| # Owner(s): ["module: primTorch"] |
| |
| import itertools |
| import torch |
| import os |
| from enum import Enum |
| from torch.overrides import resolve_name |
| from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten |
| from torch._subclasses.meta_utils import MetaConverter, assert_metadata_eq |
| import torch.utils._python_dispatch |
| from torch._dispatch.python import enable_python_dispatcher |
| from torch.testing._internal.common_utils import ( |
| TestCase, |
| skipIfCrossRef, |
| skipIfTorchDynamo, |
| suppress_warnings, |
| TEST_WITH_ASAN, |
| run_tests, |
| dtype_abbrs |
| ) |
| from torch.testing._internal.common_device_type import ( |
| ops, |
| instantiate_device_type_tests, |
| onlyCUDA, |
| onlyCPU, |
| OpDTypes, |
| ) |
| from torch.testing._internal.common_methods_invocations import op_db |
| from torchgen.yaml_utils import YamlLoader |
| from torchgen.model import OperatorName |
| |
| import sys |
| import yaml |
| import atexit |
| import re |
| from collections import defaultdict |
| import unittest |
| import warnings |
| import weakref |
| from functools import wraps |
| |
| bf16 = torch.bfloat16 |
| f64 = torch.float64 |
| f32 = torch.float32 |
| f16 = torch.float16 |
| c32 = torch.complex32 |
| c64 = torch.complex64 |
| c128 = torch.complex128 |
| i8 = torch.int8 |
| i16 = torch.int16 |
| i32 = torch.int32 |
| i64 = torch.int64 |
| b8 = torch.bool |
| u8 = torch.uint8 |
| |
| |
| class TestMetaConverter(TestCase): |
| def assertSameVersionCounter(self, m1, m2): |
| # Cannot easily test m1 and m2 have same storage due to |
| # lack of Storage bindings. Use version counter. |
| vc = m1._version |
| self.assertEqual(m2._version, vc) |
| # Doing it this way ensures that we get VC bump even with leaves |
| with torch.no_grad(): |
| m1._base.add_(3) |
| self.assertNotEqual(m1._version, vc) |
| self.assertEqual(m2._version, m1._version) |
| |
| def assertMetadataMatches(self, m1, m2): |
| assert_metadata_eq(self.assertEqual, m1, m2) |
| |
| def test_view_of_non_leaf(self): |
| x = torch.randn(4, requires_grad=True) |
| y = x.neg() |
| z1 = y[:] |
| z2 = y[:] |
| to_meta = MetaConverter() |
| m1 = to_meta(z1) |
| m2 = to_meta(z2) |
| |
| # check the test is actually testing what it claims |
| self.assertTrue(m1._is_view()) |
| self.assertFalse(m1._base.is_leaf) |
| |
| self.assertIsNot(m1, m2) |
| self.assertMetadataMatches(m1, z1) |
| self.assertMetadataMatches(m2, z2) |
| self.assertSameVersionCounter(m1, m2) |
| |
| def test_view_of_leaf(self): |
| x = torch.randn(4, requires_grad=True) |
| z1 = x[:] |
| z2 = x[:] |
| to_meta = MetaConverter() |
| m1 = to_meta(z1) |
| m2 = to_meta(z2) |
| |
| # check the test is actually testing what it claims |
| self.assertTrue(m1._is_view()) |
| self.assertTrue(m1._base.is_leaf) |
| |
| self.assertIsNot(m1, m2) |
| self.assertMetadataMatches(m1, z1) |
| self.assertMetadataMatches(m2, z2) |
| self.assertSameVersionCounter(m1, m2) |
| |
| def test_view_of_view_of_leaf(self): |
| x = torch.randn(8) |
| y = x.view(2, 4) |
| y.requires_grad = True |
| z = y.view(2, 2, 2) |
| |
| to_meta = MetaConverter() |
| mx = to_meta(x) |
| mz = to_meta(z) |
| |
| self.assertFalse(z.is_leaf) |
| |
| self.assertMetadataMatches(mx, x) |
| self.assertMetadataMatches(mz, z) |
| |
| def test_leaf(self): |
| x = torch.randn(4, requires_grad=True) |
| to_meta = MetaConverter() |
| m = to_meta(x) |
| |
| # check the test is actually testing what it claims |
| self.assertTrue(m.is_leaf) |
| self.assertTrue(m.requires_grad) |
| |
| self.assertMetadataMatches(m, x) |
| |
| def test_non_leaf(self): |
| x = torch.randn(4, requires_grad=True) |
| y = x.neg() |
| to_meta = MetaConverter() |
| m = to_meta(y) |
| |
| # check the test is actually testing what it claims |
| self.assertFalse(m.is_leaf) |
| self.assertTrue(m.requires_grad) |
| |
| self.assertMetadataMatches(m, y) |
| |
| def test_requires_grad_false(self): |
| x = torch.randn(4, requires_grad=False) |
| to_meta = MetaConverter() |
| m = to_meta(x) |
| |
| # check the test is actually testing what it claims |
| self.assertFalse(m.requires_grad) |
| |
| self.assertMetadataMatches(m, x) |
| |
| def test_channels_last(self): |
| x = torch.empty(2, 3, 4, 5, memory_format=torch.channels_last) |
| to_meta = MetaConverter() |
| m = to_meta(x) |
| |
| # check the test is actually testing what it claims |
| self.assertTrue(m.is_leaf) |
| |
| self.assertMetadataMatches(m, x) |
| |
| def test_channels_last_leaf(self): |
| x = torch.empty(2, 3, 4, 5, memory_format=torch.channels_last, requires_grad=True) |
| to_meta = MetaConverter() |
| m = to_meta(x) |
| |
| # check the test is actually testing what it claims |
| self.assertTrue(m.requires_grad) |
| self.assertTrue(m.is_leaf) |
| |
| self.assertMetadataMatches(m, x) |
| |
| def test_channels_last_non_leaf(self): |
| x = torch.empty(2, 3, 4, 5, memory_format=torch.channels_last, requires_grad=True) |
| y = x + 2 |
| |
| # sanity |
| self.assertEqual(x.stride(), y.stride()) |
| self.assertFalse(y.is_leaf) |
| |
| to_meta = MetaConverter() |
| m = to_meta(y) |
| |
| # check the test is actually testing what it claims |
| self.assertTrue(m.requires_grad) |
| self.assertFalse(m.is_leaf) |
| |
| self.assertMetadataMatches(m, y) |
| |
| # Check that we can autograd with m as input without erroring; |
| # see https://github.com/pytorch/pytorch/issues/87956 |
| loss = m.sum() |
| torch.autograd.grad(loss, m) |
| |
| def test_empty_strided_non_dense_leaf(self): |
| x = torch.empty_strided((2, 2), (4, 2), requires_grad=True) |
| |
| to_meta = MetaConverter() |
| m = to_meta(x) |
| |
| # check the test is actually testing what it claims |
| self.assertTrue(m.requires_grad) |
| self.assertTrue(m.is_leaf) |
| |
| self.assertMetadataMatches(m, x) |
| |
| def test_non_leaf_torture(self): |
| x = torch.empty(20, requires_grad=True) |
| with torch.no_grad(): |
| x.set_(x.storage(), 10, (2,), (2,)) |
| |
| to_meta = MetaConverter() |
| m = to_meta(x) |
| |
| # check the test is actually testing what it claims |
| self.assertTrue(m.requires_grad) |
| self.assertTrue(m.is_leaf) |
| |
| self.assertMetadataMatches(m, x) |
| |
| # NB: complex stuff is not actually exercised right now because |
| # we have a blanket exclusion for complex conversion |
| |
| def test_view_as_real(self): |
| x = torch.randn(4, dtype=torch.complex64) |
| y = torch.view_as_real(x) |
| m = MetaConverter()(y) |
| self.assertMetadataMatches(m, y) |
| |
| def test_complex_noncontiguous_bug(self): |
| x = torch.randn((2, 2, 4, 9), dtype=torch.complex32)[:, 0, :, :] |
| m = MetaConverter()(x) |
| self.assertMetadataMatches(m, x) |
| |
| def test_view_as_complex(self): |
| x = torch.randn((4, 2), dtype=torch.float32) |
| y = torch.view_as_complex(x) |
| m = MetaConverter()(y) |
| self.assertMetadataMatches(m, y) |
| |
| def test_view_dtype(self): |
| x = torch.randn(4, dtype=torch.float32) |
| y = x.view(dtype=torch.int32) |
| m = MetaConverter()(y) |
| self.assertMetadataMatches(m, y) |
| |
| def test_imag(self): |
| x = torch.randn(4, dtype=torch.complex64) |
| y = x.imag |
| m = MetaConverter()(y) |
| self.assertMetadataMatches(m, y) |
| |
| @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991") |
| def test_weakref(self): |
| x = torch.randn(4, 4, 4) |
| m = MetaConverter() |
| y = m(x) |
| z = m(x) |
| self.assertIs(y, z) |
| self.assertEqual(len(m.tensor_memo), 1) |
| self.assertEqual(len(m.storage_memo), 1) |
| del x |
| self.assertEqual(len(m.tensor_memo), 0) |
| m.check_for_expired_weak_storages() |
| self.assertEqual(len(m.storage_memo), 0) |
| li = [] |
| r = [] |
| for i in range(4): |
| li.append(torch.rand([i])) |
| r.append(m(li[-1])) |
| self.assertEqual(len(m.tensor_memo), 4) |
| del li |
| self.assertEqual(len(m.tensor_memo), 0) |
| m.check_for_expired_weak_storages() |
| self.assertEqual(len(m.storage_memo), 0) |
| |
| @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991") |
| def test_tensor_outlives_converter(self): |
| m = MetaConverter() |
| ref = weakref.ref(m) |
| x = torch.randn([4, 4]) |
| y = m(x) |
| del m |
| self.assertIs(ref(), None) |
| |
| aten = torch.ops.aten |
| |
| CHECK_STRIDES = { |
| torch.Tensor.__getitem__, |
| } |
| |
| CHECK_ALL_STRIDES = { |
| aten.unsqueeze.default |
| } |
| |
| CHECK_STRIDES_SKIPS = { |
| aten._conj_physical.default, |
| aten._fft_c2c.default, |
| aten._fft_c2r.default, |
| aten._fft_r2c.default, |
| aten._linalg_svd.default, |
| aten.binary_cross_entropy.default, |
| aten.complex.default, |
| aten.polar.default, |
| aten.copysign.Tensor, |
| aten.div.Tensor_mode, |
| aten.floor_divide.default, |
| aten.heaviside.default, |
| aten.lerp.Scalar, |
| aten.lerp.Tensor, |
| aten.logaddexp.default, |
| aten.logical_and.default, |
| aten.logical_or.default, |
| aten.logical_xor.default, |
| aten.pow.Scalar, |
| aten.prelu.default, |
| aten.special_xlog1py.default, |
| aten.xlogy.Tensor, |
| |
| # channel_last and channel_last_3d related failures |
| aten.convolution.default, |
| |
| # following ops fails if include_storage_offset = True, but these are a bit edge casey |
| # we should still fix them, leaving them here for tracking. |
| # aten._reshape_alias.default, # repro with test_dispatch_symbolic_meta_outplace_all_strides_matmul_cuda_float32 |
| # aten.view.default, # repro with test_dispatch_symbolic_meta_outplace_all_strides_unflatten_cuda_float32 |
| } |
| |
| class CheckStrides(Enum): |
| NONE = 0 |
| SIGNIFICANT = 1 |
| ALL = 2 |
| |
| def should_check_strides(func): |
| if func in CHECK_ALL_STRIDES: |
| return CheckStrides.ALL |
| if func in CHECK_STRIDES: |
| return CheckStrides.SIGNIFICANT |
| if func in CHECK_STRIDES_SKIPS: |
| return CheckStrides.NONE |
| if not isinstance(func, torch._ops.OpOverload): |
| return CheckStrides.NONE |
| # Prims are expected to model strides correctly |
| if func.namespace == "prims": |
| return CheckStrides.SIGNIFICANT |
| # Check if it's a view, by testing if any of the returns have |
| # a non-empty alias set |
| if any(r.alias_info.before_set for r in func._schema.returns if r.alias_info): |
| return CheckStrides.SIGNIFICANT |
| # TODO: check for TensorIterator |
| return CheckStrides.SIGNIFICANT |
| |
| def assert_ref_meta_equal(test_case, func, meta_rs, rs, msg_callable): |
| flat_meta_rs, _ = tree_flatten(meta_rs) |
| flat_rs, _ = tree_flatten(rs) |
| test_case.assertEqual(len(flat_meta_rs), len(flat_rs)) |
| for i, meta_r, r in zip(range(len(flat_rs)), flat_meta_rs, flat_rs): |
| def test_assert(cond, msg): |
| if not cond: |
| raise RuntimeError(f"output {i}: {msg_callable(msg)}") |
| if not isinstance(r, torch.Tensor): |
| continue |
| test_assert(isinstance(meta_r, torch.Tensor), f"but real {i}th result is Tensor") |
| test_assert(meta_r.dtype == r.dtype, f"but real dtype was {r.dtype}") |
| test_assert(meta_r.shape == r.shape, f"but real shape was {r.shape}") |
| # See https://github.com/pytorch/pytorch/issues/78050 |
| if should_check_strides(func) == CheckStrides.ALL: |
| same_strides, _ = torch._prims_common.check_all_strides(meta_r, r) |
| test_assert(same_strides, f"but real stride was {r.stride()}") |
| elif should_check_strides(func) == CheckStrides.SIGNIFICANT: |
| same_strides, _ = torch._prims_common.check_significant_strides(meta_r, r) |
| test_assert(same_strides, f"but real stride was {r.stride()}") |
| test_assert( |
| meta_r.storage_offset() == r.storage_offset(), |
| f"but real storage_offset was {r.storage_offset()}") |
| test_assert(meta_r.requires_grad == r.requires_grad, f"but real requires_grad was {r.requires_grad}") |
| test_assert(meta_r.is_conj() == r.is_conj(), f"but real is_conj was {r.is_conj()}") |
| test_assert(meta_r.is_neg() == r.is_neg(), f"but real is_neg was {r.is_neg()}") |
| |
| |
| # This environment variable controls whether or not we print expected failure |
| # lists at the end of a test suite run. The intended usage looks like this: |
| # |
| # 1. Run `PYTORCH_COLLECT_EXPECT=1 python test/test_meta.py` on a CUDA build |
| # of PyTorch that has LAPACK/MAGMA installed. You can filter `-k test_meta` |
| # or `-k test_dispatch_meta` to only focus on one or another list |
| # 2. Given the printed skip/xfail list, add them to the corresponding lists; |
| # torch.* entries go in meta_function and aten.* entries go in meta_dispatch. |
| # If there are preexisting entries, you need to merge in the entries. |
| # |
| # This is somewhat manual but typically you shouldn't need to do this, unless |
| # you've made a major change (e.g., added a new dtype to PyTorch) and need to |
| # refresh the lists. If you want to do it from scratch, just clear out the |
| # preexisting lists before running. |
| # |
| # WARNING: Python dict literals will silently ignore duplicate keys |
| COLLECT_EXPECT = os.getenv('PYTORCH_COLLECT_EXPECT', '0') == '1' |
| |
| seen_succeeded = {} |
| seen_failed = {} |
| failed_reasons = defaultdict(set) |
| def print_seen(): |
| expected_failures = [] |
| skips = [] |
| |
| def fmt_dtypes(dtypes): |
| r = ', '.join(sorted(dtype_abbrs[d] for d in dtypes)) |
| return '{' + r + '}' |
| |
| for op, failed_dtypes in seen_failed.items(): |
| ops = resolve_name(op) |
| succeeded_dtypes = seen_succeeded.get(op, set()) |
| expected_failures_dtypes = failed_dtypes - succeeded_dtypes |
| skips_dtypes = failed_dtypes & succeeded_dtypes |
| reasons = "" |
| if failed_reasons[op]: |
| reasons = " # " + ", ".join(sorted(failed_reasons[op])) |
| if expected_failures_dtypes: |
| expected_failures.append(f" {ops}: {fmt_dtypes(expected_failures_dtypes)},{reasons}") |
| if skips_dtypes: |
| skips.append(f" {ops}: {fmt_dtypes(skips_dtypes)},") |
| expected_failures.sort() |
| skips.sort() |
| nl = '\n' |
| print(f"""\ |
| expected_failures = {{ |
| {nl.join(expected_failures)} |
| }} |
| |
| skips = {{ |
| {nl.join(skips)} |
| }} |
| """) |
| if COLLECT_EXPECT: |
| atexit.register(print_seen) |
| |
| # Success forces pass; failure forces fail; skip unconditionally skips testing |
| TestExpect = Enum("TestExpect", ("SUCCESS", "XFAILURE", "SKIP")) |
| |
| # unlike print produce strides |
| def verbose_print(e): |
| class Lit: |
| def __init__(self, s): |
| self.s = s |
| |
| def __repr__(self): |
| return self.s |
| |
| def go(t): |
| if isinstance(t, torch.Tensor): |
| return Lit(f"{t} stride={t.stride()}") |
| else: |
| return t |
| |
| return repr(tree_map(go, e)) |
| |
| def run_meta_crossref( |
| test_case, |
| test_expect, |
| func, |
| args, |
| kwargs, |
| *, |
| dtype, |
| device_type, |
| run_symbolic_meta: bool |
| ): |
| to_meta = MetaConverter() |
| do_meta = test_expect is not TestExpect.SKIP |
| |
| if do_meta: |
| try: |
| meta_args = tree_map(to_meta, args) |
| meta_kwargs = tree_map(to_meta, kwargs) |
| except Exception as e: |
| raise RuntimeError( |
| f"failed to convert args to meta; " |
| f"originally (*{args}, **{kwargs})") from e |
| |
| try: |
| rs = func(*args, **kwargs) |
| except Exception as e: |
| # A lot of OpInfo for inplace are actually broken because |
| # they're not tested outside of gradcheck which only checks |
| # torch.float64 and torch.complex128 (which this second one |
| # often skipped as well). |
| raise unittest.SkipTest("Original OpInfo is broken") from e |
| |
| |
| # TODO: also handle cases where func raise an exception |
| |
| # For now, only attempt if we managed to convert all tensor types |
| # (if any of them failed, we're in a mixed device situation and |
| # this isn't well supported) |
| if do_meta and to_meta.successful(): |
| # Special cases |
| if func is torch.tensor_split: |
| # Use original indices_or_sections, this argument is data dependent |
| meta_args = (meta_args[0], args[1]) + meta_args[2:] |
| elif func is torch.Tensor.__getitem__: |
| # Ensure boolean tensors use original |
| assert len(args) == 2 |
| flat_args, _ = tree_flatten(args[1]) |
| flat_meta_args, spec = tree_flatten(meta_args[1]) |
| flat_new_args = [] |
| for a, ma in zip(flat_args, flat_meta_args): |
| flat_new_args.append(a if isinstance(a, torch.Tensor) and a.dtype in [torch.int8, torch.bool] else ma) |
| meta_args = (meta_args[0], tree_unflatten(flat_new_args, spec)) |
| elif func is torch.ops.aten.repeat_interleave.Tensor: |
| if kwargs.get("output_size", None) is None: |
| meta_args = args |
| elif func is torch.ops.aten.index.Tensor: |
| # Don't convert boolean tensors to meta as they will have nonzero |
| # called on them |
| indices = [] |
| for meta_index, real_index in zip(meta_args[1], args[1]): |
| if meta_index is not None and meta_index.dtype in [torch.int8, torch.bool]: |
| indices.append(real_index) |
| else: |
| indices.append(meta_index) |
| meta_args = (meta_args[0], indices) |
| |
| if kwargs.get("device", None) is not None: |
| meta_kwargs["device"] = "meta" |
| |
| try: |
| # Suppress warnings, this doesn't matter for test_meta.py |
| # but it does matter if you want to use this decorator |
| # for cross-ref testing, as some tests may be looking at |
| # errors |
| with warnings.catch_warnings(): |
| warnings.simplefilter("ignore") |
| if run_symbolic_meta: |
| # Run the decomps and meta kernels registered |
| # to the python dispatcher instead of the regular dispatcher. |
| # This should be the same set of kernels |
| # that fake tensor runs in dynamic shapes mode. |
| with enable_python_dispatcher(): |
| meta_rs = func(*meta_args, **meta_kwargs) |
| else: |
| meta_rs = func(*meta_args, **meta_kwargs) |
| except Exception as e: |
| if test_expect is TestExpect.XFAILURE: |
| return rs |
| seen_failed.setdefault(func, set()).add(dtype) |
| if isinstance(e, NotImplementedError): |
| m = RE_NOT_IMPLEMENTED_MSG.search(e.args[0]) |
| if m: |
| failed_reasons[func].add(m.group(1)) |
| if COLLECT_EXPECT: |
| return rs |
| raise RuntimeError(f"""\ |
| failed to run: {resolve_name(func)}( |
| *{verbose_print(meta_args)}, |
| **{verbose_print(meta_kwargs)} |
| )""") from e |
| else: |
| try: |
| delim = ',\n ' |
| assert_ref_meta_equal(test_case, func, meta_rs, rs, lambda msg: f"""\ |
| meta disagrees with real impl: |
| {resolve_name(func)}( |
| {delim.join(map(verbose_print, meta_args))}, |
| {delim.join(k + ": " + verbose_print(v) for k, v in meta_kwargs.items())} |
| ) = ( |
| {verbose_print(meta_rs)} |
| ) |
| {msg} |
| """) |
| except Exception: |
| if test_expect is TestExpect.XFAILURE: |
| return rs |
| seen_failed.setdefault(func, set()).add(dtype) |
| if COLLECT_EXPECT: |
| return rs |
| raise |
| else: |
| seen_succeeded.setdefault(func, set()).add(dtype) |
| if test_expect is TestExpect.XFAILURE and not COLLECT_EXPECT: |
| raise RuntimeError(f"unexpected success {resolve_name(func)} {meta_args} {meta_kwargs}") |
| |
| return rs |
| |
| |
| |
| RE_NOT_IMPLEMENTED_MSG = re.compile(r"Could not run '([^']+)' with arguments ") |
| |
| meta_function_expected_failures = { |
| torch.Tensor.to_sparse : {f64, i32, c128, i64, i16, f16, u8, c64, bf16, b8, i8, f32}, |
| torch.allclose : {f64, f16, c128, c64, bf16, f32}, |
| torch.argwhere : {f64, i32, c128, i64, i16, f16, u8, c64, bf16, b8, i8, f32}, |
| torch.combinations : {f64, i32, c128, i64, i16, f16, u8, c64, bf16, b8, i8, f32}, |
| torch.corrcoef : {f64, i32, c128, i64, i16, u8, c64, bf16, i8, f32}, |
| torch.cov : {f64, i32, c128, i64, i16, u8, c64, bf16, i8, f32}, |
| torch.functional.istft : {f64, c64, c128, f32}, |
| torch.geqrf : {f64, c64, c128, f32}, |
| torch.linalg.householder_product : {f64, c64, c128, f32}, |
| torch.masked_select : {f64, i32, c128, i64, i16, f16, u8, c64, bf16, b8, i8, f32}, |
| torch.matrix_exp : {f64, c128, c64, bf16, f32}, |
| torch.nonzero : {f64, i32, c128, i64, i16, c32, f16, u8, c64, bf16, b8, i8, f32}, |
| torch.Tensor.nonzero : {f64, i32, c128, i64, i16, c32, f16, u8, c64, bf16, b8, i8, f32}, |
| torch.ormqr : {f64, c64, c128, f32}, |
| torch.Tensor.item : {f64, i32, c128, i64, i16, f16, u8, c32, c64, bf16, b8, i8, f32}, |
| torch.bincount : {i32, i64, u8, i16, i8}, |
| torch.frexp : {f64, f16, bf16, f32}, |
| torch.functional.unique : {f64, i32, i64, u8, i16, f16, bf16, b8, i8, f32}, |
| torch.functional.unique_consecutive : {f64, i32, i64, u8, i16, f16, bf16, b8, i8, f32}, |
| torch.histc : {f64, bf16, f32}, |
| torch.histogram : {f64, f32}, |
| torch.histogramdd : {f64, f32}, |
| torch.kthvalue : {f64, i32, i64, u8, i16, bf16, i8, f32}, |
| torch.median : {f64, i32, i64, u8, i16, bf16, i8, f32}, |
| torch.mode : {f64, i32, i64, f16, u8, i16, bf16, b8, i8, f32}, |
| torch.multinomial : {f64, bf16, f32}, |
| torch.nn.functional.ctc_loss : {f64, f32}, |
| torch.nn.functional.gaussian_nll_loss : {f64, bf16, f32}, |
| torch.nn.functional.max_pool3d : {f64, f32}, |
| torch.nn.functional.max_pool3d_with_indices : {f64, f32}, |
| torch.nn.functional.max_unpool1d : {f64, f32}, |
| torch.nn.functional.max_unpool2d : {f64, f32}, |
| torch.nn.functional.max_unpool3d : {f64, f32}, |
| torch.nn.functional.multi_margin_loss : {f64, f32}, |
| torch.nn.functional.multilabel_margin_loss : {f64, f32}, |
| torch.nn.functional.one_hot : {i64}, |
| torch.nn.functional.pdist : {f64, f32}, |
| torch._segment_reduce : {f64, f16, bf16, f32}, |
| torch.searchsorted : {f64, i32, i64, f16, u8, i16, bf16, i8, f32}, |
| torch.cholesky : {f64, f32, c128, c64}, |
| torch.cholesky_inverse : {f64, f32, c128, c64}, |
| torch.cholesky_solve : {f64, f32, c128, c64}, |
| torch.linalg.eig : {f64, f32, c128, c64}, |
| torch.linalg.eigvals : {f64, f32, c128, c64}, |
| torch.linalg.lstsq : {f64, f32, c128, c64}, |
| } |
| |
| meta_function_expected_failures_only_outplace = { |
| torch.nn.functional.rrelu : {f64, bf16, f32}, |
| } |
| |
| meta_function_expected_failures_conditional = { |
| torch.repeat_interleave : (lambda dtype, *args, **kwargs: not isinstance(kwargs.get("repeats", None), int)), |
| } |
| |
| """ |
| # This is some sample code for how we could dump these dicts into YAML |
| # file for easier reading/writing |
| import yaml |
| print(yaml.dump( |
| {resolve_name(k): [dtype_abbrs[d] for d in v] |
| for k, v in meta_function_expected_failures.items()}, default_flow_style=None)) |
| import sys |
| sys.exit() |
| """ |
| |
| meta_function_skips = { |
| torch.Tensor.__rmatmul__ : {bf16, c128, f64, f32, f16, c64}, |
| torch.Tensor.matmul : {f64, f32, c128, c64}, |
| torch.functional.atleast_2d : {bf16, i8, c32, i64, u8, c128, b8, f64, i16, i32, f32, f16, c64}, |
| torch.functional.atleast_3d : {bf16, i8, c32, i64, u8, c128, b8, f64, i16, i32, f32, f16, c64}, |
| torch.functional.cartesian_prod : {bf16, i8, i64, u8, c128, b8, f64, i16, i32, f32, f16, c64}, |
| torch.functional.einsum : {bf16, c128, f64, f32, f16, c64}, |
| torch.functional.tensordot : {bf16, i8, i64, u8, c128, f64, i16, f32, i32, c64}, |
| torch.inner : {bf16, i8, i64, u8, c128, f64, i16, f32, i32, c64}, |
| torch.linalg.lu_solve : {c128, c64}, |
| torch.linalg.matrix_norm : {c128, f32, c64, f64}, |
| torch.linalg.matrix_power : {c128, c64}, |
| torch.linalg.matrix_rank : {c128, c64}, |
| torch.linalg.svd : {c128, c64}, |
| torch.matmul : {bf16, c128, f64, f32, f16, c64}, |
| torch.nanquantile : {f64, f32}, |
| torch.narrow : {bf16, i8, i64, u8, c128, b8, f64, i16, i32, f32, f16, c32, c64}, |
| torch.nn.functional.batch_norm : {f64, f32}, |
| torch.nn.functional.binary_cross_entropy : {bf16, f64, f32, f16}, |
| torch.nn.functional.dropout3d : {bf16, f64, f32, f16}, |
| torch.nn.functional.local_response_norm : {bf16, f64, f32, f16}, |
| torch.svd : {c128, c64}, |
| torch.take_along_dim : {bf16, i8, i64, u8, c128, b8, f64, i16, i32, f32, f16, c64}, |
| torch.vstack : {bf16, i8, c32, i64, u8, c128, b8, f64, i16, i32, f32, f16, c64}, |
| torch.aminmax : {i8, i64, u8, f64, b8, f32, i32, i16}, |
| torch.diff : {b8}, |
| torch.equal : {bf16, i8, c32, i64, u8, c128, b8, f64, i16, i32, f32, f16, c64}, |
| torch.nanmean : {bf16, f64, f32, f16, c32, c64, c128}, |
| torch.nn.functional.cross_entropy : {bf16, f64, f32}, |
| torch.nn.functional.interpolate : {bf16, f64, f32, u8}, |
| torch.nn.functional.nll_loss : {bf16, f64, f32}, |
| torch.linalg.pinv : {f64, f32}, |
| torch.linalg.cond : {c128, c64, f32, f64}, |
| torch.linalg.vander: {c128, c64, f32, f64, i16, i32, i64, i8, u8}, |
| torch.linalg.vecdot : {bf16, f64, f32, f16}, |
| torch.empty : {bf16, i8, c32, i64, u8, c128, b8, f64, i16, i32, f32, f16, c64}, |
| # This fails for arguments dispatched to grid_sampler_3d, but succeeds |
| # for grid_sampler_2d, so we can't just xfail it |
| torch.nn.functional.grid_sample : {f64, f32}, |
| torch.Tensor.addbmm_: {bf16, c128, c64, f32, f64, i16, i32, i64, i8, u8}, |
| } |
| |
| |
| meta_function_device_expected_failures = defaultdict(dict) |
| meta_function_device_expected_failures_only_outplace = defaultdict(dict) |
| meta_function_device_skips = defaultdict(dict) |
| |
| meta_function_device_expected_failures['cpu'] = { |
| torch.native_batch_norm: {bf16}, |
| torch._native_batch_norm_legit: {bf16}, |
| torch.native_layer_norm: {bf16}, |
| } |
| |
| meta_function_device_expected_failures['cuda'] = { |
| torch.corrcoef: {bf16, f16}, # aten::_local_scalar_dense |
| torch.cov: {f16}, # aten::_local_scalar_dense |
| torch.functional.unique: {f16}, # aten::_unique2, aten::unique_dim |
| torch.functional.unique_consecutive: {f16}, # aten::unique_consecutive |
| torch.geqrf: {f32, f64}, # aten::geqrf |
| torch.histc: {i16, i32, i64, i8}, # aten::histc, aten::histc.out |
| torch.kthvalue: {f16}, # aten::kthvalue.values |
| torch.linalg.householder_product: {f32, f64}, # aten::linalg_householder_product, aten::linalg_householder_product.out |
| torch.matrix_exp: {f16}, # aten::linalg_matrix_exp |
| torch.median: {f16}, # aten::median, aten::median.dim_values |
| torch.multinomial: {f16}, # aten::multinomial, aten::multinomial.out |
| torch.nn.functional.gaussian_nll_loss: {f16}, # aten::_local_scalar_dense |
| torch.nn.functional.max_pool3d: {bf16, f16}, # aten::max_pool3d_with_indices |
| torch.nn.functional.max_pool3d_with_indices: {bf16, f16}, # aten::max_pool3d_with_indices |
| torch.nn.functional.max_unpool1d: {f16}, # aten::max_unpool2d |
| torch.nn.functional.max_unpool2d: {f16}, # aten::max_unpool2d |
| torch.nn.functional.max_unpool3d: {f16}, # aten::max_unpool3d |
| torch.nn.functional.multi_margin_loss: {bf16, f16}, # aten::multi_margin_loss |
| torch.nn.functional.multilabel_margin_loss: {bf16, f16}, # aten::multilabel_margin_loss_forward |
| torch.ormqr: {f32, f64}, # aten::ormqr, aten::ormqr.out |
| } |
| |
| meta_function_device_expected_failures_only_outplace['cuda'] = { |
| torch.nn.functional.rrelu: {f16}, # aten::rrelu_with_noise |
| } |
| |
| meta_function_device_skips['cpu'] = { |
| torch.native_batch_norm: {f32, f64}, |
| torch._native_batch_norm_legit: {f32, f64}, |
| } |
| |
| meta_function_device_skips['cuda'] = { |
| torch.functional.tensordot: {f16}, |
| torch.inner: {f16}, |
| torch.linalg.matrix_power: {f32, f64}, |
| torch.linalg.matrix_rank: {f32, f64}, |
| torch.linalg.svd: {f32, f64}, |
| torch.nn.functional.cross_entropy: {f16}, |
| torch.nn.functional.interpolate: {f16}, |
| torch.nn.functional.nll_loss: {f16}, |
| torch.svd: {f32, f64}, |
| # This fails for arguments dispatched to grid_sampler_3d, but succeeds |
| # for grid_sampler_2d, so we can't just xfail it |
| torch.nn.functional.grid_sample : {f16}, |
| } |
| |
| # This is a __torch_function__ mode that, when enabled, interposes every |
| # Torch API call and runs the operator as normal, and then reruns it |
| # with meta inputs, and then checks that everything about the output agrees. |
| # Most of the logic deals with faithfully replicating the original tensor |
| # as a meta tensor, which is nontrivial because there are a lot of subsystems |
| # that may potentially be exercised. |
| # |
| # That being said, this class is a little overkill for what it is doing in |
| # this test file (since I could have just inlined __torch_function__ on the |
| # OpInfo call, and OpInfos generally have very regular inputs), but it will be |
| # useful for more comprehensive testing e.g., as seen in |
| # https://github.com/pytorch/pytorch/pull/75994 The big benefit is it is |
| # A LOT more efficient that torch dispatch mode (at the cost of less coverage) |
| class MetaCrossRefFunctionMode(torch.overrides.TorchFunctionMode): |
| test_case: TestCase |
| device_type: str |
| dtype: torch.dtype |
| |
| def __init__(self, test_case, *, device, dtype, inplace): |
| self.test_case = test_case |
| self.device_type = torch.device(device).type |
| self.dtype = dtype |
| self.inplace = inplace |
| |
| def __torch_function__(self, func, types, args=(), kwargs=None): |
| kwargs = kwargs or {} |
| |
| if ( |
| torch.jit.is_tracing() or isinstance(func, torch.ScriptMethod) or |
| # meta converter doesn't work correctly when no_dispatch() is on, so |
| # skip running the crossref test in this case |
| torch._C._dispatch_tls_local_exclude_set().has(torch._C.DispatchKey.Python) |
| ): |
| return func(*args, **kwargs) |
| |
| if self.dtype in meta_function_skips.get(func, set()): |
| test_expect = TestExpect.SKIP |
| elif self.dtype in meta_function_device_skips[self.device_type].get(func, set()): |
| test_expect = TestExpect.SKIP |
| elif self.dtype in meta_function_expected_failures.get(func, set()): |
| test_expect = TestExpect.XFAILURE |
| elif not self.inplace and self.dtype in meta_function_expected_failures_only_outplace.get(func, set()): |
| test_expect = TestExpect.XFAILURE |
| elif self.dtype in meta_function_device_expected_failures[self.device_type].get(func, set()): |
| test_expect = TestExpect.XFAILURE |
| elif meta_function_expected_failures_conditional.get(func, lambda *_, **__: False)(self.dtype, *args, **kwargs): |
| test_expect = TestExpect.XFAILURE |
| elif not self.inplace and \ |
| self.dtype in meta_function_device_expected_failures_only_outplace[self.device_type].get(func, set()): |
| test_expect = TestExpect.XFAILURE |
| else: |
| test_expect = TestExpect.SUCCESS |
| |
| return run_meta_crossref( |
| self.test_case, test_expect, func, args, |
| kwargs, dtype=self.dtype, device_type=self.device_type, run_symbolic_meta=False |
| ) |
| |
| # these always fail |
| meta_dispatch_expected_failures = { |
| aten.allclose.default: {f16, bf16, f32, f64, c64, c128}, # NotImplementedError: 'aten::_local_scalar_dense' |
| aten.cholesky.default : {c64, c128, f64, f32}, |
| aten.cholesky.out : {c64, c128, f64, f32}, |
| aten.cholesky_inverse.default : {c64, c128, f64, f32}, |
| aten.cholesky_inverse.out : {c64, c128, f64, f32}, |
| aten.cholesky_solve.default : {c64, c128, f64, f32}, |
| aten.cholesky_solve.out : {c64, c128, f64, f32}, |
| aten.geqrf.default : {c64, c128, f64, f32}, |
| aten.linalg_eig.default : {c64, c128, f64, f32}, |
| aten.linalg_householder_product.default : {c64, c128, f64, f32}, |
| aten.linalg_householder_product.out : {c64, c128, f64, f32}, |
| aten.linalg_lstsq.default : {c64, c128, f64, f32}, |
| aten.linalg_matrix_exp.default : {c64, bf16, f32, f64, c128}, |
| aten.masked_select.default : {c64, f16, i8, f64, c128, i64, bf16, f32, i32, b8, i16, u8}, |
| aten.masked_select.out : {c64, f16, i8, f64, c128, i64, bf16, f32, i32, b8, i16, u8}, |
| aten.nonzero.default : {c64, f16, i8, f64, c128, i64, bf16, f32, i32, c32, b8, i16, u8}, |
| aten.nonzero.out : {c64, f16, i8, f64, c128, i64, bf16, f32, i32, c32, b8, i16, u8}, |
| aten.ormqr.default : {c64, c128, f64, f32}, |
| aten.ormqr.out : {c64, c128, f64, f32}, |
| aten.tensordot.out : {c64, i8, f64, c128, i64, bf16, f32, i32, i16, u8}, |
| aten.to_sparse.default : {c64, f16, i8, f64, c128, i64, bf16, f32, i32, b8, i16, u8}, |
| aten.to_sparse.sparse_dim : {c64, f16, i8, f64, c128, i64, bf16, f32, i32, b8, i16, u8}, |
| aten._ctc_loss.default : {f32, f64}, # Shape of second output depends on data. |
| aten._ctc_loss.Tensor : {f32, f64}, # Shape of second output depends on data. |
| aten._histogramdd_bin_edges.default : {f32, f64}, |
| aten._histogramdd_from_bin_cts.default : {f32, f64}, |
| aten._histogramdd_from_bin_tensors.default : {f32, f64}, |
| aten._local_scalar_dense.default : {c32, c64, f16, i8, f64, c128, i64, bf16, f32, i32, b8, i16, u8}, |
| aten._pdist_forward.default : {f32, f64}, |
| aten._unique2.default : {i8, f64, i64, f16, bf16, f32, i32, b8, i16, u8}, |
| aten.bincount.default : {i64, i8, i32, i16, u8}, |
| aten.equal.default : {c64, f16, i8, f64, c128, i64, bf16, f32, i32, b8, i16, u8}, |
| aten.frexp.Tensor : {bf16, f32, f16, f64}, |
| aten.grid_sampler_3d.default : {f32, f64}, |
| aten.histc.default : {bf16, f32, f64}, |
| aten.histc.out : {bf16, f32, f64}, |
| aten.histogram.bin_ct : {f32, f64}, |
| aten.histogram.bins_tensor : {f32, f64}, |
| aten.kthvalue.default : {i8, f64, i64, bf16, f32, i32, i16, u8}, |
| aten.max_pool3d_with_indices.default : {f32, f64}, |
| aten.max_unpool2d.default : {f32, f64}, |
| aten.max_unpool3d.default : {f32, f64}, |
| aten.median.default : {i8, f64, i64, bf16, f32, i32, i16, u8}, |
| aten.median.dim : {i8, f64, i64, bf16, f32, i32, i16, u8}, |
| aten.mode.default : {f16, i8, f64, i64, bf16, f32, i32, b8, i16, u8}, |
| aten.multi_margin_loss.default : {f32, f64}, |
| aten.multilabel_margin_loss_forward.default : {f32, f64}, |
| aten.multinomial.default : {bf16, f32, f64}, |
| aten.multinomial.out : {bf16, f32, f64}, |
| aten.nll_loss2d_forward.default : {bf16, f32, f64}, |
| aten.rrelu_with_noise.default : {bf16, f32, f64}, |
| aten.searchsorted.Tensor : {f16, i8, f64, i64, bf16, f32, i32, i16, u8}, |
| aten.searchsorted.Tensor_out : {f16, i8, f64, i64, bf16, f32, i32, i16, u8}, |
| aten.segment_reduce.default : {bf16, f32, f16, f64}, |
| aten.unique_consecutive.default : {i8, f64, i64, f16, bf16, f32, i32, b8, i16, u8}, |
| aten.unique_dim.default : {i8, f64, i64, f16, bf16, f32, i32, b8, i16, u8}, |
| aten.upsample_nearest3d.vec : {bf16, f32, f64, u8}, |
| } |
| |
| # these sometimes pass and sometimes fail |
| meta_dispatch_skips = { |
| aten.index.Tensor: {i64, bf16, f16, u8, b8, f32, i8, f64, i16, i32, c32, c64, c128}, # at::nonzero doesn't have a Meta function |
| aten._to_copy.default: {i64, bf16, f16, u8, b8, f32, i8, f64, i16, i32, c32, c64, c128}, |
| aten.aminmax.default: {i64, u8, b8, f32, i8, f64, i16, i32}, |
| aten.linalg_lu_solve.default: {c32, c64, c128}, |
| aten.linalg_lu_solve.out: {c32, c64, c128}, |
| aten.linalg_pinv.atol_rtol_tensor: {f32, f64}, |
| aten.linalg_pinv.atol_rtol_tensor_out: {f32, f64}, |
| aten.empty.memory_format: {b8, bf16, c128, c64, c32, f16, f32, f64, i16, i32, i64, i8, u8}, |
| aten.addbmm_.default: {bf16, c128, c64, f32, f64, i16, i32, i64, i8, u8}, |
| } |
| |
| # For CompositeImplicitAutograd functions that fail before hitting the Mode |
| meta_dispatch_early_skips = set({ |
| torch.Tensor.float_power_, |
| # Errors out in one of the tests, while ProxyTensor passes... |
| torch.Tensor.cumprod_, |
| torch.Tensor.cumsum_, |
| }) |
| |
| meta_inplace_skips = set({ |
| # Errors out in one of the tests, while ProxyTensor passes... |
| torch.Tensor.cumprod_, |
| torch.Tensor.cumsum_, |
| }) |
| |
| meta_dispatch_device_expected_failures = defaultdict(dict) |
| meta_dispatch_device_skips = defaultdict(dict) |
| |
| meta_dispatch_device_expected_failures['cpu'] = { |
| aten.native_batch_norm.default: {bf16}, |
| aten._native_batch_norm_legit.default: {bf16}, |
| aten._native_batch_norm_legit.no_stats: {bf16}, |
| aten.native_layer_norm.default: {bf16}, |
| } |
| |
| meta_dispatch_device_expected_failures['cuda'] = { |
| aten._unique2.default: {f16}, # aten::_unique2 |
| aten._use_cudnn_ctc_loss.default: {f32, f64}, # aten::_use_cudnn_ctc_loss |
| aten._use_cudnn_ctc_loss.Tensor: {f32, f64}, # aten::_use_cudnn_ctc_loss.Tensor |
| aten.cudnn_grid_sampler.default: {f16, f32, f64}, # aten::cudnn_grid_sampler |
| aten.geqrf.default: {f32, f64}, # aten::geqrf |
| aten.grid_sampler_3d.default: {f16}, # aten::grid_sampler_3d |
| aten.histc.default: {i16, i32, i64, i8}, # aten::histc |
| aten.histc.out: {i16, i32, i64, i8}, # aten::histc.out |
| aten.kthvalue.default: {f16}, # aten::kthvalue.values |
| aten.linalg_eigvalsh.out: {f32, f64}, # aten::linalg_eigvalsh.out |
| aten.linalg_householder_product.default: {f32, f64}, # aten::linalg_householder_product |
| aten.linalg_householder_product.out: {f32, f64}, # aten::linalg_householder_product.out |
| aten.linalg_matrix_exp.default: {f16}, # aten::linalg_matrix_exp |
| aten.log_sigmoid_forward.default: {bf16, f16, f64, f32}, |
| aten.log_sigmoid_forward.output : {bf16, f16, f64, f32}, # aten::log_sigmoid_forward.output |
| aten.max_pool3d_with_indices.default: {bf16, f16}, # aten::max_pool3d_with_indices |
| aten.max_unpool2d.default: {f16}, # aten::max_unpool2d |
| aten.max_unpool3d.default: {f16}, # aten::max_unpool3d |
| aten.median.default: {f16}, # aten::median |
| aten.median.dim: {f16}, # aten::median.dim_values |
| aten.multi_margin_loss.default: {bf16, f16}, # aten::multi_margin_loss |
| aten.multilabel_margin_loss_forward.default: {bf16, f16}, # aten::multilabel_margin_loss_forward |
| aten.multinomial.default: {f16}, # aten::multinomial |
| aten.multinomial.out: {f16}, # aten::multinomial.out |
| aten.nll_loss2d_forward.default: {f16}, # aten::nll_loss2d_forward |
| aten.ormqr.default: {f32, f64}, # aten::ormqr |
| aten.ormqr.out: {f32, f64}, # aten::ormqr.out |
| aten.rrelu_with_noise.default: {f16}, # aten::rrelu_with_noise |
| aten.tensordot.out: {f16}, # aten::tensordot.out |
| aten.unique_consecutive.default: {f16}, # aten::unique_consecutive |
| aten.unique_dim.default: {f16}, # aten::unique_dim |
| aten.upsample_nearest3d.vec: {f16}, # aten::upsample_nearest3d.vec |
| } |
| |
| meta_dispatch_device_skips['cpu'] = { |
| aten._embedding_bag_forward_only.default: {bf16, f16, f32, f64}, |
| aten.native_batch_norm.default: {f32, f64}, |
| aten._native_batch_norm_legit.default: {f32, f64}, |
| aten._native_batch_norm_legit.no_stats: {f32, f64}, |
| } |
| |
| meta_dispatch_device_skips['cuda'] = { |
| aten._conj.default: {c32, f16}, # file issue |
| aten._linalg_svd.default: {c64, c128}, # aten::linalg_eigvalsh.out |
| aten.cudnn_batch_norm.default: {f32, f64}, |
| aten.log_softmax.int : {c32, c64}, |
| aten.softmax.int : {c32, c64}, |
| aten.softmax.int : {c32, c64}, |
| |
| # ROCm stuff; technically this should be expected failure but it's |
| # not worth it; these should get unified anyway |
| aten.miopen_batch_norm.default: {f32}, |
| } |
| |
| def get_strided_args(args): |
| |
| def get_strided_variants(t, include_storage_offset=False): |
| variants = [] |
| |
| # contiguous |
| variants.append(t) |
| |
| # transposed |
| if t.ndim > 1: |
| perm = list(reversed(range(t.ndim))) |
| transposed = torch.empty( |
| t.shape[::-1], device=t.device, dtype=t.dtype, requires_grad=t.requires_grad |
| ).permute(perm).copy_(t) |
| variants.append(transposed) |
| |
| # nondense |
| if t.ndim > 0: |
| nondense = torch.repeat_interleave(t, 2, dim=-1)[..., ::2] |
| variants.append(nondense) |
| |
| # channel_last |
| if t.ndim == 4: |
| variants.append(t.contiguous(memory_format=torch.channels_last)) |
| |
| # channel_last_3d |
| if t.ndim == 5: |
| variants.append(t.contiguous(memory_format=torch.channels_last_3d)) |
| |
| # storage_offset |
| if include_storage_offset: |
| buffer = torch.empty(t.numel() + 1, device=t.device, dtype=t.dtype, requires_grad=t.requires_grad) |
| buffer = buffer.as_strided(t.shape, t.stride(), storage_offset=1) |
| buffer.copy_(t) |
| variants.append(buffer) |
| |
| return variants |
| |
| strided_args = [] |
| for arg in args: |
| if isinstance(arg, torch.Tensor) and not arg.is_sparse_csr and arg.is_contiguous(): |
| strided_arg_variants = get_strided_variants(arg) |
| else: |
| strided_arg_variants = [arg] |
| strided_args.append(strided_arg_variants) |
| |
| yield from itertools.product(*strided_args) |
| |
| class MetaCrossRefDispatchMode(torch.utils._python_dispatch.TorchDispatchMode): |
| test_case: TestCase |
| device: torch.device |
| dtype: torch.dtype |
| |
| def __init__(self, test_case, *, device, dtype, symbolic_meta: bool): |
| self.test_case = test_case |
| # save TLS |
| self.precision = test_case.precision |
| self.rel_tol = test_case.rel_tol |
| self.device_type = torch.device(device).type |
| self.dtype = dtype |
| self.symbolic_meta = symbolic_meta |
| |
| def __torch_dispatch__(self, func, types, args=(), kwargs=None): |
| kwargs = kwargs or {} |
| |
| self.test_case.precision = self.precision |
| self.test_case.rel_tol = self.rel_tol |
| |
| if self.dtype in meta_dispatch_skips.get(func, set()): |
| test_expect = TestExpect.SKIP |
| elif self.dtype in meta_dispatch_device_skips[self.device_type].get(func, set()): |
| test_expect = TestExpect.SKIP |
| elif self.dtype in meta_dispatch_expected_failures.get(func, set()): |
| test_expect = TestExpect.XFAILURE |
| elif self.dtype in meta_dispatch_device_expected_failures[self.device_type].get(func, set()): |
| test_expect = TestExpect.XFAILURE |
| else: |
| test_expect = TestExpect.SUCCESS |
| |
| return run_meta_crossref( |
| self.test_case, |
| test_expect, |
| func, |
| args, |
| kwargs, |
| dtype=self.dtype, |
| device_type=self.device_type, |
| run_symbolic_meta=self.symbolic_meta, |
| ) |
| |
| # NB: we're running these tests only on CUDA because there are some |
| # inconsistencies between CUDA and CPU, and running on CUDA makes it easier |
| # to ignore the CPU case when inconsistencies arise. Ideally we deal |
| # with the inconsistencies but this takes time. |
| class TestMeta(TestCase): |
| # Copies inputs to inplace operations to avoid inplace modifications |
| # to leaves requiring gradient |
| def _get_safe_inplace(self, inplace_variant): |
| @wraps(inplace_variant) |
| def _fn(t, *args, **kwargs): |
| return inplace_variant(t.clone(), *args, **kwargs) |
| |
| return _fn |
| |
| @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN") |
| @skipIfCrossRef |
| @suppress_warnings |
| @ops(op_db) |
| def test_meta_outplace(self, device, dtype, op): |
| # run the OpInfo sample inputs, cross-referencing them with the |
| # meta implementation and check the results are the same. All |
| # the heavy lifting happens in MetaCrossRefFunctionMode |
| func = op.get_op() |
| samples = op.sample_inputs(device, dtype, requires_grad=False) |
| for sample_input in samples: |
| args = [sample_input.input] + list(sample_input.args) |
| kwargs = sample_input.kwargs |
| with MetaCrossRefFunctionMode(self, dtype=dtype, device=device, inplace=False): |
| expected = func(*args, **kwargs) |
| if isinstance(expected, torch.Tensor) and op.supports_out: |
| func(*args, **kwargs, out=expected) |
| |
| # Special test for functions taking "device" kwarg |
| # The crossref tests that replacing the device with "meta" works |
| # This part makes sure that *_like functions work well with a "meta" |
| # Tensor and their original device argument. |
| if "device" in kwargs and "_like" in op.name: |
| with torch.random.fork_rng(): |
| torch.manual_seed(123) |
| ref = func(*args, **kwargs) |
| |
| # *_like functions take a Tensor as first argument |
| assert isinstance(args[0], torch.Tensor) |
| with torch.random.fork_rng(): |
| torch.manual_seed(123) |
| args[0] = args[0].to(device="meta") |
| meta = func(*args, **kwargs) |
| |
| # empty_like is not deterministic |
| if op.name != "empty_like": |
| self.assertEqual(ref, meta) |
| |
| |
| |
| @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN") |
| @skipIfCrossRef |
| @suppress_warnings |
| @ops(op_db) |
| def test_meta_inplace(self, device, dtype, op): |
| func = op.get_inplace() |
| if not func: |
| self.skipTest("No inplace variable for this op") |
| if func in meta_inplace_skips: |
| self.skipTest("Skipped") |
| func = self._get_safe_inplace(func) |
| samples = op.sample_inputs(device, dtype, requires_grad=False) |
| for sample_input in samples: |
| if sample_input.broadcasts_input: |
| continue |
| args = [sample_input.input] + list(sample_input.args) |
| kwargs = sample_input.kwargs |
| with MetaCrossRefFunctionMode(self, dtype=dtype, device=device, inplace=True): |
| expected = func(*args, **kwargs) |
| |
| def _run_dispatch_meta_test(self, device, dtype, op, symbolic_meta, inplace, all_stride_variants=False): |
| if inplace: |
| func = op.get_inplace() |
| if not func: |
| self.skipTest("No inplace variable for this op") |
| else: |
| func = op.get_op() |
| |
| if func in meta_dispatch_early_skips: |
| self.skipTest("Function is in dispatch early skips") |
| |
| if inplace: |
| func = self._get_safe_inplace(func) |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=False) |
| for sample_input in samples: |
| if inplace and sample_input.broadcasts_input: |
| continue |
| |
| sample_args = [sample_input.input] + list(sample_input.args) |
| kwargs = sample_input.kwargs |
| |
| if all_stride_variants and sum(isinstance(arg, torch.Tensor) for arg in sample_args) <= 5: |
| # test inputs <= 5 tensors to avoid combinatorial explosion |
| strided_args = get_strided_args(sample_args) |
| else: |
| strided_args = [sample_args] |
| |
| for args in strided_args: |
| with MetaCrossRefDispatchMode.push(self, dtype=dtype, device=device, symbolic_meta=symbolic_meta): |
| expected = func(*args, **kwargs) |
| |
| if not inplace and isinstance(expected, torch.Tensor) and op.supports_out: |
| func(*args, **kwargs, out=expected) |
| |
| |
| @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN") |
| @skipIfCrossRef |
| @suppress_warnings |
| @ops(op_db) |
| def test_dispatch_meta_outplace(self, device, dtype, op): |
| self._run_dispatch_meta_test(device, dtype, op, symbolic_meta=False, inplace=False) |
| |
| |
| @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN") |
| @skipIfCrossRef |
| @suppress_warnings |
| @ops(op_db) |
| def test_dispatch_meta_inplace(self, device, dtype, op): |
| self._run_dispatch_meta_test(device, dtype, op, symbolic_meta=False, inplace=True) |
| |
| @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN") |
| @skipIfCrossRef |
| @suppress_warnings |
| @ops(op_db) |
| def test_dispatch_symbolic_meta_outplace(self, device, dtype, op): |
| self._run_dispatch_meta_test(device, dtype, op, symbolic_meta=True, inplace=False) |
| |
| |
| @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN") |
| @skipIfCrossRef |
| @suppress_warnings |
| @ops(op_db) |
| def test_dispatch_symbolic_meta_inplace(self, device, dtype, op): |
| self._run_dispatch_meta_test(device, dtype, op, symbolic_meta=True, inplace=True) |
| |
| @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN") |
| @skipIfCrossRef |
| @suppress_warnings |
| # only test one dtype, as output stride behavior is the same for all dtypes |
| @ops(op_db, dtypes=OpDTypes.any_common_cpu_cuda_one) |
| # Only test on CUDA, as CUDA kernel's stride is the reference |
| @onlyCUDA |
| def test_dispatch_symbolic_meta_outplace_all_strides(self, device, dtype, op): |
| self._run_dispatch_meta_test(device, dtype, op, symbolic_meta=True, inplace=False, all_stride_variants=True) |
| |
| @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN") |
| @skipIfCrossRef |
| @suppress_warnings |
| # only test one dtype, as output stride behavior is the same for all dtypes |
| @ops(op_db, dtypes=OpDTypes.any_common_cpu_cuda_one) |
| # Only test on CUDA, as CUDA kernel's stride is the reference |
| @onlyCUDA |
| def test_dispatch_symbolic_meta_inplace_all_strides(self, device, dtype, op): |
| self._run_dispatch_meta_test(device, dtype, op, symbolic_meta=True, inplace=True, all_stride_variants=True) |
| |
| |
| def test_empty_quantized(self): |
| r = torch.empty(2 ** 52, device='meta', dtype=torch.qint8) |
| self.assertEqual(r.device.type, 'meta') |
| |
| def test_nan_to_num(self): |
| t = torch.tensor([float('nan'), float('inf'), -float('inf'), 3.14], device='meta') |
| r = t.nan_to_num() |
| self.assertEqual(r.device.type, 'meta') |
| |
| @onlyCPU |
| def test_meta_autograd_no_error(self): |
| lib = torch.library.Library("meta_test", "DEF") |
| impl_cpu = torch.library.Library("meta_test", "IMPL", "CPU") |
| impl_meta = torch.library.Library("meta_test", "IMPL", "Meta") |
| |
| def foo_impl(x): |
| return x + 1 |
| |
| lib.define("foo(Tensor a) -> Tensor") |
| impl_meta.impl("foo", foo_impl) |
| impl_cpu.impl("foo", foo_impl) |
| |
| a = torch.ones(2, device='meta') |
| # The point of the test is that this should not error: |
| # We have a fallthrough kernel registered to the AutogradMeta |
| # key for custom ops, so it's fine that `foo()` doesn't have |
| # an autograd kernel. |
| b = torch.ops.meta_test.foo.default(a) |
| del impl_meta |
| del impl_cpu |
| del lib |
| |
| def test_huber_loss_backward(self): |
| inps = [torch.rand(2**52, device='meta') for _ in range(3)] |
| r = torch.ops.aten.huber_loss_backward(*inps, 0, 1.0) |
| self.assertEqual(r.device.type, 'meta') |
| self.assertEqual(r.shape, inps[0].shape) |
| |
| def test_fill__alias_relationship(self): |
| inps = torch.rand(2**52, device='meta') |
| r = torch.ops.aten.fill_(inps, 1.0) |
| # aten.fill_ returns an aliase |
| self.assertEqual(id(inps), id(r)) |
| |
| # aten.fill returns a new tensor |
| r2 = torch.ops.aten.fill(inps, 1.0) |
| self.assertNotEqual(id(inps), id(r2)) |
| |
| def test_meta__fused_moving_avg_obs_fq_helper(self, device): |
| from torch.ao.quantization import FusedMovingAvgObsFakeQuantize |
| to_meta = MetaConverter() |
| |
| x = torch.randn(5, 5, device=device) |
| running_min_op = torch.tensor(float("inf"), device=device) |
| running_max_op = torch.tensor(float("-inf"), device=device) |
| avg_const = 0.01 |
| scale = torch.tensor([1.0], device=device) |
| zero_point = torch.tensor([0], dtype=torch.int, device=device) |
| |
| mod = FusedMovingAvgObsFakeQuantize() |
| torch.ao.quantization.enable_fake_quant(mod) |
| torch.ao.quantization.enable_observer(mod) |
| mod.to(device) |
| |
| meta_x = to_meta(x) |
| |
| args = [ |
| x, |
| mod.observer_enabled, |
| mod.fake_quant_enabled, |
| running_min_op, |
| running_max_op, |
| scale, |
| zero_point, |
| avg_const, |
| 0, |
| 255, |
| 0, |
| ] |
| |
| meta_args = args.copy() |
| meta_args[0] = meta_x |
| |
| kwargss = [ |
| {}, |
| {"per_row_fake_quant": False, "symmetric_quant": False}, |
| {"per_row_fake_quant": False, "symmetric_quant": True}, |
| ] |
| |
| for kwargs in kwargss: |
| ref_out = aten._fused_moving_avg_obs_fq_helper.default(*args, **kwargs) |
| meta_out = aten._fused_moving_avg_obs_fq_helper.default(*meta_args, **kwargs) |
| |
| self.assertEqual(ref_out[0].size(), meta_out[0].size()) |
| self.assertEqual(ref_out[0].stride(), meta_out[0].stride()) |
| self.assertEqual(ref_out[1].size(), meta_out[1].size()) |
| self.assertEqual(ref_out[1].stride(), meta_out[1].stride()) |
| |
| def test_cdist_forward(self, device): |
| to_meta = MetaConverter() |
| x1 = torch.rand([3, 2], device=device) |
| x2 = torch.rand([2, 2], device=device) |
| p = 2.0 |
| for compute_mode in (None, 1, 2): |
| ref = aten._cdist_forward.default(x1, x2, p, compute_mode) |
| res = aten._cdist_forward.default(to_meta(x1), to_meta(x2), p, compute_mode) |
| self.assertEqual(res.device.type, 'meta') |
| self.assertEqual(ref.shape, res.shape) |
| |
| # opinfo test is using aten.fill_, it's not testing aten.fill |
| @onlyCUDA |
| def test_fill_stride(self): |
| to_meta = MetaConverter() |
| sample_args = [torch.rand(2, 2, 2, 2), 1.0] |
| |
| for args in get_strided_args(sample_args): |
| meta_args = to_meta(args) |
| ref_out = torch.ops.aten.fill(*args) |
| meta_out = torch.ops.aten.fill(*meta_args) |
| self.assertEqual(ref_out.size(), meta_out.size()) |
| self.assertEqual(ref_out.stride(), meta_out.stride()) |
| |
| |
| def test_map_location_deserialize(self): |
| import io |
| |
| t = torch.rand(10) |
| b = io.BytesIO() |
| |
| torch.save(t, b) |
| b.seek(0) |
| r = torch.load(b, map_location=torch.device("meta")) |
| self.assertEqual(r.device.type, 'meta') |
| self.assertEqual(r.shape, t.shape) |
| self.assertEqual(r.dtype, t.dtype) |
| self.assertEqual(r.storage().data_ptr(), 0) |
| |
| instantiate_device_type_tests(TestMeta, globals()) |
| |
| def print_op_str_if_not_supported(op_str): |
| op = OperatorName.parse(op_str) |
| packet = getattr(torch.ops.aten, str(op.name)) |
| overload = getattr(packet, op.overload_name if op.overload_name else "default") |
| if any(overload in d for d in [meta_dispatch_skips, meta_dispatch_device_skips['cuda']]): |
| print(f"{overload} # SKIP") |
| if any(overload in d for d in [meta_dispatch_expected_failures, meta_dispatch_device_expected_failures['cuda']]): |
| print(overload) |
| |
| |
| if __name__ == "__main__": |
| COMPARE_XLA = os.getenv('PYTORCH_COMPARE_XLA', None) |
| if COMPARE_XLA is not None: |
| with open(COMPARE_XLA, "r") as f: |
| d = yaml.load(f, Loader=YamlLoader) |
| ops = d.get("full_codegen", []) + d.get("supported", []) + d.get("autograd", []) |
| for op_str in ops: |
| print_op_str_if_not_supported(op_str) |
| sys.exit(0) |
| |
| COMPARE_TEXT = os.getenv('PYTORCH_COMPARE_TEXT', None) |
| if COMPARE_TEXT is not None: |
| with open(COMPARE_TEXT, "r") as f: |
| for op_str in f: |
| print_op_str_if_not_supported(op_str.strip()) |
| sys.exit(0) |
| |
| run_tests() |
