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test_torchinductor_opinfo.py
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test_torchinductor_opinfo.py
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# Owner(s): ["module: inductor"]
import atexit
import os
import sys
import unittest
from collections import defaultdict
from enum import Enum
from functools import partial
from unittest.mock import patch
import torch
import torch._dynamo
from torch.testing._internal.common_device_type import (
instantiate_device_type_tests,
onlyNativeDeviceTypes,
OpDTypes,
ops,
skipCPUIf,
skipCUDAIf,
)
from torch.testing._internal.common_methods_invocations import op_db
from torch.testing._internal.common_utils import (
dtype_abbrs,
run_tests,
skipCUDAMemoryLeakCheckIf,
skipIfCrossRef,
skipIfTorchDynamo,
suppress_warnings,
TestCase,
)
from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
try:
try:
from .test_torchinductor import check_model, check_model_cuda
except ImportError:
from test_torchinductor import check_model, check_model_cuda
except (unittest.SkipTest, ImportError) as e:
sys.stderr.write(f"{type(e)}: {e}\n")
if __name__ == "__main__":
sys.exit(0)
raise
bf16 = torch.bfloat16 # not tested
f64 = torch.float64
f32 = torch.float32
f16 = torch.float16
i8 = torch.int8 # not tested
i16 = torch.int16 # not tested
i32 = torch.int32
i64 = torch.int64
b8 = torch.bool
u8 = torch.uint8 # not tested
_ops = partial(
ops, dtypes=OpDTypes.supported, allowed_dtypes=[f16, f32, f64, i32, i64, b8]
)
# Success forces pass; failure forces fail; skip unconditionally skips testing
TestExpect = Enum("TestExpect", ("SUCCESS", "XFAILURE", "SKIP"))
COLLECT_EXPECT = os.getenv("PYTORCH_COLLECT_EXPECT", "0") == "1"
FAIL_ON_SUCCESS = os.getenv("PYTORCH_FAIL_ON_SUCCESS", "1") == "1"
ALL_SAMPLES = os.getenv("PYTORCH_ALL_SAMPLES", "0") == "1"
START = os.getenv("PYTORCH_TEST_RANGE_START", None)
END = os.getenv("PYTORCH_TEST_RANGE_END", None)
if START is not None or END is not None:
assert END is not None
assert START is not None
START = int(START)
END = int(END)
assert START < END
else:
START = 0
END = len(op_db)
seen_succeeded = defaultdict(dict)
seen_failed = defaultdict(dict)
failed_reasons = defaultdict(set)
def print_seen():
expected_failures = defaultdict(list)
def fmt_dtypes(dtypes):
r = ", ".join(sorted(dtype_abbrs[d] for d in dtypes))
return "{" + r + "}"
def process(device_type):
for op, failed_dtypes in seen_failed[device_type].items():
succeeded_dtypes = seen_succeeded.get(op, set())
expected_failures_dtypes = failed_dtypes - succeeded_dtypes
reasons = ""
if failed_reasons[op]:
reasons = " # " + ", ".join(sorted(failed_reasons[op]))
if expected_failures_dtypes:
expected_failures[device_type].append(
f' "{op}": {fmt_dtypes(expected_failures_dtypes)},{reasons}'
)
expected_failures[device_type].sort()
nl = "\n"
print(
f"""
inductor_expected_failures_single_sample[\"{device_type}\"] = {{
{nl.join(expected_failures[device_type])}
}}
"""
)
process("cpu")
process("cuda")
if COLLECT_EXPECT:
atexit.register(print_seen)
inductor_skips = defaultdict(dict)
inductor_skips["cpu"] = {
"linalg.ldl_solve": {b8, f16, f32, f64, i32, i64}, # segfault
"linalg.ldl_factor": {f32, f64}, # flaky
"__rdiv__": {b8, f16, f32, f64, i32, i64}, # flaky
}
inductor_skips["cuda"] = {
# flaky
"__rdiv__": {b8, f16, f32, f64, i32, i64},
"masked.prod": {f16, f32, f64},
"linalg.vander": {f32, f64},
"sparse.sampled_addmm": {f32, f64},
"broadcast_tensors": {f16, f32, f64},
"dsplit": {f16, f32, f64},
# Jiterator kernel is not expected to work with inductor
"jiterator_2inputs_2outputs": {b8, f16, f32, f64, i32, i64},
"jiterator_4inputs_with_extra_args": {b8, f16, f32, f64, i32, i64},
"jiterator_binary": {b8, f16, f32, f64, i32, i64},
"jiterator_binary_return_by_ref": {b8, f16, f32, f64, i32, i64},
"jiterator_unary": {b8, f16, f32, f64, i32, i64},
# Disabled on migration to core
"linalg.pinv.singular": {f32, f64},
"linalg.householder_product": {f32},
# These might be passing now?
"__getitem__": {b8, f16, f32, f64, i32, i64},
"nn.functional.conv_transpose3d": {f16},
"max.reduction_with_dim": {i32, i64},
"min.reduction_with_dim": {i32, i64},
"linalg.lu": {f32, f64},
"lu_unpack": {f32, f64},
"native_batch_norm": {f16, f32, f64},
"native_layer_norm": {f16, f32, f64},
# Issues on sm86 periodic job (complex numbers)
"cdouble": {b8, f16, f32, f64, i32, i64},
"cfloat": {b8, f16, f32, f64, i32, i64},
"randint": {b8, f16, f32, f64, i32, i64},
}
inductor_expected_failures_single_sample = defaultdict(dict)
inductor_expected_failures_single_sample["cpu"] = {
"T": {b8, f16, f32, f64, i32, i64},
"H": {b8, f16, f32, f64, i32, i64},
"mH": {b8, f16, f32, f64, i32, i64},
"mT": {b8, f16, f32, f64, i32, i64},
"__getitem__": {b8, f16, f32, f64, i32, i64},
"addr": {f16},
"allclose": {f16, f32, f64},
"amax": {f16},
"amin": {f16},
"angle": {f16, f32, f64},
"argwhere": {b8, f16, f32, f64, i32, i64},
"bernoulli": {f32, f64},
"bincount": {i32, i64},
"bucketize": {b8, f16, f32, f64, i32, i64},
"cdouble": {b8, f16, f32, f64, i32, i64},
"cfloat": {b8, f16, f32, f64, i32, i64},
"chalf": {b8, f16, f32, f64, i32, i64},
"cholesky": {f32, f64},
"combinations": {b8, f16, f32, f64, i32, i64},
"complex": {f16, f32, f64},
"constant_pad_nd": {f16, f32, f64},
"copysign": {f16},
"corrcoef": {f32, f64, i32, i64},
"cov": {f32, f64, i32, i64},
"equal": {b8, f16, f32, f64, i32, i64},
"erf": {b8, f64},
"fft.fft": {f32, f64},
"fft.fft2": {b8, f32, f64, i32, i64},
"fft.fftn": {b8, f32, f64, i32, i64},
"fft.hfft": {b8, f32, f64, i32, i64},
"fft.hfft2": {b8, f32, f64, i32, i64},
"fft.hfftn": {b8, f32, f64, i32, i64},
"fft.ifft": {f16, f32, f64},
"fft.ifft2": {b8, f32, f64, i32, i64},
"fft.ifftn": {b8, f32, f64, i32, i64},
"fft.ihfft": {f16, f32, f64},
"fft.ihfft2": {f32, f64},
"fft.ihfftn": {f32, f64},
"fft.irfft": {b8, f32, f64, i32, i64},
"fft.irfft2": {b8, f32, f64, i32, i64},
"fft.irfftn": {b8, f32, f64, i32, i64},
"fft.rfft": {f32, f64},
"fft.rfft2": {f32, f64},
"fft.rfftn": {f32, f64},
"index_add": {f16},
"index_reduce": {f16, f32, f64},
"istft": {f32, f64},
"linalg.eig": {f32, f64},
"linalg.eigh": {f32, f64},
"linalg.eigvals": {f32, f64},
"linalg.eigvalsh": {f32, f64},
"linalg.lstsq": {f32, f64},
"linalg.lstsq.grad_oriented": {f32, f64},
"linalg.matrix_rank": {f32, f64},
"linalg.matrix_rank.hermitian": {f32, f64},
"linalg.pinv.singular": {f32, f64},
"logdet": {f32, f64},
"masked.norm": {f16},
"masked.normalize": {f16},
"masked_fill": {f16},
"masked_scatter": {f16, f32, f64},
"masked_select": {b8, f16, f32, f64, i32, i64},
"max.reduction_no_dim": {f16},
"max.reduction_with_dim": {b8},
"min.reduction_no_dim": {f16},
"min.reduction_with_dim": {b8},
"multinomial": {f32, f64},
"nan_to_num": {f16},
"nanquantile": {f32, f64},
"nn.functional.avg_pool1d": {i64},
"nn.functional.avg_pool2d": {i64, f64},
"nn.functional.adaptive_avg_pool2d": {f16, f64},
"nn.functional.ctc_loss": {f32, f64},
"nn.functional.gaussian_nll_loss": {f32, f64},
"nn.functional.gelu": {f64},
"nn.functional.local_response_norm": {i64},
"nn.functional.one_hot": {i64},
"nn.functional.pairwise_distance": {f16},
"nn.functional.rrelu": {f32, f64},
"nn.functional.triplet_margin_with_distance_loss": {f32, f64, i32, i64},
"nonzero": {b8, f16, f32, f64, i32, i64},
"normal": {f16, f32, f64},
"normal.number_mean": {f16, f32, f64},
"pca_lowrank": {f32, f64},
"polar": {f32, f64},
"quantile": {f32, f64},
"rand_like": {f16, f32, f64},
"randint_like": {f16, f32, f64, i32, i64},
"randint": {f16, f32, f64, i32, i64},
"randn_like": {f16, f32, f64},
"repeat_interleave": {b8, f16, f32, f64, i32, i64},
"scatter_add": {f16},
"scatter_reduce.sum": {f16},
"scatter_reduce.prod": {f16, f32, f64},
"segment_reduce.lengths": {f16, f32, f64},
"sgn": {f16, f32, f64},
"sparse.sampled_addmm": {f32, f64},
"stft": {f32, f64},
"svd_lowrank": {f32, f64},
"tensor_split": {b8, f16, f32, f64, i32, i64},
"to": {b8, f16, f32, f64, i32, i64},
"to_sparse": {f32, f64},
"tril": {f16},
"triu": {f16},
"uniform": {f16, f32, f64},
"unique": {b8, f32, f64, i32, i64},
"unique_consecutive": {b8, f32, f64, i32, i64},
"var": {f16},
"var_mean": {f16},
"view_as_complex": {f16, f32, f64},
}
inductor_expected_failures_single_sample["cuda"] = {
"T": {b8, f16, f32, f64, i32, i64},
"H": {b8, f16, f32, f64, i32, i64},
"mH": {b8, f16, f32, f64, i32, i64},
"mT": {b8, f16, f32, f64, i32, i64},
"__getitem__": {b8, f16, f32, f64, i32, i64},
"allclose": {f16, f32, f64},
"angle": {f32, f64},
"argwhere": {b8, f16, f32, f64, i32, i64},
"baddbmm": {f16},
"bernoulli": {f16, f32, f64},
"bincount": {i32, i64},
"bucketize": {b8, f16, f32, f64, i32, i64},
"chalf": {b8, f16, f32, f64, i32, i64},
"cholesky": {f32, f64},
"combinations": {b8, f16, f32, f64, i32, i64},
"complex": {f16, f32, f64},
"corrcoef": {f16, f32, f64, i32, i64},
"cov": {f16, f32, f64, i32, i64},
"equal": {b8, f16, f32, f64, i32, i64},
"fft.fft": {f16, f32, f64},
"fft.fft2": {b8, f16, f32, f64, i32, i64},
"fft.fftn": {b8, f16, f32, f64, i32, i64},
"fft.hfft": {b8, f16, f32, f64, i32, i64},
"fft.hfft2": {b8, f16, f32, f64, i32, i64},
"fft.hfftn": {b8, f16, f32, f64, i32, i64},
"fft.ifft": {f16, f32, f64},
"fft.ifft2": {b8, f16, f32, f64, i32, i64},
"fft.ifftn": {b8, f16, f32, f64, i32, i64},
"fft.ihfft": {f16, f32, f64},
"fft.ihfft2": {f16, f32, f64},
"fft.ihfftn": {f16, f32, f64},
"fft.irfft": {b8, f16, f32, f64, i32, i64},
"fft.irfft2": {b8, f16, f32, f64, i32, i64},
"fft.irfftn": {b8, f16, f32, f64, i32, i64},
"fft.rfft": {f16, f32, f64},
"fft.rfft2": {f16, f32, f64},
"fft.rfftn": {f16, f32, f64},
"index_reduce": {f16, f32, f64},
"istft": {f32, f64},
"linalg.eig": {f32, f64},
"linalg.eigh": {f32, f64},
"linalg.eigvals": {f32, f64},
"linalg.eigvalsh": {f32, f64},
"linalg.lstsq": {f32, f64},
"linalg.lstsq.grad_oriented": {f32, f64},
"linalg.matrix_rank": {f32, f64},
"linalg.matrix_rank.hermitian": {f32, f64},
"lu_unpack": {f32, f64},
"masked.argmax": {f16, f32, f64, i32},
"masked.argmin": {f16, f32, f64, i32},
"masked_scatter": {f16, f32, f64},
"masked_select": {b8, f16, f32, f64, i32, i64},
"max.reduction_with_dim": {b8, i32, i64},
"min.reduction_with_dim": {b8, i32, i64},
"multinomial": {f16, f32, f64},
"nn.functional.adaptive_avg_pool2d": {f16},
"nn.functional.ctc_loss": {f32, f64},
"nn.functional.grid_sample": {f16},
"nn.functional.gaussian_nll_loss": {f16, f32, f64},
"nn.functional.one_hot": {i64},
"nn.functional.rrelu": {f16, f32, f64},
"nn.functional.triplet_margin_with_distance_loss": {f16, f32, f64, i32, i64},
"nonzero": {b8, f16, f32, f64, i32, i64},
"normal": {f16, f32, f64},
"normal.number_mean": {f16, f32, f64},
"pca_lowrank": {f32, f64},
"polar": {f32, f64},
"pow": {i32, i64},
"rand_like": {f16, f32, f64},
"randint_like": {f16, f32, f64, i32, i64},
"randn_like": {f16, f32, f64},
"repeat_interleave": {b8, f16, f32, f64, i32, i64},
"round.decimals_3": {f16},
"scatter_reduce.prod": {f16, f32, f64},
"segment_reduce.lengths": {f16, f32, f64},
"sgn": {f16, f32, f64},
"stft": {f32, f64},
"svd_lowrank": {f32, f64},
"tensor_split": {b8, f16, f32, f64, i32, i64},
"to": {b8, f16, f32, f64, i32, i64},
"to_sparse": {f16, f32, f64},
"uniform": {f16, f32, f64},
"unique": {b8, f16, f32, f64, i32, i64},
"unique_consecutive": {b8, f16, f32, f64, i32, i64},
"view_as_complex": {f16, f32, f64},
# AssertionError: Tensor-likes are not close!
"erf": {b8, f64},
"nn.functional.gelu": {f64},
"nn.functional.triplet_margin_loss": {f16},
}
inductor_gradient_expected_failures_single_sample = defaultdict(dict)
inductor_gradient_expected_failures_single_sample["cuda"] = {
"asin": {f16},
"cumprod": {f16},
"linalg.vector_norm": {f64, f64},
"kron": {f16},
"nanquantile": {f32, f64},
"native_batch_norm": {f16, f32, f64},
"native_layer_norm": {f16, f32, f64},
"nn.functional._scaled_dot_product_attention": {f16},
"nn.functional.avg_pool2d": {f16, f32, f64},
"nn.functional.batch_norm.without_cudnn": {f16},
"nn.functional.batch_norm": {f16},
"nn.functional.cosine_similarity": {f16},
"nn.functional.instance_norm": {f16},
"nn.functional.normalize": {f16},
"nn.functional.softsign": {f16},
"nn.functional.local_response_norm": {f16},
"outer": {f16},
"quantile": {f32, f64},
"scatter_reduce.amax": {f16, f32, f64},
"scatter_reduce.amin": {f16, f32, f64},
"tanh": {f16},
}
inductor_should_fail_with_exception = defaultdict(dict)
inductor_should_fail_with_exception["cpu"] = {}
inductor_should_fail_with_exception["cuda"] = {
"__rpow__": {
i32: "Pow input must be floating point.",
i64: "Pow input must be floating point.",
}
}
def wrapper_set_seed(op, *args, **kwargs):
"""Wrapper to set seed manually for some functions like dropout
See: https://github.com/pytorch/pytorch/pull/62315#issuecomment-896143189 for more details.
"""
torch.manual_seed(42)
return op(*args, **kwargs)
torch.testing._internal.common_methods_invocations.wrapper_set_seed = wrapper_set_seed
# This file does a global patch to `disable_global_flags()` - which we should not invoke in non testing cases.
torch._dynamo.variables.torch.tensor_dunder_fns.append(
torch.testing._internal.common_utils.disable_functorch
)
# key can be either op_name, or (op_name, deivce_type), or (op_name, device_type, dtype)
inductor_override_kwargs = {
# the return value of empty is undefined
"empty": {"assert_equal": False},
"empty_like": {"assert_equal": False},
"new_empty": {"assert_equal": False},
"new_empty_strided": {"assert_equal": False},
"randn": {"assert_equal": False},
("nn.functional.tanhshrink", "cuda", f16): {"atol": 3e-4, "rtol": 0.001},
("cummax", "cuda", f16): {"atol": 5e-4, "rtol": 0.002},
"gradient": {"check_gradient": False}, # segfault on check_gradient
# Following tests failed, and causing subsequent tests failing with unrecoverable CUDA error
"linalg.solve_triangular": {"check_gradient": False},
"linalg.lu_factor": {"check_gradient": False},
"linalg.lu_factor_ex": {"check_gradient": False},
}
# Always test with all sample for following ops
inductor_all_samples = {
"softmax.with_dtype",
"index_add",
"index_copy",
"scatter_reduce.sum",
"select_scatter",
"squeeze",
"unsqueeze",
"sum",
"amax",
"amin",
"all",
}
class TestInductorOpInfo(TestCase):
check_model = check_model
check_model_cuda = check_model_cuda
@onlyNativeDeviceTypes
@suppress_warnings
@skipCUDAMemoryLeakCheckIf(
True
) # inductor kernels failing this test intermittently
@skipCUDAIf(not HAS_CUDA, "Skipped! Triton not found")
@skipCPUIf(not HAS_CPU, "Skipped! Supported CPU compiler not found")
@skipIfTorchDynamo("Test uses dynamo already")
@skipIfCrossRef
@_ops(op_db[START:END])
@patch("torch._dynamo.config.raise_on_unsafe_aot_autograd", True)
def test_comprehensive(self, device, dtype, op):
torch._dynamo.reset()
with torch.no_grad():
torch.cuda.empty_cache()
op_name = op.name
if op.variant_test_name:
op_name += f".{op.variant_test_name}"
device_type = torch.device(device).type
assert device_type in ("cuda", "cpu")
# with open("test_output.txt", "a") as f:
# print(f"CONSIDERING OP {op_name} on {device_type} with {dtype} |
# {inductor_skips[device_type].get(op_name, set())}", flush=True, file=f)
# print(f"CONSIDERING OP {op_name} on {device_type} with {dtype} |
# {inductor_skips[device_type].get(op_name, set())}", flush=True)
if dtype in inductor_skips[device_type].get(op_name, set()):
test_expect = TestExpect.SKIP
# with open("test_output.txt", "a") as f:
# print(f"SKIPPING OP {op_name} on {device_type}", flush=True, file=f)
# print(f"SKIPPING OP {op_name} on {device_type}", flush=True)
self.skipTest(f"{op_name} in {dtype} not supported")
elif dtype in inductor_expected_failures_single_sample[device_type].get(
op_name, set()
) or dtype in inductor_gradient_expected_failures_single_sample[
device_type
].get(
op_name, set()
):
test_expect = TestExpect.XFAILURE
else:
test_expect = TestExpect.SUCCESS
overridden_kwargs = {}
if op_name in inductor_override_kwargs:
overridden_kwargs = inductor_override_kwargs[op_name]
elif (op_name, device_type) in inductor_override_kwargs:
overridden_kwargs = inductor_override_kwargs[(op_name, device_type)]
elif (op_name, device_type, dtype) in inductor_override_kwargs:
overridden_kwargs = inductor_override_kwargs[(op_name, device_type, dtype)]
func = op.get_op()
def fn(*args, **kwargs):
return func(*args, **kwargs)
requires_grad = (
op.supports_autograd
and dtype in op.supported_backward_dtypes(device_type)
# TODO: OpInfo really ought to error out for this case, but it's
# not exercised in test_ops_gradients atm. The problem is not
# complex32 per-se (which is supported by data movement only ops)
# but that when we do backwards we expect other ops like add to work
and not dtype == torch.complex32
)
samples = op.sample_inputs(device, dtype, requires_grad=requires_grad)
if op_name not in inductor_all_samples and not ALL_SAMPLES:
if isinstance(samples, (list, tuple)):
samples = [samples[0]]
else:
samples = [next(samples)]
try:
for sample_input in samples:
args = [sample_input.input] + list(sample_input.args)
kwargs = sample_input.kwargs
# UNCOMMENT TO DEBUG SEGFAULTS
# with open("test_output.txt", "a") as f:
# print(f"RUNNING OP {op_name} on {device_type} with {dtype}", flush=True, file=f)
# print(f"RUNNING OP {op_name} on {device_type} with {dtype}", flush=True)
if device_type == "cuda":
# opinfo test case have already place the input on the correct device
# so we don't need do additional copy by setting copy_to_cuda=False
adjusted_kwargs = {
"check_lowp": False,
"nopython": True,
"copy_to_cuda": False,
"reference_in_float": False,
"check_gradient": requires_grad,
}
adjusted_kwargs.update(overridden_kwargs)
self.check_model_cuda(
fn,
args,
kwargs,
**adjusted_kwargs,
)
elif device_type == "cpu":
adjusted_kwargs = {
"check_lowp": False,
"nopython": True,
# skip checking gradient on CPU for now
"check_gradient": False,
}
adjusted_kwargs.update(overridden_kwargs)
self.check_model(
fn,
args,
kwargs,
**adjusted_kwargs,
)
except Exception as e:
if test_expect is TestExpect.XFAILURE:
return
seen_failed[device_type].setdefault(op_name, set()).add(dtype)
if COLLECT_EXPECT:
return
known_failure = False
if dtype in inductor_should_fail_with_exception[device_type].get(
op_name, set()
):
failure = inductor_should_fail_with_exception[device_type][op_name][
dtype
]
if failure in str(e):
known_failure = True
if not known_failure:
raise e
# with open("test_output.txt", "a") as f:
# print(f"SUCCEEDED OP {op_name} on {device_type} with {dtype}", flush=True, file=f)
seen_succeeded[device_type].setdefault(op_name, set()).add(dtype)
if test_expect is TestExpect.XFAILURE and not COLLECT_EXPECT:
if FAIL_ON_SUCCESS:
raise RuntimeError(
f"unexpected success {op_name}, {dtype}, {device_type}"
)
instantiate_device_type_tests(TestInductorOpInfo, globals())
if __name__ == "__main__":
run_tests()