| import copy |
| import itertools |
| import functools |
| import unittest |
| |
| 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 |
| |
| # batched grad doesn't support mkldnn |
| gradcheck = functools.partial(gradcheck, check_batched_grad=False) |
| gradgradcheck = functools.partial(gradgradcheck, check_batched_grad=False) |
| |
| |
| # For OneDNN bf16 path, OneDNN requires the cpu has intel avx512 with avx512bw, |
| # avx512vl, and avx512dq at least. So we will skip the test case if one processor |
| # is not meet the requirement. |
| def has_bf16_support(): |
| import subprocess |
| try: |
| cmd = "grep avx512bw /proc/cpuinfo | grep avx512vl | grep avx512dq" |
| subprocess.check_output(cmd, shell=True) |
| return True |
| except subprocess.CalledProcessError: |
| return False |
| |
| types = [torch.float, torch.bfloat16] |
| |
| # Comment the line below to find out the CI machines having MKL-DNN build disabled |
| @unittest.skipIf(not torch._C.has_mkldnn, "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_() |
| # float cpu tensor to mkldnn float tensor or bfloat tensor. |
| 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 types: |
| 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. |
| cpu_tensor_bf16 = cpu_tensor.bfloat16() |
| for dtype1 in types: |
| mkldnn_tensor = cpu_tensor_bf16.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 types: |
| cpu_tensor_2 = mkldnn_tensor.to_dense(dtype2) |
| self.assertEqual(cpu_tensor_2.dtype, dtype2) |
| self.assertEqual(cpu_tensor_bf16, cpu_tensor_2.bfloat16(), 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 == torch.bfloat16: |
| self.assertEqual(mkldnn_tensor.element_size(), cpu_tensor_bf16.element_size()) |
| else: |
| self.assertEqual(mkldnn_tensor.element_size(), cpu_tensor_bf16.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_unsupported(self): |
| # unsupported types and unsupported types with gpu |
| for dtype in [torch.double, torch.half, 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_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], [1, 2], [1, 4]) |
| for 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() |
| mkldnn_conv = mkldnn_utils.to_mkldnn(copy.deepcopy(conv)) |
| with torch.backends.mkldnn.flags(enabled=False): |
| y_aten = conv(x) |
| y_mkldnn = mkldnn_conv(x.to_mkldnn()).to_dense() |
| self.assertEqual(y_aten, y_mkldnn) |
| |
| self._test_serialization(mkldnn_conv, (x.to_mkldnn(),)) |
| self._test_tracing(mkldnn_conv, (x.to_mkldnn(),)) |
| |
| 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) |
| |
| @unittest.skipIf(IS_WINDOWS, "Limit support for bf16 path") |
| def _test_conv_bf16_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], [1, 2], [1, 4]) |
| for 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() |
| x_bf16 = x.bfloat16() |
| if has_bf16_support(): |
| mkldnn_conv = mkldnn_utils.to_mkldnn(copy.deepcopy(conv)) |
| mkldnn_conv_bf16 = mkldnn_utils.to_mkldnn(copy.deepcopy(conv), torch.bfloat16) |
| y = mkldnn_conv(x.to_mkldnn()).to_dense() |
| y_bf16 = mkldnn_conv_bf16(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" |
| with self.assertRaisesRegex(RuntimeError, msg): |
| mkldnn_conv_bf16 = mkldnn_utils.to_mkldnn(copy.deepcopy(conv), torch.bfloat16) |
| y_bf16 = mkldnn_conv_bf16(x_bf16.to_mkldnn()).to_dense(torch.float32) |
| |
| def test_conv1d_bf16(self): |
| self._test_conv_bf16_base(dim=1) |
| |
| def test_conv2d_bf16(self): |
| self._test_conv_bf16_base(dim=2) |
| |
| def test_conv3d_bf16(self): |
| self._test_conv_bf16_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()) |
| |
| def test_relu(self): |
| x = torch.randn((4, 5), dtype=torch.float32) * 10 |
| self.assertEqual(torch.relu(x), torch.relu(x.to_mkldnn()).to_dense()) |
| |
| def test_relu_(self): |
| x1 = torch.randn((4, 5), dtype=torch.float32) * 10 |
| x2 = x1.clone().to_mkldnn() |
| self.assertEqual(torch.relu_(x1), torch.relu_(x2).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 has_bf16_support(): |
| 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_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) |
| |
| self.assertEqual( |
| max_pool(input), |
| max_pool(input.to_mkldnn()).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 has_bf16_support(): |
| 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()) |
| |
| 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) |
| |
| self.assertEqual( |
| avg_pool(input), |
| avg_pool(input.to_mkldnn()).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 has_bf16_support(): |
| 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) |
| |
| self.assertEqual( |
| adaptive_avg_pool2d(x), |
| adaptive_avg_pool2d(x.to_mkldnn()).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 has_bf16_support(): |
| 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} |
| # TODO: support training |
| for train in [False]: |
| bn = bn_module[dim](channels).float().train(train) |
| 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_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) |
| |
| 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 has_bf16_support(): |
| 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()) |
| |
| 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_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_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_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) |
| |
| @unittest.skipIf(IS_WINDOWS, "Limit support for bf16 path") |
| def test_linear_bf16(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 |
| x_bf16 = x.bfloat16() |
| |
| 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_bf16 = mkldnn_utils.to_mkldnn(copy.deepcopy(linear), torch.bfloat16) |
| if has_bf16_support(): |
| y = mkldnn_linear(x.to_mkldnn()).to_dense() |
| y_bf16 = mkldnn_linear_bf16(x_bf16.to_mkldnn()).to_dense(torch.float32) |
| self.assertEqual(y, y_bf16, atol=1e-1, rtol=1e-3) |
| else: |
| msg = "mkldnn_linear: bf16 path needs the cpu support avx512bw, avx512vl and avx512dq" |
| self.assertRaisesRegex(RuntimeError, |
| msg, |
| lambda: mkldnn_linear_bf16(x_bf16.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_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, check_trace=False) |
| 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 |
| 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(pretrained=False) |
| self._test_imagenet_model(model) |
| |
| @skipIfNoTorchVision |
| def test_resnext50_32x4d(self): |
| model = torchvision.models.resnet.resnext50_32x4d(pretrained=False) |
| self._test_imagenet_model(model) |
| |
| |
| if __name__ == '__main__': |
| run_tests() |
