Merge branch 'perplexity-vmfb' of https://github.com/nod-ai/SHARK-Pla…

…tform into perplexity-vmfb

sharktank/sharktank/layers/kv_cache.py

@@ -92,6 +92,7 @@ def __init__(
         attn_head_count: int,
         attn_head_dim: int,
         seq_length: int,
+        shard_count: int = 1,
         dtype: torch.dtype = torch.float32,
         device: Optional[torch.device] = None,
     ):
@@ -100,6 +101,7 @@ def __init__(
         self.attn_head_count = attn_head_count
         self.attn_head_dim = attn_head_dim
         self.seq_length = seq_length
+        self.shard_count = shard_count
         self.device = device
         self.dtype = dtype
 
@@ -113,15 +115,109 @@ def allocate(self, *, bs: int) -> list[torch.Tensor]:
 
         Each tensor has shape: [bs, sl, attn_head_count, attn_head_dim]
         """
-        return [
+        allocations = [
             torch.empty(
-                [bs, self.seq_length, self.attn_head_count, self.attn_head_dim],
+                [
+                    bs,
+                    self.seq_length,
+                    self.attn_head_count,
+                    self.attn_head_dim,
+                ],
                 dtype=self.dtype,
                 device=self.device,
             )
             for _ in range(2 * self.transformer_block_count)
         ]
 
+        if self.shard_count == 1:
+            return allocations
+
+        return [
+            ops.reshard_split(allocation, dim=2, count=self.shard_count)
+            for allocation in allocations
+        ]
+
+    def read(
+        self,
+        state: list[Union[torch.Tensor, SplitPrimitiveTensor]],
+        *,
+        read_into_partitions: list[Union[torch.Tensor, SplitPrimitiveTensor]],
+        transformer_block_index: int,
+        seq_len: int,
+        page_ids: Optional[Union[torch.Tensor, ReplicatedTensor]] = None,
+    ):
+        """Reads cache partitions from the page table for the given page_ids.
+
+        Args:
+        state: State struct as returned from allocate().
+        read_into_partitions: List of cache partitions to read into in-place.
+        transformer_block_index: The index of the transformer block accessing
+            the cache.
+        page_ids: Tensor of [bs, max_seqlen // block_pos_stride] of page ids
+            to access.
+
+        Returns a tuple of cache partitions (i.e. k and v caches for the transformer
+        block), linearized. Note that this reference approach to reading by
+        materializing linearly may not be terribly efficient unless if the
+        compiler can fuse the gather.
+        """
+        read_count = len(read_into_partitions)
+        reads = []
+        for i in range(read_count):
+            reads.append(
+                state[transformer_block_index * read_count + i][:, :seq_len, :, :]
+            )
+
+        return tuple(reads)
+
+    def write_timestep(
+        self,
+        state: list[Union[torch.Tensor, SplitPrimitiveTensor]],
+        # List of [bs, 1, attn_head_count, attn_head_dim]
+        cache_partitions: list[Union[torch.Tensor, SplitPrimitiveTensor]],
+        *,
+        transformer_block_index: int,
+        # [bs]
+        seq_positions: Union[torch.Tensor, ReplicatedTensor],
+        # [bs, max_seqlen // block_pos_stride]
+        page_ids: Optional[Union[torch.Tensor, ReplicatedTensor]] = None,
+    ):
+        """Writes a single batched timestep across all cache partitions.
+
+        Note that this internally loops over the batch size, which cannot be
+        dynamic.
+        """
+        bs, _, _, _ = cache_partitions[0].shape
+        update_count = len(cache_partitions)
+
+        for b in range(bs):
+            row_index = torch.tensor(b, dtype=torch.int64)
+            row_start_pos = seq_positions[row_index]
+
+            for i, update in enumerate(cache_partitions):
+                cache = state[transformer_block_index * update_count + i]
+                cache.index_put_((row_index, row_start_pos), update[row_index, 0])
+
+    def write(
+        self,
+        state: list[Union[torch.Tensor, SplitPrimitiveTensor]],
+        cache_partitions: list[Union[torch.Tensor, SplitPrimitiveTensor]],
+        *,
+        transformer_block_index: int,
+        page_ids: Optional[Union[torch.Tensor, ReplicatedTensor]] = None,
+    ):
+        """Writes cache partitions from a linear layout to the page table.
+
+        This is the inverse of the linear read. The same caveat applies if the
+        in-place scatter cannot be fused.
+        """
+        update_count = len(cache_partitions)
+
+        for idx, update_src in enumerate(cache_partitions):
+            cache_dest = state[transformer_block_index * update_count + idx]
+            _, batch_seq_len, _, _ = update_src.shape
+            cache_dest[:, :batch_seq_len, :, :] = update_src
+
 
 class PagedKVCache(BaseKVCache):
     """Implementation of a KV cache on top of a 'page table'.
@@ -238,31 +334,27 @@ def allocate(
         """Allocates tensor state for a page table for the given capacity in
         pages.
         """
+        shards = [
+            torch.empty(
+                [page_count, self.page_slab_flat_dim],
+                dtype=self.dtype,
+                device=self.device,
+            )
+            for _ in range(self.shard_count)
+        ]
+
         if self.shard_count == 1:
-            return [
-                torch.empty(
-                    [page_count, self.page_slab_flat_dim],
-                    dtype=self.dtype,
-                    device=self.device,
-                )
-            ]
-        else:
-            shards = [
-                torch.empty(
-                    [page_count, self.page_slab_flat_dim],
-                    dtype=self.dtype,
-                    device=self.device,
-                )
-                for _ in range(self.shard_count)
-            ]
-            return [SplitPrimitiveTensor(ts=shards, shard_dim=1)]
+            return shards
+
+        return [SplitPrimitiveTensor(ts=shards, shard_dim=1)]
 
     def read(
         self,
         state: list[Union[torch.Tensor, SplitPrimitiveTensor]],
         *,
         read_into_partitions: list[Union[torch.Tensor, SplitPrimitiveTensor]],
         transformer_block_index: int,
+        seq_len: int,
         page_ids: Union[torch.Tensor, ReplicatedTensor],
     ):
         """Reads cache partitions from the page table for the given page_ids.
@@ -331,6 +423,8 @@ def read_cache_partition(
         for index, read_into_partition in enumerate(read_into_partitions):
             read_cache_partition(index, read_into_partition)
 
+        return tuple([p[:, :seq_len, :] for p in read_into_partitions])
+
     def write_timestep(
         self,
         state: list[Union[torch.Tensor, SplitPrimitiveTensor]],

sharktank/sharktank/layers/paged_llama_attention_block.py

@@ -113,27 +113,16 @@ def forward(
         # Full sequence length.
         kv_seq_len = seq_block_ids.shape[1] * self.cache.block_seq_stride
 
-        if self.cache.is_paged:
-            xk, xv = self.transact_cache_paged(
-                xk_cache_update=xk,
-                xv_cache_update=xv,
-                seq_block_ids=seq_block_ids,
-                kv_seq_len=kv_seq_len,
-                start_positions=start_positions,
-                cache_state=cache_state,
-                xk_temp=xk_temp,
-                xv_temp=xv_temp,
-            )
-        elif self.cache.is_direct:
-            xk, xv = self.transact_cache_direct(
-                xk_cache_update=xk,
-                xv_cache_update=xv,
-                start_positions=start_positions,
-                kv_seq_len=kv_seq_len,
-                cache_state=cache_state,
-            )
-        else:
-            raise NotImplementedError(f"Unsupported KV cache type: {type(self.cache)}")
+        xk, xv = self.transact_cache(
+            xk_cache_update=xk,
+            xv_cache_update=xv,
+            seq_block_ids=seq_block_ids,
+            kv_seq_len=kv_seq_len,
+            start_positions=start_positions,
+            cache_state=cache_state,
+            xk_temp=xk_temp,
+            xv_temp=xv_temp,
+        )
 
         # Expand kv heads for GQA.
         gqa_n_rep = self.head_count // self.head_count_kv
@@ -202,58 +191,20 @@ def repeat_kv(x: torch.Tensor) -> torch.Tensor:
         h = h + attn_output
         return h
 
-    def transact_cache_direct(
-        self,
-        *,
-        cache_state: list[torch.Tensor],
-        xk_cache_update: torch.Tensor,
-        xv_cache_update: torch.Tensor,
-        kv_seq_len: int,
-        start_positions: Optional[torch.Tensor] = None,
-    ):
-        bs, batch_seq_len, _, _ = xk_cache_update.shape
-        cache_k = cache_state[self.block_index * 2]
-        cache_v = cache_state[self.block_index * 2 + 1]
-
-        if start_positions is None:
-            # Prefill. Write the entire cache.
-            cache_k[:, :batch_seq_len] = xk_cache_update
-            cache_v[:, :batch_seq_len] = xv_cache_update
-            return xk_cache_update, xv_cache_update
-        else:
-            # Decode. Write a single timestep.
-            # TODO: This needs to be reworked with index ops.
-            assert xk_cache_update.shape[1] == 1
-            assert xv_cache_update.shape[1] == 1
-            for b in range(bs):
-                # Make a tensor because indices must be all tensors, so we can avoid
-                # doing start_positions[row_index].item(), which generates a lot of SymInts.
-                row_index = torch.tensor(
-                    b, dtype=torch.int64, device=xk_cache_update.device
-                )
-                row_start_pos = start_positions[row_index]
-                cache_k.index_put_(
-                    (row_index, row_start_pos), xk_cache_update[row_index, 0]
-                )
-                cache_v.index_put_(
-                    (row_index, row_start_pos), xv_cache_update[row_index, 0]
-                )
-            return cache_k[:, :kv_seq_len], cache_v[:, :kv_seq_len]
-
-    def transact_cache_paged(
+    def transact_cache(
         self,
         *,
         xk_cache_update: torch.Tensor,
         xv_cache_update: torch.Tensor,
         cache_state: list[torch.Tensor],
         # [bs, batch_seq_len // block_seq_stride]
-        seq_block_ids: torch.Tensor,
+        seq_block_ids: Optional[torch.Tensor],
         kv_seq_len: int,
         start_positions: Optional[torch.Tensor] = None,
         xk_temp: Optional[torch.Tensor] = None,
         xv_temp: Optional[torch.Tensor] = None,
     ):
-        cache = self.cache.paged
+        cache = self.cache
         # Manage the cache.
         if start_positions is None:
             # Prefill: Write the entire cache.
@@ -264,46 +215,45 @@ def transact_cache_paged(
                 page_ids=seq_block_ids,
             )
             return xk_cache_update, xv_cache_update
-        else:
-            # Decode at ragged start positions.
-            # We need to initialize/read the K/V from the cache for the whole
-            # sequence. Note that at this point, it is possible to fork and
-            # use a memory efficient attention kernel that can do indirect
-            # reads, skipping this materialization. This path is taken for
-            # a decode step.
-            assert xk_temp is not None and xv_temp is not None
-            assert xk_cache_update.shape[1] == 1
-            assert xv_cache_update.shape[1] == 1
-            assert kv_seq_len == seq_block_ids.shape[1] * cache.block_seq_stride
-
-            # Write our one updated cache row into the cache.
-            cache.write_timestep(
-                cache_state,
-                cache_partitions=[
-                    xk_cache_update,
-                    xv_cache_update,
-                ],
-                transformer_block_index=self.block_index,
-                seq_positions=start_positions,
-                page_ids=seq_block_ids,
-            )
 
-            # Restore from the cache.
-            cache.read(
-                cache_state,
-                read_into_partitions=[
-                    xk_temp[:, 0:kv_seq_len, ...],
-                    xv_temp[:, 0:kv_seq_len, ...],
-                ],
-                transformer_block_index=self.block_index,
-                page_ids=seq_block_ids,
-            )
+        # Decode at ragged start positions.
+        # We need to initialize/read the K/V from the cache for the whole
+        # sequence. Note that at this point, it is possible to fork and
+        # use a memory efficient attention kernel that can do indirect
+        # reads, skipping this materialization. This path is taken for
+        # a decode step.
+        assert xk_temp is not None and xv_temp is not None
+        assert xk_cache_update.shape[1] == 1
+        assert xv_cache_update.shape[1] == 1
+        assert kv_seq_len == seq_block_ids.shape[1] * cache.block_seq_stride
+
+        # Write our one updated cache row into the cache.
+        cache.write_timestep(
+            cache_state,
+            cache_partitions=[
+                xk_cache_update,
+                xv_cache_update,
+            ],
+            transformer_block_index=self.block_index,
+            seq_positions=start_positions,
+            page_ids=seq_block_ids,
+        )
+
+        # Restore from the cache.
+        xk, xv = cache.read(
+            cache_state,
+            read_into_partitions=[
+                xk_temp[:, 0:kv_seq_len, ...],
+                xv_temp[:, 0:kv_seq_len, ...],
+            ],
+            transformer_block_index=self.block_index,
+            page_ids=seq_block_ids,
+            seq_len=kv_seq_len,
+        )
 
-            # For computation, we create a subview of the xk/xv tensors to have
-            # a sequence length covering the blocked size. This must include
-            # the newly added row (the caller is responsible for ensuring that
-            # every block has at least one row left). We'll compute on this
-            # ragged view and use an appropriate mask.
-            xk = xk_temp[:, 0:kv_seq_len, ...]
-            xv = xv_temp[:, 0:kv_seq_len, ...]
-            return xk, xv
+        # For computation, we create a subview of the xk/xv tensors to have
+        # a sequence length covering the blocked size. This must include
+        # the newly added row (the caller is responsible for ensuring that
+        # every block has at least one row left). We'll compute on this
+        # ragged view and use an appropriate mask.
+        return xk, xv

sharktank/sharktank/types/tensors.py

@@ -990,7 +990,7 @@ def _is_slicing_split_dim(self, key):
             else:
                 # Any other collection is a indexing only dimension 0.
                 return self.shard_dim == 0
-        if len(key) < self.shard_dim:
+        if len(key) <= self.shard_dim:
             return False
         if not isinstance(key[self.shard_dim], slice):
             return True