[Backend Tester] Add FACTO operator test skeleton

backends/test/harness/tester.py

@@ -361,6 +361,7 @@ def _assert_outputs_equal(model_output, ref_output, atol=1e-03, rtol=1e-03):
                     ref,
                     atol=atol,
                     rtol=rtol,
+                    equal_nan=True,
                 ), (
                     f"Output {i} does not match reference output.\n"
                     f"\tGiven atol: {atol}, rtol: {rtol}.\n"

backends/test/operators/facto_specs.py

@@ -0,0 +1,59 @@
+import facto.specdb.function as fn
+import torch
+
+from facto.inputgen.argument.type import ArgType
+from facto.inputgen.specs.model import ConstraintProducer as cp, InPosArg, OutArg, Spec
+
+"""
+This file contains FACTO operator specs for ops not in the standard FACTO db. This mainly
+includes ops not in the Core ATen op set and preserved by a backend, such as linear.
+"""
+
+LINEAR_DEFAULT_SPEC = Spec(
+    op="linear.default",  # (Tensor input, Tensor weight, Tensor? bias=None) -> Tensor
+    inspec=[
+        InPosArg(
+            ArgType.Tensor,
+            name="input",
+            deps=[1, 2],
+            constraints=[
+                cp.Dtype.Eq(lambda deps: deps[0].dtype),
+                cp.Rank.Ge(lambda deps: 2),
+                cp.Size.In(
+                    lambda deps, r, d: fn.broadcast_to(
+                        (fn.safe_size(deps[0], 0), fn.safe_size(deps[1], 1)), r, d
+                    )
+                ),
+            ],
+        ),
+        InPosArg(
+            ArgType.Tensor,
+            name="weight",
+            constraints=[
+                cp.Dtype.Ne(lambda deps: torch.bool),
+                cp.Rank.Eq(lambda deps: 2),
+            ],
+        ),
+        InPosArg(
+            ArgType.Tensor,
+            name="bias",
+            deps=[1],
+            constraints=[
+                cp.Dtype.Eq(lambda deps: deps[0].dtype),
+                cp.Rank.Eq(lambda deps: 2),
+                cp.Size.Eq(
+                    lambda deps, r, d: fn.safe_size(deps[0], 1) if d == 0 else None
+                ),
+            ],
+        ),
+    ],
+    outspec=[
+        OutArg(ArgType.Tensor),
+    ],
+)
+
+_extra_specs = [
+    LINEAR_DEFAULT_SPEC,
+]
+
+ExtraSpecDB: dict[str, Spec] = {s.op: s for s in _extra_specs}

backends/test/operators/test_facto.py

@@ -0,0 +1,292 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# 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
+
+import copy
+import functools
+import traceback
+import unittest
+from typing import Any, Callable, Sequence
+
+import torch
+from executorch.backends.test.harness.tester import Tester as TesterBase
+from executorch.backends.xnnpack.test.tester.tester import Tester as XnnpackTester
+from facto.inputgen.argtuple.gen import ArgumentTupleGenerator
+from facto.inputgen.specs.model import ConstraintProducer as cp, Spec
+from facto.inputgen.utils.random_manager import random_manager
+from facto.specdb.db import SpecDictDB
+from torch._ops import OpOverload
+
+from .facto_specs import ExtraSpecDB
+
+CombinedSpecDB = SpecDictDB | ExtraSpecDB
+
+COMMON_TENSOR_CONSTRAINTS = [
+    cp.Rank.Ge(lambda deps: 1),
+    cp.Rank.Le(lambda deps: 4),
+    cp.Size.Ge(lambda deps, r, d: 1),
+    cp.Size.Le(lambda deps, r, d: 2**9),
+]
+
+COMMON_SCALAR_CONSTRAINS = [
+    cp.Value.Ge(lambda deps, dtype: -1000),
+    cp.Value.Le(lambda deps, dtype: 1000),
+]
+
+# Operator args are treated as runtime graph inputs if the argument name is
+# in this list.
+RUNTIME_INPUT_NAMES = {
+    "self",
+    "tensor",
+    "other",
+}
+
+
+def _patch_spec(spec: Spec) -> Spec:
+    spec = copy.deepcopy(spec)
+    for inspec in spec.inspec:
+        if inspec.type.is_tensor():
+            inspec.constraints.extend(COMMON_TENSOR_CONSTRAINTS)
+        elif inspec.type.is_scalar():
+            inspec.constraints.extend(COMMON_SCALAR_CONSTRAINS)
+    return spec
+
+
+class OpModel(torch.nn.Module):
+    """
+    Wraps a single torch operator in an nn.Module.
+    """
+
+    def __init__(
+        self,
+        op: OpOverload,
+        runtime_input_count: int,
+        fixed_args: Sequence[Any],
+        fixed_kwargs: dict[str, Any],
+    ):
+        super().__init__()
+        self.op = op
+        self.runtime_input_count = runtime_input_count
+        self.fixed_kwargs = fixed_kwargs
+
+        # Register parameters for fixed tensors. Some things will choke on
+        # constant tensor weights, for example.
+        new_args = []
+        for i, arg in enumerate(fixed_args):
+            if isinstance(arg, torch.Tensor):
+                param = torch.nn.Parameter(arg, requires_grad=False)
+                param_name = f"arg_{i}_param"
+                setattr(self, param_name, param)
+                self.register_parameter(param_name, param)
+                new_args.append(param)
+            else:
+                new_args.append(arg)
+        self.fixed_args = tuple(new_args)
+
+    def forward(self, *args, **kwargs):
+        return self.op(*(args + self.fixed_args), **(kwargs | self.fixed_kwargs))
+
+
+# The convolution model has some minor wrapper logic around the actual convolution
+# operator. Most of the backends are expecting this form.
+# TODO (gjcomer) Investigate these discrepencies.
+class ConvModel(OpModel):
+    def forward(self, *args, **kwargs):
+        weight, bias, stride, padding, dilation, transposed, output_padding, groups = (
+            self.fixed_args
+        )
+
+        if not transposed:
+            if len(weight.shape) == 3:
+                op = torch.nn.functional.conv1d
+            elif len(weight.shape) == 4:
+                op = torch.nn.functional.conv2d
+            elif len(weight.shape) == 5:
+                op = torch.nn.functional.conv3d
+
+            return op(args[0], weight, bias, stride, padding, dilation, groups)
+        else:
+            if len(weight.shape) == 3:
+                op = torch.nn.functional.conv_transpose1d
+            elif len(weight.shape) == 4:
+                op = torch.nn.functional.conv_transpose2d
+            elif len(weight.shape) == 5:
+                op = torch.nn.functional.conv_transpose3d
+
+            return op(
+                args[0], weight, bias, stride, padding, output_padding, groups, dilation
+            )
+
+
+def get_module_for_op(op: OpOverload):
+    if op == torch.ops.aten.convolution.default:
+        return ConvModel
+    else:
+        return OpModel
+
+
+class FactoTestsBase(unittest.TestCase):
+    def __init__(self, tester_factory: Callable[[], TesterBase], *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._tester_factory = tester_factory
+
+    @staticmethod
+    def _generate_test(op_name: str) -> None:
+        # Find the torch op with the given name.
+        sections = op_name.split(".")
+        torch_op = functools.reduce(getattr, sections, torch.ops.aten)
+
+        test_name = "test_" + op_name.replace(".", "_")
+
+        def test_body(self):
+            self._test_op(torch_op)
+
+        setattr(FactoTestsBase, test_name, test_body)
+
+    @staticmethod
+    def get_runtime_input_count(spec: Spec):
+        # Determine which inputs are fixed at tracing time (weights, for example),
+        # vs inputs to the runtime graph. We currently assume that the runtime graph
+        # inputs start at the beginning of the arg list and are contiguous.
+        #
+        # Args are consider to be runtime inputs if they are positional and are named
+        # one of RUNTIME_INPUT_NAMES. If none match, we assume only the first arg is a
+        # runtime input.
+        runtime_input_count = 0
+        for inspec in spec.inspec:
+            is_runtime_input = (
+                inspec.type.is_tensor() and inspec.name.lower() in RUNTIME_INPUT_NAMES
+            )
+            if is_runtime_input:
+                runtime_input_count += 1
+            else:
+                break
+
+        return max(1, runtime_input_count)
+
+    def setUp(self):
+        torch.set_printoptions(threshold=3)
+
+    def _test_op(self, op: OpOverload) -> None:  # noqa
+        random_manager.seed(0)
+
+        # Strip namespace
+        op_name = op.name().split("::")[-1]
+
+        # Default to .default overload
+        if "." not in op_name:
+            op_name += ".default"
+
+        # Find and patch op spec
+        if op_name not in CombinedSpecDB:
+            raise ValueError(f"Operator {op_name} not found in SpecDictDB.")
+        spec = _patch_spec(CombinedSpecDB[op_name])
+
+        runtime_input_count = FactoTestsBase.get_runtime_input_count(spec)
+
+        print(f"Op: {op_name}, {runtime_input_count} runtime inputs")
+
+        # Run test cases
+        success_count_delegated = 0
+        success_count_undelegated = 0
+        fail_count = 0
+
+        i = 0
+        for posargs, inkwargs, _ in ArgumentTupleGenerator(spec).gen():
+            i += 1
+
+            try:
+                if isinstance(posargs[0], torch.Tensor):
+                    # Temporary for getting around XNN crashes (https://github.com/pytorch/executorch/issues/10960).
+                    # TODO Re-enable when resolved.
+                    if posargs[0].dtype in {torch.int8, torch.uint8}:
+                        print("Skipping (u)int8 case.")
+                        continue
+
+                module_cls = get_module_for_op(op)
+                model = module_cls(
+                    op, runtime_input_count, posargs[runtime_input_count:], inkwargs
+                )
+
+                # Sanity check to make sure it runs in eager. This can present nicer error
+                # messages sometimes compared to tracing.
+                try:
+                    model(*posargs[:runtime_input_count])
+                except Exception as e:
+                    print(f"Eager execution failed: {e}")
+                    continue
+
+                tester = self._tester_factory(
+                    model, tuple(posargs[:runtime_input_count])
+                )
+
+                # Dynamo will also fail to handle some patterns that are valid in eager.
+                try:
+                    tester.export()
+                except Exception:
+                    print("Export failed.")
+                    continue
+
+                tester.to_edge_transform_and_lower()
+
+                is_delegated = any(
+                    n.target == torch._higher_order_ops.executorch_call_delegate
+                    for n in tester.stages[tester.cur].graph_module.graph.nodes
+                    if n.op == "call_function"
+                )
+
+                # Only run the runtime test if the op was delegated.
+                if is_delegated:
+                    (
+                        tester.to_executorch()
+                        .serialize()
+                        .run_method_and_compare_outputs()
+                    )
+
+                if is_delegated:
+                    success_count_delegated += 1
+                else:
+                    success_count_undelegated += 1
+            except Exception:
+                fail_count += 1
+                print("Args:")
+                for arg in posargs:
+                    if isinstance(arg, torch.Tensor):
+                        print(f"  {arg.dtype} {arg.shape}")
+                    else:
+                        print(f"  {arg}")
+
+                traceback.print_exc()
+
+        print(
+            f"{success_count_delegated + success_count_undelegated} PASS, {fail_count} FAIL"
+        )
+        print(
+            f"  {success_count_delegated} DELEGATED, {success_count_undelegated} UNDELEGATED"
+        )
+
+
+# Programatically generate tests for each operator.
+for op_name in CombinedSpecDB.keys():
+    FactoTestsBase._generate_test(op_name)
+
+
+# TODO Figure out where to put these
+class FactoTestsXNNPACK(FactoTestsBase):
+    def __init__(self, *args, **kwargs):
+        super().__init__(XnnpackTester, *args, **kwargs)
+
+
+try:
+    from executorch.backends.apple.coreml.test.tester import CoreMLTester
+
+    class FactoTestsCoreML(FactoTestsBase):
+        def __init__(self, *args, **kwargs):
+            super().__init__(CoreMLTester, *args, **kwargs)
+
+except:
+    print("Skipping Core ML facto tests as Core ML AOT is not available.")

backends/xnnpack/test/tester/__init__.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-# TODO: Be more delibrate on module structure
 from executorch.backends.xnnpack.test.tester.tester import (
     Export,
     Partition,
@@ -18,13 +17,13 @@
 )
 
 __all__ = [
-    Export,
-    ToEdge,
-    Partition,
-    Quantize,
-    RunPasses,
-    ToEdgeTransformAndLower,
-    Tester,
-    Serialize,
-    ToExecutorch,
+    "Export",
+    "Partition",
+    "Quantize",
+    "RunPasses",
+    "Serialize",
+    "Tester",
+    "ToEdge",
+    "ToEdgeTransformAndLower",
+    "ToExecutorch",
 ]