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Enable bfloat16 random kernels on Windows (#44918)
Summary: Fixes https://github.com/pytorch/pytorch/issues/33793 Pull Request resolved: https://github.com/pytorch/pytorch/pull/44918 Reviewed By: pbelevich Differential Revision: D23777548 Pulled By: ngimel fbshipit-source-id: 9cf13166d7deba17bc72e402b82ed0afe347cb9b
This commit is contained in:
Gao, Xiang
Facebook GitHub Bot
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3 changed files with 3 additions and 57 deletions
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@ -273,12 +273,6 @@ namespace cuda {
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template<typename RNG>
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void random_from_to_kernel(TensorIterator& iter, uint64_t range, int64_t base, RNG gen) {
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#ifdef _WIN32
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// TODO: https://github.com/pytorch/pytorch/issues/33793
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if (iter.dtype() == ScalarType::BFloat16) {
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TORCH_CHECK(false, "random_() is not supported for bfloat16 CUDA tensors on Windows. Please see https://github.com/pytorch/pytorch/issues/33793");
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}
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#endif
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AT_DISPATCH_ALL_TYPES_AND3(at::ScalarType::Bool, at::ScalarType::Half, at::ScalarType::BFloat16, iter.dtype(), "random_from_to_kernel_cuda", [&] {
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if ((
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std::is_same<scalar_t, int64_t>::value ||
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@ -319,12 +313,6 @@ void random_from_to_kernel(TensorIterator& iter, uint64_t range, int64_t base, R
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// to(exclusive) = None (= std::numeric_limits<int64_t>::max() + 1)
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template<typename RNG>
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void random_full_64_bits_range_kernel(TensorIterator& iter, RNG gen) {
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#ifdef _WIN32
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// TODO: https://github.com/pytorch/pytorch/issues/33793
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if (iter.dtype() == ScalarType::BFloat16) {
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TORCH_CHECK(false, "random_() is not supported for bfloat16 CUDA tensors on Windows. Please see https://github.com/pytorch/pytorch/issues/33793");
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}
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#endif
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AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::BFloat16, iter.dtype(), "random_full_64_bits_range_kernel_cuda", [&] {
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if (std::is_same<scalar_t, int64_t>::value ||
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std::is_same<scalar_t, double>::value ||
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@ -361,12 +349,6 @@ struct RandomFromToKernel {
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template<typename RNG>
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void random_kernel(TensorIterator& iter, RNG gen) {
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#ifdef _WIN32
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// TODO: https://github.com/pytorch/pytorch/issues/33793
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if (iter.dtype() == ScalarType::BFloat16) {
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TORCH_CHECK(false, "random_() is not supported for bfloat16 CUDA tensors on Windows. Please see https://github.com/pytorch/pytorch/issues/33793");
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}
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#endif
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AT_DISPATCH_ALL_TYPES_AND3(at::ScalarType::Half, at::ScalarType::BFloat16, at::ScalarType::Bool, iter.dtype(), "random_kernel_cuda", [&] {
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if (std::is_same<scalar_t, double>::value || std::is_same<scalar_t, int64_t>::value) {
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auto random_func = [] __device__ (uint64_t rand) {
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@ -462,12 +444,6 @@ struct NormalKernel {
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template<typename RNG>
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void uniform_kernel(TensorIterator& iter, double from_, double to_, RNG gen) {
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#ifdef _WIN32
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// TODO: https://github.com/pytorch/pytorch/issues/33793
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if (iter.dtype() == ScalarType::BFloat16) {
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TORCH_CHECK(false, "uniform_() is not supported for bfloat16 CUDA tensors on Windows. Please see https://github.com/pytorch/pytorch/issues/33793");
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}
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#endif
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AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, iter.dtype(), "uniform_kernel_cuda", [&] {
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auto from = static_cast<scalar_t>(from_);
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auto to = static_cast<scalar_t>(to_);
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@ -7,7 +7,7 @@ import torch
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from torch.testing._internal.common_utils import \
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(TestCase, run_tests, do_test_empty_full, TEST_NUMPY, suppress_warnings,
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IS_WINDOWS, torch_to_numpy_dtype_dict, slowTest)
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torch_to_numpy_dtype_dict, slowTest)
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from torch.testing._internal.common_device_type import \
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(instantiate_device_type_tests, deviceCountAtLeast, onlyOnCPUAndCUDA,
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onlyCPU, skipCUDAIfNotRocm, largeCUDATensorTest, precisionOverride, dtypes,
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@ -822,10 +822,7 @@ class TestTensorCreation(TestCase):
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self.assertEqual(shape, torch.empty_like(torch.zeros(shape, device=device, dtype=dt)).shape)
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self.assertEqual(shape, torch.empty_strided(shape, (0,) * len(shape), device=device, dtype=dt).shape)
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if dt == torch.bfloat16 and device.startswith('cuda') and IS_WINDOWS:
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# TODO: https://github.com/pytorch/pytorch/issues/33793
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self.assertRaises(RuntimeError, lambda: torch.randint(6, shape, device=device, dtype=dt).shape)
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elif dt == torch.bool:
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if dt == torch.bool:
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self.assertEqual(shape, torch.randint(2, shape, device=device, dtype=dt).shape)
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self.assertEqual(shape, torch.randint_like(torch.zeros(shape, device=device, dtype=dt), 2).shape)
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elif dt.is_complex:
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@ -10897,10 +10897,6 @@ class TestTorchDeviceType(TestCase):
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@dtypes(torch.float, torch.double, torch.half)
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@dtypesIfCUDA(torch.float, torch.double, torch.half, torch.bfloat16)
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def test_uniform_from_to(self, device, dtype):
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# TODO: https://github.com/pytorch/pytorch/issues/33793
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if IS_WINDOWS and device.startswith('cuda') and dtype == torch.bfloat16:
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raise unittest.SkipTest("Crashes with CUDA error: unspecified launch failure")
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size = 2000
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alpha = 0.1
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@ -11119,10 +11115,6 @@ class TestTorchDeviceType(TestCase):
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@skipIfNoSciPy
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@dtypes(*torch.testing.get_all_fp_dtypes())
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def test_uniform_kstest(self, device, dtype):
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# TODO: https://github.com/pytorch/pytorch/issues/33793
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if IS_WINDOWS and device.startswith('cuda') and dtype == torch.bfloat16:
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raise unittest.SkipTest("Crashes with CUDA error: unspecified launch failure")
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from scipy import stats
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size = 1000
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for from_ in [-42, 0, 4.2]:
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@ -12244,10 +12236,7 @@ class TestTorchDeviceType(TestCase):
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def test_unfold_all_devices_and_dtypes(self, device):
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for dt in torch.testing.get_all_dtypes():
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if dt == torch.bfloat16 and device.startswith('cuda') and IS_WINDOWS:
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# TODO: https://github.com/pytorch/pytorch/issues/33793
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self.assertRaises(RuntimeError, lambda: torch.randint(5, (0, 1, 3, 0), dtype=dt, device=device))
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elif dt == torch.bool:
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if dt == torch.bool:
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x = torch.empty((0, 1, 3, 0), dtype=dt, device=device)
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self.assertEqual((0, 1, 1, 0, 3), x.unfold(2, 3, 2).shape)
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else:
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@ -17629,10 +17618,6 @@ else:
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@dtypes(*(torch.testing.get_all_int_dtypes() + torch.testing.get_all_fp_dtypes()))
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def test_random_full_range(self, device, dtype):
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# TODO: https://github.com/pytorch/pytorch/issues/33793
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if IS_WINDOWS and device.startswith('cuda') and dtype == torch.bfloat16:
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raise unittest.SkipTest("Crashes with CUDA error: unspecified launch failure")
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size = 2000
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alpha = 0.1
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@ -17667,10 +17652,6 @@ else:
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@dtypes(*(torch.testing.get_all_int_dtypes() + torch.testing.get_all_fp_dtypes()))
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def test_random_from_to(self, device, dtype):
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# TODO: https://github.com/pytorch/pytorch/issues/33793
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if IS_WINDOWS and device.startswith('cuda') and dtype == torch.bfloat16:
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raise unittest.SkipTest("Crashes with CUDA error: unspecified launch failure")
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size = 2000
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alpha = 0.1
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@ -17760,10 +17741,6 @@ else:
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@dtypes(*(torch.testing.get_all_int_dtypes() + torch.testing.get_all_fp_dtypes()))
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def test_random_to(self, device, dtype):
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# TODO: https://github.com/pytorch/pytorch/issues/33793
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if IS_WINDOWS and device.startswith('cuda') and dtype == torch.bfloat16:
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raise unittest.SkipTest("Crashes with CUDA error: unspecified launch failure")
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size = 2000
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alpha = 0.1
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@ -17822,10 +17799,6 @@ else:
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@dtypes(*(torch.testing.get_all_int_dtypes() + torch.testing.get_all_fp_dtypes()))
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def test_random_default(self, device, dtype):
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# TODO: https://github.com/pytorch/pytorch/issues/33793
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if IS_WINDOWS and device.startswith('cuda') and dtype == torch.bfloat16:
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raise unittest.SkipTest("Crashes with CUDA error: unspecified launch failure")
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size = 2000
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alpha = 0.1
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