add graph builder in oss for fuse ops

Differential Revision: D65911233

Pull Request resolved: #6877

Author: zonglinpeng

backends/cadence/aot/TARGETS

@@ -132,6 +132,18 @@ python_library(
     ],
 )
 
+python_library(
+    name = "graph_builder",
+    srcs = [
+        "graph_builder.py",
+    ],
+    typing = True,
+    deps = [
+        "fbcode//caffe2:torch",
+        "fbcode//executorch/exir:pass_base",
+    ],
+)
+
 python_library(
     name = "fuse_ops",
     srcs = [

backends/cadence/aot/graph_builder.py

@@ -0,0 +1,107 @@
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+
+# pyre-strict
+
+import logging
+from typing import Optional, Sequence, Union
+
+import torch
+from executorch.exir.pass_base import ExportPass, NodeMetadata, ProxyValue
+from torch._subclasses import FakeTensor, FakeTensorMode
+from torch.fx.node import Argument, Target
+from torch.utils import _pytree as pytree
+
+
+class GraphBuilder(ExportPass):
+    """Utility class for creating a graph module with user-specified ops.
+
+    This class allows us to create test graph modules with any ops we want
+    directly, rather than relying on decomposition or passes.
+
+    Usage:
+        builder = GraphBuilder()
+        # To insert placeholders, use builder.placeholder.
+        x = builder.placeholder("x", torch.randn(1, 3, 224, 224))
+        # To insert an op, use builder.call_operator.
+        op = builder.call_operator(
+            some_op
+            (x, other_args, ...),
+        )
+        # Insert outputs as a list of ProxyValues using builder.output.
+        builder.output([op])
+        # Get GraphModule from builder.
+        gm = builder.get_graph_module()
+    """
+
+    def __init__(self) -> None:
+        self.exporter = ExportPass()
+        self.tracer: ExportPass.ExportTracer = self.ExportTracer(
+            self, torch.fx.graph.CodeGen()
+        )
+        self.fake_tensor_mode = FakeTensorMode(allow_fallback_kernels=False)
+        self.tracer.fake_tensor_mode = self.fake_tensor_mode
+
+        # This will be called to create nodes in tracer.
+        self.interpreter = torch.fx.Interpreter(
+            torch.fx.GraphModule(torch.nn.Module(), torch.fx.Graph())
+        )
+
+    # pyre-ignore[14]: Inconsistent override.
+    def placeholder(
+        self, target: str, fake_tensor: Union[FakeTensor, torch.Tensor]
+    ) -> ProxyValue:
+        if not isinstance(fake_tensor, FakeTensor):
+            fake_tensor = self.fake_tensor_mode.from_tensor(fake_tensor)
+        logging.info(f"Creating placeholder {target} => {fake_tensor.shape}")
+        placeholder = super().placeholder(target, fake_tensor, NodeMetadata({}))
+        return placeholder
+
+    # pyre-ignore[14]: Inconsistent override.
+    def output(self, results: list[ProxyValue]) -> ProxyValue:
+        logging.info(f"Creating outputs {results}")
+        return super().output(results, NodeMetadata({}))
+
+    def get_graph_module(self) -> torch.fx.GraphModule:
+        return torch.fx.GraphModule(self.tracer.root, self.tracer.graph)
+
+    def call_operator(
+        self,
+        op,  # pyre-ignore
+        args: tuple[Argument, ...],
+        kwargs: Optional[dict[str, Argument]] = None,
+        meta: Optional[NodeMetadata] = None,
+    ) -> ProxyValue:
+        if meta is None:
+            meta = NodeMetadata({})
+        if kwargs is None:
+            kwargs = {}
+        return super().call_operator(op, args, kwargs, meta)
+
+
+def single_op_builder(
+    placeholders: Sequence[Union[torch.Tensor, FakeTensor]],
+    op: Target,
+    args: Sequence[Argument],
+    kwargs: Optional[dict[str, Argument]] = None,
+) -> torch.fx.GraphModule:
+    """Create a graph module with a single op.
+
+    Args:
+        placeholders: Placeholders to be used as inputs to the GraphModule.
+        op: The op to be inserted.
+        args: The args to be passed to the op.
+        kwargs: The kwargs to be passed to the op.
+
+    Returns:
+        A graph module with a single op
+    """
+    builder = GraphBuilder()
+    op_to_placeholder_dict = {
+        p: builder.placeholder(f"p_{i}", p) for i, p in enumerate(placeholders)
+    }
+    proxy_args, proxy_kwargs = pytree.tree_map_only(
+        (torch.Tensor, FakeTensor), lambda x: op_to_placeholder_dict[x], (args, kwargs)
+    )
+    node = builder.call_operator(op, proxy_args, proxy_kwargs)
+    builder.output([node])
+    return builder.get_graph_module()