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https://github.com/pytorch/pytorch.git
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8cde4c4d22
Summary: As part of the Variable/Tensor merge work: https://github.com/pytorch/pytorch/issues/13638, we make the following changes in this PR: 1. Remove the `Variable::Impl` class and the `DifferentiableViewImpl` class 2. Change all `Variable.data()` call sites to either use `Variable` directly, or use `Variable.tensor_data()` 3. Remove `Variable.data()` API 3. Add `Variable.variable_data()` that matches `tensor.data` in Python API, which creates a new `Variable` that shares the same storage and tensor metadata with the original `Variable`, but with a completely new autograd history. After this PR, Variable doesn't wrap a Tensor internally anymore, and both Variable and Tensor use the same TensorImpl class as its `impl_`. The only difference is that Variable always has AutogradMeta in its TensorImpl, but Tensor doesn't. **Note that this PR is BC-breaking in the following use cases:** **Use Case 1:** Previously, `x.data = y` works even if `x` and `y` are of different TensorImpl type (e.g. `x` is a CPU dense tensor whose impl is of type TensorImpl, while `y` is a CPU sparse tensor whose impl is of type SparseTensorImpl). However, after this PR, `x.data = y` doesn't work anymore if `x` and `y` are of different TensorImpl type, because the underlying implementation `variable.set_data(tensor)` no longer works if `variable` and `tensor` have different TensorImpl type. **Use Case 2:** If a tensor `x`'s `grad` is sparse, accumulating dense gradients to `x` will change the tensor that `x.grad` is pointing to. This is better illustrated with the following example: ```python params = torch.tensor([1.5, 1.5]).requires_grad_() with torch.no_grad(): # Change gradient to a sparse tensor params.grad = torch.sparse_coo_tensor(torch.tensor([[1, 1]]).long(), torch.tensor([1., 1.])) grad_saved = params.grad params.backward(torch.tensor([1.5, 1.5])) assert id(grad_saved) == id(params.grad) # This will fail after this PR ``` The assertion in the last line will fail after this PR, because adding dense gradients to sparse gradients will change the `params.grad` tensor reference. Pull Request resolved: https://github.com/pytorch/pytorch/pull/17072 Differential Revision: D14075257 Pulled By: yf225 fbshipit-source-id: 0e681df641270dea586042dd26db59f2e76b5957
288 lines
11 KiB
Python
288 lines
11 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import copy
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import unittest
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import torch
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import torch.jit
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from torch.utils import mkldnn as mkldnn_utils
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from common_utils import TestCase, run_tests, TemporaryFileName
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from torch.autograd.gradcheck import gradgradcheck, gradcheck
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# Comment the line below to find out the CI machines having MKL-DNN build disabled
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@unittest.skipIf(not torch._C.has_mkldnn, "MKL-DNN build is disabled")
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class TestMkldnn(TestCase):
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def test_conversion(self):
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for cpu_tensor in [torch.randn((1, 2, 3, 4),
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dtype=torch.float, device=torch.device('cpu')),
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torch.randn((1, 2, 3, 4, 5),
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dtype=torch.float, device=torch.device('cpu'))[:, :, :, :, 1]]:
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cpu_tensor.requires_grad_()
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mkldnn_tensor = cpu_tensor.to_mkldnn()
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cpu_tensor_1 = mkldnn_tensor.to_dense()
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self.assertEqual(cpu_tensor, cpu_tensor_1)
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self.assertEqual(mkldnn_tensor.dtype, torch.float)
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self.assertEqual(mkldnn_tensor.device, torch.device('cpu'))
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self.assertEqual(mkldnn_tensor.size(), torch.Size([1, 2, 3, 4]))
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self.assertEqual(mkldnn_tensor.numel(), cpu_tensor.numel())
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self.assertEqual(mkldnn_tensor.element_size(), cpu_tensor.element_size())
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self.assertRaisesRegex(RuntimeError,
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"Cannot access data pointer of Tensor that doesn't have storage",
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lambda: mkldnn_tensor.data_ptr() != 0)
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def test_unsupported(self):
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# unsupported types and unsupported types with gpu
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for dtype in [torch.double, torch.half, torch.uint8, torch.int8,
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torch.short, torch.int, torch.long]:
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with self.assertRaises(RuntimeError) as context:
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torch.randn(1, 2, 3, 4, dtype=dtype, device=torch.device('cpu')).to_mkldnn()
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if torch.cuda.is_available():
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with self.assertRaises(RuntimeError) as context:
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torch.randn(1, 2, 3, 4, dtype=dtype, device=torch.device('cuda')).to_mkldnn()
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# supported type with gpu
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if torch.cuda.is_available():
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with self.assertRaises(RuntimeError) as context:
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torch.randn(1, 2, 3, 4, dtype=torch.float, device=torch.device('cuda')).to_mkldnn()
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# some factory functions
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for creator in [torch.empty, torch.ones, torch.zeros, torch.randn, torch.rand]:
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with self.assertRaises(RuntimeError) as context:
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creator(1, 2, 3, 4, dtype=torch.float, device=torch.device('cpu'), layout=torch._mkldnn)
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def test_autograd_to_mkldnn(self):
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# MKLDNN only supports float32
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root = torch.randn(4, 5, dtype=torch.float32, requires_grad=True)
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def func(root):
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return root.to_mkldnn().to_dense()
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# because MKLDNN only supports float32, we need to lessen the precision.
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# these numbers are just empirical results that seem to work.
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self.assertWarnsRegex(lambda: gradcheck(func, [root], atol=4e-2, rtol=1e-2),
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'double precision floating point')
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self.assertWarnsRegex(lambda: gradgradcheck(func, [root], atol=4e-2, rtol=1e-2),
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'double precision floating point')
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def test_autograd_from_mkldnn(self):
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# MKLDNN only supports float32
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root = torch.randn(4, 5, dtype=torch.float32).to_mkldnn().requires_grad_()
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def func(root):
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return root.to_dense()
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# because MKLDNN only supports float32, we need to lessen the precision.
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# these numbers are just empirical results that seem to work.
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self.assertWarnsRegex(lambda: gradcheck(func, [root], atol=4e-2, rtol=1e-2),
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'double precision floating point')
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def test_detach(self):
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root = torch.randn(4, 5, dtype=torch.float32).to_mkldnn().requires_grad_()
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detach = root.detach()
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self.assertEqual((4, 5), detach.size())
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self.assertFalse(detach.requires_grad)
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self.assertTrue(root.requires_grad)
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detach_ = root.detach_()
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self.assertEqual((4, 5), detach_.size())
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self.assertFalse(detach_.requires_grad)
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self.assertFalse(root.requires_grad)
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def test_repr(self):
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self.assertTrue("layout=torch._mkldnn" in str(torch.randn((1, 2, 3, 4),
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dtype=torch.float, device=torch.device('cpu')).to_mkldnn()))
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def test_conv2d(self):
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for groups in [1, 4]:
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N = torch.randint(3, 10, (1,)).item()
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C = torch.randint(1, 3, (1,)).item() * groups
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M = torch.randint(1, 3, (1,)).item() * groups
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x = torch.randn(N, C, 224, 224, dtype=torch.float32) * 100
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for bias in [True, False]:
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conv2d = torch.nn.Conv2d(in_channels=C,
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out_channels=M,
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kernel_size=3,
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stride=2,
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padding=1,
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bias=bias,
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groups=groups).float()
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mkldnn_conv2d = mkldnn_utils.to_mkldnn(copy.deepcopy(conv2d))
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self.assertEqual(
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conv2d(x),
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mkldnn_conv2d(x.to_mkldnn()).to_dense())
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self._test_serialization(mkldnn_conv2d, (x.to_mkldnn(),))
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self._test_tracing(mkldnn_conv2d, (x.to_mkldnn(),))
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def test_relu(self):
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x = torch.randn((4, 5), dtype=torch.float32) * 10
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self.assertEqual(torch.relu(x), torch.relu(x.to_mkldnn()).to_dense())
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def test_relu_(self):
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x1 = torch.randn((4, 5), dtype=torch.float32) * 10
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x2 = x1.clone().to_mkldnn()
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self.assertEqual(torch.relu_(x1), torch.relu_(x2).to_dense())
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def test_max_pool2d(self):
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N = torch.randint(3, 10, (1,)).item()
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C = torch.randint(3, 10, (1,)).item()
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x = torch.randn(N, C, 64, 64, dtype=torch.float32) * 10
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max_pool2d = torch.nn.MaxPool2d(
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kernel_size=3,
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stride=2,
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padding=1)
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self.assertEqual(
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max_pool2d(x),
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max_pool2d(x.to_mkldnn()).to_dense())
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def test_avg_pool2d(self):
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N = torch.randint(3, 10, (1,)).item()
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C = torch.randint(3, 10, (1,)).item()
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x = torch.randn(N, C, 64, 64, dtype=torch.float32) * 10
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for count_include_pad in [True, False]:
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avg_pool2d = torch.nn.AvgPool2d(
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kernel_size=3,
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stride=2,
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padding=1,
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count_include_pad=count_include_pad)
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self.assertEqual(
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avg_pool2d(x),
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avg_pool2d(x.to_mkldnn()).to_dense())
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def test_adaptive_avg_pool2d(self):
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N = torch.randint(3, 10, (1,)).item()
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C = torch.randint(3, 10, (1,)).item()
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x = torch.randn(N, C, 224, 224, dtype=torch.float32) * 100
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adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d(7)
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self.assertEqual(
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adaptive_avg_pool2d(x),
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adaptive_avg_pool2d(x.to_mkldnn()).to_dense())
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def test_batch_norm2d(self):
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N = torch.randint(3, 10, (1,)).item()
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C = torch.randint(3, 100, (1,)).item()
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x = torch.randn(N, C, 35, 45, dtype=torch.float32) * 10
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# TODO: support training
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for train in [False]:
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bn = torch.nn.BatchNorm2d(C).float().train(train)
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mkldnn_bn = mkldnn_utils.to_mkldnn(copy.deepcopy(bn))
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self.assertEqual(
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bn(x),
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mkldnn_bn(x.to_mkldnn()).to_dense())
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self._test_serialization(mkldnn_bn, (x.to_mkldnn(),))
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self._test_tracing(mkldnn_bn, (x.to_mkldnn(),))
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def test_add(self):
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N = torch.randint(3, 10, (1,)).item()
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C = torch.randint(3, 100, (1,)).item()
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alpha = torch.randn(1, dtype=torch.float32).item()
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x = torch.randn(N, C, 35, 45, dtype=torch.float32) * 10
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y = torch.randn(N, C, 35, 45, dtype=torch.float32) * 10
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mx = x.to_mkldnn()
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my = y.to_mkldnn()
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# add
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self.assertEqual(
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x + y,
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(mx + my).to_dense())
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self.assertEqual(
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torch.add(x, y, alpha=alpha),
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torch.add(mx, my, alpha=alpha).to_dense())
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# add_
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x += y
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mx += my
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self.assertEqual(x, mx.to_dense())
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# add_out
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out = x.clone()
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mkldnn_out = out.to_mkldnn()
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torch.add(x, y, alpha=alpha, out=out)
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torch.add(mx, my, alpha=alpha, out=mkldnn_out)
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self.assertEqual(out, mkldnn_out.to_dense())
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def test_view(self):
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x = torch.randn(3, 4, 5, dtype=torch.float32).to_mkldnn()
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self.assertRaisesRegex(RuntimeError,
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"Change to use reshape",
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lambda: x.view(x.size(0), -1))
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def test_reshape(self):
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x = torch.randn(3, 4, 5, dtype=torch.float32) * 10
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size = (x.size(0), -1)
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self.assertEqual(
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x.reshape(size),
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x.to_mkldnn().reshape(size).to_dense(),
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)
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def test_clone(self):
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x = torch.randn(4, 5, dtype=torch.float32) * 10
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self.assertEqual(
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x.clone(),
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x.to_mkldnn().clone().to_dense(),
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)
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def test_linear(self):
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in_features = torch.randint(3, 10, (1,)).item()
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out_features = torch.randint(3, 100, (1,)).item()
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x = torch.randn(3, in_features, dtype=torch.float32) * 10
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for bias in [True, False]:
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linear = torch.nn.Linear(in_features, out_features, bias=bias).float()
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mkldnn_linear = mkldnn_utils.to_mkldnn(copy.deepcopy(linear))
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self.assertEqual(
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linear(x),
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mkldnn_linear(x.to_mkldnn()).to_dense())
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self._test_serialization(mkldnn_linear, (x.to_mkldnn(),))
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self._test_tracing(mkldnn_linear, (x.to_mkldnn(),))
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def test_sigmoid(self):
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x = torch.randn(4, 5, dtype=torch.float32) * 10
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mkldnn_x = x.to_mkldnn()
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self.assertEqual(
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torch.sigmoid(x),
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torch.sigmoid(mkldnn_x).to_dense(),
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)
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# inplace
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torch.sigmoid_(x)
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torch.sigmoid_(mkldnn_x)
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self.assertEqual(x, mkldnn_x.to_dense())
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def _test_serialization(self, module, inputs):
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with TemporaryFileName() as fname:
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torch.jit.save(module, fname)
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loaded = torch.jit.load(fname)
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self.assertEqual(
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module(*inputs).to_dense(),
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loaded(*inputs).to_dense())
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def _test_tracing(self, module, inputs):
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traced = torch.jit.trace(module, inputs, check_trace=False)
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self.assertEqual(
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module(*inputs).to_dense(),
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traced(*inputs).to_dense())
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def test_set_data_tensorimpl_type(self):
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# Dense tensor has impl of type `TensorImpl`, while MKL-DNN tensor has impl
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# of type `OpaqueTensorImpl<IDeepTensorWrapperPtr>`.
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x = torch.randn((1, 2), dtype=torch.float, device=torch.device('cpu'))
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x_mkldnn = x.to_mkldnn()
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with self.assertRaisesRegex(RuntimeError, 'different types of TensorImpl'):
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x.data = x_mkldnn
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if __name__ == '__main__':
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run_tests()
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