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9e473fd8688c9557e6a7e69d8e35ba1ba937c607
pytorch/test/test_fx.py
ekamiti 9e473fd868 Make adding Buffers more like adding Parameters (#125971) 
Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new Buffer class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the register_buffer method has not been changed. The persistent parameter in the Buffer type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new Buffer type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the Buffer type can be used as a drop in replacement for register_buffer as it just leads to register_buffer being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.

Fixes #35735

Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125971
Approved by: https://github.com/albanD, https://github.com/anijain2305, https://github.com/mlazos
2024-07-31 10:32:40 +00:00

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# Owner(s): ["module: fx"]
import builtins
import contextlib
import copy
import functools
import inspect
import math
import numbers
import io
import operator
import os
import pickle
import sys
import torch
import traceback
import typing
import types
import warnings
import unittest
from math import sqrt
from functorch.experimental import control_flow
from torch.multiprocessing import Process
from torch.testing import FileCheck
from torch.testing._internal.common_methods_invocations import op_db
from torch.testing._internal.common_device_type import ops, onlyCPU, instantiate_device_type_tests
import torch.utils._pytree as pytree
import torch.fx._pytree as fx_pytree
from torch.fx import symbolic_trace, Proxy, Node, GraphModule, Interpreter, Tracer, Transformer, Graph, wrap, PH, CodeGen
from torch.fx.node import Target, Argument, _format_arg
from torch.fx.passes import shape_prop
from torch.fx.immutable_collections import immutable_dict, immutable_list
from torch.fx.experimental.rewriter import RewritingTracer
from torch.fx.operator_schemas import get_signature_for_torch_op
from copy import deepcopy
from collections import namedtuple
from torch.fx.proxy import TraceError
from torch.fx._compatibility import _BACK_COMPAT_OBJECTS, _MARKED_WITH_COMPATIBILITY
from torch.fx._symbolic_trace import PHBase, PHWithMeta
from fx.test_subgraph_rewriter import TestSubgraphRewriter # noqa: F401
from fx.test_dce_pass import TestDCE # noqa: F401
from fx.test_fx_const_fold import TestConstFold # noqa: F401
from fx.test_fx_param_shape_control_flow import TestConstParamShapeInControlFlow # noqa: F401
from fx.test_pass_infra import TestPassManager # noqa: F401
from fx.test_common_passes import TestCommonPass # noqa: F401
from fx.test_cse_pass import TestCSEPass # noqa: F401
from fx.test_matcher_utils import TestMatcher # noqa: F401
from fx.test_source_matcher_utils import TestSourceMatcher # noqa: F401
from fx.test_gradual_type import AnnotationsTest # noqa: F401
from fx.test_gradual_type import TypeCheckerTest # noqa: F401
from typing import Any, Callable, Dict, NamedTuple, List, Optional, Set, Tuple, Union
from torch.testing._internal.common_utils import (
IS_FBCODE,
IS_MACOS,
IS_WINDOWS,
find_library_location,
run_tests,
skipIfTorchDynamo,
)
from torch.testing._internal.jit_utils import JitTestCase
from fx.named_tup import MyNamedTup
try:
from torchvision import models as torchvision_models
HAS_TORCHVISION = True
except ImportError:
HAS_TORCHVISION = False
skipIfNoTorchVision = unittest.skipIf(not HAS_TORCHVISION, "no torchvision")
from torch.testing._internal.common_quantization import skipIfNoDynamoSupport
class SimpleTest(torch.nn.Module):
def forward(self, x):
return torch.relu(x + 3.0)
def a_non_torch_leaf(a, b):
return a + b
# Used for test_autowrap_function. Autowrapped functions need to be global
def fx_int(x: float) -> int:
return int(x)
def fx_int_x2(x: float) -> int:
return int(x) * 2
# used in test_pytree. It's all the way out here because pickling a GraphModule
# that uses Point errors out if Point is local to the function
Point = namedtuple('Point', ['x', 'y'])
# Test wrap() passing both a function name as well as a function
# directly
def a_lifted_leaf(a, b):
return a[0] + a[1] + b
wrap('a_lifted_leaf')
# Test wrapping twice doesn't break anything
wrap('a_lifted_leaf')
def a_lifted_leaf2(a, b):
return a[0] + a[1] + b
wrap(a_lifted_leaf2)
wrap('len')
wrap('getattr')
def wrapped_named_tup(p1, *, p2):
return p1.x + p2.y
wrap(wrapped_named_tup)
@wrap
def wrapped_via_decorator(a):
return a + 1
wrap('wrapped_with_submodule')
def wrapped_with_submodule(x: torch.Tensor, batchnorm1d: torch.nn.BatchNorm1d):
return batchnorm1d(x)
def my_decorator(f):
@functools.wraps(f)
def wrapper_inside_decorator(*args, **kwargs):
return f(*args, **kwargs)
return wrapper_inside_decorator
@wrap
@my_decorator
def wrapped_decorated_fn(x):
return x
real_wrapped_via_decorator = wrapped_via_decorator
real_a_lifed_leaf = a_lifted_leaf
real_a_lifed_leaf2 = a_lifted_leaf2
_sqrt = sqrt
wrap('wrapper_fn')
def wrapper_fn(x):
return torch.foo(x)
class Pair(NamedTuple):
x : torch.Tensor
y : torch.Tensor
def _custom_fx_repr_fn(self) -> str:
return f"Pair(x={_format_arg(self.x)}, y={_format_arg(self.y)})"
# for testing pytrees
class Foo: # noqa: B209
def __init__(self, a, b):
self.a = a
self.b = b
class Add(torch.nn.Module):
def forward(self, x):
return x + x
@torch.fx.has_side_effect
@torch.fx.wrap
def side_effect_func(x: torch.Tensor):
print(x)
class TestFX(JitTestCase):
def setUp(self):
super().setUp()
# Checking for mutable operations whil tracing is feature flagged
# Enable it in testing but not by default
self.orig_tracer_mutable_flag = torch.fx.proxy.TracerBase.check_mutable_operations
torch.fx.proxy.TracerBase.check_mutable_operations = True
if not (IS_FBCODE or IS_WINDOWS or IS_MACOS):
lib_file_path = find_library_location('libtorchbind_test.so')
torch.ops.load_library(str(lib_file_path))
def tearDown(self):
super().tearDown()
torch.fx.proxy.TracerBase.check_mutable_operations = self.orig_tracer_mutable_flag
def checkGraphModule(self, m: torch.nn.Module, args, kwargs=None):
"""Check that an nn.Module's results match the GraphModule version
for a given set of args/kwargs.
"""
kwargs = kwargs if kwargs else {}
ref_outs = m(*args, **kwargs)
gm = symbolic_trace(m)
gm.graph.lint()
test_outs = gm(*args, **kwargs)
self.assertEqual(ref_outs, test_outs)
def test_graph_module(self):
class MySub(torch.nn.Module):
def __init__(self):
super().__init__()
self.w = torch.nn.Parameter(torch.rand(4, 3))
def forward(self, x):
return self.w + x
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.lin = torch.nn.Linear(4, 3)
self.sub_mod = MySub()
self.w = torch.nn.Parameter(torch.rand(3))
def forward(self, A, B, c):
t = torch.sigmoid(A) + self.lin(c)
return self.sub_mod(t.data + self.w + t + 1 - A + B // A + -A + A.add(B, alpha=3))
m = MyModule()
gm = symbolic_trace(m)
ms = torch.jit.script(gm)
class M2(torch.nn.Module):
def forward(self, A):
m, idx = torch.max(A, 0)
return m + 1, idx + 1
m2 = M2()
gm2 = symbolic_trace(m2)
class T(torch.nn.Module):
def forward(self, A, b=4, *args, c=5, **kwargs):
x = A + 1 + args[0] + kwargs['3']
return x
t = T()
symbolic_trace(t)
# test for issue described at https://github.com/pytorch/pytorch/issues/63883
class M3(torch.nn.Module):
def forward(self, x):
return torch.relu(x)
m3 = M3()
gm3 = symbolic_trace(m3)
new_instance = gm3.__new__(type(gm3))
new_instance.__init__(gm3, gm3.graph)
x = torch.randn(5, 3)
torch.testing.assert_close(new_instance(x), torch.relu(x))
def test_informative_co_filename(self):
class MyModule(torch.nn.Module):
def forward(self, a):
return a * 2
gm = symbolic_trace(MyModule())
self.assertIn(os.path.basename(__file__), gm.forward.__code__.co_filename)
def test_custom_import(self):
graph = torch.fx.Graph()
a = graph.placeholder('x')
b = graph.placeholder('y')
c = graph.call_function(a_non_torch_leaf, (a, b))
d = graph.call_function(torch.sin, (c,))
graph.output(d)
gm = GraphModule(torch.nn.Module(), graph)
x, y = torch.rand(1), torch.rand(1)
self.assertEqual(torch.sin(x + y), gm(x, y))
def test_args_kwargs(self):
class T(torch.nn.Module):
def forward(self, *args, **kwargs):
x = args[0] + kwargs['foo']
return x
t = T()
self.checkGraphModule(t, (torch.rand(1), torch.rand(1)), {'foo': torch.rand(1)})
def test_varargs_concrete(self):
class T(torch.nn.Module):
def forward(self, *args, **kwargs):
x = args[0] + args[1]
return x
args = (torch.rand(1), torch.rand(1))
t = T()
ref_outs = t(*args)
gm = symbolic_trace(t, concrete_args=(torch.fx.PH, torch.fx.PH))
gm.graph.lint()
test_outs = gm(*args)
self.assertEqual(ref_outs, test_outs)
def test_args_kwargs_no_self(self):
class T(torch.nn.Module):
def forward(*args, **kwargs): # noqa: B902
self = args[0]
return torch.relu(args[1])
t = T()
with self.assertRaisesRegex(RuntimeError, r'cannot be part of \*args expansion'):
self.checkGraphModule(t, (torch.rand(1), torch.rand(1)), {'foo': torch.rand(1)})
def test_fx_shifts(self):
class MyModule(torch.nn.Module):
def forward(self, x):
return x << 3, x >> 3
input = torch.LongTensor(10).random_(0, 1024)
m = MyModule()
self.checkGraphModule(m, (input,))
def test_fx_and_or(self):
class MyModule(torch.nn.Module):
def forward(self, x):
return x & x, x | x
input = torch.LongTensor(10).random_(0, 1024)
m = MyModule()
self.checkGraphModule(m, (input,))
def test_dict(self):
class MyDictMod(torch.nn.Module):
def forward(self, d):
return d['3'].relu(), {'4' : d['3'].neg()}
input_dict = {'3': torch.rand(3, 4)}
m = MyDictMod()
self.checkGraphModule(m, (input_dict,))
def test_matmul_tracing(self):
const = torch.randn(3)
def matmul_f(x):
return x @ const
mod = symbolic_trace(matmul_f)
inp = torch.randn(3)
self.assertEqual(mod(inp), matmul_f(inp))
def rmatmul_f(x):
return const @ x
mod = symbolic_trace(rmatmul_f)
inp = torch.randn(3)
self.assertEqual(mod(inp), rmatmul_f(inp))
@skipIfNoDynamoSupport
def test_control_flow_tracing(self):
def true(x, y):
return x + y
def false(x, y):
return x - y
def f(x, y):
x = control_flow.cond(x[0] == 0, true, false, [x, y])
with self.assertRaisesRegex(RuntimeError, r"Expected pred to be bool or tensor, but got Proxy\(eq\)"):
_ = symbolic_trace(f)
def test_disallow_override(self):
# Custom delegate to disallow in-place tensor operations
class NoMutableCallTracer(Tracer):
def create_node(self, kind : str, target : Union[str, Callable],
args : Tuple[Argument, ...], kwargs : Dict[str, Any], name : Optional[str] = None,
type_expr : Optional[Any] = None) -> Node:
name = target if isinstance(target, str) else torch.typename(target)
if name[-1] == '_':
raise RuntimeError('In-place operations are not supported')
return super().create_node(kind, target, args, kwargs, name)
# Test method
class MyInplaceMod(torch.nn.Module):
def forward(self, x):
x.add_(3.0)
return x
m = MyInplaceMod()
with self.assertRaisesRegex(RuntimeError, 'In-place operations'):
NoMutableCallTracer().trace(m)
# Test free function
class MyInplaceMod2(torch.nn.Module):
def forward(self, x):
torch.log_(x)
return x
m2 = MyInplaceMod2()
with self.assertRaisesRegex(RuntimeError, 'In-place operations'):
NoMutableCallTracer().trace(m2)
# Test symbolic node as an arg
class MyInplaceMod3(torch.nn.Module):
def forward(self, x):
y = torch.ones(3, 4)
y.add_(x)
return x
m3 = MyInplaceMod3()
with self.assertRaisesRegex(RuntimeError, 'In-place operations'):
NoMutableCallTracer().trace(m3)
def test_leaf_module(self):
# Custom delegate to make it so that there are no leaf modules, everything
# should get traced through
class NoLeafModulesTracer(Tracer):
def is_leaf_module(self, m, qualname):
return False
class MyReluMod(torch.nn.Module):
def __init__(self):
super().__init__()
self.relu = torch.nn.ReLU()
def forward(self, x):
return self.relu(x)
mrm = MyReluMod()
sym = NoLeafModulesTracer().trace(mrm)
for node in sym.nodes:
self.assertNotEqual(node.op, 'call_module')
sym.lint()
def test_wrap(self):
self.assertEqual(3 + 4 + 5, a_lifted_leaf((3, 4), 5))
def to_trace(y):
return a_lifted_leaf((4, y), 3) + a_lifted_leaf((3, 4), 5) + a_lifted_leaf((y, y), y)
m = symbolic_trace(to_trace)
self.assertIn('a_lifted_leaf', m.code)
self.assertEqual(27, m(2))
self.assertIs(a_lifted_leaf, real_a_lifed_leaf)
def test_wrap_fn_directly(self):
self.assertEqual(3 + 4 + 5, a_lifted_leaf2((3, 4), 5))
def to_trace(y):
return a_lifted_leaf2((4, y), 3) + a_lifted_leaf2((3, 4), 5) + a_lifted_leaf2((y, y), y)
m = symbolic_trace(to_trace)
self.assertIn('a_lifted_leaf2', m.code)
self.assertEqual(27, m(2))
self.assertIs(a_lifted_leaf2, real_a_lifed_leaf2)
def test_wrapped_via_decorator(self):
self.assertEqual(wrapped_via_decorator(0), 1)
def to_trace(y):
return wrapped_via_decorator(y)
m = symbolic_trace(to_trace)
self.assertIn('wrapped_via_decorator', m.code)
self.assertEqual(m(0), 1)
self.assertIs(wrapped_via_decorator, real_wrapped_via_decorator)
self.assertFalse(hasattr(wrapped_via_decorator, "__fx_already_patched"))
def test_wrapped_via_decorator_and_transformed(self):
self.assertEqual(wrapped_via_decorator(0), 1)
def to_trace(y):
return wrapped_via_decorator(y)
m = symbolic_trace(to_trace)
self.assertIn('wrapped_via_decorator', m.code)
self.assertEqual(m(0), 1)
self.assertIs(wrapped_via_decorator, real_wrapped_via_decorator)
self.assertFalse(hasattr(wrapped_via_decorator, "__fx_already_patched"))
transformed = torch.fx.Transformer(m).transform()
self.assertIn('wrapped_via_decorator', transformed.code)
self.assertEqual(transformed(0), 1)
self.assertIs(wrapped_via_decorator, real_wrapped_via_decorator)
self.assertFalse(hasattr(wrapped_via_decorator, "__fx_already_patched"))
def test_wrap_with_submodule(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.batchnorm1d = torch.nn.BatchNorm1d(2, affine=False)
def forward(self, x: torch.Tensor):
return wrapped_with_submodule(x, self.batchnorm1d)
m = symbolic_trace(M())
self.assertIn("wrapped_with_submodule", m.code)
input = torch.rand(3, 2)
ref_batchnorm1d = torch.nn.BatchNorm1d(2, affine=False)
self.assertEqual(ref_batchnorm1d(input), m(input))
def test_wrapped_retrace(self):
def to_trace(y):
return wrapped_via_decorator(y)
m = symbolic_trace(to_trace)
self.assertIn('wrapped_via_decorator', m.code)
self.assertEqual(m(0), 1)
retraced = symbolic_trace(m)
self.assertIn('wrapped_via_decorator', retraced.code)
self.assertEqual(retraced(0), 1)
def test_wrap_decorated_function(self):
def to_trace(y):
return wrapped_decorated_fn(y)
m = symbolic_trace(to_trace)
self.assertIn('wrapped_decorated_fn', m.code)
self.assertEqual(m(1), 1)
def test_graph_edit_with_proxy(self):
class M(torch.nn.Module):
def forward(self, a, b):
return a + b
m = M()
g = symbolic_trace(m).graph
new_g = torch.fx.Graph()
val_map : Dict[Node, Node] = {}
output_val = new_g.graph_copy(g, val_map)
t = Proxy(output_val)
# test that we can use proxy objects to generate more graph code later for things that do not need to work with modules.
new_g.output((t + t).node)
gm = GraphModule(m, new_g)
gm.graph.lint()
self.assertEqual(gm(3, 4), 14)
def test_concrete_arg_none_assert(self):
class Foo(torch.nn.Module):
def forward(self, x, val=None):
return x if val is None else x + val
f = Foo()
traced = torch.fx.symbolic_trace(f, concrete_args={'val' : None})
with self.assertRaisesRegex(AssertionError, 'val has been specialized to have value None'):
traced(torch.randn(5), torch.randn(5))
x = torch.randn(5)
torch.testing.assert_close(traced(x), f(x))
def test_trace_multiple_funcs(self):
class Foo(torch.nn.Module):
def forward(self, x, y):
return x + y
def minus_forward(self, x, y):
return x - y
def multiply_forward(self, x, y):
return x * y
f = Foo()
x, y = torch.randn(5), torch.randn(5)
print(torch.__version__)
tracer = Tracer()
torch.testing.assert_close(GraphModule(f, tracer.trace(f))(x, y), f(x, y))
tracer.traced_func_name = "minus_forward"
torch.testing.assert_close(
GraphModule(f, tracer.trace(f))(x, y),
f.minus_forward(x, y),
)
tracer.traced_func_name = "multiply_forward"
torch.testing.assert_close(
GraphModule(f, tracer.trace(f))(x, y),
f.multiply_forward(x, y),
)
tracer.traced_func_name = "add_forward"
with self.assertRaisesRegex(AssertionError, "doesn't exist in"):
tracer.trace(f)
def test_graph_unique_names(self):
class M(torch.nn.Module):
def forward(self, a, b):
return a + b
m = M()
g = symbolic_trace(m).graph
new_g = torch.fx.Graph()
val_map : Dict[Node, Node] = {}
output_val = new_g.graph_copy(g, val_map)
t = Proxy(output_val)
# test that we can use proxy objects to generate more graph code later for things that do not need to work with modules.
new_g.output((t + t).node)
gm = GraphModule(m, new_g)
seen_names : Set[str] = set()
for node in gm.graph.nodes:
assert node.name not in seen_names
seen_names.add(node.name)
def test_stack_traces(self):
class M(torch.nn.Module):
def forward(self, a, b):
return a + b
tracer = torch.fx.Tracer()
tracer.record_stack_traces = True
graph = tracer.trace(M())
# saving the original list because we will insert new nodes as a part of a test
orig_graph_nodes = list(graph.nodes)
for node in orig_graph_nodes:
if node.op == 'output':
continue
self.assertTrue(node.stack_trace is not None)
assert 'test_fx.py' in node.stack_trace
# verify that copying the node does not lose the stack trace
new_node = graph.node_copy(node)
self.assertTrue(new_node.stack_trace is not None)
assert 'test_fx.py' in new_node.stack_trace
def test_stack_traces_with_transformer(self):
class M(torch.nn.Module):
def forward(self, a, b):
return a + b
tracer = torch.fx.Tracer()
tracer.record_stack_traces = True
graph = tracer.trace(M())
gm = GraphModule(tracer.root, graph)
new_gm = Transformer(gm).transform()
# nodes after Transformer should still preserve the original node's stack trace
for node in new_gm.graph.nodes:
if node.op in {'placeholder', 'output'}:
continue
self.assertTrue(node.stack_trace is not None)
assert 'test_fx.py' in node.stack_trace
def test_lineno_map(self):
class M(torch.nn.Module):
def forward(self, a, b):
a = torch.sin(a)
b = torch.cos(b)
return a + b
tracer = torch.fx.Tracer()
graph = tracer.trace(M())
gm = GraphModule(tracer.root, graph)
expected = {1: 2, 2: 3, 3: 4, 4: 5}
self.assertTrue(set(expected.items()).issubset(set(gm._lineno_map.items())))
# test custom codegen
def transform_code(code):
return ["print('hello!')\n", *code]
gm.graph.on_generate_code(lambda _: transform_code)
gm.recompile()
expected = {2: 2, 3: 3, 4: 4, 5: 5}
self.assertTrue(set(expected.items()).issubset(set(gm._lineno_map.items())))
def test_graph_unique_names_manual(self):
graph : torch.fx.Graph = torch.fx.Graph()
a : torch.fx.Node = graph.create_node('placeholder', 'x')
b : torch.fx.Node = graph.create_node('call_module', 'linear_mod', args=(a,), name='foo_1_1')
c : torch.fx.Node = graph.create_node('get_attr', 'y_attr', name='foo_1')
d : torch.fx.Node = graph.create_node('call_function', operator.add, args=(b, c))
graph.output(d)
graph2 = torch.fx.Graph()
val_map : Dict[Node, Node] = {}
graph2.graph_copy(graph, val_map)
seen_names : Set[str] = set()
for node in graph2.nodes:
assert node.name not in seen_names
seen_names.add(node.name)
def test_unpack(self):
class M(torch.nn.Module):
def forward(self, a, b):
c, d = a
return c + d + b
a = (torch.rand(1), torch.rand(1))
b = torch.rand(1)
m = M()
self.checkGraphModule(m, (a, b))
def test_native_callable(self):
if IS_FBCODE or IS_WINDOWS or IS_MACOS:
raise unittest.SkipTest("non-portable load_library call used in test")
# This test exercises the case where we use FX to translate from Python
# code to some native callable object
#
# For the purposes of testing, we use ElementwiseInterpreter defined
# in test_custom_class.cpp.
#
# We test that we can
# 1) Construct a native callable from FX IR
# 2) Construct a drop-in replacement module that delegates to the
# native callable rather than the original code
# 3) Run both the original code and native callable wrapper with
# equivalent results
# 4) TorchScript compile the native callable wrapper and confirm
# equivalent results with the reference
# 5) TorchScript serialize and deserialize the native callable
# and confirm equivalent results with the reference
# We use this simple Module as a reference computation
class MySimpleMod(torch.nn.Module):
def forward(self, x):
return 3.0 * x + x
msm = MySimpleMod()
# This is what a lowering pass might look like: a function that takes
# a valid nn.Module, symbolically traces it, lowers the Module to some
# representation, and wraps that representation up into another
# nn.Module instance that handles dispatch to the compiled/lowered code.
def lower_to_elementwise_interpreter(orig_mod : torch.nn.Module) -> torch.nn.Module:
# ===== Stage 1: Symbolic trace the module =====
mod = symbolic_trace(orig_mod)
# ===== Stage 2: Lower GraphModule representation to the C++
# interpreter's instruction format ======
instructions = []
constant_idx = 0
constants = {}
fn_input_names = []
target_to_name = {
operator.add : "add",
operator.mul : "mul"
}
output_node : Optional[Node] = None
# For each instruction, create a triple
# (instruction_name : str, inputs : List[str], output : str)
# to feed into the C++ interpreter
for n in mod.graph.nodes:
target, args, out_name = n.target, n.args, n.name
assert len(n.kwargs) == 0, "kwargs currently not supported"
if n.op == 'placeholder':
# Placeholders specify function argument names. Save these
# for later when we generate the wrapper GraphModule
fn_input_names.append(target)
elif n.op == 'call_function':
assert target in target_to_name, "Unsupported call target " + target
arg_names = []
for arg in args:
if not isinstance(arg, Node):
# Pull out constants. These constants will later be
# fed to the interpreter C++ object via add_constant()
arg_name = f'constant_{constant_idx}'
constants[arg_name] = torch.tensor(
[arg] if isinstance(arg, numbers.Number) else arg)
arg_names.append(arg_name)
constant_idx += 1
else:
arg_names.append(arg.name)
instructions.append((target_to_name[target], arg_names, out_name))
elif n.op == 'output':
if output_node is not None:
raise RuntimeError('Multiple output nodes!')
output_node = n
else:
raise RuntimeError('Unsupported opcode ' + n.op)
interpreter = torch.classes._TorchScriptTesting._ElementwiseInterpreter()
# Load constants
for k, v in constants.items():
interpreter.add_constant(k, v)
# Specify names for positional input arguments
interpreter.set_input_names(fn_input_names)
# Load instructions
interpreter.set_instructions(instructions)
# Specify name for single output
assert isinstance(output_node.args[0], torch.fx.Node)
interpreter.set_output_name(output_node.args[0].name)
# ===== Stage 3: Create a wrapper GraphModule around the interpreter =====
class WrapperModule(torch.nn.Module):
def __init__(self, interpreter):
super().__init__()
self.interpreter = interpreter
wrapper = WrapperModule(interpreter)
# Create a graph that: 1) Takes function arguments 2) Invokes the interpreter
# 3) Returns the speficied return value
# FIXME: The following code could be greatly simplified by symbolic_trace'ing
# the wrapper with a Tracer that considers the Wrapper instance a root
# module, however, I can't get `__call__` exposed on TorchBind classes
# without it messing up Python `hasattr` for some reason. More digging
# into CPython's implementation of hasattr is probably in order...
graph = torch.fx.Graph()
# Add placeholders for fn inputs
placeholder_nodes = []
for name in fn_input_names:
placeholder_nodes.append(graph.create_node('placeholder', name))
# Get the interpreter object
interpreter_node = graph.create_node('get_attr', 'interpreter')
# Add a node to call the interpreter instance
output_node = graph.create_node(
op='call_method', target='__call__', args=(interpreter_node, placeholder_nodes))
# Register output
graph.output(output_node)
graph.lint()
# Return final GraphModule!!!
return GraphModule(wrapper, graph)
# Lower GraphModule to C++ interpreter
lowered = lower_to_elementwise_interpreter(msm)
# Compare correctness with original module
x = torch.rand(3, 4)
ref_out = msm(x)
test_out = lowered(x)
torch.testing.assert_close(test_out, ref_out)
# Test TorchScript compilation
scripted_lowered = torch.jit.script(lowered)
script_out = scripted_lowered(x)
torch.testing.assert_close(script_out, ref_out)
# Test TorchScript ser/de
import_copy = self.getExportImportCopy(scripted_lowered)
imported_out = import_copy(x)
torch.testing.assert_close(imported_out, ref_out)
def test_reserved_getattr(self):
"""Ensure that we do not name any nodes with a reserved builtin like `getattr`"""
class M(torch.nn.Module):
def forward(self, a):
return a.foo.bar.baz
m = M()
m_g = symbolic_trace(m)
m_g.graph.lint()
for node in m_g.graph.nodes:
self.assertTrue(node.name != "getattr")
@unittest.skip("Hotfix for SEV remediation")
def test_trace_buffer_slice(self):
bs, d_hid = 10, 23
class ExampleCode(torch.nn.Module):
def __init__(self):
super().__init__()
self.mm_param = torch.nn.Parameter(torch.randn(d_hid, d_hid))
self.mm_param2 = torch.nn.Parameter(torch.randn(d_hid, d_hid))
self.lin = torch.nn.Linear(d_hid, d_hid)
self.buffer = torch.nn.Buffer(torch.randn(bs + 100, d_hid))
def forward(self, x):
x = torch.mm(x, self.mm_param)
skip_connection = x
x = torch.relu(x)
x = torch.mm(x, self.mm_param) + self.buffer[:x.shape[0]]
x = self.lin(x)
x = torch.relu(x)
x = x + skip_connection
x = torch.mm(x, self.mm_param2)
x = self.lin(x)
return x
ec = ExampleCode()
traced = torch.fx.symbolic_trace(ec)
x = torch.randn(bs, d_hid)
torch.testing.assert_close(ec(x), traced(x))
def test_node_tagging(self):
class TaggingTracer(Tracer):
def create_node(self, kind : str, target : Union[str, Callable],
args : Tuple[Argument, ...], kwargs : Dict[str, Any], name : Optional[str] = None,
type_expr : Optional[Any] = None) -> Node:
n = super().create_node(kind, target, args, kwargs, name)
n.tag = 'foo'
return n
class M(torch.nn.Module):
def forward(self, a, b):
return a + b
m = M()
g = TaggingTracer().trace(m)
g.lint()
for n in g.nodes:
self.assertTrue(hasattr(n, 'tag'))
self.assertEqual(n.tag, 'foo')
def test_tensor_attribute(self):
class TensorAttribute(torch.nn.Module):
def __init__(self):
super().__init__()
self.tensor = torch.rand(3, 4)
def forward(self, x):
return torch.nn.functional.linear(x, self.tensor)
ta = TensorAttribute()
traced = symbolic_trace(ta)
traced(torch.rand(4, 4))
class WrapperForQualname(torch.nn.Module):
def __init__(self):
super().__init__()
self.ta = TensorAttribute()
def forward(self, x):
return torch.nn.functional.linear(x, self.ta.tensor)
wfq = WrapperForQualname()
traced2 = symbolic_trace(wfq)
traced2.graph.lint()
traced2(torch.rand(4, 4))
def test_tensor_attribute_coalseced(self):
def count_attrs(fx_module):
targets = set()
for node in traced.graph.nodes:
if node.op == 'get_attr':
targets.add(node.target)
return len(targets)
val = torch.tensor(5)
def f(x):
return x + val + val
traced = symbolic_trace(f)
traced.graph.lint()
self.assertEqual(count_attrs(traced), 1)
val2 = torch.tensor(5)
def f(x):
val = torch.tensor(5)
return x + val + val2
traced = symbolic_trace(f)
traced.graph.lint()
self.assertEqual(count_attrs(traced), 2)
def test_symbolic_trace_sequential(self):
class Simple(torch.nn.Module):
def forward(self, x):
return torch.neg(x)
seq = torch.nn.Sequential(
Simple(),
Simple(),
Simple()
)
traced = symbolic_trace(seq)
traced.graph.lint()
x = torch.rand(3, 4)
self.assertEqual(traced(x), seq(x))
def test_tensor_constant(self):
class ConstTensor(torch.nn.Module):
def forward(self, x):
return torch.nn.functional.linear(x, torch.zeros(3, 4))
ct = ConstTensor()
traced = symbolic_trace(ct)
traced.graph.lint()
traced(torch.rand(4, 4))
def test_pickle_graphmodule(self):
class Nested(torch.nn.Module):
def __init__(self):
super().__init__()
self.st = torch.nn.Linear(4, 4)
def forward(self, x):
return self.st(x)
n = Nested()
traced = symbolic_trace(n)
traced.graph.lint()
pickled = pickle.dumps(traced)
loaded = pickle.loads(pickled)
loaded.graph.lint()
x = torch.rand(3, 4)
self.assertEqual(loaded(x), traced(x))
def test_pickle_custom_import(self):
graph = torch.fx.Graph()
a = graph.placeholder('x')
b = graph.placeholder('y')
c = graph.call_function(a_non_torch_leaf, (a, b))
d = graph.call_function(torch.sin, (c,))
graph.output(d)
gm = GraphModule(torch.nn.Module(), graph)
pickled = pickle.dumps(gm)
loaded = pickle.loads(pickled)
loaded.graph.lint()
x, y = torch.rand(1), torch.rand(1)
self.assertEqual(loaded(x, y), gm(x, y))
def test_all_input_nodes(self):
graph : torch.fx.Graph = torch.fx.Graph()
a : torch.fx.Node = graph.placeholder('x')
b : torch.fx.Node = graph.call_module('linear_mod', args=(a,))
c : torch.fx.Node = graph.get_attr('y_attr')
d : torch.fx.Node = graph.call_function(operator.add, args=(b, c))
e : torch.fx.Node = graph.call_function(torch.unsqueeze, args=(d, 0))
graph.output(e)
graph.lint()
self.assertEqual(b.all_input_nodes, [a])
self.assertEqual(c.all_input_nodes, [])
self.assertEqual(d.all_input_nodes, [b, c])
self.assertEqual(e.all_input_nodes, [d])
def test_deepcopy_graphmodule_with_transform(self):
st = SimpleTest()
traced = symbolic_trace(st)
traced.graph.lint()
def transform(traced):
new_graph = torch.fx.Graph()
val_map : Dict[Node, Node] = {}
output_value = new_graph.graph_copy(traced.graph, val_map)
relu_out = new_graph.create_node(
op='call_method', target='neg', args=(output_value,), kwargs={})
new_graph.output(relu_out)
return GraphModule(traced, new_graph)
transformed = transform(traced)
transformed.graph.lint()
copied = copy.deepcopy(transformed)
self.assertNotEqual(id(type(transformed)), id(type(copied)))
x = torch.randn(3, 4)
self.assertEqual(copied(x), transformed(x))
def test_deepcopy_with_submods_params(self):
class Bar(torch.nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.rand(3, 4))
def forward(self, x):
return torch.relu(x) + self.param
class Baz(torch.nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.rand(3, 4))
self.bar = Bar()
def forward(self, x):
return self.bar(x) - self.param
baz = Baz()
traced = symbolic_trace(baz)
traced.graph.lint()
copied = copy.deepcopy(traced)
copied.graph.lint()
def test_deepcopy_graph_with_tracer_cls(self):
class TestTracer(Tracer):
def is_leaf_module(self, module, name):
return True
g = Graph(tracer_cls=TestTracer)
x = g.placeholder("x")
g.output(x)
h = copy.deepcopy(g)
self.assertIsNotNone(h._tracer_cls)
self.assertTrue(g._tracer_cls == h._tracer_cls)
def test_unpack_list_better_error(self):
class SomeArgs(torch.nn.Module):
def forward(self, a, b):
return torch.rand(3, 4)
class UnpacksList(torch.nn.Module):
def __init__(self):
super().__init__()
self.sa = SomeArgs()
def forward(self, x : list):
return self.sa(*x)
ul = UnpacksList()
with self.assertRaisesRegex(TraceError, 'Proxy object cannot be iterated.'):
symbolic_trace(ul)
def test_unpack_dict_better_error(self):
class SomeKwargs(torch.nn.Module):
def forward(self, x=3, y=4):
return torch.rand(3, 4)
class UnpacksDict(torch.nn.Module):
def __init__(self):
super().__init__()
self.sk = SomeKwargs()
def forward(self, x : dict):
return self.sk(**x)
ud = UnpacksDict()
with self.assertRaisesRegex(TraceError, 'Proxy object cannot be iterated.'):
symbolic_trace(ud)
def test_pretty_print_targets(self):
# Test that Graph pretty-print prints friendly name for targets
# in `operator` and `builtins`
class SomeMod(torch.nn.Module):
def forward(self, x):
return torch.add(x.foo + x.bar, 3.0)
traced = symbolic_trace(SomeMod())
graph_str = str(traced.graph)
self.assertIn('builtins.getattr', graph_str)
self.assertIn('operator.add', graph_str)
self.assertIn('torch.add', graph_str)
def test_pretty_print_node(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.param: torch.nn.Parameter = torch.nn.Parameter(
torch.rand(3, 4))
self.linear = torch.nn.Linear(4, 5)
def forward(self, x: torch.Tensor, y: int = 2):
return self.linear(x[y] + self.param).clamp(min=0.0, max=1.0)
traced = symbolic_trace(M())
all_formatted = "\n".join([n.format_node() for n in traced.graph.nodes])
FileCheck().check("x").check("placeholder") \
.check("y").check("placeholder") \
.check("getitem").check("call_function") \
.check("param").check("get_attr") \
.check("add").check("call_function") \
.check("linear").check("call_module") \
.check("clamp").check("call_method") \
.run(all_formatted)
def test_script_tensor_constant(self):
# TorchScript seems to ignore attributes that start with `__`.
# We used to call anonymous Tensor values `__tensor_constant*`, but
# they were getting ignored by script. Now they're called
# `_tensor_constant*`
class IHaveATensorConstant(torch.nn.Module):
def forward(self, x):
return x + torch.rand(3, 4)
traced = torch.fx.symbolic_trace(IHaveATensorConstant())
torch.jit.script(traced)
def test_autowrap_functions(self):
class AutowrapFnTest(torch.nn.Module):
def forward(self, x):
return fx_int(x.shape[0] / 2)
class AutowrapFnTest2(torch.nn.Module):
def forward(self, x):
return fx_int(x.shape[0] / 2) + fx_int_x2(x.shape[0] / 2)
# Check function(s) are wrapped
# `int` would normally throw a TypeError as argument can't be `Proxy`
tracer = Tracer(autowrap_functions=(fx_int,))
graph = tracer.trace(AutowrapFnTest())
traced = GraphModule(tracer.root, graph, 'test')
tracer_2 = Tracer(autowrap_functions=(fx_int, fx_int_x2))
tracer_2.trace(AutowrapFnTest2())
# Test scriptability
traced_scripted = torch.jit.script(traced)
self.assertEqual(traced_scripted(torch.rand(4)), 2)
def test_tuple_no_subscript(self):
def foo(x : Tuple):
return x[0]
traced = torch.fx.symbolic_trace(foo)
x = (torch.randn(5, 3),)
torch.testing.assert_close(traced(x), x[0])
bio = io.BytesIO()
torch.save(traced, bio)
bio.seek(0)
loaded = torch.load(bio)
torch.testing.assert_close(loaded(x), x[0])
def test_torch_fx_len(self):
class FXLenTest(torch.nn.Module):
def forward(self, x):
return len(x)
traced = symbolic_trace(FXLenTest())
self.assertEqual(traced(torch.rand(3, 4)), 3)
# Test scriptability
scripted = torch.jit.script(FXLenTest())
self.assertEqual(scripted(torch.rand(3)), 3)
traced_scripted = torch.jit.script(traced)
self.assertEqual(traced_scripted(torch.rand(3)), 3)
# Test non-proxy len
class FXLenTest2(torch.nn.Module):
def __init__(self):
super().__init__()
self.l = [3, 4, 5]
def forward(self, x):
return x + len(self.l)
traced2 = symbolic_trace(FXLenTest2())
inp = torch.rand(3, 4)
self.assertEqual(traced2(inp), inp + 3.0)
self.assertIs(len, builtins.len)
def test_torch_fx_getattr(self):
class FXGetattrTest(torch.nn.Module):
def forward(self, x):
return getattr(x, 'nonexistent_attr', torch.Tensor([2, 3]))
traced = symbolic_trace(FXGetattrTest())
self.assertEqual(traced(torch.rand(3, 4)), torch.Tensor([2, 3]))
def test_sqrt(self):
class Sqrt1(torch.nn.Module):
def forward(self, x):
return sqrt(x.size(0))
class Sqrt2(torch.nn.Module):
def forward(self, x):
return math.sqrt(x.size(0))
class Sqrt3(torch.nn.Module):
def forward(self, x):
return x + math.sqrt(2) + sqrt(2)
self.checkGraphModule(Sqrt1(), [torch.zeros(8)])
self.checkGraphModule(Sqrt2(), [torch.zeros(8)])
self.checkGraphModule(Sqrt3(), [torch.zeros(8)])
self.assertIs(sqrt, _sqrt)
self.assertIs(math.sqrt, _sqrt)
def test_torch_custom_ops(self):
class M(torch.nn.Module):
def forward(self, a):
b = torch.ops.aten.sigmoid(a)
c = torch.ops.aten.cat([a, b])
return torch.ops.aten.cat((c, c))
m = M()
input = torch.randn(3)
ref_out = m(input)
gm = symbolic_trace(m)
gm.graph.lint()
out = gm(input)
self.assertEqual(out, ref_out)
def test_torch_op_overloads(self):
class M(torch.nn.Module):
def forward(self, a):
b = torch.ops.aten.add.Tensor(a, a)
return b
m = M()
input = torch.randn(3)
ref_out = m(input)
gm = symbolic_trace(m)
gm.graph.lint()
out = gm(input)
self.assertEqual(out, ref_out)
for node in gm.graph.nodes:
if node.op == 'call_function':
assert isinstance(node.target, torch._ops.OpOverload)
assert node.target.__name__ == 'add.Tensor'
def test_pickle_torch_custom_ops(self):
class M(torch.nn.Module):
def forward(self, a):
b = torch.ops.aten.sigmoid(a)
c = torch.ops.aten.cat([a, b])
return torch.ops.aten.cat((c, c))
m = M()
input = torch.randn(3)
ref_out = m(input)
gm = symbolic_trace(m)
gm.graph.lint()
pickled = pickle.dumps(gm)
loaded = pickle.loads(pickled)
self.assertEqual(loaded(input), gm(input))
def test_pretty_print(self):
st = SimpleTest()
traced = symbolic_trace(st)
traced.graph.lint()
printed = str(traced)
assert 'SimpleTest()' in printed
assert 'torch.relu' in printed
def test_pretty_print_graph(self):
class KwargPrintTest(torch.nn.Module):
def forward(self, x):
return torch.squeeze(x + 3.0, dim=2)
st = KwargPrintTest()
traced = symbolic_trace(st)
traced.graph.lint()
stringed = str(traced.graph)
for s in ['args', 'kwargs', 'num_users']:
assert s in stringed
def test_custom_proxy_type(self):
class TensorPair:
def __init__(self, left, right):
self.left, self.right = left, right
def add(self, other):
l = self.left + other.left
r = self.right + other.right
return TensorPair(l, r)
def mul(self, other):
l = self.left * other.left
r = self.right * other.right
return TensorPair(l, r)
def use_tensor_pair(x : TensorPair, y : TensorPair):
s = x.add(y)
return s.mul(x)
x = TensorPair(torch.randn(5, 3), torch.randn(5, 3))
y = TensorPair(torch.randn(5, 3), torch.randn(5, 3))
ref_out = use_tensor_pair(x, y)
traced = symbolic_trace(use_tensor_pair)
traced_out = traced(x, y)
self.assertEqual(traced_out.left, ref_out.left)
self.assertEqual(traced_out.right, ref_out.right)
def test_custom_proxy_type_literal(self):
class TensorPair(metaclass=torch.fx.ProxyableClassMeta):
def __init__(self, left, right):
self.left, self.right = left, right
def add(self, other):
l = self.left + other.left
r = self.right + other.right
return TensorPair(l, r)
def mul(self, other):
l = self.left * other.left
r = self.right * other.right
return TensorPair(l, r)
def use_tensor_pair_literal(x : TensorPair):
s = x.add(TensorPair(torch.zeros(5, 3), torch.zeros(5, 3)))
return s.mul(x)
x = TensorPair(torch.randn(5, 3), torch.randn(5, 3))
ref_out = use_tensor_pair_literal(x)
traced = symbolic_trace(use_tensor_pair_literal)
traced_out = traced(x)
self.assertEqual(traced_out.left, ref_out.left)
self.assertEqual(traced_out.right, ref_out.right)
def test_custom_proxy_dynamic_value(self):
class TensorPair(metaclass=torch.fx.ProxyableClassMeta):
def __init__(self, left, right):
self.left, self.right = left, right
def add(self, other):
l = self.left + other.left
r = self.right + other.right
return TensorPair(l, r)
def mul(self, other):
l = self.left * other.left
r = self.right * other.right
return TensorPair(l, r)
def use_tensor_pair_ctor(x : TensorPair, y : torch.Tensor):
s = x.add(TensorPair(y, y))
return s.mul(x)
x = TensorPair(torch.randn(5, 3), torch.randn(5, 3))
y = torch.randn(5, 3)
ref_out = use_tensor_pair_ctor(x, y)
traced = symbolic_trace(use_tensor_pair_ctor)
traced_out = traced(x, y)
self.assertEqual(traced_out.left, ref_out.left)
self.assertEqual(traced_out.right, ref_out.right)
def test_custom_proxy_input_dependent_control_flow(self):
class ZeroTensor(metaclass=torch.fx.ProxyableClassMeta):
def __init__(self, inp):
if inp.sum() == 0:
self.is_zero = True
self.tensor = torch.tensor([])
else:
self.is_zero = False
self.tensor = inp
def add(self, other):
if self.is_zero:
return ZeroTensor(other.tensor)
elif other.is_zero:
return self
def use_zero_tensor(x : torch.Tensor, y : torch.Tensor):
return ZeroTensor(x + y)
x, y = torch.randn(5, 3), torch.randn(5, 3)
ref_out = use_zero_tensor(x, y)
traced = symbolic_trace(use_zero_tensor)
traced_out = traced(x, y)
self.assertEqual(traced_out.is_zero, ref_out.is_zero)
self.assertEqual(traced_out.tensor, ref_out.tensor)
def test_graph_fns(self):
g = Graph()
a = g.placeholder('a')
b = g.call_module('linear', (a,))
c = g.get_attr('bias')
d = g.call_method('add', (b, c))
e = g.call_function(torch.sin, (d,))
g.output(e)
mod = torch.nn.Module()
mod.linear = torch.nn.Linear(3, 4)
mod.bias = torch.rand(4)
gm = GraphModule(mod, g)
gm.graph.lint()
input = torch.rand(3)
r = gm(input)
ref = torch.sin(mod.linear(input) + mod.bias)
self.assertEqual(r, ref)
def test_remove_uses(self):
g : torch.fx.Graph = Graph()
x : torch.fx.Node = g.placeholder('x')
relu : torch.fx.Node = g.call_function(torch.relu, (x,))
neg : torch.fx.Node = g.call_function(torch.neg, (relu,))
g.output(neg)
neg.replace_all_uses_with(relu)
g.erase_node(neg)
self.assertTrue(neg not in relu.users)
def test_remove_uses_with_custom_filter(self):
g : torch.fx.Graph = Graph()
x : torch.fx.Node = g.placeholder('x')
relu : torch.fx.Node = g.call_function(torch.relu, (x,))
neg : torch.fx.Node = g.call_function(torch.neg, (relu,))
g.output(neg)
neg.replace_all_uses_with(relu, lambda x: x != neg)
self.assertTrue(neg in relu.users)
def test_nonetype_annotation(self):
eb = torch.nn.EmbeddingBag(3, 4)
symbolic_trace(eb)
def test_pickle_nonetype_annotation(self):
eb = torch.nn.EmbeddingBag(10, 3, mode='sum')
traced = symbolic_trace(eb)
pickled = pickle.dumps(traced)
loaded = pickle.loads(pickled)
loaded.graph.lint()
input = torch.LongTensor([1, 2, 4, 5, 4, 3, 2, 9])
offsets = torch.LongTensor([0, 4])
self.assertEqual(loaded(input, offsets), traced(input, offsets))
def test_return_tuple(self):
class M(torch.nn.Module):
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
return (x, x + x)
original = M()
traced = symbolic_trace(original)
self.assertEqual(traced(torch.ones(1)), original.forward(torch.ones(1)))
def test_construct_root_dict(self):
graph : torch.fx.Graph = torch.fx.Graph()
a : torch.fx.Node = graph.create_node('placeholder', 'x')
b : torch.fx.Node = graph.create_node('call_module', 'foo.bar.baz', args=(a,))
c : torch.fx.Node = graph.create_node('get_attr', 'zip.zap.zam')
d : torch.fx.Node = graph.create_node('call_function', operator.add, args=(b, c))
graph.output(d)
linear_mod : torch.nn.Module = torch.nn.Linear(3, 4)
add_param : torch.Tensor = torch.rand(3, 4)
gm : torch.fx.GraphModule = torch.fx.GraphModule(
{'foo.bar.baz': linear_mod, 'zip.zap.zam' : add_param}, graph)
gm.graph.lint()
assert 'self.foo.bar.baz' in gm.code
x : torch.Tensor = torch.rand(3, 3)
out : torch.Tensor = gm(x)
ref_out : torch.Tensor = linear_mod(x) + add_param
self.assertEqual(out, ref_out)
def test_symbolic_trace_assert(self):
class AssertsTensorShape(torch.nn.Module):
def forward(self, x):
torch._assert(x.shape[1] > 4, "assert_foobar")
return x
m = AssertsTensorShape()
# verify traceability
traced = symbolic_trace(m)
# verify assertion on traced model works correctly at runtime
traced(torch.rand(4, 5))
with self.assertRaisesRegex(AssertionError, "assert_foobar"):
traced(torch.rand(4, 3))
# verify the symbolically traced module is scriptable
ms = torch.jit.script(m)
with self.assertRaisesRegex(torch.jit.Error, "assert_foobar"):
ms(torch.rand(4, 3))
def test_fx_create_arg(self):
class CustomArgObject:
def __init__(self, x, y):
self.x = x
self.y = y
def __fx_create_arg__(self, tracer: torch.fx.Tracer):
return tracer.create_node(
"call_function",
CustomArgObject,
args=(
tracer.create_arg(self.x),
tracer.create_arg(self.y),
),
kwargs={},
)
class HasCustomArgObjectWhenLeaf(torch.nn.Module):
def forward(self, o: CustomArgObject):
# Not normally traceable; good reason to make
# this module a leaf.
for x in o.x:
o.y += x
return o.y
class Root(torch.nn.Module):
def __init__(self):
super().__init__()
self.inner = HasCustomArgObjectWhenLeaf()
def forward(self, x, y):
o = CustomArgObject(x, y)
return self.inner(o)
class CreateArgTracer(torch.fx.Tracer):
def is_leaf_module(self, m, module_qualified_name):
return type(m) is HasCustomArgObjectWhenLeaf
m = Root()
graph = CreateArgTracer().trace(m)
gm = torch.fx.GraphModule(m, graph)
assert "CustomArgObject(" in gm.code
def test_trace_fn_constant(self):
some_constant = torch.rand(3, 4)
def add_const(x):
return some_constant + x
traced = symbolic_trace(add_const)
input = torch.rand(3, 4)
self.assertEqual(traced(input), add_const(input))
def test_copy_no_remap(self):
traced = symbolic_trace(SimpleTest())
g = traced.graph
copied = torch.fx.Graph()
for node in g.nodes:
copied.node_copy(node)
with self.assertRaisesRegex(RuntimeError, 'does not belong to this Graph'):
copied.lint()
def test_wrong_topo(self):
graph : torch.fx.Graph = torch.fx.Graph()
a : torch.fx.Node = graph.create_node('placeholder', 'x')
b : torch.fx.Node = graph.create_node('call_module', 'foo.bar.baz', args=(a,))
c : torch.fx.Node = graph.create_node('get_attr', 'zip.zap.zam')
d : torch.fx.Node = graph.create_node('call_function', operator.add, args=(b, c))
graph.output(d)
nodes = list(graph.nodes)
nodes[3].append(nodes[2])
with self.assertRaisesRegex(RuntimeError, 'was used before it has been defined'):
graph.lint()
def test_wrong_target_type(self):
graph : torch.fx.Graph = torch.fx.Graph()
with self.assertRaises(ValueError):
n = torch.fx.Node(graph=graph, name='foo', op='call_function', target='foo',
args=(), kwargs={})
def test_example_shape_prop(self):
class TestCase(torch.nn.Module):
def __init__(self):
super().__init__()
self.attr = torch.randn(3, 4)
self.submod = torch.nn.Linear(4, 4)
def forward(self, x):
return torch.neg(self.submod(x.relu() + self.attr))
tc = TestCase()
tc_traced = symbolic_trace(tc)
ref_out = tc_traced(torch.rand(3, 4))
shape_prop.ShapeProp(tc_traced).propagate(torch.rand(3, 4))
# Make sure we're testing all opcodes
opcodes = set()
output_shape : Optional[torch.Shape] = None
output_stride : Optional[Tuple[int]] = None
for node in tc_traced.graph.nodes:
opcodes.add(node.op)
if node.op == 'output':
output_shape = node.args[0].meta['tensor_meta'].shape
output_stride = node.args[0].meta['tensor_meta'].stride
self.assertEqual(opcodes, {'placeholder', 'get_attr', 'call_function', 'call_method',
'call_module', 'output'})
# Test shape propagation and make sure results match actual
self.assertEqual(output_shape, ref_out.shape)
self.assertEqual(output_stride, ref_out.stride())
def test_shape_prop_layout(self):
class ConvTest(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv_mod = torch.nn.Conv2d(5, 5, 3)
def forward(self, x):
return self.conv_mod(x)
# contiguous layout
test_mod = ConvTest()
traced = symbolic_trace(test_mod)
x = torch.randn(5, 5, 224, 224)
shape_prop.ShapeProp(traced).propagate(x)
assert all(node.meta['tensor_meta'].memory_format is torch.contiguous_format
for node in traced.graph.nodes)
x_channels_last = x.contiguous(memory_format=torch.channels_last)
traced.to(memory_format=torch.channels_last)
shape_prop.ShapeProp(traced).propagate(x_channels_last)
for node in traced.graph.nodes:
# NB: the implementation of conv may not preserve the memory format,
# unfortunately. The best we can do is just check that the placeholder
# node is channels-last
if node.op in {'placeholder'}:
self.assertEqual(node.meta['tensor_meta'].memory_format, torch.channels_last)
def test_shape_prop_aggregate(self):
class ReturnTwo(torch.nn.Module):
def forward(self, x):
return (3, torch.sum(x))
class UnderTest(torch.nn.Module):
def __init__(self):
super().__init__()
self.rt = ReturnTwo()
def forward(self, x):
return self.rt(x)
ut = UnderTest()
class RTTracer(torch.fx.Tracer):
def is_leaf_module(self, m, module_qualified_name):
return type(m) is ReturnTwo
graph = RTTracer().trace(ut)
mod = torch.fx.GraphModule(ut, graph)
shape_prop.ShapeProp(mod).propagate(torch.rand(3, 4))
for node in mod.graph.nodes:
if node.op == 'call_module':
assert 'tensor_meta' in node.meta
tensor_meta = node.meta['tensor_meta']
assert tensor_meta[0] == 3
assert tensor_meta[1].shape == torch.Size([])
def test_shape_prop_layout_3d(self):
class ConvTest3d(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv_mod = torch.nn.Conv3d(5, 5, 3)
def forward(self, x):
return self.conv_mod(x)
test_mod_3d = ConvTest3d()
traced_3d = symbolic_trace(test_mod_3d)
x_3d = torch.randn(5, 5, 224, 224, 15)
shape_prop.ShapeProp(traced_3d).propagate(x_3d)
assert all(node.meta['tensor_meta'].memory_format is torch.contiguous_format
for node in traced_3d.graph.nodes)
x_channels_last_3d = x_3d.contiguous(memory_format=torch.channels_last_3d)
traced_3d.to(memory_format=torch.channels_last_3d)
shape_prop.ShapeProp(traced_3d).propagate(x_channels_last_3d)
for node in traced_3d.graph.nodes:
# NB: the implementation of conv may not preserve the memory format,
# unfortunately. The best we can do is just check that the placeholder
# node is channels-last
if node.op in {'placeholder'}:
self.assertEqual(node.meta['tensor_meta'].memory_format, torch.channels_last_3d)
def test_nn_module_stack(self):
class SubModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv_mod = torch.nn.Conv2d(64, 64, (3, 3), padding=1, bias=False)
def forward(self, x):
return self.conv_mod(x)
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.sub_mod = SubModule()
def forward(self, x):
return self.sub_mod(x)
m = MyModule()
gm = torch.fx.symbolic_trace(m)
mod_stack = {}
expected_stack = [('sub_mod', ('sub_mod', type(m.sub_mod))),
('sub_mod.conv_mod', ('sub_mod.conv_mod', type(m.sub_mod.conv_mod)))]
for node in gm.graph.nodes:
mod_stack = node.meta.get('nn_module_stack', {})
if mod_stack:
break
stack_list = list(mod_stack.items())
self.assertEqual(stack_list, expected_stack)
def test_transformer_preserves_nn_module_stack_for_get_attr(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.weight = torch.nn.Parameter(torch.ones(1, 1))
def forward(self, x):
return self.weight + x
tracer = torch.fx.Tracer()
graph = tracer.trace(M())
gm = GraphModule(tracer.root, graph)
for node in gm.graph.nodes:
if node.op == 'get_attr':
node.meta["nn_module_stack"] = "self"
node.meta["stack_trace"] = "stack_trace"
node.meta["source_fn_stack"] = "source_fn_stack"
new_gm = Transformer(gm).transform()
for node in new_gm.graph.nodes:
if node.op == 'get_attr':
self.assertEqual(node.meta["nn_module_stack"], "self")
self.assertEqual(node.meta["stack_trace"], "stack_trace")
self.assertEqual(node.meta["source_fn_stack"], "source_fn_stack")
def test_interpreter(self):
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.rand(3, 4))
self.linear = torch.nn.Linear(4, 5)
def forward(self, x):
return self.linear(x + self.param).clamp(min=0.0, max=1.0)
m = MyModule()
gm = torch.fx.symbolic_trace(m)
interpreter = Interpreter(gm)
input = torch.randn(3, 4)
self.assertEqual(interpreter.run(input), gm(input))
self.assertEqual(interpreter.run(input), m(input))
def test_interpreter_other_graph(self):
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.rand(3, 4))
self.linear = torch.nn.Linear(4, 5)
def forward(self, x):
return self.linear(x + self.param).clamp(min=0.0, max=1.0)
m = MyModule()
gm = torch.fx.symbolic_trace(m)
interpreter = Interpreter(gm, graph=gm.graph)
input = torch.randn(3, 4)
self.assertEqual(interpreter.run(input), gm(input))
self.assertEqual(interpreter.run(input), m(input))
def test_interpreter_run_node_override(self):
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.rand(3, 4))
self.linear = torch.nn.Linear(4, 5)
def forward(self, x):
return self.linear(x + self.param).clamp(min=0.0, max=1.0)
m = MyModule()
gm = torch.fx.symbolic_trace(m)
class RunNodeInterpreter(Interpreter):
def __init__(self, module):
super().__init__(module)
def run_node(self, n : Node) -> Any:
result = super().run_node(n)
n.cached_value = result
return result
input = torch.randn(3, 4)
RunNodeInterpreter(gm).run(input)
for node in gm.graph.nodes:
assert hasattr(node, 'cached_value')
def test_interpreter_onthefly_swap(self):
def fn(x):
return torch.sigmoid(x).neg()
gm = torch.fx.symbolic_trace(fn)
class NegSigmSwapInterpreter(Interpreter):
def call_function(self, target : Target, args : Tuple, kwargs : Dict) -> Any:
if target == torch.sigmoid:
return torch.neg(*args, **kwargs)
return super().call_function(n) # noqa: F821
def call_method(self, target : Target, args : Tuple, kwargs : Dict) -> Any:
if target == 'neg':
call_self, *args_tail = args
return call_self.sigmoid(*args_tail, **kwargs)
return super().call_method(n) # noqa: F821
input = torch.randn(3, 4)
result = NegSigmSwapInterpreter(gm).run(input)
self.assertEqual(result, torch.neg(input).sigmoid())
def test_interpreter_partial_eval(self):
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.rand(3, 4))
self.linear = torch.nn.Linear(4, 5)
def forward(self, x):
return self.linear(x + self.param).clamp(min=0.0, max=1.0)
gm = torch.fx.symbolic_trace(MyModule())
interp = Interpreter(gm)
env = {}
for node in gm.graph.nodes:
if node.op == 'call_module' and node.target == 'linear':
env[node] = torch.arange(0, 12, 1).reshape(3, 4) - 6.0
break
assert len(env) == 1
x = torch.randn(3, 4)
result = interp.run(x, initial_env=env)
self.assertEqual(result, (torch.arange(0, 12, 1).reshape(3, 4) - 6.0).clamp(0.0, 1.0))
def test_interpreter_star_args(self):
def with_star_args(x, *args):
return x + args[0]
gm = torch.fx.symbolic_trace(with_star_args)
interp = Interpreter(gm)
result = interp.run(torch.ones(3, 4), torch.ones(3, 4), torch.rand(3, 4))
self.assertEqual(result, torch.ones(3, 4) * 2.0)
@skipIfNoTorchVision
def test_interpreter_noop_resnet18(self):
rn18 = torchvision_models.resnet18()
transformed = torch.fx.Transformer(symbolic_trace(rn18)).transform()
inp = torch.randn(5, 3, 224, 224)
self.assertEqual(transformed(inp), rn18(inp))
@skipIfNoTorchVision
def test_interpreter_gc_values(self):
rn18 = torchvision_models.resnet18()
interp = Interpreter(symbolic_trace(rn18))
inp = torch.rand(5, 3, 224, 224)
out = interp.run(inp)
env_key_names = {n.name for n in interp.env.keys()}
self.assertEqual(env_key_names, {'output'})
def test_interpreter_default_args(self):
class Model(torch.nn.Module):
def forward(self, x, y=3.14159):
return x + y
model = Model()
gm = torch.fx.symbolic_trace(model)
interp = Interpreter(gm)
x = torch.randn(5, 3)
out = interp.run(x)
torch.testing.assert_close(out, x + 3.14159)
def test_interpreter_not_enough_args(self):
class Model(torch.nn.Module):
def forward(self, x, y):
return x + y
model = Model()
gm = torch.fx.symbolic_trace(model)
interp = Interpreter(gm)
x = torch.randn(5, 3)
with self.assertRaisesRegex(RuntimeError,
'Expected positional argument for parameter y, but one was not passed in'):
out = interp.run(x)
def test_transformer_noop(self):
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.rand(3, 4))
self.linear = torch.nn.Linear(4, 5)
def forward(self, x):
return self.linear(x + self.param).clamp(min=0.0, max=1.0)
m = MyModule()
gm = torch.fx.symbolic_trace(m)
new_gm = Transformer(gm).transform()
input = torch.randn(3, 4)
self.assertEqual(new_gm(input), gm(input))
def test_transformer_op_swap(self):
def fn(x):
return torch.sigmoid(x).neg()
gm = torch.fx.symbolic_trace(fn)
class NegSigmSwapXformer(Transformer):
def call_function(self, target : Target, args : Tuple, kwargs : Dict) -> Any:
if target == torch.sigmoid:
return torch.neg(*args, **kwargs)
return super().call_function(n) # noqa: F821
def call_method(self, target : Target, args : Tuple, kwargs : Dict) -> Any:
if target == 'neg':
call_self, *args_tail = args
return call_self.sigmoid(*args_tail, **kwargs)
return super().call_method(n) # noqa: F821
transformed = NegSigmSwapXformer(gm).transform()
input = torch.randn(3, 4)
self.assertEqual(transformed(input), torch.neg(input).sigmoid())
def test_transformer_multi_outputs(self):
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.rand(3, 4))
self.linear = torch.nn.Linear(4, 5)
def forward(self, x):
x = x + self.param
out = self.linear(x)
return x, out
m = MyModule()
gm = torch.fx.symbolic_trace(m)
new_gm = Transformer(gm).transform()
input = torch.randn(3, 4)
self.assertEqual(new_gm(input), gm(input))
def test_fn_type_annotations(self):
class Foo(torch.nn.Module):
def forward(self, p : Pair, z : torch.Tensor, i : int) -> Dict[str, torch.Tensor]:
return {'a': p.x + p.y + z + i}
foo_scripted = torch.jit.script(Foo())
foo_scripted(Pair(torch.rand(5), torch.rand(5)), torch.rand(5), 3)
fxed = symbolic_trace(Foo())
fxed_scripted = torch.jit.script(fxed)
fxed_scripted(Pair(torch.rand(5), torch.rand(5)), torch.rand(5), 3)
def test_fn_type_annotation_empty(self):
def forward(a : List[torch.Tensor]):
return a[0]
torch.jit.script(symbolic_trace(forward))
def test_wrapped_method(self):
def wrap_with_relu(fn):
@functools.wraps(fn)
def wrapper(*args, **kwargs):
return torch.relu(fn(*args, **kwargs))
return wrapper
class Foo(torch.nn.Module):
@wrap_with_relu
def forward(self, x, w):
return torch.matmul(x, w)
f = Foo()
traced = symbolic_trace(f)
x, w = torch.rand(3, 4), torch.rand(4, 4)
self.assertTrue(any(n.target == torch.relu for n in traced.graph.nodes))
def test_empty_graph_codegen(self):
graph = torch.fx.Graph()
gm = torch.fx.GraphModule(torch.nn.Module(), graph)
self.assertEqual(gm(), None)
def test_sequential(self):
m = torch.nn.Sequential(torch.nn.Conv2d(1, 1, 1))
gm = torch.fx.symbolic_trace(m)
gm_copy = copy.deepcopy(gm)
def test_ctx_mgr(self):
@contextlib.contextmanager
def do_nothing():
yield
class M(torch.nn.Module):
@do_nothing()
def forward(self, x):
return torch.relu(x)
m = M()
self.checkGraphModule(m, (torch.rand(3, 4),))
def test_typename_print(self):
graph : torch.fx.Graph = torch.fx.Graph()
x : torch.fx.Node = graph.create_node('placeholder', 'x')
b : torch.fx.Node = graph.create_node('call_function', target=torch.relu, args=(x,),
type_expr=List[float])
output : torch.fx.Node = graph.output(b)
self.assertTrue('typing.List[float]' in str(graph))
def test_layout(self):
class M(torch.nn.Module):
def forward(self, x):
return torch.empty_like(x, layout=torch.strided, pin_memory=False).fill_(0)
traced = symbolic_trace(M())
x = torch.rand(5, 9, 3, 4)
self.assertEqual(traced(x), torch.zeros_like(x))
def test_ellipsis(self):
class M(torch.nn.Module):
def forward(self, x, y):
return x + y[:, 1:10, ...]
traced = symbolic_trace(M())
x, y = torch.rand(5, 9, 3, 4), torch.rand(5, 15, 3, 4)
self.assertEqual(traced(x, y), x + y[:, 1:10, ...])
def test_inf_nan(self):
class FooMod(torch.nn.Module):
def forward(self, x):
return x + float('inf'), x + float('-inf'), x + float('nan')
fm = FooMod()
self.checkGraphModule(fm, (torch.rand(3, 4),))
def test_inf_nan_kwds(self):
graph : torch.fx.Graph = torch.fx.Graph()
x : torch.fx.Node = graph.create_node('placeholder', 'x')
b : torch.fx.Node = graph.create_node('call_function', operator.add, (x, float('inf')), {}, name='inf')
c : torch.fx.Node = graph.create_node('call_function', operator.add, (x, float('nan')), {}, name='nan')
graph.output((b, c))
gm = torch.fx.GraphModule(torch.nn.Module(), graph)
x = torch.rand(3, 4)
self.assertEqual(gm(x), (x + float('inf'), x + float('nan')))
def test_deepcopy_recursion_depth(self):
depth = sys.getrecursionlimit() + 20
g = torch.fx.Graph()
x = g.placeholder('x')
for i in range(depth):
x = g.call_function(torch.relu, (x,))
g.output(x)
copied_graph = copy.deepcopy(g)
val_map = {}
for orig_node, new_node in zip(g.nodes, copied_graph.nodes):
val_map[orig_node] = new_node
for orig_node, new_node in zip(g.nodes, copied_graph.nodes):
orig_users = set(orig_node.users.keys())
orig_users_equiv = {val_map[u] for u in orig_users}
new_users = set(new_node.users.keys())
self.assertEqual(orig_users_equiv, new_users)
@skipIfNoTorchVision
def test_replace_uses(self):
rn18 = torchvision_models.resnet18()
class LowerReluTracer(torch.fx.Tracer):
def is_leaf_module(self, m : torch.nn.Module, qualname : str):
if isinstance(m, torch.nn.ReLU):
return False
return super().is_leaf_module(m, qualname)
rn18_traced = GraphModule(rn18, LowerReluTracer().trace(rn18))
to_erase = []
for node in rn18_traced.graph.nodes:
if node.op == 'call_function' and node.target in [torch.relu, torch.nn.functional.relu]:
kwargs = node.kwargs.copy()
# Neg doesn't have in-place
kwargs.pop('inplace')
with rn18_traced.graph.inserting_before(node):
new_node = rn18_traced.graph.call_function(
the_function=torch.neg, args=node.args, kwargs=node.kwargs)
node.replace_all_uses_with(replace_with=new_node)
to_erase.append(node)
for node in to_erase:
rn18_traced.graph.erase_node(node)
def test_replace_input(self):
graph : torch.fx.Graph = torch.fx.Graph()
x : torch.fx.Node = graph.create_node('placeholder', 'x')
y : torch.fx.Node = graph.create_node('placeholder', 'y')
b : torch.fx.Node = graph.create_node('call_function', target=torch.relu, args=(x,))
output : torch.fx.Node = graph.output(b)
b.replace_input_with(x, y)
gm = torch.fx.GraphModule(torch.nn.Module(), graph)
input_x = torch.randn(33, 44)
input_y = torch.randn(11, 22)
self.assertEqual(gm(input_x, input_y), torch.relu(input_y))
def test_insertion_point(self):
graph : torch.fx.Graph = torch.fx.Graph()
x : torch.fx.Node = graph.create_node('placeholder', 'x')
b : torch.fx.Node = graph.create_node('call_function', target=torch.relu, args=(x,))
output : torch.fx.Node = graph.output(b)
with graph.inserting_before(b):
neg : torch.fx.Node = graph.call_function(the_function=torch.neg, args=(x,))
_, *relu_args = b.args
b.args = (neg, *relu_args)
gm = torch.fx.GraphModule(torch.nn.Module(), graph)
input = torch.randn(33, 44)
self.assertEqual(gm(input), torch.relu(torch.neg(input)))
def test_update_args_api(self):
graph : torch.fx.Graph = torch.fx.Graph()
x : torch.fx.Node = graph.create_node('placeholder', 'x')
y : torch.fx.Node = graph.create_node('placeholder', 'y')
b : torch.fx.Node = graph.create_node('call_function', target=torch.relu, args=(x,))
output : torch.fx.Node = graph.output(b)
orig_gm = torch.fx.GraphModule(torch.nn.Module(), graph)
inp_x, inp_y = torch.randn(5, 3), torch.randn(3, 5)
self.assertEqual(orig_gm(inp_x, inp_y), torch.relu(inp_x))
b.update_arg(0, y)
new_gm = torch.fx.GraphModule(torch.nn.Module(), graph)
self.assertEqual(new_gm(inp_x, inp_y), torch.relu(inp_y))
def test_update_kwargs_api(self):
graph : torch.fx.Graph = torch.fx.Graph()
x : torch.fx.Node = graph.create_node('placeholder', 'x')
y : torch.fx.Node = graph.create_node('placeholder', 'y')
b : torch.fx.Node = graph.create_node('call_function', target=torch.relu, kwargs={'input': x})
output : torch.fx.Node = graph.output(b)
orig_gm = torch.fx.GraphModule(torch.nn.Module(), graph)
inp_x, inp_y = torch.randn(5, 3), torch.randn(3, 5)
self.assertEqual(orig_gm(inp_x, inp_y), torch.relu(inp_x))
b.update_kwarg('input', y)
new_gm = torch.fx.GraphModule(torch.nn.Module(), graph)
self.assertEqual(new_gm(inp_x, inp_y), torch.relu(inp_y))
def test_immutable_list_pytree_ops(self):
rand_tensor = torch.randn(5, 3)
l = immutable_list([3, [rand_tensor, 42]])
flattened, spec = pytree.tree_flatten(l)
assert flattened == [3, rand_tensor, 42]
unflattened = pytree.tree_unflatten(flattened, spec)
assert unflattened == l
assert isinstance(unflattened, immutable_list)
def test_immutable_dict_pytree_ops(self):
rand_tensor = torch.randn(5, 3)
d = immutable_dict({'a': 3, 'b': [rand_tensor, 42]})
flattened, spec = pytree.tree_flatten(d)
assert flattened == [3, rand_tensor, 42]
unflattened = pytree.tree_unflatten(flattened, spec)
assert unflattened == d
assert isinstance(unflattened, immutable_dict)
def test_move_before(self):
graph : torch.fx.Graph = torch.fx.Graph()
x : torch.fx.Node = graph.create_node('placeholder', 'x')
b : torch.fx.Node = graph.create_node('call_function', target=torch.relu, args=(x,))
output : torch.fx.Node = graph.output(b)
neg : torch.fx.Node = graph.call_function(the_function=torch.neg, args=(x,))
_, *relu_args = b.args
b.args = (neg, *relu_args)
b.prepend(neg)
gm = torch.fx.GraphModule(torch.nn.Module(), graph)
input = torch.randn(33, 44)
self.assertEqual(gm(input), torch.relu(torch.neg(input)))
def test_prepend_self(self):
graph : torch.fx.Graph = torch.fx.Graph()
x : torch.fx.Node = graph.create_node('placeholder', 'x')
b : torch.fx.Node = graph.create_node('call_function', target=torch.relu, args=(x,))
output : torch.fx.Node = graph.output(b)
b.prepend(b)
x.append(b)
self.assertEqual(len(graph.nodes), 3)
def test_erase_node_error(self):
st = SimpleTest()
traced = symbolic_trace(st)
for node in traced.graph.nodes:
# Test deleting with uses both in another Node and at the output
if node.target in [operator.add, torch.relu]:
with self.assertRaisesRegex(RuntimeError, 'but it still had .* users in the graph'):
traced.graph.erase_node(node)
def test_copy_it(self):
d = immutable_dict([(3, 4), (5, 6)])
l = immutable_list([(3, 4), (5, 6)])
self.assertEqual(d, deepcopy(d))
self.assertEqual(l, deepcopy(l))
def test_get_torch_func_signature(self):
for key in dir(torch):
obj = getattr(torch, key)
if callable(obj):
schemas = get_signature_for_torch_op(obj)
def test_find_uses(self):
graph = torch.fx.Graph()
x = torch.fx.Proxy(graph.placeholder('x'))
y = torch.relu(x)
z = x + x
u = torch.neg(x)
graph.output((y + z + u).node)
graph.lint()
users_of_x = x.node.users
self.assertEqual(len(users_of_x), 3)
expected_ops = {'relu', 'add', 'neg'}
for use in users_of_x:
assert any(use.name.startswith(prefix) for prefix in expected_ops)
def test_inline_graph(self):
class InlineInto(torch.nn.Module):
def forward(self, x):
return torch.relu(x)
class ToInline(torch.nn.Module):
def forward(self, x):
return torch.neg(x)
inline_into = symbolic_trace(InlineInto())
to_inline = symbolic_trace(ToInline())
combined_graph = torch.fx.Graph()
output_node = combined_graph.graph_copy(inline_into.graph, {})
input_node = next(iter(to_inline.graph.nodes))
assert input_node and input_node.op == 'placeholder'
val_map = {input_node : output_node}
output = combined_graph.graph_copy(to_inline.graph, val_map)
combined_graph.output(output)
combined_module = torch.fx.GraphModule(torch.nn.Module(), combined_graph)
input = torch.rand(3, 4)
self.assertEqual(combined_module(input), input.relu().neg())
def test_multi_insert_point(self):
graph = torch.fx.Graph()
x = torch.fx.Proxy(graph.placeholder('x'))
relu = torch.relu(x)
with graph.inserting_before(relu.node):
y = torch.neg(x)
z = torch.tanh(y)
graph.output((relu.node, z.node))
graph.lint()
expected_ops = ['x', 'neg', 'tanh', 'relu']
for node, expected in zip(graph.nodes, expected_ops):
assert expected in node.name
def test_reassign_args_kwargs_uses(self):
graph = torch.fx.Graph()
x, y = Proxy(graph.placeholder('x')), Proxy(graph.placeholder('y'))
z = x + y
zed = z + z + z
graph.output(zed.node)
graph.lint()
# zed = z + z + z -> zed = z + z + x
zed.node.args = (zed.node.args[0], x.node)
self.assertEqual(list(x.node.users.keys()), [z.node, zed.node])
# z = x + y -> z = y + y
z.node.args = (y.node, y.node)
self.assertEqual(list(x.node.users.keys()), [zed.node])
def test_trace_function(self):
def foo(x, y):
return torch.relu(x) + y
x, y = torch.randn(3, 4), torch.randn(3, 4)
self.checkGraphModule(foo, (x, y))
def test_trace_return_dataclass(self):
"""
Test case for Module that return dataclass
"""
from dataclasses import dataclass
@dataclass
class MyOutput:
foo: torch.Tensor
bar: torch.Tensor
class ModuleReturnDataclass(torch.nn.Module):
def forward(self, d : torch.Tensor):
return MyOutput(foo=d + d, bar=d * 3)
module = ModuleReturnDataclass()
traced_graph = symbolic_trace(module).graph
print(traced_graph)
gm = GraphModule(module, traced_graph)
x = torch.rand(1)
self.assertEqual(module(x), gm(x))
def test_trace_return_dataclass_nested(self):
"""
Test case for Module that return dataclass
"""
from dataclasses import dataclass
@dataclass
class MyOutput:
foo: torch.Tensor
bar: torch.Tensor
class ModuleReturnDataclass(torch.nn.Module):
def forward(self, d : torch.Tensor):
return MyOutput(foo=d + d, bar=d * 3)
class CallsModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.m = ModuleReturnDataclass()
def forward(self, x):
tmp = self.m(x)
return MyOutput(foo=tmp.foo, bar=tmp.bar)
module = CallsModule()
traced_graph = symbolic_trace(module).graph
print(traced_graph)
gm = GraphModule(module, traced_graph)
x = torch.rand(1)
self.assertEqual(module(x), gm(x))
def test_trace_return_namedtuple(self):
"""
Test case for Module that return namedtuple
"""
class MyOutput(NamedTuple):
foo: torch.Tensor
bar: torch.Tensor
class ModuleReturnNamedTuple(torch.nn.Module):
def forward(self, d : torch.Tensor):
return MyOutput(foo=d, bar=d)
module = ModuleReturnNamedTuple()
traced_graph = symbolic_trace(module).graph
print(traced_graph)
gm = GraphModule(module, traced_graph)
x = torch.rand(1)
self.assertEqual(module(x), gm(x))
def test_trace_dict_int_keys(self):
class ModWithDictArg(torch.nn.Module):
def forward(self, d : Dict[int, torch.Tensor]):
return d[42]
class CallsModWithDict(torch.nn.Module):
def __init__(self):
super().__init__()
self.m = ModWithDictArg()
def forward(self, x):
return self.m({42: x})
class MyTracer(torch.fx.Tracer):
def is_leaf_module(self, m: torch.nn.Module, module_qualified_name : str) -> bool:
return isinstance(m, ModWithDictArg)
traced_graph = MyTracer().trace(CallsModWithDict())
def test_trace_dict_proxy_keys(self):
class ModWithDictArg(torch.nn.Module):
def forward(self, d : Dict[torch.Tensor, torch.Tensor]):
return d[42]
class CallsModWithDict(torch.nn.Module):
def __init__(self):
super().__init__()
self.m = ModWithDictArg()
def forward(self, x):
return self.m({x: x})
class MyTracer(torch.fx.Tracer):
def is_leaf_module(self, m: torch.nn.Module, module_qualified_name : str) -> bool:
return isinstance(m, ModWithDictArg)
with self.assertRaisesRegex(RuntimeError, 'cannot contain a Node'):
traced_graph = MyTracer().trace(CallsModWithDict())
def test_module_deepcopy_edit_nodes(self):
class Foo(torch.nn.Module):
def forward(self, x):
return torch.relu(x)
traced1 = symbolic_trace(Foo())
copied = copy.deepcopy(traced1)
for node in copied.graph.nodes:
if node.target == torch.relu:
node.target = torch.neg
copied.recompile()
traced1.recompile()
x = torch.randn(15, 15)
torch.testing.assert_close(traced1(x), torch.relu(x))
torch.testing.assert_close(copied(x), torch.neg(x))
def test_direct_param_use(self):
class TransposeTest(torch.nn.Module):
def __init__(self):
super().__init__()
self.b = torch.nn.Parameter(torch.rand(4, 3))
def forward(self, x):
return self.b
class Foo(torch.nn.Module):
def __init__(self):
super().__init__()
self.a = TransposeTest()
def forward(self, x):
return self.a.b, self.a.b.t(), self.a.b.view(12)
traced = torch.fx.symbolic_trace(Foo())
assert all('constant' not in node.target for node in traced.graph.nodes)
def test_single_default_arg(self):
class M(torch.nn.Module):
def forward(self, y=1):
return y
m = M()
self.checkGraphModule(m, ())
self.checkGraphModule(m, (3,))
def test_multiple_default_args(self):
class M(torch.nn.Module):
def forward(self, y=1, z=2):
return y + z
m = M()
self.checkGraphModule(m, ())
self.checkGraphModule(m, (3,))
self.checkGraphModule(m, (3, 4))
def test_regular_and_default_args(self):
class M(torch.nn.Module):
def forward(self, x, y=1):
return x + y
m = M()
self.checkGraphModule(m, (2,))
self.checkGraphModule(m, (2, 3))
def test_string_literal_return(self):
class M(torch.nn.Module):
def forward(self):
return "foo"
m = M()
self.checkGraphModule(m, ())
def test_namedtuple_return_qualname(self):
class NamedTupReturn(torch.nn.Module):
def forward(self, x):
return MyNamedTup(x, x)
traced = symbolic_trace(NamedTupReturn())
input = torch.rand(3, 4)
self.assertEqual(traced(input), MyNamedTup(input, input))
def test_update_args_kwargs_yells_at_you(self):
symtraced = symbolic_trace(SimpleTest())
node = next(iter(symtraced.graph.nodes))
with self.assertRaisesRegex(AttributeError, '__update_args_kwargs'):
node.__update_args_kwargs((), {})
def test_torchbind_class_attribute_in_fx(self):
if IS_FBCODE or IS_WINDOWS or IS_MACOS:
self.skipTest("torch.classes._TorchScriptTesting._StackString is registered, skipping")
class FooBar1234(torch.nn.Module):
def __init__(self):
super().__init__()
self.f = torch.classes._TorchScriptTesting._StackString(["3", "4"])
def forward(self):
return self.f.top()
m = FooBar1234()
self.checkGraphModule(m, ())
def test_torchbind_class_attribute_in_fx_tensor_arg(self):
if IS_FBCODE or IS_WINDOWS or IS_MACOS:
self.skipTest("torch.classes._TorchScriptTesting._ReLUClass is registered, skipping")
class FooBar2341(torch.nn.Module):
def __init__(self):
super().__init__()
self.f = torch.classes._TorchScriptTesting._ReLUClass()
def forward(self, x):
return self.f.run(x)
m = FooBar2341()
traced = symbolic_trace(m)
input = torch.randn(3, 4)
self.assertEqual(traced(input), m(input))
self.assertTrue(any(n.op == 'call_method' for n in traced.graph.nodes))
def test_script_method_trace(self):
class Scripted(torch.nn.Module):
def forward(self, x):
return torch.relu(x)
class Holder(torch.nn.Module):
def __init__(self):
super().__init__()
self.s = torch.jit.script(Scripted())
def forward(self, x):
return self.s(x)
h = Holder()
traced = symbolic_trace(h)
input = torch.randn(3, 4)
self.assertEqual(traced(input), h(input))
self.assertTrue(any(n.op == 'call_method' for n in traced.graph.nodes))
def test_namedtuple_return_trace(self):
class NamedTupReturn(torch.nn.Module):
def forward(self, x):
return Pair(x, x)
traced = symbolic_trace(NamedTupReturn())
input = torch.rand(3, 4)
self.assertEqual(traced(input), Pair(input, input))
def test_named_tuple_inlined(self):
class NamedTupMod(torch.nn.Module):
def forward(self, inp):
return wrapped_named_tup(Pair(inp, 1.2), p2=Pair(3.4, inp))
m = NamedTupMod()
input = torch.rand(3, 4)
ref = m(input)
traced = symbolic_trace(m)
res = traced(input)
self.assertEqual(ref, res)
# Check Pair NamedTuple works when inlined into the function call.
ph = call_func = None
for node in traced.graph.nodes:
if node.op == "placeholder":
ph = node
elif node.op == "call_function" and node.target == wrapped_named_tup:
node.update_arg(0, Pair(ph, 1.2))
node.update_kwarg("p2", Pair(3.4, ph))
call_func = node
break
self.assertTrue(call_func is not None)
self.assertTrue(isinstance(call_func.args[0], Pair))
self.assertTrue(isinstance(call_func.kwargs["p2"], Pair))
self.assertEqual(_format_arg(call_func.args[0]), "Pair(x=%inp, y=1.2)")
self.assertEqual(_format_arg(call_func.kwargs["p2"]), "Pair(x=3.4, y=%inp)")
traced.graph.eliminate_dead_code()
traced.recompile()
res = traced(input)
self.assertEqual(ref, res)
def test_return_type_exists(self):
class ReturnTypeModule(torch.nn.Module):
def other(self, x: List[str]) -> List[str]:
return x
def forward(self, x: List[str]) -> List[str]:
return self.other(x)
traced = symbolic_trace(ReturnTypeModule())
self.assertIn("-> typing_List[str]", traced._code)
scripted = torch.jit.script(traced)
self.assertIn("-> List[str]", scripted.code)
def getitem_inner(self):
class GetItemBase(torch.nn.Module):
def __init__(self):
super().__init__()
self.pe = torch.nn.Buffer(torch.randn(8, 8))
class GetItem1(GetItemBase):
def forward(self, x):
return self.pe[:, :x.size(0)]
class GetItem2(GetItemBase):
def forward(self, x):
return self.pe[x.size(0)]
class GetItem3(GetItemBase):
def forward(self, x):
return self.pe[4] # fx creates `self._tensor_constant0` here
self.checkGraphModule(GetItem1(), [torch.zeros(4)])
self.checkGraphModule(GetItem2(), [torch.zeros(4)])
self.checkGraphModule(GetItem3(), [torch.zeros(4)])
@unittest.skipUnless(os.environ.get("FX_PATCH_GETITEM") == "1",
"Will be checked in test_getitem_subproc")
def test_getitem(self):
self.getitem_inner()
def test_getitem_subproc(self):
# need to run this test in a subproc to work around:
# https://github.com/pytorch/pytorch/issues/50710
proc = Process(target=run_getitem_target)
proc.start()
proc.join()
self.assertEqual(proc.exitcode, 0)
def test_user_friendly_call_provenance_with_function(self):
def fn(x):
return wrapper_fn(x)
traced = torch.fx.symbolic_trace(fn)
with self.assertRaisesRegex(RuntimeError, "'wrapper_fn' is "
"being compiled since it was called"
" from 'fn.forward'"):
scripted = torch.jit.script(traced)
def test_user_friendly_call_provenance_with_module(self):
class M(torch.nn.Module):
def forward(self, x):
return wrapper_fn(x)
traced = torch.fx.symbolic_trace(M())
with self.assertRaisesRegex(RuntimeError, "'wrapper_fn' is "
"being compiled since it was called"
" from 'M.forward'"):
scripted = torch.jit.script(traced)
def test_snake_case(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.activations = torch.nn.ModuleDict([
["snake_case", torch.nn.ReLU()],
["PascalCase", torch.nn.LeakyReLU()],
["ALL_CAPS", torch.nn.PReLU()]
])
def forward(self, x):
a = self.activations["snake_case"](x)
b = self.activations["PascalCase"](x)
c = self.activations["ALL_CAPS"](x)
return a, b, c
traced = symbolic_trace(M())
check = [
("activations_snake_case", "activations.snake_case"),
("activations_pascal_case", "activations.PascalCase"),
("activations_all_caps", "activations.ALL_CAPS")
]
i = 0
for node in traced.graph.nodes:
if node.op == "placeholder" or node.op == "output":
continue
name = check[i][0]
target = check[i][1]
self.assertEqual(name, node.name)
self.assertEqual(target, node.target)
i += 1
self.assertEqual(i, 3)
def test_no_mutation(self):
from torch.fx.immutable_collections import immutable_list
x = immutable_list([3, 4])
with self.assertRaisesRegex(NotImplementedError, "new_args"):
x[0] = 4
def test_partial_trace(self):
class Foo(torch.nn.Module):
def forward(self, x, y):
if y:
return 2 * x
else:
return x
mod = Foo()
mod_true = symbolic_trace(mod, concrete_args={'y': True})
mod_false = symbolic_trace(mod, concrete_args={'y': False})
self.assertEqual(mod_true(3, True), 6)
print(mod_true.code)
assert any(i.target == torch._assert for i in mod_true.graph.nodes)
with self.assertRaises(AssertionError):
mod_true(3, False)
self.assertEqual(mod_false(3, False), 3)
with self.assertRaises(AssertionError):
mod_false(3, True)
def f_higher(a, f):
return f(a)
nf = symbolic_trace(f_higher, concrete_args={'f': lambda x: x * 2})
self.assertEqual(nf(3, lambda x: x * 2), 6)
def test_custom_traceback_raised_when_exception_source_is_graphmodule(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.W = torch.nn.Parameter(torch.randn(5))
def forward(self, x):
return torch.dot(self.W, x)
traced = torch.fx.symbolic_trace(M())
out = [n for n in traced.graph.nodes if n.op == "output"][-1]
with traced.graph.inserting_before(out):
relu_out = traced.graph.call_method(method_name='relu',
args=(out.args[0],))
out.args = (relu_out,)
traced.recompile()
with self.capture_stderr() as captured:
with self.assertRaises(TypeError):
traced(5)
self.assertRegex(captured[0],
r"Call using an FX-traced Module, line .* of the "
r"traced Module's generated forward function:")
def test_custom_traceback_not_raised_when_exception_source_is_submodule(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(3, 4)
def forward(self, x):
return self.linear(x)
traced = torch.fx.symbolic_trace(M())
# Do not change this to `capture_stderr` or another context
# manager without ensuring that the output is as expected
try:
traced(torch.rand(5, 5))
except RuntimeError:
captured = traceback.format_exc()
self.assertNotRegex(captured,
r"Call using an FX-traced Module, line .* of the "
r"traced Module's generated forward function:")
def test_graph_module_replicate_for_dp(self):
class Foo(torch.nn.Module):
def forward(self, x):
return torch.relu(x)
gm = torch.fx.symbolic_trace(Foo())
x = torch.randn(5, 3)
out = gm(x)
replica = gm._replicate_for_data_parallel()
out_replica = replica(x)
torch.testing.assert_close(out_replica, out)
def test_ast_rewriter_rewrites_assert(self):
class M(torch.nn.Module):
def forward(self, x: torch.Tensor, y: int, z: int):
assert y == z
return torch.add(x, x)
ast_rewriter = RewritingTracer()
graph = ast_rewriter.trace(M())
traced = GraphModule(ast_rewriter.root, graph, "gm")
traced.graph.lint()
def test_ast_rewriter_rewrites_assert_with_message(self):
class M(torch.nn.Module):
def forward(self, x: torch.Tensor, y: int, z: int):
assert y == z, "msg"
return torch.add(x, x)
ast_rewriter = RewritingTracer()
graph = ast_rewriter.trace(M())
traced = GraphModule(ast_rewriter.root, graph, "gm")
traced.graph.lint()
def test_throw_out_variant(self):
def foo(x):
y = torch.rand_like(x)
torch.sigmoid(x, out=y)
return y
class MyTracer(torch.fx.Tracer):
check_mutable_operations = True
tracer = MyTracer()
with self.assertRaisesRegex(RuntimeError, 'mutable operation aten::sigmoid.out'):
traced_graph = tracer.trace(foo)
def test_ast_rewriter_reassigns_submodules(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.bn = torch.nn.BatchNorm2d(100)
def forward(self, x: torch.Tensor):
return torch.add(x, x)
ast_rewriter = RewritingTracer()
graph = ast_rewriter.trace(M())
traced = GraphModule(ast_rewriter.root, graph, "gm")
traced.graph.lint()
def test_ast_rewriter_wrap(self):
self.assertEqual(3 + 4 + 5, a_lifted_leaf((3, 4), 5))
def to_trace(y):
return (
a_lifted_leaf((4, y), 3)
+ a_lifted_leaf((3, 4), 5)
+ a_lifted_leaf((y, y), y)
)
ast_rewriter = RewritingTracer()
graph = ast_rewriter.trace(to_trace)
traced = GraphModule(ast_rewriter.root, graph, "gm")
self.assertIn("a_lifted_leaf", traced.code)
self.assertEqual(27, traced(2))
self.assertIs(a_lifted_leaf, real_a_lifed_leaf)
def test_ast_rewriter_wrap_fn_directly(self):
self.assertEqual(3 + 4 + 5, a_lifted_leaf2((3, 4), 5))
def to_trace(y):
return (
a_lifted_leaf2((4, y), 3)
+ a_lifted_leaf2((3, 4), 5)
+ a_lifted_leaf2((y, y), y)
)
ast_rewriter = RewritingTracer()
graph = ast_rewriter.trace(to_trace)
traced = GraphModule(ast_rewriter.root, graph, "gm")
self.assertIn("a_lifted_leaf2", traced.code)
self.assertEqual(27, traced(2))
self.assertIs(a_lifted_leaf2, real_a_lifed_leaf2)
def test_profiler_ranges_side_effect(self):
g = torch.fx.Graph()
handle = g.call_function(torch.ops.profiler._record_function_enter_new, ('test_range',))
g.call_function(torch.ops.profiler._record_function_exit, (handle,))
g.output(None)
found_targets = {}
for node in g.nodes:
if node.op == 'call_function':
found_targets.setdefault(node.target)
self.assertEqual(
list(found_targets.keys()),
[torch.ops.profiler._record_function_enter_new, torch.ops.profiler._record_function_exit]
)
g.eliminate_dead_code()
found_targets = {}
for node in g.nodes:
if node.op == 'call_function':
found_targets.setdefault(node.target)
self.assertEqual(
list(found_targets.keys()),
[torch.ops.profiler._record_function_enter_new, torch.ops.profiler._record_function_exit]
)
def test_ast_rewriter_wrapped_via_decorator(self):
class F(torch.nn.Module):
def forward(self, x):
return wrapped_via_decorator(x)
ast_rewriter = RewritingTracer()
graph = ast_rewriter.trace(F())
traced = GraphModule(ast_rewriter.root, graph, "gm")
self.assertIn("wrapped_via_decorator", traced.code)
self.assertEqual(traced(0), 1)
self.assertIs(wrapped_via_decorator, real_wrapped_via_decorator)
self.assertFalse(hasattr(wrapped_via_decorator, "__fx_already_patched"))
def test_ast_rewriter_wrapped_via_decorator_and_transformed(self):
self.assertEqual(wrapped_via_decorator(0), 1)
def to_trace(y):
return wrapped_via_decorator(y)
ast_rewriter = RewritingTracer()
graph = ast_rewriter.trace(to_trace)
traced = GraphModule(ast_rewriter.root, graph, "gm")
self.assertIn("wrapped_via_decorator", traced.code)
self.assertEqual(traced(0), 1)
self.assertIs(wrapped_via_decorator, real_wrapped_via_decorator)
self.assertFalse(hasattr(wrapped_via_decorator, "__fx_already_patched"))
transformed = torch.fx.Transformer(traced).transform()
self.assertIn("wrapped_via_decorator", transformed.code)
self.assertEqual(transformed(0), 1)
self.assertIs(wrapped_via_decorator, real_wrapped_via_decorator)
self.assertFalse(hasattr(wrapped_via_decorator, "__fx_already_patched"))
def test_ast_rewriter_wrap_with_submodule(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.batchnorm1d = torch.nn.BatchNorm1d(2, affine=False)
def forward(self, x: torch.Tensor):
return wrapped_with_submodule(x, self.batchnorm1d)
ast_rewriter = RewritingTracer()
graph = ast_rewriter.trace(M())
traced = GraphModule(ast_rewriter.root, graph, "gm")
self.assertIn("wrapped_with_submodule", traced.code)
input = torch.rand(3, 2)
ref_batchnorm1d = torch.nn.BatchNorm1d(2, affine=False)
self.assertEqual(ref_batchnorm1d(input), traced(input))
def test_submodule_manipulation_API(self):
class C(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(16, 33, 3, stride=2)
self.param = torch.nn.Parameter(torch.rand(2, 3))
def forward(self, x):
return self.conv(torch.cat([self.param, x]))
class B(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(100, 200)
self.buf = torch.nn.Buffer(torch.randn(2, 3))
self.net_c = C()
def forward(self, x):
return self.linear(torch.cat([self.buf, self.net_c(x)]))
class A(torch.nn.Module):
def __init__(self):
super().__init__()
self.net_b = B()
self.param = torch.nn.Parameter(torch.rand(2, 3))
def forward(self, x):
return self.net_b(x) + self.param
a = symbolic_trace(A())
a.add_submodule("net_b.net_c.dropout", torch.nn.Dropout(p=0.2))
conv = [n for n in a.graph.nodes if n.target == "net_b.net_c.conv"][-1]
with a.graph.inserting_before(conv):
with warnings.catch_warnings(record=True) as w:
dropout = a.graph.call_module(module_name="net_b.net_c.dropout",
args=conv.args)
self.assertEqual(len(w), 0)
conv.replace_all_uses_with(dropout)
a.graph.erase_node(conv)
a.recompile()
def module_exists(gm: GraphModule, path: str) -> bool:
return any(path == name for name, _ in gm.named_modules())
def parameter_exists(gm: GraphModule, path: str) -> bool:
return (any(path == name for name, _ in gm.named_parameters())
and any(path == name for name in gm.state_dict().keys()))
def buffer_exists(gm: GraphModule, path: str) -> bool:
return (any(path == name for name, _ in gm.named_buffers())
and any(path == name for name in gm.state_dict().keys()))
# Test that we added the "dropout" submodule
self.assertTrue(module_exists(a, "net_b.net_c.dropout"))
# Test `get_submodule` with an added submodule
self.assertIsNotNone(a.get_submodule("net_b.net_c.dropout"))
# Test that the "conv" submodule is still there
self.assertTrue(module_exists(a, "net_b.net_c.conv"))
# Test `get_submodule` with an original module
self.assertIsNotNone(a.get_submodule("net_b.net_c.conv"))
# Test that the "conv" node is NOT still there
conv = [n for n in a.graph.nodes if n.target == "net_b.net_c.conv"]
self.assertEqual(conv, [])
a.delete_submodule("net_b.net_c.conv")
# Test that the "conv" submodule is now gone
self.assertFalse(module_exists(a, "net_b.net_c.conv"))
# Test `get_submodule` with a deleted submodule
with self.assertRaisesRegex(AttributeError, "has no attribute "
"`conv`"):
self.assertIsNone(a.get_submodule("net_b.net_c.conv"))
# Test `get_attr` warnings
cat = [n for n in a.graph.nodes if n.target == torch.cat][-1]
with a.graph.inserting_before(cat):
with warnings.catch_warnings(record=True) as w:
param = a.graph.get_attr(qualified_name="net_b.net_c.param")
self.assertEqual(len(w), 0)
with self.assertWarnsRegex(UserWarning, "Attempted to "
"insert a get_attr Node with no "
"underlying reference in the "
"owning GraphModule"):
bad_param = a.graph.get_attr(qualified_name="net_b.param")
a.graph.erase_node(bad_param)
cat.args = (*cat.args, param)
a.recompile()
a.graph.lint()
# Test `get_parameter`
a.get_parameter("net_b.net_c.param")
with self.assertRaisesRegex(AttributeError, "is not an "
"nn.Parameter"):
a.get_parameter("net_b.buf")
with self.assertRaisesRegex(AttributeError, "has no attribute "
"`param`"):
a.get_parameter("net_b.param")
# Test `get_buffer`
a.get_buffer("net_b.buf")
with self.assertRaisesRegex(AttributeError, "is not a "
"buffer"):
a.get_buffer("net_b.net_c.param")
with self.assertRaisesRegex(AttributeError, "has no attribute "
"`buf`"):
a.get_buffer("net_b.net_c.buf")
# Test non-nested attributes
a.get_submodule("")
a.get_parameter("param")
# Insert some unused submodules
a.add_submodule("net_b.embedding", torch.nn.Embedding(10, 3))
a.add_submodule("net_b.net_c.embedding", torch.nn.Embedding(10, 3))
a.add_submodule("net_b.net_c.rnn", torch.nn.RNN(10, 20, 2))
a.add_submodule("batch_norm_2d", torch.nn.BatchNorm2d(100))
# Garbage collection
a.delete_all_unused_submodules()
# Test that all the unused submodules are gone
self.assertFalse(module_exists(a, "net_b.embedding"))
self.assertFalse(module_exists(a, "net_b.net_c.embedding"))
self.assertFalse(module_exists(a, "net_b.net_c.rnn"))
self.assertFalse(module_exists(a, "batch_norm_2d"))
# Test that we didn't delete any unused Parameters or buffers
self.assertTrue(parameter_exists(a, "net_b.net_c.param"))
self.assertTrue(buffer_exists(a, "net_b.buf"))
a.graph.lint()
def test_delete_unused_submodules_leaf(self):
class SubModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 10)
self.relu = torch.nn.ReLU()
def forward(self, x):
x = self.linear(x)
x = self.relu(x)
return x
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.submod = SubModule()
def forward(self, x):
x = self.submod(x)
return x
model = Model()
class MyCustomTracer(torch.fx.Tracer):
def is_leaf_module(self, m: torch.nn.Module, module_qualified_name : str) -> bool:
return module_qualified_name == "submod"
inputs = torch.randn(1, 10)
traced_graph = MyCustomTracer().trace(model)
gm2 = torch.fx.GraphModule(model, traced_graph)
gm2.delete_all_unused_submodules()
torch.testing.assert_close(gm2(inputs), model(inputs))
def test_fx_stateless(self):
class MockModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.l1 = torch.nn.Linear(1, 1)
self.buffer = torch.nn.Buffer(torch.ones(1))
def forward(self, x):
return self.l1(x) + self.buffer
module = MockModule()
x = torch.rand((1, 1))
weight = torch.tensor([[1.0]], requires_grad=True)
bias = torch.tensor([0.0], requires_grad=True)
buffer = torch.tensor([0.0])
parameters = {'l1.weight': weight,
'l1.bias': bias,
'buffer': buffer}
fx_module = torch.fx.symbolic_trace(module)
res = torch.func.functional_call(fx_module, parameters, x)
res.backward()
self.assertIsNotNone(weight.grad)
self.assertIsNotNone(bias.grad)
self.assertIsNone(buffer.grad)
# Gradient was not calculated for the module stated and buffers
self.assertIsNone(module.l1.weight.grad)
self.assertIsNone(module.l1.bias.grad)
self.assertIsNone(module.buffer.grad)
def test_tracing_graphmodules_as_leaf_submodules(self):
class A(torch.nn.Module):
def forward(self, t):
return t + t
class B(torch.nn.Module):
def __init__(self):
super(type(self), self).__init__()
self.calling = False
self.called = False
def forward(self, t):
if self.calling:
return t - t
else:
return t + t
def __call__(self, *args):
self.called = True
self.calling = True
return super(type(self), self).__call__(*args)
self.calling = False
class M(torch.nn.Module):
def __init__(self, a, b):
super().__init__()
self.a = a
self.b = b
def forward(self, t):
x = self.a(t)
y = self.b(t)
return x + y
class LeafTracer(Tracer):
def is_leaf_module(self, module, name):
return True
class LeafTracerNotB(Tracer):
def is_leaf_module(self, module, name):
return False if "b" in name else True
# Recompile calls added "for fun", since they
# chain __call__ wrappers.
#
# Test: B as a regular, non-leaf module
#
a = symbolic_trace(A())
a.recompile()
m = M(a, B())
graph = LeafTracerNotB().trace(m)
gm = GraphModule(m, graph)
gm.recompile()
# Test graphmodule/submodule a is not inlined.
self.assertTrue(isinstance(gm.get_submodule("a"), GraphModule))
match = [n for n in gm.graph.nodes if n.op == "call_module" and n.target == "a"]
self.assertTrue(len(match) == 1)
# Test submodule b is not treated as leaf.
self.assertFalse(hasattr(gm, "b"))
# Test assert custom __call__ on submodule b was honored.
match = [
n
for n in gm.graph.nodes
if n.op == "call_function" and n.target == operator.sub
]
self.assertTrue(len(match) == 1)
#
# Test: B as a regular, leaf module
# symbolic_trace should only patch torch.nn.Module.__call__,
# which means B.__call__ should still execute
#
a = symbolic_trace(A())
a.recompile()
b = B()
m = M(a, b)
graph = LeafTracer().trace(m)
gm = GraphModule(m, graph)
gm.recompile()
# Test graphmodule/submodule a is not inlined.
self.assertTrue(isinstance(gm.get_submodule("a"), GraphModule))
match = [n for n in gm.graph.nodes if n.op == "call_module" and n.target == "a"]
self.assertTrue(len(match) == 1)
# Test submodule b is leaf:
self.assertTrue(isinstance(gm.get_submodule("b"), torch.nn.Module))
match = [n for n in gm.graph.nodes if n.op == "call_module" and n.target == "b"]
self.assertTrue(len(match) == 1)
# Test b.__call__ was run
self.assertTrue(b.called)
self.assertTrue(gm.get_submodule("b").called)
#
# Test: B as GraphModule leaf
# __call__ not honored since symbolic_trace directly invokes forward()
#
a = symbolic_trace(A())
a.recompile()
b = symbolic_trace(B())
b.recompile()
m = M(a, b)
graph = LeafTracer().trace(m)
gm = GraphModule(m, graph)
gm.recompile()
self.assertTrue(isinstance(gm.get_submodule("a"), GraphModule))
match = [n for n in gm.graph.nodes if n.op == "call_module" and n.target == "a"]
self.assertTrue(len(match) == 1)
self.assertTrue(isinstance(gm.get_submodule("b"), torch.nn.Module))
match = [n for n in gm.graph.nodes if n.op == "call_module" and n.target == "b"]
self.assertTrue(len(match) == 1)
def _test_graph_module_init_buffer_param_copied(self, use_dict_init: bool):
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.my_buff = torch.nn.Buffer(torch.rand(3, 4))
self.register_parameter(
"my_param", torch.nn.Parameter(torch.rand(3, 4))
)
def forward(self, x):
return x + self.my_buff + self.my_param
mod = MyModule()
mod_traced = symbolic_trace(mod)
# Create new GraphModule based on original, either w/ dict or root module.
orig_buff = mod_traced.get_buffer("my_buff")
orig_param = mod_traced.get_parameter("my_param")
mod_traced_new = GraphModule(
{"my_buff": orig_buff, "my_param": orig_param} if use_dict_init else mod,
mod_traced.graph,
)
# Check that both my_buff and my_param are found and the same.
try:
new_buff = mod_traced_new.get_buffer("my_buff")
except Exception:
self.fail("Did not find my_buff")
self.assertEqual(orig_buff, new_buff)
try:
new_param = mod_traced_new.get_parameter("my_param")
except Exception:
self.fail("Did not find my_param")
self.assertEqual(orig_param, new_param)
x = torch.rand(3, 4)
orig_out = mod_traced(x)
submodules_out = mod_traced_new(x)
self.assertEqual(orig_out, submodules_out)
def test_graph_module_init_buffer_param_copied_dict_init(self):
self._test_graph_module_init_buffer_param_copied(use_dict_init=True)
def test_graph_module_init_buffer_param_copied_mod_init(self):
self._test_graph_module_init_buffer_param_copied(use_dict_init=False)
def test_annotations_with_no_forward_references(self):
class A:
def __call__(self, x: torch.Tensor):
return torch.add(x, x)
class M(torch.nn.Module):
def forward(self, x: torch.Tensor, a: A) -> torch.Tensor:
return a(x)
self.checkGraphModule(M(), (torch.rand(2, 3), A()), kwargs=None)
def test_annotations_with_forward_references(self):
class A:
def __call__(self, x: torch.Tensor):
return torch.add(x, x)
class M(torch.nn.Module):
def forward(self, x: 'torch.Tensor', a: 'A') -> 'torch.Tensor':
return a(x)
self.checkGraphModule(M(), (torch.rand(2, 3), A()), kwargs=None)
def test_annotations_with_non_torch_reference_and_no_internal_forward_references(self):
class A:
def __call__(self, x: torch.Tensor):
return torch.add(x, x)
class M(torch.nn.Module):
def forward(self, x: List[torch.Tensor], a: A) -> torch.Tensor:
return a(x[0])
self.checkGraphModule(M(), (torch.rand(2, 3), A()), kwargs=None)
def test_annotations_with_non_torch_reference_and_internal_forward_references(self):
class A:
def __call__(self, x: torch.Tensor):
return torch.add(x, x)
class M(torch.nn.Module):
def forward(self, x: List['torch.Tensor'], a: A) -> 'torch.Tensor':
return a(x)[0]
self.checkGraphModule(M(), (torch.rand(2, 3), A()), kwargs=None)
@unittest.skipIf(sys.version_info < (3, 7), "`__future__` feature "
"`annotations` is not defined in Python <3.7")
def test_annotation_with_future(self):
try:
import fx.test_future # noqa: F401
finally:
del sys.modules["__future__"]
@unittest.skipIf(sys.version_info > (3, 11), "Does not work in 3.11")
def test_annotations_empty_tuple(self):
class Foo(torch.nn.Module):
def forward(self, x: Tuple[()], y: Tuple[str, Tuple[()]]):
return "foo"
traced = torch.fx.symbolic_trace(Foo())
x = ()
y = ("bar", ())
traced(x, y)
FileCheck().check("_Tuple[()]") \
.check("typing_Tuple[str,typing_Tuple[()]]") \
.run(traced.code)
scripted = torch.jit.script(traced)
scripted(x, y)
FileCheck().check("Tuple[()]") \
.check("Tuple[str, Tuple[()]]") \
.run(scripted.code)
@unittest.skipIf(IS_WINDOWS, "Python Windows bug? https://bugs.python.org/issue45108")
@unittest.skipIf(sys.version_info >= (3, 10), "Does not work on Python-3.10")
def test_assert(self):
def f(x):
assert x > 1
return x + 1
try:
torch.fx.proxy.TracerBase.trace_asserts = True
traced = symbolic_trace(f)
finally:
torch.fx.proxy.TracerBase.trace_asserts = False
self.assertEqual(f(2), traced(2))
with self.assertRaises(AssertionError):
traced(0)
def test_pytree(self):
# Used to test that you can use your own placeholder class
class PHTest(PHBase):
pass
def f_sum(x):
return sum(x)
def f_sum_dict(x):
out = 0
for v in x.values():
out += v
return out
def f_dict_list_map(x):
new_dict = {}
for k, v in x.items():
new_dict[k] = [i + 1 for i in v]
return new_dict
def f_dict_add(x):
return x['a'] + sum(x['z'])
def f_namedtuple_add(x):
return x.x + x.y
pytree.register_pytree_node(
Foo,
lambda x: ([x.a, x.b], None),
lambda x, _: Foo(x[0], x[1]),
)
fx_pytree.register_pytree_flatten_spec(Foo, lambda x, _: [x.a, x.b])
def f_custom(x):
return x.a + x.b
def f_custom_dict(x):
return f_sum_dict(x.a) + x.b
def f_return_custom(x):
return Foo(x.b, x.a)
tests = [
(f_sum, [PH, PH, PH]),
(f_sum, []),
(f_sum, [PHTest(), PHTest(), PHTest()]),
(f_sum_dict, {'a': PH, 'b': PH, 'c': PH}),
(f_dict_list_map, {'a': (PH, PH), 'b': [PH], 'c': []}),
(f_dict_list_map, {5: (PH, PH, PH)}),
(f_dict_add, {'a': PH, 'z': (PH, PH, PH)}),
(f_dict_add, {'a': PH, 'z': []}),
(f_custom, Foo(PH, PH)),
(f_custom, Foo(PH, 3)),
(f_custom_dict, Foo({'a': PH, 'b': PH}, PH)),
# (f_return_custom, Foo(PH, PH)), # Don't currently support output pytrees
(f_namedtuple_add, Point(PH, PH)),
]
def verify_pytree(f, inp):
val = pytree.tree_map(lambda x: torch.randn(3) if isinstance(x, PHBase) else x, inp)
num_flat_args = len(pytree.tree_leaves(inp))
orig_out = f(val)
nf = symbolic_trace(f, concrete_args={'x': inp})
self.assertEqual(nf(val), orig_out)
bare_fx = GraphModule({}, copy.deepcopy(nf.graph))
bare_fx.graph.set_codegen(CodeGen())
bare_fx.recompile()
self.assertEqual(nf.graph.process_outputs(bare_fx(*nf.graph.process_inputs(val))), orig_out)
assert num_flat_args == 0 or "tree_flatten_spec" in nf.code
assert sum(i.op == 'placeholder' for i in nf.graph.nodes) == num_flat_args
nf = symbolic_trace(nf)
self.assertEqual(nf(val), orig_out)
assert "tree_flatten_spec" not in nf.code
assert sum(i.op == 'placeholder' for i in nf.graph.nodes) == 1
nf = symbolic_trace(nf, concrete_args={'x': inp})
self.assertEqual(nf(val), orig_out)
assert num_flat_args == 0 or "tree_flatten_spec" in nf.code
assert sum(i.op == 'placeholder' for i in nf.graph.nodes) == num_flat_args
pickled = pickle.dumps(nf)
nf = pickle.loads(pickled)
self.assertEqual(nf(val), orig_out)
for f, inp in tests:
verify_pytree(f, inp)
def test_pytree_concrete(self):
def f(b, a):
if b:
return a['a']
else:
return a['z']
inp = {'a': {'a': PH, 'z': PH}, 'b': True}
nf = symbolic_trace(f, concrete_args=inp)
val = pytree.tree_map(lambda x: torch.randn(3) if x == PH else x, inp)
self.assertEqual(nf(**val), f(**val))
nf = symbolic_trace(nf)
self.assertEqual(nf(**val), f(**val))
def test_metadata_on_ph(self):
def f_sum(a: int, b: int) -> int:
return a + b
# Due to unflattening of dict, the batch argument
# will be split into two separate nodes with the names
# "batch_1" and "batch_2", referring to the keys
# "f1" and "f2" respectively in the dict.
def f_dict(a: Dict[str, str]) -> bool:
return a["f1"] == a["f2"]
def verify_metadata(gm: GraphModule, arg_names: List[str], metadata: List[str]):
for node in gm.graph.nodes:
if node.op == "placeholder":
self.assertTrue(node.name in arg_names)
self.assertTrue(node.ph_key in metadata)
verify_metadata(
gm=symbolic_trace(
f_sum,
concrete_args={"a": PHWithMeta(ph_key="a"), "b": PHWithMeta(ph_key="b")}
),
arg_names=["a_1", "b_1"],
metadata=["a", "b"]
)
verify_metadata(
gm=symbolic_trace(
f_dict,
concrete_args={"a": {"f1": PHWithMeta(ph_key="f1"), "f2": PHWithMeta(ph_key="f2")}}
),
arg_names=["a_1", "a_2"],
metadata=["f1", "f2"]
)
# Ensures that tags on nodes are NOT overwritten by PH attributes with same attr name (tag)
class TaggingTracer(Tracer):
def create_node(self, kind : str, target : Union[str, Callable],
args : Tuple[Argument, ...], kwargs : Dict[str, Any], name : Optional[str] = None,
type_expr : Optional[Any] = None) -> Node:
n = super().create_node(kind, target, args, kwargs, name)
n.tag = "foo"
return n
class PHWithTag(PHBase):
def __init__(self, tag: str):
super().__init__()
self.tag = tag
g = TaggingTracer().trace(f_sum, concrete_args={"a": PHWithTag(tag="bar"), "b": PHWithTag(tag="bar")})
for n in g.nodes:
self.assertTrue(hasattr(n, "tag"))
# Ensure that tag is still "foo" and not "bar" (from PHWithTag)
self.assertEqual(n.tag, "foo")
def test_custom_codegen(self):
class ListCodeGen(CodeGen):
def gen_fn_def(self, free_vars, maybe_return_annotation):
lst_unpack = f"""
def forward(self, args_list: List[torch.Tensor]){maybe_return_annotation}:
{', '.join(free_vars)} = args_list"""
return lst_unpack
def additional_globals(self):
return [('List', typing.List)]
def process_inputs(self, *inputs):
assert len(inputs) == 1
return inputs[0]
def f(a, b):
return a + b
nf = symbolic_trace(f)
vals = [torch.randn(3), torch.randn(3)]
self.assertEqual(nf(*vals), f(*vals))
nf.graph.set_codegen(ListCodeGen())
nf.recompile()
bare_fx = GraphModule({}, copy.deepcopy(nf.graph))
bare_fx.graph.set_codegen(CodeGen())
bare_fx.recompile()
self.assertEqual(nf(vals), f(*vals))
self.assertEqual(nf.graph.process_outputs(bare_fx(*nf.graph.process_inputs(vals))), f(*vals))
ts_f = torch.jit.script(nf)
self.assertEqual(nf(vals), ts_f(vals))
def test_custom_codegen_with_transformer(self):
class ListCodeGen(CodeGen):
def gen_fn_def(self, free_vars, maybe_return_annotation):
lst_unpack = f"""
def forward(self, args_list: List[torch.Tensor]){maybe_return_annotation}:
{', '.join(free_vars)} = args_list"""
return lst_unpack
def additional_globals(self):
return [('List', typing.List)]
def process_inputs(self, *inputs):
assert len(inputs) == 1
return inputs[0]
def f(a, b):
return a + b
nf = symbolic_trace(f)
vals = [torch.randn(3), torch.randn(3)]
self.assertEqual(nf(*vals), f(*vals))
nf.graph.set_codegen(ListCodeGen())
nf.recompile()
self.assertEqual(nf(vals), f(*vals))
transformed_gm = Transformer(nf).transform()
self.assertEqual(nf(vals), transformed_gm(vals))
def test_interpreter_with_codegen(self):
class ListCodeGen(CodeGen):
def gen_fn_def(self, free_vars, maybe_return_annotation):
lst_unpack = f"""
def forward(self, args_list: List[torch.Tensor]){maybe_return_annotation}:
{', '.join(free_vars)} = args_list"""
return lst_unpack
def additional_globals(self):
return [('List', typing.List)]
def process_inputs(self, *inputs):
assert len(inputs) == 1
return inputs[0]
def generate_output(self, output_args):
return f'return list({repr(output_args)})'
def process_outputs(self, outputs):
return list(outputs)
def f(a, b):
a = a + b
b = a + b
return a, b
nf = symbolic_trace(f)
vals = [torch.randn(3), torch.randn(3)]
nf.graph.set_codegen(ListCodeGen())
nf.recompile()
self.assertEqual(Interpreter(nf).run(vals), nf(vals))
def test_imul_code_print(self):
graph = torch.fx.Graph()
a = graph.placeholder("a")
b = graph.placeholder("b")
graph.call_function(operator.imul, (a, b), {})
graph.output(a)
gm = torch.fx.GraphModule({}, graph)
gm.recompile()
self.assertEqual(gm(2, 3), 6)
self.assertIn("a *= b", gm.code)
def test_deepcopy_tracer(self):
def fn(x, y):
return (x + y).relu().sin()
tracer = Tracer()
tracer_before = copy.deepcopy(tracer)
tracer.trace(fn)
tracer_after = copy.deepcopy(tracer)
self.assertEqual(str(tracer.graph), str(tracer_after.graph))
self.assertTrue(not hasattr(tracer_before, 'graph') or str(tracer.graph) != str(tracer_before.graph))
def test_deepcopy_graphmodule(self):
m = symbolic_trace(SimpleTest())
m.meta['hello'] = 'world'
copy_m = copy.deepcopy(m)
self.assertEqual(copy_m.meta['hello'], 'world')
def test_deepcopy_no_recursion(self):
m = symbolic_trace(SimpleTest())
m.meta['hello'] = m # circular reference
copy_m = copy.deepcopy(m) # finishes
self.assertEqual(id(copy_m), id(copy_m.meta['hello']))
def test_enum(self):
from enum import Enum
class Foo(Enum):
A = 1
B = 2
def leaf_fn(arr, enum_val):
# Use the raw enum.
arr.append(enum_val)
return arr[-1].value
def foo(x):
# Pass the enum as argument.
return leaf_fn(x, Foo.A)
traced = torch.fx.symbolic_trace(foo)
self.assertEqual(foo([]), traced([]))
def test_insert_arg(self):
m = symbolic_trace(SimpleTest())
m.buf = torch.nn.Buffer(torch.tensor(0))
output_node = next(iter(reversed(m.graph.nodes)))
with m.graph.inserting_before(output_node):
a = m.graph.get_attr("buf")
r = len(output_node.args)
output_node.insert_arg(0, a)
self.assertEqual(len(output_node.args), r + 1)
self.assertEqual(len(a.users), 1)
self.assertIs(output_node.args[0], a)
self.assertIs(next(iter(a.users.keys())), output_node)
output_node.insert_arg(2, a)
self.assertEqual(len(output_node.args), r + 2)
self.assertEqual(len(a.users), 1)
self.assertIs(output_node.args[2], a)
self.assertIs(next(iter(a.users.keys())), output_node)
m.graph.lint()
def run_getitem_target():
from torch.fx._symbolic_trace import _wrapped_methods_to_patch
_wrapped_methods_to_patch.append((torch.Tensor, "__getitem__"))
try:
TestFX().getitem_inner()
finally:
_wrapped_methods_to_patch.pop()
class TestOperatorSignatures(JitTestCase):
def setUp(self):
# Checking for mutable operations whil tracing is feature flagged
# Enable it in testing but not by default
self.orig_tracer_mutable_flag = torch.fx.proxy.TracerBase.check_mutable_operations
torch.fx.proxy.TracerBase.check_mutable_operations = True
def tearDown(self):
torch.fx.proxy.TracerBase.check_mutable_operations = self.orig_tracer_mutable_flag
@onlyCPU
@ops(op_db, allowed_dtypes=(torch.float,))
def test_get_torch_func_signature_exhaustive(self, device, dtype, op):
if not isinstance(op.op, types.BuiltinFunctionType):
raise unittest.SkipTest("This path doesn't work on Python functions")
sample_inputs_itr = op.sample_inputs(device, dtype, requires_grad=False)
schemas = get_signature_for_torch_op(op.op)
if not schemas:
raise RuntimeError('No Schemas Returned')
for sample_input in sample_inputs_itr:
# Iterate through overloads until we hit a match. If we exit this
# loop via `else`, we haven't found a match
for schema in schemas:
try:
bound_args = schema.bind(sample_input.input, *sample_input.args, **sample_input.kwargs)
bound_args.apply_defaults()
op(*bound_args.args, **bound_args.kwargs)
break
except TypeError as e:
pass
else:
raise RuntimeError(f'Did not match any schemas for op {op.name}!')
class TestFXAPIBackwardCompatibility(JitTestCase):
def setUp(self):
super().setUp()
self.maxDiff = None
# Checking for mutable operations whil tracing is feature flagged
# Enable it in testing but not by default
self.orig_tracer_mutable_flag = torch.fx.proxy.TracerBase.check_mutable_operations
torch.fx.proxy.TracerBase.check_mutable_operations = True
def tearDown(self):
super().tearDown()
torch.fx.proxy.TracerBase.check_mutable_operations = self.orig_tracer_mutable_flag
def _fn_to_stable_annotation_str(self, obj):
"""
Unfortunately we have to serialize function signatures manually since
serialization for `inspect.Signature` objects is not stable across
python versions
"""
fn_name = torch.typename(obj)
signature = inspect.signature(obj)
sig_str = f'{fn_name}{signature}'
arg_strs = []
for k, v in signature.parameters.items():
maybe_type_annotation = f': {self._annotation_type_to_stable_str(v.annotation, sig_str)}'\
if v.annotation is not inspect.Signature.empty else ''
def default_val_str(val):
if isinstance(val, (tuple, list)):
str_pieces = ['(' if isinstance(val, tuple) else '[']
str_pieces.append(', '.join(default_val_str(v) for v in val))
if isinstance(val, tuple) and len(str_pieces) == 2:
str_pieces.append(',')
str_pieces.append(')' if isinstance(val, tuple) else ']')
return ''.join(str_pieces)
# Need to fix up some default value strings.
# First case: modules. Default module `repr` contains the FS path of the module.
# Don't leak that
if isinstance(val, types.ModuleType):
return f'<module {val.__name__}>'
# Second case: callables. Callables (such as lambdas) encode their address in
# their string repr. Don't do that
if callable(val):
return f'<function {val.__name__}>'
return str(val)
if v.default is not inspect.Signature.empty:
default_val_str = default_val_str(v.default) if not isinstance(v.default, str) else f"'{v.default}'"
maybe_default = f' = {default_val_str}'
else:
maybe_default = ''
maybe_stars = ''
if v.kind == inspect.Parameter.VAR_POSITIONAL:
maybe_stars = '*'
elif v.kind == inspect.Parameter.VAR_KEYWORD:
maybe_stars = '**'
arg_strs.append(f'{maybe_stars}{k}{maybe_type_annotation}{maybe_default}')
return_annot = f' -> {self._annotation_type_to_stable_str(signature.return_annotation, sig_str)}'\
if signature.return_annotation is not inspect.Signature.empty else ''
return f'{fn_name}({", ".join(arg_strs)}){return_annot}'
def _annotation_type_to_stable_str(self, t, sig_str):
if t is inspect.Signature.empty:
return ''
# Forward ref
if isinstance(t, str):
return f"'{t}'"
if hasattr(typing, 'ForwardRef') and isinstance(t, typing.ForwardRef):
return t.__forward_arg__
if hasattr(typing, '_ForwardRef') and isinstance(t, typing._ForwardRef):
return t.__forward_arg__
trivial_mappings = {
str : 'str',
int : 'int',
float: 'float',
bool: 'bool',
torch.dtype: 'torch.dtype',
torch.Tensor: 'torch.Tensor',
torch.device: 'torch.device',
torch.memory_format: 'torch.memory_format',
slice: 'slice',
torch.nn.Module: 'torch.nn.modules.module.Module',
torch.fx.Graph : 'torch.fx.graph.Graph',
torch.fx.Node : 'torch.fx.node.Node',
torch.fx.Proxy : 'torch.fx.proxy.Proxy',
torch.fx.node.Target : 'torch.fx.node.Target',
torch.fx.node.Argument : 'torch.fx.node.Argument',
torch.fx.graph.PythonCode : 'torch.fx.graph.PythonCode',
torch.fx.graph_module.GraphModule: 'torch.fx.graph_module.GraphModule',
torch.fx.subgraph_rewriter.Match: 'torch.fx.subgraph_rewriter.Match',
Ellipsis : '...',
typing.Any: 'Any',
type(None): 'NoneType',
None: 'None',
typing.Iterator: 'Iterator',
}
mapping = trivial_mappings.get(t, None)
if mapping:
return mapping
# Handle types with contained types
contained = getattr(t, '__args__', None) or []
# Callables contain a bare List for arguments
contained = t if isinstance(t, list) else contained
# Python 3.8 puts type vars into __args__ for unbound types such as Dict
if all(isinstance(ct, typing.TypeVar) for ct in contained):
contained = []
contained_type_annots = [self._annotation_type_to_stable_str(ct, sig_str) for ct in contained]
contained_type_str = f'[{", ".join(contained_type_annots)}]' if len(contained_type_annots) > 0 else ''
origin = getattr(t, '__origin__', None)
if origin is None:
# Unbound types don't have `__origin__` in some Python versions, so fix that up here.
origin = t if t in {typing.Tuple, typing.Union, typing.Dict, typing.List, typing.Type, typing.Callable} else origin
if origin in {tuple, typing.Tuple}:
return f'Tuple{contained_type_str}'
if origin in {typing.Union}:
# Annoying hack to detect Optional
if len(contained) == 2 and (contained[0] is type(None)) ^ (contained[1] is type(None)):
not_none_param = contained[0] if contained[0] is not type(None) else contained[1]
return f'Optional[{self._annotation_type_to_stable_str(not_none_param, sig_str)}]'
return f'Union{contained_type_str}'
if origin in {dict, typing.Dict}:
return f'Dict{contained_type_str}'
if origin in {list, typing.List}:
return f'List{contained_type_str}'
if origin in {type, typing.Type}:
return f'Type{contained_type_str}'
if isinstance(t, typing.Callable):
if len(contained) > 0 and contained[0] is not Ellipsis:
return f'Callable[[{", ".join(contained_type_annots[:-1])}], {contained_type_annots[-1]}]'
else:
return f'Callable{contained_type_str}'
raise RuntimeError(f'Unrecognized type {t} used in BC-compatible type signature {sig_str}.'
f'Please add support for this type and confirm with the '
f'FX team that your signature change is valid.')
def test_function_back_compat(self):
"""
Test backward compatibility for function signatures with
@compatibility(is_backward_compatible=True). Currently this checks for
exact signature matches, which may lead to false positives. If this
becomes too annoying, we can refine this check to actually parse out
the saved schema strings and check if the change is truly backward-
incompatible.
"""
signature_strs = []
for obj in _BACK_COMPAT_OBJECTS:
if not isinstance(obj, type):
signature_strs.append(self._fn_to_stable_annotation_str(obj))
signature_strs.sort()
try:
self.assertExpected('\n'.join(signature_strs) + '\n', 'fx_backcompat_function_signatures')
except AssertionError as e:
msg = f"{e}\n****** ERROR ******\nAn FX function that has been marked " \
f"as backwards-compatible has experienced a signature change. See the " \
f"above exception context for more information. If this change was " \
f"unintended, please revert it. If it was intended, check with the FX " \
f"team to ensure that the proper deprecation protocols have been followed " \
f"and subsequently --accept the change."
raise AssertionError(msg) # noqa: B904
def test_class_member_back_compat(self):
"""
Test backward compatibility for members of classes with
@compatibility(is_backward_compatible=True). Currently this checks for
exact matches on the publicly visible members of the class.
"""
class_method_strs = []
for obj in _BACK_COMPAT_OBJECTS:
if isinstance(obj, type):
public_members = [name for name in obj.__dict__ if not name.startswith('_')]
class_method_strs.append(f'{torch.typename(obj)} {sorted(public_members)}')
class_method_strs.sort()
try:
self.assertExpected('\n'.join(class_method_strs), 'fx_backcompat_class_members')
except AssertionError as e:
msg = f"{e}\n****** ERROR ******\nAn FX class that has been marked " \
f"as backwards-compatible has experienced change in its public members. See the " \
f"above exception context for more information. If this change was " \
f"unintended, please revert it. If it was intended, check with the FX " \
f"team to ensure that the proper deprecation protocols have been followed " \
f"and subsequently --accept the change."
raise AssertionError(msg) from e
def test_public_api_surface(self):
non_back_compat_objects = {}
def check_symbols_have_bc_designation(m, seen):
if not m.__name__.startswith('torch.fx'):
return
if m.__name__.startswith('torch.fx.experimental'):
return
# It's really common for inner functions to point to random modules
# - make sure we don't recurse into modules we've already checked.
seen.add(m.__name__)
for k, v in m.__dict__.items():
if hasattr(v, '__name__') and v.__name__ in seen:
continue
if v is m:
continue
if k.startswith('_'):
continue
if isinstance(v, types.ModuleType):
check_symbols_have_bc_designation(v, seen)
elif isinstance(v, (type, types.FunctionType)):
if v not in _MARKED_WITH_COMPATIBILITY:
non_back_compat_objects.setdefault(v)
check_symbols_have_bc_designation(torch.fx, set())
check_symbols_have_bc_designation(torch.fx.passes, set())
non_back_compat_strs = [torch.typename(obj) for obj in non_back_compat_objects.keys()]
# Only want objects in torch.fx
non_back_compat_strs = [
s for s in non_back_compat_strs if s.startswith('torch.fx') and not s.startswith('torch.fx.experimental')]
# Only want objects in public namespaces
non_back_compat_strs = [
s for s in non_back_compat_strs if all(not atom.startswith('_') for atom in s.split('.'))]
non_back_compat_strs.sort()
if len(non_back_compat_strs) != 0:
raise AssertionError(f"Public FX API(s) {non_back_compat_strs} introduced but not given a "
f"backwards-compatibility classification! Please decorate these "
f"API(s) with `@torch.fx._compatibility.compatibility` to specify "
f"BC guarantees.")
def test_adding_side_effect_function(self):
class TestModule(torch.nn.Module):
def forward(self, x):
side_effect_func(x)
return x
gm = torch.fx.symbolic_trace(TestModule())
self.assertEqual(len(gm.graph.nodes), 3)
gm.graph.eliminate_dead_code()
gm.recompile()
self.assertEqual(len(gm.graph.nodes), 3)
found = False
for node in gm.graph.nodes:
if node.op == 'call_function' and node.target == side_effect_func:
found = True
self.assertTrue(found)
def test_preserve_unused_attr_after_unpickle(self):
gm = torch.fx.symbolic_trace(Add())
gm.add_submodule("foo", Add())
gm.dummy_buffer = torch.nn.Buffer(torch.empty(1))
gm.register_parameter("dummy_parameter", torch.nn.Parameter(torch.empty(1)))
b = io.BytesIO()
torch.save(gm, b)
b.seek(0)
reload_gm = torch.load(b)
self.assertTrue(hasattr(reload_gm, "foo"))
self.assertTrue(hasattr(reload_gm, "dummy_buffer"))
self.assertTrue(hasattr(reload_gm, "dummy_parameter"))
# This is failing on Python 3.12 : https://github.com/pytorch/pytorch/issues/119454
@unittest.skipIf(
sys.version_info >= (3, 12), "Failing on python 3.12+"
)
class TestFunctionalTracing(JitTestCase):
def setUp(self):
super().setUp()
# Checking for mutable operations whil tracing is feature flagged
# Enable it in testing but not by default
self.orig_tracer_mutable_flag = torch.fx.proxy.TracerBase.check_mutable_operations
torch.fx.proxy.TracerBase.check_mutable_operations = True
def tearDown(self):
super().tearDown()
torch.fx.proxy.TracerBase.check_mutable_operations = self.orig_tracer_mutable_flag
IGNORE_FUNCS = ("has_torch_function", "has_torch_function_unary",
"has_torch_function_variadic", "handle_torch_function",
"boolean_dispatch")
TO_PATCH = {"has_torch_function": None,
"has_torch_function_unary": None,
"has_torch_function_variadic": None}
BUILT_IN_FUNC = (AssertionError, "")
PROXY_ITERABLE = (TypeError, r"argument of type 'Proxy' is not iterable")
PROXY_ITERATED = (TraceError, r"Proxy object cannot be iterated")
LEN_ERROR = (RuntimeError, r"'len' is not supported in symbolic tracing by default")
ARG_TYPE_MISMATCH = (TypeError, r", not Proxy$")
CONTROL_FLOW = (TraceError, r"symbolically traced variables cannot be used as inputs to control flow")
INTERPOLATE_ARGS_CONFLICT = (ValueError, r"only one of size or scale_factor should be defined")
MUTABLE = (RuntimeError, r"Tried to trace mutable operation")
UNTRACEABLE_FUNCTIONALS = {
"adaptive_avg_pool1d": BUILT_IN_FUNC,
"avg_pool1d": BUILT_IN_FUNC,
"avg_pool2d": BUILT_IN_FUNC,
"avg_pool3d": BUILT_IN_FUNC,
"bilinear": BUILT_IN_FUNC,
"celu_": BUILT_IN_FUNC,
"channel_shuffle": BUILT_IN_FUNC,
"native_channel_shuffle": BUILT_IN_FUNC,
"conv1d": BUILT_IN_FUNC,
"conv2d": BUILT_IN_FUNC,
"conv3d": BUILT_IN_FUNC,
"conv_tbc": BUILT_IN_FUNC,
"conv_transpose1d": BUILT_IN_FUNC,
"conv_transpose2d": BUILT_IN_FUNC,
"conv_transpose3d": BUILT_IN_FUNC,
"cosine_similarity": BUILT_IN_FUNC,
"elu_": BUILT_IN_FUNC,
"gelu": BUILT_IN_FUNC,
"hardshrink": BUILT_IN_FUNC,
"hardtanh_": BUILT_IN_FUNC,
"leaky_relu_": BUILT_IN_FUNC,
"linear": BUILT_IN_FUNC,
"logsigmoid": BUILT_IN_FUNC,
"one_hot": BUILT_IN_FUNC,
"pad": ARG_TYPE_MISMATCH,
"pairwise_distance": BUILT_IN_FUNC,
"pdist": BUILT_IN_FUNC,
"pixel_shuffle": BUILT_IN_FUNC,
"pixel_unshuffle": BUILT_IN_FUNC,
"prelu": BUILT_IN_FUNC,
"relu_": BUILT_IN_FUNC,
"rrelu_": BUILT_IN_FUNC,
"selu_": BUILT_IN_FUNC,
"scaled_dot_product_attention": BUILT_IN_FUNC,
"softplus": BUILT_IN_FUNC,
"softshrink": BUILT_IN_FUNC,
"threshold_": BUILT_IN_FUNC,
"adaptive_avg_pool2d": LEN_ERROR,
"adaptive_avg_pool3d": LEN_ERROR,
"adaptive_max_pool2d_with_indices": LEN_ERROR,
"adaptive_max_pool3d_with_indices": LEN_ERROR,
"instance_norm": CONTROL_FLOW,
"adaptive_max_pool1d": PROXY_ITERABLE,
"adaptive_max_pool2d": PROXY_ITERABLE,
"adaptive_max_pool3d": PROXY_ITERABLE,
"fractional_max_pool2d": PROXY_ITERABLE,
"fractional_max_pool3d": PROXY_ITERABLE,
"max_pool1d": PROXY_ITERABLE,
"max_pool2d": PROXY_ITERABLE,
"max_pool3d": PROXY_ITERABLE,
"lp_pool2d": PROXY_ITERATED,
"lp_pool3d": PROXY_ITERATED,
"max_unpool1d": PROXY_ITERATED,
"max_unpool2d": PROXY_ITERATED,
"max_unpool3d": PROXY_ITERATED,
"fold": PROXY_ITERATED,
"unfold": PROXY_ITERATED,
"adaptive_max_pool1d_with_indices": ARG_TYPE_MISMATCH,
"fractional_max_pool2d_with_indices": ARG_TYPE_MISMATCH,
"fractional_max_pool3d_with_indices": ARG_TYPE_MISMATCH,
"layer_norm": ARG_TYPE_MISMATCH,
"rms_norm": ARG_TYPE_MISMATCH,
"lp_pool1d": ARG_TYPE_MISMATCH,
"affine_grid": CONTROL_FLOW,
"alpha_dropout": CONTROL_FLOW,
"batch_norm": CONTROL_FLOW,
"binary_cross_entropy": CONTROL_FLOW,
"binary_cross_entropy_with_logits": CONTROL_FLOW,
"celu": CONTROL_FLOW,
"cosine_embedding_loss": CONTROL_FLOW,
"cross_entropy": CONTROL_FLOW,
"ctc_loss": CONTROL_FLOW,
"dropout": CONTROL_FLOW,
"dropout1d": CONTROL_FLOW,
"dropout2d": CONTROL_FLOW,
"dropout3d": CONTROL_FLOW,
"elu": CONTROL_FLOW,
"embedding": CONTROL_FLOW,
"embedding_bag": CONTROL_FLOW,
"feature_alpha_dropout": CONTROL_FLOW,
"gaussian_nll_loss": CONTROL_FLOW,
"glu": CONTROL_FLOW,
"grid_sample": CONTROL_FLOW,
"group_norm": CONTROL_FLOW,
"gumbel_softmax": CONTROL_FLOW,
"hardsigmoid": CONTROL_FLOW,
"hardswish": CONTROL_FLOW,
"hardtanh": CONTROL_FLOW,
"hinge_embedding_loss": CONTROL_FLOW,
"huber_loss": CONTROL_FLOW,
"interpolate": CONTROL_FLOW,
"kl_div": CONTROL_FLOW,
"l1_loss": CONTROL_FLOW,
"leaky_relu": CONTROL_FLOW,
"local_response_norm": CONTROL_FLOW,
"margin_ranking_loss": CONTROL_FLOW,
"max_pool1d_with_indices": ARG_TYPE_MISMATCH,
"max_pool2d_with_indices": ARG_TYPE_MISMATCH,
"max_pool3d_with_indices": ARG_TYPE_MISMATCH,
"mse_loss": CONTROL_FLOW,
"multi_head_attention_forward": CONTROL_FLOW,
"multi_margin_loss": CONTROL_FLOW,
"multilabel_margin_loss": CONTROL_FLOW,
"multilabel_soft_margin_loss": CONTROL_FLOW,
"nll_loss": CONTROL_FLOW,
"poisson_nll_loss": CONTROL_FLOW,
"relu": CONTROL_FLOW,
"relu6": CONTROL_FLOW,
"rrelu": CONTROL_FLOW,
"selu": CONTROL_FLOW,
"silu": CONTROL_FLOW,
"mish": CONTROL_FLOW,
"smooth_l1_loss": CONTROL_FLOW,
"soft_margin_loss": CONTROL_FLOW,
"threshold": CONTROL_FLOW,
"triplet_margin_loss": CONTROL_FLOW,
"triplet_margin_with_distance_loss": CONTROL_FLOW,
"upsample": CONTROL_FLOW,
"upsample_bilinear": INTERPOLATE_ARGS_CONFLICT,
"upsample_nearest": INTERPOLATE_ARGS_CONFLICT,
}
# List of nn.functionals with Tensor inputs but not with type annotation
FUNCTIONALS_WITHOUT_ANNOTATION = (
"adaptive_max_pool1d",
"adaptive_max_pool2d",
"adaptive_max_pool3d",
"fractional_max_pool2d",
"fractional_max_pool3d",
"max_pool1d",
"max_pool2d",
"max_pool3d",
"gaussian_nll_loss",
"upsample",
"upsample_bilinear",
"upsample_nearest",
)
# Inconsistent behavior between Python 3.8 and other Python versions:
# - Python 3.8+: Re-raise internal exception like `PROXY_ITERATED`
# - Other Python: Raise `argument of type 'Proxy' is not iterable` due to the same
# internal exception above
# Use the following map to override the expected exception for Python 3.8
UNTRACEABLE_FUNCTIONALS_PY38 = {
"adaptive_max_pool1d": PROXY_ITERATED,
"adaptive_max_pool2d": PROXY_ITERATED,
"adaptive_max_pool3d": PROXY_ITERATED,
"fractional_max_pool2d": PROXY_ITERATED,
"fractional_max_pool3d": PROXY_ITERATED,
"max_pool1d": PROXY_ITERATED,
"max_pool2d": PROXY_ITERATED,
"max_pool3d": PROXY_ITERATED,
"group_norm": CONTROL_FLOW
}
@classmethod
def _get_functional(cls):
functional_list = []
for f in dir(torch.nn.functional):
if not f.islower():
continue
# Ignore internal functions
if f.startswith('_'):
continue
# Ignore supporting functions
if f in cls.IGNORE_FUNCS:
continue
fn = getattr(torch.nn.functional, f)
# Ignore non-callable object like modules
if not isinstance(fn, Callable):
continue
if f not in cls.FUNCTIONALS_WITHOUT_ANNOTATION:
try:
sig = inspect.signature(fn)
has_tensor_arg = False
for param in sig.parameters.values():
if isinstance(param.annotation, type) and issubclass(param.annotation, torch.Tensor):
has_tensor_arg = True
if not has_tensor_arg:
continue
# No signature or Object is not supported
except ValueError:
pass
functional_list.append((f, fn))
return functional_list
@classmethod
def generate_test_func(cls, func_name, fn):
def functional_test(self):
if func_name in self.UNTRACEABLE_FUNCTIONALS_PY38 and \
sys.version_info >= (3, 8) and sys.version_info < (3, 12):
exc, err = self.UNTRACEABLE_FUNCTIONALS_PY38[func_name]
with self.assertRaisesRegex(exc, err):
symbolic_trace(fn)
elif func_name in self.UNTRACEABLE_FUNCTIONALS:
exc, err = self.UNTRACEABLE_FUNCTIONALS[func_name]
with self.assertRaisesRegex(exc, err):
symbolic_trace(fn)
else:
symbolic_trace(fn)
return functional_test
@classmethod
def generate_tests(cls):
functional_list = cls._get_functional()
for func_name, fn in functional_list:
test_name = "test_nn_functional_" + func_name
functional_test = cls.generate_test_func(func_name, fn)
setattr(cls, test_name, functional_test)
@classmethod
def setUpClass(cls):
def no(*args, **kwargs):
return False
for name in cls.TO_PATCH.keys():
cls.TO_PATCH[name] = getattr(torch.nn.functional, name)
setattr(torch.nn.functional, name, no)
@classmethod
def tearDownClass(cls):
for name in cls.TO_PATCH.keys():
setattr(torch.nn.functional, name, cls.TO_PATCH[name])
TestFunctionalTracing.generate_tests()
instantiate_device_type_tests(TestOperatorSignatures, globals())
@skipIfTorchDynamo("too slow")
@skipIfNoTorchVision
class TestVisionTracing(JitTestCase):
def setUp(self):
# Checking for mutable operations while tracing is feature flagged
# Enable it in testing but not by default
self.orig_tracer_mutable_flag = torch.fx.proxy.TracerBase.check_mutable_operations
torch.fx.proxy.TracerBase.check_mutable_operations = True
def tearDown(self):
torch.fx.proxy.TracerBase.check_mutable_operations = self.orig_tracer_mutable_flag
PROXY_ITERATED = (TraceError, r"Proxy object cannot be iterated")
INCONSISTENT_TYPE = (
RuntimeError,
r"Return value was annotated as having type __torch__.torchvision.models[.\w]+ but is actually of type Tensor"
)
UNTRACEABLE_MODELS = {
"fasterrcnn_resnet50_fpn": PROXY_ITERATED,
"fasterrcnn_resnet50_fpn_v2": PROXY_ITERATED,
"fasterrcnn_mobilenet_v3_large_320_fpn": PROXY_ITERATED,
"fasterrcnn_mobilenet_v3_large_fpn": PROXY_ITERATED,
"maskrcnn_resnet50_fpn": PROXY_ITERATED,
"maskrcnn_resnet50_fpn_v2": PROXY_ITERATED,
"keypointrcnn_resnet50_fpn": PROXY_ITERATED,
"retinanet_resnet50_fpn": PROXY_ITERATED,
"retinanet_resnet50_fpn_v2": PROXY_ITERATED,
"ssd300_vgg16": PROXY_ITERATED,
"fcos_resnet50_fpn": PROXY_ITERATED,
"ssdlite320_mobilenet_v3_large": PROXY_ITERATED,
}
UNSCRIPTABLE_MODELS = {
"googlenet": INCONSISTENT_TYPE,
"inception_v3": INCONSISTENT_TYPE,
}
output_transform = {
"fcn_resnet50": lambda x: x["out"],
"fcn_resnet101": lambda x: x["out"],
"deeplabv3_resnet50": lambda x: x["out"],
"deeplabv3_resnet101": lambda x: x["out"],
"deeplabv3_mobilenet_v3_large": lambda x: x["out"],
"lraspp_mobilenet_v3_large": lambda x: x["out"],
"fasterrcnn_resnet50_fpn": lambda x: x[1],
"fasterrcnn_mobilenet_v3_large_fpn": lambda x: x[1],
"fasterrcnn_mobilenet_v3_large_320_fpn": lambda x: x[1],
"maskrcnn_resnet50_fpn": lambda x: x[1],
"keypointrcnn_resnet50_fpn": lambda x: x[1],
"retinanet_resnet50_fpn": lambda x: x[1],
}
@classmethod
def generate_test_fn(cls, name, x, kwargs):
def run_test(self):
model = torchvision_models.get_model(name, **kwargs)
model = model.eval()
if name in self.UNTRACEABLE_MODELS:
err, exc = self.UNTRACEABLE_MODELS[name]
with self.assertRaisesRegex(err, exc):
graph = symbolic_trace(model)
else:
out_transform = self.output_transform.get(name, lambda x: x)
graph : torch.fx.GraphModule = symbolic_trace(model)
a = out_transform(model(x))
b = out_transform(graph(x))
self.assertEqual(a, b)
if name in self.UNSCRIPTABLE_MODELS:
err, exc = self.UNSCRIPTABLE_MODELS[name]
with self.assertRaisesRegex(err, exc):
script = torch.jit.script(graph)
else:
script = torch.jit.script(graph)
c = out_transform(script(x))
self.assertEqual(a, c)
return run_test
@classmethod
def generate_classification_tests(cls):
for k in torchvision_models.list_models(module=torchvision_models):
test_name = 'test_torchvision_models_' + k
x = torch.rand(1, 3, 299, 299) if k in ['inception_v3'] else torch.rand(1, 3, 224, 224)
kwargs = dict(num_classes=50)
model_test = cls.generate_test_fn(k, x, kwargs)
setattr(cls, test_name, model_test)
@classmethod
def generate_segmentation_tests(cls):
for k in torchvision_models.list_models(module=torchvision_models.segmentation):
test_name = 'test_torchvision_models_segmentation_' + k
x = torch.rand(1, 3, 32, 32)
kwargs = dict(num_classes=10, pretrained_backbone=False)
model_test = cls.generate_test_fn(k, x, kwargs)
setattr(cls, test_name, model_test)
@classmethod
def generate_detection_tests(cls):
for k in torchvision_models.list_models(module=torchvision_models.detection):
test_name = 'test_torchvision_models_detection_' + k
x = [torch.rand(3, 300, 300)]
kwargs = dict(num_classes=10, pretrained_backbone=False)
model_test = cls.generate_test_fn(k, x, kwargs)
setattr(cls, test_name, model_test)
@classmethod
def generate_video_tests(cls):
for k in torchvision_models.list_models(module=torchvision_models.video):
test_name = 'test_torchvision_models_video_' + k
x = (
torch.rand(1, 3, 4, 112, 112)
if k not in {"mvit_v1_b", "mvit_v2_s", "s3d"}
else torch.rand(1, 3, 16, 224, 224)
)
kwargs = dict(num_classes=50)
model_test = cls.generate_test_fn(k, x, kwargs)
setattr(cls, test_name, model_test)
@classmethod
def generate_tests(cls):
cls.generate_classification_tests()
cls.generate_detection_tests()
cls.generate_segmentation_tests()
cls.generate_video_tests()
if HAS_TORCHVISION:
TestVisionTracing.generate_tests()
if __name__ == '__main__':
run_tests()
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