[Quant][Inductor] Enable quantization conv_binary(add/add_relu) patte…

…rn fusion inside inductor (#105456)

**Summary**
Enable the `dequant-conv2d-binary_postop(add)-unary_postop(relu)-quant` pattern fusion and lowering inside inductor.

**Test Plan**
```
clear && python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_binary
```

Pull Request resolved: #105456
Approved by: https://github.com/jgong5, https://github.com/eellison
ghstack dependencies: #104580, #104581, #104588, #104590, #105455

test/inductor/test_mkldnn_pattern_matcher.py

@@ -61,6 +61,12 @@
     lambda x, y: x.sub_(y): (1, 2, True),  # call_method
 }
 
+quantization_binary_list = [
+    lambda x, y: torch.add(x, y),
+    lambda x, y: x.add(y),
+    lambda x, y: x.add_(y),
+]
+
 
 @config.patch({"freezing": True})
 class TestPatternMatcherBase(TestCase):
@@ -391,6 +397,62 @@ def forward(self, x):
             match_nodes = 19
             self._test_common(mod, (v,), match_count, match_nodes, rtol=1e-2, atol=1e-2)
 
+    @skipIfNoDynamoSupport
+    @skipIfNoONEDNN
+    def test_qconv2d_binary(self):
+        class M(torch.nn.Module):
+            def __init__(
+                self,
+                binary_fn,
+                has_relu,
+                **kwargs,
+            ):
+                super().__init__()
+                self.conv1 = torch.nn.Conv2d(3, 6, kernel_size=3, stride=1)
+                self.conv2 = torch.nn.Conv2d(3, 6, kernel_size=3, stride=1)
+                self.binary_fn = binary_fn
+                self.has_relu = has_relu
+                self.relu = torch.nn.ReLU()
+
+            def forward(self, x):
+                x1 = self.conv1(x)
+                x2 = self.conv2(x)
+                if self.has_relu:
+                    return self.relu(self.binary_fn(x1, x2))
+                else:
+                    return self.binary_fn(x1, x2)
+
+        options = itertools.product(
+            quantization_binary_list,
+            [True, False],  # has_relu
+        )
+
+        for binary_fn, has_relu in options:
+            mod = M(binary_fn, has_relu=has_relu).eval()
+            v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=False).add(
+                1
+            )
+            # Totally 9 pattern_matcher_count, 41 pattern_matcher_nodes + 1 optional(unary post op)
+            # 1. Pair of to_int8 and to_fp32 at conv input * 2, extra input of add * 1, and graph output * 1
+            #    matched in pointless_convert pass at
+            #    torch/_inductor/fx_passes/joint_graph.py: [convert_element_type, convert_element_type_1]
+            # 2. Dequant pattern matcher for dequant promotion * 1
+            #    [convert_element_type_3, sub_1, mul_3]
+            # 3. Dequant-conv pattern matched in quantization weight prepack * 2
+            #    [convert_element_type_1, sub, mul_1, dequantize_per_channel, clone, convolution]
+            # 4. Quantization fusion in post-grad fusion pass * 1
+            #    [qconv2d_pointwise_default, div_1, round_2, add_1, clamp_min_1, clamp_max_1, convert_element_type_2]
+            # 5. Qconv2d_add * 1
+            #    [qconv2d_pointwise_default_1, convert_element_type_5, sub_2, mul_5, add_3, optional(relu),
+            #     mul_6, round_4, add_4, clamp_min_3, clamp_max_3, convert_element_type_6]
+            self._test_common(
+                mod,
+                (v,),
+                9,
+                42 if has_relu else 41,
+                check_quantization=True,
+            )
+
     @skipIfNoDynamoSupport
     @skipIfNoONEDNN
     def test_qconv2d_unary(self):
@@ -468,9 +530,8 @@ def forward(self, x):
 
         mod = M().eval()
         v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=False).add(1)
-        # For now, we have annotated conv_add in x86InductorQuantizer. But we didn't implement the lowering.
-        # TODO <leslie>: Modify the pattern matcher count after we implement the qconv2d_add lowering.
-        # Totally 10 pattern_matcher_count, 43 pattern_matcher_nodes
+
+        # Totally 11 pattern_matcher_count, 54 pattern_matcher_nodes
         # 1. Pair of to_int8 and to_fp32 at conv input * 2, extra input of add * 1, and graph output * 1
         #    matched in pointless_convert pass at
         #    torch/_inductor/fx_passes/joint_graph.py: [convert_element_type, convert_element_type_1]
@@ -480,11 +541,14 @@ def forward(self, x):
         #    [convert_element_type_1, sub, mul_1, dequantize_per_channel, clone, convolution]
         # 4. Quantization fusion in post-grad fusion pass * 2
         #    [qconv2d_pointwise_default, div_1, round_2, add_1, clamp_min_1, clamp_max_1, convert_element_type_2]
+        # 5. Qconv2d_add * 1
+        #    [qconv2d_pointwise_default_1, convert_element_type_5, sub_2, mul_5, add_3, mul_6, round_4, add_4,
+        #     clamp_min_3, clamp_max_3, convert_element_type_6]
         self._test_common(
             mod,
             (v,),
-            10,
-            43,
+            11,
+            54,
             check_quantization=True,
         )
 

torch/_inductor/fx_passes/quantization.py

@@ -69,6 +69,28 @@
     Arg(),  # algorithm
 )
 
+dequantize_accum_pattern = CallFunction(
+    aten.mul.Tensor,
+    CallFunction(
+        aten.sub.Tensor,
+        CallFunction(
+            prims.convert_element_type.default,
+            KeywordArg("accum"),
+            KeywordArg("accum_dq_dtype"),
+        ),
+        KeywordArg("accum_zp"),
+    ),
+    KeywordArg("accum_scale"),
+)
+
+
+def generate_pattern_with_binary(binary_post_op, computation_call, extra_input_pattern):
+    return CallFunction(
+        binary_post_op,
+        computation_call,
+        extra_input_pattern,
+    )
+
 
 def generate_pattern_with_unary(computation_call, unary_post_op):
     if unary_post_op is not None:
@@ -179,6 +201,67 @@ def qconv(match: Match, *args, **kwargs):
     return qconv
 
 
+def _register_quantized_conv_binary_lowering(
+    pattern,
+    pass_number,
+    computation_op,
+    fp32_output,
+    binary_unary_attr,
+):
+    @register_lowering_pattern(pattern, pass_number=pass_number)
+    def qconv_binary(match: Match, *args, **kwargs):
+        x, x_scale, x_zp = kwargs["x"], kwargs["x_scale"], kwargs["x_zp"]
+        accum, accum_scale, accum_zp = (
+            kwargs["accum"],
+            kwargs["accum_scale"],
+            kwargs["accum_zp"],
+        )
+        packed_weight, w_scale, w_zp = (
+            kwargs["packed_weight"],
+            kwargs["w_scale"],
+            kwargs["w_zp"],
+        )
+        b, stride, padding, dilation, groups = (
+            kwargs["b"],
+            kwargs["stride"],
+            kwargs["padding"],
+            kwargs["dilation"],
+            kwargs["groups"],
+        )
+        o_inv_scale, o_zero_point = (
+            kwargs["o_inv_scale"],
+            kwargs["o_zp"],
+        )
+
+        computation_args = (
+            x,
+            x_scale,
+            x_zp,
+            accum,
+            accum_scale,
+            accum_zp,
+            packed_weight,
+            w_scale,
+            w_zp,
+            b,
+            stride,
+            padding,
+            dilation,
+            groups,
+            o_inv_scale,
+            o_zero_point,
+            fp32_output,
+            binary_unary_attr.binary_op_name,
+            binary_unary_attr.alpha,
+            binary_unary_attr.unary_op_name,
+            binary_unary_attr.scalars_attr,
+            binary_unary_attr.algorithm_attr,
+        )
+        return L[computation_op](*computation_args)
+
+    return qconv_binary
+
+
 def _register_quantization_unary_fusion():
     class UnaryAttr:
         def __init__(self, op_name: str, scalars_attr=None, algorithm_attr=None):
@@ -208,8 +291,56 @@ def __init__(self, op_name: str, scalars_attr=None, algorithm_attr=None):
         )
 
 
+def _register_quantization_binary_fusion():
+    class BinaryUnaryAttr:
+        def __init__(
+            self,
+            binary_op_name: str,
+            alpha=None,
+            unary_op_name: str = "none",
+            scalars_attr=None,
+            algorithm_attr=None,
+        ):
+            self.binary_op_name = binary_op_name
+            self.alpha = alpha if alpha else 1.0
+            self.unary_op_name = unary_op_name
+            self.scalars_attr = scalars_attr if scalars_attr else []
+            self.algorithm_attr = algorithm_attr if algorithm_attr else ""
+
+    binary_replace_patterns = {
+        BinaryUnaryAttr("add", 1.0, "none", [], ""): generate_pattern_with_output_quant(
+            generate_pattern_with_binary(
+                aten.add.Tensor,
+                dequantize_qconv_pt2e_pattern,
+                dequantize_accum_pattern,
+            )
+        ),
+        BinaryUnaryAttr("add", 1.0, "relu", [], ""): generate_pattern_with_output_quant(
+            generate_pattern_with_unary(
+                generate_pattern_with_binary(
+                    aten.add.Tensor,
+                    dequantize_qconv_pt2e_pattern,
+                    dequantize_accum_pattern,
+                ),
+                aten.relu.default,
+            )
+        ),
+    }
+
+    for binary_unary_attr, patterns in binary_replace_patterns.items():
+        # Register qconv2d_binary_unary pattern for ExternKernel Lowering
+        _register_quantized_conv_binary_lowering(
+            patterns,
+            0 if binary_unary_attr.unary_op_name != "none" else 1,  # pass_number
+            torch.ops.onednn.qconv2d_pointwise.binary,  # computation_op
+            False,  # fp32_output
+            binary_unary_attr,  # binary_unary_attr
+        )
+
+
 def _register_quantization_lowerings():
     _register_quantization_unary_fusion()
+    _register_quantization_binary_fusion()
 
 
 def _is_valid_dequant_promotion_pattern(match):

torch/_inductor/graph.py

@@ -789,6 +789,7 @@ def run_node(self, n: torch.fx.Node):
                                 torch.ops.mkldnn._linear_pointwise.binary,
                                 torch.ops.aten.mkldnn_rnn_layer.default,
                                 torch.ops.onednn.qconv2d_pointwise.default,
+                                torch.ops.onednn.qconv2d_pointwise.binary,
                             ]
                             if torch._C.has_mkl:
                                 need_fixed_layout += [torch.ops.mkl._mkl_linear.default]