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pytorch/test/dynamo/test_utils.py
Joel Schlosser c4b93e6579 Replace frame_traced_fn hook with get_traced_code() util (#155249) 
#153622 introduced a hook for getting the relevant code objects after frame tracing. The idea is to have vLLM use this instead of monkey-patching `inline_call_()` to determine the source code files to hash. Unfortunately, the hook runs too late; the vLLM backend needs access to the set of source code filenames while it's running.

This PR replaces the newly-added hook with a utility function that a backend can call to get this information. I've made the change in vLLM and can verify that this allows the information to be queried at the right time.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/155249
Approved by: https://github.com/zou3519
2025-06-10 22:40:58 +00:00

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# Owner(s): ["module: dynamo"]
import dataclasses
import os
import pprint
import sys
from unittest import mock
import torch
import torch._dynamo.config as dynamo_config
import torch._inductor.config as inductor_config
from torch._dynamo import utils
from torch._inductor.test_case import TestCase
class TestUtils(TestCase):
def test_nan(self):
a = torch.Tensor([float("nan")])
b = torch.Tensor([float("nan")])
fp64_ref = torch.DoubleTensor([5.0])
res = utils.same(a, b, fp64_ref=fp64_ref, equal_nan=True)
self.assertTrue(res)
def test_larger_multiplier_for_smaller_tensor(self):
"""
Tensor numel between (10, 500]
"""
N = 100
fp64_ref = torch.full([N], 0.0, dtype=torch.double)
a = torch.full([N], 1.0)
tol = 4 * 1e-2
self.assertTrue(utils.same(a, a * 2, fp64_ref=fp64_ref, tol=tol))
self.assertFalse(utils.same(a, a * 4, fp64_ref=fp64_ref, tol=tol))
self.assertTrue(
utils.same(
a,
a * 4,
fp64_ref=fp64_ref,
use_larger_multiplier_for_smaller_tensor=True,
tol=tol,
)
)
self.assertFalse(
utils.same(
a,
a * 9,
fp64_ref=fp64_ref,
use_larger_multiplier_for_smaller_tensor=True,
tol=tol,
)
)
def test_larger_multiplier_for_even_smaller_tensor(self):
"""
Tesnor numel <=10
"""
fp64_ref = torch.DoubleTensor([0.0])
a = torch.Tensor([1.0])
tol = 4 * 1e-2
self.assertTrue(utils.same(a, a * 2, fp64_ref=fp64_ref, tol=tol))
self.assertFalse(utils.same(a, a * 7, fp64_ref=fp64_ref, tol=tol))
self.assertTrue(
utils.same(
a,
a * 7,
fp64_ref=fp64_ref,
use_larger_multiplier_for_smaller_tensor=True,
tol=tol,
)
)
self.assertFalse(
utils.same(
a,
a * 20,
fp64_ref=fp64_ref,
use_larger_multiplier_for_smaller_tensor=True,
tol=tol,
)
)
@dynamo_config.patch(
{
"log_compilation_metrics": True,
"inline_inbuilt_nn_modules": False,
}
)
def test_graph_break_counting(self):
"""
Run a compilation that includes a graph break and validate that the
graph break counter is incremented.
"""
def run_forward_backward():
model = torch.compile(TestModel())
x = torch.rand([3], requires_grad=True)
output = model(x)
loss_fn = torch.nn.MSELoss()
target = torch.tensor([1.0])
loss = loss_fn(output, target)
loss.backward()
@torch.compile
def add(x, y):
return x + y
@torch.compile
def break_it(x):
y = x.sum()
if y > 0:
return x + y.item()
return x - y.item()
@torch.compile
def break_it2(x):
y = x.sum()
if y > 0:
if y > 1:
return x * y.item()
return x + y.item()
return x - y.item()
add(torch.rand([10]), torch.rand([10]))
utils.reset_frame_count()
compilation_events = []
with mock.patch("torch._dynamo.utils.log_compilation_event") as log_event:
run_forward_backward()
compilation_events = [arg[0][0] for arg in log_event.call_args_list]
self.assertEqual(compilation_events[-1].num_graph_breaks, 0)
# We should fallback to normal mode and increment the graph break counter
torch.compile(break_it, backend="inductor")(torch.ones(3, 3))
compilation_events = [arg[0][0] for arg in log_event.call_args_list]
self.assertEqual(compilation_events[-1].num_graph_breaks, 1)
# Graph break counter should be incremented by 1 (after a reset), not 2
torch.compile(break_it, backend="inductor")(torch.ones(3, 3))
compilation_events = [arg[0][0] for arg in log_event.call_args_list]
self.assertEqual(compilation_events[-1].num_graph_breaks, 1)
# Graph break counter should be incremented by 2
torch.compile(break_it2, backend="inductor")(torch.ones(3, 3))
compilation_events = [arg[0][0] for arg in log_event.call_args_list]
self.assertEqual(compilation_events[-1].num_graph_breaks, 2)
def test_traced_code_query(self):
try:
from .utils import add, break_it
except ImportError:
from utils import add, break_it
traced_code_lists = []
def get_filenames(traced_code_lists):
return [
[code.co_filename for code in code_list]
for code_list in traced_code_lists
]
def my_backend(gm, example_inputs):
from torch._dynamo.utils import get_traced_code
nonlocal traced_code_lists
traced_code_lists.append(get_traced_code())
return gm.forward
utils_path = os.path.join(os.path.dirname(__file__), "utils.py")
# === no inlining ===
@torch.compile(backend=my_backend)
def fn(x):
return x * 2
x = torch.randn(3)
traced_code_lists = []
fn(x)
self.assertEqual(get_filenames(traced_code_lists), [[__file__]])
# === successful inlining ===
@torch.compile(backend=my_backend)
def fn(x):
return add(x) * 2
x = torch.randn(3)
traced_code_lists = []
fn(x)
utils_path = os.path.join(os.path.dirname(__file__), "utils.py")
self.assertEqual(get_filenames(traced_code_lists), [[__file__, utils_path]])
# === graph break occurs during inlining ===
@torch.compile(backend=my_backend)
def fn(x):
z = x + 1
y = break_it(z)
return y * 2
x = torch.randn(3)
traced_code_lists = []
fn(x)
self.assertEqual(get_filenames(traced_code_lists), [[__file__], [utils_path]])
# === empty graph ===
@torch.compile(backend=my_backend)
def fn(x):
return x
x = torch.randn(3)
traced_code_lists = []
fn(x)
self.assertEqual(traced_code_lists, [])
class TestModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(3, 1)
def forward(self, x):
return self.linear(x)
class TestDynamoTimed(TestCase):
"""
Test utilities surrounding dynamo_timed.
"""
def run_forward_backward(self):
model = torch.compile(TestModel())
x = torch.rand([3], requires_grad=True)
output = model(x)
loss_fn = torch.nn.MSELoss()
target = torch.tensor([1.0])
loss = loss_fn(output, target)
loss.backward()
def warmup(self):
# Helper to make sure any process-global lru_caches (e.g., torch_key())
# have already executed. Just compile something.
@torch.compile
def add(x, y):
return x + y
add(torch.rand([10]), torch.rand([10]))
utils.reset_frame_count()
torch._logging._internal.structured_logging_overhead.clear()
@dynamo_config.patch(
{
"log_compilation_metrics": True,
"inline_inbuilt_nn_modules": False,
}
)
@inductor_config.patch(
{
"bundle_triton_into_fx_graph_cache": False,
"bundled_autotune_remote_cache": False,
}
)
# We can't easily test that timing is actually accurate. Mock time to always
# return the same value; all durations will be zero.
@mock.patch("time.time", return_value=0.001)
@mock.patch("time.time_ns", return_value=100000)
@dynamo_config.patch(specialize_float=False)
def test_dynamo_timed(self, mock_time, mock_time_ns):
"""
Run a compilation that includes a forward and a backward and validate
various recorded metrics. This test could be broken into several, but the
compilation is somewhat expensive. Instead of resetting and compiling the
same thing multiple times, we may as well compile once and just check all
the things that are affected by dynamo_timed.
"""
self.warmup()
# The logging function is different for OSS vs. internal. Let's just mock
# and capture all the CompilationMetric objects logged.
compilation_events = []
with mock.patch("torch._dynamo.utils.log_compilation_event") as log_event:
self.run_forward_backward()
compilation_events = [arg[0][0] for arg in log_event.call_args_list]
# Validate utils.compile_times(). Unfortunately, we can't test the output
# reliably because it depends on whether 'tabulate' is installed. So we'll
# directly inspect the dict it prints instead:
self.assertExpectedInline(
pprint.pformat(utils.compilation_time_metrics),
"""\
{'GraphLowering.codegen': [0.0, 0.0],
'GraphLowering.compile_to_fn': [0.0, 0.0],
'GraphLowering.compile_to_module': [0.0, 0.0],
'GraphLowering.run': [0.0, 0.0],
'OutputGraph.call_user_compiler': [0.0],
'PyCodeCache.load_by_key_path': [0.0, 0.0],
'PythonWrapperCodegen.generate': [0.0, 0.0],
'Scheduler.__init__': [0.0, 0.0],
'Scheduler.codegen': [0.0, 0.0],
'Scheduler.fused_nodes': [0.0, 0.0],
'_compile.compile_inner': [0.0],
'_recursive_joint_graph_passes': [0.0],
'_recursive_post_grad_passes': [0.0, 0.0],
'_recursive_pre_grad_passes': [0.0],
'additional_fake_tensor_prop': [0.0, 0.0],
'aot_collect_metadata': [0.0],
'aot_trace_joint_graph': [0.0],
'async_compile.wait': [0.0, 0.0],
'backward._backward_impl': [0.0],
'build_guards': [0.0],
'bytecode_tracing': [0.0],
'compile_attempt_0': [0.0],
'compile_file': [0.0, 0.0],
'compile_fx.<locals>.bw_compiler': [0.0],
'compile_fx.<locals>.fw_compiler_base': [0.0],
'compile_fx_inner': [0.0, 0.0],
'create_aot_dispatcher_function': [0.0],
'fx_codegen_and_compile': [0.0, 0.0],
'gc': [0.0],
'min_cut_rematerialization_partition': [0.0]}""", # noqa: B950
)
# Now validate utils.calculate_time_spent(). Formatting the return
# value makes reading diffs much easier.
time_spent = utils.calculate_time_spent()
self.assertExpectedInline(
pprint.pformat(time_spent),
"""\
{'_recursive_joint_graph_passes': 0.0,
'_recursive_post_grad_passes': 0.0,
'_recursive_pre_grad_passes': 0.0,
'async_compile.wait': 0.0,
'backend_compile': 0.0,
'code_gen': 0.0,
'entire_backward_compile': 0.0,
'entire_frame_compile': 0.0,
'gc': 0.0,
'inductor_compile': 0.0,
'total_wall_time': 0.0}""", # noqa: B950
)
# Now validate the CompilationMetrics logs. We expect a log for the
# forward and a log for the backward.
self.assertTrue(len(compilation_events) == 2)
self.assertTrue(
all(isinstance(e, utils.CompilationMetrics) for e in compilation_events)
)
# Remove a few fields that aren't helpful for test stability.
for e in compilation_events:
e.dynamo_config = None
e.co_filename = None
e.co_firstlineno = None
e.inductor_config = None
e.cuda_version = None
e.triton_version = None
e.python_version = None
# First event is for the forward. Formatting makes reading diffs
# much easier.
raw = dataclasses.asdict(compilation_events[0])
del raw["feature_usage"]
del raw["ir_count"]
del raw["param_numel"]
del raw["param_bytes"]
del raw["param_count"]
# guard_latency_us is not deterministic
del raw["guard_latency_us"]
self.assertExpectedInline(
pprint.pformat(raw),
"""\
{'accumulated_cache_size': 0,
'aot_autograd_cumulative_compile_time_us': 0,
'backend_compile_time_s': 0.0,
'backward_cumulative_compile_time_us': None,
'cache_size': 0,
'co_filename': None,
'co_firstlineno': None,
'co_name': 'forward',
'code_gen_time_s': 0.0,
'compile_id': '1/0',
'compile_time_autotune_time_us': None,
'compliant_custom_ops': set(),
'config_inline_inbuilt_nn_modules': False,
'config_suppress_errors': False,
'cuda_version': None,
'cudagraph_skip_reason': None,
'distributed_ephemeral_timeout_us': None,
'duration_us': 0,
'dynamo_compile_time_before_restart_us': 0,
'dynamo_config': None,
'dynamo_cumulative_compile_time_us': 0,
'dynamo_time_before_restart_s': 0.0,
'end_time_us': 100,
'entire_frame_compile_time_s': 0.0,
'fail_reason': None,
'fail_type': None,
'fail_user_frame_filename': None,
'fail_user_frame_lineno': None,
'frame_key': '1',
'gc_time_us': 0,
'graph_input_count': 1,
'graph_node_count': 3,
'graph_op_count': 1,
'guard_count': 9,
'has_guarded_code': True,
'inductor_code_gen_cumulative_compile_time_us': 0,
'inductor_compile_time_s': 0.0,
'inductor_config': None,
'inductor_cumulative_compile_time_us': 0,
'inductor_fx_remote_cache_backend_type': None,
'inductor_fx_remote_cache_hit_count': None,
'inductor_fx_remote_cache_hit_keys': None,
'inductor_fx_remote_cache_miss_count': None,
'inductor_fx_remote_cache_miss_keys': None,
'is_forward': True,
'is_runtime': False,
'joint_graph_pass_time_us': 0,
'log_format_version': 3,
'non_compliant_ops': set(),
'num_graph_breaks': 0,
'num_triton_bundles': None,
'pgo_get_remote_code_state_time_us': None,
'pgo_put_remote_code_state_time_us': None,
'post_grad_pass_time_us': 0,
'pre_grad_pass_time_us': 0,
'python_version': None,
'recompile_reason': None,
'remote_cache_time_saved_s': None,
'remote_cache_version': None,
'remote_fx_graph_cache_get_time_ms': None,
'remote_fx_graph_cache_get_time_us': None,
'remote_fx_graph_cache_put_time_ms': None,
'remote_fx_graph_cache_put_time_us': None,
'restart_reasons': set(),
'runtime_cudagraphify_time_us': None,
'runtime_triton_autotune_time_us': None,
'shape_env_guard_count': 0,
'specialize_float': False,
'start_time': 0.0001,
'start_time_us': 100,
'structured_logging_overhead_s': 0.0,
'structured_logging_overhead_us': 0,
'tensorify_float_attempt': None,
'tensorify_float_failure': None,
'tensorify_float_success': None,
'triton_compile_time_us': 0,
'triton_kernel_compile_times_us': None,
'triton_version': None}""", # noqa: B950
)
# Second event is for the backward
raw = dataclasses.asdict(compilation_events[1])
del raw["feature_usage"]
del raw["ir_count"]
del raw["guard_latency_us"]
del raw["param_numel"]
del raw["param_bytes"]
del raw["param_count"]
self.assertExpectedInline(
pprint.pformat(raw),
"""\
{'accumulated_cache_size': None,
'aot_autograd_cumulative_compile_time_us': None,
'backend_compile_time_s': None,
'backward_cumulative_compile_time_us': 0,
'cache_size': None,
'co_filename': None,
'co_firstlineno': None,
'co_name': None,
'code_gen_time_s': 0.0,
'compile_id': '1/0',
'compile_time_autotune_time_us': None,
'compliant_custom_ops': None,
'config_inline_inbuilt_nn_modules': None,
'config_suppress_errors': None,
'cuda_version': None,
'cudagraph_skip_reason': None,
'distributed_ephemeral_timeout_us': None,
'duration_us': 0,
'dynamo_compile_time_before_restart_us': None,
'dynamo_config': None,
'dynamo_cumulative_compile_time_us': None,
'dynamo_time_before_restart_s': None,
'end_time_us': 100,
'entire_frame_compile_time_s': None,
'fail_reason': None,
'fail_type': None,
'fail_user_frame_filename': None,
'fail_user_frame_lineno': None,
'frame_key': None,
'gc_time_us': None,
'graph_input_count': None,
'graph_node_count': None,
'graph_op_count': None,
'guard_count': None,
'has_guarded_code': None,
'inductor_code_gen_cumulative_compile_time_us': 0,
'inductor_compile_time_s': 0.0,
'inductor_config': None,
'inductor_cumulative_compile_time_us': 0,
'inductor_fx_remote_cache_backend_type': None,
'inductor_fx_remote_cache_hit_count': None,
'inductor_fx_remote_cache_hit_keys': None,
'inductor_fx_remote_cache_miss_count': None,
'inductor_fx_remote_cache_miss_keys': None,
'is_forward': False,
'is_runtime': False,
'joint_graph_pass_time_us': None,
'log_format_version': 3,
'non_compliant_ops': None,
'num_graph_breaks': 0,
'num_triton_bundles': None,
'pgo_get_remote_code_state_time_us': None,
'pgo_put_remote_code_state_time_us': None,
'post_grad_pass_time_us': 0,
'pre_grad_pass_time_us': None,
'python_version': None,
'recompile_reason': None,
'remote_cache_time_saved_s': None,
'remote_cache_version': None,
'remote_fx_graph_cache_get_time_ms': None,
'remote_fx_graph_cache_get_time_us': None,
'remote_fx_graph_cache_put_time_ms': None,
'remote_fx_graph_cache_put_time_us': None,
'restart_reasons': None,
'runtime_cudagraphify_time_us': None,
'runtime_triton_autotune_time_us': None,
'shape_env_guard_count': None,
'specialize_float': None,
'start_time': 0.0001,
'start_time_us': 100,
'structured_logging_overhead_s': 0.0,
'structured_logging_overhead_us': 0,
'tensorify_float_attempt': None,
'tensorify_float_failure': None,
'tensorify_float_success': None,
'triton_compile_time_us': 0,
'triton_kernel_compile_times_us': None,
'triton_version': None}""", # noqa: B950
)
@dynamo_config.patch(
{
"log_compilation_metrics": True,
}
)
def test_ir_count(self):
# Different python versions have different potential IR counts.
version = (sys.version_info[0], sys.version_info[1])
self.assertIn(version, ((3, 9), (3, 10), (3, 11), (3, 12), (3, 13)))
first, second = {
(3, 9): (10, 6),
(3, 10): (10, 6),
(3, 11): (10, 6),
(3, 12): (11, 7),
(3, 13): (11, 7),
}[version]
def test1(x):
y = x + x
z = y * y
return z
compilation_events = []
with mock.patch("torch._dynamo.utils.log_compilation_event") as log_event:
torch.compile(test1)(torch.randn(10, 10))
compilation_events = [arg[0][0] for arg in log_event.call_args_list]
self.assertEqual(compilation_events[0].ir_count, first)
def test2(x):
y = x + x
return y
compilation_events = []
with mock.patch("torch._dynamo.utils.log_compilation_event") as log_event:
torch.compile(test2)(torch.randn(10, 10))
compilation_events = [arg[0][0] for arg in log_event.call_args_list]
self.assertEqual(compilation_events[0].ir_count, second)
@dynamo_config.patch({"log_compilation_metrics": True})
@inductor_config.patch({"force_disable_caches": True})
def test_dynamic_shape_feature_use(self):
compilation_events = []
with mock.patch("torch._dynamo.utils.log_compilation_event") as log_event:
@torch.compile()
def f(x):
return x * x
f(torch.randn(4))
f(torch.randn(3))
compilation_events = [
arg[0][0].feature_usage for arg in log_event.call_args_list
]
self.assertIn(
("dynamo.automatic_dynamic_shapes", True), compilation_events[1].items()
)
compilation_events = []
with dynamo_config.patch({"automatic_dynamic_shapes": False}), mock.patch(
"torch._dynamo.utils.log_compilation_event"
) as log_event:
@torch.compile()
def f(x):
return x * x
f(torch.randn(4))
f(torch.randn(3))
compilation_events = [
arg[0][0].feature_usage for arg in log_event.call_args_list
]
self.assertIn(
("dynamo.automatic_dynamic_shapes", False), compilation_events[1].items()
)
@dynamo_config.patch({"log_compilation_metrics": True})
def test_num_params(self):
import torch.nn as nn
import torch.nn.functional as F
class ModelSimple(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
def forward(self, x):
return F.relu(self.conv1(x))
self.assertEqual([x.numel() for x in ModelSimple().parameters()], [500, 20])
compilation_events = []
with mock.patch("torch._dynamo.utils.log_compilation_event") as log_event:
m = ModelSimple()
torch.compile(m)(torch.randn(1, 10, 10))
compilation_events = [arg[0][0] for arg in log_event.call_args_list]
self.assertEqual(compilation_events[0].param_numel, 520)
self.assertEqual(compilation_events[0].param_bytes, 4 * 520)
self.assertEqual(compilation_events[0].param_count, 2)
class ModelWrapped(nn.Module):
def __init__(self) -> None:
super().__init__()
self.m1 = ModelSimple()
self.m2 = ModelSimple()
def forward(self, x):
return self.m1(x) + self.m2(x)
compilation_events = []
with mock.patch("torch._dynamo.utils.log_compilation_event") as log_event:
m = ModelWrapped()
torch.compile(m)(torch.randn(1, 10, 10))
compilation_events = [arg[0][0] for arg in log_event.call_args_list]
self.assertEqual(compilation_events[0].param_numel, 1040)
self.assertEqual(compilation_events[0].param_bytes, 4 * 1040)
self.assertEqual(compilation_events[0].param_count, 4)
# Test a tied module
l1 = nn.Linear(4, 4)
l2 = nn.Linear(4, 4)
m = nn.Sequential(l1, nn.Sequential(l1, l2))
self.assertEqual([x.numel() for x in m.parameters()], [16, 4, 16, 4])
with mock.patch("torch._dynamo.utils.log_compilation_event") as log_event:
torch.compile(m)(torch.randn(4, 4))
compilation_events = [arg[0][0] for arg in log_event.call_args_list]
self.assertEqual(compilation_events[0].param_numel, 40)
self.assertEqual(compilation_events[0].param_bytes, 4 * 40)
self.assertEqual(compilation_events[0].param_count, 4)
# Test tied weights
l1 = nn.Linear(4, 4)
l2 = nn.Linear(4, 4)
l1.weight = l2.weight
m = nn.Sequential(l1, nn.Sequential(l2))
self.assertEqual([x.numel() for x in m.parameters()], [16, 4, 4])
with mock.patch("torch._dynamo.utils.log_compilation_event") as log_event:
torch.compile(m)(torch.randn(4, 4))
compilation_events = [arg[0][0] for arg in log_event.call_args_list]
self.assertEqual(compilation_events[0].param_numel, 24)
self.assertEqual(compilation_events[0].param_bytes, 4 * 24)
self.assertEqual(compilation_events[0].param_count, 3)
class TestInductorConfigParsingForLogging(TestCase):
"""
Test for parsing inductor config for logging in CompilationMetrics.
"""
class TestObject:
def __init__(self, a, b):
self.a = a
self.b = b
def test_inductor_config_jsonify(self):
"""
Sanity check if the actual inductor config is parsed correctly
"""
inductor_config_json = utils._scrubbed_inductor_config_for_logging()
self.assertTrue(isinstance(inductor_config_json, str))
self.assertIn('trace"', inductor_config_json)
@mock.patch("torch._dynamo.utils.torch._inductor.config")
def test_inductor_config_parsing_non_conforming_items(self, mocked_inductor_config):
"""
Test if the inductor config is parsed correctly when the config is
- None
- not a dict
- not json serializable
- complex unserializable objects
"""
obj = TestCase
test_mock_config = {
"some": {"name": obj, "some": True},
"data": {"name": obj, "some": True},
"list": [
{"name": obj, "some": True},
{"name": obj, "some": True},
],
"object": {
"name": obj,
"some": True,
"data": {"name": obj, "some": True},
},
}
expected = (
"""{"data": {"name": "Value is not JSON serializable", "some": true}, """
""""list": [{"name": "Value is not JSON serializable", "some": true}, """
"""{"name": "Value is not JSON serializable", "some": true}], """
""""object": {"data": {"name": "Value is not JSON serializable", "some": true}, """
""""name": "Value is not JSON serializable", "some": true}, """
""""some": {"name": "Value is not JSON serializable", "some": true}}"""
)
mocked_inductor_config.get_config_copy.return_value = test_mock_config
inductor_config_json = utils._scrubbed_inductor_config_for_logging()
self.assertEqual(inductor_config_json, expected)
expected = "{}"
mocked_inductor_config.get_config_copy.return_value = {obj: obj}
inductor_config_json = utils._scrubbed_inductor_config_for_logging()
self.assertEqual(inductor_config_json, expected)
expected = "Inductor Config is not JSON serializable"
mocked_inductor_config.get_config_copy.return_value = obj
inductor_config_json = utils._scrubbed_inductor_config_for_logging()
self.assertEqual(inductor_config_json, expected)
expected = None
mocked_inductor_config.get_config_copy.return_value = None
inductor_config_json = utils._scrubbed_inductor_config_for_logging()
self.assertEqual(inductor_config_json, expected)
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
run_tests()
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