Move and rename GranularityType -> Granularity (#1038)

* Make module swap the main QAT flow again

Summary: Following #987, this
commit makes module swap the main QAT flow today. We remove all
tensor subclass fake quantize injection logic since this is not
needed in both the long term and the short term plans for QAT.
In the short term, we will continue to use a full module swap
flow, and only migrate to the long term flow once there is
general distributed support for tensor subclasses and when
tensor subclass composability provides meaningful benefits.

Test Plan:
python test/quantization/test_qat.py

[ghstack-poisoned]

* Move and rename GranularityType -> Granularity

Summary: Move GranularityType to quant_primitives.py to be
consistent with other similar fields like MappingType and
ZeroPointDomain.

Test Plan: CI

[ghstack-poisoned]

* Update on "Move and rename GranularityType -> Granularity"


Summary: Move GranularityType to quant_primitives.py to be
consistent with other similar fields like MappingType and
ZeroPointDomain.

Test Plan: CI

[ghstack-poisoned]

* Update on "Move and rename GranularityType -> Granularity"


Summary: Move GranularityType to quant_primitives.py to be
consistent with other similar fields like MappingType and
ZeroPointDomain.

Test Plan: CI

[ghstack-poisoned]

* Update on "Move and rename GranularityType -> Granularity"


Summary: Move GranularityType to quant_primitives.py to be
consistent with other similar fields like MappingType and
ZeroPointDomain.

Test Plan: CI

[ghstack-poisoned]

* Update base for Update on "Move and rename GranularityType -> Granularity"


Summary: Move GranularityType to quant_primitives.py to be
consistent with other similar fields like MappingType and
ZeroPointDomain.

Test Plan: CI

[ghstack-poisoned]

test/dtypes/test_affine_quantized_float.py

@@ -26,11 +26,17 @@
     float8_weight_only,
     quantize_,
 )
-from torchao.quantization.observer import PerRow, PerTensor
+from torchao.quantization.granularity import (
+    PerRow,
+    PerTensor,
+)
 from torchao.quantization.quant_api import (
     float8_static_activation_float8_weight,
 )
-from torchao.quantization.quant_primitives import MappingType, choose_qparams_affine
+from torchao.quantization.quant_primitives import (
+    MappingType,
+    choose_qparams_affine,
+)
 
 random.seed(0)
 torch.manual_seed(0)

test/quantization/test_observer.py

@@ -9,11 +9,13 @@
 from torch.testing._internal import common_utils
 from torch.testing._internal.common_utils import TestCase
 
-from torchao.quantization.observer import (
-    AffineQuantizedMinMaxObserver,
+from torchao.quantization.granularity import (
     PerAxis,
     PerTensor,
 )
+from torchao.quantization.observer import (
+    AffineQuantizedMinMaxObserver,
+)
 from torchao.quantization.quant_api import (
     insert_observers_,
 )
@@ -42,7 +44,7 @@ def test_min_max_per_tensor_affine(self):
         obs = AffineQuantizedMinMaxObserver(
             MappingType.ASYMMETRIC,
             torch.uint8,
-            granularity_type=PerTensor(),
+            granularity=PerTensor(),
             eps=torch.finfo(torch.float32).eps,
             scale_dtype=torch.float,
             zero_point_dtype=torch.int,
@@ -54,7 +56,7 @@ def test_min_max_per_channel_affine(self):
         obs = AffineQuantizedMinMaxObserver(
             MappingType.ASYMMETRIC,
             torch.uint8,
-            granularity_type=PerAxis(axis=0),
+            granularity=PerAxis(axis=0),
             eps=torch.finfo(torch.float32).eps,
             scale_dtype=torch.float,
             zero_point_dtype=torch.int,
@@ -68,7 +70,7 @@ def test_block_size_calc_success(self):
         obs = AffineQuantizedMinMaxObserver(
             MappingType.SYMMETRIC,
             torch.float8_e4m3fn,
-            granularity_type=PerTensor(),
+            granularity=PerTensor(),
             eps=torch.finfo(torch.float32).eps,
             scale_dtype=torch.float,
             zero_point_dtype=torch.int,
@@ -87,7 +89,7 @@ def test_block_size_calc_success(self):
         obs = AffineQuantizedMinMaxObserver(
             MappingType.SYMMETRIC,
             torch.float8_e4m3fn,
-            granularity_type=PerAxis(1),
+            granularity=PerAxis(1),
             eps=torch.finfo(torch.float32).eps,
             scale_dtype=torch.float,
             zero_point_dtype=torch.int,
@@ -102,7 +104,7 @@ def test_block_size_row_errors(self):
         obs = AffineQuantizedMinMaxObserver(
             MappingType.SYMMETRIC,
             torch.float8_e4m3fn,
-            granularity_type=PerAxis(0),
+            granularity=PerAxis(0),
             eps=torch.finfo(torch.float32).eps,
             scale_dtype=torch.float,
             zero_point_dtype=torch.int,
@@ -121,7 +123,7 @@ def test_block_size_row_errors(self):
         obs = AffineQuantizedMinMaxObserver(
             MappingType.SYMMETRIC,
             torch.float8_e4m3fn,
-            granularity_type=PerAxis(1),
+            granularity=PerAxis(1),
             eps=torch.finfo(torch.float32).eps,
             scale_dtype=torch.float,
             zero_point_dtype=torch.int,
@@ -149,7 +151,7 @@ def test_linear_observer_tensor(self, observe_weight: bool):
         input_observer = AffineQuantizedMinMaxObserver(
             MappingType.SYMMETRIC,
             torch.float8_e4m3fn,
-            granularity_type=PerTensor(),
+            granularity=PerTensor(),
             eps=torch.finfo(torch.float32).eps,
             scale_dtype=torch.float,
             zero_point_dtype=torch.int,
@@ -159,7 +161,7 @@ def test_linear_observer_tensor(self, observe_weight: bool):
             weight_observer = AffineQuantizedMinMaxObserver(
                 MappingType.SYMMETRIC,
                 torch.float8_e4m3fn,
-                granularity_type=PerTensor(),
+                granularity=PerTensor(),
                 eps=torch.finfo(torch.float32).eps,
                 scale_dtype=torch.float,
                 zero_point_dtype=torch.int,

torchao/_models/llama/eval.py

@@ -24,9 +24,9 @@
     float8_dynamic_activation_float8_weight,
     float8_static_activation_float8_weight,
 )
-from torchao.quantization.observer import PerRow, PerTensor
 from torchao._models._eval import TransformerEvalWrapper, InputRecorder
 from torchao._models.llama.model import prepare_inputs_for_model
+from torchao.quantization.granularity import PerRow, PerTensor
 
 from tokenizer import get_tokenizer
 import time
@@ -255,4 +255,4 @@ def run_evaluation(
         args.calibration_limit,
         args.calibration_seq_length,
         args.pad_calibration_inputs,
-    )
+    )

torchao/_models/llama/generate.py

@@ -216,7 +216,7 @@ def main(
             float8_weight_only,
             float8_dynamic_activation_float8_weight,
         )
-        from torchao.quantization.observer import PerTensor, PerRow
+        from torchao.quantization.granularity import PerTensor, PerRow
         if "int8wo" in quantization:
             quantize_(model, int8_weight_only())
         if "int8dq" in quantization:

torchao/prototype/awq/api.py

@@ -1,13 +1,13 @@
 import torch
 import torch.nn.functional as F
 
+from torchao.quantization.granularity import PerGroup
 from torchao.quantization.quant_primitives import (
     MappingType,
     ZeroPointDomain,
      _DTYPE_TO_QVALUE_BOUNDS,
 )
 from torchao.quantization import to_weight_tensor_with_linear_activation_scale_metadata
-from torchao.quantization.observer import PerGroup
 from torchao.quantization.quant_api import _replace_with_custom_fn_if_matches_filter
 from torchao.dtypes.uintx import _DTYPE_TO_BIT_WIDTH, UintxLayoutType
 from torchao.dtypes import( 

torchao/prototype/awq/core.py

@@ -7,20 +7,21 @@
 from torch.utils._python_dispatch import return_and_correct_aliasing
 from torchao.dtypes.uintx import _DTYPE_TO_BIT_WIDTH, UintxLayoutType
 from torchao.dtypes import to_affine_quantized_intx
+from torchao.quantization.granularity import Granularity
 from torchao.quantization.quant_primitives import (
     MappingType,
     ZeroPointDomain,
 )
 from torchao.quantization.observer import (
-    AffineQuantizedObserverBase, GranularityType
+    AffineQuantizedObserverBase,
 )
 
 
 class AWQObserver(AffineQuantizedObserverBase):
     def __init__(self,
         weight: torch.Tensor,
         bias: torch.Tensor,
-        quantization_granularity: GranularityType,
+        quantization_granularity: Granularity,
         mapping_type: MappingType,
         target_dtype: torch.dtype,
         n_validation_examples: int,
@@ -40,7 +41,7 @@ def __init__(self,
         Args:
             weight: The weight tensor to be observed.
             bias: The bias tensor to be observed.
-            quantization_granularity: Granularity type which specifies how many weights share the same scale/zero point
+            quantization_granularity: Granularity which specifies how many weights share the same scale/zero point
             input_dtype: The data type of the input tensor.
             mapping_type: Always set to asymmetric 
             target_dtype: The target data type of the quantized tensor
@@ -153,4 +154,4 @@ def from_float(cls, float_linear: torch.nn.Linear, act_obs: AWQObserver):
         observed_linear = cls(float_linear.in_features, float_linear.out_features, act_obs, False, device=float_linear.weight.device, dtype=float_linear.weight.dtype)
         observed_linear.weight = float_linear.weight
         observed_linear.bias = float_linear.bias
-        return observed_linear
+        return observed_linear

torchao/quantization/README.md

@@ -137,7 +137,7 @@ change_linear_weights_to_int8_dqtensors(model)
 ```python
 # for torch 2.4+
 from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight
-from torchao.quantization.observer import PerTensor
+from torchao.quantization.quant_api import PerTensor
 quantize_(model, float8_dynamic_activation_float8_weight(granularity=PerTensor()))
 ```
 

torchao/quantization/autoquant.py

@@ -12,12 +12,14 @@
 from torchao.dtypes import AffineQuantizedTensor, PlainLayoutType, TensorCoreTiledLayoutType, Float8LayoutType
 from torchao.quantization.linear_activation_quantized_tensor import LinearActivationQuantizedTensor
 from torch.utils._python_dispatch import return_and_correct_aliasing
-from .quant_primitives import (
-    safe_int_mm,
+from .granularity import (
+    PerAxis,
+    PerRow,
+    PerTensor,
 )
+from .quant_primitives import safe_int_mm
 from torchao.utils import TORCH_VERSION_AT_LEAST_2_3, TORCH_VERSION_AT_LEAST_2_5
 from torchao.quantization.utils import quantize_activation_per_token_absmax
-from torchao.quantization.observer import PerAxis, PerTensor, PerRow
 from torchao.float8.inference import Float8MMConfig
 
 import torch.nn.functional as F

torchao/quantization/granularity.py

@@ -0,0 +1,76 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class Granularity:
+    """
+    Base class for representing the granularity of quantization.
+
+    This class serves as a parent for specific granularity types used in 
+    quantization operations, such as per-tensor or per-axis quantization.
+    """
+    pass
+
+@dataclass(frozen=True)
+class PerTensor(Granularity):
+    """
+    Represents per-tensor granularity in quantization.
+
+    This granularity type calcualtes the quantization parameters
+    based off the entire tensor.
+    """
+    pass
+
+@dataclass(frozen=True)
+class PerAxis(Granularity):
+    """
+    Represents per-axis granularity in quantization.
+
+    This granularity type calcualtes different quantization parameters
+    along a specified axis of the tensor.
+
+    For example if the input tensor is shape [8, 16] and axis=0, then
+    the quantization parameters are calculated for each row of the tensor.
+    Giving a total of 8 quantization parameters.
+
+
+    Attributes:
+        axis (int): The axis along which reduction is performed.
+    """
+    axis: int
+
+@dataclass(frozen=True)
+
+class PerGroup(Granularity):
+    """
+    Represents per-channel group granularity in quantization.
+
+    This granularity type calcualtes different quantization parameters
+    for each group of <group_size> elements.
+
+    For example if the input tensor is shape [8, 16], and the group size is 4, then
+    the input tensor is reshaped to [64, 4]
+    quantization parameters are calculated for each group of 4 elements,
+    giving a total of 64 quantization parameters.
+
+    Attributes:
+        group_size (int): The size of each quantization group
+
+    """
+    group_size: int
+
+class PerRow(Granularity):
+    """
+    Represents row-wise granularity in quantization.
+
+    This is a special case of per-axis quantization and is unique to Float8 matmuls
+    where the input is quantized with a block_size of (1, ..., input.shape[-1]). And the weight
+    is quantized with a block_size of (1, weight.shape[1]).
+    """
+    pass