weight and optional bias. +NOTE: unlike PyTorch, this implementation is not limited to only 2d convolutions and instead works for any number of dimensions.

+

[function] !convnd

+

(!convnd x weight &key (bias nil) (groups 1) (stride 1) (dilation 1) (padding 0))
+
+Applies a convolutional layer over a tensor x with a given weight and optional bias.

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.nn.criterion/index.html b/packages/caten.nn.criterion/index.html new file mode 100644 index 000000000..4ead255f9 --- /dev/null +++ b/packages/caten.nn.criterion/index.html @@ -0,0 +1,599 @@ + + + + + + + + + + + + + + + + + + + + + + Criterion | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Criterion

+ + + + +
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+
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+ + +

Criterion

+

[class] L1NormLoss

+
(L1NormLoss &key (reduction :mean))
+
+

Implements L1Norm Loss:

+
l(x, y) = L = {l_1, ..., l_n}^\intercal, l_n = abs(x_n - y_n)
+
+

reduction is one of :mean or :sum.

+

[function] !l1norm

+
(!l1norm a b &key (reduction :mean))
+
+

Computes L1Norm

+

[class] MSELoss

+
(MSELoss &key (reduction :mean))
+
+

Implements MSE Loss:

+
l(x, y) = L = {l_1, ..., l_n}^\intercal, l_n = (x_n - y_n)^2
+
+

reduction is one of :mean or :sum.

+

[function] !mse

+
(!mse a b &key (reduction :mean))
+
+

Computes MSE Loss between a and b.

+

[class] CrossEntropyLoss

+
(CrossEntropyLoss &key (reduction :mean) (delta 1e-5))
+
+

Returns a tensor that measures the Cross-Entropy-Error between each element in the x and labels. labels are one-hot encoded.

+
L_i = -p_ilog(x_i + delta)
+
+
\begin{equation}
+  out_i=
+  \begin{cases}
+    sum(L)  & \text{reduction = sum} \\
+    mean(L) & \text{reduction = mean} \\
+    L       & \text{otherwise}
+  \end{cases}
+\end{equation}
+
+

[function] !cross-entropy

+

(!cross-entropy x labels &key (reduction :mean) (delta 1e-5))
+
+Computes Cross Entropy Loss between x and labels. labels are one-hot encoded.

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.nn.embeddings/index.html b/packages/caten.nn.embeddings/index.html new file mode 100644 index 000000000..39fa7a795 --- /dev/null +++ b/packages/caten.nn.embeddings/index.html @@ -0,0 +1,532 @@ + + + + + + + + + + + + + + + + + + + + + + Embedding | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Embedding

+ + + + +
+
+ +
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+ +
+
+
+ +
+ + +

Embeddings

+

[class] Embedding

+

(Embedding vocab-size embedding-dim)
+
+Implements an embedding layer.

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.nn.encoding/index.html b/packages/caten.nn.encoding/index.html new file mode 100644 index 000000000..9aad69071 --- /dev/null +++ b/packages/caten.nn.encoding/index.html @@ -0,0 +1,542 @@ + + + + + + + + + + + + + + + + + + + + + + Encoding | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Encoding

+ + + + +
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+ +
+
+
+ +
+ + +

Encoding

+

[class] RoPE

+

(RoPE dim &key (base 10000))
+
+Implements Rotation Positional Encoding (RoPE).

+

[function] !rope

+

(!rope x dim &key (base 10000))
+
+Applies Rotation Positional Encoding (RoPE) to the input tensor x with the specified dim.

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.nn.linears/index.html b/packages/caten.nn.linears/index.html new file mode 100644 index 000000000..de2aefdbb --- /dev/null +++ b/packages/caten.nn.linears/index.html @@ -0,0 +1,532 @@ + + + + + + + + + + + + + + + + + + + + + + Linear | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Linear

+ + + + +
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+ +
+
+
+ +
+ + +

Linear

+

[class] Linear

+

(Linear in-featrues out-features &key (bias t))
+
+Implements a linear transformation.

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.nn.normalizations/index.html b/packages/caten.nn.normalizations/index.html new file mode 100644 index 000000000..09a5b094a --- /dev/null +++ b/packages/caten.nn.normalizations/index.html @@ -0,0 +1,582 @@ + + + + + + + + + + + + + + + + + + + + + + Normalization | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Normalization

+ + + + +
+
+ +
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+ +
+
+
+ +
+ + +

Normalizations

+

[class] BatchNorm

+

(BatchNorm dims &key (eps 1e-5) (affine t) (bias t))
+
+Batch Normalization Layer

+

[function] !batch-norm

+

(!batch-norm x &optional weight bias mean invstd (axis 1) (eps 1e-5))
+
+Computes the batch normalization of a tensor x with optional weight and bias tensors.

+

[class] LayerNorm

+

(LayerNorm dims &key (eps 1e-5) (affine t) (bias t))
+
+Layer Normalization Layer

+

[function] !layer-norm

+

(!layer-norm x normalized-shape &key (weight) (bias) (eps 1e-5))
+
+Computes the layer normalization of a tensor x with optional weight and bias tensors.

+

[class] RMSNorm

+

(RMSNorm dims &key (eps 1e-5) (weight t))
+
+Root Mean Square Normalization Layer

+

[function] !rms-norm

+

(!rms-norm x normalized-shape &key (weight) (eps 1e-5))
+
+Computes the root mean square normalization of a tensor x with optional weight tensor.

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
+
+
+
+ +
+ +
+
+
+ + + + + + \ No newline at end of file diff --git a/packages/caten.nn.optimizers/index.html b/packages/caten.nn.optimizers/index.html new file mode 100644 index 000000000..14df8c88f --- /dev/null +++ b/packages/caten.nn.optimizers/index.html @@ -0,0 +1,580 @@ + + + + + + + + + + + + + + + + + + + + + + Optimizers | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Optimizers

+ + + + +
+
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+ +
+
+
+ +
+ + +

Optimizers

+

[class] AbstractOptimizer

+

AbstractOptimizer is the base class for all optimizers. One AbstractOptimizer corresponds to one Tensor class with :requires-grad=T. (optimizer-param optimizer) to get the corresponding tensor.

+

[generic] step-optimizer

+

NIL

+

[function] hook-optimizers

+
(hook-optimizers avm hooker)
+
+

This function is used to hook the optimizers in the recognised parameters in avm-params-to-optimize. hooker is an function that takes one argument, which is the tensor that requires-grad=T, returns the AbstractOptimizer.

+

A list of created optimizers are returned.

+

[function] zero-grad

+

(zero-grad optimizer)
+
+Fills the gradient of the optimizer with zeros.

+

[class] SGD

+

Implements SGD Optimizer:

+
Param_{new}\gets{Param - Param_{grad}\times{lr}}
+
+

where the initarg :lr is the learning rate.

+

[function] SGD

+

(SGD :lr 1e-3)
+
+Returns a lambda function that takes one argument, which is the parameter tensor, and returns an instance of SGD optimizer.

+ + + +
+
+ + + + + + + + + + + +
+
+ + + + + +
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+ +
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+
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+ + + + + + \ No newline at end of file diff --git a/packages/caten.nn.padding/index.html b/packages/caten.nn.padding/index.html new file mode 100644 index 000000000..a7b34eb8c --- /dev/null +++ b/packages/caten.nn.padding/index.html @@ -0,0 +1,559 @@ + + + + + + + + + + + + + + + + + + + + + + Padding | Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ + + + +
+
+
+ + + +
+
+ +
+
+ + + + + +
+
+ In this article +
+ +
+
+ + +
+ +

Padding

+ + + + +
+
+ +
+ + + + + + +
+
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Padding

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[class] Padding

+

The Func Padding pads the input_tensor with the specified padding. The argument is given in the following format. +

(output_tensor, input_tensor, *index_components, *padding_starts, *padding_ends)
+
+where each (length index_components), (length padding_starts), and (length padding_ends) == ndim(input_tensor) == ndim(output_tensor).

+

With JIT=1, everything is expected to be fused in a single EXPR like: +

out = where(start_0 < index_components[0] < end_0 and start_1 < index_components[1] < end_1 and ..., input_tensor[i=i'-start_0, j=j'_start_1, ...], value)
+

+

[function] !padding

+

(!padding x padding &key (value 0.0))
+
+Pads the tensor x with the specified padding and value.

+

Padding is specified as: ((pad_0_1 pad_0_2) (pad_1_0 pad_1_0) ...). If T is provided instead of (pad_0_0 pad_0_1), that means no padding is applied to the axis. (that is, T = (0 0))

+

Each start_0 and pad_0 is expected as a positive integer (caten will not check this!), or a scalar int32/int64 tensor.

+

[function] !padding2d

+

(!padding2d x padding &key (value 0.0))
+
+Pads the last two dimension of the tensor x with the specified padding and value..

+

padding is specified as: (padding_left padding_right padding_top padding_bottom).

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Pooling

+

[class] AvgPool

+

(AvgPool kernel-size &key (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling))
+
+Implements Average Pooling layer.

+

[function] !avgpool

+

(!avgpool x &key (kernel-size `(2 2)) (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling))
+
+Applies average pooling over the tensor.

+

[class] MaxPool

+

(MaxPool kernel-size &key (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling))
+
+Implements Max Pooling layer.

+

[function] !maxpool

+

(!maxpool x &key (kernel-size `(2 2)) (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling))
+
+Applies max pooling over the tensor.

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+ + + + + + \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000..9d3cd8f3e --- /dev/null +++ b/requirements.txt @@ -0,0 +1,2 @@ +markdown-katex +lantana==2.11.2 diff --git a/search/lunr.js b/search/lunr.js new file mode 100644 index 000000000..aca0a167f --- /dev/null +++ b/search/lunr.js @@ -0,0 +1,3475 @@ +/** + * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9 + * Copyright (C) 2020 Oliver Nightingale + * @license MIT + */ + +;(function(){ + +/** + * A convenience function for configuring and constructing + * a new lunr Index. + * + * A lunr.Builder instance is created and the pipeline setup + * with a trimmer, stop word filter and stemmer. + * + * This builder object is yielded to the configuration function + * that is passed as a parameter, allowing the list of fields + * and other builder parameters to be customised. + * + * All documents _must_ be added within the passed config function. + * + * @example + * var idx = lunr(function () { + * this.field('title') + * this.field('body') + * this.ref('id') + * + * documents.forEach(function (doc) { + * this.add(doc) + * }, this) + * }) + * + * @see {@link lunr.Builder} + * @see {@link lunr.Pipeline} + * @see {@link lunr.trimmer} + * @see {@link lunr.stopWordFilter} + * @see {@link lunr.stemmer} + * @namespace {function} lunr + */ +var lunr = function (config) { + var builder = new lunr.Builder + + builder.pipeline.add( + lunr.trimmer, + lunr.stopWordFilter, + lunr.stemmer + ) + + builder.searchPipeline.add( + lunr.stemmer + ) + + config.call(builder, builder) + return builder.build() +} + +lunr.version = "2.3.9" +/*! + * lunr.utils + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A namespace containing utils for the rest of the lunr library + * @namespace lunr.utils + */ +lunr.utils = {} + +/** + * Print a warning message to the console. + * + * @param {String} message The message to be printed. + * @memberOf lunr.utils + * @function + */ +lunr.utils.warn = (function (global) { + /* eslint-disable no-console */ + return function (message) { + if (global.console && console.warn) { + console.warn(message) + } + } + /* eslint-enable no-console */ +})(this) + +/** + * Convert an object to a string. + * + * In the case of `null` and `undefined` the function returns + * the empty string, in all other cases the result of calling + * `toString` on the passed object is returned. + * + * @param {Any} obj The object to convert to a string. + * @return {String} string representation of the passed object. + * @memberOf lunr.utils + */ +lunr.utils.asString = function (obj) { + if (obj === void 0 || obj === null) { + return "" + } else { + return obj.toString() + } +} + +/** + * Clones an object. + * + * Will create a copy of an existing object such that any mutations + * on the copy cannot affect the original. + * + * Only shallow objects are supported, passing a nested object to this + * function will cause a TypeError. + * + * Objects with primitives, and arrays of primitives are supported. + * + * @param {Object} obj The object to clone. + * @return {Object} a clone of the passed object. + * @throws {TypeError} when a nested object is passed. + * @memberOf Utils + */ +lunr.utils.clone = function (obj) { + if (obj === null || obj === undefined) { + return obj + } + + var clone = Object.create(null), + keys = Object.keys(obj) + + for (var i = 0; i < keys.length; i++) { + var key = keys[i], + val = obj[key] + + if (Array.isArray(val)) { + clone[key] = val.slice() + continue + } + + if (typeof val === 'string' || + typeof val === 'number' || + typeof val === 'boolean') { + clone[key] = val + continue + } + + throw new TypeError("clone is not deep and does not support nested objects") + } + + return clone +} +lunr.FieldRef = function (docRef, fieldName, stringValue) { + this.docRef = docRef + this.fieldName = fieldName + this._stringValue = stringValue +} + +lunr.FieldRef.joiner = "/" + +lunr.FieldRef.fromString = function (s) { + var n = s.indexOf(lunr.FieldRef.joiner) + + if (n === -1) { + throw "malformed field ref string" + } + + var fieldRef = s.slice(0, n), + docRef = s.slice(n + 1) + + return new lunr.FieldRef (docRef, fieldRef, s) +} + +lunr.FieldRef.prototype.toString = function () { + if (this._stringValue == undefined) { + this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef + } + + return this._stringValue +} +/*! + * lunr.Set + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A lunr set. + * + * @constructor + */ +lunr.Set = function (elements) { + this.elements = Object.create(null) + + if (elements) { + this.length = elements.length + + for (var i = 0; i < this.length; i++) { + this.elements[elements[i]] = true + } + } else { + this.length = 0 + } +} + +/** + * A complete set that contains all elements. + * + * @static + * @readonly + * @type {lunr.Set} + */ +lunr.Set.complete = { + intersect: function (other) { + return other + }, + + union: function () { + return this + }, + + contains: function () { + return true + } +} + +/** + * An empty set that contains no elements. + * + * @static + * @readonly + * @type {lunr.Set} + */ +lunr.Set.empty = { + intersect: function () { + return this + }, + + union: function (other) { + return other + }, + + contains: function () { + return false + } +} + +/** + * Returns true if this set contains the specified object. + * + * @param {object} object - Object whose presence in this set is to be tested. + * @returns {boolean} - True if this set contains the specified object. + */ +lunr.Set.prototype.contains = function (object) { + return !!this.elements[object] +} + +/** + * Returns a new set containing only the elements that are present in both + * this set and the specified set. + * + * @param {lunr.Set} other - set to intersect with this set. + * @returns {lunr.Set} a new set that is the intersection of this and the specified set. + */ + +lunr.Set.prototype.intersect = function (other) { + var a, b, elements, intersection = [] + + if (other === lunr.Set.complete) { + return this + } + + if (other === lunr.Set.empty) { + return other + } + + if (this.length < other.length) { + a = this + b = other + } else { + a = other + b = this + } + + elements = Object.keys(a.elements) + + for (var i = 0; i < elements.length; i++) { + var element = elements[i] + if (element in b.elements) { + intersection.push(element) + } + } + + return new lunr.Set (intersection) +} + +/** + * Returns a new set combining the elements of this and the specified set. + * + * @param {lunr.Set} other - set to union with this set. + * @return {lunr.Set} a new set that is the union of this and the specified set. + */ + +lunr.Set.prototype.union = function (other) { + if (other === lunr.Set.complete) { + return lunr.Set.complete + } + + if (other === lunr.Set.empty) { + return this + } + + return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements))) +} +/** + * A function to calculate the inverse document frequency for + * a posting. This is shared between the builder and the index + * + * @private + * @param {object} posting - The posting for a given term + * @param {number} documentCount - The total number of documents. + */ +lunr.idf = function (posting, documentCount) { + var documentsWithTerm = 0 + + for (var fieldName in posting) { + if (fieldName == '_index') continue // Ignore the term index, its not a field + documentsWithTerm += Object.keys(posting[fieldName]).length + } + + var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5) + + return Math.log(1 + Math.abs(x)) +} + +/** + * A token wraps a string representation of a token + * as it is passed through the text processing pipeline. + * + * @constructor + * @param {string} [str=''] - The string token being wrapped. + * @param {object} [metadata={}] - Metadata associated with this token. + */ +lunr.Token = function (str, metadata) { + this.str = str || "" + this.metadata = metadata || {} +} + +/** + * Returns the token string that is being wrapped by this object. + * + * @returns {string} + */ +lunr.Token.prototype.toString = function () { + return this.str +} + +/** + * A token update function is used when updating or optionally + * when cloning a token. + * + * @callback lunr.Token~updateFunction + * @param {string} str - The string representation of the token. + * @param {Object} metadata - All metadata associated with this token. + */ + +/** + * Applies the given function to the wrapped string token. + * + * @example + * token.update(function (str, metadata) { + * return str.toUpperCase() + * }) + * + * @param {lunr.Token~updateFunction} fn - A function to apply to the token string. + * @returns {lunr.Token} + */ +lunr.Token.prototype.update = function (fn) { + this.str = fn(this.str, this.metadata) + return this +} + +/** + * Creates a clone of this token. Optionally a function can be + * applied to the cloned token. + * + * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token. + * @returns {lunr.Token} + */ +lunr.Token.prototype.clone = function (fn) { + fn = fn || function (s) { return s } + return new lunr.Token (fn(this.str, this.metadata), this.metadata) +} +/*! + * lunr.tokenizer + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A function for splitting a string into tokens ready to be inserted into + * the search index. Uses `lunr.tokenizer.separator` to split strings, change + * the value of this property to change how strings are split into tokens. + * + * This tokenizer will convert its parameter to a string by calling `toString` and + * then will split this string on the character in `lunr.tokenizer.separator`. + * Arrays will have their elements converted to strings and wrapped in a lunr.Token. + * + * Optional metadata can be passed to the tokenizer, this metadata will be cloned and + * added as metadata to every token that is created from the object to be tokenized. + * + * @static + * @param {?(string|object|object[])} obj - The object to convert into tokens + * @param {?object} metadata - Optional metadata to associate with every token + * @returns {lunr.Token[]} + * @see {@link lunr.Pipeline} + */ +lunr.tokenizer = function (obj, metadata) { + if (obj == null || obj == undefined) { + return [] + } + + if (Array.isArray(obj)) { + return obj.map(function (t) { + return new lunr.Token( + lunr.utils.asString(t).toLowerCase(), + lunr.utils.clone(metadata) + ) + }) + } + + var str = obj.toString().toLowerCase(), + len = str.length, + tokens = [] + + for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) { + var char = str.charAt(sliceEnd), + sliceLength = sliceEnd - sliceStart + + if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) { + + if (sliceLength > 0) { + var tokenMetadata = lunr.utils.clone(metadata) || {} + tokenMetadata["position"] = [sliceStart, sliceLength] + tokenMetadata["index"] = tokens.length + + tokens.push( + new lunr.Token ( + str.slice(sliceStart, sliceEnd), + tokenMetadata + ) + ) + } + + sliceStart = sliceEnd + 1 + } + + } + + return tokens +} + +/** + * The separator used to split a string into tokens. Override this property to change the behaviour of + * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens. + * + * @static + * @see lunr.tokenizer + */ +lunr.tokenizer.separator = /[\s\-]+/ +/*! + * lunr.Pipeline + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.Pipelines maintain an ordered list of functions to be applied to all + * tokens in documents entering the search index and queries being ran against + * the index. + * + * An instance of lunr.Index created with the lunr shortcut will contain a + * pipeline with a stop word filter and an English language stemmer. Extra + * functions can be added before or after either of these functions or these + * default functions can be removed. + * + * When run the pipeline will call each function in turn, passing a token, the + * index of that token in the original list of all tokens and finally a list of + * all the original tokens. + * + * The output of functions in the pipeline will be passed to the next function + * in the pipeline. To exclude a token from entering the index the function + * should return undefined, the rest of the pipeline will not be called with + * this token. + * + * For serialisation of pipelines to work, all functions used in an instance of + * a pipeline should be registered with lunr.Pipeline. Registered functions can + * then be loaded. If trying to load a serialised pipeline that uses functions + * that are not registered an error will be thrown. + * + * If not planning on serialising the pipeline then registering pipeline functions + * is not necessary. + * + * @constructor + */ +lunr.Pipeline = function () { + this._stack = [] +} + +lunr.Pipeline.registeredFunctions = Object.create(null) + +/** + * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token + * string as well as all known metadata. A pipeline function can mutate the token string + * or mutate (or add) metadata for a given token. + * + * A pipeline function can indicate that the passed token should be discarded by returning + * null, undefined or an empty string. This token will not be passed to any downstream pipeline + * functions and will not be added to the index. + * + * Multiple tokens can be returned by returning an array of tokens. Each token will be passed + * to any downstream pipeline functions and all will returned tokens will be added to the index. + * + * Any number of pipeline functions may be chained together using a lunr.Pipeline. + * + * @interface lunr.PipelineFunction + * @param {lunr.Token} token - A token from the document being processed. + * @param {number} i - The index of this token in the complete list of tokens for this document/field. + * @param {lunr.Token[]} tokens - All tokens for this document/field. + * @returns {(?lunr.Token|lunr.Token[])} + */ + +/** + * Register a function with the pipeline. + * + * Functions that are used in the pipeline should be registered if the pipeline + * needs to be serialised, or a serialised pipeline needs to be loaded. + * + * Registering a function does not add it to a pipeline, functions must still be + * added to instances of the pipeline for them to be used when running a pipeline. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @param {String} label - The label to register this function with + */ +lunr.Pipeline.registerFunction = function (fn, label) { + if (label in this.registeredFunctions) { + lunr.utils.warn('Overwriting existing registered function: ' + label) + } + + fn.label = label + lunr.Pipeline.registeredFunctions[fn.label] = fn +} + +/** + * Warns if the function is not registered as a Pipeline function. + * + * @param {lunr.PipelineFunction} fn - The function to check for. + * @private + */ +lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) { + var isRegistered = fn.label && (fn.label in this.registeredFunctions) + + if (!isRegistered) { + lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn) + } +} + +/** + * Loads a previously serialised pipeline. + * + * All functions to be loaded must already be registered with lunr.Pipeline. + * If any function from the serialised data has not been registered then an + * error will be thrown. + * + * @param {Object} serialised - The serialised pipeline to load. + * @returns {lunr.Pipeline} + */ +lunr.Pipeline.load = function (serialised) { + var pipeline = new lunr.Pipeline + + serialised.forEach(function (fnName) { + var fn = lunr.Pipeline.registeredFunctions[fnName] + + if (fn) { + pipeline.add(fn) + } else { + throw new Error('Cannot load unregistered function: ' + fnName) + } + }) + + return pipeline +} + +/** + * Adds new functions to the end of the pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline. + */ +lunr.Pipeline.prototype.add = function () { + var fns = Array.prototype.slice.call(arguments) + + fns.forEach(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + this._stack.push(fn) + }, this) +} + +/** + * Adds a single function after a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.after = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + pos = pos + 1 + this._stack.splice(pos, 0, newFn) +} + +/** + * Adds a single function before a function that already exists in the + * pipeline. + * + * Logs a warning if the function has not been registered. + * + * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. + * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. + */ +lunr.Pipeline.prototype.before = function (existingFn, newFn) { + lunr.Pipeline.warnIfFunctionNotRegistered(newFn) + + var pos = this._stack.indexOf(existingFn) + if (pos == -1) { + throw new Error('Cannot find existingFn') + } + + this._stack.splice(pos, 0, newFn) +} + +/** + * Removes a function from the pipeline. + * + * @param {lunr.PipelineFunction} fn The function to remove from the pipeline. + */ +lunr.Pipeline.prototype.remove = function (fn) { + var pos = this._stack.indexOf(fn) + if (pos == -1) { + return + } + + this._stack.splice(pos, 1) +} + +/** + * Runs the current list of functions that make up the pipeline against the + * passed tokens. + * + * @param {Array} tokens The tokens to run through the pipeline. + * @returns {Array} + */ +lunr.Pipeline.prototype.run = function (tokens) { + var stackLength = this._stack.length + + for (var i = 0; i < stackLength; i++) { + var fn = this._stack[i] + var memo = [] + + for (var j = 0; j < tokens.length; j++) { + var result = fn(tokens[j], j, tokens) + + if (result === null || result === void 0 || result === '') continue + + if (Array.isArray(result)) { + for (var k = 0; k < result.length; k++) { + memo.push(result[k]) + } + } else { + memo.push(result) + } + } + + tokens = memo + } + + return tokens +} + +/** + * Convenience method for passing a string through a pipeline and getting + * strings out. This method takes care of wrapping the passed string in a + * token and mapping the resulting tokens back to strings. + * + * @param {string} str - The string to pass through the pipeline. + * @param {?object} metadata - Optional metadata to associate with the token + * passed to the pipeline. + * @returns {string[]} + */ +lunr.Pipeline.prototype.runString = function (str, metadata) { + var token = new lunr.Token (str, metadata) + + return this.run([token]).map(function (t) { + return t.toString() + }) +} + +/** + * Resets the pipeline by removing any existing processors. + * + */ +lunr.Pipeline.prototype.reset = function () { + this._stack = [] +} + +/** + * Returns a representation of the pipeline ready for serialisation. + * + * Logs a warning if the function has not been registered. + * + * @returns {Array} + */ +lunr.Pipeline.prototype.toJSON = function () { + return this._stack.map(function (fn) { + lunr.Pipeline.warnIfFunctionNotRegistered(fn) + + return fn.label + }) +} +/*! + * lunr.Vector + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A vector is used to construct the vector space of documents and queries. These + * vectors support operations to determine the similarity between two documents or + * a document and a query. + * + * Normally no parameters are required for initializing a vector, but in the case of + * loading a previously dumped vector the raw elements can be provided to the constructor. + * + * For performance reasons vectors are implemented with a flat array, where an elements + * index is immediately followed by its value. E.g. [index, value, index, value]. This + * allows the underlying array to be as sparse as possible and still offer decent + * performance when being used for vector calculations. + * + * @constructor + * @param {Number[]} [elements] - The flat list of element index and element value pairs. + */ +lunr.Vector = function (elements) { + this._magnitude = 0 + this.elements = elements || [] +} + + +/** + * Calculates the position within the vector to insert a given index. + * + * This is used internally by insert and upsert. If there are duplicate indexes then + * the position is returned as if the value for that index were to be updated, but it + * is the callers responsibility to check whether there is a duplicate at that index + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @returns {Number} + */ +lunr.Vector.prototype.positionForIndex = function (index) { + // For an empty vector the tuple can be inserted at the beginning + if (this.elements.length == 0) { + return 0 + } + + var start = 0, + end = this.elements.length / 2, + sliceLength = end - start, + pivotPoint = Math.floor(sliceLength / 2), + pivotIndex = this.elements[pivotPoint * 2] + + while (sliceLength > 1) { + if (pivotIndex < index) { + start = pivotPoint + } + + if (pivotIndex > index) { + end = pivotPoint + } + + if (pivotIndex == index) { + break + } + + sliceLength = end - start + pivotPoint = start + Math.floor(sliceLength / 2) + pivotIndex = this.elements[pivotPoint * 2] + } + + if (pivotIndex == index) { + return pivotPoint * 2 + } + + if (pivotIndex > index) { + return pivotPoint * 2 + } + + if (pivotIndex < index) { + return (pivotPoint + 1) * 2 + } +} + +/** + * Inserts an element at an index within the vector. + * + * Does not allow duplicates, will throw an error if there is already an entry + * for this index. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + */ +lunr.Vector.prototype.insert = function (insertIdx, val) { + this.upsert(insertIdx, val, function () { + throw "duplicate index" + }) +} + +/** + * Inserts or updates an existing index within the vector. + * + * @param {Number} insertIdx - The index at which the element should be inserted. + * @param {Number} val - The value to be inserted into the vector. + * @param {function} fn - A function that is called for updates, the existing value and the + * requested value are passed as arguments + */ +lunr.Vector.prototype.upsert = function (insertIdx, val, fn) { + this._magnitude = 0 + var position = this.positionForIndex(insertIdx) + + if (this.elements[position] == insertIdx) { + this.elements[position + 1] = fn(this.elements[position + 1], val) + } else { + this.elements.splice(position, 0, insertIdx, val) + } +} + +/** + * Calculates the magnitude of this vector. + * + * @returns {Number} + */ +lunr.Vector.prototype.magnitude = function () { + if (this._magnitude) return this._magnitude + + var sumOfSquares = 0, + elementsLength = this.elements.length + + for (var i = 1; i < elementsLength; i += 2) { + var val = this.elements[i] + sumOfSquares += val * val + } + + return this._magnitude = Math.sqrt(sumOfSquares) +} + +/** + * Calculates the dot product of this vector and another vector. + * + * @param {lunr.Vector} otherVector - The vector to compute the dot product with. + * @returns {Number} + */ +lunr.Vector.prototype.dot = function (otherVector) { + var dotProduct = 0, + a = this.elements, b = otherVector.elements, + aLen = a.length, bLen = b.length, + aVal = 0, bVal = 0, + i = 0, j = 0 + + while (i < aLen && j < bLen) { + aVal = a[i], bVal = b[j] + if (aVal < bVal) { + i += 2 + } else if (aVal > bVal) { + j += 2 + } else if (aVal == bVal) { + dotProduct += a[i + 1] * b[j + 1] + i += 2 + j += 2 + } + } + + return dotProduct +} + +/** + * Calculates the similarity between this vector and another vector. + * + * @param {lunr.Vector} otherVector - The other vector to calculate the + * similarity with. + * @returns {Number} + */ +lunr.Vector.prototype.similarity = function (otherVector) { + return this.dot(otherVector) / this.magnitude() || 0 +} + +/** + * Converts the vector to an array of the elements within the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toArray = function () { + var output = new Array (this.elements.length / 2) + + for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) { + output[j] = this.elements[i] + } + + return output +} + +/** + * A JSON serializable representation of the vector. + * + * @returns {Number[]} + */ +lunr.Vector.prototype.toJSON = function () { + return this.elements +} +/* eslint-disable */ +/*! + * lunr.stemmer + * Copyright (C) 2020 Oliver Nightingale + * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt + */ + +/** + * lunr.stemmer is an english language stemmer, this is a JavaScript + * implementation of the PorterStemmer taken from http://tartarus.org/~martin + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token - The string to stem + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + * @function + */ +lunr.stemmer = (function(){ + var step2list = { + "ational" : "ate", + "tional" : "tion", + "enci" : "ence", + "anci" : "ance", + "izer" : "ize", + "bli" : "ble", + "alli" : "al", + "entli" : "ent", + "eli" : "e", + "ousli" : "ous", + "ization" : "ize", + "ation" : "ate", + "ator" : "ate", + "alism" : "al", + "iveness" : "ive", + "fulness" : "ful", + "ousness" : "ous", + "aliti" : "al", + "iviti" : "ive", + "biliti" : "ble", + "logi" : "log" + }, + + step3list = { + "icate" : "ic", + "ative" : "", + "alize" : "al", + "iciti" : "ic", + "ical" : "ic", + "ful" : "", + "ness" : "" + }, + + c = "[^aeiou]", // consonant + v = "[aeiouy]", // vowel + C = c + "[^aeiouy]*", // consonant sequence + V = v + "[aeiou]*", // vowel sequence + + mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0 + meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1 + mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1 + s_v = "^(" + C + ")?" + v; // vowel in stem + + var re_mgr0 = new RegExp(mgr0); + var re_mgr1 = new RegExp(mgr1); + var re_meq1 = new RegExp(meq1); + var re_s_v = new RegExp(s_v); + + var re_1a = /^(.+?)(ss|i)es$/; + var re2_1a = /^(.+?)([^s])s$/; + var re_1b = /^(.+?)eed$/; + var re2_1b = /^(.+?)(ed|ing)$/; + var re_1b_2 = /.$/; + var re2_1b_2 = /(at|bl|iz)$/; + var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$"); + var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var re_1c = /^(.+?[^aeiou])y$/; + var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + + var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + + var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + var re2_4 = /^(.+?)(s|t)(ion)$/; + + var re_5 = /^(.+?)e$/; + var re_5_1 = /ll$/; + var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + + var porterStemmer = function porterStemmer(w) { + var stem, + suffix, + firstch, + re, + re2, + re3, + re4; + + if (w.length < 3) { return w; } + + firstch = w.substr(0,1); + if (firstch == "y") { + w = firstch.toUpperCase() + w.substr(1); + } + + // Step 1a + re = re_1a + re2 = re2_1a; + + if (re.test(w)) { w = w.replace(re,"$1$2"); } + else if (re2.test(w)) { w = w.replace(re2,"$1$2"); } + + // Step 1b + re = re_1b; + re2 = re2_1b; + if (re.test(w)) { + var fp = re.exec(w); + re = re_mgr0; + if (re.test(fp[1])) { + re = re_1b_2; + w = w.replace(re,""); + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = re_s_v; + if (re2.test(stem)) { + w = stem; + re2 = re2_1b_2; + re3 = re3_1b_2; + re4 = re4_1b_2; + if (re2.test(w)) { w = w + "e"; } + else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); } + else if (re4.test(w)) { w = w + "e"; } + } + } + + // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say) + re = re_1c; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + w = stem + "i"; + } + + // Step 2 + re = re_2; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step2list[suffix]; + } + } + + // Step 3 + re = re_3; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = re_mgr0; + if (re.test(stem)) { + w = stem + step3list[suffix]; + } + } + + // Step 4 + re = re_4; + re2 = re2_4; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + if (re.test(stem)) { + w = stem; + } + } else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = re_mgr1; + if (re2.test(stem)) { + w = stem; + } + } + + // Step 5 + re = re_5; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = re_mgr1; + re2 = re_meq1; + re3 = re3_5; + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) { + w = stem; + } + } + + re = re_5_1; + re2 = re_mgr1; + if (re.test(w) && re2.test(w)) { + re = re_1b_2; + w = w.replace(re,""); + } + + // and turn initial Y back to y + + if (firstch == "y") { + w = firstch.toLowerCase() + w.substr(1); + } + + return w; + }; + + return function (token) { + return token.update(porterStemmer); + } +})(); + +lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer') +/*! + * lunr.stopWordFilter + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.generateStopWordFilter builds a stopWordFilter function from the provided + * list of stop words. + * + * The built in lunr.stopWordFilter is built using this generator and can be used + * to generate custom stopWordFilters for applications or non English languages. + * + * @function + * @param {Array} token The token to pass through the filter + * @returns {lunr.PipelineFunction} + * @see lunr.Pipeline + * @see lunr.stopWordFilter + */ +lunr.generateStopWordFilter = function (stopWords) { + var words = stopWords.reduce(function (memo, stopWord) { + memo[stopWord] = stopWord + return memo + }, {}) + + return function (token) { + if (token && words[token.toString()] !== token.toString()) return token + } +} + +/** + * lunr.stopWordFilter is an English language stop word list filter, any words + * contained in the list will not be passed through the filter. + * + * This is intended to be used in the Pipeline. If the token does not pass the + * filter then undefined will be returned. + * + * @function + * @implements {lunr.PipelineFunction} + * @params {lunr.Token} token - A token to check for being a stop word. + * @returns {lunr.Token} + * @see {@link lunr.Pipeline} + */ +lunr.stopWordFilter = lunr.generateStopWordFilter([ + 'a', + 'able', + 'about', + 'across', + 'after', + 'all', + 'almost', + 'also', + 'am', + 'among', + 'an', + 'and', + 'any', + 'are', + 'as', + 'at', + 'be', + 'because', + 'been', + 'but', + 'by', + 'can', + 'cannot', + 'could', + 'dear', + 'did', + 'do', + 'does', + 'either', + 'else', + 'ever', + 'every', + 'for', + 'from', + 'get', + 'got', + 'had', + 'has', + 'have', + 'he', + 'her', + 'hers', + 'him', + 'his', + 'how', + 'however', + 'i', + 'if', + 'in', + 'into', + 'is', + 'it', + 'its', + 'just', + 'least', + 'let', + 'like', + 'likely', + 'may', + 'me', + 'might', + 'most', + 'must', + 'my', + 'neither', + 'no', + 'nor', + 'not', + 'of', + 'off', + 'often', + 'on', + 'only', + 'or', + 'other', + 'our', + 'own', + 'rather', + 'said', + 'say', + 'says', + 'she', + 'should', + 'since', + 'so', + 'some', + 'than', + 'that', + 'the', + 'their', + 'them', + 'then', + 'there', + 'these', + 'they', + 'this', + 'tis', + 'to', + 'too', + 'twas', + 'us', + 'wants', + 'was', + 'we', + 'were', + 'what', + 'when', + 'where', + 'which', + 'while', + 'who', + 'whom', + 'why', + 'will', + 'with', + 'would', + 'yet', + 'you', + 'your' +]) + +lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter') +/*! + * lunr.trimmer + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.trimmer is a pipeline function for trimming non word + * characters from the beginning and end of tokens before they + * enter the index. + * + * This implementation may not work correctly for non latin + * characters and should either be removed or adapted for use + * with languages with non-latin characters. + * + * @static + * @implements {lunr.PipelineFunction} + * @param {lunr.Token} token The token to pass through the filter + * @returns {lunr.Token} + * @see lunr.Pipeline + */ +lunr.trimmer = function (token) { + return token.update(function (s) { + return s.replace(/^\W+/, '').replace(/\W+$/, '') + }) +} + +lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer') +/*! + * lunr.TokenSet + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * A token set is used to store the unique list of all tokens + * within an index. Token sets are also used to represent an + * incoming query to the index, this query token set and index + * token set are then intersected to find which tokens to look + * up in the inverted index. + * + * A token set can hold multiple tokens, as in the case of the + * index token set, or it can hold a single token as in the + * case of a simple query token set. + * + * Additionally token sets are used to perform wildcard matching. + * Leading, contained and trailing wildcards are supported, and + * from this edit distance matching can also be provided. + * + * Token sets are implemented as a minimal finite state automata, + * where both common prefixes and suffixes are shared between tokens. + * This helps to reduce the space used for storing the token set. + * + * @constructor + */ +lunr.TokenSet = function () { + this.final = false + this.edges = {} + this.id = lunr.TokenSet._nextId + lunr.TokenSet._nextId += 1 +} + +/** + * Keeps track of the next, auto increment, identifier to assign + * to a new tokenSet. + * + * TokenSets require a unique identifier to be correctly minimised. + * + * @private + */ +lunr.TokenSet._nextId = 1 + +/** + * Creates a TokenSet instance from the given sorted array of words. + * + * @param {String[]} arr - A sorted array of strings to create the set from. + * @returns {lunr.TokenSet} + * @throws Will throw an error if the input array is not sorted. + */ +lunr.TokenSet.fromArray = function (arr) { + var builder = new lunr.TokenSet.Builder + + for (var i = 0, len = arr.length; i < len; i++) { + builder.insert(arr[i]) + } + + builder.finish() + return builder.root +} + +/** + * Creates a token set from a query clause. + * + * @private + * @param {Object} clause - A single clause from lunr.Query. + * @param {string} clause.term - The query clause term. + * @param {number} [clause.editDistance] - The optional edit distance for the term. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromClause = function (clause) { + if ('editDistance' in clause) { + return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance) + } else { + return lunr.TokenSet.fromString(clause.term) + } +} + +/** + * Creates a token set representing a single string with a specified + * edit distance. + * + * Insertions, deletions, substitutions and transpositions are each + * treated as an edit distance of 1. + * + * Increasing the allowed edit distance will have a dramatic impact + * on the performance of both creating and intersecting these TokenSets. + * It is advised to keep the edit distance less than 3. + * + * @param {string} str - The string to create the token set from. + * @param {number} editDistance - The allowed edit distance to match. + * @returns {lunr.Vector} + */ +lunr.TokenSet.fromFuzzyString = function (str, editDistance) { + var root = new lunr.TokenSet + + var stack = [{ + node: root, + editsRemaining: editDistance, + str: str + }] + + while (stack.length) { + var frame = stack.pop() + + // no edit + if (frame.str.length > 0) { + var char = frame.str.charAt(0), + noEditNode + + if (char in frame.node.edges) { + noEditNode = frame.node.edges[char] + } else { + noEditNode = new lunr.TokenSet + frame.node.edges[char] = noEditNode + } + + if (frame.str.length == 1) { + noEditNode.final = true + } + + stack.push({ + node: noEditNode, + editsRemaining: frame.editsRemaining, + str: frame.str.slice(1) + }) + } + + if (frame.editsRemaining == 0) { + continue + } + + // insertion + if ("*" in frame.node.edges) { + var insertionNode = frame.node.edges["*"] + } else { + var insertionNode = new lunr.TokenSet + frame.node.edges["*"] = insertionNode + } + + if (frame.str.length == 0) { + insertionNode.final = true + } + + stack.push({ + node: insertionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str + }) + + // deletion + // can only do a deletion if we have enough edits remaining + // and if there are characters left to delete in the string + if (frame.str.length > 1) { + stack.push({ + node: frame.node, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(1) + }) + } + + // deletion + // just removing the last character from the str + if (frame.str.length == 1) { + frame.node.final = true + } + + // substitution + // can only do a substitution if we have enough edits remaining + // and if there are characters left to substitute + if (frame.str.length >= 1) { + if ("*" in frame.node.edges) { + var substitutionNode = frame.node.edges["*"] + } else { + var substitutionNode = new lunr.TokenSet + frame.node.edges["*"] = substitutionNode + } + + if (frame.str.length == 1) { + substitutionNode.final = true + } + + stack.push({ + node: substitutionNode, + editsRemaining: frame.editsRemaining - 1, + str: frame.str.slice(1) + }) + } + + // transposition + // can only do a transposition if there are edits remaining + // and there are enough characters to transpose + if (frame.str.length > 1) { + var charA = frame.str.charAt(0), + charB = frame.str.charAt(1), + transposeNode + + if (charB in frame.node.edges) { + transposeNode = frame.node.edges[charB] + } else { + transposeNode = new lunr.TokenSet + frame.node.edges[charB] = transposeNode + } + + if (frame.str.length == 1) { + transposeNode.final = true + } + + stack.push({ + node: transposeNode, + editsRemaining: frame.editsRemaining - 1, + str: charA + frame.str.slice(2) + }) + } + } + + return root +} + +/** + * Creates a TokenSet from a string. + * + * The string may contain one or more wildcard characters (*) + * that will allow wildcard matching when intersecting with + * another TokenSet. + * + * @param {string} str - The string to create a TokenSet from. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.fromString = function (str) { + var node = new lunr.TokenSet, + root = node + + /* + * Iterates through all characters within the passed string + * appending a node for each character. + * + * When a wildcard character is found then a self + * referencing edge is introduced to continually match + * any number of any characters. + */ + for (var i = 0, len = str.length; i < len; i++) { + var char = str[i], + final = (i == len - 1) + + if (char == "*") { + node.edges[char] = node + node.final = final + + } else { + var next = new lunr.TokenSet + next.final = final + + node.edges[char] = next + node = next + } + } + + return root +} + +/** + * Converts this TokenSet into an array of strings + * contained within the TokenSet. + * + * This is not intended to be used on a TokenSet that + * contains wildcards, in these cases the results are + * undefined and are likely to cause an infinite loop. + * + * @returns {string[]} + */ +lunr.TokenSet.prototype.toArray = function () { + var words = [] + + var stack = [{ + prefix: "", + node: this + }] + + while (stack.length) { + var frame = stack.pop(), + edges = Object.keys(frame.node.edges), + len = edges.length + + if (frame.node.final) { + /* In Safari, at this point the prefix is sometimes corrupted, see: + * https://github.com/olivernn/lunr.js/issues/279 Calling any + * String.prototype method forces Safari to "cast" this string to what + * it's supposed to be, fixing the bug. */ + frame.prefix.charAt(0) + words.push(frame.prefix) + } + + for (var i = 0; i < len; i++) { + var edge = edges[i] + + stack.push({ + prefix: frame.prefix.concat(edge), + node: frame.node.edges[edge] + }) + } + } + + return words +} + +/** + * Generates a string representation of a TokenSet. + * + * This is intended to allow TokenSets to be used as keys + * in objects, largely to aid the construction and minimisation + * of a TokenSet. As such it is not designed to be a human + * friendly representation of the TokenSet. + * + * @returns {string} + */ +lunr.TokenSet.prototype.toString = function () { + // NOTE: Using Object.keys here as this.edges is very likely + // to enter 'hash-mode' with many keys being added + // + // avoiding a for-in loop here as it leads to the function + // being de-optimised (at least in V8). From some simple + // benchmarks the performance is comparable, but allowing + // V8 to optimize may mean easy performance wins in the future. + + if (this._str) { + return this._str + } + + var str = this.final ? '1' : '0', + labels = Object.keys(this.edges).sort(), + len = labels.length + + for (var i = 0; i < len; i++) { + var label = labels[i], + node = this.edges[label] + + str = str + label + node.id + } + + return str +} + +/** + * Returns a new TokenSet that is the intersection of + * this TokenSet and the passed TokenSet. + * + * This intersection will take into account any wildcards + * contained within the TokenSet. + * + * @param {lunr.TokenSet} b - An other TokenSet to intersect with. + * @returns {lunr.TokenSet} + */ +lunr.TokenSet.prototype.intersect = function (b) { + var output = new lunr.TokenSet, + frame = undefined + + var stack = [{ + qNode: b, + output: output, + node: this + }] + + while (stack.length) { + frame = stack.pop() + + // NOTE: As with the #toString method, we are using + // Object.keys and a for loop instead of a for-in loop + // as both of these objects enter 'hash' mode, causing + // the function to be de-optimised in V8 + var qEdges = Object.keys(frame.qNode.edges), + qLen = qEdges.length, + nEdges = Object.keys(frame.node.edges), + nLen = nEdges.length + + for (var q = 0; q < qLen; q++) { + var qEdge = qEdges[q] + + for (var n = 0; n < nLen; n++) { + var nEdge = nEdges[n] + + if (nEdge == qEdge || qEdge == '*') { + var node = frame.node.edges[nEdge], + qNode = frame.qNode.edges[qEdge], + final = node.final && qNode.final, + next = undefined + + if (nEdge in frame.output.edges) { + // an edge already exists for this character + // no need to create a new node, just set the finality + // bit unless this node is already final + next = frame.output.edges[nEdge] + next.final = next.final || final + + } else { + // no edge exists yet, must create one + // set the finality bit and insert it + // into the output + next = new lunr.TokenSet + next.final = final + frame.output.edges[nEdge] = next + } + + stack.push({ + qNode: qNode, + output: next, + node: node + }) + } + } + } + } + + return output +} +lunr.TokenSet.Builder = function () { + this.previousWord = "" + this.root = new lunr.TokenSet + this.uncheckedNodes = [] + this.minimizedNodes = {} +} + +lunr.TokenSet.Builder.prototype.insert = function (word) { + var node, + commonPrefix = 0 + + if (word < this.previousWord) { + throw new Error ("Out of order word insertion") + } + + for (var i = 0; i < word.length && i < this.previousWord.length; i++) { + if (word[i] != this.previousWord[i]) break + commonPrefix++ + } + + this.minimize(commonPrefix) + + if (this.uncheckedNodes.length == 0) { + node = this.root + } else { + node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child + } + + for (var i = commonPrefix; i < word.length; i++) { + var nextNode = new lunr.TokenSet, + char = word[i] + + node.edges[char] = nextNode + + this.uncheckedNodes.push({ + parent: node, + char: char, + child: nextNode + }) + + node = nextNode + } + + node.final = true + this.previousWord = word +} + +lunr.TokenSet.Builder.prototype.finish = function () { + this.minimize(0) +} + +lunr.TokenSet.Builder.prototype.minimize = function (downTo) { + for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) { + var node = this.uncheckedNodes[i], + childKey = node.child.toString() + + if (childKey in this.minimizedNodes) { + node.parent.edges[node.char] = this.minimizedNodes[childKey] + } else { + // Cache the key for this node since + // we know it can't change anymore + node.child._str = childKey + + this.minimizedNodes[childKey] = node.child + } + + this.uncheckedNodes.pop() + } +} +/*! + * lunr.Index + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * An index contains the built index of all documents and provides a query interface + * to the index. + * + * Usually instances of lunr.Index will not be created using this constructor, instead + * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be + * used to load previously built and serialized indexes. + * + * @constructor + * @param {Object} attrs - The attributes of the built search index. + * @param {Object} attrs.invertedIndex - An index of term/field to document reference. + * @param {Object} attrs.fieldVectors - Field vectors + * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens. + * @param {string[]} attrs.fields - The names of indexed document fields. + * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms. + */ +lunr.Index = function (attrs) { + this.invertedIndex = attrs.invertedIndex + this.fieldVectors = attrs.fieldVectors + this.tokenSet = attrs.tokenSet + this.fields = attrs.fields + this.pipeline = attrs.pipeline +} + +/** + * A result contains details of a document matching a search query. + * @typedef {Object} lunr.Index~Result + * @property {string} ref - The reference of the document this result represents. + * @property {number} score - A number between 0 and 1 representing how similar this document is to the query. + * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match. + */ + +/** + * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple + * query language which itself is parsed into an instance of lunr.Query. + * + * For programmatically building queries it is advised to directly use lunr.Query, the query language + * is best used for human entered text rather than program generated text. + * + * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported + * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello' + * or 'world', though those that contain both will rank higher in the results. + * + * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can + * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding + * wildcards will increase the number of documents that will be found but can also have a negative + * impact on query performance, especially with wildcards at the beginning of a term. + * + * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term + * hello in the title field will match this query. Using a field not present in the index will lead + * to an error being thrown. + * + * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term + * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported + * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2. + * Avoid large values for edit distance to improve query performance. + * + * Each term also supports a presence modifier. By default a term's presence in document is optional, however + * this can be changed to either required or prohibited. For a term's presence to be required in a document the + * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and + * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not + * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'. + * + * To escape special characters the backslash character '\' can be used, this allows searches to include + * characters that would normally be considered modifiers, e.g. `foo\~2` will search for a term "foo~2" instead + * of attempting to apply a boost of 2 to the search term "foo". + * + * @typedef {string} lunr.Index~QueryString + * @example Simple single term query + * hello + * @example Multiple term query + * hello world + * @example term scoped to a field + * title:hello + * @example term with a boost of 10 + * hello^10 + * @example term with an edit distance of 2 + * hello~2 + * @example terms with presence modifiers + * -foo +bar baz + */ + +/** + * Performs a search against the index using lunr query syntax. + * + * Results will be returned sorted by their score, the most relevant results + * will be returned first. For details on how the score is calculated, please see + * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}. + * + * For more programmatic querying use lunr.Index#query. + * + * @param {lunr.Index~QueryString} queryString - A string containing a lunr query. + * @throws {lunr.QueryParseError} If the passed query string cannot be parsed. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.search = function (queryString) { + return this.query(function (query) { + var parser = new lunr.QueryParser(queryString, query) + parser.parse() + }) +} + +/** + * A query builder callback provides a query object to be used to express + * the query to perform on the index. + * + * @callback lunr.Index~queryBuilder + * @param {lunr.Query} query - The query object to build up. + * @this lunr.Query + */ + +/** + * Performs a query against the index using the yielded lunr.Query object. + * + * If performing programmatic queries against the index, this method is preferred + * over lunr.Index#search so as to avoid the additional query parsing overhead. + * + * A query object is yielded to the supplied function which should be used to + * express the query to be run against the index. + * + * Note that although this function takes a callback parameter it is _not_ an + * asynchronous operation, the callback is just yielded a query object to be + * customized. + * + * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query. + * @returns {lunr.Index~Result[]} + */ +lunr.Index.prototype.query = function (fn) { + // for each query clause + // * process terms + // * expand terms from token set + // * find matching documents and metadata + // * get document vectors + // * score documents + + var query = new lunr.Query(this.fields), + matchingFields = Object.create(null), + queryVectors = Object.create(null), + termFieldCache = Object.create(null), + requiredMatches = Object.create(null), + prohibitedMatches = Object.create(null) + + /* + * To support field level boosts a query vector is created per + * field. An empty vector is eagerly created to support negated + * queries. + */ + for (var i = 0; i < this.fields.length; i++) { + queryVectors[this.fields[i]] = new lunr.Vector + } + + fn.call(query, query) + + for (var i = 0; i < query.clauses.length; i++) { + /* + * Unless the pipeline has been disabled for this term, which is + * the case for terms with wildcards, we need to pass the clause + * term through the search pipeline. A pipeline returns an array + * of processed terms. Pipeline functions may expand the passed + * term, which means we may end up performing multiple index lookups + * for a single query term. + */ + var clause = query.clauses[i], + terms = null, + clauseMatches = lunr.Set.empty + + if (clause.usePipeline) { + terms = this.pipeline.runString(clause.term, { + fields: clause.fields + }) + } else { + terms = [clause.term] + } + + for (var m = 0; m < terms.length; m++) { + var term = terms[m] + + /* + * Each term returned from the pipeline needs to use the same query + * clause object, e.g. the same boost and or edit distance. The + * simplest way to do this is to re-use the clause object but mutate + * its term property. + */ + clause.term = term + + /* + * From the term in the clause we create a token set which will then + * be used to intersect the indexes token set to get a list of terms + * to lookup in the inverted index + */ + var termTokenSet = lunr.TokenSet.fromClause(clause), + expandedTerms = this.tokenSet.intersect(termTokenSet).toArray() + + /* + * If a term marked as required does not exist in the tokenSet it is + * impossible for the search to return any matches. We set all the field + * scoped required matches set to empty and stop examining any further + * clauses. + */ + if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) { + for (var k = 0; k < clause.fields.length; k++) { + var field = clause.fields[k] + requiredMatches[field] = lunr.Set.empty + } + + break + } + + for (var j = 0; j < expandedTerms.length; j++) { + /* + * For each term get the posting and termIndex, this is required for + * building the query vector. + */ + var expandedTerm = expandedTerms[j], + posting = this.invertedIndex[expandedTerm], + termIndex = posting._index + + for (var k = 0; k < clause.fields.length; k++) { + /* + * For each field that this query term is scoped by (by default + * all fields are in scope) we need to get all the document refs + * that have this term in that field. + * + * The posting is the entry in the invertedIndex for the matching + * term from above. + */ + var field = clause.fields[k], + fieldPosting = posting[field], + matchingDocumentRefs = Object.keys(fieldPosting), + termField = expandedTerm + "/" + field, + matchingDocumentsSet = new lunr.Set(matchingDocumentRefs) + + /* + * if the presence of this term is required ensure that the matching + * documents are added to the set of required matches for this clause. + * + */ + if (clause.presence == lunr.Query.presence.REQUIRED) { + clauseMatches = clauseMatches.union(matchingDocumentsSet) + + if (requiredMatches[field] === undefined) { + requiredMatches[field] = lunr.Set.complete + } + } + + /* + * if the presence of this term is prohibited ensure that the matching + * documents are added to the set of prohibited matches for this field, + * creating that set if it does not yet exist. + */ + if (clause.presence == lunr.Query.presence.PROHIBITED) { + if (prohibitedMatches[field] === undefined) { + prohibitedMatches[field] = lunr.Set.empty + } + + prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet) + + /* + * Prohibited matches should not be part of the query vector used for + * similarity scoring and no metadata should be extracted so we continue + * to the next field + */ + continue + } + + /* + * The query field vector is populated using the termIndex found for + * the term and a unit value with the appropriate boost applied. + * Using upsert because there could already be an entry in the vector + * for the term we are working with. In that case we just add the scores + * together. + */ + queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b }) + + /** + * If we've already seen this term, field combo then we've already collected + * the matching documents and metadata, no need to go through all that again + */ + if (termFieldCache[termField]) { + continue + } + + for (var l = 0; l < matchingDocumentRefs.length; l++) { + /* + * All metadata for this term/field/document triple + * are then extracted and collected into an instance + * of lunr.MatchData ready to be returned in the query + * results + */ + var matchingDocumentRef = matchingDocumentRefs[l], + matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field), + metadata = fieldPosting[matchingDocumentRef], + fieldMatch + + if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) { + matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata) + } else { + fieldMatch.add(expandedTerm, field, metadata) + } + + } + + termFieldCache[termField] = true + } + } + } + + /** + * If the presence was required we need to update the requiredMatches field sets. + * We do this after all fields for the term have collected their matches because + * the clause terms presence is required in _any_ of the fields not _all_ of the + * fields. + */ + if (clause.presence === lunr.Query.presence.REQUIRED) { + for (var k = 0; k < clause.fields.length; k++) { + var field = clause.fields[k] + requiredMatches[field] = requiredMatches[field].intersect(clauseMatches) + } + } + } + + /** + * Need to combine the field scoped required and prohibited + * matching documents into a global set of required and prohibited + * matches + */ + var allRequiredMatches = lunr.Set.complete, + allProhibitedMatches = lunr.Set.empty + + for (var i = 0; i < this.fields.length; i++) { + var field = this.fields[i] + + if (requiredMatches[field]) { + allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field]) + } + + if (prohibitedMatches[field]) { + allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field]) + } + } + + var matchingFieldRefs = Object.keys(matchingFields), + results = [], + matches = Object.create(null) + + /* + * If the query is negated (contains only prohibited terms) + * we need to get _all_ fieldRefs currently existing in the + * index. This is only done when we know that the query is + * entirely prohibited terms to avoid any cost of getting all + * fieldRefs unnecessarily. + * + * Additionally, blank MatchData must be created to correctly + * populate the results. + */ + if (query.isNegated()) { + matchingFieldRefs = Object.keys(this.fieldVectors) + + for (var i = 0; i < matchingFieldRefs.length; i++) { + var matchingFieldRef = matchingFieldRefs[i] + var fieldRef = lunr.FieldRef.fromString(matchingFieldRef) + matchingFields[matchingFieldRef] = new lunr.MatchData + } + } + + for (var i = 0; i < matchingFieldRefs.length; i++) { + /* + * Currently we have document fields that match the query, but we + * need to return documents. The matchData and scores are combined + * from multiple fields belonging to the same document. + * + * Scores are calculated by field, using the query vectors created + * above, and combined into a final document score using addition. + */ + var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]), + docRef = fieldRef.docRef + + if (!allRequiredMatches.contains(docRef)) { + continue + } + + if (allProhibitedMatches.contains(docRef)) { + continue + } + + var fieldVector = this.fieldVectors[fieldRef], + score = queryVectors[fieldRef.fieldName].similarity(fieldVector), + docMatch + + if ((docMatch = matches[docRef]) !== undefined) { + docMatch.score += score + docMatch.matchData.combine(matchingFields[fieldRef]) + } else { + var match = { + ref: docRef, + score: score, + matchData: matchingFields[fieldRef] + } + matches[docRef] = match + results.push(match) + } + } + + /* + * Sort the results objects by score, highest first. + */ + return results.sort(function (a, b) { + return b.score - a.score + }) +} + +/** + * Prepares the index for JSON serialization. + * + * The schema for this JSON blob will be described in a + * separate JSON schema file. + * + * @returns {Object} + */ +lunr.Index.prototype.toJSON = function () { + var invertedIndex = Object.keys(this.invertedIndex) + .sort() + .map(function (term) { + return [term, this.invertedIndex[term]] + }, this) + + var fieldVectors = Object.keys(this.fieldVectors) + .map(function (ref) { + return [ref, this.fieldVectors[ref].toJSON()] + }, this) + + return { + version: lunr.version, + fields: this.fields, + fieldVectors: fieldVectors, + invertedIndex: invertedIndex, + pipeline: this.pipeline.toJSON() + } +} + +/** + * Loads a previously serialized lunr.Index + * + * @param {Object} serializedIndex - A previously serialized lunr.Index + * @returns {lunr.Index} + */ +lunr.Index.load = function (serializedIndex) { + var attrs = {}, + fieldVectors = {}, + serializedVectors = serializedIndex.fieldVectors, + invertedIndex = Object.create(null), + serializedInvertedIndex = serializedIndex.invertedIndex, + tokenSetBuilder = new lunr.TokenSet.Builder, + pipeline = lunr.Pipeline.load(serializedIndex.pipeline) + + if (serializedIndex.version != lunr.version) { + lunr.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + lunr.version + "' does not match serialized index '" + serializedIndex.version + "'") + } + + for (var i = 0; i < serializedVectors.length; i++) { + var tuple = serializedVectors[i], + ref = tuple[0], + elements = tuple[1] + + fieldVectors[ref] = new lunr.Vector(elements) + } + + for (var i = 0; i < serializedInvertedIndex.length; i++) { + var tuple = serializedInvertedIndex[i], + term = tuple[0], + posting = tuple[1] + + tokenSetBuilder.insert(term) + invertedIndex[term] = posting + } + + tokenSetBuilder.finish() + + attrs.fields = serializedIndex.fields + + attrs.fieldVectors = fieldVectors + attrs.invertedIndex = invertedIndex + attrs.tokenSet = tokenSetBuilder.root + attrs.pipeline = pipeline + + return new lunr.Index(attrs) +} +/*! + * lunr.Builder + * Copyright (C) 2020 Oliver Nightingale + */ + +/** + * lunr.Builder performs indexing on a set of documents and + * returns instances of lunr.Index ready for querying. + * + * All configuration of the index is done via the builder, the + * fields to index, the document reference, the text processing + * pipeline and document scoring parameters are all set on the + * builder before indexing. + * + * @constructor + * @property {string} _ref - Internal reference to the document reference field. + * @property {string[]} _fields - Internal reference to the document fields to index. + * @property {object} invertedIndex - The inverted index maps terms to document fields. + * @property {object} documentTermFrequencies - Keeps track of document term frequencies. + * @property {object} documentLengths - Keeps track of the length of documents added to the index. + * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing. + * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing. + * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index. + * @property {number} documentCount - Keeps track of the total number of documents indexed. + * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75. + * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2. + * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space. + * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index. + */ +lunr.Builder = function () { + this._ref = "id" + this._fields = Object.create(null) + this._documents = Object.create(null) + this.invertedIndex = Object.create(null) + this.fieldTermFrequencies = {} + this.fieldLengths = {} + this.tokenizer = lunr.tokenizer + this.pipeline = new lunr.Pipeline + this.searchPipeline = new lunr.Pipeline + this.documentCount = 0 + this._b = 0.75 + this._k1 = 1.2 + this.termIndex = 0 + this.metadataWhitelist = [] +} + +/** + * Sets the document field used as the document reference. Every document must have this field. + * The type of this field in the document should be a string, if it is not a string it will be + * coerced into a string by calling toString. + * + * The default ref is 'id'. + * + * The ref should _not_ be changed during indexing, it should be set before any documents are + * added to the index. Changing it during indexing can lead to inconsistent results. + * + * @param {string} ref - The name of the reference field in the document. + */ +lunr.Builder.prototype.ref = function (ref) { + this._ref = ref +} + +/** + * A function that is used to extract a field from a document. + * + * Lunr expects a field to be at the top level of a document, if however the field + * is deeply nested within a document an extractor function can be used to extract + * the right field for indexing. + * + * @callback fieldExtractor + * @param {object} doc - The document being added to the index. + * @returns {?(string|object|object[])} obj - The object that will be indexed for this field. + * @example Extracting a nested field + * function (doc) { return doc.nested.field } + */ + +/** + * Adds a field to the list of document fields that will be indexed. Every document being + * indexed should have this field. Null values for this field in indexed documents will + * not cause errors but will limit the chance of that document being retrieved by searches. + * + * All fields should be added before adding documents to the index. Adding fields after + * a document has been indexed will have no effect on already indexed documents. + * + * Fields can be boosted at build time. This allows terms within that field to have more + * importance when ranking search results. Use a field boost to specify that matches within + * one field are more important than other fields. + * + * @param {string} fieldName - The name of a field to index in all documents. + * @param {object} attributes - Optional attributes associated with this field. + * @param {number} [attributes.boost=1] - Boost applied to all terms within this field. + * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document. + * @throws {RangeError} fieldName cannot contain unsupported characters '/' + */ +lunr.Builder.prototype.field = function (fieldName, attributes) { + if (/\//.test(fieldName)) { + throw new RangeError ("Field '" + fieldName + "' contains illegal character '/'") + } + + this._fields[fieldName] = attributes || {} +} + +/** + * A parameter to tune the amount of field length normalisation that is applied when + * calculating relevance scores. A value of 0 will completely disable any normalisation + * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b + * will be clamped to the range 0 - 1. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.b = function (number) { + if (number < 0) { + this._b = 0 + } else if (number > 1) { + this._b = 1 + } else { + this._b = number + } +} + +/** + * A parameter that controls the speed at which a rise in term frequency results in term + * frequency saturation. The default value is 1.2. Setting this to a higher value will give + * slower saturation levels, a lower value will result in quicker saturation. + * + * @param {number} number - The value to set for this tuning parameter. + */ +lunr.Builder.prototype.k1 = function (number) { + this._k1 = number +} + +/** + * Adds a document to the index. + * + * Before adding fields to the index the index should have been fully setup, with the document + * ref and all fields to index already having been specified. + * + * The document must have a field name as specified by the ref (by default this is 'id') and + * it should have all fields defined for indexing, though null or undefined values will not + * cause errors. + * + * Entire documents can be boosted at build time. Applying a boost to a document indicates that + * this document should rank higher in search results than other documents. + * + * @param {object} doc - The document to add to the index. + * @param {object} attributes - Optional attributes associated with this document. + * @param {number} [attributes.boost=1] - Boost applied to all terms within this document. + */ +lunr.Builder.prototype.add = function (doc, attributes) { + var docRef = doc[this._ref], + fields = Object.keys(this._fields) + + this._documents[docRef] = attributes || {} + this.documentCount += 1 + + for (var i = 0; i < fields.length; i++) { + var fieldName = fields[i], + extractor = this._fields[fieldName].extractor, + field = extractor ? extractor(doc) : doc[fieldName], + tokens = this.tokenizer(field, { + fields: [fieldName] + }), + terms = this.pipeline.run(tokens), + fieldRef = new lunr.FieldRef (docRef, fieldName), + fieldTerms = Object.create(null) + + this.fieldTermFrequencies[fieldRef] = fieldTerms + this.fieldLengths[fieldRef] = 0 + + // store the length of this field for this document + this.fieldLengths[fieldRef] += terms.length + + // calculate term frequencies for this field + for (var j = 0; j < terms.length; j++) { + var term = terms[j] + + if (fieldTerms[term] == undefined) { + fieldTerms[term] = 0 + } + + fieldTerms[term] += 1 + + // add to inverted index + // create an initial posting if one doesn't exist + if (this.invertedIndex[term] == undefined) { + var posting = Object.create(null) + posting["_index"] = this.termIndex + this.termIndex += 1 + + for (var k = 0; k < fields.length; k++) { + posting[fields[k]] = Object.create(null) + } + + this.invertedIndex[term] = posting + } + + // add an entry for this term/fieldName/docRef to the invertedIndex + if (this.invertedIndex[term][fieldName][docRef] == undefined) { + this.invertedIndex[term][fieldName][docRef] = Object.create(null) + } + + // store all whitelisted metadata about this token in the + // inverted index + for (var l = 0; l < this.metadataWhitelist.length; l++) { + var metadataKey = this.metadataWhitelist[l], + metadata = term.metadata[metadataKey] + + if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) { + this.invertedIndex[term][fieldName][docRef][metadataKey] = [] + } + + this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata) + } + } + + } +} + +/** + * Calculates the average document length for this index + * + * @private + */ +lunr.Builder.prototype.calculateAverageFieldLengths = function () { + + var fieldRefs = Object.keys(this.fieldLengths), + numberOfFields = fieldRefs.length, + accumulator = {}, + documentsWithField = {} + + for (var i = 0; i < numberOfFields; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + field = fieldRef.fieldName + + documentsWithField[field] || (documentsWithField[field] = 0) + documentsWithField[field] += 1 + + accumulator[field] || (accumulator[field] = 0) + accumulator[field] += this.fieldLengths[fieldRef] + } + + var fields = Object.keys(this._fields) + + for (var i = 0; i < fields.length; i++) { + var fieldName = fields[i] + accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName] + } + + this.averageFieldLength = accumulator +} + +/** + * Builds a vector space model of every document using lunr.Vector + * + * @private + */ +lunr.Builder.prototype.createFieldVectors = function () { + var fieldVectors = {}, + fieldRefs = Object.keys(this.fieldTermFrequencies), + fieldRefsLength = fieldRefs.length, + termIdfCache = Object.create(null) + + for (var i = 0; i < fieldRefsLength; i++) { + var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), + fieldName = fieldRef.fieldName, + fieldLength = this.fieldLengths[fieldRef], + fieldVector = new lunr.Vector, + termFrequencies = this.fieldTermFrequencies[fieldRef], + terms = Object.keys(termFrequencies), + termsLength = terms.length + + + var fieldBoost = this._fields[fieldName].boost || 1, + docBoost = this._documents[fieldRef.docRef].boost || 1 + + for (var j = 0; j < termsLength; j++) { + var term = terms[j], + tf = termFrequencies[term], + termIndex = this.invertedIndex[term]._index, + idf, score, scoreWithPrecision + + if (termIdfCache[term] === undefined) { + idf = lunr.idf(this.invertedIndex[term], this.documentCount) + termIdfCache[term] = idf + } else { + idf = termIdfCache[term] + } + + score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf) + score *= fieldBoost + score *= docBoost + scoreWithPrecision = Math.round(score * 1000) / 1000 + // Converts 1.23456789 to 1.234. + // Reducing the precision so that the vectors take up less + // space when serialised. Doing it now so that they behave + // the same before and after serialisation. Also, this is + // the fastest approach to reducing a number's precision in + // JavaScript. + + fieldVector.insert(termIndex, scoreWithPrecision) + } + + fieldVectors[fieldRef] = fieldVector + } + + this.fieldVectors = fieldVectors +} + +/** + * Creates a token set of all tokens in the index using lunr.TokenSet + * + * @private + */ +lunr.Builder.prototype.createTokenSet = function () { + this.tokenSet = lunr.TokenSet.fromArray( + Object.keys(this.invertedIndex).sort() + ) +} + +/** + * Builds the index, creating an instance of lunr.Index. + * + * This completes the indexing process and should only be called + * once all documents have been added to the index. + * + * @returns {lunr.Index} + */ +lunr.Builder.prototype.build = function () { + this.calculateAverageFieldLengths() + this.createFieldVectors() + this.createTokenSet() + + return new lunr.Index({ + invertedIndex: this.invertedIndex, + fieldVectors: this.fieldVectors, + tokenSet: this.tokenSet, + fields: Object.keys(this._fields), + pipeline: this.searchPipeline + }) +} + +/** + * Applies a plugin to the index builder. + * + * A plugin is a function that is called with the index builder as its context. + * Plugins can be used to customise or extend the behaviour of the index + * in some way. A plugin is just a function, that encapsulated the custom + * behaviour that should be applied when building the index. + * + * The plugin function will be called with the index builder as its argument, additional + * arguments can also be passed when calling use. The function will be called + * with the index builder as its context. + * + * @param {Function} plugin The plugin to apply. + */ +lunr.Builder.prototype.use = function (fn) { + var args = Array.prototype.slice.call(arguments, 1) + args.unshift(this) + fn.apply(this, args) +} +/** + * Contains and collects metadata about a matching document. + * A single instance of lunr.MatchData is returned as part of every + * lunr.Index~Result. + * + * @constructor + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + * @property {object} metadata - A cloned collection of metadata associated with this document. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData = function (term, field, metadata) { + var clonedMetadata = Object.create(null), + metadataKeys = Object.keys(metadata || {}) + + // Cloning the metadata to prevent the original + // being mutated during match data combination. + // Metadata is kept in an array within the inverted + // index so cloning the data can be done with + // Array#slice + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + clonedMetadata[key] = metadata[key].slice() + } + + this.metadata = Object.create(null) + + if (term !== undefined) { + this.metadata[term] = Object.create(null) + this.metadata[term][field] = clonedMetadata + } +} + +/** + * An instance of lunr.MatchData will be created for every term that matches a + * document. However only one instance is required in a lunr.Index~Result. This + * method combines metadata from another instance of lunr.MatchData with this + * objects metadata. + * + * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one. + * @see {@link lunr.Index~Result} + */ +lunr.MatchData.prototype.combine = function (otherMatchData) { + var terms = Object.keys(otherMatchData.metadata) + + for (var i = 0; i < terms.length; i++) { + var term = terms[i], + fields = Object.keys(otherMatchData.metadata[term]) + + if (this.metadata[term] == undefined) { + this.metadata[term] = Object.create(null) + } + + for (var j = 0; j < fields.length; j++) { + var field = fields[j], + keys = Object.keys(otherMatchData.metadata[term][field]) + + if (this.metadata[term][field] == undefined) { + this.metadata[term][field] = Object.create(null) + } + + for (var k = 0; k < keys.length; k++) { + var key = keys[k] + + if (this.metadata[term][field][key] == undefined) { + this.metadata[term][field][key] = otherMatchData.metadata[term][field][key] + } else { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key]) + } + + } + } + } +} + +/** + * Add metadata for a term/field pair to this instance of match data. + * + * @param {string} term - The term this match data is associated with + * @param {string} field - The field in which the term was found + * @param {object} metadata - The metadata recorded about this term in this field + */ +lunr.MatchData.prototype.add = function (term, field, metadata) { + if (!(term in this.metadata)) { + this.metadata[term] = Object.create(null) + this.metadata[term][field] = metadata + return + } + + if (!(field in this.metadata[term])) { + this.metadata[term][field] = metadata + return + } + + var metadataKeys = Object.keys(metadata) + + for (var i = 0; i < metadataKeys.length; i++) { + var key = metadataKeys[i] + + if (key in this.metadata[term][field]) { + this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key]) + } else { + this.metadata[term][field][key] = metadata[key] + } + } +} +/** + * A lunr.Query provides a programmatic way of defining queries to be performed + * against a {@link lunr.Index}. + * + * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method + * so the query object is pre-initialized with the right index fields. + * + * @constructor + * @property {lunr.Query~Clause[]} clauses - An array of query clauses. + * @property {string[]} allFields - An array of all available fields in a lunr.Index. + */ +lunr.Query = function (allFields) { + this.clauses = [] + this.allFields = allFields +} + +/** + * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause. + * + * This allows wildcards to be added to the beginning and end of a term without having to manually do any string + * concatenation. + * + * The wildcard constants can be bitwise combined to select both leading and trailing wildcards. + * + * @constant + * @default + * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour + * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists + * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists + * @see lunr.Query~Clause + * @see lunr.Query#clause + * @see lunr.Query#term + * @example query term with trailing wildcard + * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING }) + * @example query term with leading and trailing wildcard + * query.term('foo', { + * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING + * }) + */ + +lunr.Query.wildcard = new String ("*") +lunr.Query.wildcard.NONE = 0 +lunr.Query.wildcard.LEADING = 1 +lunr.Query.wildcard.TRAILING = 2 + +/** + * Constants for indicating what kind of presence a term must have in matching documents. + * + * @constant + * @enum {number} + * @see lunr.Query~Clause + * @see lunr.Query#clause + * @see lunr.Query#term + * @example query term with required presence + * query.term('foo', { presence: lunr.Query.presence.REQUIRED }) + */ +lunr.Query.presence = { + /** + * Term's presence in a document is optional, this is the default value. + */ + OPTIONAL: 1, + + /** + * Term's presence in a document is required, documents that do not contain + * this term will not be returned. + */ + REQUIRED: 2, + + /** + * Term's presence in a document is prohibited, documents that do contain + * this term will not be returned. + */ + PROHIBITED: 3 +} + +/** + * A single clause in a {@link lunr.Query} contains a term and details on how to + * match that term against a {@link lunr.Index}. + * + * @typedef {Object} lunr.Query~Clause + * @property {string[]} fields - The fields in an index this clause should be matched against. + * @property {number} [boost=1] - Any boost that should be applied when matching this clause. + * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be. + * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline. + * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended. + * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents. + */ + +/** + * Adds a {@link lunr.Query~Clause} to this query. + * + * Unless the clause contains the fields to be matched all fields will be matched. In addition + * a default boost of 1 is applied to the clause. + * + * @param {lunr.Query~Clause} clause - The clause to add to this query. + * @see lunr.Query~Clause + * @returns {lunr.Query} + */ +lunr.Query.prototype.clause = function (clause) { + if (!('fields' in clause)) { + clause.fields = this.allFields + } + + if (!('boost' in clause)) { + clause.boost = 1 + } + + if (!('usePipeline' in clause)) { + clause.usePipeline = true + } + + if (!('wildcard' in clause)) { + clause.wildcard = lunr.Query.wildcard.NONE + } + + if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) { + clause.term = "*" + clause.term + } + + if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) { + clause.term = "" + clause.term + "*" + } + + if (!('presence' in clause)) { + clause.presence = lunr.Query.presence.OPTIONAL + } + + this.clauses.push(clause) + + return this +} + +/** + * A negated query is one in which every clause has a presence of + * prohibited. These queries require some special processing to return + * the expected results. + * + * @returns boolean + */ +lunr.Query.prototype.isNegated = function () { + for (var i = 0; i < this.clauses.length; i++) { + if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) { + return false + } + } + + return true +} + +/** + * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause} + * to the list of clauses that make up this query. + * + * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion + * to a token or token-like string should be done before calling this method. + * + * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an + * array, each term in the array will share the same options. + * + * @param {object|object[]} term - The term(s) to add to the query. + * @param {object} [options] - Any additional properties to add to the query clause. + * @returns {lunr.Query} + * @see lunr.Query#clause + * @see lunr.Query~Clause + * @example adding a single term to a query + * query.term("foo") + * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard + * query.term("foo", { + * fields: ["title"], + * boost: 10, + * wildcard: lunr.Query.wildcard.TRAILING + * }) + * @example using lunr.tokenizer to convert a string to tokens before using them as terms + * query.term(lunr.tokenizer("foo bar")) + */ +lunr.Query.prototype.term = function (term, options) { + if (Array.isArray(term)) { + term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this) + return this + } + + var clause = options || {} + clause.term = term.toString() + + this.clause(clause) + + return this +} +lunr.QueryParseError = function (message, start, end) { + this.name = "QueryParseError" + this.message = message + this.start = start + this.end = end +} + +lunr.QueryParseError.prototype = new Error +lunr.QueryLexer = function (str) { + this.lexemes = [] + this.str = str + this.length = str.length + this.pos = 0 + this.start = 0 + this.escapeCharPositions = [] +} + +lunr.QueryLexer.prototype.run = function () { + var state = lunr.QueryLexer.lexText + + while (state) { + state = state(this) + } +} + +lunr.QueryLexer.prototype.sliceString = function () { + var subSlices = [], + sliceStart = this.start, + sliceEnd = this.pos + + for (var i = 0; i < this.escapeCharPositions.length; i++) { + sliceEnd = this.escapeCharPositions[i] + subSlices.push(this.str.slice(sliceStart, sliceEnd)) + sliceStart = sliceEnd + 1 + } + + subSlices.push(this.str.slice(sliceStart, this.pos)) + this.escapeCharPositions.length = 0 + + return subSlices.join('') +} + +lunr.QueryLexer.prototype.emit = function (type) { + this.lexemes.push({ + type: type, + str: this.sliceString(), + start: this.start, + end: this.pos + }) + + this.start = this.pos +} + +lunr.QueryLexer.prototype.escapeCharacter = function () { + this.escapeCharPositions.push(this.pos - 1) + this.pos += 1 +} + +lunr.QueryLexer.prototype.next = function () { + if (this.pos >= this.length) { + return lunr.QueryLexer.EOS + } + + var char = this.str.charAt(this.pos) + this.pos += 1 + return char +} + +lunr.QueryLexer.prototype.width = function () { + return this.pos - this.start +} + +lunr.QueryLexer.prototype.ignore = function () { + if (this.start == this.pos) { + this.pos += 1 + } + + this.start = this.pos +} + +lunr.QueryLexer.prototype.backup = function () { + this.pos -= 1 +} + +lunr.QueryLexer.prototype.acceptDigitRun = function () { + var char, charCode + + do { + char = this.next() + charCode = char.charCodeAt(0) + } while (charCode > 47 && charCode < 58) + + if (char != lunr.QueryLexer.EOS) { + this.backup() + } +} + +lunr.QueryLexer.prototype.more = function () { + return this.pos < this.length +} + +lunr.QueryLexer.EOS = 'EOS' +lunr.QueryLexer.FIELD = 'FIELD' +lunr.QueryLexer.TERM = 'TERM' +lunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE' +lunr.QueryLexer.BOOST = 'BOOST' +lunr.QueryLexer.PRESENCE = 'PRESENCE' + +lunr.QueryLexer.lexField = function (lexer) { + lexer.backup() + lexer.emit(lunr.QueryLexer.FIELD) + lexer.ignore() + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexTerm = function (lexer) { + if (lexer.width() > 1) { + lexer.backup() + lexer.emit(lunr.QueryLexer.TERM) + } + + lexer.ignore() + + if (lexer.more()) { + return lunr.QueryLexer.lexText + } +} + +lunr.QueryLexer.lexEditDistance = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.EDIT_DISTANCE) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexBoost = function (lexer) { + lexer.ignore() + lexer.acceptDigitRun() + lexer.emit(lunr.QueryLexer.BOOST) + return lunr.QueryLexer.lexText +} + +lunr.QueryLexer.lexEOS = function (lexer) { + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } +} + +// This matches the separator used when tokenising fields +// within a document. These should match otherwise it is +// not possible to search for some tokens within a document. +// +// It is possible for the user to change the separator on the +// tokenizer so it _might_ clash with any other of the special +// characters already used within the search string, e.g. :. +// +// This means that it is possible to change the separator in +// such a way that makes some words unsearchable using a search +// string. +lunr.QueryLexer.termSeparator = lunr.tokenizer.separator + +lunr.QueryLexer.lexText = function (lexer) { + while (true) { + var char = lexer.next() + + if (char == lunr.QueryLexer.EOS) { + return lunr.QueryLexer.lexEOS + } + + // Escape character is '\' + if (char.charCodeAt(0) == 92) { + lexer.escapeCharacter() + continue + } + + if (char == ":") { + return lunr.QueryLexer.lexField + } + + if (char == "~") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexEditDistance + } + + if (char == "^") { + lexer.backup() + if (lexer.width() > 0) { + lexer.emit(lunr.QueryLexer.TERM) + } + return lunr.QueryLexer.lexBoost + } + + // "+" indicates term presence is required + // checking for length to ensure that only + // leading "+" are considered + if (char == "+" && lexer.width() === 1) { + lexer.emit(lunr.QueryLexer.PRESENCE) + return lunr.QueryLexer.lexText + } + + // "-" indicates term presence is prohibited + // checking for length to ensure that only + // leading "-" are considered + if (char == "-" && lexer.width() === 1) { + lexer.emit(lunr.QueryLexer.PRESENCE) + return lunr.QueryLexer.lexText + } + + if (char.match(lunr.QueryLexer.termSeparator)) { + return lunr.QueryLexer.lexTerm + } + } +} + +lunr.QueryParser = function (str, query) { + this.lexer = new lunr.QueryLexer (str) + this.query = query + this.currentClause = {} + this.lexemeIdx = 0 +} + +lunr.QueryParser.prototype.parse = function () { + this.lexer.run() + this.lexemes = this.lexer.lexemes + + var state = lunr.QueryParser.parseClause + + while (state) { + state = state(this) + } + + return this.query +} + +lunr.QueryParser.prototype.peekLexeme = function () { + return this.lexemes[this.lexemeIdx] +} + +lunr.QueryParser.prototype.consumeLexeme = function () { + var lexeme = this.peekLexeme() + this.lexemeIdx += 1 + return lexeme +} + +lunr.QueryParser.prototype.nextClause = function () { + var completedClause = this.currentClause + this.query.clause(completedClause) + this.currentClause = {} +} + +lunr.QueryParser.parseClause = function (parser) { + var lexeme = parser.peekLexeme() + + if (lexeme == undefined) { + return + } + + switch (lexeme.type) { + case lunr.QueryLexer.PRESENCE: + return lunr.QueryParser.parsePresence + case lunr.QueryLexer.FIELD: + return lunr.QueryParser.parseField + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expected either a field or a term, found " + lexeme.type + + if (lexeme.str.length >= 1) { + errorMessage += " with value '" + lexeme.str + "'" + } + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } +} + +lunr.QueryParser.parsePresence = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + switch (lexeme.str) { + case "-": + parser.currentClause.presence = lunr.Query.presence.PROHIBITED + break + case "+": + parser.currentClause.presence = lunr.Query.presence.REQUIRED + break + default: + var errorMessage = "unrecognised presence operator'" + lexeme.str + "'" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + var errorMessage = "expecting term or field, found nothing" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.FIELD: + return lunr.QueryParser.parseField + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expecting term or field, found '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseField = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + if (parser.query.allFields.indexOf(lexeme.str) == -1) { + var possibleFields = parser.query.allFields.map(function (f) { return "'" + f + "'" }).join(', '), + errorMessage = "unrecognised field '" + lexeme.str + "', possible fields: " + possibleFields + + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.fields = [lexeme.str] + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + var errorMessage = "expecting term, found nothing" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + return lunr.QueryParser.parseTerm + default: + var errorMessage = "expecting term, found '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseTerm = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + parser.currentClause.term = lexeme.str.toLowerCase() + + if (lexeme.str.indexOf("*") != -1) { + parser.currentClause.usePipeline = false + } + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseEditDistance = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var editDistance = parseInt(lexeme.str, 10) + + if (isNaN(editDistance)) { + var errorMessage = "edit distance must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.editDistance = editDistance + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + +lunr.QueryParser.parseBoost = function (parser) { + var lexeme = parser.consumeLexeme() + + if (lexeme == undefined) { + return + } + + var boost = parseInt(lexeme.str, 10) + + if (isNaN(boost)) { + var errorMessage = "boost must be numeric" + throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) + } + + parser.currentClause.boost = boost + + var nextLexeme = parser.peekLexeme() + + if (nextLexeme == undefined) { + parser.nextClause() + return + } + + switch (nextLexeme.type) { + case lunr.QueryLexer.TERM: + parser.nextClause() + return lunr.QueryParser.parseTerm + case lunr.QueryLexer.FIELD: + parser.nextClause() + return lunr.QueryParser.parseField + case lunr.QueryLexer.EDIT_DISTANCE: + return lunr.QueryParser.parseEditDistance + case lunr.QueryLexer.BOOST: + return lunr.QueryParser.parseBoost + case lunr.QueryLexer.PRESENCE: + parser.nextClause() + return lunr.QueryParser.parsePresence + default: + var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" + throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) + } +} + + /** + * export the module via AMD, CommonJS or as a browser global + * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js + */ + ;(function (root, factory) { + if (typeof define === 'function' && define.amd) { + // AMD. Register as an anonymous module. + define(factory) + } else if (typeof exports === 'object') { + /** + * Node. Does not work with strict CommonJS, but + * only CommonJS-like environments that support module.exports, + * like Node. + */ + module.exports = factory() + } else { + // Browser globals (root is window) + root.lunr = factory() + } + }(this, function () { + /** + * Just return a value to define the module export. + * This example returns an object, but the module + * can return a function as the exported value. + */ + return lunr + })) +})(); diff --git a/search/main.js b/search/main.js new file mode 100644 index 000000000..a5e469d7c --- /dev/null +++ b/search/main.js @@ -0,0 +1,109 @@ +function getSearchTermFromLocation() { + var sPageURL = window.location.search.substring(1); + var sURLVariables = sPageURL.split('&'); + for (var i = 0; i < sURLVariables.length; i++) { + var sParameterName = sURLVariables[i].split('='); + if (sParameterName[0] == 'q') { + return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20')); + } + } +} + +function joinUrl (base, path) { + if (path.substring(0, 1) === "/") { + // path starts with `/`. Thus it is absolute. + return path; + } + if (base.substring(base.length-1) === "/") { + // base ends with `/` + return base + path; + } + return base + "/" + path; +} + +function escapeHtml (value) { + return value.replace(/&/g, '&') + .replace(/"/g, '"') + .replace(//g, '>'); +} + +function formatResult (location, title, summary) { + return ''; +} + +function displayResults (results) { + var search_results = document.getElementById("mkdocs-search-results"); + while (search_results.firstChild) { + search_results.removeChild(search_results.firstChild); + } + if (results.length > 0){ + for (var i=0; i < results.length; i++){ + var result = results[i]; + var html = formatResult(result.location, result.title, result.summary); + search_results.insertAdjacentHTML('beforeend', html); + } + } else { + var noResultsText = search_results.getAttribute('data-no-results-text'); + if (!noResultsText) { + noResultsText = "No results found"; + } + search_results.insertAdjacentHTML('beforeend', '

' + noResultsText + '

'); + } +} + +function doSearch () { + var query = document.getElementById('mkdocs-search-query').value; + if (query.length > min_search_length) { + if (!window.Worker) { + displayResults(search(query)); + } else { + searchWorker.postMessage({query: query}); + } + } else { + // Clear results for short queries + displayResults([]); + } +} + +function initSearch () { + var search_input = document.getElementById('mkdocs-search-query'); + if (search_input) { + search_input.addEventListener("keyup", doSearch); + } + var term = getSearchTermFromLocation(); + if (term) { + search_input.value = term; + doSearch(); + } +} + +function onWorkerMessage (e) { + if (e.data.allowSearch) { + initSearch(); + } else if (e.data.results) { + var results = e.data.results; + displayResults(results); + } else if (e.data.config) { + min_search_length = e.data.config.min_search_length-1; + } +} + +if (!window.Worker) { + console.log('Web Worker API not supported'); + // load index in main thread + $.getScript(joinUrl(base_url, "search/worker.js")).done(function () { + console.log('Loaded worker'); + init(); + window.postMessage = function (msg) { + onWorkerMessage({data: msg}); + }; + }).fail(function (jqxhr, settings, exception) { + console.error('Could not load worker.js'); + }); +} else { + // Wrap search in a web worker + var searchWorker = new Worker(joinUrl(base_url, "search/worker.js")); + searchWorker.postMessage({init: true}); + searchWorker.onmessage = onWorkerMessage; +} diff --git a/search/search_index.json b/search/search_index.json new file mode 100644 index 000000000..1183bfe15 --- /dev/null +++ b/search/search_index.json @@ -0,0 +1 @@ +{"config":{"indexing":"full","lang":["en"],"min_search_length":2,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Home Welcome to Caten docs","title":"Home"},{"location":"#home","text":"Welcome to Caten docs","title":"Home"},{"location":"development/","text":"Development","title":"Development"},{"location":"development/#development","text":"","title":"Development"},{"location":"quickstart/","text":"QuickStart Install","title":"Quickstart"},{"location":"quickstart/#quickstart","text":"","title":"QuickStart"},{"location":"quickstart/#install","text":"","title":"Install"},{"location":"packages/caten.aasm/","text":"caten/aasm UnaryOps :CAST The node :CAST casts the first read tensor into :dtype , writing the result into the first write. When optimizing cast in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE , :NOT The node :NOT computes the logical-not of the given tensor if the input is a boolean, otherwise (integer) computes a bitwise-not. out = not(x) (if boolean) out = lognot(x) (if integer) When optimizing not in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE , :SQRT The node :SQRT computes square-root of the first read tensor, writing the result to the first write. out = sqrt(x); When optimizing sqrt in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE , :LOG2 The node :LOG2 computes log2 of the first read tensor, writing the result to the first write. out = log2(x); When optimizing log2 in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE , :EXP2 The node :EXP2 computes exp2 of the first read tensor, writing the result to the first write. out = exp2(x); When optimizing exp2 in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE , :SIN The node :SIN computes sine of the first read tensor, writing the result to the first write. out = sin(x); When optimizing sin in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE , :RECIP The node :RECIP computes the reciprocal of the first read tensor, writing the result to the first write. out = (1/x); When optimizing recip in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE , :NEG The node :NEG flips the sign of the first read tensor, writing the result to the first write. out = (-x); When optimizing neg in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE , BinaryOps :GCD Computes the greatest common divisor of two integer tensors in read, writing the result to the first write. (Note: This computation should only applied to scalar tensors, and used for only computing the dynamic shaped tensor indexing.) out <- gcd(x, y) When optimizing gcd in-place, the 0th read is consumed. superclasses = BINARYOPS , :MAX Computes the maximum value of two tensors in read, writing the result to the first write. out <- max(x, y) When optimizing max in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE , :MOVE Moves the second read into the first read, setting the result to first write. out <- move(x, y) where move(x, y) is x = y When optimizing move in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE , :XOR The node :XOR computes the bit-wise xor of two tensors in read if they are integer, otherwise (boolean) computes the logical-xor. out <- x ^ y (if boolean) out <- x ^ y (if integer) When optimizing xor in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE , :OR The node :OR computes the bit-wise or of two tensors in read if they are integer, otherwise (boolean) computes the logical-or. out <- x || y (if boolean) out <- x | y (if integer) When optimizing or in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE , :AND The node :AND computes the bit-wise and of two tensors in read if they are integer, otherwise (boolean) computes the logical-and. out <- x && y (if boolean) out <- x & y (if integer) When optimizing and in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE , :IDIV The node :IDIV divides the first tensor in read by the second tensor in read , writing the result to the first write . Unlike other BinaryOps, :IDIV assumes two tensors to be an integer typed tensor. out <- x / y When optimizing idiv in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE , :MOD The node :MOD finds the reminder of the first tensor in read divided by the second tensor in read . out <- x % y When optimizing mod in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE , :MUL The node :MUL multiplies the two tensors in read and writes the result to the first write . out <- x + y When optimizing mul in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE , :ADD The node :ADD adds the two tensors in read and writes the result to the first write . out <- x + y When optimizing add in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE , TernaryOps :WMMA The node :WMMA is generated during optimization (simplifiers.lisp) by AJIT and represents a fused computation of :ADD and :MUL. WMMA is not generated during VM execution. WMMA is used to optimize the gemm computation: WMMA(c, a, b) is the equivalent to: c += a * b (if reduction = t) out = c + a * b (if reduction = nil) When optimizing wmma in-place, the 0th read is consumed. superclasses = TERNARYOPS , JITABLE , :WHERE If the result of the first read (boolean) is true, the second read is selected, and if false, the third read is selected and written to the first write. When optimizing in-place, note that the value of the second read is used as a placeholder since choosing the first read would result in a data type mismatch with write. in_dtype = dtype_of(y); out[in_dtype] = x[boolean] ? y[in_dtype] : z[in_dtype]; When optimizing where in-place, the 1th read is consumed. superclasses = TERNARYOPS , JITABLE , :< Compares the second and third tensors in read with < , writing the result to the first write. The first read tensor is an placeholder for the first write tensor and is always boolean. x = y < z; out = x; When optimizing < in-place, the 0th read is consumed. superclasses = TERNARYOPS , JITABLE , :!= Compares the second and third tensors in read with not-equal , writing the result to the first write. The first read tensor is an placeholder for the first write tensor and is always boolean. x = y != z; out = x; When optimizing != in-place, the 0th read is consumed. superclasses = TERNARYOPS , JITABLE , Buffer :VIEW Creates a view object of the tensor in a first read. View object can modify the multi-dimensional offset of tensors, strides, shapes, and strides without copying. out = view(x, *shape-new, *upfrom, *below, *by, *stride-new) upfrom and below describes the multi-dimensional offset of the tensor. Caten applies an operation to out in the range of [upfrom, below) . by indicates the step of stride. the out tensor is reinitialized with shape-new and stride-new . View has an attribute broadcast[list]`, this indicates the stride of thecorresponding axis is recognised as 0 if set to T. nrank[(unsigned-byte 32)] the rank of viewed tensor. broadcast[list] broadcasting order. permute[list] is an optional parameter and does nothing in VM, but requires to apply Polyhedral Compiler. If the view was created in caten/apis:!permute , set the argument to this attribute. When optimizing view in-place, the 0th read is consumed. superclasses = BUFFEROPS , JITABLE , :STORE Just like a :MOVE, moves the second tensor in read into the first tensor in read, writing the result to the first write. (Note: :STORE can be removed in the future refactoring) When optimizing store in-place, the 0th read is consumed. superclasses = BUFFEROPS , BINARYOPS , JITABLE , :LOAD Fills the first tensor in read with value , writing the result into the first write. The first read can be either of tensor or scalar. x[...] = value; out = x; value[symbol or number] initial value. When optimizing load in-place, the 0th read is consumed. superclasses = BUFFEROPS , JITABLE , :ALLOCATE Allocates a new matrix of scalar value in the VM. out = allocate(*shape, *stride) :Allocate is defined as described above. The first through nrank -th read scalar tensors represent the size of the Tensor, and from the nrank -th read to the last, they represent the stride of the allocated Tensor. (they can be sliced using subseq ) dtype[dtype-t] dtype to allocate. nrank[(unsigned-byte 32)] a rank of tensor. If set to 0, allocates a scalar. from[symbol or buffer or null] If specified, instead of allocating, an already allocated Buffer is used. If a symbol is specified, a buffer is already defined in the variable table of AVM. If buffer is specified, use the buffer directly. pool[null or Buffer] A place to store the result of the previous allocation. Allocation will be performed only after this slot is set to nil, or size are different due to dynamic shape. When optimizing allocate in-place, the 0th read is consumed. superclasses = BUFFEROPS , JITABLE , Indexing :INDEX-COMPONENTS The node :INDEX-COMPONENTS Indicates which element-wise computation of the Tensor is being performed. Typically, it should return the argument used when performing Aref on the Tensor with the corresponding strides . out <- index_components(x, *strides) is compiled as: for i=0..N for j=0..M out[i, j] = stride[0] * i + j; When optimizing index-components in-place, the 0th read is consumed. superclasses = INDEXING , JITABLE , JIT Specific Ops :JIT_KERNEL The node :JIT_KERNEL is an instruction that calls a jit-compiled kernel from the VM. When optimizing jit_kernel in-place, the 0th read is consumed. :EXPR The node :EXPR is a data structure used by the JIT compiler, representing a node that fuses multiple composable nodes. - Expr[caten/codegen/expr:EXPR] a tree structure comprised of caten/codegen/expr:EXPR - reduction[boolean] When optimizing expr in-place, the 0th read is consumed. superclasses = JITABLE ,","title":"caten/aasm"},{"location":"packages/caten.aasm/#catenaasm","text":"","title":"caten/aasm"},{"location":"packages/caten.aasm/#unaryops","text":"","title":"UnaryOps"},{"location":"packages/caten.aasm/#cast","text":"The node :CAST casts the first read tensor into :dtype , writing the result into the first write. When optimizing cast in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE ,","title":":CAST"},{"location":"packages/caten.aasm/#not","text":"The node :NOT computes the logical-not of the given tensor if the input is a boolean, otherwise (integer) computes a bitwise-not. out = not(x) (if boolean) out = lognot(x) (if integer) When optimizing not in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE ,","title":":NOT"},{"location":"packages/caten.aasm/#sqrt","text":"The node :SQRT computes square-root of the first read tensor, writing the result to the first write. out = sqrt(x); When optimizing sqrt in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE ,","title":":SQRT"},{"location":"packages/caten.aasm/#log2","text":"The node :LOG2 computes log2 of the first read tensor, writing the result to the first write. out = log2(x); When optimizing log2 in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE ,","title":":LOG2"},{"location":"packages/caten.aasm/#exp2","text":"The node :EXP2 computes exp2 of the first read tensor, writing the result to the first write. out = exp2(x); When optimizing exp2 in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE ,","title":":EXP2"},{"location":"packages/caten.aasm/#sin","text":"The node :SIN computes sine of the first read tensor, writing the result to the first write. out = sin(x); When optimizing sin in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE ,","title":":SIN"},{"location":"packages/caten.aasm/#recip","text":"The node :RECIP computes the reciprocal of the first read tensor, writing the result to the first write. out = (1/x); When optimizing recip in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE ,","title":":RECIP"},{"location":"packages/caten.aasm/#neg","text":"The node :NEG flips the sign of the first read tensor, writing the result to the first write. out = (-x); When optimizing neg in-place, the 0th read is consumed. superclasses = UNARYOPS , JITABLE ,","title":":NEG"},{"location":"packages/caten.aasm/#binaryops","text":"","title":"BinaryOps"},{"location":"packages/caten.aasm/#gcd","text":"Computes the greatest common divisor of two integer tensors in read, writing the result to the first write. (Note: This computation should only applied to scalar tensors, and used for only computing the dynamic shaped tensor indexing.) out <- gcd(x, y) When optimizing gcd in-place, the 0th read is consumed. superclasses = BINARYOPS ,","title":":GCD"},{"location":"packages/caten.aasm/#max","text":"Computes the maximum value of two tensors in read, writing the result to the first write. out <- max(x, y) When optimizing max in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE ,","title":":MAX"},{"location":"packages/caten.aasm/#move","text":"Moves the second read into the first read, setting the result to first write. out <- move(x, y) where move(x, y) is x = y When optimizing move in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE ,","title":":MOVE"},{"location":"packages/caten.aasm/#xor","text":"The node :XOR computes the bit-wise xor of two tensors in read if they are integer, otherwise (boolean) computes the logical-xor. out <- x ^ y (if boolean) out <- x ^ y (if integer) When optimizing xor in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE ,","title":":XOR"},{"location":"packages/caten.aasm/#or","text":"The node :OR computes the bit-wise or of two tensors in read if they are integer, otherwise (boolean) computes the logical-or. out <- x || y (if boolean) out <- x | y (if integer) When optimizing or in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE ,","title":":OR"},{"location":"packages/caten.aasm/#and","text":"The node :AND computes the bit-wise and of two tensors in read if they are integer, otherwise (boolean) computes the logical-and. out <- x && y (if boolean) out <- x & y (if integer) When optimizing and in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE ,","title":":AND"},{"location":"packages/caten.aasm/#idiv","text":"The node :IDIV divides the first tensor in read by the second tensor in read , writing the result to the first write . Unlike other BinaryOps, :IDIV assumes two tensors to be an integer typed tensor. out <- x / y When optimizing idiv in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE ,","title":":IDIV"},{"location":"packages/caten.aasm/#mod","text":"The node :MOD finds the reminder of the first tensor in read divided by the second tensor in read . out <- x % y When optimizing mod in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE ,","title":":MOD"},{"location":"packages/caten.aasm/#mul","text":"The node :MUL multiplies the two tensors in read and writes the result to the first write . out <- x + y When optimizing mul in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE ,","title":":MUL"},{"location":"packages/caten.aasm/#add","text":"The node :ADD adds the two tensors in read and writes the result to the first write . out <- x + y When optimizing add in-place, the 0th read is consumed. superclasses = BINARYOPS , JITABLE ,","title":":ADD"},{"location":"packages/caten.aasm/#ternaryops","text":"","title":"TernaryOps"},{"location":"packages/caten.aasm/#wmma","text":"The node :WMMA is generated during optimization (simplifiers.lisp) by AJIT and represents a fused computation of :ADD and :MUL. WMMA is not generated during VM execution. WMMA is used to optimize the gemm computation: WMMA(c, a, b) is the equivalent to: c += a * b (if reduction = t) out = c + a * b (if reduction = nil) When optimizing wmma in-place, the 0th read is consumed. superclasses = TERNARYOPS , JITABLE ,","title":":WMMA"},{"location":"packages/caten.aasm/#where","text":"If the result of the first read (boolean) is true, the second read is selected, and if false, the third read is selected and written to the first write. When optimizing in-place, note that the value of the second read is used as a placeholder since choosing the first read would result in a data type mismatch with write. in_dtype = dtype_of(y); out[in_dtype] = x[boolean] ? y[in_dtype] : z[in_dtype]; When optimizing where in-place, the 1th read is consumed. superclasses = TERNARYOPS , JITABLE ,","title":":WHERE"},{"location":"packages/caten.aasm/#_1","text":"Compares the second and third tensors in read with < , writing the result to the first write. The first read tensor is an placeholder for the first write tensor and is always boolean. x = y < z; out = x; When optimizing < in-place, the 0th read is consumed. superclasses = TERNARYOPS , JITABLE ,","title":":<"},{"location":"packages/caten.aasm/#_2","text":"Compares the second and third tensors in read with not-equal , writing the result to the first write. The first read tensor is an placeholder for the first write tensor and is always boolean. x = y != z; out = x; When optimizing != in-place, the 0th read is consumed. superclasses = TERNARYOPS , JITABLE ,","title":":!="},{"location":"packages/caten.aasm/#buffer","text":"","title":"Buffer"},{"location":"packages/caten.aasm/#view","text":"Creates a view object of the tensor in a first read. View object can modify the multi-dimensional offset of tensors, strides, shapes, and strides without copying. out = view(x, *shape-new, *upfrom, *below, *by, *stride-new) upfrom and below describes the multi-dimensional offset of the tensor. Caten applies an operation to out in the range of [upfrom, below) . by indicates the step of stride. the out tensor is reinitialized with shape-new and stride-new . View has an attribute broadcast[list]`, this indicates the stride of thecorresponding axis is recognised as 0 if set to T. nrank[(unsigned-byte 32)] the rank of viewed tensor. broadcast[list] broadcasting order. permute[list] is an optional parameter and does nothing in VM, but requires to apply Polyhedral Compiler. If the view was created in caten/apis:!permute , set the argument to this attribute. When optimizing view in-place, the 0th read is consumed. superclasses = BUFFEROPS , JITABLE ,","title":":VIEW"},{"location":"packages/caten.aasm/#store","text":"Just like a :MOVE, moves the second tensor in read into the first tensor in read, writing the result to the first write. (Note: :STORE can be removed in the future refactoring) When optimizing store in-place, the 0th read is consumed. superclasses = BUFFEROPS , BINARYOPS , JITABLE ,","title":":STORE"},{"location":"packages/caten.aasm/#load","text":"Fills the first tensor in read with value , writing the result into the first write. The first read can be either of tensor or scalar. x[...] = value; out = x; value[symbol or number] initial value. When optimizing load in-place, the 0th read is consumed. superclasses = BUFFEROPS , JITABLE ,","title":":LOAD"},{"location":"packages/caten.aasm/#allocate","text":"Allocates a new matrix of scalar value in the VM. out = allocate(*shape, *stride) :Allocate is defined as described above. The first through nrank -th read scalar tensors represent the size of the Tensor, and from the nrank -th read to the last, they represent the stride of the allocated Tensor. (they can be sliced using subseq ) dtype[dtype-t] dtype to allocate. nrank[(unsigned-byte 32)] a rank of tensor. If set to 0, allocates a scalar. from[symbol or buffer or null] If specified, instead of allocating, an already allocated Buffer is used. If a symbol is specified, a buffer is already defined in the variable table of AVM. If buffer is specified, use the buffer directly. pool[null or Buffer] A place to store the result of the previous allocation. Allocation will be performed only after this slot is set to nil, or size are different due to dynamic shape. When optimizing allocate in-place, the 0th read is consumed. superclasses = BUFFEROPS , JITABLE ,","title":":ALLOCATE"},{"location":"packages/caten.aasm/#indexing","text":"","title":"Indexing"},{"location":"packages/caten.aasm/#index-components","text":"The node :INDEX-COMPONENTS Indicates which element-wise computation of the Tensor is being performed. Typically, it should return the argument used when performing Aref on the Tensor with the corresponding strides . out <- index_components(x, *strides) is compiled as: for i=0..N for j=0..M out[i, j] = stride[0] * i + j; When optimizing index-components in-place, the 0th read is consumed. superclasses = INDEXING , JITABLE ,","title":":INDEX-COMPONENTS"},{"location":"packages/caten.aasm/#jit-specific-ops","text":"","title":"JIT Specific Ops"},{"location":"packages/caten.aasm/#jit_kernel","text":"The node :JIT_KERNEL is an instruction that calls a jit-compiled kernel from the VM. When optimizing jit_kernel in-place, the 0th read is consumed.","title":":JIT_KERNEL"},{"location":"packages/caten.aasm/#expr","text":"The node :EXPR is a data structure used by the JIT compiler, representing a node that fuses multiple composable nodes. - Expr[caten/codegen/expr:EXPR] a tree structure comprised of caten/codegen/expr:EXPR - reduction[boolean] When optimizing expr in-place, the 0th read is consumed. superclasses = JITABLE ,","title":":EXPR"},{"location":"packages/caten.air/","text":"aIR(= A bstract IR ) is a Caten's internal IR and is used to represent computations expressed as either Let-Binding Based IR or a DAG. Each nodes are represented using a Node structure, and their list (or tree structure) is maintained by the Graph CLOS class. The compilation is performed by efficiently manipulating and optimizing the graph using a pattern matcher. Note that All nodes must be declared using the defnode macro before compilation. This allows node-related errors to be detected and reported during compilation. Node [struct] Node A node is a computation with input sources (reads) and outputs (writes) . Read1 Read2 Read3 Read4 \\ | | / [Operation] | writes1 writes2 ... Before creating a node using make-node , the node must be defined using defnode . Attributes are defined by defnode. node-class[keyword] indicates the classification of the node (e.g.: :BinaryOps, :UnaryOps ). This information is used for debugging purposes. node-type[keyword] indicates the type of the node (e.g.: :Add, :Sub, :Mul, :Div ). node-id[symbol] a unique symbol that exists only once in the namespaces. (usually generated by gensym) node-writes[list] a list of symbols that represent the output buffers. node-reads[list] a list of symbols or numbers that represent the input buffers. (usually connected with the (node-writes) ) node-attr[Attribute] additional information about the node. (e.g.: reduction, dtype, type inference etc...) out-nth[fixnum] node->id will point to the out-nth element of the writes list. [macro] defnode Defines a new node. ( defnode ( class type ) ( &rest direct-superclasses ) description &key ( placeholder 0 ) ( verify 'identity ) ( slots )) class[keyword] an identifier of the node class. type[keyword] an identifier of the node type. direct-superclasses[list of keyword] a list of superclasses. description[string] a description of the node. placeholder[(unsigned-byte 32) or -1] when mutating the node in-place, the compiler consumes the placeholder-th read buffer. verify[function] a function to verify the arguments. slots[list of (symbol &rest slot-options)] a list of slot definitions. [function] make-node ( make-node class type writes reads &rest attrs ) Create a node with the given class , type , writes , reads , and attrs . class and type should be defined by defnode , otherwise the function will produce an error. class[keyword] indicates the classification of the node (e.g.: :BinaryOps, :UnaryOps ). This information is used for debugging purposes. type[keyword] indicates the type of the node (e.g.: :Add, :Sub, :Mul, :Div ). writes[list] a list of symbols that represent the output buffers. reads[list] a list of symbols or numbers that represent the input buffers. (usually connected with the (node-writes) ) attrs[keyword and value pairs] initializers for the Attribute class. [function] copy-node (copy-node node) Create a deepcopy of the given node. [function] getattrs (getattrs node) Return the list of attributes of the node. [function] getattr (getattr node id &key (allow-undefined nil)) Return the value of the attribute id of the node. If the attribute is not defined, it will produce an error. If allow-undefined is t, it returns nil without error. This function is setfable. [function] remattr (remattr node id &key (allow-undefined nil)) Equivalent to: (setf (getattr node id :allow-undefined allow-undefined) nil) Examples lisp CATEN-USER> ( make-node :BinaryOps :ADD ( list 'x ) ( list 'y 'z ) :reduction t ) Result Result Graph [class] Graph A Graph is a data structure used to handle a collection of nodes. Graph has a list of nodes (nodes), node inputs (seen), and node outputs (outputs). Unlike FastGraph , Graph does not assume that the nodes form a DAG. Additionally, it guarantees that the nodes will not be sorted during each process of the Pattern Matcher. Graph structures, such as Let-Binding, are represented using Graph. Note: Using Graph may lead to performance degradation if the graph is a DAG. Please use FastGraph instead. [class] FastGraph FastGraph is a subclass of Graph that implements faster node searches based on the assumption that the nodes form a DAG. (It is approximately 20 times faster than Graph, in the larger scale.) Since FastGraph stores nodes as a hash-table, there are some constraints on node operations. If necessary, converting between (->fast-graph graph) and (->graph graph) can be done frequently with minimal impact on performance. [generic] copy-graph (copy-graph graph) Creates a copy of the given graph. [function] make-graph (make-graph &rest nodes) Creates a new Graph object with the given nodes. [generic] graph-nodes NIL [generic] id->value (id->value graph id) Returns a node whose node-writes includes the given id. [generic] id->users (id->users graph id) Returns a list of nodes whose node-reads includes the given id. [generic] remnode (remnode graph id) Removes all node where node-id = id from the given graph. [generic] insert-nodes (insert-nodes graph nodes) Inserts the given nodes (list) into the graph. [function] ->fast-graph (->fast-graph graph) Creates a FastGraph object from the given graph. [generic] ->graph (->graph graph) Converts the given graph to a fast graph. [generic] verify-graph (verify-graph graph &key no-purge) Verify the consistency of the graphs and simplify them by doing following things: - Checks if all variables are immutable - All variables appeared in read, are also defined in writes. - Purge all isolated graph - Sort nodes by time. - Set no-purge=T to ignore purge isolated graph step. This has no effect on FastGraph. Simplifier [macro] defsimplifier (defsimplifier (name &key (speed 3)) &rest rules) Defines a new simplifier named name . The defined function has a following form: (name graph &key (no-verify nil) (return-changed-p nil)) The graph is a graph to simplify. The no-verify is a flag to skip the verification process. The return-changed-p is a flag to return the result of the simplification process, or a boolean indicating that the graph was changed during simplification process.. The speed is an optimization level. The rules are a list of simplification rules. Each rule has a form: (Node_Name (Read_Args) Attrs) (TODO: Documentation) (See also: ./source/aasm/constant-folding.lisp ) Visualizer [function] ->dot (->dot graph &key (pathname \"/tmp/graph.dot\") (open t) (title \"node\")) Visualizes the graph using graphviz(requirement). Set open=t to open the resulting image in the default browser. A tmp file is created at the pathname location. The graph is saved as a .png and .html file. The title is used in the html file. [function] pprint-graph (pprint-graph graph &key (screen-width 140) (stream t)) The function pprint-graph prints the graph in a tree-like structure. screen-width controls the width of the output, if the graph is too wide and cannot fit in the screen, it will be split into multiple pages. lisp CATEN-USER> ( pprint-graph ( avm-graph ( caten ( !gelu ( make-tensor ` ( 3 3 ) :id 'x )))) :stream nil ) Result Result ============================================================================================================================================ === [P: 0] ================================================================================================================================= [P=0, ID=0]: :PAUSE/BACKWARD {0} \u2514 :MUL {N1} \u251c :MUL {N2} \u2502 \u2514 Allocate[:float32] (3 3) \u2502 \u251c :MOVE {N3} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N4} \u2502 \u251c load(0.5) \u2502 \u2514 Allocate[:float32] NIL \u2514 :ADD {N5} \u251c :MOVE {N6} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N7} \u2502 \u251c load(1.0) \u2502 \u2514 Allocate[:float32] NIL \u2514 :ADD {N8} \u2514 :VIEW {N9} \u2502 \u251c load(-1.0) \u2502 \u2514 Allocate[:float32] NIL \u251c :MUL {N10} \u251c :MOVE {N11} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N12} \u2502 \u251c load(2.0) \u2502 \u2514 Allocate[:float32] NIL \u2514 :RECIP {N13} \u2514 :ADD {N14} \u251c :MOVE {N15} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N16} \u2502 \u251c load(1.0) \u2502 \u2514 Allocate[:float32] NIL \u2514 :EXP2 {N17} \u2514 :MUL {N18} \u2514 load(1.442695) \u2502 \u2514 Allocate[:float32] NIL \u251c :MUL {N19} \u2514 :VIEW {N20} \u2502 \u251c load(0.6931472) \u2502 \u2514 Allocate[:float32] NIL \u251c :MUL {N21} \u2514 :VIEW {N22} \u2502 \u251c load(-1.442695) \u2502 \u2514 Allocate[:float32] NIL \u251c :MUL {N23} \u251c :MOVE {N24} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N25} \u2502 \u251c load(2.0) \u2514 :MUL {N26} \u251c :MOVE {N27} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N28} \u2502 \u251c load(0.7978846) \u2502 \u2514 Allocate[:float32] NIL \u2514 :ADD {N29} \u251c Allocate[:float32] (3 3) \u2514 :MUL {N30} \u2514 :MUL {N31} \u2502 \u2514 Allocate[:float32] (3 3) \u2502 \u251c :MUL {N32} \u2502 \u251c Allocate[:float32] (3 3) \u2502 Allocate[:float32] (3 3) \u251c :MOVE {N33} \u251c Allocate[:float32] (3 3) \u2514 :VIEW {N34} \u251c load(0.044715) \u2514 Allocate[:float32] NIL ============================================================================================================================================","title":"caten/air"},{"location":"packages/caten.air/#node","text":"","title":"Node"},{"location":"packages/caten.air/#struct-node","text":"A node is a computation with input sources (reads) and outputs (writes) . Read1 Read2 Read3 Read4 \\ | | / [Operation] | writes1 writes2 ... Before creating a node using make-node , the node must be defined using defnode . Attributes are defined by defnode. node-class[keyword] indicates the classification of the node (e.g.: :BinaryOps, :UnaryOps ). This information is used for debugging purposes. node-type[keyword] indicates the type of the node (e.g.: :Add, :Sub, :Mul, :Div ). node-id[symbol] a unique symbol that exists only once in the namespaces. (usually generated by gensym) node-writes[list] a list of symbols that represent the output buffers. node-reads[list] a list of symbols or numbers that represent the input buffers. (usually connected with the (node-writes) ) node-attr[Attribute] additional information about the node. (e.g.: reduction, dtype, type inference etc...) out-nth[fixnum] node->id will point to the out-nth element of the writes list.","title":"[struct] Node"},{"location":"packages/caten.air/#macro-defnode","text":"Defines a new node. ( defnode ( class type ) ( &rest direct-superclasses ) description &key ( placeholder 0 ) ( verify 'identity ) ( slots )) class[keyword] an identifier of the node class. type[keyword] an identifier of the node type. direct-superclasses[list of keyword] a list of superclasses. description[string] a description of the node. placeholder[(unsigned-byte 32) or -1] when mutating the node in-place, the compiler consumes the placeholder-th read buffer. verify[function] a function to verify the arguments. slots[list of (symbol &rest slot-options)] a list of slot definitions.","title":"[macro] defnode"},{"location":"packages/caten.air/#function-make-node","text":"( make-node class type writes reads &rest attrs ) Create a node with the given class , type , writes , reads , and attrs . class and type should be defined by defnode , otherwise the function will produce an error. class[keyword] indicates the classification of the node (e.g.: :BinaryOps, :UnaryOps ). This information is used for debugging purposes. type[keyword] indicates the type of the node (e.g.: :Add, :Sub, :Mul, :Div ). writes[list] a list of symbols that represent the output buffers. reads[list] a list of symbols or numbers that represent the input buffers. (usually connected with the (node-writes) ) attrs[keyword and value pairs] initializers for the Attribute class.","title":"[function] make-node"},{"location":"packages/caten.air/#function-copy-node","text":"(copy-node node) Create a deepcopy of the given node.","title":"[function] copy-node"},{"location":"packages/caten.air/#function-getattrs","text":"(getattrs node) Return the list of attributes of the node.","title":"[function] getattrs"},{"location":"packages/caten.air/#function-getattr","text":"(getattr node id &key (allow-undefined nil)) Return the value of the attribute id of the node. If the attribute is not defined, it will produce an error. If allow-undefined is t, it returns nil without error. This function is setfable.","title":"[function] getattr"},{"location":"packages/caten.air/#function-remattr","text":"(remattr node id &key (allow-undefined nil)) Equivalent to: (setf (getattr node id :allow-undefined allow-undefined) nil)","title":"[function] remattr"},{"location":"packages/caten.air/#examples","text":"lisp CATEN-USER> ( make-node :BinaryOps :ADD ( list 'x ) ( list 'y 'z ) :reduction t ) Result Result ","title":"Examples"},{"location":"packages/caten.air/#graph","text":"","title":"Graph"},{"location":"packages/caten.air/#class-graph","text":"A Graph is a data structure used to handle a collection of nodes. Graph has a list of nodes (nodes), node inputs (seen), and node outputs (outputs). Unlike FastGraph , Graph does not assume that the nodes form a DAG. Additionally, it guarantees that the nodes will not be sorted during each process of the Pattern Matcher. Graph structures, such as Let-Binding, are represented using Graph. Note: Using Graph may lead to performance degradation if the graph is a DAG. Please use FastGraph instead.","title":"[class] Graph"},{"location":"packages/caten.air/#class-fastgraph","text":"FastGraph is a subclass of Graph that implements faster node searches based on the assumption that the nodes form a DAG. (It is approximately 20 times faster than Graph, in the larger scale.) Since FastGraph stores nodes as a hash-table, there are some constraints on node operations. If necessary, converting between (->fast-graph graph) and (->graph graph) can be done frequently with minimal impact on performance.","title":"[class] FastGraph"},{"location":"packages/caten.air/#generic-copy-graph","text":"(copy-graph graph) Creates a copy of the given graph.","title":"[generic] copy-graph"},{"location":"packages/caten.air/#function-make-graph","text":"(make-graph &rest nodes) Creates a new Graph object with the given nodes.","title":"[function] make-graph"},{"location":"packages/caten.air/#generic-graph-nodes","text":"NIL","title":"[generic] graph-nodes"},{"location":"packages/caten.air/#generic-id-value","text":"(id->value graph id) Returns a node whose node-writes includes the given id.","title":"[generic] id->value"},{"location":"packages/caten.air/#generic-id-users","text":"(id->users graph id) Returns a list of nodes whose node-reads includes the given id.","title":"[generic] id->users"},{"location":"packages/caten.air/#generic-remnode","text":"(remnode graph id) Removes all node where node-id = id from the given graph.","title":"[generic] remnode"},{"location":"packages/caten.air/#generic-insert-nodes","text":"(insert-nodes graph nodes) Inserts the given nodes (list) into the graph.","title":"[generic] insert-nodes"},{"location":"packages/caten.air/#function-fast-graph","text":"(->fast-graph graph) Creates a FastGraph object from the given graph.","title":"[function] ->fast-graph"},{"location":"packages/caten.air/#generic-graph","text":"(->graph graph) Converts the given graph to a fast graph.","title":"[generic] ->graph"},{"location":"packages/caten.air/#generic-verify-graph","text":"(verify-graph graph &key no-purge) Verify the consistency of the graphs and simplify them by doing following things: - Checks if all variables are immutable - All variables appeared in read, are also defined in writes. - Purge all isolated graph - Sort nodes by time. - Set no-purge=T to ignore purge isolated graph step. This has no effect on FastGraph.","title":"[generic] verify-graph"},{"location":"packages/caten.air/#simplifier","text":"","title":"Simplifier"},{"location":"packages/caten.air/#macro-defsimplifier","text":"(defsimplifier (name &key (speed 3)) &rest rules) Defines a new simplifier named name . The defined function has a following form: (name graph &key (no-verify nil) (return-changed-p nil)) The graph is a graph to simplify. The no-verify is a flag to skip the verification process. The return-changed-p is a flag to return the result of the simplification process, or a boolean indicating that the graph was changed during simplification process.. The speed is an optimization level. The rules are a list of simplification rules. Each rule has a form: (Node_Name (Read_Args) Attrs) (TODO: Documentation) (See also: ./source/aasm/constant-folding.lisp )","title":"[macro] defsimplifier"},{"location":"packages/caten.air/#visualizer","text":"","title":"Visualizer"},{"location":"packages/caten.air/#function-dot","text":"(->dot graph &key (pathname \"/tmp/graph.dot\") (open t) (title \"node\")) Visualizes the graph using graphviz(requirement). Set open=t to open the resulting image in the default browser. A tmp file is created at the pathname location. The graph is saved as a .png and .html file. The title is used in the html file.","title":"[function] ->dot"},{"location":"packages/caten.air/#function-pprint-graph","text":"(pprint-graph graph &key (screen-width 140) (stream t)) The function pprint-graph prints the graph in a tree-like structure. screen-width controls the width of the output, if the graph is too wide and cannot fit in the screen, it will be split into multiple pages. lisp CATEN-USER> ( pprint-graph ( avm-graph ( caten ( !gelu ( make-tensor ` ( 3 3 ) :id 'x )))) :stream nil ) Result Result ============================================================================================================================================ === [P: 0] ================================================================================================================================= [P=0, ID=0]: :PAUSE/BACKWARD {0} \u2514 :MUL {N1} \u251c :MUL {N2} \u2502 \u2514 Allocate[:float32] (3 3) \u2502 \u251c :MOVE {N3} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N4} \u2502 \u251c load(0.5) \u2502 \u2514 Allocate[:float32] NIL \u2514 :ADD {N5} \u251c :MOVE {N6} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N7} \u2502 \u251c load(1.0) \u2502 \u2514 Allocate[:float32] NIL \u2514 :ADD {N8} \u2514 :VIEW {N9} \u2502 \u251c load(-1.0) \u2502 \u2514 Allocate[:float32] NIL \u251c :MUL {N10} \u251c :MOVE {N11} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N12} \u2502 \u251c load(2.0) \u2502 \u2514 Allocate[:float32] NIL \u2514 :RECIP {N13} \u2514 :ADD {N14} \u251c :MOVE {N15} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N16} \u2502 \u251c load(1.0) \u2502 \u2514 Allocate[:float32] NIL \u2514 :EXP2 {N17} \u2514 :MUL {N18} \u2514 load(1.442695) \u2502 \u2514 Allocate[:float32] NIL \u251c :MUL {N19} \u2514 :VIEW {N20} \u2502 \u251c load(0.6931472) \u2502 \u2514 Allocate[:float32] NIL \u251c :MUL {N21} \u2514 :VIEW {N22} \u2502 \u251c load(-1.442695) \u2502 \u2514 Allocate[:float32] NIL \u251c :MUL {N23} \u251c :MOVE {N24} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N25} \u2502 \u251c load(2.0) \u2514 :MUL {N26} \u251c :MOVE {N27} \u2502 \u251c Allocate[:float32] (3 3) \u2502 \u2514 :VIEW {N28} \u2502 \u251c load(0.7978846) \u2502 \u2514 Allocate[:float32] NIL \u2514 :ADD {N29} \u251c Allocate[:float32] (3 3) \u2514 :MUL {N30} \u2514 :MUL {N31} \u2502 \u2514 Allocate[:float32] (3 3) \u2502 \u251c :MUL {N32} \u2502 \u251c Allocate[:float32] (3 3) \u2502 Allocate[:float32] (3 3) \u251c :MOVE {N33} \u251c Allocate[:float32] (3 3) \u2514 :VIEW {N34} \u251c load(0.044715) \u2514 Allocate[:float32] NIL ============================================================================================================================================","title":"[function] pprint-graph"},{"location":"packages/caten.apis.aot/","text":"AOT TODO","title":"AOT"},{"location":"packages/caten.apis.aot/#aot","text":"TODO","title":"AOT"},{"location":"packages/caten.apis.differentiable_ops/","text":"Func Func Class [class] Func A CLOS class that represents a computation. [Func] -> -> [FastGraph] -> -> [Completed] Func is a syntax-sugar for generating lowered instructions defined in the caten/aasm package. To properly lower the Func , You need to implement the following three methods: lower: Lower the Func into a list of caten/air:node . This should return caten/air:graph . forward: Create the type for the Tensor after computation. Be mindful of its lazy evaluation nature; do not perform the actual computation. ShapeTracker might help you. (use the st macro) backward: Create the graph for backward of op given prev-grad. Return: (values input_1.grad input_2.grad ...) . [generic] lower (lower op &rest nodes) Lowers the Func into a list of caten/air:node . This should return caten/air:graph. - op[Func] Func to lower. - nodes[list] list of previous nodes (each position corresponds to the position of the variables in the Func). [generic] forward (forward op &rest tensors) Create the type for the Tensor after computation. Be mindful of its lazy evaluation nature; do not perform the actual computation. Use the st macro to create a new tensor. op[Func] Func to forward. tensors[list] list of input tensors. [generic] backward (backward op &optional prev-grad) Create the graph for backward of op given prev-grad. Return: (values input_1.grad input_2.grad ...) . save-for-backward is determined automatically, so you do not have to consider about in-place operation. op[Func] Func to backward. prev-grad[Tensor] previous gradient tensor. Differentiable Ops (built_in) [function] !identity (!identity tensor) Equivalent to #'identity, but it is used to create a lazy computation node. lisp CATEN-USER> ( proceed ( !identity ( make-tensor ` ( 3 3 ) :initial-element 1.0 ))) Result Result {Tensor[float32] :shape (3 3) :id STC98724 ((1.0 1.0 1.0) (1.0 1.0 1.0) (1.0 1.0 1.0)) :op # :requires-grad NIL :variables (STC98663) :tracker #} [function] !view (!view base &rest subscripts) Create a view node from the base tensor and subscripts. We refer to VIEW as a node creating tensor whose buffers are shared with the base tensor, but shapes, strides, dtypes, offsets, or dtypes are different. Subscripts has the following notation: t keep using the base view. fixnum refers to the specified element. e.g.: A[3] (a b) slices in the range of [a, b) (a b c) slices in the range of [a, b) with step c . c can be negative. In that case, b must be larger than a. For example: (10 0 -1) to reverse the elements in the axis. (:~ n) to broadcast the axis, with the size of n It is supported to compose mutliple views; the viewed tensors can be created from the viewed tensors. lisp CATEN-USER> ( proceed ( !contiguous ( !view ( ax+b ` ( 10 10 ) 1 0 ) ` ( 3 6 ) ` ( 3 6 )))) Result Result {Tensor[float32] :shape (3 3) :id STC99611 ((33.0 34.0 35.0) (43.0 44.0 45.0) (53.0 54.0 55.0)) :op # :requires-grad NIL :variables (STC98928) :tracker #} [function] !permute (!permute tensor &rest order) Returns a tensor that is a permutation of the original tensor. The new tensor has the same data as the original tensor but with the dimensions permuted according to the order specified. order can be passed as a list or separated arguments. That is, both of (!permute x 0 1) or (!permute x (list 0 1)) are valid. lisp CATEN-USER> ( proceed ( !contiguous ( !permute ( ax+b ` ( 10 10 ) 1 0 ) ` ( 1 0 )))) Result Result {Tensor[float32] :shape (10 10) :id STC99803 ((0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0) (1.0 11.0 21.0 31.0 41.0 51.0 61.0 71.0 81.0 91.0) (2.0 12.0 22.0 32.0 42.0 52.0 62.0 72.0 82.0 92.0) (3.0 13.0 23.0 33.0 43.0 53.0 63.0 73.0 83.0 93.0) (4.0 14.0 24.0 34.0 44.0 54.0 64.0 74.0 84.0 94.0) (5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0) (6.0 16.0 26.0 36.0 46.0 56.0 66.0 76.0 86.0 96.0) (7.0 17.0 27.0 37.0 47.0 57.0 67.0 77.0 87.0 97.0) (8.0 18.0 28.0 38.0 48.0 58.0 68.0 78.0 88.0 98.0) (9.0 19.0 29.0 39.0 49.0 59.0 69.0 79.0 89.0 99.0)) :op # :requires-grad NIL :variables (TID99619) :tracker #} [function] !t (!t tensor) Transposes the last two axes of the tensor lisp CATEN-USER> ( proceed ( !contiguous ( !t ( ax+b ` ( 10 10 ) 1 0 )))) Result Result {Tensor[float32] :shape (10 10) :id STC99995 ((0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0) (1.0 11.0 21.0 31.0 41.0 51.0 61.0 71.0 81.0 91.0) (2.0 12.0 22.0 32.0 42.0 52.0 62.0 72.0 82.0 92.0) (3.0 13.0 23.0 33.0 43.0 53.0 63.0 73.0 83.0 93.0) (4.0 14.0 24.0 34.0 44.0 54.0 64.0 74.0 84.0 94.0) (5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0) (6.0 16.0 26.0 36.0 46.0 56.0 66.0 76.0 86.0 96.0) (7.0 17.0 27.0 37.0 47.0 57.0 67.0 77.0 87.0 97.0) (8.0 18.0 28.0 38.0 48.0 58.0 68.0 78.0 88.0 98.0) (9.0 19.0 29.0 39.0 49.0 59.0 69.0 79.0 89.0 99.0)) :op # :requires-grad NIL :variables (TID99811) :tracker #} [function] !transpose (!transpose tensor &optional (dim0 1) (dim1 0)) Transposes the dim0 and dim1 . lisp CATEN-USER> ( proceed ( !contiguous ( !transpose ( ax+b ` ( 10 10 ) 1 0 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC100187 ((0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0) (1.0 11.0 21.0 31.0 41.0 51.0 61.0 71.0 81.0 91.0) (2.0 12.0 22.0 32.0 42.0 52.0 62.0 72.0 82.0 92.0) (3.0 13.0 23.0 33.0 43.0 53.0 63.0 73.0 83.0 93.0) (4.0 14.0 24.0 34.0 44.0 54.0 64.0 74.0 84.0 94.0) (5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0) (6.0 16.0 26.0 36.0 46.0 56.0 66.0 76.0 86.0 96.0) (7.0 17.0 27.0 37.0 47.0 57.0 67.0 77.0 87.0 97.0) (8.0 18.0 28.0 38.0 48.0 58.0 68.0 78.0 88.0 98.0) (9.0 19.0 29.0 39.0 49.0 59.0 69.0 79.0 89.0 99.0)) :op # :requires-grad NIL :variables (TID100003) :tracker #} [function] !contiguous (!contiguous x &key (force nil)) If the tensor is viewed, creates a copy of tensor with contiguous memory. Otherwise, returns the original tensor. If force is set to T, it always creates a copy. lisp CATEN-USER> ( proceed ( !contiguous ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC100287 ((0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0) (10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0) (20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0) (30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0) (40.0 41.0 42.0 43.0 44.0 45.0 46.0 47.0 48.0 49.0) (50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0) (60.0 61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0) (70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0) (80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0) (90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0)) :op # :requires-grad NIL :variables (STC100194) :tracker #} [function] !copy (!copy x) Creates a copy of the tensor. In Caten, the in-place operations are automatically determined, so in general, you do not have to consider using it. lisp CATEN-USER> ( proceed ( !copy ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC100418 ((0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0) (10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0) (20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0) (30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0) (40.0 41.0 42.0 43.0 44.0 45.0 46.0 47.0 48.0 49.0) (50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0) (60.0 61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0) (70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0) (80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0) (90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0)) :op # :requires-grad NIL :variables (STC100299) :tracker #} [function] !reshape (!reshape x &rest shape) Returns a same tensor but shape is changed. shape can be passed as a list or separated arguments. That is, both of (!reshape x '(1 2 3)) or (!reshape x 1 2 3) are valid. Shape is a list of integers, symbols, or tensors. If x is a viewed tensor, it creates a copy of the tensor with contiguous memory (but later JIT will try to eliminate this). lisp CATEN-USER> ( proceed ( !reshape ( ax+b ` ( 10 10 ) 1 0 ) ` ( 5 20 ))) Result Result {Tensor[float32] :shape (5 20) :id STC100616 ((0.0 1.0 2.0 3.0 4.0 ~ 15.0 16.0 17.0 18.0 19.0) (20.0 21.0 22.0 23.0 24.0 ~ 35.0 36.0 37.0 38.0 39.0) (40.0 41.0 42.0 43.0 44.0 ~ 55.0 56.0 57.0 58.0 59.0) (60.0 61.0 62.0 63.0 64.0 ~ 75.0 76.0 77.0 78.0 79.0) (80.0 81.0 82.0 83.0 84.0 ~ 95.0 96.0 97.0 98.0 99.0)) :op # :requires-grad NIL :variables (TID100426) :tracker #} [function] !flatten (!flatten x &key (axis 1)) Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). lisp CATEN-USER> ( proceed ( !flatten ( make-tensor ` ( 3 3 3 3 )) :axis 1 )) Result Result {Tensor[float32] :shape (3 27) :id STC100922 ((0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)) :op # :requires-grad NIL :variables (TID100657) :tracker #} [function] !uprank (!uprank x n) Returns a tensor with one is inserted at the beginning of the shape of x for n times. lisp CATEN-USER> ( proceed ( !uprank ( ax+b ` ( 10 10 ) 1 0 ) 2 )) Result Result {Tensor[float32] :shape (1 1 10 10) :id STC101186 ((((0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0) (10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0) (20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0) (30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0) (40.0 41.0 42.0 43.0 44.0 45.0 46.0 47.0 48.0 49.0) (50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0) (60.0 61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0) (70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0) (80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0) (90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0)))) :op # :requires-grad NIL :variables (TID100930) :tracker #} [function] !repeat (!repeat x &rest repeats) Returns a tensor with the shape of x broadcasted by repeats . lisp CATEN-USER> ( proceed ( !repeat ( ax+b ` ( 1 10 ) 1 0 ) 10 1 )) Result Result {Tensor[float32] :shape (10 10) :id STC103674 <> :op # :requires-grad NIL :variables (VID101556) :tracker #} [function] !expand (!expand x &rest shape) Returns a tensor that is expanded to the shape that is specified. Expand can also increase the number of dimensions that a tensor has. lisp CATEN-USER> ( proceed ( !expand ( ax+b ` ( 1 10 ) 1 0 ) ` ( 10 10 ))) Result Result {Tensor[float32] :shape (1 10) :id STC104313 ((0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0)) :op # :requires-grad NIL :variables (VID103682) :tracker #} [function] !move (!move a b &key (reduce nil)) Moves the element of b into a, returning a. If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !move ( ax+b ` ( 10 10 ) 0 0 ) ( ax+b ` ( 10 10 ) 0 2 ))) Result Result {Tensor[float32] :shape (10 10) :id STC104482 ((2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)) :op # :requires-grad NIL :variables (STC104328) :tracker #} [function] !assign (!assign a b) Equivalent to doing (!move a b :reduce t) . Useful when you want to the value of lazy ops to an pre-allocated buffer, like KV-Cache. lisp CATEN-USER> ( proceed ( !assign ( ax+b ` ( 10 10 ) 0 0 ) ( ax+b ` ( 10 10 ) 0 2 ))) Result Result {Tensor[float32] :shape (10 10) :id STC104651 ((2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)) :op # :requires-grad NIL :variables (STC104497) :tracker #} [function] !add (!add a b &key (reduce nil)) ;; or (!+ &rest tensors) Adds a and b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !add ( ax+b ` ( 10 10 ) 1 0 ) ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC104821 ((0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0) (20.0 22.0 24.0 26.0 28.0 30.0 32.0 34.0 36.0 38.0) (40.0 42.0 44.0 46.0 48.0 50.0 52.0 54.0 56.0 58.0) (60.0 62.0 64.0 66.0 68.0 70.0 72.0 74.0 76.0 78.0) (80.0 82.0 84.0 86.0 88.0 90.0 92.0 94.0 96.0 98.0) (100.0 102.0 104.0 106.0 108.0 110.0 112.0 114.0 116.0 118.0) (120.0 122.0 124.0 126.0 128.0 130.0 132.0 134.0 136.0 138.0) (140.0 142.0 144.0 146.0 148.0 150.0 152.0 154.0 156.0 158.0) (160.0 162.0 164.0 166.0 168.0 170.0 172.0 174.0 176.0 178.0) (180.0 182.0 184.0 186.0 188.0 190.0 192.0 194.0 196.0 198.0)) :op # :requires-grad NIL :variables (STC104666) :tracker #} [function] !sub (!sub a b &key (reduce nil)) ;; or (!- &rest tensors) Subtracts b from a . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !sub ( ax+b ` ( 10 10 ) 1 0 ) ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC104997 ((0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0)) :op # :requires-grad NIL :variables (STC104837) :tracker #} [function] !mul (!mul a b &key (reduce nil)) ;; or (!* &rest tensors) Multiplies a and b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !mul ( ax+b ` ( 10 10 ) 1 0 ) ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC105173 ((0.0 1.0 4.0 9.0 16.0 25.0 36.0 49.0 64.0 81.0) (100.0 121.0 144.0 169.0 196.0 225.0 256.0 289.0 324.0 361.0) (400.0 441.0 484.0 529.0 576.0 625.0 676.0 729.0 784.0 841.0) (900.0 961.0 1024.0 1089.0 1156.0 1225.0 1296.0 1369.0 1444.0 1521.0) (1600.0 1681.0 1764.0 1849.0 1936.0 2025.0 2116.0 2209.0 2304.0 2401.0) (2500.0 2601.0 2704.0 2809.0 2916.0 3025.0 3136.0 3249.0 3364.0 3481.0) (3600.0 3721.0 3844.0 3969.0 4096.0 4225.0 4356.0 4489.0 4624.0 4761.0) (4900.0 5041.0 5184.0 5329.0 5476.0 5625.0 5776.0 5929.0 6084.0 6241.0) (6400.0 6561.0 6724.0 6889.0 7056.0 7225.0 7396.0 7569.0 7744.0 7921.0) (8100.0 8281.0 8464.0 8649.0 8836.0 9025.0 9216.0 9409.0 9604.0 9801.0)) :op # :requires-grad NIL :variables (STC105012) :tracker #} [function] !div (!div a b &key (reduce nil)) ;; or (!/ &rest tensors) Divides a by b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !div ( ax+b ` ( 10 10 ) 1 1 ) ( ax+b ` ( 10 10 ) 1 1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC105364 ((1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (0.99999994 1.0 1.0 1.0 1.0 1.0 0.99999994 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 0.99999994 1.0 1.0 1.0 1.0 1.0) (0.99999994 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 0.99999994 0.99999994 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 0.99999994 1.0 1.0 0.99999994 1.0 1.0 1.0)) :op # :requires-grad NIL :variables (STC105189) :tracker #} [function] !mod (!mod a b &key (reduce nil)) Computes the remainder of the division of a by b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !mod ( ax+b ` ( 10 10 ) 1 1 :dtype :int32 ) ( ax+b ` ( 10 10 ) 1 1 :dtype :int32 ))) Result Result {Tensor[int32] :shape (10 10) :id STC105538 ((0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0)) :op # :requires-grad NIL :variables (STC105379) :tracker #} [function] !idiv (!idiv a b &key (reduce nil)) Assuming both of a and b are the integer, divides a by b and returns the integer part. If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !idiv ( ax+b ` ( 10 10 ) 3 1 :dtype :uint32 ) ( ax+b ` ( 10 10 ) 0 2 :dtype :uint32 ))) Result Result {Tensor[uint32] :shape (10 10) :id STC105703 ((0 2 3 5 6 8 9 11 12 14) (15 17 18 20 21 23 24 26 27 29) (30 32 33 35 36 38 39 41 42 44) (45 47 48 50 51 53 54 56 57 59) (60 62 63 65 66 68 69 71 72 74) (75 77 78 80 81 83 84 86 87 89) (90 92 93 95 96 98 99 101 102 104) (105 107 108 110 111 113 114 116 117 119) (120 122 123 125 126 128 129 131 132 134) (135 137 138 140 141 143 144 146 147 149)) :op # :requires-grad NIL :variables (STC105553) :tracker #} [function] !maximum (!maximum a b &key (reduce nil)) Returns the maximum of a and b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !maximum ( rand ` ( 3 3 )) ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC130379 ((0.35161772 0.27699816 0.13698694) (0.25436726 0.8601034 0.39287245) (0.4397496 0.71887904 0.36415708)) :op # :requires-grad NIL :variables (STC129657) :tracker #} [function] !minimum (!minimum a b &key (reduce nil)) Returns the minimum of a and b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !minimum ( rand ` ( 3 3 )) ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC131132 ((-0.45117822 0.61168617 -1.7840805) (0.6194901 -0.007118067 -0.24680464) (0.17250395 -1.4365431 0.30330306)) :op # :requires-grad NIL :variables (STC130395) :tracker #} [function] !gcd (!gcd a b &key (reduce nil)) Returns the greatest common divisor of a and b . If reduce is T, it will reduce the result. (Broadcast) a , b are expected to be integer scalars. (dedicated to the view computation) lisp CATEN-USER> ( ctx:with-contextvar ( :jit 0 ) ( proceed ( !gcd ( iconst 8 ) ( iconst 4 )))) Result Result {Tensor[int64] :shape NIL :id STC131154 4 :op # :requires-grad NIL :variables (STC131135) :tracker #} [function] !lcm (!lcm a b) Returns the least common multiple of a and b . a , b are expected to be integer scalars. lisp CATEN-USER> ( ctx:with-contextvar ( :jit 0 ) ( proceed ( !lcm ( iconst 8 ) ( iconst 4 )))) Result Result {Tensor[int64] :shape NIL :id STC131217 8 :op # :requires-grad NIL :variables (STC131160) :tracker #} [function] !exp (!exp x) Computes (exp x) . lisp CATEN-USER> ( proceed ( !exp ( ax+b ` ( 10 10 ) 0.01 0.0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC131340 ((1.0 1.0100502 1.0202013 1.0304545 1.0408108 1.0512711 1.0618366 1.0725082 1.0832871 1.0941743) (1.105171 1.116278 1.1274968 1.1388284 1.1502738 1.1618342 1.1735109 1.1853049 1.1972173 1.2092496) (1.2214028 1.2336781 1.2460767 1.2586 1.2712492 1.2840254 1.2969301 1.3099644 1.3231298 1.3364275) (1.3498588 1.3634251 1.3771278 1.3909681 1.4049476 1.4190675 1.4333293 1.4477346 1.4622846 1.4769808) (1.4918246 1.5068177 1.5219616 1.5372574 1.5527072 1.5683122 1.5840739 1.5999942 1.6160744 1.6323161) (1.6487212 1.6652912 1.6820276 1.6989322 1.7160068 1.733253 1.7506725 1.768267 1.7860384 1.8039883) (1.8221188 1.8404315 1.8589281 1.8776106 1.8964808 1.9155407 1.9347923 1.9542372 1.9738777 1.9937155) (2.0137527 2.033991 2.054433 2.0750806 2.0959353 2.1169999 2.138276 2.1597662 2.181472 2.2033963) (2.2255409 2.2479079 2.2704997 2.2933185 2.316367 2.3396468 2.3631606 2.386911 2.4108996 2.4351294) (2.4596028 2.4843223 2.5092902 2.5345092 2.5599813 2.5857096 2.6116965 2.6379445 2.664456 2.691234)) :op # :requires-grad NIL :variables (STC131225) :tracker #} [function] !log (!log x) Computes (log x) . lisp CATEN-USER> ( proceed ( !log ( ax+b ` ( 10 10 ) 0.01 0.001 ))) Result Result {Tensor[float32] :shape (10 10) :id STC131452 ((-6.9077554 -4.50986 -3.8632329 -3.4737678 -3.1941833 -2.97593 -2.7968814 -2.6450753 -2.5133061 -2.396896) (-2.2926347 -2.198225 -2.1119647 -2.032558 -1.9589953 -1.8904755 -1.8263509 -1.7660917 -1.7092583 -1.6554819) (-1.6044505 -1.555897 -1.5095925 -1.4653376 -1.4229584 -1.3823024 -1.343235 -1.3056365 -1.2694006 -1.2344321) (-1.2006451 -1.1679624 -1.1363143 -1.105637 -1.0758728 -1.046969 -1.0188774 -0.99155325 -0.964956 -0.93904775) (-0.9137939 -0.8891622 -0.8651225 -0.84164727 -0.81871045 -0.796288 -0.7743574 -0.7528972 -0.73188806 -0.7113112) (-0.6911492 -0.6713857 -0.6520053 -0.63299334 -0.6143361 -0.59602046 -0.5780344 -0.5603661 -0.5430046 -0.52593935) (-0.5091604 -0.49265832 -0.47642422 -0.46044946 -0.44472587 -0.42924568 -0.41400146 -0.39898625 -0.38419297 -0.36961547) (-0.35524744 -0.3410829 -0.3271162 -0.3133419 -0.29975465 -0.28634965 -0.27312195 -0.26006696 -0.24718018 -0.23445737) (-0.22189441 -0.20948724 -0.19723219 -0.18512551 -0.17316367 -0.1613432 -0.14966084 -0.1381133 -0.12669767 -0.11541088) (-0.10425006 -0.093212426 -0.082295306 -0.07149601 -0.060812157 -0.050241243 -0.039780907 -0.029428856 -0.019182874 -0.009040808)) :op # :requires-grad NIL :variables (STC131348) :tracker #} [function] !sqrt (!sqrt x) Computes (sqrt x) . lisp CATEN-USER> ( proceed ( !sqrt ( ax+b ` ( 10 10 ) 0.01 0.0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC131733 ((0.0 0.1 0.14142135 0.17320508 0.2 0.2236068 0.24494897 0.26457512 0.2828427 0.29999998) (0.31622776 0.33166248 0.34641016 0.3605551 0.37416574 0.38729832 0.4 0.41231057 0.42426407 0.4358899) (0.4472136 0.45825756 0.4690416 0.47958314 0.48989794 0.5 0.50990194 0.51961523 0.52915025 0.53851646) (0.5477225 0.55677646 0.5656854 0.5744563 0.5830952 0.591608 0.59999996 0.60827625 0.61644137 0.6244998) (0.6324555 0.64031243 0.64807403 0.65574384 0.66332495 0.67082036 0.67823297 0.6855655 0.6928203 0.7) (0.70710677 0.71414286 0.7211102 0.72801095 0.7348469 0.7416199 0.7483315 0.7549834 0.7615773 0.76811457) (0.77459663 0.781025 0.7874008 0.7937254 0.8 0.8062258 0.8124038 0.81853527 0.82462114 0.83066237) (0.83666 0.84261495 0.84852815 0.85440034 0.86023253 0.8660254 0.8717798 0.8774964 0.8831761 0.8888194) (0.8944272 0.9 0.9055385 0.91104335 0.9165151 0.92195445 0.92736185 0.9327379 0.9380832 0.9433981) (0.94868326 0.9539392 0.9591663 0.96436507 0.96953595 0.9746794 0.9797959 0.98488575 0.98994946 0.9949874)) :op # :requires-grad NIL :variables (STC131460) :tracker #} [function] !neg (!neg tensor) !neg computes the negative value of the tensor. lisp CATEN-USER> ( proceed ( !neg ( ax+b ` ( 10 10 ) 0.01 0.0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC131837 ((-0.0 -0.01 -0.02 -0.03 -0.04 -0.049999997 -0.06 -0.07 -0.08 -0.089999996) (-0.099999994 -0.11 -0.12 -0.13 -0.14 -0.14999999 -0.16 -0.17 -0.17999999 -0.19) (-0.19999999 -0.21 -0.22 -0.22999999 -0.24 -0.25 -0.26 -0.26999998 -0.28 -0.29) (-0.29999998 -0.31 -0.32 -0.32999998 -0.34 -0.35 -0.35999998 -0.37 -0.38 -0.39) (-0.39999998 -0.41 -0.42 -0.42999998 -0.44 -0.45 -0.45999998 -0.47 -0.48 -0.48999998) (-0.5 -0.51 -0.52 -0.53 -0.53999996 -0.55 -0.56 -0.57 -0.58 -0.59) (-0.59999996 -0.61 -0.62 -0.63 -0.64 -0.65 -0.65999997 -0.66999996 -0.68 -0.69) (-0.7 -0.71 -0.71999997 -0.72999996 -0.74 -0.75 -0.76 -0.77 -0.78 -0.78999996) (-0.79999995 -0.81 -0.82 -0.83 -0.84 -0.84999996 -0.85999995 -0.87 -0.88 -0.89) (-0.9 -0.90999997 -0.91999996 -0.93 -0.94 -0.95 -0.96 -0.96999997 -0.97999996 -0.98999995)) :op # :requires-grad NIL :variables (STC131741) :tracker #} [function] !recip (!recip tensor) !recip computes the reciprocal of the tensor. lisp CATEN-USER> ( proceed ( !recip ( ax+b ` ( 10 10 ) 0.01 0.1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC131949 ((10.0 9.090909 8.333333 7.692308 7.142857 6.6666665 6.25 5.882353 5.5555553 5.263158) (5.0000005 4.7619047 4.5454545 4.3478265 4.1666665 4.0 3.846154 3.7037036 3.5714285 3.448276) (3.3333335 3.2258065 3.125 3.0303032 2.9411764 2.857143 2.777778 2.702703 2.631579 2.5641026) (2.5000002 2.4390244 2.3809524 2.3255816 2.2727273 2.2222223 2.1739132 2.1276596 2.0833335 2.0408163) (2.0000002 1.9607843 1.923077 1.8867925 1.8518518 1.8181818 1.7857143 1.754386 1.724138 1.6949153) (1.6666666 1.6393442 1.6129032 1.5873016 1.5625 1.5384614 1.5151515 1.4925373 1.4705882 1.4492754) (1.4285715 1.4084506 1.3888888 1.369863 1.3513514 1.3333334 1.3157895 1.2987013 1.2820512 1.2658228) (1.25 1.2345679 1.2195122 1.2048193 1.1904762 1.1764705 1.1627907 1.1494253 1.1363636 1.1235955) (1.1111112 1.098901 1.0869565 1.0752689 1.0638298 1.0526316 1.0416667 1.0309278 1.0204082 1.010101) (1.0 0.990099 0.98039216 0.97087383 0.9615385 0.952381 0.9433963 0.9345795 0.92592597 0.91743124)) :op # :requires-grad NIL :variables (STC131845) :tracker #} [function] !square (!square x) Computes the x*x lisp CATEN-USER> ( proceed ( !square ( ax+b ` ( 10 10 ) 0.01 0.1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC132058 ((0.010000001 0.0121 0.014400001 0.0169 0.0196 0.0225 0.0256 0.028900001 0.0324 0.0361) (0.039999995 0.044100005 0.0484 0.052899994 0.057600003 0.0625 0.0676 0.072900005 0.0784 0.08409999) (0.08999999 0.0961 0.1024 0.10889999 0.115600005 0.122499995 0.12959999 0.13689998 0.1444 0.15209998) (0.15999998 0.1681 0.17639999 0.18489999 0.1936 0.20249999 0.21159998 0.2209 0.2304 0.24009998) (0.24999997 0.26009998 0.2704 0.28089997 0.29160002 0.3025 0.3136 0.3249 0.33639997 0.34809998) (0.36 0.37210003 0.3844 0.3969 0.4096 0.42250004 0.43560004 0.4489 0.46240002 0.4761) (0.48999998 0.5041 0.5184 0.53290004 0.54760003 0.5625 0.5776 0.5929 0.60840005 0.6241) (0.64000005 0.6561 0.6724 0.6889 0.7056001 0.7225 0.7396 0.7569 0.7744 0.79209995) (0.80999994 0.8281 0.8464 0.8649 0.8836 0.9025 0.9216 0.9409 0.96040004 0.98010004) (1.0 1.0201 1.0403999 1.0609 1.0816 1.1024998 1.1235999 1.1448998 1.1663998 1.1880999)) :op # :requires-grad NIL :variables (STC131957) :tracker #} [function] !rsqrt (!rsqrt x) Computes the reciprocal of sqrt x. lisp CATEN-USER> ( proceed ( !rsqrt ( ax+b ` ( 10 10 ) 0.01 0.1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC132353 ((3.1622777 3.0151134 2.8867514 2.7735012 2.6726124 2.5819888 2.5 2.4253561 2.3570225 2.2941573) (2.236068 2.1821787 2.1320071 2.0851443 2.0412414 2.0 1.9611614 1.924501 1.8898224 1.8569535) (1.825742 1.7960529 1.7677671 1.7407765 1.7149858 1.6903085 1.6666667 1.6439899 1.6222143 1.6012815) (1.5811388 1.5617375 1.5430336 1.5249858 1.5075567 1.490712 1.4744196 1.4586499 1.4433757 1.4285715) (1.4142135 1.40028 1.3867506 1.3736057 1.3608276 1.3483996 1.3363062 1.3245324 1.3130643 1.3018892) (1.2909944 1.2803688 1.2700013 1.2598816 1.25 1.2403474 1.2309148 1.2216945 1.2126781 1.2038586) (1.1952286 1.1867816 1.1785113 1.1704115 1.1624764 1.1547005 1.1470786 1.1396058 1.132277 1.1250879) (1.118034 1.1111112 1.1043153 1.0976427 1.0910894 1.0846523 1.0783278 1.0721126 1.0660036 1.0599979) (1.0540926 1.0482849 1.0425721 1.0369517 1.0314213 1.0259783 1.0206207 1.0153462 1.0101525 1.0050378) (1.0 0.99503714 0.99014753 0.98532933 0.9805807 0.9759001 0.97128594 0.96673656 0.9622505 0.95782626)) :op # :requires-grad NIL :variables (STC132067) :tracker #} [function] !signum (!signum x) Returns the sign of the tensor. If the tensor is positive, it returns 1. If the tensor is negative, it returns -1. If the tensor is zero, it returns 0. Note that this function is not differentiable. lisp CATEN-USER> ( proceed ( !signum ( ax+b ` ( 10 10 ) 0.02 -0.1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC135515 ((-1.0 -1.0 -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0)) :op # :requires-grad NIL :variables (STC132953) :tracker #} [function] !gid (!gid tensor rank &key (dtype *default-float*)) Finds the rank th index components of the tensor. For example (!gid x 1) for (3 3) tensor is a `(0 1 2). (As a tip) by combining !gid with !where, you can implement pseudo a random accessing of the tensor. For example: lisp CATEN-USER> ( proceed ( !gid ( make-tensor ` ( 3 3 )) -1 )) Result Result {Tensor[float32] :shape (3 3) :id STC136141 ((0.0 1.0 2.0) (0.0 1.0 2.0) (0.0 1.0 2.0)) :op # :requires-grad NIL :variables (VID135525) :tracker #} [function] !normalize-axis (!normalize-axis ndim axis) Creates a tensor graph which normalizes the axis. If the axis is negative, it will be normalized to the positive axis. lisp CATEN-USER> ( proceed ( !normalize-axis 3 -1 )) Result Result {Tensor[int64] :shape NIL :id STC136198 2 :op # :requires-grad NIL :variables (STC136148) :tracker #} [function] !abs (!abs x) Returns the absolute value of the tensor. lisp CATEN-USER> ( proceed ( !abs ( ax+b ` ( 10 10 ) 0.02 -0.1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC139369 ((0.1 0.08 0.060000002 0.040000003 0.020000003 7.450581e-9 0.019999996 0.04 0.059999995 0.07999999) (0.09999999 0.12 0.13999999 0.16 0.18 0.19999999 0.22 0.24000001 0.26 0.28) (0.29999998 0.32 0.34 0.35999998 0.38 0.4 0.42 0.43999997 0.46 0.48) (0.49999997 0.52 0.53999996 0.55999994 0.58 0.59999996 0.61999995 0.64 0.65999997 0.67999995) (0.6999999 0.71999997 0.73999995 0.75999993 0.78 0.79999995 0.81999993 0.84 0.85999995 0.87999994) (0.9 0.91999996 0.93999994 0.9599999 0.9799999 1.0 1.02 1.04 1.06 1.0799999) (1.0999999 1.12 1.14 1.16 1.18 1.1999999 1.2199999 1.2399999 1.26 1.28) (1.3 1.3199999 1.3399999 1.3599999 1.38 1.4 1.42 1.4399999 1.4599999 1.4799999) (1.4999999 1.52 1.54 1.56 1.5799999 1.5999999 1.6199999 1.64 1.66 1.68) (1.6999999 1.7199999 1.7399999 1.76 1.78 1.8 1.8199999 1.8399999 1.8599999 1.8799999)) :op # :requires-grad NIL :variables (STC136799) :tracker #} [function] !> (!> x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !> ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC140821 ((1 0 0) (0 0 0) (1 1 0)) :op # :requires-grad NIL :variables (STC139582) :tracker #} [function] !< (!< x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !< ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC142265 ((0 0 0) (0 0 0) (0 0 1)) :op # :requires-grad NIL :variables (STC141034) :tracker #} [function] !>= (!>= x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !>= ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC143723 ((1 1 1) (1 1 0) (1 0 0)) :op # :requires-grad NIL :variables (STC142478) :tracker #} [function] !<= (!<= x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !<= ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC145173 ((1 1 0) (0 0 1) (1 0 1)) :op # :requires-grad NIL :variables (STC143936) :tracker #} [function] !eq (!EQ x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !eq ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC146619 ((0 0 0) (0 0 0) (0 0 0)) :op # :requires-grad NIL :variables (STC145386) :tracker #} [function] !neq (!NEQ x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !neq ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC148063 ((1 1 1) (1 1 1) (1 1 1)) :op # :requires-grad NIL :variables (STC146832) :tracker #} [function] !and (!AND x y) Computes the logical/bitwise and of the tensor. lisp CATEN-USER> ( proceed ( !and ( iconst 5 ) ( iconst 3 ))) Result Result {Tensor[int64] :shape NIL :id STC148081 1 :op # :requires-grad NIL :variables (STC148066) :tracker #} [function] !xor (!XOR x y) Computes the logical/bitwise xor of the tensor. lisp CATEN-USER> ( proceed ( !xor ( iconst 5 ) ( iconst 3 ))) Result Result {Tensor[int64] :shape NIL :id STC148099 6 :op # :requires-grad NIL :variables (STC148084) :tracker #} [function] !or (!OR x y) Computes the logical/bitwise or of the tensor. lisp CATEN-USER> ( proceed ( !or ( iconst 5 ) ( iconst 3 ))) Result Result {Tensor[int64] :shape NIL :id STC148117 7 :op # :requires-grad NIL :variables (STC148102) :tracker #} [function] !where (!where condition x y) Selects elements from x or y based on the condition . If the condition is true, it selects the element from x , otherwise from y . lisp CATEN-USER> ( proceed ( !where ( !eq ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC149563 ((0 0 0) (0 0 0) (0 0 0)) :op # :requires-grad NIL :variables (STC148330) :tracker #} [function] !const (!const tensor value) Creates a constant tensor with the specified value from the tensor. lisp CATEN-USER> ( proceed ( !const ( make-tensor ` ( 3 3 )) 1.0 )) Result Result {Tensor[float32] :shape NIL :id STC149578 1.0 :op # :requires-grad NIL :variables (SID149568) :tracker #} [generic] !index-components (!index-components object) Returns the index components of the tensor. object can be either of tensor or list. lisp CATEN-USER> ( proceed ( !index-components ( make-tensor ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC149661 ((0.0 1.0 2.0) (3.0 4.0 5.0) (6.0 7.0 8.0)) :op # :requires-grad NIL :variables (STC149583) :tracker #} lisp CATEN-USER> ( proceed ( !index-components ` ( 1 3 ))) Result Result {Tensor[float32] :shape (1 3) :id STC149744 ((0.0 1.0 2.0)) :op # :requires-grad NIL :variables (STC149666) :tracker #}","title":"Func"},{"location":"packages/caten.apis.differentiable_ops/#func","text":"","title":"Func"},{"location":"packages/caten.apis.differentiable_ops/#func-class","text":"","title":"Func Class"},{"location":"packages/caten.apis.differentiable_ops/#class-func","text":"A CLOS class that represents a computation. [Func] -> -> [FastGraph] -> -> [Completed] Func is a syntax-sugar for generating lowered instructions defined in the caten/aasm package. To properly lower the Func , You need to implement the following three methods: lower: Lower the Func into a list of caten/air:node . This should return caten/air:graph . forward: Create the type for the Tensor after computation. Be mindful of its lazy evaluation nature; do not perform the actual computation. ShapeTracker might help you. (use the st macro) backward: Create the graph for backward of op given prev-grad. Return: (values input_1.grad input_2.grad ...) .","title":"[class] Func"},{"location":"packages/caten.apis.differentiable_ops/#generic-lower","text":"(lower op &rest nodes) Lowers the Func into a list of caten/air:node . This should return caten/air:graph. - op[Func] Func to lower. - nodes[list] list of previous nodes (each position corresponds to the position of the variables in the Func).","title":"[generic] lower"},{"location":"packages/caten.apis.differentiable_ops/#generic-forward","text":"(forward op &rest tensors) Create the type for the Tensor after computation. Be mindful of its lazy evaluation nature; do not perform the actual computation. Use the st macro to create a new tensor. op[Func] Func to forward. tensors[list] list of input tensors.","title":"[generic] forward"},{"location":"packages/caten.apis.differentiable_ops/#generic-backward","text":"(backward op &optional prev-grad) Create the graph for backward of op given prev-grad. Return: (values input_1.grad input_2.grad ...) . save-for-backward is determined automatically, so you do not have to consider about in-place operation. op[Func] Func to backward. prev-grad[Tensor] previous gradient tensor.","title":"[generic] backward"},{"location":"packages/caten.apis.differentiable_ops/#differentiable-ops-built_in","text":"","title":"Differentiable Ops (built_in)"},{"location":"packages/caten.apis.differentiable_ops/#function-identity","text":"(!identity tensor) Equivalent to #'identity, but it is used to create a lazy computation node. lisp CATEN-USER> ( proceed ( !identity ( make-tensor ` ( 3 3 ) :initial-element 1.0 ))) Result Result {Tensor[float32] :shape (3 3) :id STC98724 ((1.0 1.0 1.0) (1.0 1.0 1.0) (1.0 1.0 1.0)) :op # :requires-grad NIL :variables (STC98663) :tracker #}","title":"[function] !identity"},{"location":"packages/caten.apis.differentiable_ops/#function-view","text":"(!view base &rest subscripts) Create a view node from the base tensor and subscripts. We refer to VIEW as a node creating tensor whose buffers are shared with the base tensor, but shapes, strides, dtypes, offsets, or dtypes are different. Subscripts has the following notation: t keep using the base view. fixnum refers to the specified element. e.g.: A[3] (a b) slices in the range of [a, b) (a b c) slices in the range of [a, b) with step c . c can be negative. In that case, b must be larger than a. For example: (10 0 -1) to reverse the elements in the axis. (:~ n) to broadcast the axis, with the size of n It is supported to compose mutliple views; the viewed tensors can be created from the viewed tensors. lisp CATEN-USER> ( proceed ( !contiguous ( !view ( ax+b ` ( 10 10 ) 1 0 ) ` ( 3 6 ) ` ( 3 6 )))) Result Result {Tensor[float32] :shape (3 3) :id STC99611 ((33.0 34.0 35.0) (43.0 44.0 45.0) (53.0 54.0 55.0)) :op # :requires-grad NIL :variables (STC98928) :tracker #}","title":"[function] !view"},{"location":"packages/caten.apis.differentiable_ops/#function-permute","text":"(!permute tensor &rest order) Returns a tensor that is a permutation of the original tensor. The new tensor has the same data as the original tensor but with the dimensions permuted according to the order specified. order can be passed as a list or separated arguments. That is, both of (!permute x 0 1) or (!permute x (list 0 1)) are valid. lisp CATEN-USER> ( proceed ( !contiguous ( !permute ( ax+b ` ( 10 10 ) 1 0 ) ` ( 1 0 )))) Result Result {Tensor[float32] :shape (10 10) :id STC99803 ((0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0) (1.0 11.0 21.0 31.0 41.0 51.0 61.0 71.0 81.0 91.0) (2.0 12.0 22.0 32.0 42.0 52.0 62.0 72.0 82.0 92.0) (3.0 13.0 23.0 33.0 43.0 53.0 63.0 73.0 83.0 93.0) (4.0 14.0 24.0 34.0 44.0 54.0 64.0 74.0 84.0 94.0) (5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0) (6.0 16.0 26.0 36.0 46.0 56.0 66.0 76.0 86.0 96.0) (7.0 17.0 27.0 37.0 47.0 57.0 67.0 77.0 87.0 97.0) (8.0 18.0 28.0 38.0 48.0 58.0 68.0 78.0 88.0 98.0) (9.0 19.0 29.0 39.0 49.0 59.0 69.0 79.0 89.0 99.0)) :op # :requires-grad NIL :variables (TID99619) :tracker #}","title":"[function] !permute"},{"location":"packages/caten.apis.differentiable_ops/#function-t","text":"(!t tensor) Transposes the last two axes of the tensor lisp CATEN-USER> ( proceed ( !contiguous ( !t ( ax+b ` ( 10 10 ) 1 0 )))) Result Result {Tensor[float32] :shape (10 10) :id STC99995 ((0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0) (1.0 11.0 21.0 31.0 41.0 51.0 61.0 71.0 81.0 91.0) (2.0 12.0 22.0 32.0 42.0 52.0 62.0 72.0 82.0 92.0) (3.0 13.0 23.0 33.0 43.0 53.0 63.0 73.0 83.0 93.0) (4.0 14.0 24.0 34.0 44.0 54.0 64.0 74.0 84.0 94.0) (5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0) (6.0 16.0 26.0 36.0 46.0 56.0 66.0 76.0 86.0 96.0) (7.0 17.0 27.0 37.0 47.0 57.0 67.0 77.0 87.0 97.0) (8.0 18.0 28.0 38.0 48.0 58.0 68.0 78.0 88.0 98.0) (9.0 19.0 29.0 39.0 49.0 59.0 69.0 79.0 89.0 99.0)) :op # :requires-grad NIL :variables (TID99811) :tracker #}","title":"[function] !t"},{"location":"packages/caten.apis.differentiable_ops/#function-transpose","text":"(!transpose tensor &optional (dim0 1) (dim1 0)) Transposes the dim0 and dim1 . lisp CATEN-USER> ( proceed ( !contiguous ( !transpose ( ax+b ` ( 10 10 ) 1 0 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC100187 ((0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0) (1.0 11.0 21.0 31.0 41.0 51.0 61.0 71.0 81.0 91.0) (2.0 12.0 22.0 32.0 42.0 52.0 62.0 72.0 82.0 92.0) (3.0 13.0 23.0 33.0 43.0 53.0 63.0 73.0 83.0 93.0) (4.0 14.0 24.0 34.0 44.0 54.0 64.0 74.0 84.0 94.0) (5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0) (6.0 16.0 26.0 36.0 46.0 56.0 66.0 76.0 86.0 96.0) (7.0 17.0 27.0 37.0 47.0 57.0 67.0 77.0 87.0 97.0) (8.0 18.0 28.0 38.0 48.0 58.0 68.0 78.0 88.0 98.0) (9.0 19.0 29.0 39.0 49.0 59.0 69.0 79.0 89.0 99.0)) :op # :requires-grad NIL :variables (TID100003) :tracker #}","title":"[function] !transpose"},{"location":"packages/caten.apis.differentiable_ops/#function-contiguous","text":"(!contiguous x &key (force nil)) If the tensor is viewed, creates a copy of tensor with contiguous memory. Otherwise, returns the original tensor. If force is set to T, it always creates a copy. lisp CATEN-USER> ( proceed ( !contiguous ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC100287 ((0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0) (10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0) (20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0) (30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0) (40.0 41.0 42.0 43.0 44.0 45.0 46.0 47.0 48.0 49.0) (50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0) (60.0 61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0) (70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0) (80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0) (90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0)) :op # :requires-grad NIL :variables (STC100194) :tracker #}","title":"[function] !contiguous"},{"location":"packages/caten.apis.differentiable_ops/#function-copy","text":"(!copy x) Creates a copy of the tensor. In Caten, the in-place operations are automatically determined, so in general, you do not have to consider using it. lisp CATEN-USER> ( proceed ( !copy ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC100418 ((0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0) (10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0) (20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0) (30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0) (40.0 41.0 42.0 43.0 44.0 45.0 46.0 47.0 48.0 49.0) (50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0) (60.0 61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0) (70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0) (80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0) (90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0)) :op # :requires-grad NIL :variables (STC100299) :tracker #}","title":"[function] !copy"},{"location":"packages/caten.apis.differentiable_ops/#function-reshape","text":"(!reshape x &rest shape) Returns a same tensor but shape is changed. shape can be passed as a list or separated arguments. That is, both of (!reshape x '(1 2 3)) or (!reshape x 1 2 3) are valid. Shape is a list of integers, symbols, or tensors. If x is a viewed tensor, it creates a copy of the tensor with contiguous memory (but later JIT will try to eliminate this). lisp CATEN-USER> ( proceed ( !reshape ( ax+b ` ( 10 10 ) 1 0 ) ` ( 5 20 ))) Result Result {Tensor[float32] :shape (5 20) :id STC100616 ((0.0 1.0 2.0 3.0 4.0 ~ 15.0 16.0 17.0 18.0 19.0) (20.0 21.0 22.0 23.0 24.0 ~ 35.0 36.0 37.0 38.0 39.0) (40.0 41.0 42.0 43.0 44.0 ~ 55.0 56.0 57.0 58.0 59.0) (60.0 61.0 62.0 63.0 64.0 ~ 75.0 76.0 77.0 78.0 79.0) (80.0 81.0 82.0 83.0 84.0 ~ 95.0 96.0 97.0 98.0 99.0)) :op # :requires-grad NIL :variables (TID100426) :tracker #}","title":"[function] !reshape"},{"location":"packages/caten.apis.differentiable_ops/#function-flatten","text":"(!flatten x &key (axis 1)) Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, ... d_n) then the output will have shape (d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn). lisp CATEN-USER> ( proceed ( !flatten ( make-tensor ` ( 3 3 3 3 )) :axis 1 )) Result Result {Tensor[float32] :shape (3 27) :id STC100922 ((0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)) :op # :requires-grad NIL :variables (TID100657) :tracker #}","title":"[function] !flatten"},{"location":"packages/caten.apis.differentiable_ops/#function-uprank","text":"(!uprank x n) Returns a tensor with one is inserted at the beginning of the shape of x for n times. lisp CATEN-USER> ( proceed ( !uprank ( ax+b ` ( 10 10 ) 1 0 ) 2 )) Result Result {Tensor[float32] :shape (1 1 10 10) :id STC101186 ((((0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0) (10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0) (20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0) (30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0) (40.0 41.0 42.0 43.0 44.0 45.0 46.0 47.0 48.0 49.0) (50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0) (60.0 61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0) (70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0) (80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0) (90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0)))) :op # :requires-grad NIL :variables (TID100930) :tracker #}","title":"[function] !uprank"},{"location":"packages/caten.apis.differentiable_ops/#function-repeat","text":"(!repeat x &rest repeats) Returns a tensor with the shape of x broadcasted by repeats . lisp CATEN-USER> ( proceed ( !repeat ( ax+b ` ( 1 10 ) 1 0 ) 10 1 )) Result Result {Tensor[float32] :shape (10 10) :id STC103674 <> :op # :requires-grad NIL :variables (VID101556) :tracker #}","title":"[function] !repeat"},{"location":"packages/caten.apis.differentiable_ops/#function-expand","text":"(!expand x &rest shape) Returns a tensor that is expanded to the shape that is specified. Expand can also increase the number of dimensions that a tensor has. lisp CATEN-USER> ( proceed ( !expand ( ax+b ` ( 1 10 ) 1 0 ) ` ( 10 10 ))) Result Result {Tensor[float32] :shape (1 10) :id STC104313 ((0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0)) :op # :requires-grad NIL :variables (VID103682) :tracker #}","title":"[function] !expand"},{"location":"packages/caten.apis.differentiable_ops/#function-move","text":"(!move a b &key (reduce nil)) Moves the element of b into a, returning a. If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !move ( ax+b ` ( 10 10 ) 0 0 ) ( ax+b ` ( 10 10 ) 0 2 ))) Result Result {Tensor[float32] :shape (10 10) :id STC104482 ((2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)) :op # :requires-grad NIL :variables (STC104328) :tracker #}","title":"[function] !move"},{"location":"packages/caten.apis.differentiable_ops/#function-assign","text":"(!assign a b) Equivalent to doing (!move a b :reduce t) . Useful when you want to the value of lazy ops to an pre-allocated buffer, like KV-Cache. lisp CATEN-USER> ( proceed ( !assign ( ax+b ` ( 10 10 ) 0 0 ) ( ax+b ` ( 10 10 ) 0 2 ))) Result Result {Tensor[float32] :shape (10 10) :id STC104651 ((2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0) (2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0)) :op # :requires-grad NIL :variables (STC104497) :tracker #}","title":"[function] !assign"},{"location":"packages/caten.apis.differentiable_ops/#function-add","text":"(!add a b &key (reduce nil)) ;; or (!+ &rest tensors) Adds a and b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !add ( ax+b ` ( 10 10 ) 1 0 ) ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC104821 ((0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0) (20.0 22.0 24.0 26.0 28.0 30.0 32.0 34.0 36.0 38.0) (40.0 42.0 44.0 46.0 48.0 50.0 52.0 54.0 56.0 58.0) (60.0 62.0 64.0 66.0 68.0 70.0 72.0 74.0 76.0 78.0) (80.0 82.0 84.0 86.0 88.0 90.0 92.0 94.0 96.0 98.0) (100.0 102.0 104.0 106.0 108.0 110.0 112.0 114.0 116.0 118.0) (120.0 122.0 124.0 126.0 128.0 130.0 132.0 134.0 136.0 138.0) (140.0 142.0 144.0 146.0 148.0 150.0 152.0 154.0 156.0 158.0) (160.0 162.0 164.0 166.0 168.0 170.0 172.0 174.0 176.0 178.0) (180.0 182.0 184.0 186.0 188.0 190.0 192.0 194.0 196.0 198.0)) :op # :requires-grad NIL :variables (STC104666) :tracker #}","title":"[function] !add"},{"location":"packages/caten.apis.differentiable_ops/#function-sub","text":"(!sub a b &key (reduce nil)) ;; or (!- &rest tensors) Subtracts b from a . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !sub ( ax+b ` ( 10 10 ) 1 0 ) ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC104997 ((0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0)) :op # :requires-grad NIL :variables (STC104837) :tracker #}","title":"[function] !sub"},{"location":"packages/caten.apis.differentiable_ops/#function-mul","text":"(!mul a b &key (reduce nil)) ;; or (!* &rest tensors) Multiplies a and b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !mul ( ax+b ` ( 10 10 ) 1 0 ) ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC105173 ((0.0 1.0 4.0 9.0 16.0 25.0 36.0 49.0 64.0 81.0) (100.0 121.0 144.0 169.0 196.0 225.0 256.0 289.0 324.0 361.0) (400.0 441.0 484.0 529.0 576.0 625.0 676.0 729.0 784.0 841.0) (900.0 961.0 1024.0 1089.0 1156.0 1225.0 1296.0 1369.0 1444.0 1521.0) (1600.0 1681.0 1764.0 1849.0 1936.0 2025.0 2116.0 2209.0 2304.0 2401.0) (2500.0 2601.0 2704.0 2809.0 2916.0 3025.0 3136.0 3249.0 3364.0 3481.0) (3600.0 3721.0 3844.0 3969.0 4096.0 4225.0 4356.0 4489.0 4624.0 4761.0) (4900.0 5041.0 5184.0 5329.0 5476.0 5625.0 5776.0 5929.0 6084.0 6241.0) (6400.0 6561.0 6724.0 6889.0 7056.0 7225.0 7396.0 7569.0 7744.0 7921.0) (8100.0 8281.0 8464.0 8649.0 8836.0 9025.0 9216.0 9409.0 9604.0 9801.0)) :op # :requires-grad NIL :variables (STC105012) :tracker #}","title":"[function] !mul"},{"location":"packages/caten.apis.differentiable_ops/#function-div","text":"(!div a b &key (reduce nil)) ;; or (!/ &rest tensors) Divides a by b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !div ( ax+b ` ( 10 10 ) 1 1 ) ( ax+b ` ( 10 10 ) 1 1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC105364 ((1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (0.99999994 1.0 1.0 1.0 1.0 1.0 0.99999994 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 0.99999994 1.0 1.0 1.0 1.0 1.0) (0.99999994 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 0.99999994 0.99999994 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 0.99999994 1.0 1.0 0.99999994 1.0 1.0 1.0)) :op # :requires-grad NIL :variables (STC105189) :tracker #}","title":"[function] !div"},{"location":"packages/caten.apis.differentiable_ops/#function-mod","text":"(!mod a b &key (reduce nil)) Computes the remainder of the division of a by b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !mod ( ax+b ` ( 10 10 ) 1 1 :dtype :int32 ) ( ax+b ` ( 10 10 ) 1 1 :dtype :int32 ))) Result Result {Tensor[int32] :shape (10 10) :id STC105538 ((0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0) (0 0 0 0 0 0 0 0 0 0)) :op # :requires-grad NIL :variables (STC105379) :tracker #}","title":"[function] !mod"},{"location":"packages/caten.apis.differentiable_ops/#function-idiv","text":"(!idiv a b &key (reduce nil)) Assuming both of a and b are the integer, divides a by b and returns the integer part. If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !idiv ( ax+b ` ( 10 10 ) 3 1 :dtype :uint32 ) ( ax+b ` ( 10 10 ) 0 2 :dtype :uint32 ))) Result Result {Tensor[uint32] :shape (10 10) :id STC105703 ((0 2 3 5 6 8 9 11 12 14) (15 17 18 20 21 23 24 26 27 29) (30 32 33 35 36 38 39 41 42 44) (45 47 48 50 51 53 54 56 57 59) (60 62 63 65 66 68 69 71 72 74) (75 77 78 80 81 83 84 86 87 89) (90 92 93 95 96 98 99 101 102 104) (105 107 108 110 111 113 114 116 117 119) (120 122 123 125 126 128 129 131 132 134) (135 137 138 140 141 143 144 146 147 149)) :op # :requires-grad NIL :variables (STC105553) :tracker #}","title":"[function] !idiv"},{"location":"packages/caten.apis.differentiable_ops/#function-maximum","text":"(!maximum a b &key (reduce nil)) Returns the maximum of a and b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !maximum ( rand ` ( 3 3 )) ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC130379 ((0.35161772 0.27699816 0.13698694) (0.25436726 0.8601034 0.39287245) (0.4397496 0.71887904 0.36415708)) :op # :requires-grad NIL :variables (STC129657) :tracker #}","title":"[function] !maximum"},{"location":"packages/caten.apis.differentiable_ops/#function-minimum","text":"(!minimum a b &key (reduce nil)) Returns the minimum of a and b . If reduce is T, it will reduce the result. (Broadcast) lisp CATEN-USER> ( proceed ( !minimum ( rand ` ( 3 3 )) ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC131132 ((-0.45117822 0.61168617 -1.7840805) (0.6194901 -0.007118067 -0.24680464) (0.17250395 -1.4365431 0.30330306)) :op # :requires-grad NIL :variables (STC130395) :tracker #}","title":"[function] !minimum"},{"location":"packages/caten.apis.differentiable_ops/#function-gcd","text":"(!gcd a b &key (reduce nil)) Returns the greatest common divisor of a and b . If reduce is T, it will reduce the result. (Broadcast) a , b are expected to be integer scalars. (dedicated to the view computation) lisp CATEN-USER> ( ctx:with-contextvar ( :jit 0 ) ( proceed ( !gcd ( iconst 8 ) ( iconst 4 )))) Result Result {Tensor[int64] :shape NIL :id STC131154 4 :op # :requires-grad NIL :variables (STC131135) :tracker #}","title":"[function] !gcd"},{"location":"packages/caten.apis.differentiable_ops/#function-lcm","text":"(!lcm a b) Returns the least common multiple of a and b . a , b are expected to be integer scalars. lisp CATEN-USER> ( ctx:with-contextvar ( :jit 0 ) ( proceed ( !lcm ( iconst 8 ) ( iconst 4 )))) Result Result {Tensor[int64] :shape NIL :id STC131217 8 :op # :requires-grad NIL :variables (STC131160) :tracker #}","title":"[function] !lcm"},{"location":"packages/caten.apis.differentiable_ops/#function-exp","text":"(!exp x) Computes (exp x) . lisp CATEN-USER> ( proceed ( !exp ( ax+b ` ( 10 10 ) 0.01 0.0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC131340 ((1.0 1.0100502 1.0202013 1.0304545 1.0408108 1.0512711 1.0618366 1.0725082 1.0832871 1.0941743) (1.105171 1.116278 1.1274968 1.1388284 1.1502738 1.1618342 1.1735109 1.1853049 1.1972173 1.2092496) (1.2214028 1.2336781 1.2460767 1.2586 1.2712492 1.2840254 1.2969301 1.3099644 1.3231298 1.3364275) (1.3498588 1.3634251 1.3771278 1.3909681 1.4049476 1.4190675 1.4333293 1.4477346 1.4622846 1.4769808) (1.4918246 1.5068177 1.5219616 1.5372574 1.5527072 1.5683122 1.5840739 1.5999942 1.6160744 1.6323161) (1.6487212 1.6652912 1.6820276 1.6989322 1.7160068 1.733253 1.7506725 1.768267 1.7860384 1.8039883) (1.8221188 1.8404315 1.8589281 1.8776106 1.8964808 1.9155407 1.9347923 1.9542372 1.9738777 1.9937155) (2.0137527 2.033991 2.054433 2.0750806 2.0959353 2.1169999 2.138276 2.1597662 2.181472 2.2033963) (2.2255409 2.2479079 2.2704997 2.2933185 2.316367 2.3396468 2.3631606 2.386911 2.4108996 2.4351294) (2.4596028 2.4843223 2.5092902 2.5345092 2.5599813 2.5857096 2.6116965 2.6379445 2.664456 2.691234)) :op # :requires-grad NIL :variables (STC131225) :tracker #}","title":"[function] !exp"},{"location":"packages/caten.apis.differentiable_ops/#function-log","text":"(!log x) Computes (log x) . lisp CATEN-USER> ( proceed ( !log ( ax+b ` ( 10 10 ) 0.01 0.001 ))) Result Result {Tensor[float32] :shape (10 10) :id STC131452 ((-6.9077554 -4.50986 -3.8632329 -3.4737678 -3.1941833 -2.97593 -2.7968814 -2.6450753 -2.5133061 -2.396896) (-2.2926347 -2.198225 -2.1119647 -2.032558 -1.9589953 -1.8904755 -1.8263509 -1.7660917 -1.7092583 -1.6554819) (-1.6044505 -1.555897 -1.5095925 -1.4653376 -1.4229584 -1.3823024 -1.343235 -1.3056365 -1.2694006 -1.2344321) (-1.2006451 -1.1679624 -1.1363143 -1.105637 -1.0758728 -1.046969 -1.0188774 -0.99155325 -0.964956 -0.93904775) (-0.9137939 -0.8891622 -0.8651225 -0.84164727 -0.81871045 -0.796288 -0.7743574 -0.7528972 -0.73188806 -0.7113112) (-0.6911492 -0.6713857 -0.6520053 -0.63299334 -0.6143361 -0.59602046 -0.5780344 -0.5603661 -0.5430046 -0.52593935) (-0.5091604 -0.49265832 -0.47642422 -0.46044946 -0.44472587 -0.42924568 -0.41400146 -0.39898625 -0.38419297 -0.36961547) (-0.35524744 -0.3410829 -0.3271162 -0.3133419 -0.29975465 -0.28634965 -0.27312195 -0.26006696 -0.24718018 -0.23445737) (-0.22189441 -0.20948724 -0.19723219 -0.18512551 -0.17316367 -0.1613432 -0.14966084 -0.1381133 -0.12669767 -0.11541088) (-0.10425006 -0.093212426 -0.082295306 -0.07149601 -0.060812157 -0.050241243 -0.039780907 -0.029428856 -0.019182874 -0.009040808)) :op # :requires-grad NIL :variables (STC131348) :tracker #}","title":"[function] !log"},{"location":"packages/caten.apis.differentiable_ops/#function-sqrt","text":"(!sqrt x) Computes (sqrt x) . lisp CATEN-USER> ( proceed ( !sqrt ( ax+b ` ( 10 10 ) 0.01 0.0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC131733 ((0.0 0.1 0.14142135 0.17320508 0.2 0.2236068 0.24494897 0.26457512 0.2828427 0.29999998) (0.31622776 0.33166248 0.34641016 0.3605551 0.37416574 0.38729832 0.4 0.41231057 0.42426407 0.4358899) (0.4472136 0.45825756 0.4690416 0.47958314 0.48989794 0.5 0.50990194 0.51961523 0.52915025 0.53851646) (0.5477225 0.55677646 0.5656854 0.5744563 0.5830952 0.591608 0.59999996 0.60827625 0.61644137 0.6244998) (0.6324555 0.64031243 0.64807403 0.65574384 0.66332495 0.67082036 0.67823297 0.6855655 0.6928203 0.7) (0.70710677 0.71414286 0.7211102 0.72801095 0.7348469 0.7416199 0.7483315 0.7549834 0.7615773 0.76811457) (0.77459663 0.781025 0.7874008 0.7937254 0.8 0.8062258 0.8124038 0.81853527 0.82462114 0.83066237) (0.83666 0.84261495 0.84852815 0.85440034 0.86023253 0.8660254 0.8717798 0.8774964 0.8831761 0.8888194) (0.8944272 0.9 0.9055385 0.91104335 0.9165151 0.92195445 0.92736185 0.9327379 0.9380832 0.9433981) (0.94868326 0.9539392 0.9591663 0.96436507 0.96953595 0.9746794 0.9797959 0.98488575 0.98994946 0.9949874)) :op # :requires-grad NIL :variables (STC131460) :tracker #}","title":"[function] !sqrt"},{"location":"packages/caten.apis.differentiable_ops/#function-neg","text":"(!neg tensor) !neg computes the negative value of the tensor. lisp CATEN-USER> ( proceed ( !neg ( ax+b ` ( 10 10 ) 0.01 0.0 ))) Result Result {Tensor[float32] :shape (10 10) :id STC131837 ((-0.0 -0.01 -0.02 -0.03 -0.04 -0.049999997 -0.06 -0.07 -0.08 -0.089999996) (-0.099999994 -0.11 -0.12 -0.13 -0.14 -0.14999999 -0.16 -0.17 -0.17999999 -0.19) (-0.19999999 -0.21 -0.22 -0.22999999 -0.24 -0.25 -0.26 -0.26999998 -0.28 -0.29) (-0.29999998 -0.31 -0.32 -0.32999998 -0.34 -0.35 -0.35999998 -0.37 -0.38 -0.39) (-0.39999998 -0.41 -0.42 -0.42999998 -0.44 -0.45 -0.45999998 -0.47 -0.48 -0.48999998) (-0.5 -0.51 -0.52 -0.53 -0.53999996 -0.55 -0.56 -0.57 -0.58 -0.59) (-0.59999996 -0.61 -0.62 -0.63 -0.64 -0.65 -0.65999997 -0.66999996 -0.68 -0.69) (-0.7 -0.71 -0.71999997 -0.72999996 -0.74 -0.75 -0.76 -0.77 -0.78 -0.78999996) (-0.79999995 -0.81 -0.82 -0.83 -0.84 -0.84999996 -0.85999995 -0.87 -0.88 -0.89) (-0.9 -0.90999997 -0.91999996 -0.93 -0.94 -0.95 -0.96 -0.96999997 -0.97999996 -0.98999995)) :op # :requires-grad NIL :variables (STC131741) :tracker #}","title":"[function] !neg"},{"location":"packages/caten.apis.differentiable_ops/#function-recip","text":"(!recip tensor) !recip computes the reciprocal of the tensor. lisp CATEN-USER> ( proceed ( !recip ( ax+b ` ( 10 10 ) 0.01 0.1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC131949 ((10.0 9.090909 8.333333 7.692308 7.142857 6.6666665 6.25 5.882353 5.5555553 5.263158) (5.0000005 4.7619047 4.5454545 4.3478265 4.1666665 4.0 3.846154 3.7037036 3.5714285 3.448276) (3.3333335 3.2258065 3.125 3.0303032 2.9411764 2.857143 2.777778 2.702703 2.631579 2.5641026) (2.5000002 2.4390244 2.3809524 2.3255816 2.2727273 2.2222223 2.1739132 2.1276596 2.0833335 2.0408163) (2.0000002 1.9607843 1.923077 1.8867925 1.8518518 1.8181818 1.7857143 1.754386 1.724138 1.6949153) (1.6666666 1.6393442 1.6129032 1.5873016 1.5625 1.5384614 1.5151515 1.4925373 1.4705882 1.4492754) (1.4285715 1.4084506 1.3888888 1.369863 1.3513514 1.3333334 1.3157895 1.2987013 1.2820512 1.2658228) (1.25 1.2345679 1.2195122 1.2048193 1.1904762 1.1764705 1.1627907 1.1494253 1.1363636 1.1235955) (1.1111112 1.098901 1.0869565 1.0752689 1.0638298 1.0526316 1.0416667 1.0309278 1.0204082 1.010101) (1.0 0.990099 0.98039216 0.97087383 0.9615385 0.952381 0.9433963 0.9345795 0.92592597 0.91743124)) :op # :requires-grad NIL :variables (STC131845) :tracker #}","title":"[function] !recip"},{"location":"packages/caten.apis.differentiable_ops/#function-square","text":"(!square x) Computes the x*x lisp CATEN-USER> ( proceed ( !square ( ax+b ` ( 10 10 ) 0.01 0.1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC132058 ((0.010000001 0.0121 0.014400001 0.0169 0.0196 0.0225 0.0256 0.028900001 0.0324 0.0361) (0.039999995 0.044100005 0.0484 0.052899994 0.057600003 0.0625 0.0676 0.072900005 0.0784 0.08409999) (0.08999999 0.0961 0.1024 0.10889999 0.115600005 0.122499995 0.12959999 0.13689998 0.1444 0.15209998) (0.15999998 0.1681 0.17639999 0.18489999 0.1936 0.20249999 0.21159998 0.2209 0.2304 0.24009998) (0.24999997 0.26009998 0.2704 0.28089997 0.29160002 0.3025 0.3136 0.3249 0.33639997 0.34809998) (0.36 0.37210003 0.3844 0.3969 0.4096 0.42250004 0.43560004 0.4489 0.46240002 0.4761) (0.48999998 0.5041 0.5184 0.53290004 0.54760003 0.5625 0.5776 0.5929 0.60840005 0.6241) (0.64000005 0.6561 0.6724 0.6889 0.7056001 0.7225 0.7396 0.7569 0.7744 0.79209995) (0.80999994 0.8281 0.8464 0.8649 0.8836 0.9025 0.9216 0.9409 0.96040004 0.98010004) (1.0 1.0201 1.0403999 1.0609 1.0816 1.1024998 1.1235999 1.1448998 1.1663998 1.1880999)) :op # :requires-grad NIL :variables (STC131957) :tracker #}","title":"[function] !square"},{"location":"packages/caten.apis.differentiable_ops/#function-rsqrt","text":"(!rsqrt x) Computes the reciprocal of sqrt x. lisp CATEN-USER> ( proceed ( !rsqrt ( ax+b ` ( 10 10 ) 0.01 0.1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC132353 ((3.1622777 3.0151134 2.8867514 2.7735012 2.6726124 2.5819888 2.5 2.4253561 2.3570225 2.2941573) (2.236068 2.1821787 2.1320071 2.0851443 2.0412414 2.0 1.9611614 1.924501 1.8898224 1.8569535) (1.825742 1.7960529 1.7677671 1.7407765 1.7149858 1.6903085 1.6666667 1.6439899 1.6222143 1.6012815) (1.5811388 1.5617375 1.5430336 1.5249858 1.5075567 1.490712 1.4744196 1.4586499 1.4433757 1.4285715) (1.4142135 1.40028 1.3867506 1.3736057 1.3608276 1.3483996 1.3363062 1.3245324 1.3130643 1.3018892) (1.2909944 1.2803688 1.2700013 1.2598816 1.25 1.2403474 1.2309148 1.2216945 1.2126781 1.2038586) (1.1952286 1.1867816 1.1785113 1.1704115 1.1624764 1.1547005 1.1470786 1.1396058 1.132277 1.1250879) (1.118034 1.1111112 1.1043153 1.0976427 1.0910894 1.0846523 1.0783278 1.0721126 1.0660036 1.0599979) (1.0540926 1.0482849 1.0425721 1.0369517 1.0314213 1.0259783 1.0206207 1.0153462 1.0101525 1.0050378) (1.0 0.99503714 0.99014753 0.98532933 0.9805807 0.9759001 0.97128594 0.96673656 0.9622505 0.95782626)) :op # :requires-grad NIL :variables (STC132067) :tracker #}","title":"[function] !rsqrt"},{"location":"packages/caten.apis.differentiable_ops/#function-signum","text":"(!signum x) Returns the sign of the tensor. If the tensor is positive, it returns 1. If the tensor is negative, it returns -1. If the tensor is zero, it returns 0. Note that this function is not differentiable. lisp CATEN-USER> ( proceed ( !signum ( ax+b ` ( 10 10 ) 0.02 -0.1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC135515 ((-1.0 -1.0 -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0) (1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0)) :op # :requires-grad NIL :variables (STC132953) :tracker #}","title":"[function] !signum"},{"location":"packages/caten.apis.differentiable_ops/#function-gid","text":"(!gid tensor rank &key (dtype *default-float*)) Finds the rank th index components of the tensor. For example (!gid x 1) for (3 3) tensor is a `(0 1 2). (As a tip) by combining !gid with !where, you can implement pseudo a random accessing of the tensor. For example: lisp CATEN-USER> ( proceed ( !gid ( make-tensor ` ( 3 3 )) -1 )) Result Result {Tensor[float32] :shape (3 3) :id STC136141 ((0.0 1.0 2.0) (0.0 1.0 2.0) (0.0 1.0 2.0)) :op # :requires-grad NIL :variables (VID135525) :tracker #}","title":"[function] !gid"},{"location":"packages/caten.apis.differentiable_ops/#function-normalize-axis","text":"(!normalize-axis ndim axis) Creates a tensor graph which normalizes the axis. If the axis is negative, it will be normalized to the positive axis. lisp CATEN-USER> ( proceed ( !normalize-axis 3 -1 )) Result Result {Tensor[int64] :shape NIL :id STC136198 2 :op # :requires-grad NIL :variables (STC136148) :tracker #}","title":"[function] !normalize-axis"},{"location":"packages/caten.apis.differentiable_ops/#function-abs","text":"(!abs x) Returns the absolute value of the tensor. lisp CATEN-USER> ( proceed ( !abs ( ax+b ` ( 10 10 ) 0.02 -0.1 ))) Result Result {Tensor[float32] :shape (10 10) :id STC139369 ((0.1 0.08 0.060000002 0.040000003 0.020000003 7.450581e-9 0.019999996 0.04 0.059999995 0.07999999) (0.09999999 0.12 0.13999999 0.16 0.18 0.19999999 0.22 0.24000001 0.26 0.28) (0.29999998 0.32 0.34 0.35999998 0.38 0.4 0.42 0.43999997 0.46 0.48) (0.49999997 0.52 0.53999996 0.55999994 0.58 0.59999996 0.61999995 0.64 0.65999997 0.67999995) (0.6999999 0.71999997 0.73999995 0.75999993 0.78 0.79999995 0.81999993 0.84 0.85999995 0.87999994) (0.9 0.91999996 0.93999994 0.9599999 0.9799999 1.0 1.02 1.04 1.06 1.0799999) (1.0999999 1.12 1.14 1.16 1.18 1.1999999 1.2199999 1.2399999 1.26 1.28) (1.3 1.3199999 1.3399999 1.3599999 1.38 1.4 1.42 1.4399999 1.4599999 1.4799999) (1.4999999 1.52 1.54 1.56 1.5799999 1.5999999 1.6199999 1.64 1.66 1.68) (1.6999999 1.7199999 1.7399999 1.76 1.78 1.8 1.8199999 1.8399999 1.8599999 1.8799999)) :op # :requires-grad NIL :variables (STC136799) :tracker #}","title":"[function] !abs"},{"location":"packages/caten.apis.differentiable_ops/#function","text":"(!> x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !> ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC140821 ((1 0 0) (0 0 0) (1 1 0)) :op # :requires-grad NIL :variables (STC139582) :tracker #}","title":"[function] !>"},{"location":"packages/caten.apis.differentiable_ops/#function_1","text":"(!< x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !< ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC142265 ((0 0 0) (0 0 0) (0 0 1)) :op # :requires-grad NIL :variables (STC141034) :tracker #}","title":"[function] !<"},{"location":"packages/caten.apis.differentiable_ops/#function_2","text":"(!>= x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !>= ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC143723 ((1 1 1) (1 1 0) (1 0 0)) :op # :requires-grad NIL :variables (STC142478) :tracker #}","title":"[function] !>="},{"location":"packages/caten.apis.differentiable_ops/#function_3","text":"(!<= x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !<= ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC145173 ((1 1 0) (0 0 1) (1 0 1)) :op # :requires-grad NIL :variables (STC143936) :tracker #}","title":"[function] !<="},{"location":"packages/caten.apis.differentiable_ops/#function-eq","text":"(!EQ x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !eq ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC146619 ((0 0 0) (0 0 0) (0 0 0)) :op # :requires-grad NIL :variables (STC145386) :tracker #}","title":"[function] !eq"},{"location":"packages/caten.apis.differentiable_ops/#function-neq","text":"(!NEQ x y) Compares x and y element-wise and returns the result as a boolean tensor. lisp CATEN-USER> ( proceed ( !where ( !neq ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC148063 ((1 1 1) (1 1 1) (1 1 1)) :op # :requires-grad NIL :variables (STC146832) :tracker #}","title":"[function] !neq"},{"location":"packages/caten.apis.differentiable_ops/#function-and","text":"(!AND x y) Computes the logical/bitwise and of the tensor. lisp CATEN-USER> ( proceed ( !and ( iconst 5 ) ( iconst 3 ))) Result Result {Tensor[int64] :shape NIL :id STC148081 1 :op # :requires-grad NIL :variables (STC148066) :tracker #}","title":"[function] !and"},{"location":"packages/caten.apis.differentiable_ops/#function-xor","text":"(!XOR x y) Computes the logical/bitwise xor of the tensor. lisp CATEN-USER> ( proceed ( !xor ( iconst 5 ) ( iconst 3 ))) Result Result {Tensor[int64] :shape NIL :id STC148099 6 :op # :requires-grad NIL :variables (STC148084) :tracker #}","title":"[function] !xor"},{"location":"packages/caten.apis.differentiable_ops/#function-or","text":"(!OR x y) Computes the logical/bitwise or of the tensor. lisp CATEN-USER> ( proceed ( !or ( iconst 5 ) ( iconst 3 ))) Result Result {Tensor[int64] :shape NIL :id STC148117 7 :op # :requires-grad NIL :variables (STC148102) :tracker #}","title":"[function] !or"},{"location":"packages/caten.apis.differentiable_ops/#function-where","text":"(!where condition x y) Selects elements from x or y based on the condition . If the condition is true, it selects the element from x , otherwise from y . lisp CATEN-USER> ( proceed ( !where ( !eq ( rand ` ( 3 3 )) ( randn ` ( 3 3 ))) ( iconst 1 ) ( iconst 0 ))) Result Result {Tensor[int64] :shape (3 3) :id STC149563 ((0 0 0) (0 0 0) (0 0 0)) :op # :requires-grad NIL :variables (STC148330) :tracker #}","title":"[function] !where"},{"location":"packages/caten.apis.differentiable_ops/#function-const","text":"(!const tensor value) Creates a constant tensor with the specified value from the tensor. lisp CATEN-USER> ( proceed ( !const ( make-tensor ` ( 3 3 )) 1.0 )) Result Result {Tensor[float32] :shape NIL :id STC149578 1.0 :op # :requires-grad NIL :variables (SID149568) :tracker #}","title":"[function] !const"},{"location":"packages/caten.apis.differentiable_ops/#generic-index-components","text":"(!index-components object) Returns the index components of the tensor. object can be either of tensor or list. lisp CATEN-USER> ( proceed ( !index-components ( make-tensor ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC149661 ((0.0 1.0 2.0) (3.0 4.0 5.0) (6.0 7.0 8.0)) :op # :requires-grad NIL :variables (STC149583) :tracker #} lisp CATEN-USER> ( proceed ( !index-components ` ( 1 3 ))) Result Result {Tensor[float32] :shape (1 3) :id STC149744 ((0.0 1.0 2.0)) :op # :requires-grad NIL :variables (STC149666) :tracker #}","title":"[generic] !index-components"},{"location":"packages/caten.apis.facet_apis/","text":"Facet APIs We are going to see how to access the Tensor as a lisp object. [generic] change-facet (change-facet obj direction) change-facet converts obj to the data type specified by direction. During the conversion process, it attempts to synchronize the Buffer (i.e., no copying is performed). In default, :direction could be one of :tensor, :simple-array, :array. Users can extend this method if needed. lisp CATEN-USER> ( change-facet 1 :tensor ) Result Result {Tensor[int64] :shape NIL :id STC315298 1 :op # :requires-grad NIL :variables (SID315288) :tracker #} lisp CATEN-USER> ( change-facet ' (( 1.0 2.0 3.0 ) ( 4.0 5.0 6.0 )) :tensor ) Result Result {Tensor[float32] :shape (2 3) :id TID315299 ((1.0 2.0 3.0) (4.0 5.0 6.0)) :op # :requires-grad NIL :variables NIL :tracker #} lisp CATEN-USER> ( change-facet #( 1 2 3 ) :tensor ) Result Result {Tensor[int64] :shape (3) :id TID315303 (1 2 3) :op # :requires-grad NIL :variables NIL :tracker #} lisp CATEN-USER> ( change-facet ( rand ` ( 2 2 )) :simple-array ) Result Result #(0.044882257 0.29727465 0.3416528 0.28447717) lisp CATEN-USER> ( change-facet ( rand ` ( 2 2 )) :array ) Result Result #2A((0.20724466 0.4652091) (0.09929559 0.52421707)) [macro] with-facet (with-facet (bind (object &key (direction :array))) &body body) Binds the result of (change-facet object direction) to the bind . [macro] with-facets (with-facets ((&rest input-forms) &body body)) Expands to a series of with-facet forms. If you want to access an individual element of Tensor, it is wiser to convert it into an Array. The following code snippet initializes the diagonal of a Tensor to 0.0 without creating a copy: lisp CATEN-USER> ( let (( x ( rand ` ( 3 3 )))) ( with-facet ( a ( x :direction :array )) ( setf ( aref a 0 0 ) 0.0 ( aref a 1 1 ) 0.0 ( aref a 2 2 ) 0.0 )) ( print x )) Result Result {Tensor[float32] :shape (3 3) :id TID315317 ((0.0 0.96047294 0.079053536) (0.29605937 0.0 0.07566601) (0.6344492 0.97433025 0.0)) :op # :requires-grad NIL :variables NIL :tracker #}","title":"Facet APIs"},{"location":"packages/caten.apis.facet_apis/#facet-apis","text":"We are going to see how to access the Tensor as a lisp object.","title":"Facet APIs"},{"location":"packages/caten.apis.facet_apis/#generic-change-facet","text":"(change-facet obj direction) change-facet converts obj to the data type specified by direction. During the conversion process, it attempts to synchronize the Buffer (i.e., no copying is performed). In default, :direction could be one of :tensor, :simple-array, :array. Users can extend this method if needed. lisp CATEN-USER> ( change-facet 1 :tensor ) Result Result {Tensor[int64] :shape NIL :id STC315298 1 :op # :requires-grad NIL :variables (SID315288) :tracker #} lisp CATEN-USER> ( change-facet ' (( 1.0 2.0 3.0 ) ( 4.0 5.0 6.0 )) :tensor ) Result Result {Tensor[float32] :shape (2 3) :id TID315299 ((1.0 2.0 3.0) (4.0 5.0 6.0)) :op # :requires-grad NIL :variables NIL :tracker #} lisp CATEN-USER> ( change-facet #( 1 2 3 ) :tensor ) Result Result {Tensor[int64] :shape (3) :id TID315303 (1 2 3) :op # :requires-grad NIL :variables NIL :tracker #} lisp CATEN-USER> ( change-facet ( rand ` ( 2 2 )) :simple-array ) Result Result #(0.044882257 0.29727465 0.3416528 0.28447717) lisp CATEN-USER> ( change-facet ( rand ` ( 2 2 )) :array ) Result Result #2A((0.20724466 0.4652091) (0.09929559 0.52421707))","title":"[generic] change-facet"},{"location":"packages/caten.apis.facet_apis/#macro-with-facet","text":"(with-facet (bind (object &key (direction :array))) &body body) Binds the result of (change-facet object direction) to the bind .","title":"[macro] with-facet"},{"location":"packages/caten.apis.facet_apis/#macro-with-facets","text":"(with-facets ((&rest input-forms) &body body)) Expands to a series of with-facet forms. If you want to access an individual element of Tensor, it is wiser to convert it into an Array. The following code snippet initializes the diagonal of a Tensor to 0.0 without creating a copy: lisp CATEN-USER> ( let (( x ( rand ` ( 3 3 )))) ( with-facet ( a ( x :direction :array )) ( setf ( aref a 0 0 ) 0.0 ( aref a 1 1 ) 0.0 ( aref a 2 2 ) 0.0 )) ( print x )) Result Result {Tensor[float32] :shape (3 3) :id TID315317 ((0.0 0.96047294 0.079053536) (0.29605937 0.0 0.07566601) (0.6344492 0.97433025 0.0)) :op # :requires-grad NIL :variables NIL :tracker #}","title":"[macro] with-facets"},{"location":"packages/caten.apis.initializers/","text":"Initializers [function] set-manual-seed (set-manual-seed &key (seed 0)) Sets the seed for random operations. [macro] with-manual-seed (with-manual-seed (seed) &body body) Sets the seed for random operations within the scope of the body. [function] ax+b (ax+b shape a b &key (out nil) (dtype *default-float*) (order *default-order*)) Generates an array sampled from this formula: x_i = a * index_components(i) + b. There is a linspace function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( proceed ( ax+b ` ( 3 3 ) 2 1 )) Result Result {Tensor[float32] :shape (3 3) :id STC203205 ((1.0 3.0 5.0) (7.0 9.0 11.0) (13.0 15.0 17.0)) :op # :requires-grad NIL :variables (STC203115) :tracker #} Static CATEN-USER> ( linspace ` ( 3 3 ) 1 0 ) Result Result {Tensor[float32] :shape (3 3) :id TID203206 ((0.0 1.0 2.0) (3.0 4.0 5.0) (6.0 7.0 8.0)) :op # :requires-grad NIL :variables NIL :tracker #} [function] !rand (!rand shape &key (dtype *default-float*) (order *default-order*) (out nil)) Creates a tensor whose elements are randomly sampled from a uniform distribution over the interval [0, 1) . There is a rand function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( proceed ( !rand ` ( 3 3 ))) Result Result {Tensor[float32] :shape (3 3) :id STC210745 ((0.5561414 0.5751883 0.832701) (0.7853549 0.37660313 0.84738994) (0.55430925 0.9354172 0.9739074)) :op # :requires-grad NIL :variables (STC203277) :tracker #} Static CATEN-USER> ( rand ` ( 3 3 )) Result Result {Tensor[float32] :shape (3 3) :id TID210746 ((0.53696483 0.6626895 0.9899716) (0.33544847 0.3335809 0.6769852) (0.041966714 0.015882287 0.753615)) :op # :requires-grad NIL :variables NIL :tracker #} [function] !normal (!normal shape &key (mean 0.0) (std 1.0) (dtype *default-float*) (order *default-order*) (out nil)) Creates a tensor whose elements are randomly sampled from a normal distribution with the given mean and std . There is a normal function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( proceed ( !normal ` ( 3 3 ) :mean 0.0 :std 2.0 )) Result Result {Tensor[float32] :shape (3 3) :id STC226682 ((0.22197889 -4.817684 0.18407516) (-1.9114828 -0.43231335 -0.90131146) (-2.5488627 -0.80741 -1.066406)) :op # :requires-grad NIL :variables (STC210758) :tracker #} Static CATEN-USER> ( normal ` ( 3 3 ) :mean 0.0 :std 1.0 ) Result Result {Tensor[float32] :shape (3 3) :id TID226683 ((0.20325683 1.9010925 0.11178739) (0.06818088 0.10441013 0.98638976) (0.6173678 -1.0524902 -0.3097188)) :op # :requires-grad NIL :variables NIL :tracker #} [function] !randn (!randn shape &key (dtype *default-float*) (order *default-order*) (out nil)) Creates a tensor whose elements are randomly sampled from a normal distribution with a mean of 0 and a standard deviation of 1. There is a randn function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( proceed ( !randn ` ( 3 3 ))) Result Result {Tensor[float32] :shape (3 3) :id STC241391 ((-1.0663555 -0.2564156 0.08828225) (2.0705404 -0.094690986 1.1788496) (-1.4148906 1.0167512 -0.6521854)) :op # :requires-grad NIL :variables (STC226693) :tracker #} Static CATEN-USER> ( randn ` ( 3 3 )) Result Result {Tensor[float32] :shape (3 3) :id TID241392 ((0.8697504 -2.7880201 1.4020109) (-0.47702768 0.6844121 -0.68407243) (1.3354832 -0.36871713 -0.22123063)) :op # :requires-grad NIL :variables NIL :tracker #} [function] !uniform (!uniform shape &key (low 0.0) (high 1.0) (dtype *default-float*) (order *default-order*) (out nil)) Creates a tensor whose elements are randomly sampled from a uniform distribution over the interval [low, high) There is a uniform function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( proceed ( !uniform ` ( 3 3 ) :low 1.0 :high 2.0 )) Result Result {Tensor[float32] :shape (3 3) :id STC250162 ((1.590658 1.0057554 1.2562647) (1.1458193 1.6777649 1.8310666) (1.8900917 1.3517946 1.3587387)) :op # :requires-grad NIL :variables (STC241404) :tracker #} Static CATEN-USER> ( uniform ` ( 3 3 ) :low 1.0 :high 2.0 ) Result Result {Tensor[float32] :shape (3 3) :id TID250163 ((1.8804288 1.6129477 1.8870113) (1.0641054 1.7846086 1.8476219) (1.3157417 1.4555242 1.7016529)) :op # :requires-grad NIL :variables NIL :tracker #} [function] !randint (!randint shape &key (low 0) (high 1) (dtype *default-int*) (order *default-order*) (out nil)) Creates a tensor whose elements are randomly sampled from a uniform distribution over the interval [low, high) . There is a randint function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( ctx:with-contextvar ( :jit 1 ) ( proceed ( !randint ` ( 3 3 ) :low 1 :high 10 ))) Result Result {Tensor[int64] :shape (3 3) :id STC287365 ((6 4 4) (9 3 6) (9 6 9)) :op # :requires-grad NIL :variables (STC257801) :tracker #} Static CATEN-USER> ( ctx:with-contextvar ( :jit 1 ) ( randint ` ( 3 3 ) :low 1 :high 10 :dtype :int32 )) Result Result {Tensor[int32] :shape (3 3) :id TID287366 ((3 6 4) (8 9 4) (7 8 6)) :op # :requires-grad NIL :variables NIL :tracker #} [function] !full (!full shape fill-value &key (dtype *default-float*) (order *default-order*)) Initializes a tensor filled with fill-value . lisp CATEN-USER> ( proceed ( !full ` ( 3 3 ) ( inf ))) Result Result {Tensor[float32] :shape (3 3) :id STC315261 ((#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY)) :op # :requires-grad NIL :variables (TID315199) :tracker #} [function] xavier-uniform No description provided [function] xavier-gaussian No description provided","title":"Initializers"},{"location":"packages/caten.apis.initializers/#initializers","text":"","title":"Initializers"},{"location":"packages/caten.apis.initializers/#function-set-manual-seed","text":"(set-manual-seed &key (seed 0)) Sets the seed for random operations.","title":"[function] set-manual-seed"},{"location":"packages/caten.apis.initializers/#macro-with-manual-seed","text":"(with-manual-seed (seed) &body body) Sets the seed for random operations within the scope of the body.","title":"[macro] with-manual-seed"},{"location":"packages/caten.apis.initializers/#function-axb","text":"(ax+b shape a b &key (out nil) (dtype *default-float*) (order *default-order*)) Generates an array sampled from this formula: x_i = a * index_components(i) + b. There is a linspace function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( proceed ( ax+b ` ( 3 3 ) 2 1 )) Result Result {Tensor[float32] :shape (3 3) :id STC203205 ((1.0 3.0 5.0) (7.0 9.0 11.0) (13.0 15.0 17.0)) :op # :requires-grad NIL :variables (STC203115) :tracker #} Static CATEN-USER> ( linspace ` ( 3 3 ) 1 0 ) Result Result {Tensor[float32] :shape (3 3) :id TID203206 ((0.0 1.0 2.0) (3.0 4.0 5.0) (6.0 7.0 8.0)) :op # :requires-grad NIL :variables NIL :tracker #}","title":"[function] ax+b"},{"location":"packages/caten.apis.initializers/#function-rand","text":"(!rand shape &key (dtype *default-float*) (order *default-order*) (out nil)) Creates a tensor whose elements are randomly sampled from a uniform distribution over the interval [0, 1) . There is a rand function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( proceed ( !rand ` ( 3 3 ))) Result Result {Tensor[float32] :shape (3 3) :id STC210745 ((0.5561414 0.5751883 0.832701) (0.7853549 0.37660313 0.84738994) (0.55430925 0.9354172 0.9739074)) :op # :requires-grad NIL :variables (STC203277) :tracker #} Static CATEN-USER> ( rand ` ( 3 3 )) Result Result {Tensor[float32] :shape (3 3) :id TID210746 ((0.53696483 0.6626895 0.9899716) (0.33544847 0.3335809 0.6769852) (0.041966714 0.015882287 0.753615)) :op # :requires-grad NIL :variables NIL :tracker #}","title":"[function] !rand"},{"location":"packages/caten.apis.initializers/#function-normal","text":"(!normal shape &key (mean 0.0) (std 1.0) (dtype *default-float*) (order *default-order*) (out nil)) Creates a tensor whose elements are randomly sampled from a normal distribution with the given mean and std . There is a normal function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( proceed ( !normal ` ( 3 3 ) :mean 0.0 :std 2.0 )) Result Result {Tensor[float32] :shape (3 3) :id STC226682 ((0.22197889 -4.817684 0.18407516) (-1.9114828 -0.43231335 -0.90131146) (-2.5488627 -0.80741 -1.066406)) :op # :requires-grad NIL :variables (STC210758) :tracker #} Static CATEN-USER> ( normal ` ( 3 3 ) :mean 0.0 :std 1.0 ) Result Result {Tensor[float32] :shape (3 3) :id TID226683 ((0.20325683 1.9010925 0.11178739) (0.06818088 0.10441013 0.98638976) (0.6173678 -1.0524902 -0.3097188)) :op # :requires-grad NIL :variables NIL :tracker #}","title":"[function] !normal"},{"location":"packages/caten.apis.initializers/#function-randn","text":"(!randn shape &key (dtype *default-float*) (order *default-order*) (out nil)) Creates a tensor whose elements are randomly sampled from a normal distribution with a mean of 0 and a standard deviation of 1. There is a randn function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( proceed ( !randn ` ( 3 3 ))) Result Result {Tensor[float32] :shape (3 3) :id STC241391 ((-1.0663555 -0.2564156 0.08828225) (2.0705404 -0.094690986 1.1788496) (-1.4148906 1.0167512 -0.6521854)) :op # :requires-grad NIL :variables (STC226693) :tracker #} Static CATEN-USER> ( randn ` ( 3 3 )) Result Result {Tensor[float32] :shape (3 3) :id TID241392 ((0.8697504 -2.7880201 1.4020109) (-0.47702768 0.6844121 -0.68407243) (1.3354832 -0.36871713 -0.22123063)) :op # :requires-grad NIL :variables NIL :tracker #}","title":"[function] !randn"},{"location":"packages/caten.apis.initializers/#function-uniform","text":"(!uniform shape &key (low 0.0) (high 1.0) (dtype *default-float*) (order *default-order*) (out nil)) Creates a tensor whose elements are randomly sampled from a uniform distribution over the interval [low, high) There is a uniform function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( proceed ( !uniform ` ( 3 3 ) :low 1.0 :high 2.0 )) Result Result {Tensor[float32] :shape (3 3) :id STC250162 ((1.590658 1.0057554 1.2562647) (1.1458193 1.6777649 1.8310666) (1.8900917 1.3517946 1.3587387)) :op # :requires-grad NIL :variables (STC241404) :tracker #} Static CATEN-USER> ( uniform ` ( 3 3 ) :low 1.0 :high 2.0 ) Result Result {Tensor[float32] :shape (3 3) :id TID250163 ((1.8804288 1.6129477 1.8870113) (1.0641054 1.7846086 1.8476219) (1.3157417 1.4555242 1.7016529)) :op # :requires-grad NIL :variables NIL :tracker #}","title":"[function] !uniform"},{"location":"packages/caten.apis.initializers/#function-randint","text":"(!randint shape &key (low 0) (high 1) (dtype *default-int*) (order *default-order*) (out nil)) Creates a tensor whose elements are randomly sampled from a uniform distribution over the interval [low, high) . There is a randint function for the same purpose, but it is not lazy. Lazy CATEN-USER> ( ctx:with-contextvar ( :jit 1 ) ( proceed ( !randint ` ( 3 3 ) :low 1 :high 10 ))) Result Result {Tensor[int64] :shape (3 3) :id STC287365 ((6 4 4) (9 3 6) (9 6 9)) :op # :requires-grad NIL :variables (STC257801) :tracker #} Static CATEN-USER> ( ctx:with-contextvar ( :jit 1 ) ( randint ` ( 3 3 ) :low 1 :high 10 :dtype :int32 )) Result Result {Tensor[int32] :shape (3 3) :id TID287366 ((3 6 4) (8 9 4) (7 8 6)) :op # :requires-grad NIL :variables NIL :tracker #}","title":"[function] !randint"},{"location":"packages/caten.apis.initializers/#function-full","text":"(!full shape fill-value &key (dtype *default-float*) (order *default-order*)) Initializes a tensor filled with fill-value . lisp CATEN-USER> ( proceed ( !full ` ( 3 3 ) ( inf ))) Result Result {Tensor[float32] :shape (3 3) :id STC315261 ((#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY)) :op # :requires-grad NIL :variables (TID315199) :tracker #}","title":"[function] !full"},{"location":"packages/caten.apis.initializers/#function-xavier-uniform","text":"No description provided","title":"[function] xavier-uniform"},{"location":"packages/caten.apis.initializers/#function-xavier-gaussian","text":"No description provided","title":"[function] xavier-gaussian"},{"location":"packages/caten.apis/","text":"Overview Welcome to Caten/APIs WIP proceed is a function that evaluates a computational graph. lisp CATEN-USER> ( proceed ( make-tensor ` ( 3 3 ) :initial-element 1.0 )) Result Result {Tensor[float32] :shape (3 3) :id STC24811 ((1.0 1.0 1.0) (1.0 1.0 1.0) (1.0 1.0 1.0)) :op # :requires-grad NIL :variables (TID24749) :tracker #} ctx:with-contextvar is a macro that sets the context variable. JIT=1 to use JIT, JIT_DEBUG=1 to see the generated code. DOT=1 to debug the pattern matcher on your browser. (need graphviz) lisp CATEN-USER> ( ctx:with-contextvar ( :jit 1 :jit_debug 1 :dot 0 ) ( caten ( forward ( Embedding 10 10 ) ( make-tensor ` ( b c ))))) Result Result #S(AVM :GRAPH Graph[seen=NIL, outputs=(val_35)] { ) where :nrank=2 :dtype=FLOAT32 :_type_relay=NIL :_read_views=NIL :_output_type=NIL> :dtypes=(FLOAT32 INT64 INT64 FLOAT32 FLOAT32)> } :NAME MAIN60122 :FW-OUTPUTS (val_35) :BW-OUTPUTS NIL :ID2TENSOR # :TAPE-LENGTH 14 :PC 0 :VARIABLES # :PARAMS-TO-OPTIMIZE NIL :DUMPED NIL)","title":"Overview"},{"location":"packages/caten.apis/#overview","text":"Welcome to Caten/APIs WIP proceed is a function that evaluates a computational graph. lisp CATEN-USER> ( proceed ( make-tensor ` ( 3 3 ) :initial-element 1.0 )) Result Result {Tensor[float32] :shape (3 3) :id STC24811 ((1.0 1.0 1.0) (1.0 1.0 1.0) (1.0 1.0 1.0)) :op # :requires-grad NIL :variables (TID24749) :tracker #} ctx:with-contextvar is a macro that sets the context variable. JIT=1 to use JIT, JIT_DEBUG=1 to see the generated code. DOT=1 to debug the pattern matcher on your browser. (need graphviz) lisp CATEN-USER> ( ctx:with-contextvar ( :jit 1 :jit_debug 1 :dot 0 ) ( caten ( forward ( Embedding 10 10 ) ( make-tensor ` ( b c ))))) Result Result #S(AVM :GRAPH Graph[seen=NIL, outputs=(val_35)] { ) where :nrank=2 :dtype=FLOAT32 :_type_relay=NIL :_read_views=NIL :_output_type=NIL> :dtypes=(FLOAT32 INT64 INT64 FLOAT32 FLOAT32)> } :NAME MAIN60122 :FW-OUTPUTS (val_35) :BW-OUTPUTS NIL :ID2TENSOR # :TAPE-LENGTH 14 :PC 0 :VARIABLES # :PARAMS-TO-OPTIMIZE NIL :DUMPED NIL)","title":"Overview"},{"location":"packages/caten.apis.models/","text":"Models TODO [macro] defcall (defcall (model-bind model) (&rest inputs) body0 A macro to write defmethod call in a more concise way. Example ( defcall ( model Transformer ) ( Tokens[Batch Seq-Len] Start-Pos[] ) ( with-slots (( wte wte ) ( wpe wpe ) ( h h ) ( ln-f ln-f ) ( lm-head lm-head )) model ( let* (( token-emb ( forward wte tokens )) ( pos-emb ( forward wpe ( !cast ( !add start-pos ( !index-components ` ( 1 , seq-len ))) ( dtype-of tokens )))) ( hi ( !add token-emb pos-emb )) ( mask ( !triu ( !full ` ( 1 1 , seq-len , ( !+ start-pos ( iconst seq-len ))) ( -inf )) :diagonal ( !+ ( iconst 1 ) start-pos ))) ( _ ( dolist ( hn h ) ( setf hi ( forward hn hi mask start-pos )))) ( logits ( forward lm-head ( forward ln-f hi )))) ( declare ( ignore _ )) ;; (!argmax (!view logits t -1 t)) ( !argmax logits )))) [macro] defsequence ( defsequence ( name ( &rest args ) &optional docstring &rest nodes )) Defines a model which the definition is given as a sequence of nodes. Note that models defined by this macro only accept one input. Example ( defsequence MLP ( in-features hidden-dim out-features &key ( activation #' !relu )) ( Linear in-features hidden-dim ) ( asnode activation ) ( Linear hidden-dim hidden-dim ) ( asnode activation ) ( Linear hidden-dim out-features )) [function] asnode (asnode function &rest rest-args) Wraps the function as a callable node by (forward ...) . function is a function which takes one argument and returns one value. rest-args is a place to pass additional arguments like: (asnode #'!leaky-relu :neg-slope 1e-2)","title":"Model"},{"location":"packages/caten.apis.models/#models","text":"TODO","title":"Models"},{"location":"packages/caten.apis.models/#macro-defcall","text":"(defcall (model-bind model) (&rest inputs) body0 A macro to write defmethod call in a more concise way.","title":"[macro] defcall"},{"location":"packages/caten.apis.models/#example","text":"( defcall ( model Transformer ) ( Tokens[Batch Seq-Len] Start-Pos[] ) ( with-slots (( wte wte ) ( wpe wpe ) ( h h ) ( ln-f ln-f ) ( lm-head lm-head )) model ( let* (( token-emb ( forward wte tokens )) ( pos-emb ( forward wpe ( !cast ( !add start-pos ( !index-components ` ( 1 , seq-len ))) ( dtype-of tokens )))) ( hi ( !add token-emb pos-emb )) ( mask ( !triu ( !full ` ( 1 1 , seq-len , ( !+ start-pos ( iconst seq-len ))) ( -inf )) :diagonal ( !+ ( iconst 1 ) start-pos ))) ( _ ( dolist ( hn h ) ( setf hi ( forward hn hi mask start-pos )))) ( logits ( forward lm-head ( forward ln-f hi )))) ( declare ( ignore _ )) ;; (!argmax (!view logits t -1 t)) ( !argmax logits ))))","title":"Example"},{"location":"packages/caten.apis.models/#macro-defsequence","text":"( defsequence ( name ( &rest args ) &optional docstring &rest nodes )) Defines a model which the definition is given as a sequence of nodes. Note that models defined by this macro only accept one input.","title":"[macro] defsequence"},{"location":"packages/caten.apis.models/#example_1","text":"( defsequence MLP ( in-features hidden-dim out-features &key ( activation #' !relu )) ( Linear in-features hidden-dim ) ( asnode activation ) ( Linear hidden-dim hidden-dim ) ( asnode activation ) ( Linear hidden-dim out-features ))","title":"Example"},{"location":"packages/caten.apis.models/#function-asnode","text":"(asnode function &rest rest-args) Wraps the function as a callable node by (forward ...) . function is a function which takes one argument and returns one value. rest-args is a place to pass additional arguments like: (asnode #'!leaky-relu :neg-slope 1e-2)","title":"[function] asnode"},{"location":"packages/caten.apis.module/","text":"Module [macro] defmodule (defmodule (name ((&rest constructor-args) &rest attrs) &key (where nil) (direct-superclasses nil)) (&rest slots) &key (documentation \"\") (impl nil) (forward nil) (backward nil)) Define a module named name . In Caten, Module is a CLOS class that represents a set of Funcs and is defined as a subclass of Func itself. It is used to represent computational nodes that can be expressed through compositions of Funcs. Consequently, as a subclass of Func, Module utilizes the following three methods for manipulation: [method] impl (impl (op Module) &rest tensors) tensors[list] a list of the input tensor. In the impl method, please describe the process for constructing the computational graph of the Module using a combination of Func and Module . The computational graph must begin with the inputs. If there are multiple outputs, bind them with cl:values . If you need to record Tensors for the backward process, now is the time to do so. [method] forward (forward (op Module) &rest tensors) tensors[List] a list of the input tensor. In the forward method, describe the operation to create a Tensor after computation. Be mindful of its lazy evaluation nature; do not perform the actual computation at this stage. The st macro in ShapeTracker is quite useful for creating the Tensor after the operation. If necessary, you may also include checks for additional attributes or slots here. If you specify ShapeTracker in :where , the defmodule macro will automatically generate the forward. Therefore, you must describe either :where or :forward . [method] backward (optional) (backward (op Module) prev-grad) -> (values input_1.grad input_2.grad ...) prev-grad[Tensor] In the backward method, describe the gradient computation for the Module using a combination of Func and Module . The arguments are fixed as (op prev-grad) , where op = module instance, prev-grad is a tensor. If you need the value of the Tensor at the input stage for the gradient calculation, temporarily store it using the module-sv4bws accessor while calling the impl method. The compiler does not depend on module-sv4bws , so you are free to choose how to store the Tensor. In Caten, since save-for-backward is automatically determined, there is no need to be concerned about in-place operations. Note that backward is optional . If it is not provided, AD will be applied based on the computational graph from impl . [method] lower The lower method is automatically written by the defmodule , so there is no need to consider it when describing the module. However, it is necessary to understand how it is lowered for when defining simplifiers for the Module . lower produces the following node: (make-node :Graph (intern (symbol-name (symb 'graph/ name)) \"KEYWORD\") outputs inputs &rest attrs) Nodes whose class is :Graph are completely eliminated during lower by impl . Syntax forward , backward , impl are described in one of the following format. forward := ((op &rest args) &body body) forward := (lambda (&rest args) &body body) forward := fname Effects it defines a class named name . it defines a function named name . it works as a constructor. Arguments name[symbol] the name of module constructor-args[list] arguments for the constructor attrs[list] define attrs for the lowered graph based on the constructor-args variables using the following format: (:key1 value1 :key1 value2 ...) . slots[list] slots for the defined class. where[nil or string] ShapeTracker documentation[string] documentation Notes The methods are called in the order of forward->impl->backward during compilation impl is performed recursively, so modules must not be co-dependent within the impl method. (e.g.: do not define a module A that depends on B that depends on A ...) Modules (built_in) [function] !sum (!sum x &key (axis t) (keepdims nil)) Compute the sum of the tensor. lisp CATEN-USER> ( proceed ( !sum ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (1 1) :id STC151141 ((4950.0)) :op # :requires-grad NIL :variables (VID149847) :tracker #} [function] !mean (!mean x &key (axis t) (keepdims nil)) Compute the mean of the tensor. lisp CATEN-USER> ( proceed ( !mean ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (1 1) :id STC152868 ((49.5)) :op # :requires-grad NIL :variables (VID151244) :tracker #} [function] !max (!max x &key (axis t) (keepdims nil)) Compute the maximum of the tensor. lisp CATEN-USER> ( proceed ( !max ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (1 1) :id STC154939 ((99.0)) :op # :requires-grad NIL :variables (VID152971) :tracker #} [function] !min (!min x &key (axis t) (keepdims nil)) Compute the minimum of the tensor. lisp CATEN-USER> ( proceed ( !min ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (1 1) :id STC157028 ((0.0)) :op # :requires-grad NIL :variables (VID155042) :tracker #} [function] !matmul (!matmul a b) Performs matrix multiplication between two tensors a and b . lisp CATEN-USER> ( proceed ( !matmul ( rand ` ( 32 64 )) ( rand ` ( 64 128 )))) Result Result {Tensor[float32] :shape (32 128) :id STC161494 ((15.361223 15.828152 14.417058 15.882298 15.45585 ~ 13.062644 16.183292 12.734098 16.734056 15.52915) (14.835705 14.845199 16.404129 16.800152 16.33881 ~ 14.190987 15.975574 14.354835 15.514871 15.666682) (15.371529 15.849551 15.536994 15.782414 16.02211 ~ 13.076694 15.905483 13.951732 14.709311 16.353771) (17.939054 17.170252 19.312342 18.902992 19.101515 ~ 14.990455 17.7107 13.865579 17.309923 18.022947) (15.27028 14.820869 15.772669 15.838371 15.730969 ~ 13.963487 16.55142 13.431679 14.782211 15.346079) ... (14.130519 14.243071 16.591045 15.483922 15.168219 ~ 12.99691 14.225608 13.279083 14.397368 14.895684) (14.720093 15.086392 16.987844 16.13062 15.741239 ~ 14.447361 16.014956 13.712358 14.148518 15.63789) (18.153896 17.43257 17.920296 18.44725 17.450092 ~ 15.145452 18.734285 15.93504 17.834435 17.902695) (14.291782 15.027209 17.237173 16.649723 16.29012 ~ 13.699114 16.332775 15.594537 15.32779 16.408815) (13.0013075 12.744859 14.356513 13.985053 14.217204 ~ 11.638837 14.574662 12.32615 11.624662 14.840029)) :op # :requires-grad NIL :variables (STC157041) :tracker #} [function] !sinh (!sinh x) lisp CATEN-USER> ( proceed ( !sinh ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC162259 ((-0.07758549 1.7127867 0.5037463) (-0.431553 -0.93981755 -2.0931177) (0.3535945 0.108312786 -0.27719477)) :op # :requires-grad NIL :variables (STC161501) :tracker #} [function] !cosh (!cosh x) lisp CATEN-USER> ( proceed ( !cosh ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC163018 ((2.2316532 1.5844023 1.1880606) (4.6957664 1.0567473 1.0100629) (2.2520223 1.0415792 1.0012665)) :op # :requires-grad NIL :variables (STC162266) :tracker #} [function] !tanh (!tanh x) lisp CATEN-USER> ( proceed ( !tanh ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC165777 ((0.7690325 -0.59365016 0.81341624) (-0.17309564 0.36871922 -0.34405994) (0.64914274 -0.7870514 -0.19824558)) :op # :requires-grad NIL :variables (STC163025) :tracker #} [function] !cos (!cos x) lisp CATEN-USER> ( proceed ( !cos ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC166463 ((0.99518865 0.95479304 0.7415326) (0.5100029 -0.06835051 0.84506404) (0.99555033 -0.061528657 0.97121775)) :op # :requires-grad NIL :variables (STC165784) :tracker #} [function] !tan (!tan x) lisp CATEN-USER> ( proceed ( !tan ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC167188 ((-1.348232 -0.47360304 -0.19166948) (-0.45079947 0.096330844 -0.0046434165) (-1.4844729 -0.74011093 -1.0703554)) :op # :requires-grad NIL :variables (STC166470) :tracker #} [function] !log2 (!log2 x) lisp CATEN-USER> ( proceed ( !log2 ( ax+b ` ( 3 3 ) 1 0.1 ))) Result Result {Tensor[float32] :shape (3 3) :id STC167945 ((-3.321928 0.13750356 1.0703893) (1.6322682 2.0356238 2.3504972) (2.6088092 2.827819 3.017922)) :op # :requires-grad NIL :variables (STC167196) :tracker #} [function] !exp2 (!exp2 x) lisp CATEN-USER> ( proceed ( !exp2 ( ax+b ` ( 3 3 ) 1 0.1 ))) Result Result {Tensor[float32] :shape (3 3) :id STC168685 ((1.0717734 2.143547 4.2870936) (8.574187 17.148375 34.29675) (68.593475 137.18695 274.37408)) :op # :requires-grad NIL :variables (STC167953) :tracker #} [function] !expt (!expt base power) Computes the power of base with power. lisp CATEN-USER> ( proceed ( !expt ( ax+b ` ( 3 3 ) 1 0.1 ) 2 )) Result Result {Tensor[float32] :shape (3 3) :id STC170701 ((0.010000001 1.21 4.4099994) (9.61 16.81 26.009998) (37.21 50.41 65.61001)) :op # :requires-grad NIL :variables (STC168988) :tracker #} [function] !truncate (!truncate x) lisp CATEN-USER> ( proceed ( !truncate ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC170894 ((0.0 0.0 1.0) (0.0 0.0 0.0) (1.0 -1.0 0.0)) :op # :requires-grad NIL :variables (STC170708) :tracker #} [function] !ceiling (!ceiling x) lisp CATEN-USER> ( proceed ( !ceiling ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC171819 ((1.0 -1.0 1.0) (1.0 0.0 0.0) (0.0 0.0 1.0)) :op # :requires-grad NIL :variables (STC170901) :tracker #} [function] !floor (!floor x) lisp CATEN-USER> ( proceed ( !floor ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC172740 ((0.0 0.0 1.0) (0.0 -1.0 -1.0) (-1.0 0.0 -2.0)) :op # :requires-grad NIL :variables (STC171826) :tracker #} [function] !triu (!triu x &key (diagonal 0)) Returns the upper triangular part of the tensor (>= 2D) or batch of matrices input. lisp CATEN-USER> ( proceed ( !triu ( rand ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC174099 ((0.498627 0.5731704 0.24880171) (0.0 0.044707708 0.7780621) (0.0 0.0 0.45015103)) :op # :requires-grad NIL :variables (STC172747) :tracker #} [function] !tril (!tril x &key (diagonal 0)) Returns the lower triangular part of the tensor (>= 2D) or batch of matrices input. lisp CATEN-USER> ( proceed ( !tril ( rand ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC175466 ((0.5187954 0.0 0.0) (0.6216609 0.9618312 0.0) (0.020816622 0.5980801 0.5765489)) :op # :requires-grad NIL :variables (STC174106) :tracker #} [function] !argmax (!argmax x &key (axis -1) (keepdims nil)) Returns the indices of the maximum values along an axis. lisp CATEN-USER> ( proceed ( !argmax ( rand ` ( 3 3 )))) Result Result {Tensor[int64] :shape (3 1) :id STC182222 ((2) (2) (2)) :op # :requires-grad NIL :variables (VID175568) :tracker #} [function] !argmin (!argmin x &key (axis -1) (keepdims nil)) Returns the indices of the minimum values along an axis. lisp CATEN-USER> ( proceed ( !argmin ( rand ` ( 3 3 )))) Result Result {Tensor[int64] :shape (3 1) :id STC189167 ((0) (1) (2)) :op # :requires-grad NIL :variables (VID182324) :tracker #} [function] !clip (!clip x min max) !clip limits the given input within an interval. The interval is specified by the inputs 'min' and 'max'. min/max is either a number, a symbol, or a tensor. The implementation follows the ONNX specification. https://github.com/onnx/onnx/blob/main/docs/Changelog.md#clip-13 lisp CATEN-USER> ( proceed ( !clip ( randn ` ( 3 3 )) -0.3 0.3 )) Result Result {Tensor[float32] :shape (3 3) :id STC191761 ((0.3 -0.3 0.3) (0.3 -0.3 0.3) (-0.22691669 -0.3 0.3)) :op # :requires-grad NIL :variables (STC189364) :tracker #} [function] !erf (!erf x) Computes the error function of x. lisp CATEN-USER> ( proceed ( !erf ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC203108 ((0.984875 0.81764907 -0.99786603) (-0.8090681 0.9998888 -0.67663276) (0.99501437 0.98214996 0.49436408)) :op # :requires-grad NIL :variables (STC191768) :tracker #}","title":"Module"},{"location":"packages/caten.apis.module/#module","text":"","title":"Module"},{"location":"packages/caten.apis.module/#macro-defmodule","text":"(defmodule (name ((&rest constructor-args) &rest attrs) &key (where nil) (direct-superclasses nil)) (&rest slots) &key (documentation \"\") (impl nil) (forward nil) (backward nil)) Define a module named name . In Caten, Module is a CLOS class that represents a set of Funcs and is defined as a subclass of Func itself. It is used to represent computational nodes that can be expressed through compositions of Funcs. Consequently, as a subclass of Func, Module utilizes the following three methods for manipulation:","title":"[macro] defmodule"},{"location":"packages/caten.apis.module/#method-impl","text":"(impl (op Module) &rest tensors) tensors[list] a list of the input tensor. In the impl method, please describe the process for constructing the computational graph of the Module using a combination of Func and Module . The computational graph must begin with the inputs. If there are multiple outputs, bind them with cl:values . If you need to record Tensors for the backward process, now is the time to do so.","title":"[method] impl"},{"location":"packages/caten.apis.module/#method-forward","text":"(forward (op Module) &rest tensors) tensors[List] a list of the input tensor. In the forward method, describe the operation to create a Tensor after computation. Be mindful of its lazy evaluation nature; do not perform the actual computation at this stage. The st macro in ShapeTracker is quite useful for creating the Tensor after the operation. If necessary, you may also include checks for additional attributes or slots here. If you specify ShapeTracker in :where , the defmodule macro will automatically generate the forward. Therefore, you must describe either :where or :forward .","title":"[method] forward"},{"location":"packages/caten.apis.module/#method-backward-optional","text":"(backward (op Module) prev-grad) -> (values input_1.grad input_2.grad ...) prev-grad[Tensor] In the backward method, describe the gradient computation for the Module using a combination of Func and Module . The arguments are fixed as (op prev-grad) , where op = module instance, prev-grad is a tensor. If you need the value of the Tensor at the input stage for the gradient calculation, temporarily store it using the module-sv4bws accessor while calling the impl method. The compiler does not depend on module-sv4bws , so you are free to choose how to store the Tensor. In Caten, since save-for-backward is automatically determined, there is no need to be concerned about in-place operations. Note that backward is optional . If it is not provided, AD will be applied based on the computational graph from impl .","title":"[method] backward (optional)"},{"location":"packages/caten.apis.module/#method-lower","text":"The lower method is automatically written by the defmodule , so there is no need to consider it when describing the module. However, it is necessary to understand how it is lowered for when defining simplifiers for the Module . lower produces the following node: (make-node :Graph (intern (symbol-name (symb 'graph/ name)) \"KEYWORD\") outputs inputs &rest attrs) Nodes whose class is :Graph are completely eliminated during lower by impl .","title":"[method] lower"},{"location":"packages/caten.apis.module/#syntax","text":"forward , backward , impl are described in one of the following format. forward := ((op &rest args) &body body) forward := (lambda (&rest args) &body body) forward := fname","title":"Syntax"},{"location":"packages/caten.apis.module/#effects","text":"it defines a class named name . it defines a function named name . it works as a constructor.","title":"Effects"},{"location":"packages/caten.apis.module/#arguments","text":"name[symbol] the name of module constructor-args[list] arguments for the constructor attrs[list] define attrs for the lowered graph based on the constructor-args variables using the following format: (:key1 value1 :key1 value2 ...) . slots[list] slots for the defined class. where[nil or string] ShapeTracker documentation[string] documentation","title":"Arguments"},{"location":"packages/caten.apis.module/#notes","text":"The methods are called in the order of forward->impl->backward during compilation impl is performed recursively, so modules must not be co-dependent within the impl method. (e.g.: do not define a module A that depends on B that depends on A ...)","title":"Notes"},{"location":"packages/caten.apis.module/#modules-built_in","text":"","title":"Modules (built_in)"},{"location":"packages/caten.apis.module/#function-sum","text":"(!sum x &key (axis t) (keepdims nil)) Compute the sum of the tensor. lisp CATEN-USER> ( proceed ( !sum ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (1 1) :id STC151141 ((4950.0)) :op # :requires-grad NIL :variables (VID149847) :tracker #}","title":"[function] !sum"},{"location":"packages/caten.apis.module/#function-mean","text":"(!mean x &key (axis t) (keepdims nil)) Compute the mean of the tensor. lisp CATEN-USER> ( proceed ( !mean ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (1 1) :id STC152868 ((49.5)) :op # :requires-grad NIL :variables (VID151244) :tracker #}","title":"[function] !mean"},{"location":"packages/caten.apis.module/#function-max","text":"(!max x &key (axis t) (keepdims nil)) Compute the maximum of the tensor. lisp CATEN-USER> ( proceed ( !max ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (1 1) :id STC154939 ((99.0)) :op # :requires-grad NIL :variables (VID152971) :tracker #}","title":"[function] !max"},{"location":"packages/caten.apis.module/#function-min","text":"(!min x &key (axis t) (keepdims nil)) Compute the minimum of the tensor. lisp CATEN-USER> ( proceed ( !min ( ax+b ` ( 10 10 ) 1 0 ))) Result Result {Tensor[float32] :shape (1 1) :id STC157028 ((0.0)) :op # :requires-grad NIL :variables (VID155042) :tracker #}","title":"[function] !min"},{"location":"packages/caten.apis.module/#function-matmul","text":"(!matmul a b) Performs matrix multiplication between two tensors a and b . lisp CATEN-USER> ( proceed ( !matmul ( rand ` ( 32 64 )) ( rand ` ( 64 128 )))) Result Result {Tensor[float32] :shape (32 128) :id STC161494 ((15.361223 15.828152 14.417058 15.882298 15.45585 ~ 13.062644 16.183292 12.734098 16.734056 15.52915) (14.835705 14.845199 16.404129 16.800152 16.33881 ~ 14.190987 15.975574 14.354835 15.514871 15.666682) (15.371529 15.849551 15.536994 15.782414 16.02211 ~ 13.076694 15.905483 13.951732 14.709311 16.353771) (17.939054 17.170252 19.312342 18.902992 19.101515 ~ 14.990455 17.7107 13.865579 17.309923 18.022947) (15.27028 14.820869 15.772669 15.838371 15.730969 ~ 13.963487 16.55142 13.431679 14.782211 15.346079) ... (14.130519 14.243071 16.591045 15.483922 15.168219 ~ 12.99691 14.225608 13.279083 14.397368 14.895684) (14.720093 15.086392 16.987844 16.13062 15.741239 ~ 14.447361 16.014956 13.712358 14.148518 15.63789) (18.153896 17.43257 17.920296 18.44725 17.450092 ~ 15.145452 18.734285 15.93504 17.834435 17.902695) (14.291782 15.027209 17.237173 16.649723 16.29012 ~ 13.699114 16.332775 15.594537 15.32779 16.408815) (13.0013075 12.744859 14.356513 13.985053 14.217204 ~ 11.638837 14.574662 12.32615 11.624662 14.840029)) :op # :requires-grad NIL :variables (STC157041) :tracker #}","title":"[function] !matmul"},{"location":"packages/caten.apis.module/#function-sinh","text":"(!sinh x) lisp CATEN-USER> ( proceed ( !sinh ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC162259 ((-0.07758549 1.7127867 0.5037463) (-0.431553 -0.93981755 -2.0931177) (0.3535945 0.108312786 -0.27719477)) :op # :requires-grad NIL :variables (STC161501) :tracker #}","title":"[function] !sinh"},{"location":"packages/caten.apis.module/#function-cosh","text":"(!cosh x) lisp CATEN-USER> ( proceed ( !cosh ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC163018 ((2.2316532 1.5844023 1.1880606) (4.6957664 1.0567473 1.0100629) (2.2520223 1.0415792 1.0012665)) :op # :requires-grad NIL :variables (STC162266) :tracker #}","title":"[function] !cosh"},{"location":"packages/caten.apis.module/#function-tanh","text":"(!tanh x) lisp CATEN-USER> ( proceed ( !tanh ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC165777 ((0.7690325 -0.59365016 0.81341624) (-0.17309564 0.36871922 -0.34405994) (0.64914274 -0.7870514 -0.19824558)) :op # :requires-grad NIL :variables (STC163025) :tracker #}","title":"[function] !tanh"},{"location":"packages/caten.apis.module/#function-cos","text":"(!cos x) lisp CATEN-USER> ( proceed ( !cos ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC166463 ((0.99518865 0.95479304 0.7415326) (0.5100029 -0.06835051 0.84506404) (0.99555033 -0.061528657 0.97121775)) :op # :requires-grad NIL :variables (STC165784) :tracker #}","title":"[function] !cos"},{"location":"packages/caten.apis.module/#function-tan","text":"(!tan x) lisp CATEN-USER> ( proceed ( !tan ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC167188 ((-1.348232 -0.47360304 -0.19166948) (-0.45079947 0.096330844 -0.0046434165) (-1.4844729 -0.74011093 -1.0703554)) :op # :requires-grad NIL :variables (STC166470) :tracker #}","title":"[function] !tan"},{"location":"packages/caten.apis.module/#function-log2","text":"(!log2 x) lisp CATEN-USER> ( proceed ( !log2 ( ax+b ` ( 3 3 ) 1 0.1 ))) Result Result {Tensor[float32] :shape (3 3) :id STC167945 ((-3.321928 0.13750356 1.0703893) (1.6322682 2.0356238 2.3504972) (2.6088092 2.827819 3.017922)) :op # :requires-grad NIL :variables (STC167196) :tracker #}","title":"[function] !log2"},{"location":"packages/caten.apis.module/#function-exp2","text":"(!exp2 x) lisp CATEN-USER> ( proceed ( !exp2 ( ax+b ` ( 3 3 ) 1 0.1 ))) Result Result {Tensor[float32] :shape (3 3) :id STC168685 ((1.0717734 2.143547 4.2870936) (8.574187 17.148375 34.29675) (68.593475 137.18695 274.37408)) :op # :requires-grad NIL :variables (STC167953) :tracker #}","title":"[function] !exp2"},{"location":"packages/caten.apis.module/#function-expt","text":"(!expt base power) Computes the power of base with power. lisp CATEN-USER> ( proceed ( !expt ( ax+b ` ( 3 3 ) 1 0.1 ) 2 )) Result Result {Tensor[float32] :shape (3 3) :id STC170701 ((0.010000001 1.21 4.4099994) (9.61 16.81 26.009998) (37.21 50.41 65.61001)) :op # :requires-grad NIL :variables (STC168988) :tracker #}","title":"[function] !expt"},{"location":"packages/caten.apis.module/#function-truncate","text":"(!truncate x) lisp CATEN-USER> ( proceed ( !truncate ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC170894 ((0.0 0.0 1.0) (0.0 0.0 0.0) (1.0 -1.0 0.0)) :op # :requires-grad NIL :variables (STC170708) :tracker #}","title":"[function] !truncate"},{"location":"packages/caten.apis.module/#function-ceiling","text":"(!ceiling x) lisp CATEN-USER> ( proceed ( !ceiling ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC171819 ((1.0 -1.0 1.0) (1.0 0.0 0.0) (0.0 0.0 1.0)) :op # :requires-grad NIL :variables (STC170901) :tracker #}","title":"[function] !ceiling"},{"location":"packages/caten.apis.module/#function-floor","text":"(!floor x) lisp CATEN-USER> ( proceed ( !floor ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC172740 ((0.0 0.0 1.0) (0.0 -1.0 -1.0) (-1.0 0.0 -2.0)) :op # :requires-grad NIL :variables (STC171826) :tracker #}","title":"[function] !floor"},{"location":"packages/caten.apis.module/#function-triu","text":"(!triu x &key (diagonal 0)) Returns the upper triangular part of the tensor (>= 2D) or batch of matrices input. lisp CATEN-USER> ( proceed ( !triu ( rand ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC174099 ((0.498627 0.5731704 0.24880171) (0.0 0.044707708 0.7780621) (0.0 0.0 0.45015103)) :op # :requires-grad NIL :variables (STC172747) :tracker #}","title":"[function] !triu"},{"location":"packages/caten.apis.module/#function-tril","text":"(!tril x &key (diagonal 0)) Returns the lower triangular part of the tensor (>= 2D) or batch of matrices input. lisp CATEN-USER> ( proceed ( !tril ( rand ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC175466 ((0.5187954 0.0 0.0) (0.6216609 0.9618312 0.0) (0.020816622 0.5980801 0.5765489)) :op # :requires-grad NIL :variables (STC174106) :tracker #}","title":"[function] !tril"},{"location":"packages/caten.apis.module/#function-argmax","text":"(!argmax x &key (axis -1) (keepdims nil)) Returns the indices of the maximum values along an axis. lisp CATEN-USER> ( proceed ( !argmax ( rand ` ( 3 3 )))) Result Result {Tensor[int64] :shape (3 1) :id STC182222 ((2) (2) (2)) :op # :requires-grad NIL :variables (VID175568) :tracker #}","title":"[function] !argmax"},{"location":"packages/caten.apis.module/#function-argmin","text":"(!argmin x &key (axis -1) (keepdims nil)) Returns the indices of the minimum values along an axis. lisp CATEN-USER> ( proceed ( !argmin ( rand ` ( 3 3 )))) Result Result {Tensor[int64] :shape (3 1) :id STC189167 ((0) (1) (2)) :op # :requires-grad NIL :variables (VID182324) :tracker #}","title":"[function] !argmin"},{"location":"packages/caten.apis.module/#function-clip","text":"(!clip x min max) !clip limits the given input within an interval. The interval is specified by the inputs 'min' and 'max'. min/max is either a number, a symbol, or a tensor. The implementation follows the ONNX specification. https://github.com/onnx/onnx/blob/main/docs/Changelog.md#clip-13 lisp CATEN-USER> ( proceed ( !clip ( randn ` ( 3 3 )) -0.3 0.3 )) Result Result {Tensor[float32] :shape (3 3) :id STC191761 ((0.3 -0.3 0.3) (0.3 -0.3 0.3) (-0.22691669 -0.3 0.3)) :op # :requires-grad NIL :variables (STC189364) :tracker #}","title":"[function] !clip"},{"location":"packages/caten.apis.module/#function-erf","text":"(!erf x) Computes the error function of x. lisp CATEN-USER> ( proceed ( !erf ( randn ` ( 3 3 )))) Result Result {Tensor[float32] :shape (3 3) :id STC203108 ((0.984875 0.81764907 -0.99786603) (-0.8090681 0.9998888 -0.67663276) (0.99501437 0.98214996 0.49436408)) :op # :requires-grad NIL :variables (STC191768) :tracker #}","title":"[function] !erf"},{"location":"packages/caten.apis.shapetracker/","text":"ShapeTracker TODO","title":"ShapeTracker"},{"location":"packages/caten.apis.shapetracker/#shapetracker","text":"TODO","title":"ShapeTracker"},{"location":"packages/caten.apis.state-dict/","text":"State-Dict [struct] State-Dict A structure representing the state dictionary of a neural network model. Entry[Hash-Table]: A hash table containing the parameters of the model, where: Key[string]: A string representing the parameter name, following a naming convention that reflects the model's architecture. Value[Tensor]: A tensor containing the parameter values associated with the key. This hash table stores all the parameters of the model, enabling easy saving, loading, and manipulation of model weights. [generic] ->state-dict (->state-dict module parents) Generates a list of cons cells containing the keys and values for creating a State-Dict from any given Module/Class . Return: A list of cons cells in the form (coms (list module_names ...) . tensor) , representing parameters and their corresponding tensors. module_names are the list of symbols representing the path to the parameter slot from the root module. TL;DR. this function traverses all slots of the Module/Class and recognizes the following objects as parameters: Tensor Module/Class List of Tensors List of Modules/Classes By default, the keys are created according to the following rules: Tensor If a slot contains a Tensor, create a cons cell with the key as (append parent (list slot_name)) and the value as the Tensor. Module If a slot contains a Module, recursively apply (->state-dict slot_value parent) to that Module with setting parent = (append parent (list slot_name)) as the new Parent. List of Tensors/Modules If a slot contains a list of Tensors or Modules, apply the above rules to each element. Keys are created by appending the slot name and the index (e.g., slot_name 0 , slot_name 1 , ...) to the Parent. To recognize other objects as parameters, please extend this method by adding methods for the desired classes. [function] get-state-dict (get-state-dict module &key (key-mapper #'pytorch-style-dict-key)) Constructs a State-Dict by recursively exploring all paramters of the given Module/Class. Module[Module/Class] The module from which to extract parameters. Key-Mapper[Function] A function that takes a list of names (the first element of the cons cell) and returns a string to be used as the key in the StateDict. Defaults to pytorch-style-dict-key . which must be #'(lambda (x) ...) Returns: State-Dict [function] load-state-dict (load-state-dict module state-dict &key (silent nil) (key-mapper #'pytorch-style-dict-key)) Loads the parameters from the given state-dict into the module, returning the given module. - silent[boolean] If set to t, suppresses warnings about unused keys, dtype mismatches, shape mismatches, and uninitialized tensors. - key-mapper[function] A function used to map the keys in the state-dict to the keys in the module. Defaults to pytorch-style-dict-key . (see: get-state-dict) Example: Transformer lisp CATEN-USER> ( progn ( ql:quickload :caten/llm ) ( get-state-dict ( caten/llm:Transformer 32 2 2 1e-5 32 ))) Result Result # (32 32) wpe.weight -> (1024 32) h.0.attn.c_attn.weight -> (96 32) h.0.attn.c_attn.bias -> (96) h.0.attn.c_proj.weight -> (32 32) h.0.attn.c_proj.bias -> (32) h.0.mlp.c_fc.weight -> (128 32) h.0.mlp.c_fc.bias -> (128) h.0.mlp.c_proj.weight -> (32 128) h.0.mlp.c_proj.bias -> (32) h.0.ln_1.affine -> (32) h.0.ln_1.bias -> (32) h.0.ln_2.affine -> (32) h.0.ln_2.bias -> (32) h.1.attn.c_attn.weight -> (96 32) h.1.attn.c_attn.bias -> (96) h.1.attn.c_proj.weight -> (32 32) h.1.attn.c_proj.bias -> (32) h.1.mlp.c_fc.weight -> (128 32) h.1.mlp.c_fc.bias -> (128) h.1.mlp.c_proj.weight -> (32 128) h.1.mlp.c_proj.bias -> (32) h.1.ln_1.affine -> (32) h.1.ln_1.bias -> (32) h.1.ln_2.affine -> (32) h.1.ln_2.bias -> (32) ln_f.affine -> (32) ln_f.bias -> (32) lm_head.weight -> (32 32) } {1003ECC7D3}>","title":"StateDict"},{"location":"packages/caten.apis.state-dict/#state-dict","text":"","title":"State-Dict"},{"location":"packages/caten.apis.state-dict/#struct-state-dict","text":"A structure representing the state dictionary of a neural network model. Entry[Hash-Table]: A hash table containing the parameters of the model, where: Key[string]: A string representing the parameter name, following a naming convention that reflects the model's architecture. Value[Tensor]: A tensor containing the parameter values associated with the key. This hash table stores all the parameters of the model, enabling easy saving, loading, and manipulation of model weights.","title":"[struct] State-Dict"},{"location":"packages/caten.apis.state-dict/#generic-state-dict","text":"(->state-dict module parents) Generates a list of cons cells containing the keys and values for creating a State-Dict from any given Module/Class . Return: A list of cons cells in the form (coms (list module_names ...) . tensor) , representing parameters and their corresponding tensors. module_names are the list of symbols representing the path to the parameter slot from the root module. TL;DR. this function traverses all slots of the Module/Class and recognizes the following objects as parameters: Tensor Module/Class List of Tensors List of Modules/Classes By default, the keys are created according to the following rules: Tensor If a slot contains a Tensor, create a cons cell with the key as (append parent (list slot_name)) and the value as the Tensor. Module If a slot contains a Module, recursively apply (->state-dict slot_value parent) to that Module with setting parent = (append parent (list slot_name)) as the new Parent. List of Tensors/Modules If a slot contains a list of Tensors or Modules, apply the above rules to each element. Keys are created by appending the slot name and the index (e.g., slot_name 0 , slot_name 1 , ...) to the Parent. To recognize other objects as parameters, please extend this method by adding methods for the desired classes.","title":"[generic] ->state-dict"},{"location":"packages/caten.apis.state-dict/#function-get-state-dict","text":"(get-state-dict module &key (key-mapper #'pytorch-style-dict-key)) Constructs a State-Dict by recursively exploring all paramters of the given Module/Class. Module[Module/Class] The module from which to extract parameters. Key-Mapper[Function] A function that takes a list of names (the first element of the cons cell) and returns a string to be used as the key in the StateDict. Defaults to pytorch-style-dict-key . which must be #'(lambda (x) ...) Returns: State-Dict","title":"[function] get-state-dict"},{"location":"packages/caten.apis.state-dict/#function-load-state-dict","text":"(load-state-dict module state-dict &key (silent nil) (key-mapper #'pytorch-style-dict-key)) Loads the parameters from the given state-dict into the module, returning the given module. - silent[boolean] If set to t, suppresses warnings about unused keys, dtype mismatches, shape mismatches, and uninitialized tensors. - key-mapper[function] A function used to map the keys in the state-dict to the keys in the module. Defaults to pytorch-style-dict-key . (see: get-state-dict)","title":"[function] load-state-dict"},{"location":"packages/caten.apis.state-dict/#example-transformer","text":"lisp CATEN-USER> ( progn ( ql:quickload :caten/llm ) ( get-state-dict ( caten/llm:Transformer 32 2 2 1e-5 32 ))) Result Result # (32 32) wpe.weight -> (1024 32) h.0.attn.c_attn.weight -> (96 32) h.0.attn.c_attn.bias -> (96) h.0.attn.c_proj.weight -> (32 32) h.0.attn.c_proj.bias -> (32) h.0.mlp.c_fc.weight -> (128 32) h.0.mlp.c_fc.bias -> (128) h.0.mlp.c_proj.weight -> (32 128) h.0.mlp.c_proj.bias -> (32) h.0.ln_1.affine -> (32) h.0.ln_1.bias -> (32) h.0.ln_2.affine -> (32) h.0.ln_2.bias -> (32) h.1.attn.c_attn.weight -> (96 32) h.1.attn.c_attn.bias -> (96) h.1.attn.c_proj.weight -> (32 32) h.1.attn.c_proj.bias -> (32) h.1.mlp.c_fc.weight -> (128 32) h.1.mlp.c_fc.bias -> (128) h.1.mlp.c_proj.weight -> (32 128) h.1.mlp.c_proj.bias -> (32) h.1.ln_1.affine -> (32) h.1.ln_1.bias -> (32) h.1.ln_2.affine -> (32) h.1.ln_2.bias -> (32) ln_f.affine -> (32) ln_f.bias -> (32) lm_head.weight -> (32 32) } {1003ECC7D3}>","title":"Example: Transformer"},{"location":"packages/caten.apis.tensor/","text":"Tensor [struct] Tensor A struct tensor is a multi-dimensional, and strided matrix (and nrank=0 to scalar value) containing elements of the single dtype. Also the tensor has following slots: shape[list] A list of number, symbol, or Tensor . buffer[Buffer] A buffer of the tensor. Realized arrays are stored here. dtype[keyword] A dtype of the tensor. order[order] A memory layout of the tensor, selected from either of :row or :column. id[symbol] A unique identifier of the tensor. (usually created by gensym) op[Func] Func object that represents the operation of the tensor. views[list] A list of ViewRange objects, determining the bound of loops. requires-grad[boolean] A flag to determine whether the tensor requires a gradient. grad[Tensor] A gradient and realized tensor of the tensor. grad-id[symbol] A unique identifier of the gradient tensor. variables[list] A list of Tensor objects that are used in the operation of the tensor. Tensors listed here are involved in the compilation. [function] make-tensor (make-tensor shape &key (dtype *default-float*) (order *default-order*) (id (gensym \"TID\")) (requires-grad nil) (initial-element nil) (views nil) (from nil)) Create a new lazy tensor. shape[list] A list of number, symbol, or Tensor . dtype[keyword] A dtype of the tensor. order[order] A memory layout of the tensor, selected from either of :row or :column. id[symbol] A unique identifier of the tensor. (usually created by gensym) requires-grad[boolean] A flag to determine whether the tensor requires a gradient. initial-element[null|number|symbol|ScalarTensor] An initial value of the tensor. views[list] A list of ViewRange objects, determining the bound of loops. from[null|Buffer|Symbol] A buffer used to initialize the tensor. If symbol is given, the buffer is taken from the variable table. [function] make-scalar (make-scalar value &key (dtype *default-float*) (order *default-order*) (id (gensym \"SID\")) (requires-grad nil)) Create a new scalar tensor. ScalarTensor in Caten is a tensor with a rank of 0. value[number|symbol] A value of the scalar tensor. dtype[keyword] A dtype of the tensor. order[order] A memory layout of the tensor, selected from either of :row or :column. id[symbol] A unique identifier of the tensor. (usually created by gensym) requires-grad[boolean] A flag to determine whether the tensor requires a gradient. Tips: You can use fconst , uconst , and iconst to create a scalar tensor with a default dtype. (arguments are the same as make-scalar .) [function] grad (grad tensor) Returns a gradient of the tensor [function] shape (shape tensor) Returns a copy of the shape of the tensor [function] ndim (ndim tensor) Returns a rank of the tensor [function] dtype-of (dtype-of tensor) Returns a dtype of the tensor [function] order (order tensor) Returns a memory-layout of the tensor. [function] caten ( caten tensors &key ( jit ( = 1 ( ctx:getenv :JIT ))) ( name :main ) ( simplifiers *external-simplifiers* )) Compiles the given tensors, returning an AVM struct. tensor[Tensor|List] toplevel tensors. jit[boolean] If set to 0, caten only applies the graph-level compilation. If set to 1, caten calls %jit to generate the fast kernel supported by caten/codegen . This parameter should be specified using the (ctx:with-contextvar) macro. name[keyword] the name of compiled avm. rewriters[list] a list of graph rewriters called each time the compiler will lower the module. simplifiers[list] a list of external simplifiers used in the graph-level compilation. (defined by defsimplifier) Pass the function name. [function] tensor-graph (tensor-graph tensor) Lowers the given tensors into an aasm graph, only the constant folding is applied. For some convenience, this function returns the first tensor if the input is not a tensor. [function] tensor-lowered-graph (tensor-lowered-graph &rest tensors) Creates a lowered graph from the given tensors. (= an input grpah to JIT=1) ### [method] forward (forward AVM &rest params) Compute the forward pass of the compiled computational graph (AVM). The params are additional parameters that are used to initialize the AVM variable table, passed in the following format: - `(symbol . number)` Loaded as a `*default-int*` scalar tensor, used to determine the shape of dynamic shaped tensors. - `(symbol . buffer)` or `(symbol . tensor)` Used to assign the initial elements of the tensor specified by the name in `form` in `(make-tensor ... :from x)` Here's an example. #### Examples ```lisp title=\"Example of forward with dynamic shaped\" (let ((model (caten (!randn `(a b))))) (forward model `(a . 10) `(b . 10))) Result Result {Tensor[float32] :shape (A B) :id STC98223 ((-0.61595285 -0.031014156 2.2868037 -0.8684415 -0.56618977 -0.7328551 -0.2807455 2.3028476 0.79900587 1.8557153) (-1.7908674 -0.46563956 -0.20578465 -0.35169426 -1.4918212 0.11561119 0.30114642 1.7040857 -2.916635 -1.5369263) (0.9987553 0.45711854 1.0495216 -1.0601267 -0.54998857 0.22259901 0.15503313 -0.9351286 2.9082751 -0.12879859) (0.49433577 0.56157786 0.4193139 0.72080606 0.19486764 0.35958967 0.46794757 -0.8435187 0.09039084 -0.88607216) (0.239293 -0.3790358 0.439364 -0.10819176 -0.07170133 -2.9237595 -0.10269515 -0.3924638 -1.0511456 -0.93730205) (-0.2857653 0.6431336 0.47663587 1.4265004 0.37391514 -0.8274696 -0.39017528 0.31971508 0.06125845 0.13821441) (0.9454282 -1.7564898 0.09252599 0.7165159 0.775417 -0.00204291 0.99140173 0.1647516 0.77457863 0.6881668) (0.5833384 -1.8209114 0.37789294 0.030298902 -0.2962195 2.3168423 0.72732586 -0.20335685 0.8696461 0.38297242) (1.1594744 -0.035024546 1.1469852 0.55789834 2.0441046 -1.1500646 -0.091196336 0.8264797 0.7891973 0.112303734) (-1.1058131 0.29724032 0.5923294 -0.1579682 0.18783261 0.33747807 -0.026869688 -0.4813399 0.6008693 -0.69397247)) :op # :requires-grad NIL :variables (STC76067) :tracker #} [method] backward (forward AVM &optional prev-dout) Compute the backward pass of the compiled computational graph (AVM). Note that the prev-dout is ignored. Forward pass must be computed first. Gradients are automatically reset to zero before the forward pass. [function] proceed (proceed &rest tensors) Compiles the given tensors, returning an evaluated tensors. [macro] with-no-grad (with-no-grad &body body) Under the scope of with-no-grad , the gradient computation is disabled. This parameter should be set during inference, and the compiler will generate a more efficient code. [variable] inference-mode When *inference-mode* is set to T, it explicitly indicates that the code is being executed in inference mode. Additionally, it has the following effects: Static Random Generation (e.g., rand) with :requires-grad=T does nothing (it is expected to load a parameter from the state dictionary). The behavior of certain NN operations, such as BatchNorm and Dropout, changes. [macro] with-inference-mode (with-inference-mode (() &body body)) Sets *inference-mode*=T and *no-grad*=T within the scope of the body. Examples Tensor Creation lisp CATEN-USER> ( make-tensor ` ( 30 30 )) Result Result {Tensor[float32] :shape (30 30) :id TID98224 :buffer nil :op # :requires-grad NIL :variables NIL :tracker #} Realize lisp CATEN-USER> ( proceed ( make-tensor ` ( 30 30 ))) Result Result {Tensor[float32] :shape (30 30) :id STC98288 ((0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) ... (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)) :op # :requires-grad NIL :variables (TID98228) :tracker #} Creating a computational graph (Lazy) lisp CATEN-USER> ( !add ( ax+b ` ( 3 3 ) 0 1 ) ( ax+b ` ( 3 3 ) 0 1 )) Result Result {Tensor[float32] :shape (3 3) :id STC98303 :buffer nil :op # :requires-grad NIL :variables (STC98295 STC98302) :tracker #} Evaluating a computational graph. lisp CATEN-USER> ( proceed ( !add ( ax+b ` ( 3 3 ) 0 1 ) ( ax+b ` ( 3 3 ) 0 1 ))) Result Result {Tensor[float32] :shape (3 3) :id STC98469 ((2.0 2.0 2.0) (2.0 2.0 2.0) (2.0 2.0 2.0)) :op # :requires-grad NIL :variables (STC98318) :tracker #} Floating Features [function] inf (inf &key (dtype *default-float*)) Returns a positive infinity of the dtype for the current Common Lisp implementation. This feature is supported by float-features lisp CATEN-USER> ( inf ) Result Result #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY lisp CATEN-USER> ( proceed ( !full ` ( 3 3 ) ( inf ))) Result Result {Tensor[float32] :shape (3 3) :id STC98532 ((#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY)) :op # :requires-grad NIL :variables (TID98470) :tracker #} [function] -inf (-inf &key (dtype *default-float*)) Returns a negative infinity of the dtype for the current Common Lisp implementation. This feature is supported by float-features lisp CATEN-USER> ( -inf ) Result Result #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY lisp CATEN-USER> ( proceed ( !full ` ( 3 3 ) ( -inf ))) Result Result {Tensor[float32] :shape (3 3) :id STC98595 ((#.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY)) :op # :requires-grad NIL :variables (TID98533) :tracker #} [function] nan (nan &key (dtype *default-float*)) Returns a NaN of the dtype for the current Common Lisp implementation. This feature is supported by float-features lisp CATEN-USER> ( nan ) Result Result # lisp CATEN-USER> ( proceed ( !full ` ( 3 3 ) ( nan ))) Result Result {Tensor[float32] :shape (3 3) :id STC98658 ((# # #) (# # #) (# # #)) :op # :requires-grad NIL :variables (TID98596) :tracker #} [function] float-type-of (float-type-of x) Returns :INF if the number is a negative infinity, :-INF if the number is a negative infinity, :nan if the number is NaN, or T otherwise.","title":"Tensor"},{"location":"packages/caten.apis.tensor/#tensor","text":"","title":"Tensor"},{"location":"packages/caten.apis.tensor/#struct-tensor","text":"A struct tensor is a multi-dimensional, and strided matrix (and nrank=0 to scalar value) containing elements of the single dtype. Also the tensor has following slots: shape[list] A list of number, symbol, or Tensor . buffer[Buffer] A buffer of the tensor. Realized arrays are stored here. dtype[keyword] A dtype of the tensor. order[order] A memory layout of the tensor, selected from either of :row or :column. id[symbol] A unique identifier of the tensor. (usually created by gensym) op[Func] Func object that represents the operation of the tensor. views[list] A list of ViewRange objects, determining the bound of loops. requires-grad[boolean] A flag to determine whether the tensor requires a gradient. grad[Tensor] A gradient and realized tensor of the tensor. grad-id[symbol] A unique identifier of the gradient tensor. variables[list] A list of Tensor objects that are used in the operation of the tensor. Tensors listed here are involved in the compilation.","title":"[struct] Tensor"},{"location":"packages/caten.apis.tensor/#function-make-tensor","text":"(make-tensor shape &key (dtype *default-float*) (order *default-order*) (id (gensym \"TID\")) (requires-grad nil) (initial-element nil) (views nil) (from nil)) Create a new lazy tensor. shape[list] A list of number, symbol, or Tensor . dtype[keyword] A dtype of the tensor. order[order] A memory layout of the tensor, selected from either of :row or :column. id[symbol] A unique identifier of the tensor. (usually created by gensym) requires-grad[boolean] A flag to determine whether the tensor requires a gradient. initial-element[null|number|symbol|ScalarTensor] An initial value of the tensor. views[list] A list of ViewRange objects, determining the bound of loops. from[null|Buffer|Symbol] A buffer used to initialize the tensor. If symbol is given, the buffer is taken from the variable table.","title":"[function] make-tensor"},{"location":"packages/caten.apis.tensor/#function-make-scalar","text":"(make-scalar value &key (dtype *default-float*) (order *default-order*) (id (gensym \"SID\")) (requires-grad nil)) Create a new scalar tensor. ScalarTensor in Caten is a tensor with a rank of 0. value[number|symbol] A value of the scalar tensor. dtype[keyword] A dtype of the tensor. order[order] A memory layout of the tensor, selected from either of :row or :column. id[symbol] A unique identifier of the tensor. (usually created by gensym) requires-grad[boolean] A flag to determine whether the tensor requires a gradient. Tips: You can use fconst , uconst , and iconst to create a scalar tensor with a default dtype. (arguments are the same as make-scalar .)","title":"[function] make-scalar"},{"location":"packages/caten.apis.tensor/#function-grad","text":"(grad tensor) Returns a gradient of the tensor","title":"[function] grad"},{"location":"packages/caten.apis.tensor/#function-shape","text":"(shape tensor) Returns a copy of the shape of the tensor","title":"[function] shape"},{"location":"packages/caten.apis.tensor/#function-ndim","text":"(ndim tensor) Returns a rank of the tensor","title":"[function] ndim"},{"location":"packages/caten.apis.tensor/#function-dtype-of","text":"(dtype-of tensor) Returns a dtype of the tensor","title":"[function] dtype-of"},{"location":"packages/caten.apis.tensor/#function-order","text":"(order tensor) Returns a memory-layout of the tensor.","title":"[function] order"},{"location":"packages/caten.apis.tensor/#function-caten","text":"( caten tensors &key ( jit ( = 1 ( ctx:getenv :JIT ))) ( name :main ) ( simplifiers *external-simplifiers* )) Compiles the given tensors, returning an AVM struct. tensor[Tensor|List] toplevel tensors. jit[boolean] If set to 0, caten only applies the graph-level compilation. If set to 1, caten calls %jit to generate the fast kernel supported by caten/codegen . This parameter should be specified using the (ctx:with-contextvar) macro. name[keyword] the name of compiled avm. rewriters[list] a list of graph rewriters called each time the compiler will lower the module. simplifiers[list] a list of external simplifiers used in the graph-level compilation. (defined by defsimplifier) Pass the function name.","title":"[function] caten"},{"location":"packages/caten.apis.tensor/#function-tensor-graph","text":"(tensor-graph tensor) Lowers the given tensors into an aasm graph, only the constant folding is applied. For some convenience, this function returns the first tensor if the input is not a tensor.","title":"[function] tensor-graph"},{"location":"packages/caten.apis.tensor/#function-tensor-lowered-graph","text":"(tensor-lowered-graph &rest tensors) Creates a lowered graph from the given tensors. (= an input grpah to JIT=1) ### [method] forward (forward AVM &rest params) Compute the forward pass of the compiled computational graph (AVM). The params are additional parameters that are used to initialize the AVM variable table, passed in the following format: - `(symbol . number)` Loaded as a `*default-int*` scalar tensor, used to determine the shape of dynamic shaped tensors. - `(symbol . buffer)` or `(symbol . tensor)` Used to assign the initial elements of the tensor specified by the name in `form` in `(make-tensor ... :from x)` Here's an example. #### Examples ```lisp title=\"Example of forward with dynamic shaped\" (let ((model (caten (!randn `(a b))))) (forward model `(a . 10) `(b . 10))) Result Result {Tensor[float32] :shape (A B) :id STC98223 ((-0.61595285 -0.031014156 2.2868037 -0.8684415 -0.56618977 -0.7328551 -0.2807455 2.3028476 0.79900587 1.8557153) (-1.7908674 -0.46563956 -0.20578465 -0.35169426 -1.4918212 0.11561119 0.30114642 1.7040857 -2.916635 -1.5369263) (0.9987553 0.45711854 1.0495216 -1.0601267 -0.54998857 0.22259901 0.15503313 -0.9351286 2.9082751 -0.12879859) (0.49433577 0.56157786 0.4193139 0.72080606 0.19486764 0.35958967 0.46794757 -0.8435187 0.09039084 -0.88607216) (0.239293 -0.3790358 0.439364 -0.10819176 -0.07170133 -2.9237595 -0.10269515 -0.3924638 -1.0511456 -0.93730205) (-0.2857653 0.6431336 0.47663587 1.4265004 0.37391514 -0.8274696 -0.39017528 0.31971508 0.06125845 0.13821441) (0.9454282 -1.7564898 0.09252599 0.7165159 0.775417 -0.00204291 0.99140173 0.1647516 0.77457863 0.6881668) (0.5833384 -1.8209114 0.37789294 0.030298902 -0.2962195 2.3168423 0.72732586 -0.20335685 0.8696461 0.38297242) (1.1594744 -0.035024546 1.1469852 0.55789834 2.0441046 -1.1500646 -0.091196336 0.8264797 0.7891973 0.112303734) (-1.1058131 0.29724032 0.5923294 -0.1579682 0.18783261 0.33747807 -0.026869688 -0.4813399 0.6008693 -0.69397247)) :op # :requires-grad NIL :variables (STC76067) :tracker #}","title":"[function] tensor-lowered-graph"},{"location":"packages/caten.apis.tensor/#method-backward","text":"(forward AVM &optional prev-dout) Compute the backward pass of the compiled computational graph (AVM). Note that the prev-dout is ignored. Forward pass must be computed first. Gradients are automatically reset to zero before the forward pass.","title":"[method] backward"},{"location":"packages/caten.apis.tensor/#function-proceed","text":"(proceed &rest tensors) Compiles the given tensors, returning an evaluated tensors.","title":"[function] proceed"},{"location":"packages/caten.apis.tensor/#macro-with-no-grad","text":"(with-no-grad &body body) Under the scope of with-no-grad , the gradient computation is disabled. This parameter should be set during inference, and the compiler will generate a more efficient code.","title":"[macro] with-no-grad"},{"location":"packages/caten.apis.tensor/#variable-inference-mode","text":"When *inference-mode* is set to T, it explicitly indicates that the code is being executed in inference mode. Additionally, it has the following effects: Static Random Generation (e.g., rand) with :requires-grad=T does nothing (it is expected to load a parameter from the state dictionary). The behavior of certain NN operations, such as BatchNorm and Dropout, changes.","title":"[variable] inference-mode"},{"location":"packages/caten.apis.tensor/#macro-with-inference-mode","text":"(with-inference-mode (() &body body)) Sets *inference-mode*=T and *no-grad*=T within the scope of the body.","title":"[macro] with-inference-mode"},{"location":"packages/caten.apis.tensor/#examples","text":"","title":"Examples"},{"location":"packages/caten.apis.tensor/#tensor-creation","text":"lisp CATEN-USER> ( make-tensor ` ( 30 30 )) Result Result {Tensor[float32] :shape (30 30) :id TID98224 :buffer nil :op # :requires-grad NIL :variables NIL :tracker #}","title":"Tensor Creation"},{"location":"packages/caten.apis.tensor/#realize","text":"lisp CATEN-USER> ( proceed ( make-tensor ` ( 30 30 ))) Result Result {Tensor[float32] :shape (30 30) :id STC98288 ((0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) ... (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0) (0.0 0.0 0.0 0.0 0.0 ~ 0.0 0.0 0.0 0.0 0.0)) :op # :requires-grad NIL :variables (TID98228) :tracker #}","title":"Realize"},{"location":"packages/caten.apis.tensor/#creating-a-computational-graph-lazy","text":"lisp CATEN-USER> ( !add ( ax+b ` ( 3 3 ) 0 1 ) ( ax+b ` ( 3 3 ) 0 1 )) Result Result {Tensor[float32] :shape (3 3) :id STC98303 :buffer nil :op # :requires-grad NIL :variables (STC98295 STC98302) :tracker #}","title":"Creating a computational graph (Lazy)"},{"location":"packages/caten.apis.tensor/#evaluating-a-computational-graph","text":"lisp CATEN-USER> ( proceed ( !add ( ax+b ` ( 3 3 ) 0 1 ) ( ax+b ` ( 3 3 ) 0 1 ))) Result Result {Tensor[float32] :shape (3 3) :id STC98469 ((2.0 2.0 2.0) (2.0 2.0 2.0) (2.0 2.0 2.0)) :op # :requires-grad NIL :variables (STC98318) :tracker #}","title":"Evaluating a computational graph."},{"location":"packages/caten.apis.tensor/#floating-features","text":"","title":"Floating Features"},{"location":"packages/caten.apis.tensor/#function-inf","text":"(inf &key (dtype *default-float*)) Returns a positive infinity of the dtype for the current Common Lisp implementation. This feature is supported by float-features lisp CATEN-USER> ( inf ) Result Result #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY lisp CATEN-USER> ( proceed ( !full ` ( 3 3 ) ( inf ))) Result Result {Tensor[float32] :shape (3 3) :id STC98532 ((#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-POSITIVE-INFINITY)) :op # :requires-grad NIL :variables (TID98470) :tracker #}","title":"[function] inf"},{"location":"packages/caten.apis.tensor/#function-inf_1","text":"(-inf &key (dtype *default-float*)) Returns a negative infinity of the dtype for the current Common Lisp implementation. This feature is supported by float-features lisp CATEN-USER> ( -inf ) Result Result #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY lisp CATEN-USER> ( proceed ( !full ` ( 3 3 ) ( -inf ))) Result Result {Tensor[float32] :shape (3 3) :id STC98595 ((#.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY) (#.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY #.SB-EXT:SINGLE-FLOAT-NEGATIVE-INFINITY)) :op # :requires-grad NIL :variables (TID98533) :tracker #}","title":"[function] -inf"},{"location":"packages/caten.apis.tensor/#function-nan","text":"(nan &key (dtype *default-float*)) Returns a NaN of the dtype for the current Common Lisp implementation. This feature is supported by float-features lisp CATEN-USER> ( nan ) Result Result # lisp CATEN-USER> ( proceed ( !full ` ( 3 3 ) ( nan ))) Result Result {Tensor[float32] :shape (3 3) :id STC98658 ((# # #) (# # #) (# # #)) :op # :requires-grad NIL :variables (TID98596) :tracker #}","title":"[function] nan"},{"location":"packages/caten.apis.tensor/#function-float-type-of","text":"(float-type-of x) Returns :INF if the number is a negative infinity, :-INF if the number is a negative infinity, :nan if the number is NaN, or T otherwise.","title":"[function] float-type-of"},{"location":"packages/caten.apps.gpt2/","text":"caten/apps.gpt2 Implements GPT2 text generation from the pre-trained model. [function] make-gpt2 (make-gpt2 model-type &key max-seq-len) Creates a compiled GPT2 model from the pre-trained gguf file. The model-type must be one of :gpt2 , :gpt2-medium , :gpt2-large , or :gpt2-xl . The max-seq-len is the maximum sequence length for the model. Since GPT2 is a heavy model, you should consider JIT=1 as a prerequisite. The pretrained model is downloaded from the following HuggingFace repository: ( defun url ( model-type ) ( format nil \"https://huggingface.co/hikettei/gpt2-gguf/resolve/main/~(~a~)-f32.gguf?download=true\" model-type )) [function] gpt2-inference (gpt2-inference model input) Takes a compiled GPT2 model and a string input, and generates a text output.","title":"caten/apps.gpt2"},{"location":"packages/caten.apps.gpt2/#catenappsgpt2","text":"Implements GPT2 text generation from the pre-trained model.","title":"caten/apps.gpt2"},{"location":"packages/caten.apps.gpt2/#function-make-gpt2","text":"(make-gpt2 model-type &key max-seq-len) Creates a compiled GPT2 model from the pre-trained gguf file. The model-type must be one of :gpt2 , :gpt2-medium , :gpt2-large , or :gpt2-xl . The max-seq-len is the maximum sequence length for the model. Since GPT2 is a heavy model, you should consider JIT=1 as a prerequisite. The pretrained model is downloaded from the following HuggingFace repository: ( defun url ( model-type ) ( format nil \"https://huggingface.co/hikettei/gpt2-gguf/resolve/main/~(~a~)-f32.gguf?download=true\" model-type ))","title":"[function] make-gpt2"},{"location":"packages/caten.apps.gpt2/#function-gpt2-inference","text":"(gpt2-inference model input) Takes a compiled GPT2 model and a string input, and generates a text output.","title":"[function] gpt2-inference"},{"location":"packages/caten.apps/","text":"Overview Do you want to integrate Caten's features into your Common Lisp application? With caten/apps , you can effortlessly experiment with inference using ready-made implementations of various deep learning models. caten/apps is a suite of packages that implement pipelines to smoothly perform the following tasks: Automatically download pretrained weights Automatically optimize and compile models for inference For instance, text generation inference provided by caten/apps.gpt2 can be accomplished in just two lines: ( defparameter *model* ( make-gpt2 :gpt2 )) ( gpt2-generate *model* \"What is the answer to life, and the world?\" ) If you're considering contributing to Caten, we warmly welcome PRs that add new models to caten/apps . If you've implemented a model using Caten, please consider adding it to caten/apps ! (Feel free to create a PR!)","title":"Overview"},{"location":"packages/caten.apps/#overview","text":"Do you want to integrate Caten's features into your Common Lisp application? With caten/apps , you can effortlessly experiment with inference using ready-made implementations of various deep learning models. caten/apps is a suite of packages that implement pipelines to smoothly perform the following tasks: Automatically download pretrained weights Automatically optimize and compile models for inference For instance, text generation inference provided by caten/apps.gpt2 can be accomplished in just two lines: ( defparameter *model* ( make-gpt2 :gpt2 )) ( gpt2-generate *model* \"What is the answer to life, and the world?\" ) If you're considering contributing to Caten, we warmly welcome PRs that add new models to caten/apps . If you've implemented a model using Caten, please consider adding it to caten/apps ! (Feel free to create a PR!)","title":"Overview"},{"location":"packages/caten.avm/","text":"caten/avm TODO","title":"caten/avm"},{"location":"packages/caten.avm/#catenavm","text":"TODO","title":"caten/avm"},{"location":"packages/caten.codegen/","text":"caten/codegen Hackable Kernel Generator The caten/codegen is a system designed to JIT-compile AASM Graphs into other languages, such as C or Metal, to achieve faster computation. This package has the following two objectives: Device Independence : It imposes no constraints on the execution device, allowing users to extend it with only 200\u2013300 lines of code. Performance : It ensures computations run fairly fast on each target device. Caten's code generation is divided into two stages based on the intended purpose: caten/codegen Generates unoptimized code (e.g., Scheduler, Lowerer, op fusion, etc.). caten/polyhedral (Optional) Performs advanced kernel loop transformations and optimizations on the scheduled code, such as Auto Scheduler. This document focuses on the first stage, caten/codegen , and provides related information and some examples. How to enable JIT To enable JIT, you need to set the JIT contextvar before running the function caten . The following code snippet demonstrates how to enable JIT on repl: ;; Option1: Set globally (setf (ctx:getenv :JIT) 1) ;; Option2: Set locally (ctx:with-contextvar (:JIT 1) ;; ... ) After setting the contextvar, you can run the function caten to JIT-compile the AASM Graphs, e.g.: (caten (!rand `(3 3))) How to debug JIT You can set the JIT_DEBUG contextvar to debug the JIT compilation process. The debugging level ranges from 0 to 4, as shown below: JIT_DEBUG | Effect ----------|------- 0 | None 1 | Nothing for now 2 | Displays blueprint and scop 3 | Displays schedule-graph 4 | Displays memory-planner and the compiled code How to learn JIT You can start by reading ./caten/docs/getting-started.lisp to understand the basic concepts of caten/codegen through a running example. Additionally, the following sections provide documentation and explanations for each JIT mechanism, along with simple executable examples. Shape Inference The JIT compilation starts by inferring the shape of each points in the graph. caten/codegen/shape-inference is responsible for this task. [function] run-type-infer (run-type-infer avm) Run the shape inference to the given AVM, returning Type-Reporter [struct] Inferred-Type A structure Inferred-Type contains the shape/stride/view information of the node at each points. Also, it containts the iteration space information that is used to render the kernel. If the shape inference is successfully done and properly deployed to the target graph by the rewriting-rule , the function (read-type-relay node) will return the Inferred-Type structure. relay-reads returns the list of the buffer corresponding to the node-reads. (If node-reads a number, nil is set) relay-writes returns the list of the buffer corresponding to the node-writes. relay-read-iters returns the list of the iteration space corresponding to the node-reads. relay-write-iters returns the list of the iteration space corresponding to the node-writes. [struct] Iteration-Space Iteration-Space is a structure that contains the shape/stride/view information of the each buffer. It is used to render the kernel. iteration-space-shape to get the shape, iteration-space-strides to get the stride, iteration-space-views to get the offsets and increments, and iteration-space-procedure to get how the iteraton-space is collapsed or permuted. Each elements for shape/stride are Expr. iteration-space-expr-aref to render the aref. (iteration-space-expr-aref iteration-space buffer gids) gids corresponds for the loop idx in the kernel. Rewriting Rules TODO: apply-rewriting-rules doc Optimization for gemm TODO: WMMA Rewriting Docs Scheduler The caten/codegen/scheduler is responsible for assigining the execution order of the computation graph in the aasm. Occasionally they merges or rewrites a view in order to schedule multiple nodes into the same schedule if possible. The function graph-schedule is an entry point, and takes a shape-inferred aasm graph as input, performs scheduling, and returns a schedule graph (called Schedule-Graph) whose each node is composed of Schedule-Item. One Schedule-Item corresponds to one kernel in GPU, graph-schedule must ensure that each item is merged only within the bounds that can be lowered into a single kernel.## Schedule-Item :SCHEDULE-ITEM Schedule-Item is an intermidate object to represent a one kernel in GPU. f = cache_name or name write_ids = f(*[storage_id_dst], *[dynamic_shape], *[inputs]) ^ can be modified by the memory-planner It has a unique name , and cache-name . If cache-name was assigned, the compiler will fail to compile this schedule-item and reuse the kernel named cache-name instead. In order to lowering the computation graph as the foreign language, items must be consisted of JITAble operations (except for special irs and :allocate). If it qualifies, jitable is set to T. Otherwise, the scheduled items are relocated to the compiled avm directly. Specifially, if the item was :ALLOCATE, :allocated-p is set to T. blueprint[list] is a lowered schedule-item polyhedral[list] is a Polyhedral IR obtained by lowering blueprint auto-schedule-p[list] is set to T if it is worth to run auto-scheduler. (If there is a symbolic incremental, loop is not an affine and cannot run isl scheduler) items[list] are the scheduled items items-to-cache[list] are the copies of items but having the unique read/write. It is used to determine the equivalence of the two schedule-items. rank[fixnum] is the highest rank of the iteration space. storage-id-src[list] is the list of the storage-id of the source buffer (optimized by running memory-planner) storage-id-dst[list] is the list of the storage-id of the destination buffer (optimized by running memory-planner) namespace[list] a list of symbols appeared in the items. It is used to map a new blueprint from the cached blueprint. When optimizing schedule-item in-place, the 0th read is consumed. [function] graph-schedule (graph-schedule graph) Creates a schedule-graph(FastGraph) from the given graph . Example (Scheduler + Shape Inference + Rewriting Rules) This code snippet demonstrates how to create a schedule-graph from AASM Graph. AASM Graph is obtained by running caten with JIT=0. lisp CATEN-USER> ( ctx:with-contextvar ( :JIT 0 ) ( pprint-graph ( avm-graph ( caten ( !relu ( !matmul ( make-tensor ` ( 3 3 )) ( make-tensor ` ( 3 3 )))))))) Result Result ============================================================================================================================================ === [P: 0] ================================================================================================================================= [P=0, ID=0]: :PAUSE/BACKWARD {0} \u2514 :MAX {N1} \u2514 :VIEW {N2} \u2502 \u251c load(0.0) \u2502 \u2514 Allocate[:float32] NIL \u251c :VIEW {N3} \u251c :MOVE {N4} \u251c Allocate[:float32] (3 3 1) \u2514 :VIEW {N5} \u251c :ADD {N6} \u251c :VIEW {N7} \u2502 \u251c load(0.0) \u2502 \u2514 Allocate[:float32] (3 3 1) \u2514 :MUL {N8} \u2514 :VIEW {N9} \u2502 \u251c Allocate[:float32] (3 3) \u251c :MOVE {N10} \u251c Allocate[:float32] (3 3 3) \u2514 :VIEW {N11} \u251c Allocate[:float32] (3 3) ============================================================================================================================================ Example ( let* (( graph ( ctx:with-contextvar ( :JIT 0 ) ( avm-graph ( caten ( !relu ( !matmul ( make-tensor ` ( 3 3 )) ( make-tensor ` ( 3 3 )))))))) ( vm ( make-avm graph :example nil ( graph-outputs graph ) nil ))) ( run-type-infer vm ) ;; Running the shape inference ( apply-rewriting-rules vm ) ;; Running the rewriting rules ;; graph-schedule to finally run the scheduler ( pprint-graph ( graph-schedule ( avm-graph vm )))) Result Result ============================================================================================================================================ === [P: 0] ================================================================================================================================= [P=0, ID=0]: :PAUSE/BACKWARD {N1} \u2514 [KERNEL] FUSED_RELU_SUMNODE_MATMUL18987 \u251c Allocate[:float32] (3 3 3) \u251c Allocate[:float32] (3 3) \u251c Allocate[:float32] (3 3) \u251c Allocate[:float32] (3 3 1) \u251c Allocate[:float32] (3 3 1) \u2514 Allocate[:float32] NIL ============================================================================================================================================ Lowerer (Blueprint) The package caten/codegen/blueprint is responsible for lowering the schedule-item into a blueprint. A blueprint is an IR that represents a computation graph with explicit loop bounds. The lower-schedule-item method infers loop boundaries based on Schedule-item and performs lowering into a format that includes :FOR/:ENDFOR nodes.### [function] LOWER-SCHEDULE-ITEM (lower-schedule-item node base-graph scheduled-graph) Lowers the Schedule-Item into blueprint. node is a schedule-item to lower which belongs to scheduled-graph. base-graph is the graph you passed to the graph-schedule. scheduled-graph is the scheduled graph obtained by graph-schedule. Example (Scheduler + Lowerer) This code snippet demonstrates how to lower the schedule-graph into a blueprint created in the previous section. Example ( let* (( graph ( ctx:with-contextvar ( :JIT 0 ) ( avm-graph ( caten ( !relu ( !matmul ( make-tensor ` ( 3 3 )) ( make-tensor ` ( 3 3 )))))))) ( vm ( make-avm graph :example nil ( graph-outputs graph ) nil ))) ( run-type-infer vm ) ;; Running the shape inference ( apply-rewriting-rules vm ) ;; Running the rewriting rules ;; graph-schedule to finally run the scheduler ( let (( schedule-graph ( graph-schedule ( avm-graph vm )))) ( caten/codegen/expr-cache::with-expr-cache () ;; need this to forcibly keep the loop affine. ( let (( bp ( lower-schedule-item ( nth 1 ( graph-nodes schedule-graph )) graph schedule-graph ))) ( print-blueprint bp nil ))))) Result Result { } Renderer TODO (Docs for Renderer/rendre-expr/extensible polyhedral compiler/etc) Here is a list of nodes used to render the kernel. Render IRs :DEFINE-GLOBAL The node :DEFINE-GLOBAL declares a global variable in the kernel. (it corresponds to the argument of the kernel.) When optimizing define-global in-place, the 0th read is consumed. :AREF :AREF corresponds to the following code: ID[*space] storage-id[symbol] An index to the reference pointer optimized by the memory-planner. buffer[Buffer] The buffer to be accessed. space[Iteration-Space] The iteration space :AREF belongs to. When optimizing aref in-place, the 0th read is consumed. :ENDIF } // endif When optimizing endif in-place, the 0th read is consumed. :IF if(condition) When optimizing if in-place, the 0th read is consumed. :ENDFOR } // idx When optimizing endfor in-place, the 0th read is consumed. :FOR for(int idx=upfrom, below, by) scope[keyword] If :global , the loop is parallelizable. One of :global, :local. When optimizing for in-place, the 0th read is consumed. EXPR TODO Memory Planner TODO: Docs (MIP Solver) Scop Analyzer TODO: Docs for scop","title":"caten/codegen"},{"location":"packages/caten.codegen/#catencodegen","text":"","title":"caten/codegen"},{"location":"packages/caten.codegen/#hackable-kernel-generator","text":"The caten/codegen is a system designed to JIT-compile AASM Graphs into other languages, such as C or Metal, to achieve faster computation. This package has the following two objectives: Device Independence : It imposes no constraints on the execution device, allowing users to extend it with only 200\u2013300 lines of code. Performance : It ensures computations run fairly fast on each target device. Caten's code generation is divided into two stages based on the intended purpose: caten/codegen Generates unoptimized code (e.g., Scheduler, Lowerer, op fusion, etc.). caten/polyhedral (Optional) Performs advanced kernel loop transformations and optimizations on the scheduled code, such as Auto Scheduler. This document focuses on the first stage, caten/codegen , and provides related information and some examples.","title":"Hackable Kernel Generator"},{"location":"packages/caten.codegen/#how-to-enable-jit","text":"To enable JIT, you need to set the JIT contextvar before running the function caten . The following code snippet demonstrates how to enable JIT on repl: ;; Option1: Set globally (setf (ctx:getenv :JIT) 1) ;; Option2: Set locally (ctx:with-contextvar (:JIT 1) ;; ... ) After setting the contextvar, you can run the function caten to JIT-compile the AASM Graphs, e.g.: (caten (!rand `(3 3)))","title":"How to enable JIT"},{"location":"packages/caten.codegen/#how-to-debug-jit","text":"You can set the JIT_DEBUG contextvar to debug the JIT compilation process. The debugging level ranges from 0 to 4, as shown below: JIT_DEBUG | Effect ----------|------- 0 | None 1 | Nothing for now 2 | Displays blueprint and scop 3 | Displays schedule-graph 4 | Displays memory-planner and the compiled code","title":"How to debug JIT"},{"location":"packages/caten.codegen/#how-to-learn-jit","text":"You can start by reading ./caten/docs/getting-started.lisp to understand the basic concepts of caten/codegen through a running example. Additionally, the following sections provide documentation and explanations for each JIT mechanism, along with simple executable examples.","title":"How to learn JIT"},{"location":"packages/caten.codegen/#shape-inference","text":"The JIT compilation starts by inferring the shape of each points in the graph. caten/codegen/shape-inference is responsible for this task.","title":"Shape Inference"},{"location":"packages/caten.codegen/#function-run-type-infer","text":"(run-type-infer avm) Run the shape inference to the given AVM, returning Type-Reporter","title":"[function] run-type-infer"},{"location":"packages/caten.codegen/#struct-inferred-type","text":"A structure Inferred-Type contains the shape/stride/view information of the node at each points. Also, it containts the iteration space information that is used to render the kernel. If the shape inference is successfully done and properly deployed to the target graph by the rewriting-rule , the function (read-type-relay node) will return the Inferred-Type structure. relay-reads returns the list of the buffer corresponding to the node-reads. (If node-reads a number, nil is set) relay-writes returns the list of the buffer corresponding to the node-writes. relay-read-iters returns the list of the iteration space corresponding to the node-reads. relay-write-iters returns the list of the iteration space corresponding to the node-writes.","title":"[struct] Inferred-Type"},{"location":"packages/caten.codegen/#struct-iteration-space","text":"Iteration-Space is a structure that contains the shape/stride/view information of the each buffer. It is used to render the kernel. iteration-space-shape to get the shape, iteration-space-strides to get the stride, iteration-space-views to get the offsets and increments, and iteration-space-procedure to get how the iteraton-space is collapsed or permuted. Each elements for shape/stride are Expr. iteration-space-expr-aref to render the aref. (iteration-space-expr-aref iteration-space buffer gids) gids corresponds for the loop idx in the kernel.","title":"[struct] Iteration-Space"},{"location":"packages/caten.codegen/#rewriting-rules","text":"TODO: apply-rewriting-rules doc","title":"Rewriting Rules"},{"location":"packages/caten.codegen/#optimization-for-gemm","text":"TODO: WMMA Rewriting Docs","title":"Optimization for gemm"},{"location":"packages/caten.codegen/#scheduler","text":"The caten/codegen/scheduler is responsible for assigining the execution order of the computation graph in the aasm. Occasionally they merges or rewrites a view in order to schedule multiple nodes into the same schedule if possible. The function graph-schedule is an entry point, and takes a shape-inferred aasm graph as input, performs scheduling, and returns a schedule graph (called Schedule-Graph) whose each node is composed of Schedule-Item. One Schedule-Item corresponds to one kernel in GPU, graph-schedule must ensure that each item is merged only within the bounds that can be lowered into a single kernel.## Schedule-Item","title":"Scheduler"},{"location":"packages/caten.codegen/#schedule-item","text":"Schedule-Item is an intermidate object to represent a one kernel in GPU. f = cache_name or name write_ids = f(*[storage_id_dst], *[dynamic_shape], *[inputs]) ^ can be modified by the memory-planner It has a unique name , and cache-name . If cache-name was assigned, the compiler will fail to compile this schedule-item and reuse the kernel named cache-name instead. In order to lowering the computation graph as the foreign language, items must be consisted of JITAble operations (except for special irs and :allocate). If it qualifies, jitable is set to T. Otherwise, the scheduled items are relocated to the compiled avm directly. Specifially, if the item was :ALLOCATE, :allocated-p is set to T. blueprint[list] is a lowered schedule-item polyhedral[list] is a Polyhedral IR obtained by lowering blueprint auto-schedule-p[list] is set to T if it is worth to run auto-scheduler. (If there is a symbolic incremental, loop is not an affine and cannot run isl scheduler) items[list] are the scheduled items items-to-cache[list] are the copies of items but having the unique read/write. It is used to determine the equivalence of the two schedule-items. rank[fixnum] is the highest rank of the iteration space. storage-id-src[list] is the list of the storage-id of the source buffer (optimized by running memory-planner) storage-id-dst[list] is the list of the storage-id of the destination buffer (optimized by running memory-planner) namespace[list] a list of symbols appeared in the items. It is used to map a new blueprint from the cached blueprint. When optimizing schedule-item in-place, the 0th read is consumed.","title":":SCHEDULE-ITEM"},{"location":"packages/caten.codegen/#function-graph-schedule","text":"(graph-schedule graph) Creates a schedule-graph(FastGraph) from the given graph .","title":"[function] graph-schedule"},{"location":"packages/caten.codegen/#example-scheduler-shape-inference-rewriting-rules","text":"This code snippet demonstrates how to create a schedule-graph from AASM Graph. AASM Graph is obtained by running caten with JIT=0. lisp CATEN-USER> ( ctx:with-contextvar ( :JIT 0 ) ( pprint-graph ( avm-graph ( caten ( !relu ( !matmul ( make-tensor ` ( 3 3 )) ( make-tensor ` ( 3 3 )))))))) Result Result ============================================================================================================================================ === [P: 0] ================================================================================================================================= [P=0, ID=0]: :PAUSE/BACKWARD {0} \u2514 :MAX {N1} \u2514 :VIEW {N2} \u2502 \u251c load(0.0) \u2502 \u2514 Allocate[:float32] NIL \u251c :VIEW {N3} \u251c :MOVE {N4} \u251c Allocate[:float32] (3 3 1) \u2514 :VIEW {N5} \u251c :ADD {N6} \u251c :VIEW {N7} \u2502 \u251c load(0.0) \u2502 \u2514 Allocate[:float32] (3 3 1) \u2514 :MUL {N8} \u2514 :VIEW {N9} \u2502 \u251c Allocate[:float32] (3 3) \u251c :MOVE {N10} \u251c Allocate[:float32] (3 3 3) \u2514 :VIEW {N11} \u251c Allocate[:float32] (3 3) ============================================================================================================================================ Example ( let* (( graph ( ctx:with-contextvar ( :JIT 0 ) ( avm-graph ( caten ( !relu ( !matmul ( make-tensor ` ( 3 3 )) ( make-tensor ` ( 3 3 )))))))) ( vm ( make-avm graph :example nil ( graph-outputs graph ) nil ))) ( run-type-infer vm ) ;; Running the shape inference ( apply-rewriting-rules vm ) ;; Running the rewriting rules ;; graph-schedule to finally run the scheduler ( pprint-graph ( graph-schedule ( avm-graph vm )))) Result Result ============================================================================================================================================ === [P: 0] ================================================================================================================================= [P=0, ID=0]: :PAUSE/BACKWARD {N1} \u2514 [KERNEL] FUSED_RELU_SUMNODE_MATMUL18987 \u251c Allocate[:float32] (3 3 3) \u251c Allocate[:float32] (3 3) \u251c Allocate[:float32] (3 3) \u251c Allocate[:float32] (3 3 1) \u251c Allocate[:float32] (3 3 1) \u2514 Allocate[:float32] NIL ============================================================================================================================================","title":"Example (Scheduler + Shape Inference + Rewriting Rules)"},{"location":"packages/caten.codegen/#lowerer-blueprint","text":"The package caten/codegen/blueprint is responsible for lowering the schedule-item into a blueprint. A blueprint is an IR that represents a computation graph with explicit loop bounds. The lower-schedule-item method infers loop boundaries based on Schedule-item and performs lowering into a format that includes :FOR/:ENDFOR nodes.### [function] LOWER-SCHEDULE-ITEM (lower-schedule-item node base-graph scheduled-graph) Lowers the Schedule-Item into blueprint. node is a schedule-item to lower which belongs to scheduled-graph. base-graph is the graph you passed to the graph-schedule. scheduled-graph is the scheduled graph obtained by graph-schedule.","title":"Lowerer (Blueprint)"},{"location":"packages/caten.codegen/#example-scheduler-lowerer","text":"This code snippet demonstrates how to lower the schedule-graph into a blueprint created in the previous section. Example ( let* (( graph ( ctx:with-contextvar ( :JIT 0 ) ( avm-graph ( caten ( !relu ( !matmul ( make-tensor ` ( 3 3 )) ( make-tensor ` ( 3 3 )))))))) ( vm ( make-avm graph :example nil ( graph-outputs graph ) nil ))) ( run-type-infer vm ) ;; Running the shape inference ( apply-rewriting-rules vm ) ;; Running the rewriting rules ;; graph-schedule to finally run the scheduler ( let (( schedule-graph ( graph-schedule ( avm-graph vm )))) ( caten/codegen/expr-cache::with-expr-cache () ;; need this to forcibly keep the loop affine. ( let (( bp ( lower-schedule-item ( nth 1 ( graph-nodes schedule-graph )) graph schedule-graph ))) ( print-blueprint bp nil ))))) Result Result { }","title":"Example (Scheduler + Lowerer)"},{"location":"packages/caten.codegen/#renderer","text":"TODO (Docs for Renderer/rendre-expr/extensible polyhedral compiler/etc) Here is a list of nodes used to render the kernel.","title":"Renderer"},{"location":"packages/caten.codegen/#render-irs","text":"","title":"Render IRs"},{"location":"packages/caten.codegen/#define-global","text":"The node :DEFINE-GLOBAL declares a global variable in the kernel. (it corresponds to the argument of the kernel.) When optimizing define-global in-place, the 0th read is consumed.","title":":DEFINE-GLOBAL"},{"location":"packages/caten.codegen/#aref","text":":AREF corresponds to the following code: ID[*space] storage-id[symbol] An index to the reference pointer optimized by the memory-planner. buffer[Buffer] The buffer to be accessed. space[Iteration-Space] The iteration space :AREF belongs to. When optimizing aref in-place, the 0th read is consumed.","title":":AREF"},{"location":"packages/caten.codegen/#endif","text":"} // endif When optimizing endif in-place, the 0th read is consumed.","title":":ENDIF"},{"location":"packages/caten.codegen/#if","text":"if(condition) When optimizing if in-place, the 0th read is consumed.","title":":IF"},{"location":"packages/caten.codegen/#endfor","text":"} // idx When optimizing endfor in-place, the 0th read is consumed.","title":":ENDFOR"},{"location":"packages/caten.codegen/#for","text":"for(int idx=upfrom, below, by) scope[keyword] If :global , the loop is parallelizable. One of :global, :local. When optimizing for in-place, the 0th read is consumed.","title":":FOR"},{"location":"packages/caten.codegen/#expr","text":"TODO","title":"EXPR"},{"location":"packages/caten.codegen/#memory-planner","text":"TODO: Docs (MIP Solver)","title":"Memory Planner"},{"location":"packages/caten.codegen/#scop-analyzer","text":"TODO: Docs for scop","title":"Scop Analyzer"},{"location":"packages/caten.external.gguf/","text":"GGUF Looking to work with quantized models? The caten/gguf package enables you to read quantized model weights directly from GGUF files, construct a StateDict, and create tokenizers for caten/llm . It's a convenient tool for integrating quantized models into your projects. Please note that the quantization functionality currently supports only a limited number of bit depths. We appreciate your understanding and are continuously working to expand this feature. (As of this writing, fp32/fp16 only, Int8 Quant will be added soon!) [class] GGUF A class that represents the GGUF file format. (gguf-version gguf) returns a fixnum indicating the version of the GGUF file. (gguf-tensor-count gguf) returns the number of tensors in the GGUF file. (gguf-metadata-kv-count gguf) returns the number of metadata key-value pairs in the GGUF file. (gguf-metadata gguf) returns a list of metadata key-value pairs. (gguf-tensor-info gguf) returns a list of tensor information. (gguf-metadata-get gguf key) to get the corresponding value of the key where key is a string. [function] make-gguf Creates GGUF from the given stream. The definition is described in the following link: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#file-structure In short, this function accepts the following format: [Magic Number (4 Byte)] | [GGUF Version (4 Byte)] | [Tensor_Count (8 Byte)] | [Metadata_KV_Count (8 Byte)] | [Rest_data] [function] load-gguf (load-gguf pathname) Creates a gguf file from the given pathname. [function] load-gguf-url (load-gguf-url url filename &optional output-directory) Creates a gguf file from the given URL. The downloaded file will be saved in the output-directory named as filename. If the file already exists, it will not download the file again. [function] gguf->state-dict (gguf->state-dict gguf) Creates a caten/state-dict from the given gguf file's tensor-info. [function] gguf->bpe-tokenizer (gguf->bpe-tokenizer gguf &key (metadata-tokens \"tokenizer.ggml.tokens\") (metadata-merges \"tokenizer.ggml.merges\")) Creates a BPE tokenizer (which is caten/llm:Tokenizer) from the given gguf file's metadata. [struct] Metadata A structure to represent metadata in GGUF file. (metadata-key metadata) to access the key typed as string. (metadata-value-type metadata) to access the type of value typed as a keyword. (metadata-value metadata) to access the value typed as a number, boolean, or simple-array. [struct] Tensor-Info Tensor-Info stores the information of a tensor in the gguf file. (tensor-info-name tensor-info) returns the name of the tensor which is a string, (tensor-info-n-dimension tensor-info) returns the rank of the tensor. (tensor-info-dimensions tensor-info) returns the shape of the tensor. (tensor-info-ggml-type tensor-info) returns the data type of the tensor which is a keyword. (tensor-info-relative-offset tensor-info) returns the offset of the tensor in the gguf file. (tensor-info-absolute-offset tensor-info) returns the absolute offset of the tensor in the stream, (inconviniently buffers are stored with this offset but caten will precompute them). (tensor-info-buffer tensor-info) returns the parsed buffer of the tensor.","title":"caten/gguf"},{"location":"packages/caten.external.gguf/#gguf","text":"Looking to work with quantized models? The caten/gguf package enables you to read quantized model weights directly from GGUF files, construct a StateDict, and create tokenizers for caten/llm . It's a convenient tool for integrating quantized models into your projects. Please note that the quantization functionality currently supports only a limited number of bit depths. We appreciate your understanding and are continuously working to expand this feature. (As of this writing, fp32/fp16 only, Int8 Quant will be added soon!)","title":"GGUF"},{"location":"packages/caten.external.gguf/#class-gguf","text":"A class that represents the GGUF file format. (gguf-version gguf) returns a fixnum indicating the version of the GGUF file. (gguf-tensor-count gguf) returns the number of tensors in the GGUF file. (gguf-metadata-kv-count gguf) returns the number of metadata key-value pairs in the GGUF file. (gguf-metadata gguf) returns a list of metadata key-value pairs. (gguf-tensor-info gguf) returns a list of tensor information. (gguf-metadata-get gguf key) to get the corresponding value of the key where key is a string.","title":"[class] GGUF"},{"location":"packages/caten.external.gguf/#function-make-gguf","text":"Creates GGUF from the given stream. The definition is described in the following link: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#file-structure In short, this function accepts the following format: [Magic Number (4 Byte)] | [GGUF Version (4 Byte)] | [Tensor_Count (8 Byte)] | [Metadata_KV_Count (8 Byte)] | [Rest_data]","title":"[function] make-gguf"},{"location":"packages/caten.external.gguf/#function-load-gguf","text":"(load-gguf pathname) Creates a gguf file from the given pathname.","title":"[function] load-gguf"},{"location":"packages/caten.external.gguf/#function-load-gguf-url","text":"(load-gguf-url url filename &optional output-directory) Creates a gguf file from the given URL. The downloaded file will be saved in the output-directory named as filename. If the file already exists, it will not download the file again.","title":"[function] load-gguf-url"},{"location":"packages/caten.external.gguf/#function-gguf-state-dict","text":"(gguf->state-dict gguf) Creates a caten/state-dict from the given gguf file's tensor-info.","title":"[function] gguf->state-dict"},{"location":"packages/caten.external.gguf/#function-gguf-bpe-tokenizer","text":"(gguf->bpe-tokenizer gguf &key (metadata-tokens \"tokenizer.ggml.tokens\") (metadata-merges \"tokenizer.ggml.merges\")) Creates a BPE tokenizer (which is caten/llm:Tokenizer) from the given gguf file's metadata.","title":"[function] gguf->bpe-tokenizer"},{"location":"packages/caten.external.gguf/#struct-metadata","text":"A structure to represent metadata in GGUF file. (metadata-key metadata) to access the key typed as string. (metadata-value-type metadata) to access the type of value typed as a keyword. (metadata-value metadata) to access the value typed as a number, boolean, or simple-array.","title":"[struct] Metadata"},{"location":"packages/caten.external.gguf/#struct-tensor-info","text":"Tensor-Info stores the information of a tensor in the gguf file. (tensor-info-name tensor-info) returns the name of the tensor which is a string, (tensor-info-n-dimension tensor-info) returns the rank of the tensor. (tensor-info-dimensions tensor-info) returns the shape of the tensor. (tensor-info-ggml-type tensor-info) returns the data type of the tensor which is a keyword. (tensor-info-relative-offset tensor-info) returns the offset of the tensor in the gguf file. (tensor-info-absolute-offset tensor-info) returns the absolute offset of the tensor in the stream, (inconviniently buffers are stored with this offset but caten will precompute them). (tensor-info-buffer tensor-info) returns the parsed buffer of the tensor.","title":"[struct] Tensor-Info"},{"location":"packages/caten.external.llm/","text":"Overview TODO: Docs Tokenizers TODO: Docs [class] Tokenizer A tokenizer is a class that can encode and decode strings into and from. - (encode tokenizer string) -> (list fixnum ...) encodes a string into a tokenized form. - (decode tokenizer (list fixnum ...)) -> string decodes a tokenized form into a string. [class] BPETokenizer An implementation of Byte Pair Encoding tokenizer. Initialized by the make-bpe-tokenizer function. [generic] encode NIL [generic] decode NIL Models (TODO: Complete docs) Definitions are here: https://github.com/hikettei/Caten/blob/main/external/llm/layers.lisp Currently, the following models are implemented and tested: Attention Scaled-Dot-Product-Attention FeedForward TransformerBlock Transformer","title":"caten/llm"},{"location":"packages/caten.external.llm/#overview","text":"TODO: Docs","title":"Overview"},{"location":"packages/caten.external.llm/#tokenizers","text":"TODO: Docs","title":"Tokenizers"},{"location":"packages/caten.external.llm/#class-tokenizer","text":"A tokenizer is a class that can encode and decode strings into and from. - (encode tokenizer string) -> (list fixnum ...) encodes a string into a tokenized form. - (decode tokenizer (list fixnum ...)) -> string decodes a tokenized form into a string.","title":"[class] Tokenizer"},{"location":"packages/caten.external.llm/#class-bpetokenizer","text":"An implementation of Byte Pair Encoding tokenizer. Initialized by the make-bpe-tokenizer function.","title":"[class] BPETokenizer"},{"location":"packages/caten.external.llm/#generic-encode","text":"NIL","title":"[generic] encode"},{"location":"packages/caten.external.llm/#generic-decode","text":"NIL","title":"[generic] decode"},{"location":"packages/caten.external.llm/#models","text":"(TODO: Complete docs) Definitions are here: https://github.com/hikettei/Caten/blob/main/external/llm/layers.lisp Currently, the following models are implemented and tested: Attention Scaled-Dot-Product-Attention FeedForward TransformerBlock Transformer","title":"Models"},{"location":"packages/caten.external.onnx/","text":"ONNX Looking to compile Caten models directly from ONNX files? The caten/onnx package has you covered. It supports dynamic shapes and weight loading, allowing for flexible and efficient model integration. Currently, caten/onnx supports a limited set of operations. If you encounter any unsupported operations, we warmly welcome your contributions to expand its functionality. Please consider submitting a PR to add them! We'd like to support all opsets which is impossible! Requirement: cl-onnx requires cl-protobufs , and cl-protobufs may require protocol-compiler to be installed on your system. (Follow the instruction in cl-protobufs first) # Example $ sudo apt-get install protobuf-compiler [function] from-onnx (from-onnx path &key opset) Constructs a caten ir from an onnx model file at path . opset specifies the opset version to use. If not specified, uses the opset version in the file. Inputs or dynamic shapes for caten is created as a keyword (so pass the input tensor like (:input . , (rand (list 3 3))) ...","title":"caten/oonx"},{"location":"packages/caten.external.onnx/#onnx","text":"Looking to compile Caten models directly from ONNX files? The caten/onnx package has you covered. It supports dynamic shapes and weight loading, allowing for flexible and efficient model integration. Currently, caten/onnx supports a limited set of operations. If you encounter any unsupported operations, we warmly welcome your contributions to expand its functionality. Please consider submitting a PR to add them! We'd like to support all opsets which is impossible! Requirement: cl-onnx requires cl-protobufs , and cl-protobufs may require protocol-compiler to be installed on your system. (Follow the instruction in cl-protobufs first) # Example $ sudo apt-get install protobuf-compiler","title":"ONNX"},{"location":"packages/caten.external.onnx/#function-from-onnx","text":"(from-onnx path &key opset) Constructs a caten ir from an onnx model file at path . opset specifies the opset version to use. If not specified, uses the opset version in the file. Inputs or dynamic shapes for caten is created as a keyword (so pass the input tensor like (:input . , (rand (list 3 3))) ...","title":"[function] from-onnx"},{"location":"packages/caten.nn.activations/","text":"Activations [function] !sigmoid (!sigmoid x) Applies the Sigmoid function element-wise to the input tensor. [function] !hard-sigmoid (!hard-sigmoid x &key (alpha 0.2) (beta 0.5)) Applies the HardSigmoid function element-wise to the input tensor. [function] !relu (!relu x) Applies the ReLU function element-wise to the input tensor. [function] !leaky-relu (!leaky-relu x &key (neg-slope 1e-3)) Applies the LeakyReLU function element-wise to the input tensor. [function] !log-softmax (!log-softmax x &key (axis -1)) Applies the LogSoftmax function element-wise to the input tensor. [function] !elu (!elu x &key (alpha 1.0)) Applies the ELU function element-wise to the input tensor. [function] !relu6 (!relu6 x) Applies the ReLU6 function element-wise to the input tensor. [function] !softmax (!softmax x &key (axis -1)) Applies the Softmax function to the input tensor. [function] !softplus (!softplus x &key (beta 1.0)) Applies the Softplus function element-wise to the input tensor. [function] !softsign (!softsign x) Applies the Softsign function element-wise to the input tensor. [function] !softshrink (!softshrink x &key (lmd 0.5)) Applies the SoftShrink function element-wise to the input tensor. [function] !celu (!celu x &key (alpha 1.0)) Applies the CeLU function element-wise to the input tensor. [function] !silu (!silu x) Applies the SiLU function element-wise to the input tensor. [function] !logsigmoid (!logsigmoid x) Applies the LogSigmoid function element-wise to the input tensor. [function] !gelu (!gelu x &key (approx :tanh)) Applies the GeLU activation to the input tensor. There are two ways to approximate the GeLU function. :tanh to use tanh. :sigmoid to use sigmoid. [function] !selu (!selu x) Applies the SeLU function element-wise to the input tensor. [function] !mish (!mish x) Applies the Mish function element-wise to the input tensor. [function] !hardswish (!hardswish x) Applies the HardSwish function element-wise to the input tensor. [function] !hardtanh (!hardtanh x &key (min_val -1.0) (max_val 1.0)) Applies the HardTanh function element-wise to the input tensor. [function] !softmin (!softmin x) Applies the Softmin function element-wise to the input tensor.","title":"Activation"},{"location":"packages/caten.nn.activations/#activations","text":"","title":"Activations"},{"location":"packages/caten.nn.activations/#function-sigmoid","text":"(!sigmoid x) Applies the Sigmoid function element-wise to the input tensor.","title":"[function] !sigmoid"},{"location":"packages/caten.nn.activations/#function-hard-sigmoid","text":"(!hard-sigmoid x &key (alpha 0.2) (beta 0.5)) Applies the HardSigmoid function element-wise to the input tensor.","title":"[function] !hard-sigmoid"},{"location":"packages/caten.nn.activations/#function-relu","text":"(!relu x) Applies the ReLU function element-wise to the input tensor.","title":"[function] !relu"},{"location":"packages/caten.nn.activations/#function-leaky-relu","text":"(!leaky-relu x &key (neg-slope 1e-3)) Applies the LeakyReLU function element-wise to the input tensor.","title":"[function] !leaky-relu"},{"location":"packages/caten.nn.activations/#function-log-softmax","text":"(!log-softmax x &key (axis -1)) Applies the LogSoftmax function element-wise to the input tensor.","title":"[function] !log-softmax"},{"location":"packages/caten.nn.activations/#function-elu","text":"(!elu x &key (alpha 1.0)) Applies the ELU function element-wise to the input tensor.","title":"[function] !elu"},{"location":"packages/caten.nn.activations/#function-relu6","text":"(!relu6 x) Applies the ReLU6 function element-wise to the input tensor.","title":"[function] !relu6"},{"location":"packages/caten.nn.activations/#function-softmax","text":"(!softmax x &key (axis -1)) Applies the Softmax function to the input tensor.","title":"[function] !softmax"},{"location":"packages/caten.nn.activations/#function-softplus","text":"(!softplus x &key (beta 1.0)) Applies the Softplus function element-wise to the input tensor.","title":"[function] !softplus"},{"location":"packages/caten.nn.activations/#function-softsign","text":"(!softsign x) Applies the Softsign function element-wise to the input tensor.","title":"[function] !softsign"},{"location":"packages/caten.nn.activations/#function-softshrink","text":"(!softshrink x &key (lmd 0.5)) Applies the SoftShrink function element-wise to the input tensor.","title":"[function] !softshrink"},{"location":"packages/caten.nn.activations/#function-celu","text":"(!celu x &key (alpha 1.0)) Applies the CeLU function element-wise to the input tensor.","title":"[function] !celu"},{"location":"packages/caten.nn.activations/#function-silu","text":"(!silu x) Applies the SiLU function element-wise to the input tensor.","title":"[function] !silu"},{"location":"packages/caten.nn.activations/#function-logsigmoid","text":"(!logsigmoid x) Applies the LogSigmoid function element-wise to the input tensor.","title":"[function] !logsigmoid"},{"location":"packages/caten.nn.activations/#function-gelu","text":"(!gelu x &key (approx :tanh)) Applies the GeLU activation to the input tensor. There are two ways to approximate the GeLU function. :tanh to use tanh. :sigmoid to use sigmoid.","title":"[function] !gelu"},{"location":"packages/caten.nn.activations/#function-selu","text":"(!selu x) Applies the SeLU function element-wise to the input tensor.","title":"[function] !selu"},{"location":"packages/caten.nn.activations/#function-mish","text":"(!mish x) Applies the Mish function element-wise to the input tensor.","title":"[function] !mish"},{"location":"packages/caten.nn.activations/#function-hardswish","text":"(!hardswish x) Applies the HardSwish function element-wise to the input tensor.","title":"[function] !hardswish"},{"location":"packages/caten.nn.activations/#function-hardtanh","text":"(!hardtanh x &key (min_val -1.0) (max_val 1.0)) Applies the HardTanh function element-wise to the input tensor.","title":"[function] !hardtanh"},{"location":"packages/caten.nn.activations/#function-softmin","text":"(!softmin x) Applies the Softmin function element-wise to the input tensor.","title":"[function] !softmin"},{"location":"packages/caten.nn.convolutions/","text":"Convolutions [class] ConvND (ConvND in-channels out-channels kernel-size &key (groups 1) (stride 1) (dilation 1) (padding 0) (bias t)) Applies a convolution over a tensor with a given weight and optional bias . NOTE: unlike PyTorch, this implementation is not limited to only 2d convolutions and instead works for any number of dimensions. [function] !convnd (!convnd x weight &key (bias nil) (groups 1) (stride 1) (dilation 1) (padding 0)) Applies a convolutional layer over a tensor x with a given weight and optional bias .","title":"Convolution"},{"location":"packages/caten.nn.convolutions/#convolutions","text":"","title":"Convolutions"},{"location":"packages/caten.nn.convolutions/#class-convnd","text":"(ConvND in-channels out-channels kernel-size &key (groups 1) (stride 1) (dilation 1) (padding 0) (bias t)) Applies a convolution over a tensor with a given weight and optional bias . NOTE: unlike PyTorch, this implementation is not limited to only 2d convolutions and instead works for any number of dimensions.","title":"[class] ConvND"},{"location":"packages/caten.nn.convolutions/#function-convnd","text":"(!convnd x weight &key (bias nil) (groups 1) (stride 1) (dilation 1) (padding 0)) Applies a convolutional layer over a tensor x with a given weight and optional bias .","title":"[function] !convnd"},{"location":"packages/caten.nn.criterion/","text":"Criterion [class] L1NormLoss (L1NormLoss &key (reduction :mean)) Implements L1Norm Loss: l(x, y) = L = {l_1, ..., l_n}^\\intercal, l_n = abs(x_n - y_n) reduction is one of :mean or :sum. [function] !l1norm (!l1norm a b &key (reduction :mean)) Computes L1Norm [class] MSELoss (MSELoss &key (reduction :mean)) Implements MSE Loss: l(x, y) = L = {l_1, ..., l_n}^\\intercal, l_n = (x_n - y_n)^2 reduction is one of :mean or :sum. [function] !mse (!mse a b &key (reduction :mean)) Computes MSE Loss between a and b. [class] CrossEntropyLoss (CrossEntropyLoss &key (reduction :mean) (delta 1e-5)) Returns a tensor that measures the Cross-Entropy-Error between each element in the x and labels. labels are one-hot encoded. L_i = -p_ilog(x_i + delta) \\begin{equation} out_i= \\begin{cases} sum(L) & \\text{reduction = sum} \\\\ mean(L) & \\text{reduction = mean} \\\\ L & \\text{otherwise} \\end{cases} \\end{equation} [function] !cross-entropy (!cross-entropy x labels &key (reduction :mean) (delta 1e-5)) Computes Cross Entropy Loss between x and labels. labels are one-hot encoded.","title":"Criterion"},{"location":"packages/caten.nn.criterion/#criterion","text":"","title":"Criterion"},{"location":"packages/caten.nn.criterion/#class-l1normloss","text":"(L1NormLoss &key (reduction :mean)) Implements L1Norm Loss: l(x, y) = L = {l_1, ..., l_n}^\\intercal, l_n = abs(x_n - y_n) reduction is one of :mean or :sum.","title":"[class] L1NormLoss"},{"location":"packages/caten.nn.criterion/#function-l1norm","text":"(!l1norm a b &key (reduction :mean)) Computes L1Norm","title":"[function] !l1norm"},{"location":"packages/caten.nn.criterion/#class-mseloss","text":"(MSELoss &key (reduction :mean)) Implements MSE Loss: l(x, y) = L = {l_1, ..., l_n}^\\intercal, l_n = (x_n - y_n)^2 reduction is one of :mean or :sum.","title":"[class] MSELoss"},{"location":"packages/caten.nn.criterion/#function-mse","text":"(!mse a b &key (reduction :mean)) Computes MSE Loss between a and b.","title":"[function] !mse"},{"location":"packages/caten.nn.criterion/#class-crossentropyloss","text":"(CrossEntropyLoss &key (reduction :mean) (delta 1e-5)) Returns a tensor that measures the Cross-Entropy-Error between each element in the x and labels. labels are one-hot encoded. L_i = -p_ilog(x_i + delta) \\begin{equation} out_i= \\begin{cases} sum(L) & \\text{reduction = sum} \\\\ mean(L) & \\text{reduction = mean} \\\\ L & \\text{otherwise} \\end{cases} \\end{equation}","title":"[class] CrossEntropyLoss"},{"location":"packages/caten.nn.criterion/#function-cross-entropy","text":"(!cross-entropy x labels &key (reduction :mean) (delta 1e-5)) Computes Cross Entropy Loss between x and labels. labels are one-hot encoded.","title":"[function] !cross-entropy"},{"location":"packages/caten.nn.embeddings/","text":"Embeddings [class] Embedding (Embedding vocab-size embedding-dim) Implements an embedding layer.","title":"Embedding"},{"location":"packages/caten.nn.embeddings/#embeddings","text":"","title":"Embeddings"},{"location":"packages/caten.nn.embeddings/#class-embedding","text":"(Embedding vocab-size embedding-dim) Implements an embedding layer.","title":"[class] Embedding"},{"location":"packages/caten.nn.encoding/","text":"Encoding [class] RoPE (RoPE dim &key (base 10000)) Implements Rotation Positional Encoding (RoPE). [function] !rope (!rope x dim &key (base 10000)) Applies Rotation Positional Encoding (RoPE) to the input tensor x with the specified dim .","title":"Encoding"},{"location":"packages/caten.nn.encoding/#encoding","text":"","title":"Encoding"},{"location":"packages/caten.nn.encoding/#class-rope","text":"(RoPE dim &key (base 10000)) Implements Rotation Positional Encoding (RoPE).","title":"[class] RoPE"},{"location":"packages/caten.nn.encoding/#function-rope","text":"(!rope x dim &key (base 10000)) Applies Rotation Positional Encoding (RoPE) to the input tensor x with the specified dim .","title":"[function] !rope"},{"location":"packages/caten.nn.linears/","text":"Linear [class] Linear (Linear in-featrues out-features &key (bias t)) Implements a linear transformation.","title":"Linear"},{"location":"packages/caten.nn.linears/#linear","text":"","title":"Linear"},{"location":"packages/caten.nn.linears/#class-linear","text":"(Linear in-featrues out-features &key (bias t)) Implements a linear transformation.","title":"[class] Linear"},{"location":"packages/caten.nn.normalizations/","text":"Normalizations [class] BatchNorm (BatchNorm dims &key (eps 1e-5) (affine t) (bias t)) Batch Normalization Layer [function] !batch-norm (!batch-norm x &optional weight bias mean invstd (axis 1) (eps 1e-5)) Computes the batch normalization of a tensor x with optional weight and bias tensors. [class] LayerNorm (LayerNorm dims &key (eps 1e-5) (affine t) (bias t)) Layer Normalization Layer [function] !layer-norm (!layer-norm x normalized-shape &key (weight) (bias) (eps 1e-5)) Computes the layer normalization of a tensor x with optional weight and bias tensors. [class] RMSNorm (RMSNorm dims &key (eps 1e-5) (weight t)) Root Mean Square Normalization Layer [function] !rms-norm (!rms-norm x normalized-shape &key (weight) (eps 1e-5)) Computes the root mean square normalization of a tensor x with optional weight tensor.","title":"Normalization"},{"location":"packages/caten.nn.normalizations/#normalizations","text":"","title":"Normalizations"},{"location":"packages/caten.nn.normalizations/#class-batchnorm","text":"(BatchNorm dims &key (eps 1e-5) (affine t) (bias t)) Batch Normalization Layer","title":"[class] BatchNorm"},{"location":"packages/caten.nn.normalizations/#function-batch-norm","text":"(!batch-norm x &optional weight bias mean invstd (axis 1) (eps 1e-5)) Computes the batch normalization of a tensor x with optional weight and bias tensors.","title":"[function] !batch-norm"},{"location":"packages/caten.nn.normalizations/#class-layernorm","text":"(LayerNorm dims &key (eps 1e-5) (affine t) (bias t)) Layer Normalization Layer","title":"[class] LayerNorm"},{"location":"packages/caten.nn.normalizations/#function-layer-norm","text":"(!layer-norm x normalized-shape &key (weight) (bias) (eps 1e-5)) Computes the layer normalization of a tensor x with optional weight and bias tensors.","title":"[function] !layer-norm"},{"location":"packages/caten.nn.normalizations/#class-rmsnorm","text":"(RMSNorm dims &key (eps 1e-5) (weight t)) Root Mean Square Normalization Layer","title":"[class] RMSNorm"},{"location":"packages/caten.nn.normalizations/#function-rms-norm","text":"(!rms-norm x normalized-shape &key (weight) (eps 1e-5)) Computes the root mean square normalization of a tensor x with optional weight tensor.","title":"[function] !rms-norm"},{"location":"packages/caten.nn.optimizers/","text":"Optimizers [class] AbstractOptimizer AbstractOptimizer is the base class for all optimizers. One AbstractOptimizer corresponds to one Tensor class with :requires-grad=T . (optimizer-param optimizer) to get the corresponding tensor. [generic] step-optimizer NIL [function] hook-optimizers (hook-optimizers avm hooker) This function is used to hook the optimizers in the recognised parameters in avm-params-to-optimize. hooker is an function that takes one argument, which is the tensor that requires-grad=T, returns the AbstractOptimizer. A list of created optimizers are returned. [function] zero-grad (zero-grad optimizer) Fills the gradient of the optimizer with zeros. [class] SGD Implements SGD Optimizer: Param_{new}\\gets{Param - Param_{grad}\\times{lr}} where the initarg :lr is the learning rate. [function] SGD (SGD :lr 1e-3) Returns a lambda function that takes one argument, which is the parameter tensor, and returns an instance of SGD optimizer.","title":"Optimizers"},{"location":"packages/caten.nn.optimizers/#optimizers","text":"","title":"Optimizers"},{"location":"packages/caten.nn.optimizers/#class-abstractoptimizer","text":"AbstractOptimizer is the base class for all optimizers. One AbstractOptimizer corresponds to one Tensor class with :requires-grad=T . (optimizer-param optimizer) to get the corresponding tensor.","title":"[class] AbstractOptimizer"},{"location":"packages/caten.nn.optimizers/#generic-step-optimizer","text":"NIL","title":"[generic] step-optimizer"},{"location":"packages/caten.nn.optimizers/#function-hook-optimizers","text":"(hook-optimizers avm hooker) This function is used to hook the optimizers in the recognised parameters in avm-params-to-optimize. hooker is an function that takes one argument, which is the tensor that requires-grad=T, returns the AbstractOptimizer. A list of created optimizers are returned.","title":"[function] hook-optimizers"},{"location":"packages/caten.nn.optimizers/#function-zero-grad","text":"(zero-grad optimizer) Fills the gradient of the optimizer with zeros.","title":"[function] zero-grad"},{"location":"packages/caten.nn.optimizers/#class-sgd","text":"Implements SGD Optimizer: Param_{new}\\gets{Param - Param_{grad}\\times{lr}} where the initarg :lr is the learning rate.","title":"[class] SGD"},{"location":"packages/caten.nn.optimizers/#function-sgd","text":"(SGD :lr 1e-3) Returns a lambda function that takes one argument, which is the parameter tensor, and returns an instance of SGD optimizer.","title":"[function] SGD"},{"location":"packages/caten.nn.padding/","text":"Padding [class] Padding The Func Padding pads the input_tensor with the specified padding. The argument is given in the following format. (output_tensor, input_tensor, *index_components, *padding_starts, *padding_ends) where each (length index_components), (length padding_starts), and (length padding_ends) == ndim(input_tensor) == ndim(output_tensor). With JIT=1, everything is expected to be fused in a single EXPR like: out = where(start_0 < index_components[0] < end_0 and start_1 < index_components[1] < end_1 and ..., input_tensor[i=i'-start_0, j=j'_start_1, ...], value) [function] !padding (!padding x padding &key (value 0.0)) Pads the tensor x with the specified padding and value. Padding is specified as: ((pad_0_1 pad_0_2) (pad_1_0 pad_1_0) ...) . If T is provided instead of (pad_0_0 pad_0_1) , that means no padding is applied to the axis. (that is, T = (0 0) ) Each start_0 and pad_0 is expected as a positive integer (caten will not check this!), or a scalar int32/int64 tensor. [function] !padding2d (!padding2d x padding &key (value 0.0)) Pads the last two dimension of the tensor x with the specified padding and value.. padding is specified as: (padding_left padding_right padding_top padding_bottom).","title":"Padding"},{"location":"packages/caten.nn.padding/#padding","text":"","title":"Padding"},{"location":"packages/caten.nn.padding/#class-padding","text":"The Func Padding pads the input_tensor with the specified padding. The argument is given in the following format. (output_tensor, input_tensor, *index_components, *padding_starts, *padding_ends) where each (length index_components), (length padding_starts), and (length padding_ends) == ndim(input_tensor) == ndim(output_tensor). With JIT=1, everything is expected to be fused in a single EXPR like: out = where(start_0 < index_components[0] < end_0 and start_1 < index_components[1] < end_1 and ..., input_tensor[i=i'-start_0, j=j'_start_1, ...], value)","title":"[class] Padding"},{"location":"packages/caten.nn.padding/#function-padding","text":"(!padding x padding &key (value 0.0)) Pads the tensor x with the specified padding and value. Padding is specified as: ((pad_0_1 pad_0_2) (pad_1_0 pad_1_0) ...) . If T is provided instead of (pad_0_0 pad_0_1) , that means no padding is applied to the axis. (that is, T = (0 0) ) Each start_0 and pad_0 is expected as a positive integer (caten will not check this!), or a scalar int32/int64 tensor.","title":"[function] !padding"},{"location":"packages/caten.nn.padding/#function-padding2d","text":"(!padding2d x padding &key (value 0.0)) Pads the last two dimension of the tensor x with the specified padding and value.. padding is specified as: (padding_left padding_right padding_top padding_bottom).","title":"[function] !padding2d"},{"location":"packages/caten.nn.pooling/","text":"Pooling [class] AvgPool (AvgPool kernel-size &key (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling)) Implements Average Pooling layer. [function] !avgpool (!avgpool x &key (kernel-size `(2 2)) (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling)) Applies average pooling over the tensor. [class] MaxPool (MaxPool kernel-size &key (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling)) Implements Max Pooling layer. [function] !maxpool (!maxpool x &key (kernel-size `(2 2)) (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling)) Applies max pooling over the tensor.","title":"Pooling"},{"location":"packages/caten.nn.pooling/#pooling","text":"","title":"Pooling"},{"location":"packages/caten.nn.pooling/#class-avgpool","text":"(AvgPool kernel-size &key (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling)) Implements Average Pooling layer.","title":"[class] AvgPool"},{"location":"packages/caten.nn.pooling/#function-avgpool","text":"(!avgpool x &key (kernel-size `(2 2)) (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling)) Applies average pooling over the tensor.","title":"[function] !avgpool"},{"location":"packages/caten.nn.pooling/#class-maxpool","text":"(MaxPool kernel-size &key (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling)) Implements Max Pooling layer.","title":"[class] MaxPool"},{"location":"packages/caten.nn.pooling/#function-maxpool","text":"(!maxpool x &key (kernel-size `(2 2)) (stride nil) (dilation 1) (padding 0) (ceiling #'ceiling)) Applies max pooling over the tensor.","title":"[function] !maxpool"}]} \ No newline at end of file diff --git a/search/worker.js b/search/worker.js new file mode 100644 index 000000000..8628dbce9 --- /dev/null +++ b/search/worker.js @@ -0,0 +1,133 @@ +var base_path = 'function' === typeof importScripts ? '.' : '/search/'; +var allowSearch = false; +var index; +var documents = {}; +var lang = ['en']; +var data; + +function getScript(script, callback) { + console.log('Loading script: ' + script); + $.getScript(base_path + script).done(function () { + callback(); + }).fail(function (jqxhr, settings, exception) { + console.log('Error: ' + exception); + }); +} + +function getScriptsInOrder(scripts, callback) { + if (scripts.length === 0) { + callback(); + return; + } + getScript(scripts[0], function() { + getScriptsInOrder(scripts.slice(1), callback); + }); +} + +function loadScripts(urls, callback) { + if( 'function' === typeof importScripts ) { + importScripts.apply(null, urls); + callback(); + } else { + getScriptsInOrder(urls, callback); + } +} + +function onJSONLoaded () { + data = JSON.parse(this.responseText); + var scriptsToLoad = ['lunr.js']; + if (data.config && data.config.lang && data.config.lang.length) { + lang = data.config.lang; + } + if (lang.length > 1 || lang[0] !== "en") { + scriptsToLoad.push('lunr.stemmer.support.js'); + if (lang.length > 1) { + scriptsToLoad.push('lunr.multi.js'); + } + if (lang.includes("ja") || lang.includes("jp")) { + scriptsToLoad.push('tinyseg.js'); + } + for (var i=0; i < lang.length; i++) { + if (lang[i] != 'en') { + scriptsToLoad.push(['lunr', lang[i], 'js'].join('.')); + } + } + } + loadScripts(scriptsToLoad, onScriptsLoaded); +} + +function onScriptsLoaded () { + console.log('All search scripts loaded, building Lunr index...'); + if (data.config && data.config.separator && data.config.separator.length) { + lunr.tokenizer.separator = new RegExp(data.config.separator); + } + + if (data.index) { + index = lunr.Index.load(data.index); + data.docs.forEach(function (doc) { + documents[doc.location] = doc; + }); + console.log('Lunr pre-built index loaded, search ready'); + } else { + index = lunr(function () { + if (lang.length === 1 && lang[0] !== "en" && lunr[lang[0]]) { + this.use(lunr[lang[0]]); + } else if (lang.length > 1) { + this.use(lunr.multiLanguage.apply(null, lang)); // spread operator not supported in all browsers: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Spread_operator#Browser_compatibility + } + this.field('title'); + this.field('text'); + this.ref('location'); + + for (var i=0; i < data.docs.length; i++) { + var doc = data.docs[i]; + this.add(doc); + documents[doc.location] = doc; + } + }); + console.log('Lunr index built, search ready'); + } + allowSearch = true; + postMessage({config: data.config}); + postMessage({allowSearch: allowSearch}); +} + +function init () { + var oReq = new XMLHttpRequest(); + oReq.addEventListener("load", onJSONLoaded); + var index_path = base_path + '/search_index.json'; + if( 'function' === typeof importScripts ){ + index_path = 'search_index.json'; + } + oReq.open("GET", index_path); + oReq.send(); +} + +function search (query) { + if (!allowSearch) { + console.error('Assets for search still loading'); + return; + } + + var resultDocuments = []; + var results = index.search(query); + for (var i=0; i < results.length; i++){ + var result = results[i]; + doc = documents[result.ref]; + doc.summary = doc.text.substring(0, 200); + resultDocuments.push(doc); + } + return resultDocuments; +} + +if( 'function' === typeof importScripts ) { + onmessage = function (e) { + if (e.data.init) { + init(); + } else if (e.data.query) { + postMessage({ results: search(e.data.query) }); + } else { + console.error("Worker - Unrecognized message: " + e); + } + }; +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 000000000..0f8724efd --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,3 @@ + + + \ No newline at end of file diff --git a/sitemap.xml.gz b/sitemap.xml.gz new file mode 100644 index 000000000..3981dbe44 Binary files /dev/null and b/sitemap.xml.gz differ diff --git a/statics.html b/statics.html new file mode 100644 index 000000000..aec3fd02d --- /dev/null +++ b/statics.html @@ -0,0 +1,461 @@ + + + + + + + + + + + + + + + + + + + + + + Caten Documentation + + + + + + + + + + + + + + + + + + + + + + + +
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サイト名Caten Documentation
記事の総数33
サイトの最終更新日(UTC)2024-12-22 08:34:12.931162+00:00
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