| # Owner(s): ["module: mkldnn"] |
| |
| import copy |
| import itertools |
| import functools |
| import unittest |
| from contextlib import nullcontext |
| |
| try: |
| import torchvision |
| HAS_TORCHVISION = True |
| except ImportError: |
| HAS_TORCHVISION = False |
| |
| skipIfNoTorchVision = unittest.skipIf(not HAS_TORCHVISION, "no torchvision") |
| |
| import torch |
| import torch.nn.functional as F |
| import torch.jit |
| import torch.backends.mkldnn |
| from torch.utils import mkldnn as mkldnn_utils |
| from torch.testing._internal.common_utils import TestCase, \ |
| run_tests, TemporaryFileName, gradcheck, gradgradcheck, IS_WINDOWS, \ |
| skipIfTorchDynamo, xfailIfTorchDynamo |
| from torch.testing._internal.common_device_type import ( |
| instantiate_device_type_tests, |
| dtypes, |
| ) |
| |
| # batched grad doesn't support mkldnn |
| gradcheck = functools.partial(gradcheck, check_batched_grad=False) |
| gradgradcheck = functools.partial(gradgradcheck, check_batched_grad=False) |
| |
| |
| types = [torch.float, torch.bfloat16, torch.half] |
| |
| # Comment the line below to find out the CI machines having MKL-DNN build disabled |
| @unittest.skipIf(not torch.backends.mkldnn.is_available(), "MKL-DNN build is disabled") |
| class TestMkldnn(TestCase): |
| def test_conversion(self): |
| for cpu_tensor in [torch.randn((1, 2, 3, 4), |
| dtype=torch.float, device=torch.device('cpu')), |
| torch.randn((1, 2, 3, 4, 5), |
| dtype=torch.float, device=torch.device('cpu'))[:, :, :, :, 1]]: |
| cpu_tensor.requires_grad_() |
| convert_dtypes = {torch.half: [torch.half, torch.float], |
| torch.bfloat16: [torch.bfloat16, torch.float], |
| torch.float: [torch.bfloat16, torch.half]} |
| # float/bfloat16/half cpu tensor to mkldnn tensortensor. |
| for dtype1 in types: |
| mkldnn_tensor = cpu_tensor.to_mkldnn(dtype1) |
| self.assertEqual(mkldnn_tensor.dtype, dtype1) |
| cpu_tensor_1 = mkldnn_tensor.to_dense() |
| # not given dtype for to_dense, mkldnn tensor has same dtype with cpu tensor |
| self.assertEqual(mkldnn_tensor.dtype, cpu_tensor_1.dtype) |
| # mkldnn float/bfloat tensor to cpu float or bfloat tensor |
| for dtype2 in convert_dtypes[dtype1]: |
| cpu_tensor_2 = mkldnn_tensor.to_dense(dtype2) |
| self.assertEqual(cpu_tensor_2.dtype, dtype2) |
| atol = 1e-5 if dtype1 == torch.float and dtype2 == torch.float else 1e-2 |
| self.assertEqual(cpu_tensor, cpu_tensor_2.float(), atol=atol, rtol=0) |
| |
| self.assertEqual(mkldnn_tensor.device, torch.device('cpu')) |
| self.assertEqual(mkldnn_tensor.size(), torch.Size([1, 2, 3, 4])) |
| self.assertEqual(mkldnn_tensor.numel(), cpu_tensor.numel()) |
| if dtype1 == torch.float: |
| self.assertEqual(mkldnn_tensor.element_size(), cpu_tensor.element_size()) |
| else: |
| self.assertEqual(mkldnn_tensor.element_size(), cpu_tensor.element_size() / 2) |
| self.assertRaisesRegex(RuntimeError, |
| "Cannot access data pointer of Tensor that doesn't have storage", |
| lambda: mkldnn_tensor.data_ptr() != 0) |
| |
| # bfloat cpu tensor to mkldnn float tensor or bfloat tensor. |
| for orig_dtype in [torch.half, torch.bfloat16]: |
| cpu_tensor_lower = cpu_tensor.to(dtype=orig_dtype) |
| for dtype1 in convert_dtypes[orig_dtype]: |
| mkldnn_tensor = cpu_tensor_lower.to_mkldnn(dtype1) |
| self.assertEqual(mkldnn_tensor.dtype, dtype1) |
| cpu_tensor_1 = mkldnn_tensor.to_dense() |
| # not given dtype for to_dense, mkldnn tensor has same dtype with cpu tensor |
| self.assertEqual(mkldnn_tensor.dtype, cpu_tensor_1.dtype) |
| # mkldnn float/bfloat/half tensor to cpu float/bfloat/half tensor |
| for dtype2 in convert_dtypes[cpu_tensor_lower.dtype]: |
| cpu_tensor_2 = mkldnn_tensor.to_dense(dtype2) |
| self.assertEqual(cpu_tensor_2.dtype, dtype2) |
| self.assertEqual(cpu_tensor_lower, |
| cpu_tensor_2.to(dtype=cpu_tensor_lower.dtype), atol=1e-5, rtol=0) |
| |
| self.assertEqual(mkldnn_tensor.device, torch.device('cpu')) |
| self.assertEqual(mkldnn_tensor.size(), torch.Size([1, 2, 3, 4])) |
| self.assertEqual(mkldnn_tensor.numel(), cpu_tensor.numel()) |
| if dtype1 in [torch.bfloat16, torch.half]: |
| self.assertEqual(mkldnn_tensor.element_size(), cpu_tensor_lower.element_size()) |
| else: |
| self.assertEqual(mkldnn_tensor.element_size(), cpu_tensor_lower.element_size() * 2) |
| self.assertRaisesRegex(RuntimeError, |
| "Cannot access data pointer of Tensor that doesn't have storage", |
| lambda: mkldnn_tensor.data_ptr() != 0) |
| |
| def test_conversion_byte_char(self): |
| int8_types = [torch.int8, torch.uint8] |
| for int8_type in int8_types: |
| low = -100 if int8_type is torch.int8 else 0 |
| high = 100 |
| for cpu_tensor in [torch.randint( |
| low=low, |
| high=high, |
| size=(1, 2, 3, 4), |
| dtype=torch.int64, |
| device=torch.device('cpu')), |
| torch.randint( |
| low=low, |
| high=high, |
| size=(1, 2, 3, 4, 5), |
| dtype=torch.int64, |
| device=torch.device('cpu'))[:, :, :, :, :]]: |
| |
| cpu_tensor = cpu_tensor.to(dtype=int8_type) |
| mkldnn_tensor = cpu_tensor.to_mkldnn(int8_type) |
| self.assertEqual(mkldnn_tensor.dtype, int8_type) |
| cpu_tensor_1 = mkldnn_tensor.to_dense() |
| self.assertEqual(mkldnn_tensor.dtype, cpu_tensor_1.dtype) |
| self.assertEqual(cpu_tensor, cpu_tensor_1) |
| self.assertEqual(mkldnn_tensor.device, torch.device('cpu')) |
| self.assertEqual(mkldnn_tensor.size(), cpu_tensor.size()) |
| self.assertEqual(mkldnn_tensor.numel(), cpu_tensor.numel()) |
| self.assertEqual(mkldnn_tensor.element_size(), cpu_tensor.element_size()) |
| self.assertRaisesRegex(RuntimeError, |
| "Cannot access data pointer of Tensor that doesn't have storage", |
| lambda: mkldnn_tensor.data_ptr() != 0) |
| |
| def test_copy(self): |
| x = torch.randn(4, 5, dtype=torch.float32) |
| mkldnn_x = x.to_mkldnn() |
| mkldnn_y = torch.randn(4, 5, dtype=torch.float32).to_mkldnn() |
| mkldnn_z = torch.randn(4, 10, dtype=torch.float32).to_mkldnn() |
| mkldnn_y.copy_(mkldnn_x) |
| self.assertEqual(x, mkldnn_y.to_dense()) |
| self.assertRaisesRegex(RuntimeError, |
| "copy_mkldnn_: only support same size tensor.", |
| lambda: mkldnn_z.copy_(mkldnn_x)) |
| self.assertRaisesRegex(RuntimeError, |
| "copy_mkldnn_: between mkldnn layout and dense Tensors is not implemented! " |
| "Found self type = torch.FloatTensor and src type = Mkldnntorch.FloatTensor", |
| lambda: x.copy_(mkldnn_x)) |
| self.assertRaisesRegex(RuntimeError, |
| "copy_mkldnn_: between mkldnn layout and dense Tensors is not implemented! " |
| "Found self type = Mkldnntorch.FloatTensor and src type = torch.FloatTensor", |
| lambda: mkldnn_x.copy_(x)) |
| |
| def test_unsupported(self): |
| # unsupported types and unsupported types with gpu |
| for dtype in [torch.double, torch.uint8, torch.int8, |
| torch.short, torch.int, torch.long]: |
| with self.assertRaises(RuntimeError) as context: |
| torch.randn(1, 2, 3, 4, dtype=dtype, device=torch.device('cpu')).to_mkldnn() |
| if torch.cuda.is_available(): |
| with self.assertRaises(RuntimeError) as context: |
| torch.randn(1, 2, 3, 4, dtype=dtype, device=torch.device('cuda')).to_mkldnn() |
| # supported type with gpu |
| if torch.cuda.is_available(): |
| with self.assertRaises(RuntimeError) as context: |
| torch.randn(1, 2, 3, 4, dtype=torch.float, device=torch.device('cuda')).to_mkldnn() |
| # some factory functions |
| for creator in [torch.ones, torch.randn, torch.rand]: |
| with self.assertRaises(RuntimeError) as context: |
| creator(1, 2, 3, 4, dtype=torch.float, device=torch.device('cpu'), layout=torch._mkldnn) |
| |
| def test_mkldnn_conv_shapecheck(self): |
| input = torch.full((1, 1, 1, 24,), 1, dtype=torch.float32) |
| w1 = torch.full((1, 1, 1, 24,), 1, dtype=torch.float32) |
| b1 = torch.full((1,), 1, dtype=torch.float32) |
| w2 = torch.full((1, 1, 2, 24,), 1, dtype=torch.float32) |
| b2 = torch.full((2,), 1, dtype=torch.float32) |
| options = zip([-1, 0, 0, 0, 0, 0, 0], # padding |
| [1, 0, 1, 1, 1, 1, 1], # stride |
| [1, 1, 0, 1, 1, 1, 1], # dilation |
| [1, 1, 1, 0, 2, 1, 1], # groups |
| [w1, w1, w1, w1, w1, w1, w2], # weight |
| [b1, b1, b1, b1, b1, b2, b1]) # bias |
| for pad, st, dil, gr, w, b in options: |
| with self.assertRaises(RuntimeError) as _: |
| torch.mkldnn_convolution(input, w, b, [pad] * 2, [st] * 2, [dil] * 2, gr) |
| |
| def test_autograd_to_mkldnn(self): |
| # MKLDNN only supports float32 |
| root = torch.randn(4, 5, dtype=torch.float32, requires_grad=True) |
| |
| def func(root): |
| return root.to_mkldnn().to_dense() |
| |
| # because MKLDNN only supports float32, we need to lessen the precision. |
| # these numbers are just empirical results that seem to work. |
| self.assertWarnsRegex(UserWarning, |
| 'double precision floating point', |
| lambda: gradcheck(func, [root], atol=4e-2, rtol=1e-2)) |
| self.assertWarnsRegex(UserWarning, |
| 'double precision floating point', |
| lambda: gradgradcheck(func, [root], atol=4e-2, rtol=1e-2)) |
| |
| def test_autograd_from_mkldnn(self): |
| # MKLDNN only supports float32 |
| root = torch.randn(4, 5, dtype=torch.float32).to_mkldnn().requires_grad_() |
| |
| def func(root): |
| return root.to_dense() |
| |
| # because MKLDNN only supports float32, we need to lessen the precision. |
| # these numbers are just empirical results that seem to work. |
| self.assertWarnsRegex(UserWarning, |
| 'double precision floating point', |
| lambda: gradcheck(func, [root], atol=4e-2, rtol=1e-2)) |
| |
| def test_detach(self): |
| root = torch.randn(4, 5, dtype=torch.float32).to_mkldnn().requires_grad_() |
| |
| detach = root.detach() |
| self.assertEqual((4, 5), detach.size()) |
| self.assertFalse(detach.requires_grad) |
| self.assertTrue(root.requires_grad) |
| |
| detach_ = root.detach_() |
| self.assertEqual((4, 5), detach_.size()) |
| self.assertFalse(detach_.requires_grad) |
| self.assertFalse(root.requires_grad) |
| |
| def test_repr(self): |
| self.assertTrue("layout=torch._mkldnn" in str(torch.randn((1, 2, 3, 4), |
| dtype=torch.float, device=torch.device('cpu')).to_mkldnn())) |
| |
| def _test_conv_base(self, dim): |
| conv_module = {1: torch.nn.Conv1d, 2: torch.nn.Conv2d, 3: torch.nn.Conv3d} |
| input_shapes = {1: (224,), 2: (224, 224), 3: (55, 55, 55)} |
| options = itertools.product([True, False], [True, False], [1, 2], [1, 4]) |
| for train, bias, dilation, groups in options: |
| N = torch.randint(3, 10, (1,)).item() |
| M = torch.randint(1, 3, (1,)).item() * groups |
| C = torch.randint(1, 3, (1,)).item() * groups |
| x_shape = (N, C) + input_shapes[dim] |
| x = torch.randn(x_shape, dtype=torch.float32) |
| conv = conv_module[dim](in_channels=C, |
| out_channels=M, |
| kernel_size=3, |
| stride=2, |
| padding=1, |
| dilation=dilation, |
| bias=bias, |
| groups=groups).float() |
| x1 = x.clone() |
| x2 = x.clone().to_mkldnn() |
| if not train: |
| mkldnn_conv = mkldnn_utils.to_mkldnn(copy.deepcopy(conv)) |
| elif train and dim != 1: |
| # TODO: enable conv1d training. |
| x1.requires_grad_() |
| x2.requires_grad_() |
| mkldnn_conv = copy.deepcopy(conv) |
| with torch.backends.mkldnn.flags(enabled=False): |
| y_aten = conv(x1) |
| if train and dim != 1: |
| loss1 = y_aten.sum() |
| loss1.backward() |
| if not train or (train and dim != 1): |
| y_mkldnn = mkldnn_conv(x2).to_dense() |
| self.assertEqual(y_aten, y_mkldnn) |
| if not train: |
| self._test_serialization(mkldnn_conv, (x.to_mkldnn(),)) |
| self._test_tracing(mkldnn_conv, (x.to_mkldnn(),)) |
| elif dim != 1: |
| loss2 = y_mkldnn.sum() |
| loss2.backward() |
| self.assertTrue(x2.grad.is_mkldnn) |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| self.assertEqual(conv.weight.grad, |
| mkldnn_conv.weight.grad, |
| atol=1e-3, |
| rtol=1e-3) |
| if bias: |
| self.assertEqual(conv.bias.grad, mkldnn_conv.bias.grad) |
| |
| def test_conv1d(self): |
| self._test_conv_base(dim=1) |
| |
| def test_conv2d(self): |
| self._test_conv_base(dim=2) |
| |
| def test_conv3d(self): |
| self._test_conv_base(dim=3) |
| |
| def _test_conv_deconv_lower_precision_base(self, dim, conv_module, dtype): |
| input_shapes = {1: (224,), 2: (224, 224), 3: (55, 55, 55)} |
| options = itertools.product([True, False], [1, 2], [1, 4]) |
| for bias, dilation, groups in options: |
| N = torch.randint(1, 3, (1,)).item() |
| M = torch.randint(1, 3, (1,)).item() * groups |
| C = torch.randint(1, 3, (1,)).item() * groups |
| x_shape = (N, C) + input_shapes[dim] |
| x = torch.randn(x_shape, dtype=torch.float32) |
| # TODO: remove this when group depthwise is supported: |
| if conv_module in [torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d, |
| torch.nn.ConvTranspose3d] and groups > 1 and C == groups: |
| continue |
| conv = conv_module(in_channels=C, |
| out_channels=M, |
| kernel_size=3, |
| stride=2, |
| padding=1, |
| dilation=dilation, |
| bias=bias, |
| groups=groups).float() |
| x_lower = x.to(dtype=dtype) |
| if (dtype == torch.bfloat16 and torch.ops.mkldnn._is_mkldnn_bf16_supported()) or \ |
| (dtype == torch.half and torch.ops.mkldnn._is_mkldnn_fp16_supported()): |
| mkldnn_conv = mkldnn_utils.to_mkldnn(copy.deepcopy(conv)) |
| mkldnn_conv_lower = mkldnn_utils.to_mkldnn(copy.deepcopy(conv), dtype) |
| y = mkldnn_conv(x.to_mkldnn()).to_dense() |
| y_lower = mkldnn_conv_lower(x_lower.to_mkldnn()).to_dense(torch.float32) |
| self.assertEqual(y, y_lower, atol=1e-1, rtol=1e-3) |
| else: |
| msg = { |
| torch.bfloat16: r"bf16 path needs the cpu support avx_ne_convert or avx512bw, avx512vl and avx512dq", |
| torch.half: r"fp16 path needs the cpu support avx_ne_convert or avx512_fp16", |
| } |
| with self.assertRaisesRegex(RuntimeError, msg[dtype]): |
| mkldnn_conv_lower = mkldnn_utils.to_mkldnn(copy.deepcopy(conv), dtype) |
| y_lower = mkldnn_conv_lower(x_lower.to_mkldnn()).to_dense(torch.float32) |
| # test thnn impl |
| conv_lower = copy.deepcopy(conv).to(dtype=dtype) |
| conv_ref = copy.deepcopy(conv_lower).float() |
| with torch.backends.mkldnn.flags(enabled=False): |
| x_ref = x_lower.clone().float().detach().requires_grad_() |
| x_lower.requires_grad_() |
| y = conv_ref(x_ref) |
| y_lower = conv_lower(x_lower).float() |
| self.assertEqual(y, y_lower, atol=5e-2, rtol=5e-3) |
| |
| @dtypes(torch.float16, torch.bfloat16) |
| def test_conv_deconv_1d_lower_precision(self, dtype): |
| self._test_conv_deconv_lower_precision_base(1, torch.nn.Conv1d, dtype=dtype) |
| self._test_conv_deconv_lower_precision_base(1, torch.nn.ConvTranspose1d, dtype=dtype) |
| |
| @dtypes(torch.float16, torch.bfloat16) |
| def test_conv_deconv_2d_lower_precision(self, dtype): |
| self._test_conv_deconv_lower_precision_base(2, torch.nn.Conv2d, dtype=dtype) |
| self._test_conv_deconv_lower_precision_base(2, torch.nn.ConvTranspose2d, dtype=dtype) |
| |
| @dtypes(torch.float16, torch.bfloat16) |
| def test_conv_deconv_3d_lower_precision(self, dtype): |
| self._test_conv_deconv_lower_precision_base(3, torch.nn.Conv3d, dtype=dtype) |
| self._test_conv_deconv_lower_precision_base(3, torch.nn.ConvTranspose3d, dtype=dtype) |
| |
| def _test_conv_deconv_nhwc_base(self, conv_module, weight_memory_format, dtype, prec=None): |
| input_shapes = {2: (55, 55), 3: (14, 14, 14)} |
| options = itertools.product([True, False], [True, False], [1, 2], [1, 4]) |
| if conv_module in [torch.nn.Conv2d, torch.nn.ConvTranspose2d]: |
| cl_format = torch.channels_last |
| input_shape = input_shapes[2] |
| elif conv_module in [torch.nn.Conv3d, torch.nn.ConvTranspose3d]: |
| cl_format = torch.channels_last_3d |
| input_shape = input_shapes[3] |
| |
| for train, bias, dilation, groups in options: |
| N = torch.randint(3, 10, (1,)).item() |
| M = torch.randint(1, 3, (1,)).item() * groups |
| C = torch.randint(1, 3, (1,)).item() * groups |
| x_shape = (N, C) + input_shape |
| x = torch.randn(x_shape, dtype=dtype) |
| |
| # conv1: mkldnn conv/deconv in contiguous memory format (nchw) |
| # conv2: mkldnn conv/deconv in channels last memory format (nhwc) |
| conv1 = conv_module(in_channels=C, |
| out_channels=M, |
| kernel_size=3, |
| stride=2, |
| padding=1, |
| dilation=dilation, |
| bias=bias, |
| groups=groups).to(dtype=dtype) |
| conv2 = copy.deepcopy(conv1).to(memory_format=weight_memory_format) |
| x1 = x.clone() |
| x2 = x.clone().to(memory_format=cl_format) |
| if train: |
| x1.requires_grad_() |
| x2.requires_grad_() |
| y1 = conv1(x1) |
| y2 = conv2(x2) |
| self.assertEqual(y1, y2, atol=prec, rtol=prec) |
| |
| if train: |
| y1.sum().backward() |
| y2.sum().backward() |
| self.assertTrue(x2.grad.is_contiguous(memory_format=cl_format)) |
| self.assertEqual(conv1.weight.grad, |
| conv2.weight.grad, |
| atol=1e-3, |
| rtol=1e-3) |
| if bias: |
| self.assertEqual(conv1.bias.grad, conv2.bias.grad, atol=prec, rtol=prec) |
| self.assertEqual(x1.grad, x2.grad, atol=prec, rtol=prec) |
| |
| def test_conv_nhwc_fp32(self): |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv2d, torch.contiguous_format, dtype=torch.float32) |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv2d, torch.channels_last, dtype=torch.float32) |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv3d, torch.contiguous_format, dtype=torch.float32) |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv3d, torch.channels_last_3d, dtype=torch.float32) |
| |
| @dtypes(torch.float16, torch.bfloat16) |
| def test_conv_nhwc_lower_precision(self, dtype): |
| # when torch.ops.mkldnn._is_mkldnn_bf16_supported() or torch.ops.mkldnn._is_mkldnn_fp16_supported() |
| # returns false, bf16/fp16 CPU conv will fall back to thnn impl |
| support_checks = { |
| torch.bfloat16: torch.ops.mkldnn._is_mkldnn_bf16_supported, |
| torch.float16: torch.ops.mkldnn._is_mkldnn_fp16_supported |
| } |
| if support_checks[dtype](): |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv2d, torch.contiguous_format, dtype=dtype) |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv2d, torch.channels_last, dtype=dtype) |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv3d, torch.contiguous_format, dtype=dtype) |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv3d, torch.channels_last_3d, dtype=dtype) |
| |
| # BF16/FP16 fallback implementations are divided into two parts im2col+gemm, |
| # and the number of data type conversions in the middle is more than that of onednn's direct conv, |
| # resulting in additional accuracy loss. |
| precisions = { |
| torch.bfloat16: 1e-2, |
| torch.float16: 2e-3, |
| } |
| prec = precisions[dtype] |
| with torch.backends.mkldnn.flags(enabled=False): |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv2d, torch.contiguous_format, dtype=dtype, prec=prec) |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv2d, torch.channels_last, dtype=dtype, prec=prec) |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv3d, torch.contiguous_format, dtype=dtype, prec=prec) |
| self._test_conv_deconv_nhwc_base(torch.nn.Conv3d, torch.channels_last_3d, dtype=dtype, prec=prec) |
| |
| |
| def test_conv_transpose_nhwc_fp32(self): |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose2d, torch.contiguous_format, dtype=torch.float32) |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose2d, torch.channels_last, dtype=torch.float32) |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose3d, torch.contiguous_format, dtype=torch.float32) |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose3d, torch.channels_last_3d, dtype=torch.float32) |
| |
| @dtypes(torch.float16, torch.bfloat16) |
| def test_conv_transpose_nhwc_lower_precision(self, dtype): |
| # when torch.ops.mkldnn._is_mkldnn_bf16_supported() or torch.ops.mkldnn._is_mkldnn_fp16_supported() |
| # returns false, bf16/fp16 CPU conv will fall back to thnn impl |
| support_checks = { |
| torch.bfloat16: torch.ops.mkldnn._is_mkldnn_bf16_supported, |
| torch.float16: torch.ops.mkldnn._is_mkldnn_fp16_supported |
| } |
| if support_checks[dtype](): |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose2d, torch.contiguous_format, dtype=dtype) |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose2d, torch.channels_last, dtype=dtype) |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose3d, torch.contiguous_format, dtype=dtype) |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose3d, torch.channels_last_3d, dtype=dtype) |
| |
| # BF16/FP16 fallback implementations are divided into two parts col2im+gemm, |
| # and the number of data type conversions in the middle is more than that of onednn's direct conv, |
| # resulting in additional accuracy loss. |
| precisions = { |
| torch.bfloat16: 2e-2, |
| torch.float16: 3e-3, |
| } |
| prec = precisions[dtype] |
| with torch.backends.mkldnn.flags(enabled=False): |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose2d, torch.contiguous_format, dtype=dtype, prec=prec) |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose2d, torch.channels_last, dtype=dtype, prec=prec) |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose3d, torch.contiguous_format, dtype=dtype, prec=prec) |
| self._test_conv_deconv_nhwc_base(torch.nn.ConvTranspose3d, torch.channels_last_3d, dtype=dtype, prec=prec) |
| |
| def _test_conv_transpose_base(self, dim): |
| conv_module = { |
| 1: torch.nn.ConvTranspose1d, |
| 2: torch.nn.ConvTranspose2d, |
| 3: torch.nn.ConvTranspose3d |
| } |
| input_shapes = {1: (55,), 2: (28, 28), 3: (14, 14, 14)} |
| options = itertools.product([True, False], [True, False], [1, 2], [1, 4]) |
| for train, bias, dilation, groups in options: |
| N = torch.randint(3, 10, (1,)).item() |
| M = torch.randint(1, 3, (1,)).item() * groups |
| C = torch.randint(1, 3, (1,)).item() * groups |
| x_shape = (N, C) + input_shapes[dim] |
| data = torch.randn(x_shape, dtype=torch.float32) |
| # conv: mkldnn tranpose conv fp32 |
| # conv_ref: thnn transpose conv fp32 |
| conv = conv_module[dim](in_channels=C, |
| out_channels=M, |
| kernel_size=3, |
| stride=1, |
| padding=1, |
| dilation=dilation, |
| bias=bias, |
| groups=groups).to(dtype=torch.float32) |
| x = data.clone() |
| x_ref = x.clone() |
| if train: |
| x.requires_grad_() |
| x_ref.requires_grad_() |
| |
| conv_ref = copy.deepcopy(conv) |
| with torch.backends.mkldnn.flags(enabled=False): |
| y_ref = conv_ref(x_ref) |
| if train: |
| y_ref.sum().backward() |
| |
| y = conv(x) |
| if train: |
| y.sum().backward() |
| |
| self.assertEqual(y, y_ref) |
| if train: |
| self.assertEqual(x.grad, x_ref.grad) |
| self.assertEqual(conv.weight.grad, |
| conv_ref.weight.grad, |
| atol=1e-3, |
| rtol=1e-3) |
| if bias: |
| self.assertEqual(conv.bias.grad, conv_ref.bias.grad) |
| |
| def test_conv_transpose1d(self): |
| self._test_conv_transpose_base(dim=1) |
| |
| def test_conv_transpose2d(self): |
| self._test_conv_transpose_base(dim=2) |
| |
| def test_conv_transpose3d(self): |
| self._test_conv_transpose_base(dim=3) |
| |
| def test_conv2d_legacy_jit_model(self): |
| """ |
| MKLDNN integration used to serialize models with 5d weight for grouped |
| convolutions, we'd like to preserve this behavior |
| """ |
| g = 4 |
| conv2d = torch.nn.Conv2d(16, 16, 3, groups=g) |
| conv2d_mkldnn = torch.utils.mkldnn.to_mkldnn(conv2d) |
| |
| # contrive legacy conv2d module with a 5-d weight |
| o, i, h, w = conv2d.weight.shape |
| weight_5d = conv2d.weight.reshape((g, o // g, i, h, w)) |
| conv2d_mkldnn.weight = weight_5d.to_mkldnn() |
| |
| x = torch.randn(1, 16, 8, 8) |
| |
| with TemporaryFileName() as fname: |
| torch.jit.save(conv2d_mkldnn, fname) |
| conv2d_loaded = torch.jit.load(fname) |
| |
| self.assertEqual(conv2d_mkldnn.weight.ndimension(), 5) |
| self.assertEqual(conv2d_loaded.weight.ndimension(), 4) |
| self.assertEqual( |
| conv2d(x), |
| conv2d_loaded(x.to_mkldnn()).to_dense()) |
| |
| # This test is to check whether 1D conv is supported for mkldnn tensor, |
| # which is exposed by Issue https://github.com/pytorch/pytorch/issues/68034. |
| def test_conv1d_functional(self): |
| input = torch.randn(2, 3, 10).to_mkldnn() |
| weight = torch.randn(3, 3, 3).to_mkldnn() |
| bias = torch.randn(3).to_mkldnn() |
| output = torch.nn.functional.conv1d(input, weight, bias) |
| self.assertEqual(output.size(), torch.Size([2, 3, 8])) |
| |
| def test_relu(self): |
| x = torch.randn((4, 5), dtype=torch.float32) * 10 |
| x1 = x.clone().requires_grad_() |
| x2 = x.clone().to_mkldnn().requires_grad_() |
| y1 = torch.relu(x1) |
| y2 = torch.relu(x2).to_dense() |
| loss1 = y1.sum() |
| loss2 = y2.sum() |
| loss1.backward() |
| loss2.backward() |
| self.assertEqual(y1, y2) |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| |
| def test_relu_(self): |
| x = torch.randn((4, 5), dtype=torch.float32) * 10 |
| x1 = x.clone().requires_grad_() |
| x2 = x.clone().to_mkldnn().requires_grad_() |
| y1 = torch.relu_(x1.clone()) |
| y2 = torch.relu_(x2.clone()).to_dense() |
| loss1 = y1.sum() |
| loss2 = y2.sum() |
| loss1.backward() |
| loss2.backward() |
| self.assertEqual(y1, y2) |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| |
| @unittest.skipIf(IS_WINDOWS, "Limit support for bf16 path") |
| def _test_relu_bf16_base(self, name): |
| x = torch.randn((4, 5), dtype=torch.float32) * 10 |
| x_bf16 = x.bfloat16() |
| fn = getattr(torch, name) |
| if torch.ops.mkldnn._is_mkldnn_bf16_supported(): |
| y = fn(x.to_mkldnn()).to_dense() |
| y_bf16 = fn(x_bf16.to_mkldnn()).to_dense(torch.float32) |
| self.assertEqual(y, y_bf16, atol=1e-1, rtol=1e-3) |
| else: |
| msg = r"bf16 path needs the cpu support avx512bw, avx512vl and avx512dq" |
| self.assertRaisesRegex(RuntimeError, |
| msg, |
| lambda: fn(x_bf16.to_mkldnn())) |
| |
| def test_relu_bf16(self): |
| self._test_relu_bf16_base("relu") |
| |
| def test_relu_inplace_bf16(self): |
| self._test_relu_bf16_base("relu_") |
| |
| def test_gelu(self): |
| m = torch.nn.GELU() |
| x = torch.randn((4, 5), dtype=torch.float32) * 10 |
| x1 = x.clone().requires_grad_() |
| x2 = x.clone().to_mkldnn().requires_grad_() |
| y1 = m(x1) |
| y2 = m(x2).to_dense() |
| loss1 = y1.sum() |
| loss2 = y2.sum() |
| loss1.backward() |
| loss2.backward() |
| self.assertEqual(y1, y2) |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| |
| @unittest.skipIf(IS_WINDOWS, "Limit support for bf16 path") |
| def test_gelu_bf16(self): |
| m = torch.nn.GELU() |
| x = torch.randn((4, 5), dtype=torch.float32) * 10 |
| x1 = x.clone().to_mkldnn().requires_grad_() |
| x2 = x.clone().to_mkldnn(torch.bfloat16).requires_grad_() |
| if torch.ops.mkldnn._is_mkldnn_bf16_supported(): |
| y1 = m(x1).to_dense() |
| y2 = m(x2).to_dense() |
| loss1 = y1.sum() |
| loss2 = y2.sum() |
| loss1.backward() |
| loss2.backward() |
| self.assertEqual(y1, y2.to(torch.float32), atol=1e-1, rtol=0) |
| self.assertEqual(x1.grad.to_dense(), x2.grad.to_dense(torch.float32), atol=1e-2, rtol=0) |
| else: |
| msg = r"bf16 path needs the cpu support avx512bw, avx512vl and avx512dq" |
| self.assertRaisesRegex(RuntimeError, |
| msg, |
| lambda: m(x2)) |
| |
| def _test_prelu_base(self, size, num_channels): |
| x = torch.randn(size, dtype=torch.float32) |
| x1 = x.clone().requires_grad_() |
| x2 = x.clone().to_mkldnn().requires_grad_() |
| x3 = x.clone().to_mkldnn().requires_grad_() |
| m1 = torch.nn.PReLU(num_channels) |
| m2 = mkldnn_utils.to_mkldnn(copy.deepcopy(m1)) |
| m3 = copy.deepcopy(m1) |
| y1 = m1(x1) |
| y2 = m2(x2).to_dense() |
| y3 = m3(x3).to_dense() # Only convert data to mkldnn, weight is Aten tensor |
| loss1 = y1.sum() |
| loss1.backward() |
| loss2 = y2.sum() |
| loss2.backward() |
| loss3 = y3.sum() |
| loss3.backward() |
| self.assertEqual(y1, y2) |
| self.assertEqual(y1, y3) |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| self.assertEqual(x1.grad, x3.grad.to_dense()) |
| |
| def test_prelu(self): |
| self._test_prelu_base(torch.Size([16]), 1) |
| self._test_prelu_base(torch.Size([16, 64]), 1) |
| self._test_prelu_base(torch.Size([16, 64]), 64) |
| self._test_prelu_base(torch.Size([16, 64, 112]), 1) |
| self._test_prelu_base(torch.Size([16, 64, 112]), 64) |
| self._test_prelu_base(torch.Size([16, 64, 112, 112]), 1) |
| self._test_prelu_base(torch.Size([16, 64, 112, 112]), 64) |
| self._test_prelu_base(torch.Size([16, 64, 112, 112, 1]), 1) |
| self._test_prelu_base(torch.Size([16, 64, 112, 112, 1]), 64) |
| |
| @unittest.skipIf(IS_WINDOWS, "Limit support for bf16 path") |
| def _test_prelu_bf16_base(self, size, num_channels): |
| if torch.ops.mkldnn._is_mkldnn_bf16_supported(): |
| x = torch.randn(size, dtype=torch.float32) |
| x_fp32 = x.clone().to_mkldnn().requires_grad_() |
| x_bf16 = x.clone().to_mkldnn(torch.bfloat16).requires_grad_() |
| m = mkldnn_utils.to_mkldnn(torch.nn.PReLU()) |
| m_bf16 = mkldnn_utils.to_mkldnn(torch.nn.PReLU(), torch.bfloat16) |
| |
| y = m(x_fp32).to_dense() |
| y_bf16 = m_bf16(x_bf16).to_dense() |
| self.assertEqual(y, y_bf16.to(torch.float32), atol=1e-1, rtol=1e-3) |
| |
| loss = y.sum() |
| loss.backward() |
| loss_bf16 = y_bf16.sum() |
| loss_bf16.backward() |
| self.assertEqual(x_fp32.grad.to_dense(), x_bf16.grad.to_dense(torch.float32)) |
| else: |
| x_bf16 = torch.randn(size, dtype=torch.bfloat16).requires_grad_() |
| m_bf16 = mkldnn_utils.to_mkldnn(torch.nn.PReLU(), torch.bfloat16) |
| msg = r"bf16 path needs the cpu support avx512bw, avx512vl and avx512dq" |
| self.assertRaisesRegex(RuntimeError, |
| msg, |
| lambda: m_bf16(x_bf16)) |
| |
| def test_prelu_bf16(self): |
| self._test_prelu_bf16_base(torch.Size([16]), 1) |
| self._test_prelu_bf16_base(torch.Size([16, 64]), 1) |
| self._test_prelu_bf16_base(torch.Size([16, 64]), 64) |
| self._test_prelu_bf16_base(torch.Size([16, 64, 112]), 1) |
| self._test_prelu_bf16_base(torch.Size([16, 64, 112]), 64) |
| self._test_prelu_bf16_base(torch.Size([16, 64, 112, 112, 1]), 1) |
| self._test_prelu_bf16_base(torch.Size([16, 64, 112, 112, 1]), 64) |
| |
| def _test_max_pool_base(self, dim, input): |
| pool_module = {2: torch.nn.MaxPool2d, 3: torch.nn.MaxPool3d} |
| for stride in [1, 2, 3]: |
| for ceil_mode in [False, True]: |
| max_pool = pool_module[dim]( |
| kernel_size=3 if not ceil_mode else 7, |
| stride=stride, |
| padding=1, |
| ceil_mode=ceil_mode) |
| |
| x1 = input.clone().requires_grad_() |
| x2 = input.clone().to_mkldnn().requires_grad_() |
| y1 = max_pool(x1) |
| y2 = max_pool(x2).to_dense() |
| loss1 = y1.sum() |
| loss2 = y2.sum() |
| loss1.backward() |
| loss2.backward() |
| self.assertEqual(y1, y2) |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| |
| def test_max_pool2d(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| for H, W in [(64, 64), (35, 39), (16, 19), [7, 8]]: |
| x = torch.randn(N, C, H, W, dtype=torch.float32) * 10 |
| self._test_max_pool_base(dim=2, input=x) |
| |
| def test_max_pool3d(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| for D, H, W in [(64, 64, 64), (35, 39, 35), (16, 19, 20), [7, 8, 9]]: |
| x = torch.randn(N, C, D, H, W, dtype=torch.float32) * 10 |
| self._test_max_pool_base(dim=3, input=x) |
| |
| |
| @unittest.skipIf(IS_WINDOWS, "Limit support for bf16 path") |
| def _test_max_pool_bf16_base(self, dim, input): |
| pool_module = {2: torch.nn.MaxPool2d, 3: torch.nn.MaxPool3d} |
| x_bf16 = input.bfloat16() |
| for stride in [1, 2, 3]: |
| for ceil_mode in [False, True]: |
| max_pool = pool_module[dim]( |
| kernel_size=3 if not ceil_mode else 7, |
| stride=stride, |
| padding=1, |
| ceil_mode=ceil_mode) |
| |
| if torch.ops.mkldnn._is_mkldnn_bf16_supported(): |
| y = max_pool(input.to_mkldnn()).to_dense() |
| y_bf16 = max_pool(x_bf16.to_mkldnn()).to_dense(torch.float32) |
| self.assertEqual(y, y_bf16, atol=0.1, rtol=1e-3) |
| else: |
| msg = "mkldnn_max_pool%dd: bf16 path needs the cpu support avx512bw, avx512vl and avx512dq" % dim |
| self.assertRaisesRegex(RuntimeError, |
| msg, |
| lambda: max_pool(x_bf16.to_mkldnn())) |
| |
| def test_max_pool2d_bf16(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| for H, W in [(64, 64), (35, 39), (16, 19), [7, 8]]: |
| x = torch.randn(N, C, H, W, dtype=torch.float32) * 10 |
| self._test_max_pool_bf16_base(dim=2, input=x) |
| |
| def test_max_pool3d_bf16(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| for D, H, W in [(64, 64, 64), (35, 39, 35), (16, 19, 20), [7, 8, 9]]: |
| x = torch.randn(N, C, D, H, W, dtype=torch.float32) * 10 |
| self._test_max_pool_bf16_base(dim=3, input=x) |
| |
| def test_max_pool2d_stride_none(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| |
| for H, W in [(64, 64), (35, 39), (16, 19), [7, 8]]: |
| x = torch.randn(N, C, H, W, dtype=torch.float32) * 10 |
| for ceil_mode in [False, True]: |
| y1 = F.max_pool2d( |
| x, |
| kernel_size=3 if not ceil_mode else 7, |
| stride=None, |
| padding=1, |
| ceil_mode=ceil_mode) |
| |
| y2 = F.max_pool2d( |
| x.to_mkldnn(), |
| kernel_size=3 if not ceil_mode else 7, |
| stride=None, |
| padding=1, |
| ceil_mode=ceil_mode) |
| |
| self.assertEqual(y1, y2.to_dense()) |
| |
| # https://github.com/pytorch/pytorch/issues/127111 |
| @xfailIfTorchDynamo |
| def test_max_pool_unsupported(self): |
| # OneDNN not support dilation max_pooling, will be avilabled in v2.0. |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| |
| # 2d dilation case |
| x = torch.randn(N, C, 7, 7, dtype=torch.float32).to_mkldnn() |
| max_pool2d = torch.nn.MaxPool2d( |
| kernel_size=3, |
| stride=3, |
| padding=1, |
| dilation=2) |
| self.assertRaisesRegex(RuntimeError, |
| 'mkldnn_max_pool2d does not support dilation case', |
| lambda: max_pool2d(x)) |
| |
| # 3d dilation case |
| x = torch.randn(N, C, 7, 7, 7, dtype=torch.float32).to_mkldnn() |
| max_pool3d = torch.nn.MaxPool3d( |
| kernel_size=3, |
| stride=3, |
| padding=1, |
| dilation=2) |
| self.assertRaisesRegex(RuntimeError, |
| 'mkldnn_max_pool3d does not support dilation case', |
| lambda: max_pool3d(x)) |
| |
| def _test_avg_pool_base(self, dim, input): |
| avg_module = {2: torch.nn.AvgPool2d, 3: torch.nn.AvgPool3d} |
| for count_include_pad in [True, False]: |
| avg_pool = avg_module[dim]( |
| kernel_size=3, |
| stride=2, |
| padding=1, |
| count_include_pad=count_include_pad) |
| |
| x1 = input.clone().requires_grad_() |
| x2 = input.clone().to_mkldnn().requires_grad_() |
| y1 = avg_pool(x1) |
| y2 = avg_pool(x2).to_dense() |
| loss1 = y1.sum() |
| loss2 = y2.sum() |
| loss1.backward() |
| loss2.backward() |
| self.assertEqual(y1, y2) |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| |
| def test_avg_pool2d(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| x = torch.randn(N, C, 64, 64, dtype=torch.float32) * 10 |
| self._test_avg_pool_base(dim=2, input=x) |
| |
| def test_avg_pool3d(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| x = torch.randn(N, C, 64, 64, 64, dtype=torch.float32) * 10 |
| self._test_avg_pool_base(dim=3, input=x) |
| |
| @unittest.skipIf(IS_WINDOWS, "Limit support for bf16 path") |
| def _test_avg_pool_bf16_base(self, dim, input): |
| avg_module = {2: torch.nn.AvgPool2d, 3: torch.nn.AvgPool3d} |
| x_bf16 = input.bfloat16() |
| for count_include_pad in [True, False]: |
| avg_pool = avg_module[dim]( |
| kernel_size=3, |
| stride=2, |
| padding=1, |
| count_include_pad=count_include_pad) |
| if torch.ops.mkldnn._is_mkldnn_bf16_supported(): |
| y = avg_pool(input.to_mkldnn()).to_dense() |
| y_bf16 = avg_pool(x_bf16.to_mkldnn()).to_dense(torch.float) |
| self.assertEqual(y, y_bf16, atol=1e-1, rtol=1e-3) |
| else: |
| msg = "mkldnn_avg_pool%dd: bf16 path needs the cpu support avx512bw, avx512vl and avx512dq" % dim |
| self.assertRaisesRegex(RuntimeError, |
| msg, |
| lambda: avg_pool(x_bf16.to_mkldnn())) |
| |
| def test_avg_pool2d_bf16(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| x = torch.randn(N, C, 64, 64, dtype=torch.float32) * 10 |
| self._test_avg_pool_bf16_base(dim=2, input=x) |
| |
| def test_avg_pool3d_bf16(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| x = torch.randn(N, C, 64, 64, 64, dtype=torch.float32) * 10 |
| self._test_avg_pool_bf16_base(dim=3, input=x) |
| |
| def test_avg_pool2d_stride_none(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| x = torch.randn(N, C, 64, 64, dtype=torch.float32) * 10 |
| |
| for count_include_pad in [True, False]: |
| y1 = F.avg_pool2d( |
| x, |
| kernel_size=3, |
| stride=None, |
| padding=1, |
| count_include_pad=count_include_pad) |
| y2 = F.avg_pool2d( |
| x.to_mkldnn(), |
| kernel_size=3, |
| stride=None, |
| padding=1, |
| count_include_pad=count_include_pad) |
| |
| self.assertEqual(y1, y2.to_dense()) |
| |
| def test_adaptive_avg_pool2d(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| x = torch.randn(N, C, 224, 224, dtype=torch.float32) * 100 |
| |
| adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d(7) |
| x1 = x.clone().requires_grad_() |
| x2 = x.clone().to_mkldnn().requires_grad_() |
| y1 = adaptive_avg_pool2d(x1) |
| y2 = adaptive_avg_pool2d(x2).to_dense() |
| |
| loss1 = y1.sum() |
| loss2 = y2.sum() |
| loss1.backward() |
| loss2.backward() |
| |
| self.assertEqual(y1, y2) |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| |
| @unittest.skipIf(IS_WINDOWS, "Limit support for bf16 path") |
| def test_adaptive_avg_pool2d_bf16(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 10, (1,)).item() |
| x = torch.randn(N, C, 224, 224, dtype=torch.float32) * 100 |
| |
| x_bf16 = x.bfloat16() |
| adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d(7) |
| |
| if torch.ops.mkldnn._is_mkldnn_bf16_supported(): |
| y = adaptive_avg_pool2d(x.to_mkldnn()).to_dense() |
| y_bf16 = adaptive_avg_pool2d(x.to_mkldnn()).to_dense(torch.float32) |
| self.assertEqual(y, y_bf16, atol=1e-1, rtol=1e-3) |
| else: |
| msg = "mkldnn_adaptive_avg_pool2d: bf16 path needs the cpu support avx512bw, avx512vl and avx512dq" |
| self.assertRaisesRegex(RuntimeError, |
| msg, |
| lambda: adaptive_avg_pool2d(x_bf16.to_mkldnn())) |
| |
| def _test_batch_norm_base(self, dim, channels, input): |
| bn_module = {2 : torch.nn.BatchNorm2d, 3 : torch.nn.BatchNorm3d} |
| bn = bn_module[dim](channels).float().train(False) |
| mkldnn_bn = mkldnn_utils.to_mkldnn(copy.deepcopy(bn)) |
| self.assertEqual( |
| bn(input), |
| mkldnn_bn(input.to_mkldnn()).to_dense()) |
| |
| self._test_serialization(mkldnn_bn, (input.to_mkldnn(),)) |
| self._test_tracing(mkldnn_bn, (input.to_mkldnn(),)) |
| |
| def _test_batch_norm_train_base(self, dim, channels, input): |
| # TODO: support 3d batchnorm training. |
| bn_module = {2 : torch.nn.BatchNorm2d} |
| # TODO: support none affine. |
| options = itertools.product([True], [True, False]) |
| for affine, track_running_stats in options: |
| bn = bn_module[dim]( |
| num_features=channels, |
| affine=affine, |
| track_running_stats=track_running_stats).float().train(True) |
| mkldnn_bn = copy.deepcopy(bn) |
| x1 = input.clone().requires_grad_() |
| x2 = input.clone().to_mkldnn().requires_grad_() |
| y1 = bn(x1) |
| y2 = mkldnn_bn(x2).to_dense() |
| loss1 = y1.sum() |
| loss2 = y2.sum() |
| loss1.backward() |
| loss2.backward() |
| self.assertEqual(y1, y2) |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| self.assertEqual(bn.weight.grad, mkldnn_bn.weight.grad, rtol=1e-3, atol=1e-3) |
| if track_running_stats: |
| self.assertEqual(bn.running_mean, mkldnn_bn.running_mean) |
| self.assertEqual(bn.running_var, mkldnn_bn.running_var, rtol=1e-5, atol=1e-5) |
| |
| def test_batch_norm_2d(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 100, (1,)).item() |
| x = torch.randn(N, C, 35, 45, dtype=torch.float32) * 10 |
| self._test_batch_norm_base(dim=2, channels=C, input=x) |
| self._test_batch_norm_train_base(dim=2, channels=C, input=x) |
| |
| def test_batch_norm_3d(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 100, (1,)).item() |
| x = torch.randn(N, C, 30, 30, 30, dtype=torch.float32) * 10 |
| self._test_batch_norm_base(dim=3, channels=C, input=x) |
| |
| @unittest.skipIf(IS_WINDOWS, "Limit support for bf16 path") |
| def _test_batch_norm_bf16_base(self, dim, channels, input): |
| bn_module = {2 : torch.nn.BatchNorm2d, 3 : torch.nn.BatchNorm3d} |
| x_bf16 = input.bfloat16() |
| # TODO: support training |
| for train in [False]: |
| bn = bn_module[dim](channels).float().train(train) |
| mkldnn_bn = mkldnn_utils.to_mkldnn(copy.deepcopy(bn)) |
| if torch.ops.mkldnn._is_mkldnn_bf16_supported(): |
| y = bn(input.to_mkldnn().to_dense()) |
| y_bf16 = bn(input.to_mkldnn().to_dense(torch.float)) |
| self.assertEqual(y, y_bf16, atol=1e-1, rtol=1e-3) |
| else: |
| msg = "mkldnn_batch_norm: bf16 path needs the cpu support avx512bw, avx512vl and avx512dq" |
| self.assertRaisesRegex(RuntimeError, |
| msg, |
| lambda: bn(x_bf16.to_mkldnn())) |
| |
| def test_batch_norm_2d_bf16(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 100, (1,)).item() |
| x = torch.randn(N, C, 35, 45, dtype=torch.float32) * 10 |
| self._test_batch_norm_bf16_base(dim=2, channels=C, input=x) |
| |
| def test_batch_norm_3d_bf16(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 100, (1,)).item() |
| x = torch.randn(N, C, 30, 30, 30, dtype=torch.float32) * 10 |
| self._test_batch_norm_bf16_base(dim=3, channels=C, input=x) |
| |
| def test_add(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 100, (1,)).item() |
| alpha = torch.randn(1, dtype=torch.float32).item() |
| |
| x = torch.randn(N, C, 35, 45, dtype=torch.float32) * 10 |
| y = torch.randn(N, C, 35, 45, dtype=torch.float32) * 10 |
| mx = x.to_mkldnn() |
| my = y.to_mkldnn() |
| |
| # add |
| self.assertEqual( |
| x + y, |
| (mx + my).to_dense()) |
| |
| self.assertEqual( |
| torch.add(x, y, alpha=alpha), |
| torch.add(mx, my, alpha=alpha).to_dense()) |
| |
| # add_ |
| x += y |
| mx += my |
| self.assertEqual(x, mx.to_dense()) |
| |
| # add_out |
| out = x.clone() |
| mkldnn_out = out.to_mkldnn() |
| torch.add(x, y, alpha=alpha, out=out) |
| torch.add(mx, my, alpha=alpha, out=mkldnn_out) |
| self.assertEqual(out, mkldnn_out.to_dense()) |
| |
| # add_out inplace case: first input |
| torch.add(x, y, alpha=alpha, out=x) |
| torch.add(mx, my, alpha=alpha, out=mx) |
| self.assertEqual(x, mx.to_dense()) |
| |
| # add_out inplace case: second input |
| torch.add(x, y, alpha=alpha, out=y) |
| torch.add(mx, my, alpha=alpha, out=my) |
| self.assertEqual(y, my.to_dense()) |
| |
| def test_mul(self): |
| N = torch.randint(3, 10, (1,)).item() |
| C = torch.randint(3, 100, (1,)).item() |
| value = torch.randn(1, dtype=torch.float32).item() |
| |
| x = torch.randn(N, C, 35, 45, dtype=torch.float32) * 10 |
| y = torch.randn(N, C, 35, 45, dtype=torch.float32) * 10 |
| mx = x.to_mkldnn() |
| my = y.to_mkldnn() |
| |
| # mul |
| self.assertEqual( |
| x * y, |
| (mx * my).to_dense()) |
| |
| self.assertEqual( |
| x * value, |
| (mx * value).to_dense()) |
| |
| self.assertEqual( |
| torch.mul(x, y), |
| torch.mul(mx, my).to_dense()) |
| |
| self.assertEqual( |
| torch.mul(x, value), |
| torch.mul(mx, value).to_dense()) |
| |
| # mul_ |
| x *= y |
| mx *= my |
| self.assertEqual(x, mx.to_dense()) |
| |
| x *= value |
| mx *= value |
| self.assertEqual(x, mx.to_dense()) |
| |
| # mul_out |
| out = x.clone() |
| mkldnn_out = out.to_mkldnn() |
| torch.mul(x, y, out=out) |
| torch.mul(mx, my, out=mkldnn_out) |
| self.assertEqual(out, mkldnn_out.to_dense()) |
| |
| out = x.clone() |
| mkldnn_out = out.to_mkldnn() |
| torch.mul(x, value, out=out) |
| torch.mul(mx, value, out=mkldnn_out) |
| self.assertEqual(out, mkldnn_out.to_dense()) |
| |
| def test_0_dimension_tensor(self): |
| x = torch.rand([20, 20, 1, 1], dtype=torch.float) |
| y = torch.rand([20, 20, 0, 1], dtype=torch.float) |
| |
| # unary ops work without modification |
| out_relu = torch.relu(y) |
| out_relu_mkldnn = torch.relu(y.to_mkldnn()).to_dense() |
| self.assertEqual(out_relu, out_relu_mkldnn) |
| |
| out_mul = x * y |
| out_mul_mkldnn = (x.to_mkldnn() * y.to_mkldnn()).to_dense() |
| self.assertEqual(out_mul, out_mul_mkldnn) |
| |
| out_add = x + y |
| out_add_mkldnn = (x.to_mkldnn() + y.to_mkldnn()).to_dense() |
| self.assertEqual(out_add, out_add_mkldnn) |
| |
| x.requires_grad_(True) |
| y.requires_grad_(True) |
| with self.assertRaisesRegex(RuntimeError, "0-dimension Tensor in training"): |
| x.to_mkldnn() + y.to_mkldnn() |
| |
| with self.assertRaisesRegex(RuntimeError, "must match"): |
| torch.rand([5]).to_mkldnn() + torch.rand([0]).to_mkldnn() |
| |
| C = 7 |
| m = torch.nn.Conv2d(C, C, 3) |
| x = torch.randn(0, C, C, 8, dtype=torch.float) |
| out_eager = m(x) |
| out_mkldnn = mkldnn_utils.to_mkldnn(m)(x) |
| self.assertEqual(out_eager, out_mkldnn) |
| |
| # https://github.com/pytorch/pytorch/issues/127111 |
| @xfailIfTorchDynamo |
| def test_view(self): |
| x = torch.randn(3, 4, 5, dtype=torch.float32).to_mkldnn() |
| self.assertRaisesRegex(RuntimeError, |
| "Change to use reshape", |
| lambda: x.view(x.size(0), -1)) |
| |
| def test_reshape(self): |
| x = torch.randn(3, 4, 5, dtype=torch.float32) * 10 |
| size = (x.size(0), -1) |
| |
| self.assertEqual( |
| x.reshape(size), |
| x.to_mkldnn().reshape(size).to_dense(), |
| ) |
| # test whether share same memory for plain format tensor |
| y = x.to_mkldnn() |
| z = y.reshape(size).add_(y.reshape(size)) |
| self.assertEqual( |
| y.reshape(size).to_dense(), |
| z.to_dense(), |
| ) |
| |
| def test_reshape_blocked_format(self): |
| # construct an mkldnn blocked tensor with mkldnn conv2d |
| C = 7 |
| m = mkldnn_utils.to_mkldnn(torch.nn.Conv2d(C, C, 3)) |
| x = torch.randn(1, C, 8, 8).to_mkldnn() |
| |
| # mkldnn tensor w/ blocked format |
| y_block = m(x) |
| # aten tensor w/ plain format |
| y_plain = y_block.to_dense() |
| |
| y_block_reshape = y_block.reshape(C, -1) |
| y_plain_reshape = y_plain.reshape(C, -1) |
| |
| self.assertEqual(y_plain_reshape, y_block_reshape.to_dense()) |
| |
| def test_reshape_backward(self): |
| x = torch.randn(3, 4, 5, dtype=torch.float32) * 10 |
| size = (x.size(0), -1) |
| |
| x1 = x.clone().requires_grad_() |
| x2 = x.clone().to_mkldnn().requires_grad_() |
| in_features = 20 |
| out_features = torch.randint(3, 100, (1,)).item() |
| linear = torch.nn.Linear(in_features, out_features).float() |
| |
| y1 = linear(x1.reshape(size)).sum() |
| y2 = linear(x2.reshape(size).to_dense()).sum() |
| y1.backward() |
| y2.backward() |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| |
| def test_clone(self): |
| x = torch.randn(4, 5, dtype=torch.float32) * 10 |
| self.assertEqual( |
| x.clone(), |
| x.to_mkldnn().clone().to_dense(), |
| ) |
| # test whether share same memory |
| y = x.to_mkldnn() |
| z = y.clone().add_(y) |
| self.assertNotEqual( |
| y.to_dense(), |
| z.to_dense(), |
| ) |
| |
| def test_transpose(self): |
| x = torch.randn(3, 4, 5, dtype=torch.float32) * 10 |
| for dim1 in range(x.ndim): |
| for dim2 in range(x.ndim): |
| self.assertEqual( |
| x.transpose(dim1, dim2), |
| x.to_mkldnn().transpose(dim1, dim2).to_dense(), |
| ) |
| |
| def test_transpose_invalid_dime(self): |
| x = torch.randn(3, 4, 5, dtype=torch.float32).to_mkldnn() |
| with self.assertRaisesRegex(IndexError, "Dimension out of range"): |
| torch._mkldnn_transpose(x, 0, 12) |
| |
| def test_linear_non_contiguous_weight(self): |
| in_features = torch.randint(3, 10, (1,)).item() |
| out_features = torch.randint(3, 100, (1,)).item() |
| x = torch.randn(3, in_features, dtype=torch.float32) * 10 |
| w = torch.randn(in_features, out_features, dtype=torch.float32) |
| for bias in [True, False]: |
| x1 = x.clone().requires_grad_() |
| x2 = x.clone().to_mkldnn().requires_grad_() |
| linear = torch.nn.Linear(in_features, out_features).float() |
| linear.weight = torch.nn.Parameter(w.t()) |
| mkldnn_linear = copy.deepcopy(linear) |
| y1 = linear(x1).sum() |
| y2 = mkldnn_linear(x2).to_dense().sum() |
| y1.backward() |
| y2.backward() |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| self.assertEqual(linear.weight.grad, mkldnn_linear.weight.grad) |
| if bias: |
| self.assertEqual(linear.bias.grad, mkldnn_linear.bias.grad) |
| |
| def test_linear(self): |
| in_features = torch.randint(3, 10, (1,)).item() |
| out_features = torch.randint(3, 100, (1,)).item() |
| x = torch.randn(3, in_features, dtype=torch.float32) * 10 |
| |
| for bias in [True, False]: |
| linear = torch.nn.Linear(in_features, out_features, bias=bias).float() |
| mkldnn_linear = mkldnn_utils.to_mkldnn(copy.deepcopy(linear)) |
| self.assertEqual( |
| linear(x), |
| mkldnn_linear(x.to_mkldnn()).to_dense()) |
| |
| self._test_serialization(mkldnn_linear, (x.to_mkldnn(),)) |
| self._test_tracing(mkldnn_linear, (x.to_mkldnn(),)) |
| |
| def test_linear_backward(self): |
| in_features = torch.randint(3, 10, (1,)).item() |
| out_features = torch.randint(3, 100, (1,)).item() |
| x = torch.randn(3, in_features, dtype=torch.float32) * 10 |
| for bias in [True, False]: |
| x1 = x.clone().requires_grad_() |
| x2 = x.clone().to_mkldnn().requires_grad_() |
| linear = torch.nn.Linear(in_features, out_features).float() |
| mkldnn_linear = copy.deepcopy(linear) |
| y1 = linear(x1).sum() |
| y2 = mkldnn_linear(x2).to_dense().sum() |
| y1.backward() |
| y2.backward() |
| self.assertEqual(x1.grad, x2.grad.to_dense()) |
| self.assertEqual(linear.weight.grad, mkldnn_linear.weight.grad) |
| if bias: |
| self.assertEqual(linear.bias.grad, mkldnn_linear.bias.grad) |
| |
| @dtypes(torch.float16, torch.bfloat16) |
| def test_linear_lowp(self, dtype): |
| in_features = torch.randint(3, 10, (1,)).item() |
| out_features = torch.randint(3, 100, (1,)).item() |
| x = torch.randn(3, in_features, dtype=torch.float32) * 10 |
| x_lowp = x.to(dtype=dtype) |
| |
| for bias in [True, False]: |
| linear = torch.nn.Linear(in_features, out_features, bias=bias).float() |
| mkldnn_linear = mkldnn_utils.to_mkldnn(copy.deepcopy(linear)) |
| mkldnn_linear_lowp = mkldnn_utils.to_mkldnn( |
| copy.deepcopy(linear), dtype |
| ) |
| lowp_support = { |
| torch.bfloat16: torch.ops.mkldnn._is_mkldnn_bf16_supported, |
| torch.half: torch.ops.mkldnn._is_mkldnn_fp16_supported, |
| } |
| if lowp_support[dtype](): |
| y = mkldnn_linear(x.to_mkldnn()).to_dense() |
| y_lowp = mkldnn_linear_lowp(x_lowp.to_mkldnn()).to_dense( |
| torch.float32 |
| ) |
| if dtype == torch.bfloat16: |
| self.assertEqual(y, y_lowp, atol=1e-1, rtol=1e-3) |
| else: |
| self.assertEqual(y, y_lowp, atol=5e-3, rtol=1e-3) |
| else: |
| msg = { |
| torch.bfloat16: r"bf16 path needs the cpu support avx_ne_convert or avx512bw, avx512vl and avx512dq", |
| torch.half: r"fp16 path needs the cpu support avx_ne_convert or avx512_fp16", |
| } |
| self.assertRaisesRegex( |
| RuntimeError, |
| msg[dtype], |
| lambda: mkldnn_linear_lowp(x_lowp.to_mkldnn()), |
| ) |
| |
| def test_softmax(self): |
| x = torch.randn(3, 4, 5, dtype=torch.float32) * 10 |
| for dim in range(x.ndim): |
| softmax = torch.nn.Softmax(dim=dim) |
| self.assertEqual( |
| softmax(x), |
| softmax(x.to_mkldnn()).to_dense()) |
| |
| def test_sigmoid(self): |
| x = torch.randn(4, 5, dtype=torch.float32) * 10 |
| mkldnn_x = x.to_mkldnn() |
| self.assertEqual( |
| torch.sigmoid(x), |
| torch.sigmoid(mkldnn_x).to_dense(), |
| ) |
| # inplace |
| torch.sigmoid_(x) |
| torch.sigmoid_(mkldnn_x) |
| self.assertEqual(x, mkldnn_x.to_dense()) |
| |
| def test_tanh(self): |
| x = torch.randn(4, 5, dtype=torch.float32) * 10 |
| mkldnn_x = x.to_mkldnn() |
| self.assertEqual( |
| torch.tanh(x), |
| torch.tanh(mkldnn_x).to_dense(), |
| ) |
| # inplace |
| torch.tanh_(x) |
| torch.tanh_(mkldnn_x) |
| self.assertEqual(x, mkldnn_x.to_dense()) |
| |
| def _test_serialization(self, module, inputs): |
| with TemporaryFileName() as fname: |
| torch.jit.save(module, fname) |
| loaded = torch.jit.load(fname) |
| self.assertEqual( |
| module(*inputs).to_dense(), |
| loaded(*inputs).to_dense()) |
| |
| def _test_tracing(self, module, inputs): |
| traced = torch.jit.trace(module, inputs) |
| self.assertEqual( |
| module(*inputs).to_dense(), |
| traced(*inputs).to_dense()) |
| |
| def test_set_data_tensorimpl_type(self): |
| # Dense tensor has impl of type `TensorImpl`, while MKL-DNN tensor has impl |
| # of type `OpaqueTensorImpl<IDeepTensorWrapperPtr>`. |
| x = torch.randn((1, 2), dtype=torch.float, device=torch.device('cpu')) |
| x_mkldnn = x.to_mkldnn() |
| with self.assertRaisesRegex(RuntimeError, 'incompatible tensor type'): |
| x.data = x_mkldnn |
| |
| def test_empty(self): |
| x1 = torch.empty(4, 5, 2, 3, dtype=torch.float32) |
| x2 = torch.empty(4, 5, 2, 3, dtype=torch.float32, layout=torch._mkldnn) |
| self.assertEqual(x1.size(), x2.to_dense().size()) |
| self.assertEqual(x1.dtype, x2.to_dense().dtype) |
| |
| def test_zero_(self): |
| x1 = torch.randn(4, 5, dtype=torch.float32) * 10 |
| x2 = x1.clone().to_mkldnn() |
| self.assertEqual( |
| x1.zero_(), |
| x2.zero_().to_dense(), |
| ) |
| |
| def test_is_mkldnn(self): |
| x = torch.randn(1, dtype=torch.float32) |
| self.assertFalse(x.is_mkldnn) |
| self.assertTrue(x.to_mkldnn().is_mkldnn) |
| |
| # legacy constructor/new doesn't support mkldnn tensors |
| @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1992") |
| def test_legacy_new_failure(self): |
| x = torch.randn(1, dtype=torch.float32) |
| x_mkldnn = x.to_mkldnn() |
| self.assertRaises(RuntimeError, lambda: x_mkldnn.new(device='cpu')) |
| self.assertRaises(RuntimeError, lambda: x_mkldnn.new(x.storage())) |
| self.assertRaises(RuntimeError, lambda: x_mkldnn.new(x)) |
| self.assertRaises(RuntimeError, lambda: x_mkldnn.new(torch.Size([2, 3]))) |
| self.assertRaises(RuntimeError, lambda: x_mkldnn.new([6])) |
| |
| def test_is_mkldnn_jit(self): |
| class EnsureMkldnn(torch.jit.ScriptModule): |
| @torch.jit.script_method |
| def forward(self, x): |
| if not x.is_mkldnn: |
| x = x.to_mkldnn() |
| return x |
| |
| m = EnsureMkldnn() |
| x = torch.randn(1, dtype=torch.float32) |
| self.assertTrue(m(x).is_mkldnn) |
| self.assertTrue(m(x.to_mkldnn()).is_mkldnn) |
| |
| def _test_imagenet_model(self, model): |
| model = model.train(False).float() |
| mkldnn_model = mkldnn_utils.to_mkldnn(copy.deepcopy(model)) |
| x = torch.randn(1, 3, 224, 224, dtype=torch.float32) |
| with torch.no_grad(): |
| self.assertEqual( |
| model(x), |
| mkldnn_model(x.to_mkldnn()).to_dense(), |
| ) |
| |
| @skipIfNoTorchVision |
| def test_resnet18(self): |
| model = torchvision.models.resnet.resnet18(weights=None) |
| self._test_imagenet_model(model) |
| |
| @skipIfNoTorchVision |
| def test_resnext50_32x4d(self): |
| model = torchvision.models.resnet.resnext50_32x4d(weights=None) |
| self._test_imagenet_model(model) |
| |
| def _lstm_params_list(self): |
| params_dict = { |
| "input_size": [1, 5], |
| "hidden_size": [5, 16], |
| "num_layers": [1, 3], |
| "bidirectional": [False, True], |
| "bias": [False, True], |
| "batch_first": [False, True], |
| "dropout": [0, 0.4, 0.7, 1], |
| "batch_size": [1, 2], |
| "seq_len": [1, 3], |
| "training": [False, True] |
| } |
| |
| params_list = list(params_dict.values()) |
| return params_list |
| |
| def _cast_dtype(self, input, dtype): |
| if dtype == torch.bfloat16: |
| input = input.to(torch.bfloat16) |
| elif dtype == torch.half: |
| input = input.to(torch.half) |
| return input |
| |
| def test_lstm(self): |
| seed = 2023 |
| torch.manual_seed(seed) |
| |
| params_list = self._lstm_params_list() |
| for dtype in types: |
| bf16 = dtype == torch.bfloat16 |
| fp16 = dtype == torch.half |
| rtol = 1.3e-6 |
| atol = 1e-5 |
| |
| if bf16: |
| rtol = 0.02 |
| atol = 0.02 |
| if fp16: |
| rtol = 1e-3 |
| atol = 1e-3 |
| for input_size, hidden_size, num_layers, bidirectional, bias, batch_first, dropout, batch_size, seq_len, training \ |
| in itertools.product(*params_list): |
| num_directions = 2 if bidirectional else 1 |
| if batch_first: |
| input = torch.randn(batch_size, seq_len, input_size, dtype=torch.float32) |
| else: |
| input = torch.randn(seq_len, batch_size, input_size, dtype=torch.float32) |
| h = torch.randn(num_layers * num_directions, batch_size, hidden_size, dtype=torch.float32) |
| c = torch.randn(num_layers * num_directions, batch_size, hidden_size, dtype=torch.float32) |
| if fp16: |
| # TODO add traing support when oneDNN support lstm FP16 training |
| training = False |
| model = torch.nn.LSTM(input_size, hidden_size, num_layers, bidirectional=bidirectional, |
| bias=bias, dropout=dropout, batch_first=batch_first).float() |
| model.train() if training else model.eval() |
| input1 = input.clone().requires_grad_(training) |
| input2 = input.clone().requires_grad_(training) |
| |
| h1 = h.clone().requires_grad_(training) |
| h2 = h.clone().requires_grad_(training) |
| c1 = c.clone().requires_grad_(training) |
| c2 = c.clone().requires_grad_(training) |
| |
| model1 = copy.deepcopy(model) |
| model2 = copy.deepcopy(model) |
| with torch.no_grad() if not training else nullcontext(): |
| with torch.backends.mkldnn.flags(enabled=False): |
| torch.manual_seed(seed) |
| output1, (hn1, cn1) = self._cast_dtype(model1, dtype)( |
| self._cast_dtype(input1, dtype), |
| ( |
| self._cast_dtype(h1, dtype), |
| self._cast_dtype(c1, dtype), |
| ), |
| ) |
| |
| torch.manual_seed(seed) |
| output2, (hn2, cn2) = self._cast_dtype(model2, dtype)( |
| self._cast_dtype(input2, dtype), |
| ( |
| self._cast_dtype(h2, dtype), |
| self._cast_dtype(c2, dtype), |
| ), |
| ) |
| self.assertEqual(output1, output2, rtol=rtol, atol=atol) |
| self.assertEqual(hn1, hn2, rtol=rtol, atol=atol) |
| self.assertEqual(cn1, cn2, rtol=rtol, atol=atol) |
| |
| if training: |
| with torch.backends.mkldnn.flags(enabled=False): |
| torch.manual_seed(seed) |
| output1.sum().backward(retain_graph=True) |
| |
| torch.manual_seed(seed) |
| output2.sum().backward(retain_graph=True) |
| |
| self.assertEqual(input1.grad, input2.grad, rtol=rtol, atol=atol) |
| for name, para in model1.named_parameters(): |
| self.assertEqual(para, getattr(model2, name)) |
| self.assertEqual( |
| para.grad, |
| getattr(model2, name).grad, |
| rtol=rtol, |
| atol=atol, |
| ) |
| |
| with torch.backends.mkldnn.flags(enabled=False): |
| torch.manual_seed(seed) |
| hn1.sum().backward(retain_graph=True) |
| torch.manual_seed(seed) |
| hn2.sum().backward(retain_graph=True) |
| self.assertEqual(h1.grad, h2.grad, rtol=rtol, atol=atol) |
| |
| with torch.backends.mkldnn.flags(enabled=False): |
| torch.manual_seed(seed) |
| cn1.sum().backward(retain_graph=True) |
| torch.manual_seed(seed) |
| cn2.sum().backward(retain_graph=True) |
| self.assertEqual(c1.grad, c2.grad, rtol=rtol, atol=atol) |
| |
| @dtypes(torch.float16, torch.bfloat16) |
| def test_matmul_lower_precision(self, dtype): |
| support_check = { |
| torch.bfloat16: torch.ops.mkldnn._is_mkldnn_bf16_supported, |
| torch.float16: torch.ops.mkldnn._is_mkldnn_fp16_supported, |
| } |
| |
| def common(self, shape1, shape2, op, dtype): |
| a = torch.randn(shape1, dtype=dtype) |
| a_ref = a.float() |
| b = torch.randn(shape2, dtype=dtype) |
| b_ref = b.float() |
| |
| y = op(a, b) |
| y_ref = op(a_ref, b_ref) |
| self.assertEqual(y, y_ref, exact_dtype=False) |
| |
| if support_check[dtype](): |
| a1 = torch.randn([64, 1, 33], dtype=dtype) |
| # a2 is contiguous tensor but it's strides |
| # is not default contiguous strides. |
| a2 = torch.as_strided(a1.clone(), [64, 1, 33], [33, 3, 1]) |
| self.assertTrue(a2.is_contiguous()) |
| b = torch.randn(64, 33, 256).to(dtype=dtype) |
| y1 = torch.ops.aten.bmm(a1, b) |
| y2 = torch.bmm(a2, b) |
| self.assertEqual(y1, y2) |
| |
| for shape1, shape2, op in [ |
| ((33, 77), (77, 22), torch.matmul), |
| ((128, 256), (256, 10), torch.matmul), |
| ((7, 300), (300, 3), torch.matmul), |
| ((1, 100), (100, 60), torch.matmul), |
| ((100, 1), (1, 100), torch.matmul), |
| ((20, 54, 78), (20, 78, 10), torch.bmm), |
| ((1, 300, 1), (1, 1, 300), torch.bmm), |
| ]: |
| common(self, shape1, shape2, op, dtype) |
| |
| |
| instantiate_device_type_tests(TestMkldnn, globals(), only_for=('cpu',)) |
| |
| if __name__ == '__main__': |
| run_tests() |
