test_mkldnn.py

from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import unittest

import torch
from torch.utils import mkldnn as mkldnn_utils
from common_utils import TestCase, run_tests
from torch.autograd.gradcheck import gradgradcheck, gradcheck


# 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_()
            mkldnn_tensor = cpu_tensor.to_mkldnn()
            cpu_tensor_1 = mkldnn_tensor.to_dense()
            self.assertEqual(cpu_tensor, cpu_tensor_1)
            self.assertEqual(mkldnn_tensor.dtype, torch.float)
            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())
            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_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.empty, torch.ones, torch.zeros, 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(lambda: gradcheck(func, [root], atol=4e-2, rtol=1e-2),
                              'double precision floating point')
        self.assertWarnsRegex(lambda: gradgradcheck(func, [root], atol=4e-2, rtol=1e-2),
                              'double precision floating point')

    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(lambda: gradcheck(func, [root], atol=4e-2, rtol=1e-2),
                              'double precision floating point')

    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_conv2d(self):
        for groups in [1, 4]:
            N = torch.randint(3, 10, (1,)).item()
            C = torch.randint(1, 3, (1,)).item() * groups
            M = torch.randint(1, 3, (1,)).item() * groups
            x = torch.randn(N, C, 224, 224, dtype=torch.float32) * 100
            for bias in [True, False]:
                conv2d = torch.nn.Conv2d(in_channels=C,
                                         out_channels=M,
                                         kernel_size=3,
                                         stride=2,
                                         padding=1,
                                         bias=bias,
                                         groups=groups).float()
                mkldnn_conv2d = mkldnn_utils.to_mkldnn(copy.deepcopy(conv2d))
                self.assertEqual(
                    conv2d(x),
                    mkldnn_conv2d(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())

    def test_max_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

        max_pool2d = torch.nn.MaxPool2d(
            kernel_size=3,
            stride=2,
            padding=1)

        self.assertEqual(
            max_pool2d(x),
            max_pool2d(x.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

        for count_include_pad in [True, False]:
            avg_pool2d = torch.nn.AvgPool2d(
                kernel_size=3,
                stride=2,
                padding=1,
                count_include_pad=count_include_pad)

            self.assertEqual(
                avg_pool2d(x),
                avg_pool2d(x.to_mkldnn()).to_dense())

    def test_batch_norm2d(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

        # TODO: support training
        for train in [False]:
            bn = torch.nn.BatchNorm2d(C).float().train(train)
            mkldnn_bn = mkldnn_utils.to_mkldnn(copy.deepcopy(bn))
            self.assertEqual(
                bn(x),
                mkldnn_bn(x.to_mkldnn()).to_dense())

    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_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(),
        )

    def test_clone(self):
        x = torch.randn(4, 5, dtype=torch.float32) * 10
        self.assertEqual(
            x.clone(),
            x.to_mkldnn().clone().to_dense(),
        )

    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).float()
            mkldnn_linear = mkldnn_utils.to_mkldnn(copy.deepcopy(linear))
            self.assertEqual(
                linear(x),
                mkldnn_linear(x.to_mkldnn()).to_dense())


if __name__ == '__main__':
    run_tests()