VAD perf improvements - Improve aten::full performance (#3974)

Summary: Pull Request resolved: #3974

Reviewed By: dulinriley, zonglinpengmeta

Differential Revision: D56524303

fbshipit-source-id: 5bdeb29d473446d0350d397e48eb70eeeaed16d3

backends/cadence/aot/passes.py

@@ -4,20 +4,34 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+from typing import Any, Dict, Tuple
+
 import torch
 from executorch.backends.cadence.aot.utils import get_edge_overload_packet
 from executorch.exir.dialects._ops import ops as exir_ops
-from executorch.exir.pass_base import ExportPass, ProxyValue
+from executorch.exir.pass_base import ExportPass, NodeMetadata, ProxyValue
 from torch._subclasses import FakeTensor
 from torch.utils._pytree import tree_map_only
 
 
+# pyre-strict
+
+# Similar to what's done in executorch/exir/pass_base.py
+Argument = Any  # pyre-ignore
+
+
 class ReplacePT2QuantWithCadenceQuantPass(ExportPass):
     """
     Replace the pt2 quantization ops with custom cadence quantization ops.
     """
 
-    def call_operator(self, op, args, kwargs, meta):
+    def call_operator(
+        self,
+        op,  # pyre-ignore
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Argument],
+        meta: NodeMetadata,
+    ) -> ProxyValue:
         if op not in {exir_ops.edge.quantized_decomposed.quantize_per_tensor.default}:
             return super().call_operator(op, args, kwargs, meta)
 
@@ -34,7 +48,13 @@ class ReplacePT2DequantWithCadenceDequantPass(ExportPass):
     Replace the pt2 dequantization ops with custom cadence dequantization ops.
     """
 
-    def call_operator(self, op, args, kwargs, meta):
+    def call_operator(
+        self,
+        op,  # pyre-ignore
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Argument],
+        meta: NodeMetadata,
+    ) -> ProxyValue:
         if op not in {exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default}:
             return super().call_operator(op, args, kwargs, meta)
 
@@ -51,7 +71,13 @@ class ReplaceScalarTensorWithFullPass(ExportPass):
     aten.scalar_tensor can be replaced by aten.full with a shape of [1].
     """
 
-    def call_operator(self, op, args, kwargs, meta):
+    def call_operator(
+        self,
+        op,  # pyre-ignore
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Argument],
+        meta: NodeMetadata,
+    ) -> ProxyValue:
         if op not in {
             exir_ops.edge.aten.scalar_tensor.default,
             torch.ops.aten.scalar_tensor.default,
@@ -64,7 +90,7 @@ def call_operator(self, op, args, kwargs, meta):
                 [1],
                 args[0],
             ),
-            {},
+            {"dtype": torch.float32},
             meta,
         )
 
@@ -75,7 +101,13 @@ class ReplaceSqueezeAndUnsqueezeWithViewPass(ExportPass):
     view_copy op
     """
 
-    def call_operator(self, op, args, kwargs, meta):
+    def call_operator(
+        self,
+        op,  # pyre-ignore
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Argument],
+        meta: NodeMetadata,
+    ) -> ProxyValue:
         # Instead of testing EdgeOpOverload, test EdgeOpOverloadPacket,
         # which allows us to cover all overloads.
         if get_edge_overload_packet(op) not in {
@@ -99,7 +131,13 @@ def call_operator(self, op, args, kwargs, meta):
 
 
 class RemoveZeroSizedCatArgsPass(ExportPass):
-    def call_operator(self, op, args, kwargs, meta):
+    def call_operator(
+        self,
+        op,  # pyre-ignore
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Argument],
+        meta: NodeMetadata,
+    ) -> ProxyValue:
         if op != exir_ops.edge.aten.cat.default:
             return super().call_operator(op, args, kwargs, meta)
 
@@ -122,6 +160,7 @@ def call_operator(self, op, args, kwargs, meta):
             # TODO(matthiascremon): confirm this is the best way to do this.
             if isinstance(result, FakeTensor):
                 result.constant = result
+            # pyre-ignore[7]: Incompatible return type.
             return torch.empty_like(result)
 
         # If there was only one tensor in the new_args list,
@@ -130,7 +169,7 @@ def call_operator(self, op, args, kwargs, meta):
             return new_args[0]
 
         # Otherwise, we replace args[0] with new_args.
-        args = list(args)
-        args[0] = new_args
+        init_args = list(args)
+        init_args[0] = new_args
         args = tuple(args)
         return super().call_operator(op, args, kwargs, meta)

backends/cadence/aot/quantizer/fusion_pass.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-strict
+
 from typing import Any, Dict, List, Tuple
 
 import torch
@@ -31,6 +33,11 @@
 from torch.fx.passes.utils.fuser_utils import legalize_graph
 
 
+# Use this to avoid pyre errors
+# pyre-ignore[33]: `_ModelInputsType` cannot alias to `Any`.
+ArgsType = Any
+
+
 # Helper function to get the args and kwargs for the linear replacement op
 def get_args_and_kwargs_linear(
     graph_module: GraphModule,
@@ -40,7 +47,7 @@ def get_args_and_kwargs_linear(
     dequants_weights: List[fx.Node],
     bias_inputs: List[fx.Node],
     quant_node: fx.Node,
-) -> Tuple[Tuple[Any], Dict[str, Any]]:
+) -> Tuple[Tuple[ArgsType], Dict[str, ArgsType]]:
     """
     Returns the args and kwargs for the linear replacement op.
     """
@@ -98,7 +105,7 @@ def get_args_and_kwargs_layer_norm(
     dequants_inputs: List[fx.Node],
     other_inputs: List[fx.Node],
     quant_node: fx.Node,
-) -> Tuple[Tuple[Any], Dict[str, Any]]:
+) -> Tuple[Tuple[ArgsType], Dict[str, ArgsType]]:
     """
     Returns the args and kwargs for the layer norm replacement op.
     """
@@ -167,7 +174,7 @@ def get_args_and_kwargs_matmul(
     inputs_inputs: List[fx.Node],
     dequants_inputs: List[fx.Node],
     quant_node: fx.Node,
-) -> Tuple[Tuple[Any, ...], Dict[str, Any]]:
+) -> Tuple[Tuple[ArgsType, ...], Dict[str, ArgsType]]:
     requantize_scale = (
         # pyre-ignore[58]: Unsupported operand
         dequants_inputs[0].args[1]
@@ -203,7 +210,7 @@ def get_args_and_kwargs_conv(
     bias_inputs: List[fx.Node],
     quant_node: fx.Node,
     op_node: fx.Node,
-):
+) -> Tuple[Tuple[ArgsType], Dict[str, ArgsType]]:
     weight_scale = dequants_weights[0].args[1]
     weight_zero_point = dequants_weights[0].args[2]
     # pyre-fixme[58]: Unsupported operand types
@@ -277,12 +284,14 @@ def get_args_and_kwargs_relu(
     graph_module: GraphModule,
     inputs_inputs: List[fx.Node],
     dequants_inputs: List[fx.Node],
-):
+) -> Tuple[Tuple[ArgsType], Dict[str, ArgsType]]:
     # Make the args and kwargs for the replacement op
     args = tuple(inputs_inputs)
 
     X_zero_point = graph_module.graph.call_function(
-        torch.ops.aten.full.default, ([1], dequants_inputs[0].args[2])
+        torch.ops.aten.full.default,
+        ([1], dequants_inputs[0].args[2]),
+        {"dtype": torch.int32},
     )
 
     kwargs = {
@@ -292,8 +301,10 @@ def get_args_and_kwargs_relu(
 
 
 class QuantFusion(ExportPass):
-    def __init__(self, patterns):
+    # pyre-ignore[2]: Parameter `patterns` has no type specified
+    def __init__(self, patterns) -> None:
         super().__init__()
+        # pyre-ignore[4]: Parameter `patterns` of class `QuantFusion` has no type specified
         self.patterns = patterns
 
     def call(self, graph_module: fx.GraphModule) -> PassResult:  # noqa: C901
@@ -427,10 +438,12 @@ def call(self, graph_module: fx.GraphModule) -> PassResult:  # noqa: C901
             graph_module.recompile()
 
     @classmethod
+    # pyre-ignore[2]: Parameter `nodes` has no type specified
     def is_fused(cls, nodes) -> bool:
         return any(cls.__qualname__ in n.meta for n in nodes)
 
     @classmethod
+    # pyre-ignore[2]: Parameter `nodes` has no type specified
     def mark_fused(cls, nodes) -> bool:
         for n in nodes:
             # pyre-fixme[7]: Incompatible return type