Add general fake_quantize_affine op (pytorch#492)

Summary: Add a general `fake_quantize_affine` op that simulates
`quantize_affine` + `dequantize_affine` but without casting the
intermediate quantized values to lower bit-widths, intended for
quantization-aware training (QAT).

Test Plan:
python test/quantization/test_quant_primitives.py -k test_fake_quantize_affine

test/quantization/test_quant_primitives.py

@@ -9,6 +9,7 @@
 import unittest
 import torch
 from torchao.quantization.quant_primitives import (
+    fake_quantize_affine,
     quantize_affine,
     dequantize_affine,
     choose_qparams_affine,
@@ -503,5 +504,24 @@ def test_groupwise_affine_dequantize_tensor_from_qparams(self):
 
         self.assertTrue(torch.equal(w_bf16, w_bf16_ref))
 
+    @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "skipping when torch version is 2.4 or lower")
+    def test_fake_quantize_affine(self):
+        input = torch.randn(10, 10)
+
+        mapping_type = MappingType.SYMMETRIC
+        block_size = list(input.shape)
+        for i in range(len(block_size) - 1):
+            block_size[i] = 1
+        dtype = torch.int8
+        eps = 1e-5
+        quant_min = -127
+        quant_max = 127
+        scale, zero_point = choose_qparams_affine(input, mapping_type, block_size, dtype, quant_min, quant_max, eps=eps, scale_dtype=torch.float)
+
+        quantized = quantize_affine(input, block_size, scale, zero_point, dtype, quant_min, quant_max)
+        dequantized = dequantize_affine(quantized, block_size, scale, zero_point, dtype, quant_min, quant_max)
+        fake_quantized = fake_quantize_affine(input, block_size, scale, zero_point, dtype, quant_min, quant_max)
+        torch.testing.assert_close(dequantized, fake_quantized)
+
 if __name__ == "__main__":
     unittest.main()

torchao/quantization/quant_primitives.py

@@ -23,6 +23,7 @@
     "choose_qparams_affine",
     "quantize_affine",
     "dequantize_affine",
+    "fake_quantize_affine",
 ]
 
 class MappingType(Enum):
@@ -203,14 +204,34 @@ def _quantize_affine(
     output_dtype: torch.dtype,
     quant_min: Optional[int] = None,
     quant_max: Optional[int] = None,
-    zero_point_domain: str = "INT",
+    zero_point_domain: str = ZeroPointDomain.INT.name,
 ) -> torch.Tensor:
     """op definition that has compatible signatures with custom op library
     """
+    quant_min, quant_max = _get_and_check_qmin_qmax(output_dtype, quant_min, quant_max)
+    return _quantize_affine_no_dtype_cast(
+        input,
+        block_size,
+        scale,
+        zero_point,
+        quant_min,
+        quant_max,
+        zero_point_domain,
+    ).to(output_dtype)
+
+
+def _quantize_affine_no_dtype_cast(
+    input: torch.Tensor,
+    block_size: List[int],
+    scale: torch.Tensor,
+    zero_point: Optional[torch.Tensor],
+    quant_min: int,
+    quant_max: int,
+    zero_point_domain: str = ZeroPointDomain.INT.name,
+) -> torch.Tensor:
     # TODO: validations
     # TODO: validate scale/zero_point dimensions are compatible with block_size
     assert input.dtype in [torch.float32, torch.float16, torch.bfloat16], f"Unsupported input dtype: {input.dtype}"
-    quant_min, quant_max = _get_and_check_qmin_qmax(output_dtype, quant_min, quant_max)
     shape_for_reduction, reduction_dims = _get_reduction_params(block_size, input.size())
     original_shape = input.shape
     input = input.view(shape_for_reduction)
@@ -224,7 +245,7 @@ def _quantize_affine(
     if zero_point_domain == ZeroPointDomain.INT.name:
         quant = torch.clamp(
             torch.round(input * (1.0 / scale)) + zero_point, quant_min, quant_max
-        ).to(output_dtype)
+        )
     else:
         assert zero_point_domain == ZeroPointDomain.FLOAT.name
         mid_point = (quant_max + quant_min + 1) / 2
@@ -233,11 +254,12 @@ def _quantize_affine(
             torch.clamp(
                 torch.round((input - min_val) / scale),
                 quant_min, quant_max)
-        ).to(output_dtype)
+        )
     quant = quant.view(original_shape)
 
     return quant
 
+
 def dequantize_affine(
     input: torch.Tensor,
     block_size: Tuple[int, ...],
@@ -283,6 +305,7 @@ def dequantize_affine(
         output_dtype=output_dtype,
     )
 
+
 @register_custom_op
 def _dequantize_affine(
     input: torch.Tensor,
@@ -292,7 +315,7 @@ def _dequantize_affine(
     input_dtype: torch.dtype,
     quant_min: Optional[int] = None,
     quant_max: Optional[int] = None,
-    zero_point_domain: str = "INT",
+    zero_point_domain: str = ZeroPointDomain.INT.name,
     output_dtype: torch.dtype = torch.float32,
 ) -> torch.Tensor:
     """op definition that has compatible signatures with custom op library
@@ -303,7 +326,28 @@ def _dequantize_affine(
     assert input.dtype == input_dtype, f"Expected: {input_dtype}, got: {input.dtype}"
     assert output_dtype in [torch.float32, torch.float16, torch.bfloat16], f"Unsupported output dtype: {output_dtype}"
     quant_min, quant_max = _get_and_check_qmin_qmax(input_dtype, quant_min, quant_max)
+    return _dequantize_affine_no_dtype_check(
+        input,
+        block_size,
+        scale,
+        zero_point,
+        quant_min,
+        quant_max,
+        zero_point_domain,
+        output_dtype,
+    )
+
 
+def _dequantize_affine_no_dtype_check(
+    input: torch.Tensor,
+    block_size: List[int],
+    scale: torch.Tensor,
+    zero_point: Optional[torch.Tensor],
+    quant_min: int,
+    quant_max: int,
+    zero_point_domain: str = ZeroPointDomain.INT.name,
+    output_dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
     shape_for_reduction, reduction_dims = _get_reduction_params(block_size, input.size())
     original_shape = input.shape
     input = input.view(shape_for_reduction)
@@ -335,6 +379,62 @@ def _dequantize_affine(
 
     return dequant.view(original_shape).to(output_dtype)
 
+
+def fake_quantize_affine(
+    input: torch.Tensor,
+    block_size: Tuple[int, ...],
+    scale: torch.Tensor,
+    zero_point: Optional[torch.Tensor],
+    quant_dtype: torch.dtype,
+    quant_min: Optional[int] = None,
+    quant_max: Optional[int] = None,
+    zero_point_domain: ZeroPointDomain = ZeroPointDomain.INT,
+) -> torch.Tensor:
+    """
+    General fake quantize op for quantization-aware training (QAT).
+    This is equivalent to calling `quantize_affine` + `dequantize_affine`
+    but without the dtype casts.
+
+    Args:
+      input (torch.Tensor): original float32, float16 or bfloat16 Tensor
+      block_size: (Tuple[int, ...]): granularity of quantization, this means the size of the tensor elements that's sharing the same qparam
+           e.g. when size is the same as the input tensor dimension, we are using per tensor quantization
+      scale (float): quantization parameter for affine quantization
+      zero_point (int): quantization parameter for affine quantization
+      quant_dtype (torch.dtype): desired quantized dtype for determining and validating quant_min and quant_max values.
+      quant_min (Optional[int]): minimum quantized value for output Tensor, if not specified, it will be derived from dtype
+      quant_max (Optional[int]): maximum quantized value for output Tensor, if not specified, it will be derived from dtype
+      zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be eitehr integer or float
+        if zero_point is in integer domain, zero point is added to the quantized integer value during
+        quantization
+        if zero_point is in floating point domain, zero point is subtracted from the floating point (unquantized)
+        value during quantization
+        default is ZeroPointDomain.INT
+    """
+    input_dtype = input.dtype
+    quant_min, quant_max = _get_and_check_qmin_qmax(quant_dtype, quant_min, quant_max)
+    q = _quantize_affine_no_dtype_cast(
+        input,
+        block_size,
+        scale,
+        zero_point,
+        quant_min,
+        quant_max,
+        zero_point_domain.name,
+    )
+    dq = _dequantize_affine_no_dtype_check(
+        q,
+        block_size,
+        scale,
+        zero_point,
+        quant_min,
+        quant_max,
+        zero_point_domain.name,
+        output_dtype=input_dtype,
+    )
+    return dq
+
+
 def choose_qparams_affine(
    input: torch.Tensor,
    mapping_type: MappingType,