Move and rename GranularityType -> Granularity

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