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[Inductor UT] Generalize inductor UT for intel GPU (Part 2) (#134556)
[Inductor UT] Reuse Inductor test case for Intel GPU. Reuse `test/inductor/test_torchinductor_opinfo.py` Reuse `test/inductor/test_minifier_isolate.py` Pull Request resolved: https://github.com/pytorch/pytorch/pull/134556 Approved by: https://github.com/etaf, https://github.com/eellison
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xingyuan li
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PyTorch MergeBot
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e54b559e88
commit
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@ -8,12 +8,11 @@ from torch.testing._internal.common_utils import (
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IS_MACOS,
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skipIfRocm,
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skipIfWindows,
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skipIfXpu,
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TEST_WITH_ASAN,
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)
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from torch.testing._internal.inductor_utils import HAS_CUDA
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requires_cuda = unittest.skipUnless(HAS_CUDA, "requires cuda")
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from torch.testing._internal.inductor_utils import GPU_TYPE
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from torch.testing._internal.triton_utils import requires_gpu
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# These minifier tests are slow, because they must be run in separate
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@ -41,10 +40,11 @@ inner(torch.randn(2, 2).to("{device}"))
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self._test_after_aot_runtime_error("cpu", "")
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@skipIfRocm
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@requires_cuda
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@skipIfXpu
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@requires_gpu
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@inductor_config.patch("triton.inject_relu_bug_TESTING_ONLY", "runtime_error")
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def test_after_aot_cuda_runtime_error(self):
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self._test_after_aot_runtime_error("cuda", "device-side assert")
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def test_after_aot_gpu_runtime_error(self):
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self._test_after_aot_runtime_error(GPU_TYPE, "device-side assert")
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if __name__ == "__main__":
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@ -26,6 +26,7 @@ from torch.testing._internal.common_device_type import (
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ops,
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skipCPUIf,
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skipCUDAIf,
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skipXPUIf,
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)
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from torch.testing._internal.common_methods_invocations import op_db, skipOps
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from torch.testing._internal.common_utils import (
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@ -40,7 +41,7 @@ from torch.testing._internal.common_utils import (
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TEST_WITH_ASAN,
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TEST_WITH_ROCM,
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)
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from torch.testing._internal.inductor_utils import GPU_TYPE, HAS_CPU, HAS_CUDA
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from torch.testing._internal.inductor_utils import GPU_TYPE, HAS_CPU, HAS_CUDA, HAS_XPU
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from torch.utils._python_dispatch import TorchDispatchMode
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from torch.utils._pytree import tree_map
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@ -207,6 +208,8 @@ if TEST_WITH_ROCM:
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inductor_skips["cuda"]["logcumsumexp"] = {f32}
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inductor_skips["cuda"]["special.modified_bessel_i1"] = {f64}
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inductor_skips["xpu"] = {}
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inductor_expected_failures_single_sample = defaultdict(dict)
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inductor_expected_failures_single_sample["cpu"] = {
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@ -256,6 +259,109 @@ inductor_expected_failures_single_sample["cuda"] = {
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}, # NYI: could not find kernel for aten.view.default at dispatch key DispatchKey.SparseCUDA
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}
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inductor_expected_failures_single_sample["xpu"] = {
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"_upsample_bilinear2d_aa": {f16, f32, f64},
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"cholesky": {f32, f64},
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"multinomial": {f16, f32, f64},
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("normal", "in_place"): {f16, f32, f64},
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("normal", "number_mean"): {f16, f32, f64},
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"normal": {f16, f32, f64},
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"sparse.sampled_addmm": {f32, f64},
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"tan": {f16},
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"torch.ops.aten._flash_attention_forward": {f16},
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"torch.ops.aten._efficient_attention_forward": {f16, f32},
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"to_sparse": {f16, f32, f64, b8, i32, i64},
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"linalg.eig": {f32, f64},
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"linalg.eigvals": {f32, f64},
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# Double and complex datatype matmul is not supported in oneDNN
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"__rmatmul__": {f64},
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("addmm", "decomposed"): {f64},
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"addr": {f64},
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"baddbmm": {f64},
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"bmm": {f64},
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"byte": {f16, f32},
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"cdist": {f64},
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"corrcoef": {f64},
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"cov": {f64},
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"einsum": {f64},
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"inner": {f64},
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"linalg.cholesky_ex": {f64},
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"linalg.cholesky": {f64},
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("linalg.det", "singular"): {f64},
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"linalg.ldl_factor_ex": {f64},
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"linalg.ldl_factor": {f64},
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"linalg.ldl_solve": {f64},
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"linalg.matrix_power": {f64},
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"linalg.multi_dot": {f64},
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"matmul": {f64},
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"mm": {f64},
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"mv": {f64},
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"nn.functional.bilinear": {f64},
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"nn.functional.linear": {f64},
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"pca_lowrank": {f64},
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"svd_lowrank": {f64},
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"tensordot": {f64},
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"triangular_solve": {f64},
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"svd": {f64},
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"qr": {f64},
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"pinverse": {f64},
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"ormqr": {f64},
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("norm", "nuc"): {f64},
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"lu": {f64},
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"lu_solve": {f64},
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"logdet": {f64},
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"linalg.tensorsolve": {f64},
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"linalg.tensorinv": {f64},
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"linalg.svdvals": {f64},
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"linalg.svd": {f64},
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"linalg.solve": {f64},
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"linalg.solve_triangular": {f64},
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"linalg.solve_ex": {f64},
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"linalg.slogdet": {f64},
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"linalg.qr": {f64},
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"linalg.pinv": {f64},
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("linalg.pinv", "hermitian"): {f64},
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"linalg.norm": {f64},
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("linalg.norm", "subgradients_at_zero"): {f64},
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"linalg.matrix_rank": {f64},
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("linalg.matrix_rank", "hermitian"): {f64},
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"linalg.matrix_norm": {f64},
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"linalg.lu": {f64},
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"linalg.lu_solve": {f64},
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"linalg.lu_factor": {f64},
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"linalg.lu_factor_ex": {f64},
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"linalg.lstsq": {f64},
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("linalg.lstsq", "grad_oriented"): {f64},
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"linalg.inv": {f64},
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"linalg.inv_ex": {f64},
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"linalg.householder_product": {f64},
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"linalg.eigvalsh": {f64},
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"linalg.eigh": {f64},
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"linalg.det": {f64},
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"linalg.cond": {f64},
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"geqrf": {f64},
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"cholesky_solve": {f64},
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"cholesky_inverse": {f64},
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# could not create a primitive
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"addbmm": {f16, f32, f64},
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"addmm": {f16, f32, f64},
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"addmv": {f32, f64},
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# could not create a primitive descriptor for
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# a deconvolution forward propagation primitive
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"nn.functional.conv_transpose2d": {f32, f64},
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"nn.functional.conv_transpose3d": {f32, f64},
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# rrelu not supported on XPU now
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"nn.functional.rrelu": {f16, f32, f64},
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"histc": {i32, i64},
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# not implemented for 'Half'
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"nn.functional.multilabel_margin_loss": {f16},
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"nn.functional.multi_margin_loss": {f16},
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"nn.functional.avg_pool3d": {f16},
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"nn.functional.adaptive_max_pool3d": {f16},
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# not implemented for 'Bool'
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"nn.functional.unfold": {b8},
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}
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# intentionally not handled
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intentionally_not_handled = {
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@ -278,11 +384,13 @@ if not functorch_config.view_replay_for_aliased_outputs:
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}
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inductor_expected_failures_single_sample["cuda"].update(intentionally_not_handled)
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inductor_expected_failures_single_sample["xpu"].update(intentionally_not_handled)
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inductor_gradient_expected_failures_single_sample = defaultdict(dict)
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inductor_gradient_expected_failures_single_sample["cuda"] = {}
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inductor_gradient_expected_failures_single_sample["xpu"] = {}
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if not TEST_MKL:
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inductor_expected_failures_single_sample["cpu"].update({})
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@ -290,6 +398,7 @@ if not TEST_MKL:
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inductor_should_fail_with_exception = defaultdict(dict)
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inductor_should_fail_with_exception["cpu"] = {}
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inductor_should_fail_with_exception["cuda"] = {}
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inductor_should_fail_with_exception["xpu"] = {}
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def get_skips_and_xfails(from_dict, xfails=True):
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@ -324,9 +433,10 @@ torch.testing._internal.common_methods_invocations.wrapper_set_seed = (
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wrapper_noop_set_seed
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)
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# key can be either op_name, or (op_name, dtype)
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inductor_override_kwargs = defaultdict(dict)
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# key can be either op_name, or (op_name, deivce_type), or (op_name, device_type, dtype)
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inductor_override_kwargs = {
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inductor_override_kwargs["cpu"] = {
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# the return value of empty is undefined
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"empty": {"assert_equal": False},
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"empty_permuted": {"assert_equal": False},
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@ -335,92 +445,222 @@ inductor_override_kwargs = {
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"empty_strided": {"assert_equal": False},
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"new_empty_strided": {"assert_equal": False},
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"randn": {"assert_equal": False},
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("cross", "cuda", f16): {"reference_in_float": True},
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("linalg.cross", "cuda", f16): {"reference_in_float": True},
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("addr", "cuda", f16): {"reference_in_float": True},
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("baddbmm", "cuda", f16): {"atol": 2e-3, "rtol": 0.002}, # decomp affects accuracy
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("angle", "cuda", f64): {"reference_in_float": True},
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("asin", "cuda", f16): {"reference_in_float": True},
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("atanh", "cuda", f16): {"reference_in_float": True},
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("cauchy", "cuda"): {"reference_in_float": True},
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("cummax", "cuda", f16): {"atol": 5e-4, "rtol": 0.002},
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("cumsum", "cuda", f16): {"reference_in_float": True},
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("cumprod", "cuda"): {"reference_in_float": True, "atol": 7e-5, "rtol": 0.002},
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("logcumsumexp", "cuda"): {"grad_atol": 8e-4, "grad_rtol": 0.001},
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("exponential", "cuda"): {"reference_in_float": True},
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("geometric", "cuda"): {"reference_in_float": True},
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("kron", "cuda", f16): {"reference_in_float": True},
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("log_normal", "cuda"): {"reference_in_float": True},
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("masked.softmin", "cuda", f16): {"atol": 1e-4, "rtol": 0.01},
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("nn.functional.batch_norm", "cuda", f16): {"reference_in_float": True},
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("nn.functional.batch_norm.without_cudnn", "cuda", f16): {
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"reference_in_float": True
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},
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("nn.functional.cosine_similarity", "cuda", f16): {"reference_in_float": True},
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("nn.functional.instance_norm", "cuda", f16): {"reference_in_float": True},
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("nn.functional.local_response_norm", "cuda", f16): {"reference_in_float": True},
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("nn.functional.normalize", "cuda", f16): {"atol": 1e-3, "rtol": 0.05},
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("nn.functional.rms_norm", "cuda", f16): {"reference_in_float": True},
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("nn.functional.soft_margin_loss", "cuda", f16): {"reference_in_float": True},
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("nn.functional.softmin", "cuda", f16): {"atol": 1e-4, "rtol": 0.01},
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("nn.functional.softsign", "cuda", f16): {"reference_in_float": True},
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("nn.functional.tanhshrink", "cuda", f16): {"atol": 3e-4, "rtol": 0.001},
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("nn.functional.multilabel_soft_margin_loss", "cpu", f16): {
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("nn.functional.multilabel_soft_margin_loss", f16): {
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"atol": 3e-4,
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"rtol": 0.002,
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},
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("outer", "cuda", f16): {"reference_in_float": True},
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("round.decimals_3", "cuda", f16): {"reference_in_float": True},
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("nn.functional.triplet_margin_loss", "cuda", f16): {"atol": 1e-4, "rtol": 0.02},
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("nn.functional.triplet_margin_with_distance_loss", "cuda", f16): {
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"atol": 1e-4,
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"rtol": 0.02,
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},
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("sinc", "cuda", f16): {"atol": 0.008, "rtol": 0.002},
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("torch.ops.aten._safe_softmax.default", "cuda", f16): {"atol": 5e-4, "rtol": 0.02},
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("softmax", "cpu", f16): {"atol": 1e-4, "rtol": 0.02},
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("softmax", "cuda", f16): {"atol": 1e-4, "rtol": 0.02},
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("_softmax_backward_data", "cuda", f16): {"atol": 0.008, "rtol": 0.002},
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("special.log_ndtr", "cuda", f64): {"atol": 1e-6, "rtol": 1e-5},
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("polygamma.polygamma_n_0", "cpu", f32): {"atol": 1e-3, "rtol": 1e-4},
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("polygamma.polygamma_n_1", "cpu", f32): {"atol": 1e-3, "rtol": 1e-4},
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("polygamma.polygamma_n_2", "cpu", f32): {"atol": 1e-3, "rtol": 1e-4},
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("polygamma.polygamma_n_3", "cpu", f32): {"atol": 1e-3, "rtol": 1e-4},
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("polygamma.polygamma_n_4", "cpu", f32): {"atol": 1e-3, "rtol": 1e-4},
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("special.polygamma.special_polygamma_n_0", "cpu", f32): {
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("softmax", f16): {"atol": 1e-4, "rtol": 0.02},
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("polygamma.polygamma_n_0", f32): {"atol": 1e-3, "rtol": 1e-4},
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("polygamma.polygamma_n_1", f32): {"atol": 1e-3, "rtol": 1e-4},
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("polygamma.polygamma_n_2", f32): {"atol": 1e-3, "rtol": 1e-4},
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("polygamma.polygamma_n_3", f32): {"atol": 1e-3, "rtol": 1e-4},
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("polygamma.polygamma_n_4", f32): {"atol": 1e-3, "rtol": 1e-4},
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("special.polygamma.special_polygamma_n_0", f32): {
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"atol": 1e-3,
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"rtol": 1e-4,
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},
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("std_mean.unbiased", "cuda", f16): {"reference_in_float": True},
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("uniform", "cuda"): {"reference_in_float": True},
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("_unsafe_masked_index_put_accumulate", "cuda", f16): {"atol": 1e-4, "rtol": 0.01},
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("_unsafe_masked_index_put_accumulate", "cpu", f16): {"atol": 1e-4, "rtol": 0.01},
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("_unsafe_masked_index_put_accumulate", f16): {"atol": 1e-4, "rtol": 0.01},
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# Following tests are failing with strict comparision but atol=1 is acceptable due roundings errors
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("nn.functional.interpolate.bilinear", "cpu", u8): {"atol": 1, "rtol": 0},
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("nn.functional.upsample_bilinear", "cpu", u8): {"atol": 1, "rtol": 0},
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("nn.functional.interpolate.bicubic", "cpu", u8): {"atol": 1, "rtol": 0},
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("nn.functional.interpolate.bilinear", u8): {"atol": 1, "rtol": 0},
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("nn.functional.upsample_bilinear", u8): {"atol": 1, "rtol": 0},
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("nn.functional.interpolate.bicubic", u8): {"atol": 1, "rtol": 0},
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# High atol due to precision loss
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("nn.functional.interpolate.bilinear", "cuda", f64): {"atol": 5e-4, "rtol": 0},
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("nn.functional.upsample_bilinear", "cuda", f64): {"atol": 5e-4, "rtol": 0},
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("nn.functional.interpolate.bicubic", "cpu", f32): {"atol": 5e-3, "rtol": 0},
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("nn.functional.interpolate.bicubic", "cuda", f64): {"atol": 1e-3, "rtol": 0},
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# Unreasonably high atol requirement:
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("index_reduce.mean", "cuda", f16): {"check_gradient": False},
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("index_reduce.mean", "cuda", f32): {"check_gradient": False},
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("index_reduce.mean", "cuda", f64): {"check_gradient": False},
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# Gradient contains non-finite entries:
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("index_reduce.amin", "cuda", f64): {"check_gradient": False},
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("index_reduce.amin", "cuda", f32): {"check_gradient": False},
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("index_reduce.amin", "cuda", f16): {"check_gradient": False},
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("index_reduce.amax", "cuda", f64): {"check_gradient": False},
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("index_reduce.amax", "cuda", f32): {"check_gradient": False},
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("index_reduce.amax", "cuda", f16): {"check_gradient": False},
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("tanh", "cuda", f16): {"atol": 1e-4, "rtol": 1e-2},
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("nn.functional.interpolate.bicubic", f32): {"atol": 5e-3, "rtol": 0},
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}
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inductor_override_kwargs["cuda"] = {
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# the return value of empty is undefined
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"empty": {"assert_equal": False},
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"empty_permuted": {"assert_equal": False},
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"empty_like": {"assert_equal": False},
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"new_empty": {"assert_equal": False},
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"empty_strided": {"assert_equal": False},
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"new_empty_strided": {"assert_equal": False},
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"randn": {"assert_equal": False},
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("cross", f16): {"reference_in_float": True},
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("linalg.cross", f16): {"reference_in_float": True},
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("addr", f16): {"reference_in_float": True},
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("baddbmm", f16): {"atol": 2e-3, "rtol": 0.002}, # decomp affects accuracy
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("angle", f64): {"reference_in_float": True},
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("asin", f16): {"reference_in_float": True},
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("atanh", f16): {"reference_in_float": True},
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"cauchy": {"reference_in_float": True},
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("cummax", f16): {"atol": 5e-4, "rtol": 0.002},
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("cumsum", f16): {"reference_in_float": True},
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"cumprod": {"reference_in_float": True, "atol": 7e-5, "rtol": 0.002},
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"logcumsumexp": {"grad_atol": 8e-4, "grad_rtol": 0.001},
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"exponential": {"reference_in_float": True},
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"geometric": {"reference_in_float": True},
|
||||
("kron", f16): {"reference_in_float": True},
|
||||
"log_normal": {"reference_in_float": True},
|
||||
("masked.softmin", f16): {"atol": 1e-4, "rtol": 0.01},
|
||||
("nn.functional.batch_norm", f16): {"reference_in_float": True},
|
||||
("nn.functional.batch_norm.without_cudnn", f16): {"reference_in_float": True},
|
||||
("nn.functional.cosine_similarity", f16): {"reference_in_float": True},
|
||||
("nn.functional.instance_norm", f16): {"reference_in_float": True},
|
||||
("nn.functional.local_response_norm", f16): {"reference_in_float": True},
|
||||
("nn.functional.normalize", f16): {"atol": 1e-3, "rtol": 0.05},
|
||||
("nn.functional.rms_norm", f16): {"reference_in_float": True},
|
||||
("nn.functional.soft_margin_loss", f16): {"reference_in_float": True},
|
||||
("nn.functional.softmin", f16): {"atol": 1e-4, "rtol": 0.01},
|
||||
("nn.functional.softsign", f16): {"reference_in_float": True},
|
||||
("nn.functional.tanhshrink", f16): {"atol": 3e-4, "rtol": 0.001},
|
||||
("outer", f16): {"reference_in_float": True},
|
||||
("round.decimals_3", f16): {"reference_in_float": True},
|
||||
("nn.functional.triplet_margin_loss", f16): {"atol": 1e-4, "rtol": 0.02},
|
||||
("nn.functional.triplet_margin_with_distance_loss", f16): {
|
||||
"atol": 1e-4,
|
||||
"rtol": 0.02,
|
||||
},
|
||||
("sinc", f16): {"atol": 0.008, "rtol": 0.002},
|
||||
("torch.ops.aten._safe_softmax.default", f16): {"atol": 5e-4, "rtol": 0.02},
|
||||
("softmax", f16): {"atol": 1e-4, "rtol": 0.02},
|
||||
("_softmax_backward_data", f16): {"atol": 0.008, "rtol": 0.002},
|
||||
("special.log_ndtr", f64): {"atol": 1e-6, "rtol": 1e-5},
|
||||
("std_mean.unbiased", f16): {"reference_in_float": True},
|
||||
"uniform": {"reference_in_float": True},
|
||||
("_unsafe_masked_index_put_accumulate", f16): {"atol": 1e-4, "rtol": 0.01},
|
||||
# High atol due to precision loss
|
||||
("nn.functional.interpolate.bilinear", f64): {"atol": 5e-4, "rtol": 0},
|
||||
("nn.functional.upsample_bilinear", f64): {"atol": 5e-4, "rtol": 0},
|
||||
("nn.functional.interpolate.bicubic", f64): {"atol": 1e-3, "rtol": 0},
|
||||
# Unreasonably high atol requirement:
|
||||
("index_reduce.mean", f16): {"check_gradient": False},
|
||||
("index_reduce.mean", f32): {"check_gradient": False},
|
||||
("index_reduce.mean", f64): {"check_gradient": False},
|
||||
# Gradient contains non-finite entries:
|
||||
("index_reduce.amin", f64): {"check_gradient": False},
|
||||
("index_reduce.amin", f32): {"check_gradient": False},
|
||||
("index_reduce.amin", f16): {"check_gradient": False},
|
||||
("index_reduce.amax", f64): {"check_gradient": False},
|
||||
("index_reduce.amax", f32): {"check_gradient": False},
|
||||
("index_reduce.amax", f16): {"check_gradient": False},
|
||||
("tanh", f16): {"atol": 1e-4, "rtol": 1e-2},
|
||||
}
|
||||
|
||||
inductor_override_kwargs["xpu"] = {
|
||||
# the return value of empty is undefined
|
||||
"empty": {"assert_equal": False},
|
||||
"empty_permuted": {"assert_equal": False},
|
||||
"empty_like": {"assert_equal": False},
|
||||
"new_empty": {"assert_equal": False},
|
||||
"empty_strided": {"assert_equal": False},
|
||||
"new_empty_strided": {"assert_equal": False},
|
||||
"randn": {"assert_equal": False},
|
||||
# XPU
|
||||
("cross", f16): {"reference_in_float": True},
|
||||
("addr", f16): {"reference_in_float": True},
|
||||
("baddbmm", f16): {"atol": 2e-3, "rtol": 0.002}, # decomp affects accuracy
|
||||
("angle", f64): {"reference_in_float": True},
|
||||
("asin", f16): {"reference_in_float": True},
|
||||
("atanh", f16): {"reference_in_float": True},
|
||||
"cauchy": {"reference_in_float": True},
|
||||
("cummax", f16): {"atol": 5e-4, "rtol": 0.002},
|
||||
("cumsum", f16): {"reference_in_float": True},
|
||||
"cumprod": {"reference_in_float": True, "atol": 7e-5, "rtol": 0.002},
|
||||
("dot", f16): {"atol": 1e-5, "rtol": 0.002},
|
||||
"logcumsumexp": {"grad_atol": 8e-4, "grad_rtol": 0.001},
|
||||
"exponential": {"reference_in_float": True},
|
||||
"geometric": {"reference_in_float": True},
|
||||
("kron", f16): {"reference_in_float": True},
|
||||
("linalg.cross", f16): {"reference_in_float": True},
|
||||
("linalg.vecdot", f16): {"atol": 1e-5, "rtol": 2e-2},
|
||||
"log_normal": {"reference_in_float": True},
|
||||
("logsumexp", f16): {"atol": 1e-5, "rtol": 1e-2},
|
||||
("masked.cumprod", f16): {"atol": 1e-5, "rtol": 5e-2},
|
||||
("masked.cumsum", f16): {"atol": 1e-5, "rtol": 5e-3},
|
||||
("masked.softmin", f16): {"atol": 1e-4, "rtol": 0.01},
|
||||
("masked.softmax", f16): {"atol": 2e-4, "rtol": 0.01},
|
||||
("masked.var", f16): {"atol": 2e-5, "rtol": 5e-3},
|
||||
("native_batch_norm", f64): {"atol": 1e-7, "rtol": 1e-5},
|
||||
("_native_batch_norm_legit", f64): {"atol": 1e-7, "rtol": 5e-6},
|
||||
("_batch_norm_with_update", f64): {"atol": 1e-7, "rtol": 1e-6},
|
||||
("native_layer_norm", f16): {"atol": 5e-3, "rtol": 5e-3},
|
||||
("native_layer_norm", f32): {"atol": 5e-3, "rtol": 5e-3},
|
||||
("nn.functional.batch_norm", f16): {"reference_in_float": True},
|
||||
("nn.functional.batch_norm", f64): {"atol": 1e-6, "rtol": 1e-6},
|
||||
("nn.functional.batch_norm.without_cudnn", f16): {"reference_in_float": True},
|
||||
("nn.functional.conv1d", f16): {"atol": 1e-5, "rtol": 6e-3},
|
||||
("nn.functional.conv3d", f16): {"atol": 1e-5, "rtol": 2e-3},
|
||||
("nn.functional.conv_transpose2d", f16): {"atol": 1e-5, "rtol": 2e-3},
|
||||
("nn.functional.conv_transpose3d", f16): {"atol": 1e-5, "rtol": 5e-3},
|
||||
("nn.functional.cosine_embedding_loss", f16): {"atol": 1e-5, "rtol": 2e-3},
|
||||
("nn.functional.cosine_similarity", f16): {
|
||||
"reference_in_float": True,
|
||||
"atol": 1e-5,
|
||||
"rtol": 5e-3,
|
||||
},
|
||||
("nn.functional.instance_norm", f16): {"reference_in_float": True},
|
||||
("nn.functional.instance_norm", f64): {"atol": 1e-6, "rtol": 1e-6},
|
||||
("nn.functional.layer_norm", f16): {"atol": 5e-3, "rtol": 2e-3},
|
||||
("nn.functional.layer_norm", f32): {"atol": 5e-5, "rtol": 2e-3},
|
||||
("nn.functional.local_response_norm", f16): {"reference_in_float": True},
|
||||
("nn.functional.multilabel_soft_margin_loss", f16): {
|
||||
"atol": 3e-4,
|
||||
"rtol": 2e-3,
|
||||
},
|
||||
("nn.functional.normalize", f16): {"atol": 1e-3, "rtol": 0.05},
|
||||
("nn.functional.rms_norm", f16): {"reference_in_float": True},
|
||||
("nn.functional.soft_margin_loss", f16): {"reference_in_float": True},
|
||||
("nn.functional.softmin", f16): {"atol": 1e-4, "rtol": 0.01},
|
||||
("nn.functional.softsign", f16): {
|
||||
"reference_in_float": True,
|
||||
"atol": 1e-5,
|
||||
"rtol": 0.005,
|
||||
},
|
||||
("nn.functional.tanhshrink", f16): {"atol": 3e-4, "rtol": 0.001},
|
||||
("outer", f16): {"reference_in_float": True},
|
||||
("round.decimals_3", f16): {"reference_in_float": True},
|
||||
("nn.functional.triplet_margin_loss", f16): {"atol": 1e-4, "rtol": 0.02},
|
||||
("nn.functional.triplet_margin_with_distance_loss", f16): {
|
||||
"atol": 1e-4,
|
||||
"rtol": 0.02,
|
||||
},
|
||||
("remainder", f16): {"atol": 1e-4, "rtol": 0.005},
|
||||
("nn.functional.upsample_bilinear", f16): {"atol": 1e-5, "rtol": 0.002},
|
||||
("sinc", f16): {"atol": 0.008, "rtol": 0.002},
|
||||
("softmax", f16): {"atol": 1e-4, "rtol": 0.02},
|
||||
("_softmax_backward_data", f16): {"atol": 0.008, "rtol": 0.002},
|
||||
("special.log_ndtr", f64): {"atol": 1e-6, "rtol": 1e-5},
|
||||
("std_mean.unbiased", f16): {
|
||||
"reference_in_float": True,
|
||||
"atol": 5e-5,
|
||||
"rtol": 5e-3,
|
||||
},
|
||||
("trapezoid", f16): {"atol": 1e-5, "rtol": 5e-3},
|
||||
("trapz", f16): {"atol": 1e-5, "rtol": 5e-3},
|
||||
"uniform": {"reference_in_float": True},
|
||||
("var_mean", f16): {"atol": 1e-5, "rtol": 2e-3},
|
||||
("var_mean.unbiased", f16): {"atol": 1e-5, "rtol": 2e-3},
|
||||
("vdot", f16): {"atol": 1e-5, "rtol": 2e-3},
|
||||
# Following tests are failing with strict comparision but atol=1 is acceptable due roundings errors
|
||||
# High atol due to precision loss
|
||||
("nn.functional.interpolate.bilinear", f64): {"atol": 5e-4, "rtol": 0},
|
||||
("nn.functional.upsample_bilinear", f64): {"atol": 5e-4, "rtol": 0},
|
||||
("nn.functional.interpolate.bicubic", f64): {"atol": 1e-3, "rtol": 0},
|
||||
# Unreasonably high atol requirement:
|
||||
("index_reduce.mean", f16): {"check_gradient": False},
|
||||
("index_reduce.mean", f32): {"check_gradient": False},
|
||||
("index_reduce.mean", f64): {"check_gradient": False},
|
||||
# Gradient contains non-finite entries:
|
||||
("index_reduce.amin", f64): {"check_gradient": False},
|
||||
("index_reduce.amin", f32): {"check_gradient": False},
|
||||
("index_reduce.amin", f16): {"check_gradient": False},
|
||||
("index_reduce.amax", f64): {"check_gradient": False},
|
||||
("index_reduce.amax", f32): {"check_gradient": False},
|
||||
("index_reduce.amax", f16): {"check_gradient": False},
|
||||
("tanh", f16): {"atol": 1e-4, "rtol": 1e-2},
|
||||
("nn.functional.embedding_bag", f16): {"check_gradient": False},
|
||||
("nn.functional.embedding_bag", f32): {"check_gradient": False},
|
||||
("nn.functional.embedding_bag", f64): {"check_gradient": False},
|
||||
("_unsafe_masked_index", f16): {"atol": 1e-5, "rtol": 2e-3},
|
||||
("_unsafe_masked_index_put_accumulate", f16): {"atol": 1e-5, "rtol": 5e-3},
|
||||
}
|
||||
|
||||
# Test with one sample only for following ops
|
||||
inductor_one_sample = {
|
||||
inductor_one_sample = defaultdict(dict)
|
||||
|
||||
inductor_one_sample["cpu"] = {
|
||||
"_segment_reduce.lengths": {f16},
|
||||
"_segment_reduce.offsets": {f16},
|
||||
"addmv": {f16},
|
||||
@ -456,89 +696,244 @@ inductor_one_sample = {
|
||||
"normal": {f16, f32, f64},
|
||||
"put": {f16, f32, f64},
|
||||
"take": {b8, f16, f32, f64, i32, i64},
|
||||
("__rdiv__", "cuda"): {f16},
|
||||
("__rmod__", "cuda"): {f16, i64},
|
||||
("__rmul__", "cuda"): {f16},
|
||||
("__rpow__", "cuda"): {f16},
|
||||
("_unsafe_masked_index", "cuda"): {f16},
|
||||
("_unsafe_masked_index_put_accumulate", "cuda"): {f16},
|
||||
("addcdiv", "cuda"): {f16},
|
||||
("addcmul", "cuda"): {f16},
|
||||
("atan2", "cuda"): {f16},
|
||||
("cumsum", "cuda"): {f16},
|
||||
("cumulative_trapezoid", "cuda"): {f16},
|
||||
("dist", "cuda"): {f16},
|
||||
("div.no_rounding_mode", "cuda"): {f16},
|
||||
("fmod", "cuda"): {f16},
|
||||
("grid_sampler_2d", "cuda"): {f16},
|
||||
("index_fill", "cuda"): {f16, f32, f64},
|
||||
("ldexp", "cuda"): {f16},
|
||||
("lerp", "cuda"): {f16},
|
||||
("linalg.householder_product", "cuda"): {f32},
|
||||
("linalg.matrix_norm", "cuda"): {f16},
|
||||
("linalg.vector_norm", "cuda"): {f16},
|
||||
("logspace", "cuda"): {i32, i64},
|
||||
("masked.cumsum", "cuda"): {f16},
|
||||
("masked.logsumexp", "cuda"): {f16},
|
||||
("masked.mean", "cuda"): {b8},
|
||||
("masked.normalize", "cuda"): {f16},
|
||||
("masked.prod", "cuda"): {f16},
|
||||
("masked.std", "cuda"): {f16},
|
||||
("masked.var", "cuda"): {f16},
|
||||
("mul", "cuda"): {f16},
|
||||
("nn.functional.alpha_dropout", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.avg_pool1d", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.avg_pool2d", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.avg_pool3d", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.binary_cross_entropy", "cuda"): {f16},
|
||||
("nn.functional.binary_cross_entropy_with_logits", "cuda"): {f16},
|
||||
("nn.functional.conv2d", "cuda"): {f16},
|
||||
("nn.functional.cosine_embedding_loss", "cuda"): {f16},
|
||||
("nn.functional.dropout2d", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.dropout3d", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.dropout", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.feature_alpha_dropout.with_train", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.fractional_max_pool2d", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.fractional_max_pool3d", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.grid_sample", "cuda"): {f16},
|
||||
("nn.functional.group_norm", "cuda"): {f16},
|
||||
("nn.functional.hinge_embedding_loss", "cuda"): {f16},
|
||||
}
|
||||
|
||||
inductor_one_sample["cuda"] = {
|
||||
"_segment_reduce.lengths": {f16},
|
||||
"_segment_reduce.offsets": {f16},
|
||||
"addmv": {f16},
|
||||
"as_strided.partial_views": {f16},
|
||||
"corrcoef": {f16},
|
||||
"diff": {f16},
|
||||
"einsum": {f16, i32},
|
||||
"gradient": {f16},
|
||||
"histogram": {f32, f64},
|
||||
"histogramdd": {f32, f64},
|
||||
"index_put": {f16, f32, f64},
|
||||
"linalg.eig": {f32, f64},
|
||||
"linspace": {f16, i32, i64},
|
||||
"linspace.tensor_overload": {f16, f32, f64, i32, i64},
|
||||
"logspace": {f16, i32, i64},
|
||||
"logspace.tensor_overload": {f16, f32, f64, i32, i64},
|
||||
"masked_logsumexp": {i64},
|
||||
"max_pool2d_with_indices_backward": {f16, f32, f64},
|
||||
"new_empty_strided": {f16},
|
||||
"nn.functional.adaptive_avg_pool3d": {f16},
|
||||
"nn.functional.adaptive_max_pool1d": {f16, f32},
|
||||
"nn.functional.adaptive_max_pool2d": {f16, f32},
|
||||
"nn.functional.bilinear": {f16},
|
||||
"nn.functional.conv_transpose1d": {f16},
|
||||
"nn.functional.conv_transpose2d": {f16},
|
||||
"nn.functional.conv_transpose3d": {f16},
|
||||
"nn.functional.cosine_similarity": {f16},
|
||||
"nn.functional.cross_entropy": {f16, f32, f64},
|
||||
"nn.functional.gaussian_nll_loss": {f16},
|
||||
"nn.functional.grid_sample": {f16, f32, f64},
|
||||
"nn.functional.interpolate.area": {f16},
|
||||
"nn.functional.nll_loss": {f16, f32, f64},
|
||||
"normal": {f16, f32, f64},
|
||||
"put": {f16, f32, f64},
|
||||
"take": {b8, f16, f32, f64, i32, i64},
|
||||
"__rdiv__": {f16},
|
||||
"__rmod__": {f16, i64},
|
||||
"__rmul__": {f16},
|
||||
"__rpow__": {f16},
|
||||
"_unsafe_masked_index": {f16},
|
||||
"_unsafe_masked_index_put_accumulate": {f16},
|
||||
"addcdiv": {f16},
|
||||
"addcmul": {f16},
|
||||
"atan2": {f16},
|
||||
"cumsum": {f16},
|
||||
"cumulative_trapezoid": {f16},
|
||||
"dist": {f16},
|
||||
"div.no_rounding_mode": {f16},
|
||||
"fmod": {f16},
|
||||
"grid_sampler_2d": {f16},
|
||||
"index_fill": {f16, f32, f64},
|
||||
"ldexp": {f16},
|
||||
"lerp": {f16},
|
||||
"linalg.householder_product": {f32},
|
||||
"linalg.matrix_norm": {f16},
|
||||
"linalg.vector_norm": {f16},
|
||||
"masked.cumsum": {f16},
|
||||
"masked.logsumexp": {f16},
|
||||
"masked.mean": {b8},
|
||||
"masked.normalize": {f16},
|
||||
"masked.prod": {f16},
|
||||
"masked.std": {f16},
|
||||
"masked.var": {f16},
|
||||
"mul": {f16},
|
||||
"nn.functional.alpha_dropout": {f16, f32, f64},
|
||||
"nn.functional.avg_pool1d": {f16, f32, f64},
|
||||
"nn.functional.avg_pool2d": {f16, f32, f64},
|
||||
"nn.functional.avg_pool3d": {f16, f32, f64},
|
||||
"nn.functional.binary_cross_entropy": {f16},
|
||||
"nn.functional.binary_cross_entropy_with_logits": {f16},
|
||||
"nn.functional.conv2d": {f16},
|
||||
"nn.functional.cosine_embedding_loss": {f16},
|
||||
"nn.functional.dropout2d": {f16, f32, f64},
|
||||
"nn.functional.dropout3d": {f16, f32, f64},
|
||||
"nn.functional.dropout": {f16, f32, f64},
|
||||
"nn.functional.feature_alpha_dropout.with_train": {f16, f32, f64},
|
||||
"nn.functional.fractional_max_pool2d": {f16, f32, f64},
|
||||
"nn.functional.fractional_max_pool3d": {f16, f32, f64},
|
||||
"nn.functional.group_norm": {f16},
|
||||
"nn.functional.hinge_embedding_loss": {f16},
|
||||
# Enabling all tests for this test fails randomly
|
||||
# See https://github.com/pytorch/pytorch/issues/129238
|
||||
("nn.functional.huber_loss", "cuda"): {f16},
|
||||
("nn.functional.interpolate.bicubic", "cuda"): {f16},
|
||||
("nn.functional.interpolate.bilinear", "cuda"): {f16},
|
||||
("nn.functional.interpolate.trilinear", "cuda"): {f16},
|
||||
("nn.functional.kl_div", "cuda"): {f16},
|
||||
("nn.functional.margin_ranking_loss", "cuda"): {f16},
|
||||
("nn.functional.max_pool1d", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.max_pool3d", "cuda"): {f16},
|
||||
("nn.functional.mse_loss", "cuda"): {f16},
|
||||
("nn.functional.multi_margin_loss", "cuda"): {f16},
|
||||
("nn.functional.multilabel_margin_loss", "cuda"): {f16},
|
||||
("nn.functional.multilabel_soft_margin_loss", "cuda"): {f16},
|
||||
("nn.functional.normalize", "cuda"): {f16},
|
||||
("nn.functional.pad.replicate", "cuda"): {f16, f32, f64},
|
||||
("nn.functional.pad.reflect", "cuda"): {f16},
|
||||
("nn.functional.pairwise_distance", "cuda"): {f16},
|
||||
("nn.functional.poisson_nll_loss", "cuda"): {f16},
|
||||
("nn.functional.rms_norm", "cuda"): {f16},
|
||||
("norm", "cuda"): {f16},
|
||||
("pow", "cuda"): {f16},
|
||||
("prod", "cuda"): {f16},
|
||||
("scatter_reduce.amax", "cuda"): {f16, f32, f64},
|
||||
("scatter_reduce.amin", "cuda"): {f16, f32, f64},
|
||||
("scatter_reduce.mean", "cuda"): {f16, f32, f64},
|
||||
("special.xlog1py", "cuda"): {f16},
|
||||
("std", "cuda"): {f16},
|
||||
("std_mean", "cuda"): {f16},
|
||||
("svd_lowrank", "cuda"): {f32, f64},
|
||||
("trapezoid", "cuda"): {f16},
|
||||
("trapz", "cuda"): {f16},
|
||||
("true_divide", "cuda"): {f16},
|
||||
("var", "cuda"): {f16},
|
||||
("var_mean", "cuda"): {f16},
|
||||
("xlogy", "cuda"): {f16},
|
||||
"nn.functional.huber_loss": {f16},
|
||||
"nn.functional.interpolate.bicubic": {f16},
|
||||
"nn.functional.interpolate.bilinear": {f16},
|
||||
"nn.functional.interpolate.trilinear": {f16},
|
||||
"nn.functional.kl_div": {f16},
|
||||
"nn.functional.margin_ranking_loss": {f16},
|
||||
"nn.functional.max_pool1d": {f16, f32, f64},
|
||||
"nn.functional.max_pool3d": {f16},
|
||||
"nn.functional.mse_loss": {f16},
|
||||
"nn.functional.multi_margin_loss": {f16},
|
||||
"nn.functional.multilabel_margin_loss": {f16},
|
||||
"nn.functional.multilabel_soft_margin_loss": {f16},
|
||||
"nn.functional.normalize": {f16},
|
||||
"nn.functional.pad.replicate": {f16, f32, f64},
|
||||
"nn.functional.pad.reflect": {f16},
|
||||
"nn.functional.pairwise_distance": {f16},
|
||||
"nn.functional.poisson_nll_loss": {f16},
|
||||
"nn.functional.rms_norm": {f16},
|
||||
"norm": {f16},
|
||||
"pow": {f16},
|
||||
"prod": {f16},
|
||||
"scatter_reduce.amax": {f16, f32, f64},
|
||||
"scatter_reduce.amin": {f16, f32, f64},
|
||||
"scatter_reduce.mean": {f16, f32, f64},
|
||||
"special.xlog1py": {f16},
|
||||
"std": {f16},
|
||||
"std_mean": {f16},
|
||||
"svd_lowrank": {f32, f64},
|
||||
"trapezoid": {f16},
|
||||
"trapz": {f16},
|
||||
"true_divide": {f16},
|
||||
"var": {f16},
|
||||
"var_mean": {f16},
|
||||
"xlogy": {f16},
|
||||
}
|
||||
|
||||
inductor_one_sample["xpu"] = {
|
||||
"_segment_reduce.lengths": {f16},
|
||||
"_segment_reduce.offsets": {f16},
|
||||
"addmv": {f16},
|
||||
"as_strided.partial_views": {f16},
|
||||
"corrcoef": {f16},
|
||||
"diff": {f16},
|
||||
"einsum": {f16, i32},
|
||||
"gradient": {f16},
|
||||
"histogram": {f32, f64},
|
||||
"histogramdd": {f32, f64},
|
||||
"index_put": {f16, f32, f64},
|
||||
"linalg.eig": {f32, f64},
|
||||
"linspace": {f16, i32, i64},
|
||||
"linspace.tensor_overload": {f16, f32, f64, i32, i64},
|
||||
"logspace": {f16, i32, i64},
|
||||
"logspace.tensor_overload": {f16, f32, f64, i32, i64},
|
||||
"masked_logsumexp": {i64},
|
||||
"max_pool2d_with_indices_backward": {f16, f32, f64},
|
||||
"new_empty_strided": {f16},
|
||||
"nn.functional.adaptive_avg_pool3d": {f16},
|
||||
"nn.functional.adaptive_max_pool1d": {f16, f32},
|
||||
"nn.functional.adaptive_max_pool2d": {f16, f32},
|
||||
"nn.functional.bilinear": {f16},
|
||||
"nn.functional.conv_transpose1d": {f16},
|
||||
"nn.functional.conv_transpose2d": {f16},
|
||||
"nn.functional.conv_transpose3d": {f16},
|
||||
"nn.functional.cosine_similarity": {f16},
|
||||
"nn.functional.cross_entropy": {f16, f32, f64},
|
||||
"nn.functional.gaussian_nll_loss": {f16},
|
||||
"nn.functional.grid_sample": {f16, f32, f64},
|
||||
"nn.functional.interpolate.area": {f16},
|
||||
"nn.functional.nll_loss": {f16, f32, f64},
|
||||
"normal": {f16, f32, f64},
|
||||
"put": {f16, f32, f64},
|
||||
"take": {b8, f16, f32, f64, i32, i64},
|
||||
"__rdiv__": {f16},
|
||||
"__rmod__": {f16, i64},
|
||||
"__rmul__": {f16},
|
||||
"__rpow__": {f16},
|
||||
"_unsafe_masked_index": {f16},
|
||||
"_unsafe_masked_index_put_accumulate": {f16},
|
||||
"addcdiv": {f16},
|
||||
"addcmul": {f16},
|
||||
"atan2": {f16},
|
||||
"cumsum": {f16},
|
||||
"cumulative_trapezoid": {f16},
|
||||
"dist": {f16},
|
||||
"div.no_rounding_mode": {f16},
|
||||
"fmod": {f16},
|
||||
"grid_sampler_2d": {f16},
|
||||
"index_fill": {f16, f32, f64},
|
||||
"ldexp": {f16},
|
||||
"lerp": {f16},
|
||||
"linalg.householder_product": {f32},
|
||||
"linalg.matrix_norm": {f16},
|
||||
"linalg.vector_norm": {f16},
|
||||
"masked.cumsum": {f16},
|
||||
"masked.logsumexp": {f16},
|
||||
"masked.mean": {b8},
|
||||
"masked.normalize": {f16},
|
||||
"masked.prod": {f16},
|
||||
"masked.std": {f16},
|
||||
"masked.var": {f16},
|
||||
"mul": {f16},
|
||||
"nn.functional.alpha_dropout": {f16, f32, f64},
|
||||
"nn.functional.avg_pool1d": {f16, f32, f64},
|
||||
"nn.functional.avg_pool2d": {f16, f32, f64},
|
||||
"nn.functional.avg_pool3d": {f16, f32, f64},
|
||||
"nn.functional.binary_cross_entropy": {f16},
|
||||
"nn.functional.binary_cross_entropy_with_logits": {f16},
|
||||
"nn.functional.conv2d": {f16},
|
||||
"nn.functional.cosine_embedding_loss": {f16},
|
||||
"nn.functional.dropout2d": {f16, f32, f64},
|
||||
"nn.functional.dropout3d": {f16, f32, f64},
|
||||
"nn.functional.dropout": {f16, f32, f64},
|
||||
"nn.functional.feature_alpha_dropout.with_train": {f16, f32, f64},
|
||||
"nn.functional.fractional_max_pool2d": {f16, f32, f64},
|
||||
"nn.functional.fractional_max_pool3d": {f16, f32, f64},
|
||||
"nn.functional.group_norm": {f16},
|
||||
"nn.functional.hinge_embedding_loss": {f16},
|
||||
# Enabling all tests for this test fails randomly
|
||||
# See https://github.com/pytorch/pytorch/issues/129238
|
||||
"nn.functional.huber_loss": {f16},
|
||||
"nn.functional.interpolate.bicubic": {f16},
|
||||
"nn.functional.interpolate.bilinear": {f16},
|
||||
"nn.functional.interpolate.trilinear": {f16},
|
||||
"nn.functional.kl_div": {f16},
|
||||
"nn.functional.margin_ranking_loss": {f16},
|
||||
"nn.functional.max_pool1d": {f16, f32, f64},
|
||||
"nn.functional.max_pool3d": {f16},
|
||||
"nn.functional.mse_loss": {f16},
|
||||
"nn.functional.multi_margin_loss": {f16},
|
||||
"nn.functional.multilabel_margin_loss": {f16},
|
||||
"nn.functional.multilabel_soft_margin_loss": {f16},
|
||||
"nn.functional.normalize": {f16},
|
||||
"nn.functional.pad.replicate": {f16, f32, f64},
|
||||
"nn.functional.pad.reflect": {f16},
|
||||
"nn.functional.pairwise_distance": {f16},
|
||||
"nn.functional.poisson_nll_loss": {f16},
|
||||
"nn.functional.rms_norm": {f16},
|
||||
"norm": {f16},
|
||||
"pow": {f16},
|
||||
"prod": {f16},
|
||||
"scatter_reduce.amax": {f16, f32, f64},
|
||||
"scatter_reduce.amin": {f16, f32, f64},
|
||||
"scatter_reduce.mean": {f16, f32, f64},
|
||||
"special.xlog1py": {f16},
|
||||
"std": {f16},
|
||||
"std_mean": {f16},
|
||||
"svd_lowrank": {f32, f64},
|
||||
"trapezoid": {f16},
|
||||
"trapz": {f16},
|
||||
"true_divide": {f16},
|
||||
"var": {f16},
|
||||
"var_mean": {f16},
|
||||
"xlogy": {f16},
|
||||
}
|
||||
|
||||
|
||||
@ -572,6 +967,7 @@ class TestInductorOpInfo(TestCase):
|
||||
True
|
||||
) # inductor kernels failing this test intermittently
|
||||
@skipCUDAIf(not HAS_CUDA, "Skipped! Triton not found")
|
||||
@skipXPUIf(not HAS_XPU, "Skipped! Supported XPU compiler not found")
|
||||
@skipCPUIf(not HAS_CPU, "Skipped! Supported CPU compiler not found")
|
||||
@unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
|
||||
@skipIfTorchDynamo("Test uses dynamo already")
|
||||
@ -593,6 +989,8 @@ class TestInductorOpInfo(TestCase):
|
||||
# TODO: should we move empty_cache to the common device interface
|
||||
if device_type == "cuda":
|
||||
torch.cuda.empty_cache()
|
||||
elif device == "xpu":
|
||||
torch.xpu.empty_cache()
|
||||
op_name = op.name
|
||||
if op.variant_test_name:
|
||||
op_name += f".{op.variant_test_name}"
|
||||
@ -630,12 +1028,12 @@ class TestInductorOpInfo(TestCase):
|
||||
test_expect = ExpectedTestResult.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)]
|
||||
overridden_kwargs.update(
|
||||
inductor_override_kwargs.get(device_type, {}).get(op_name, {})
|
||||
)
|
||||
overridden_kwargs.update(
|
||||
inductor_override_kwargs.get(device_type, {}).get((op_name, dtype), {})
|
||||
)
|
||||
func = op.get_op()
|
||||
|
||||
def fn(*args, **kwargs):
|
||||
@ -653,8 +1051,7 @@ class TestInductorOpInfo(TestCase):
|
||||
samples = op.sample_inputs(device, dtype, requires_grad=requires_grad)
|
||||
|
||||
if (
|
||||
dtype in inductor_one_sample.get(op_name, {})
|
||||
or dtype in inductor_one_sample.get((op_name, device_type), {})
|
||||
dtype in inductor_one_sample.get(device_type, {}).get(op_name, {})
|
||||
) and not ALL_SAMPLES:
|
||||
if isinstance(samples, (list, tuple)):
|
||||
samples = [samples[0]]
|
||||
@ -796,7 +1193,7 @@ class TestInductorOpInfo(TestCase):
|
||||
# print(f"SUCCEEDED OP {op_name} on {device_type} with {dtype}", flush=True, file=f)
|
||||
|
||||
|
||||
instantiate_device_type_tests(TestInductorOpInfo, globals())
|
||||
instantiate_device_type_tests(TestInductorOpInfo, globals(), allow_xpu=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_tests()
|
||||
|
||||
Reference in New Issue
Block a user