Add support for quantized bmm

backends/cadence/aot/TARGETS

@@ -28,6 +28,7 @@ python_library(
         "compiler.py",
     ],
     deps = [
+        "fbsource//third-party/pypi/pyre-extensions:pyre-extensions",
         ":passes",
         ":utils",
         "//caffe2:torch",

backends/cadence/aot/compiler.py

@@ -18,9 +18,13 @@
     ReplaceSqueezeAndUnsqueezeWithViewPass,
 )
 from executorch.backends.cadence.aot.quantizer.fusion_pass import QuantFusion
-from executorch.backends.cadence.aot.quantizer.quantizer import CadenceQuantizer
+from executorch.backends.cadence.aot.quantizer.quantizer import (
+    CadenceGenericQuantizer,
+    CadenceQuantizer,
+)
 from executorch.backends.cadence.aot.utils import model_is_quantized
 from executorch.exir import EdgeCompileConfig, EdgeProgramManager, to_edge
+from pyre_extensions import assert_is_instance
 from torch._export import capture_pre_autograd_graph
 from torch.ao.quantization.pt2e.export_utils import model_is_exported
 from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
@@ -53,8 +57,10 @@ def quantize_pt2(
     converted_model = convert_pt2e(prepared_model)
 
     # Get patterns and apply fusion of dq -> op -> q to qop
-    # pyre-fixme[16]: Pyre doesn't get that CadenceQuantizer has a patterns attribute
-    patterns = [q.pattern for q in quantizer.quantizers]
+    patterns = [
+        assert_is_instance(q, CadenceGenericQuantizer).pattern
+        for q in quantizer.quantizers
+    ]
     QuantFusion(patterns)(converted_model)
 
     return converted_model

backends/cadence/aot/quantizer/fusion_pass.py

@@ -11,6 +11,7 @@
 import torch
 from executorch.backends.cadence.aot.quantizer.patterns import (
     AddmmPattern,
+    BmmPattern,
     Conv1dPattern,
     Conv2dPattern,
     LayerNormFunctionalPattern,
@@ -361,9 +362,7 @@ def call(self, graph_module: fx.GraphModule) -> PassResult:  # noqa: C901
                         inputs_inputs + weights_inputs + other_inputs + bias_inputs
                     )
                     kwargs = {}
-                    if isinstance(pattern, Conv1dPattern) or isinstance(
-                        pattern, Conv2dPattern
-                    ):
+                    if isinstance(pattern, (Conv1dPattern, Conv2dPattern)):
                         args, kwargs = get_args_and_kwargs_conv(
                             graph_module,
                             inputs_inputs,
@@ -396,7 +395,7 @@ def call(self, graph_module: fx.GraphModule) -> PassResult:  # noqa: C901
                             other_inputs,
                             quant_node,
                         )
-                    elif isinstance(pattern, MatmulPattern):
+                    elif isinstance(pattern, (BmmPattern, MatmulPattern)):
                         args, kwargs = get_args_and_kwargs_matmul(
                             inputs_inputs,
                             dequants_inputs,

backends/cadence/aot/quantizer/patterns.py

@@ -95,6 +95,26 @@ def replacement_op(self):
         return torch.ops.cadence.quantized_linear
 
 
+class BmmPattern(QuantizationPattern):
+    def partition_types(self) -> List[Callable[..., torch.Tensor]]:
+        return [torch.bmm]
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> PartitionAnchors:
+        bmm_node = fused_partition[0].nodes[-1]
+
+        return PartitionAnchors(
+            inputs=[(bmm_node, 0), (bmm_node, 1)],
+            weights=[],
+            biases=[],
+            output=[(bmm_node,)],
+        )
+
+    def replacement_op(self):
+        return torch.ops.cadence.quantized_matmul.default
+
+
 class Conv1dPattern(QuantizationPattern):
     def partition_types(self) -> List[Type[torch.nn.Module]]:
         return [torch.nn.Conv1d]

backends/cadence/aot/quantizer/quantizer.py

@@ -9,6 +9,7 @@
 import torch
 from executorch.backends.cadence.aot.quantizer.patterns import (
     AddmmPattern,
+    BmmPattern,
     Conv1dPattern,
     Conv2dPattern,
     LayerNormFunctionalPattern,
@@ -133,6 +134,7 @@ def __init__(self):
         super().__init__(
             [
                 CadenceGenericQuantizer(AddmmPattern(), static_qconfig),
+                CadenceGenericQuantizer(BmmPattern(), static_qconfig),
                 CadenceGenericQuantizer(Conv1dPattern(), static_qconfig),
                 CadenceGenericQuantizer(Conv2dPattern(), static_qconfig),
                 CadenceGenericQuantizer(LayerNormPattern(), static_qconfig),