Add decorator for custom op and inductor decomp registration

Summary:
This PR adds a decorator to register custom op and also an inductor dcomposition.

The goal is for torch.export path to be able to see high level ops like quantize_affine instead of breaking down the op, this is because some backends like xnnpack wants to work with these higher level ops.

This is a redo for pytorch#408, difference is we can preserve the enums on the python side in this PR

Test Plan:
regression tests:
python test/quantization/test_quant_api.py
python test/integration/test_integration.py

also need to check performance with python tutorials/quantize_vit/run_vit_b_quant.py

Reviewers:

Subscribers:

Tasks:

Tags:

Author: jerryzh168

test/integration/test_integration.py

@@ -1244,7 +1244,7 @@ def test_autoquant_manual(self, device, dtype):
         out3 = mod(example_input)
         sqnr2 = SQNR(out, out3)
         self.assertTrue(sqnr2 >= 30)
-    
+
 
     @parameterized.expand(combine_parameters(COMMON_DEVICE_DTYPE,
         [
@@ -1376,7 +1376,7 @@ class TestExport(unittest.TestCase):
         list(itertools.product(TENSOR_SUBCLASS_APIS, COMMON_DEVICES, COMMON_DTYPES)),
     )
     @run_supported_device_dtype
-    def test_aoti(self, api, test_device, test_dtype):
+    def test_export(self, api, test_device, test_dtype):
         if not TORCH_VERSION_AFTER_2_4:
             self.skipTest("aoti compatibility requires 2.4+.")
 
@@ -1413,9 +1413,20 @@ def forward(self, x):
 
         # make sure it compiles
         example_inputs = (x,)
-        model = torch.export.export(model, example_inputs).module()
+        from torch._export import capture_pre_autograd_graph
+        # TODO: export changes numerics right now, this is because of functionalization according to Zhengxu
+        # we can re-enable this after non-functional IR is enabled in export
+        # model = torch.export.export(model, example_inputs).module()
+        model = capture_pre_autograd_graph(model, example_inputs)
         after_export = model(x)
         self.assertTrue(torch.equal(after_export, ref))
+        if api is _int8da_int8w_api:
+            targets = [n.target for n in model.graph.nodes]
+            self.assertTrue(torch.ops.quant.choose_qparams_affine.default in targets)
+            self.assertTrue(torch.ops.quant.quantize_affine.default in targets)
+
+
+
 
 class TestUtils(unittest.TestCase):
     @parameterized.expand(COMMON_DEVICE_DTYPE)

torchao/quantization/quant_primitives.py

@@ -4,13 +4,16 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
-from enum import Enum
+from enum import Enum, auto
 from typing import List, Optional, Tuple, Dict
 import torch
 
 from torchao.kernel.intmm import int_scaled_matmul
 from torchao.kernel.intmm import safe_int_mm
-from torchao.utils import TORCH_VERSION_AFTER_2_3
+from torchao.utils import (
+    TORCH_VERSION_AFTER_2_3,
+    TORCH_VERSION_AFTER_2_5,
+)
 
 
 __all__ = [
@@ -34,17 +37,17 @@ class MappingType(Enum):
     based on this mapping
     e.g. scale = (10.2 - (-3.5)) / (7 - (-8))
     """
-    SYMMETRIC = 0
-    ASYMMETRIC = 1
+    SYMMETRIC = auto()
+    ASYMMETRIC = auto()
 
 class ZeroPointDomain(Enum):
     """Enum that indicate whether zero_point is in integer domain or floating point domain
 
     integer domain: quantized_val = (float_val / scale) (integer) + zero_point (integer)
     float domain: quantized_val = (float_val - (zero_point (float) - scale * mid_point)) / scale
     """
-    INT = 0
-    FLOAT = 1
+    INT = auto()
+    FLOAT = auto()
 
 """
 Map from dtype to the bound value of integers
@@ -69,6 +72,90 @@ class ZeroPointDomain(Enum):
     })
 
 
+# def register_custom_op(name: str):
+#     from torch._inductor.decomposition import register_decomposition
+
+#     def decorator(fn):
+#         if TORCH_VERSION_AFTER_2_5:
+#             opdef = torch.library.custom_op(name, mutates_args=())(fn)
+#             opdef.register_fake(fn)
+#             register_decomposition([opdef._opoverload])(fn)
+#             return opdef
+#         else:
+#             return fn
+
+#     return decorator
+
+quant_lib = torch.library.Library("quant", "FRAGMENT")
+
+# def register_custom_op(lib, schema: str):
+#     """This decorator is used to preserve some high level operators for torch.export.export
+#     while still allow them to be decomposed for inductor path
+
+#     NOTE: This should be applied at the top, after all other decorators have been applied
+#     """
+#     from torch._inductor.decomposition import register_decomposition
+
+#     def decorator(fn):
+#         if TORCH_VERSION_AFTER_2_5:
+#             # TODO: change order
+#             lib_namespace = lib.ns
+#             op_name = schema.split("(")[0]
+#             lib.define(schema)
+#             lib.impl(op_name, fn, "CompositeImplicitAutograd")
+#             op = getattr(getattr(torch.ops, lib_namespace), op_name)
+#             register_decomposition([op])(fn)
+#             return op
+#         else:
+#             return fn
+
+#     return decorator
+
+def register_custom_op(lib):
+    """This decorator is used to preserve some high level operators for torch.export.export
+    while still allow them to be decomposed for inductor path
+
+    requirement: make sure `fn.__name__[1:]` is the operator name you want to register
+
+    NOTE: This should be applied at the top, after all other decorators have been applied
+    NOTE: We haven't tested the case when `fn` accepts tensor subclass instance as input,
+    e.g. uint4 tensor subclass instance, and we'll probably need to figure out what would make
+    sense for downstream system (like executorch) to accept as well
+
+    Example:
+        lib = torch.library.Library("my_namespace', "FRAGMENT")
+        @register_custom_op(lib)
+        def _the_op_that_needs_to_be_preserved(...)
+            ...
+
+        # after this, `_the_op_that_needs_to_be_preserved` will be preserved as
+        # torch.ops.my_namespace.the_op_that_needs_to_be_preserved operator after
+        # torch.export.export
+
+    """
+    from torch._inductor.decomposition import register_decomposition
+
+    def decorator(fn):
+        if TORCH_VERSION_AFTER_2_5:
+            from torch._library.infer_schema import infer_schema
+
+            # assuming fn.__name__ starts with `_` and we want to take the rest
+            # to be the name of the custom op
+            op_name = fn.__name__[1:]
+            schema = op_name + infer_schema(fn)
+            lib.define(schema)
+            lib.impl(op_name, fn, "CompositeImplicitAutograd")
+
+            lib_namespace = lib.ns
+            op = getattr(getattr(torch.ops, lib_namespace), op_name)
+            register_decomposition([op])(fn)
+            return op
+        else:
+            return fn
+
+    return decorator
+
+
 # TODO: decide on if we want to allow custom quant_min/quant_max here
 def _get_and_check_qmin_qmax(dtype, quant_min, quant_max):
     """Get quant_min and quant_max args based on dtype and also
@@ -140,7 +227,7 @@ def quantize_affine(
     quant_min: Optional[int] = None,
     quant_max: Optional[int] = None,
     zero_point_domain: ZeroPointDomain = ZeroPointDomain.INT,
-):
+) -> torch.Tensor:
     """
     Args:
       input (torch.Tensor): original float32, float16 or bfloat16 Tensor
@@ -174,6 +261,31 @@ def quantize_affine(
     Output:
       quantized tensor with requested dtype
     """
+    return _quantize_affine(
+        input,
+        block_size,
+        scale,
+        zero_point,
+        output_dtype,
+        quant_min,
+        quant_max,
+        zero_point_domain.name,
+    )
+
+
+@register_custom_op(quant_lib)
+def _quantize_affine(
+    input: torch.Tensor,
+    block_size: List[int],
+    scale: torch.Tensor,
+    zero_point: Optional[torch.Tensor],
+    output_dtype: torch.dtype,
+    quant_min: Optional[int] = None,
+    quant_max: Optional[int] = None,
+    zero_point_domain: str = "INT",
+) -> torch.Tensor:
+    """op definition that has compatible signatures with custom op library
+    """
     # 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}"
@@ -188,12 +300,12 @@ def quantize_affine(
     if zero_point is not None:
         zero_point = zero_point.view(shape_after_reduction)
 
-    if zero_point_domain == ZeroPointDomain.INT:
+    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
+        assert zero_point_domain == ZeroPointDomain.FLOAT.name
         mid_point = (quant_max + quant_min + 1) / 2
         min_val = zero_point - scale * mid_point
         quant = (
@@ -216,7 +328,7 @@ def dequantize_affine(
     zero_point_domain: ZeroPointDomain = ZeroPointDomain.INT,
     *,
     output_dtype: torch.dtype = torch.float32,
-):
+) -> torch.Tensor:
     """
     Args:
       input (torch.Tensor): quantized tensor, should match the dtype `dtype` argument
@@ -238,6 +350,34 @@ def dequantize_affine(
     Output:
       dequantized Tensor, with requested dtype or fp32
     """
+    return _dequantize_affine(
+        input,
+        block_size,
+        scale,
+        zero_point,
+        input_dtype,
+        quant_min,
+        quant_max,
+        zero_point_domain.name,
+        output_dtype=output_dtype,
+    )
+
+
+# @register_custom_op(quant_lib, 'dequantize_affine(Tensor input, int[] block_size, Tensor scale, Tensor zero_point, ScalarType input_dtype, int? quant_min=None, int? quant_max=None, str zero_point_domain="INT", ScalarType output_dtype=float) -> Tensor')
+@register_custom_op(quant_lib)
+def _dequantize_affine(
+    input: torch.Tensor,
+    block_size: List[int],
+    scale: torch.Tensor,
+    zero_point: Optional[torch.Tensor],
+    input_dtype: torch.dtype,
+    quant_min: Optional[int] = None,
+    quant_max: Optional[int] = None,
+    zero_point_domain: str = "INT",
+    output_dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    """op definition that has compatible signatures with custom op library
+    """
 
     # TODO: validations
     # TODO: validate scale/zero_point dimensions are compatible with block_size
@@ -255,16 +395,16 @@ def dequantize_affine(
     if zero_point is not None:
         zero_point = zero_point.view(shape_after_reduction)
 
-    if zero_point_domain == ZeroPointDomain.INT:
+    if zero_point_domain == ZeroPointDomain.INT.name:
         # Force a copy to avoid input modification due
         # to upcoming in-place operations.
         dequant = input.to(torch.int32, copy=True)
         if zero_point is not None:
-            dequant -= zero_point.to(torch.int32)
+            dequant = dequant - zero_point.to(torch.int32)
         dequant = dequant.to(output_dtype)
-        dequant *= scale
+        dequant = dequant * scale
     else:
-        assert zero_point_domain == ZeroPointDomain.FLOAT, f"Unexpected zero point domain: {zero_point_domain}"
+        assert zero_point_domain == ZeroPointDomain.FLOAT.name, f"Unexpected zero point domain: {zero_point_domain}"
         mid_point = (quant_max + quant_min + 1) / 2
         # This should allocate new memory and avoid input modification
         dequant = input - mid_point
@@ -320,8 +460,39 @@ def choose_qparams_affine(
     Output:
         Tuple of scales and zero_points Tensor with requested dtype
     """
+    return _choose_qparams_affine(
+        input,
+        mapping_type.name,
+        block_size,
+        target_dtype,
+        quant_min,
+        quant_max,
+        eps,
+        scale_dtype,
+        zero_point_dtype,
+        preserve_zero,
+        zero_point_domain.name
+    )
+
+# @register_custom_op(quant_lib, 'choose_qparams_affine(Tensor input, str mapping_type, int[] block_size, ScalarType target_dtype, int? quant_min=None, int? quant_max=None, float? eps=None, ScalarType? scale_dtype=None, ScalarType? zero_point_dtype=None, bool preserve_zero=True, str zero_point_domain="INT") -> (Tensor, Tensor)')
+@register_custom_op(quant_lib)
+def _choose_qparams_affine(
+   input: torch.Tensor,
+   mapping_type: str,
+   block_size: List[int],
+   target_dtype: torch.dtype,
+   quant_min: Optional[int] = None,
+   quant_max: Optional[int] = None,
+   eps: Optional[float] = None,
+   scale_dtype: Optional[torch.dtype] = None,
+   zero_point_dtype: Optional[torch.dtype] = None,
+   preserve_zero: bool = True,
+   zero_point_domain: str = "INT",
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """op definition that has compatible signatures with custom op library
+    """
     quant_min, quant_max = _get_and_check_qmin_qmax(target_dtype, quant_min, quant_max)
-    assert mapping_type in [MappingType.SYMMETRIC, MappingType.ASYMMETRIC], f"Unsupported mapping type: {mapping_type}"
+    assert mapping_type in [MappingType.SYMMETRIC.name, MappingType.ASYMMETRIC.name], f"Unsupported mapping type: {mapping_type}"
 
     if scale_dtype is None:
         scale_dtype = input.dtype
@@ -342,21 +513,22 @@ def choose_qparams_affine(
         min_val_neg = min_val
         max_val_pos = max_val
 
-    if mapping_type == MappingType.SYMMETRIC:
+    if mapping_type == MappingType.SYMMETRIC.name:
         max_val_pos = torch.max(-min_val_neg, max_val_pos)
         scale = max_val_pos / (float(quant_max - quant_min) / 2)
         if not preserve_zero:
             raise ValueError("preserve_zero == False is not supported for symmetric quantization")
-        if zero_point_domain != ZeroPointDomain.INT:
+        if zero_point_domain != ZeroPointDomain.INT.name:
             raise ValueError("zero_point_domain != ZeroPointDomain.INT is not supported for symmetric quantization")
         zero_point = torch.full_like(scale, int((quant_max + quant_min + 1) / 2))
     else:
+        assert mapping_type == MappingType.ASYMMETRIC.name
         scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min)
         if preserve_zero:
             zero_point = quant_min - torch.round(min_val_neg / scale)
             zero_point = torch.clamp(zero_point, quant_min, quant_max)
         else:
-            assert zero_point_domain == ZeroPointDomain.FLOAT, "if not preserve_zero, zero_point must be in FLOAT domain"
+            assert zero_point_domain == ZeroPointDomain.FLOAT.name, "if not preserve_zero, zero_point must be in FLOAT domain"
             mid_point = (quant_max + quant_min + 1) / 2
             zero_point = min_val_neg + scale * mid_point