Add fuse_dq_q_pass in exir/passes and also add it to HTP backend (#2295)

Summary:

There are passes such as these https://fburl.com/code/vs6n4vcv that remove noops from the graph. The problem is that after this pass runs it still leaves in the dq->q nodes in the graph which then potentially get delegated to the backend causing perf regressions.

This pass will remove the dq->q ops if their qparams are of the same value. If not it won't touch them.

Reviewed By: kimishpatel

Differential Revision: D54543323

Author: tarun292

exir/passes/TARGETS

@@ -155,6 +155,7 @@ python_library(
     deps = [
         "//caffe2:torch",
         "//executorch/exir:pass_base",
+        "//executorch/exir/dialects:lib",
     ],
 )
 

exir/passes/remove_noop_pass.py

@@ -6,38 +6,88 @@
 
 # pyre-strict
 
+from typing import List, Tuple
+
 import torch
-from executorch.exir.pass_base import ExportPass, ProxyValue
-from torch.utils import _pytree as pytree
+from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.pass_base import ExportPass, PassResult
+from torch.fx import GraphModule
+
+_DEQUANT_OPS: Tuple[torch._ops.OpOverload] = (
+    torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+    torch.ops.quantized_decomposed.dequantize_per_channel.default,
+    exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
+    exir_ops.edge.quantized_decomposed.dequantize_per_channel.default,
+)
+_QUANT_OPS: Tuple[torch._ops.OpOverload] = (
+    torch.ops.quantized_decomposed.quantize_per_tensor.default,
+    torch.ops.quantized_decomposed.quantize_per_channel.default,
+    exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
+    exir_ops.edge.quantized_decomposed.quantize_per_channel.default,
+)
+
+
+def eliminate_dq_q(
+    graph_module: GraphModule,
+    dequant_nodes: List[torch.fx.Node],
+    check_qparams: bool = True,
+) -> None:
+    for node in dequant_nodes:
+        assert node.target in _DEQUANT_OPS
+        for user in list(node.users):
+            if user.target in _QUANT_OPS:
+                # Drop the input arg and check that the qparams are the same.
+                qparams_dq = list(node.args)[1:]
+                qparams_q = list(user.args)[1:]
+                if check_qparams and (qparams_dq != qparams_q):
+                    continue
+                user.replace_all_uses_with(node.args[0])
 
 
 class RemoveNoopPass(ExportPass):
     """
     Removes noops that pass through arguments.
     """
 
-    # pyre-ignore
-    def call_operator(self, op, args, kwargs, meta):
-        if op not in (
-            torch.ops.aten.to.dtype,
-            torch.ops.aten.dropout.default,
-            torch.ops.aten.slice_copy.Tensor,
-        ):
-            return super().call_operator(op, args, kwargs, meta)
-
-        args_data, kwargs_data = pytree.tree_map_only(
-            ProxyValue, lambda x: x.data, (args, kwargs)
-        )
-        orig_tensor = (
-            args[0].to_tensor() if isinstance(args[0], ProxyValue) else args[0]
-        )
-
-        if orig_tensor is op(*args_data, **kwargs_data):
-            return args[0]
-
-        if op == torch.ops.aten.slice_copy.Tensor:
-            result = op(*args_data, **kwargs_data)
-            if orig_tensor.size() == result.size():
-                return args[0]
-
-        return super().call_operator(op, args, kwargs, meta)
+    def call(self, graph_module: GraphModule) -> PassResult:
+
+        # In this list we'll collect all the dequant nodes that are inputs to ops that
+        # are removed in this pass and later check for redundant dq->q patterns and
+        # remove them.
+        dequant_nodes = []
+
+        for node in graph_module.graph.nodes:
+            if node.op != "call_function":
+                continue
+
+            if node.target not in (
+                torch.ops.aten.to.dtype,
+                torch.ops.aten.dropout.default,
+                torch.ops.aten.slice_copy.Tensor,
+            ):
+                continue
+
+            orig_tensor = node.args[0].meta["val"]
+
+            if orig_tensor is node.meta["val"]:
+                # If the graph is quantized, we must remove the entire pattern consisting of dq->op->q.
+                # Otherwise, removing only the op will suffice.
+                if node.args[0].target in _DEQUANT_OPS:
+                    dequant_nodes += [node.args[0]]
+                node.replace_all_uses_with(node.args[0])
+                continue
+
+            if node.target == torch.ops.aten.slice_copy.Tensor:
+                if orig_tensor.size() == node.meta["val"].size():
+                    # If the graph is quantized, we must remove the entire pattern consisting of dq->op->q.
+                    # Otherwise, removing only the op will suffice.
+                    if node.args[0].target in _DEQUANT_OPS:
+                        dequant_nodes += [node.args[0]]
+                    node.replace_all_uses_with(node.args[0])
+
+        graph_module.graph.eliminate_dead_code()
+        eliminate_dq_q(graph_module, dequant_nodes)
+        graph_module.graph.lint()
+        graph_module.graph.eliminate_dead_code()
+
+        return PassResult(graph_module, True)

exir/tests/test_passes.py

@@ -15,7 +15,7 @@
 # Import passes
 import executorch.exir.memory_planning  # noqa
 import torch
-from executorch.exir import EdgeCompileConfig, memory, to_edge
+from executorch.exir import EdgeCompileConfig, EdgeProgramManager, memory, to_edge
 from executorch.exir.dialects._ops import bind_pattern_to_op, ops, ops as exir_ops
 from executorch.exir.dialects.edge._ops import EdgeOpOverload
 from executorch.exir.emit import emit_program
@@ -50,6 +50,12 @@
 from functorch.experimental import control_flow
 
 from torch import nn
+
+from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
+from torch.ao.quantization.quantizer.xnnpack_quantizer import (
+    get_symmetric_quantization_config,
+    XNNPACKQuantizer,
+)
 from torch.export import export
 from torch.fx import GraphModule, subgraph_rewriter
 from torch.fx.experimental.proxy_tensor import make_fx
@@ -1244,3 +1250,134 @@ def forward(self, x):
         #     %copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%arg0_1, %aten_add_tensor_1), kwargs = {})
         #     return (copy__default, aten_add_tensor)
         self.assertEqual(count_copies(gm), 1)
+
+    def test_remove_quantized_op_noop_pass(self) -> None:
+        class TestAddSliceNoop(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x):
+                x = x + x
+                x = x + x[:]
+                return x
+
+        class TestAddSliceNotNoop(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x):
+                x = x + x
+                x = x + x[:1]
+                return x
+
+        def count_dq_nodes(gm: torch.fx.GraphModule) -> int:
+            return sum(
+                (
+                    node.target
+                    in (
+                        torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+                        exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
+                    )
+                )
+                for node in gm.graph.nodes
+            )
+
+        def count_q_nodes(gm: torch.fx.GraphModule) -> int:
+            return sum(
+                (
+                    node.target
+                    in (
+                        torch.ops.quantized_decomposed.quantize_per_tensor.default,
+                        exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
+                    )
+                )
+                for node in gm.graph.nodes
+            )
+
+        def quantize_model(
+            m_eager: torch.nn.Module, example_inputs: Tuple[torch.Tensor]
+        ) -> Tuple[EdgeProgramManager, int, int]:
+            # program capture
+            m = torch._export.capture_pre_autograd_graph(
+                m_eager,
+                example_inputs,
+            )
+
+            quantizer = XNNPACKQuantizer()
+            quantization_config = get_symmetric_quantization_config()
+            quantizer.set_global(quantization_config)
+            m = prepare_pt2e(m, quantizer)
+            m = convert_pt2e(m, fold_quantize=True)
+            ep = torch.export.export(m, example_inputs)
+            dq_nodes_pre = count_dq_nodes(ep.graph_module)
+            q_nodes_pre = count_q_nodes(ep.graph_module)
+            edge = to_edge(
+                ep, compile_config=EdgeCompileConfig(_check_ir_validity=False)
+            )
+            return edge, dq_nodes_pre, q_nodes_pre
+
+        example_inputs = (torch.randn(9, 8),)
+        model = TestAddSliceNoop()
+        m_eager = model.eval()
+        edge, dq_nodes_pre, q_nodes_pre = quantize_model(m_eager, example_inputs)
+
+        dq_nodes_post = count_dq_nodes(edge.exported_program().graph_module)
+        q_nodes_post = count_q_nodes(edge.exported_program().graph_module)
+        # One dq and one q node around the slice copy should have been removed.
+        self.assertEqual(dq_nodes_pre - dq_nodes_post, 1)
+        self.assertEqual(q_nodes_pre - q_nodes_post, 1)
+
+        # Check that the slice_copy is removed by the RemoveNoopPass.
+        for node in edge.exported_program().graph_module.graph.nodes:
+            self.assertFalse("slice" in str(node.target))
+
+        model = TestAddSliceNotNoop()
+        m_eager = model.eval()
+        edge, dq_nodes_pre, q_nodes_pre = quantize_model(m_eager, example_inputs)
+
+        dq_nodes_post = count_dq_nodes(edge.exported_program().graph_module)
+        q_nodes_post = count_q_nodes(edge.exported_program().graph_module)
+        # One dq and one q node around the slice copy should have been removed.
+        self.assertEqual(dq_nodes_pre, dq_nodes_post)
+        self.assertEqual(q_nodes_pre, q_nodes_post)
+
+        print(edge.exported_program().graph_module.graph)
+        # Check that the slice_copy is not removed by the RemoveNoopPass.
+        self.assertTrue(
+            any(
+                "slice" in str(node.target)
+                for node in edge.exported_program().graph_module.graph.nodes
+            )
+        )
+
+    def test_dq_q_no_op_pass(self) -> None:
+        class TestDqQ(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x):
+                dq = torch.ops.quantized_decomposed.dequantize_per_tensor.default(
+                    x, 1.0, 0, -128, 127, torch.int8
+                )
+                q = torch.ops.quantized_decomposed.quantize_per_tensor.default(
+                    dq, 1.0, 0, -128, 127, torch.int8
+                )
+                return q
+
+        model = TestDqQ()
+        m_eager = model.eval()
+        ep = torch.export.export(m_eager, (torch.randn(9, 8),))
+        edge = to_edge(ep)
+        # Check that the dq and q nodes are not touched by the RemoveNoopPass.
+        self.assertTrue(
+            any(
+                "dequantize" in str(node.target)
+                for node in edge.exported_program().graph_module.graph.nodes
+            )
+        )
+        self.assertTrue(
+            any(
+                "quantize" in str(node.target)
+                for node in edge.exported_program().graph_module.graph.nodes
+            )
+        )