# Owner(s): ["module: intel"] import itertools import math import unittest from itertools import product import torch import torch.backends.cudnn as cudnn import torch.nn as nn import torch.nn.functional as F from torch._C._dynamo.guards import assert_size_stride from torch.testing import make_tensor from torch.testing._internal.common_cuda import tf32_is_not_fp32 from torch.testing._internal.common_device_type import ( dtypes, instantiate_device_type_tests, onlyXPU, ) from torch.testing._internal.common_dtype import floating_types_and from torch.testing._internal.common_nn import _test_module_empty_input, NNTestCase from torch.testing._internal.common_utils import ( dtype2prec_DONTUSE, gradcheck, gradgradcheck, parametrize as parametrize_test, run_tests, set_default_dtype, TEST_SCIPY, TEST_WITH_ROCM, ) AMPERE_OR_ROCM = TEST_WITH_ROCM or tf32_is_not_fp32() if TEST_SCIPY: import scipy.ndimage import scipy.signal class TestConvolutionNNDeviceType(NNTestCase): def run_conv_double_back_test( self, kern, stride, padding, chan_in, chan_out, batch_size, inp_size, dilation, no_weight, groups=1, use_xpu=False, use_bias=True, dtype=torch.double, ): device = torch.device("xpu" if use_xpu else "cpu") x = torch.randn( batch_size, chan_in, inp_size, inp_size, device=device, dtype=dtype, requires_grad=True, ) weight = torch.randn( chan_out, chan_in // groups, kern, kern, device=device, dtype=dtype, requires_grad=not no_weight, ) if use_bias: bias = torch.randn(chan_out, device=device, dtype=dtype, requires_grad=True) else: bias = None def func(*inputs): if use_bias: lx, lweight, lbias = inputs else: lx, lweight = inputs lbias = None out = F.conv2d(lx, lweight, lbias, stride, padding, dilation, groups) return out if use_bias: inputs = x, weight, bias else: inputs = x, weight dummy_out = func(*inputs) grad_y = torch.randn_like( dummy_out, device=device, dtype=dtype, requires_grad=True ) if dtype == torch.float: (g,) = torch.autograd.grad(dummy_out.sum(), x, create_graph=True) return g.requires_grad return gradgradcheck(func, inputs, (grad_y,)) @dtypes(*floating_types_and(torch.half, torch.bfloat16)) def test_Conv2d_large_workspace(self, device, dtype): sizes = [ (1, 256, 109, 175), (1, 256, 80, 128), (1, 256, 120, 192), ] def run_test(benchmark): conv = torch.nn.Conv2d(256, 256, kernel_size=3, padding=1).to(device, dtype) for size in sizes: x = torch.randn(size, device=device, dtype=dtype) out = conv(x.detach().clone().requires_grad_()) out.backward(torch.ones_like(out)) run_test(benchmark=False) run_test(benchmark=True) @dtypes(torch.half, torch.float) def test_ConvTranspose2d_large_output_padding(self, device, dtype): net1 = torch.nn.ConvTranspose2d( 128, 64, kernel_size=3, stride=2, padding=1, output_padding=1 ).to(device=device, dtype=dtype) net2 = torch.nn.ConvTranspose2d( 64, 32, kernel_size=3, stride=2, padding=1, output_padding=1 ).to(device=device, dtype=dtype) net3 = torch.nn.ConvTranspose2d( 32, 3, kernel_size=3, stride=2, padding=1, output_padding=1 ).to(device=device, dtype=dtype) x = torch.rand(1, 128, 6, 6, device=device, dtype=dtype, requires_grad=True) x = net1(x) x = net2(x) x = net3(x) x.backward(torch.randn_like(x)) @dtypes(torch.float, torch.double, torch.half) def test_Conv2d_depthwise_naive_groups(self, device, dtype): if dtype == torch.half and "xpu" in device: self.skipTest( "The accuracy issue of dtype fp16 would be fixed in oneDNN v3.4" ) for depth_multiplier in [1, 2]: m = nn.Conv2d(2, 2 * depth_multiplier, kernel_size=3, groups=2).to( device, dtype ) i = ( torch.randn(2, 2, 6, 6, device=device, dtype=dtype) .div_(2) .requires_grad_() ) output = m(i) grad_output = ( torch.randn(2, 2 * depth_multiplier, 4, 4, device=device, dtype=dtype) / 2 ) output.backward(grad_output) offset = 1 * depth_multiplier m1 = nn.Conv2d(1, 1 * depth_multiplier, kernel_size=3).to(device, dtype) m1.weight.data = m.weight.data[:offset].clone() m1.bias.data = m.bias.data[:offset].clone() i1 = i.detach()[:, :1].clone().requires_grad_() output1 = m1(i1) output1.backward(grad_output[:, :offset].contiguous()) m2 = nn.Conv2d(1, 1 * depth_multiplier, kernel_size=3).to(device, dtype) m2.weight.data.copy_(m.weight.data[offset:]) m2.bias.data.copy_(m.bias.data[offset:]) i2 = i.detach()[:, 1:].clone().requires_grad_() output2 = m2(i2) output2.backward(grad_output[:, offset:].contiguous()) self.assertEqual( output, torch.cat([output1, output2], 1), atol=dtype2prec_DONTUSE[dtype], rtol=0, ) self.assertEqual( i.grad.data, torch.cat([i1.grad.data, i2.grad.data], 1), atol=dtype2prec_DONTUSE[dtype], rtol=0, ) self.assertEqual( m.bias.grad.data, torch.cat([m1.bias.grad.data, m2.bias.grad.data], 0), atol=dtype2prec_DONTUSE[dtype], rtol=0, ) self.assertEqual( m.weight.grad.data, torch.cat([m1.weight.grad.data, m2.weight.grad.data], 0), atol=dtype2prec_DONTUSE[dtype], rtol=0, ) @dtypes(torch.float, torch.double, torch.half) def test_Conv3d_depthwise_naive_groups(self, device, dtype): if dtype == torch.half and "xpu" in device: self.skipTest( "The accuracy issue of dtype fp16 would be fixed in oneDNN v3.4" ) for depth_multiplier in [1, 2]: m = nn.Conv3d(2, 2 * depth_multiplier, kernel_size=3, groups=2).to( device, dtype ) i = ( torch.randn(2, 2, 6, 6, 6, device=device, dtype=dtype) .div_(2) .requires_grad_() ) output = m(i) grad_output = ( torch.randn( 2, 2 * depth_multiplier, 4, 4, 4, device=device, dtype=dtype ) / 2 ) output.backward(grad_output) offset = 1 * depth_multiplier m1 = nn.Conv3d(1, 1 * depth_multiplier, kernel_size=3).to(device, dtype) m1.weight.data = m.weight.data[:offset].clone() m1.bias.data = m.bias.data[:offset].clone() i1 = i.detach()[:, :1].clone().requires_grad_() output1 = m1(i1) output1.backward(grad_output[:, :offset].contiguous()) m2 = nn.Conv3d(1, 1 * depth_multiplier, kernel_size=3).to(device, dtype) m2.weight.data.copy_(m.weight.data[offset:]) m2.bias.data.copy_(m.bias.data[offset:]) i2 = i.detach()[:, 1:].clone().requires_grad_() output2 = m2(i2) output2.backward(grad_output[:, offset:].contiguous()) atol, rtol = (3e-4, 3e-2) self.assertEqual( output, torch.cat([output1, output2], 1), atol=atol, rtol=rtol ) self.assertEqual( i.grad.data, torch.cat([i1.grad.data, i2.grad.data], 1), atol=dtype2prec_DONTUSE[dtype], rtol=0, ) self.assertEqual( m.bias.grad.data, torch.cat([m1.bias.grad.data, m2.bias.grad.data], 0), atol=dtype2prec_DONTUSE[dtype], rtol=0, ) self.assertEqual( m.weight.grad.data, torch.cat([m1.weight.grad.data, m2.weight.grad.data], 0), atol=atol, rtol=rtol, ) @dtypes(torch.float, torch.double, torch.half) def test_noncontig_conv_grad(self, device, dtype): module = nn.Conv2d(3, 5, kernel_size=3, padding=1).to(device, dtype) input = torch.randn( 2, 3, 10, 10, dtype=dtype, device=device, requires_grad=True ) output = module(input) grad = torch.randn(2, 2, 5, 10, 10, dtype=dtype, device=device)[:, 1] assert not grad.is_contiguous() output.backward(grad, retain_graph=True) self.assertIsNotNone(input.grad) result = input.grad.data.clone() input.grad.data.zero_() output.backward(grad.contiguous()) self.assertEqual( result, input.grad.data, atol=dtype2prec_DONTUSE[dtype], rtol=0 ) @dtypes(torch.double) def test_conv_double_backward(self, device, dtype): with torch.backends.cudnn.flags(enabled=True, deterministic=True): batch_size = 1 for kern, inp_size, dilations in [(3, 5, [1, 2]), (4, 9, [1])]: for stride, padding, chan_in, chan_out, dilation in product( [1], [2], [2], [3], dilations ): no_weight = stride == 2 result = self.run_conv_double_back_test( kern, stride, padding, chan_in, chan_out, batch_size, inp_size, dilation, no_weight, use_xpu=True, dtype=dtype, ) self.assertTrue(result, "Conv double backward test failed") def test_conv_double_backward_no_bias(self): kern, stride = 3, 2 chan_in, chan_out = 2, 4 batch_size, inp_size = 2, 5 padding, dilation = 1, 1 no_weight, use_bias = False, True result = self.run_conv_double_back_test( kern, stride, padding, chan_in, chan_out, batch_size, inp_size, dilation, no_weight, use_bias=use_bias, ) self.assertTrue(result, "Conv double backward test failed") def test_conv_double_backward_groups(self): kern, stride, padding = 3, 1, 2 chan_in, chan_out = 2, 4 batch_size, inp_size, dilation = 2, 6, 1 no_weight = False groups = 2 result = self.run_conv_double_back_test( kern, stride, padding, chan_in * groups, chan_out * groups, batch_size, inp_size, dilation, no_weight, groups=groups, ) self.assertTrue(result, "Conv double backward test failed") def test_conv_double_backward_stride(self): batch_size = 2 for kern, inp_size, dilations in [(3, 5, [1, 2]), (3, 7, [1])]: for stride, padding, chan_in, chan_out, dilation in product( [2], [0, 1], [1], [2], dilations ): no_weight = False self.run_conv_double_back_test( kern, stride, padding, chan_in, chan_out, batch_size, inp_size, dilation, no_weight, ) @dtypes(torch.float) def test_conv1d_same_padding(self, device, dtype): test_args = [ range(50, 55), [1, 2, 3, 8], range(1, 4), [1], ] for in_size, k_size, dilation, stride in itertools.product(*test_args): x = torch.rand(1, 1, in_size, device=device, dtype=dtype) y = torch.rand(1, 1, k_size, device=device, dtype=dtype) z = F.conv1d(x, y, padding="same", dilation=dilation, stride=stride) self.assertEqual(z.size(2), int(math.ceil(in_size / stride))) x = torch.rand(1, 1, 12, device=device, dtype=dtype) y = torch.rand(1, 1, 3, device=device, dtype=dtype) expect = F.conv1d(x, y, padding=1) actual = F.conv1d(x, y, padding="same") self.assertEqual(expect, actual) x = torch.rand(1, 1, 12, device=device, dtype=dtype) y = torch.rand(1, 1, 4, device=device, dtype=dtype) expect = F.conv1d(x, y, padding=3, dilation=2) actual = F.conv1d(x, y, padding="same", dilation=2) self.assertEqual(expect, actual) expect = F.conv1d(x, y, padding=5, dilation=3)[..., 1:] actual = F.conv1d(x, y, padding="same", dilation=3) self.assertEqual(expect, actual) @dtypes(torch.float) def test_conv3d_same_padding(self, device, dtype): rtol, atol = None, None x = torch.rand(1, 1, 10, 11, 12, device=device, dtype=dtype) y = torch.rand(1, 1, 1, 2, 5, device=device, dtype=dtype) expect = F.conv3d(x, y, padding=(0, 1, 2))[..., :, 1:, :] actual = F.conv3d(x, y, padding="same") self.assertEqual(expect, actual, rtol=rtol, atol=atol) expect = F.conv3d(x, y, padding=(0, 1, 4), dilation=2) actual = F.conv3d(x, y, padding="same", dilation=2) self.assertEqual(expect, actual, rtol=rtol, atol=atol) y = torch.rand(1, 1, 4, 4, 4, device=device, dtype=dtype) expect = F.conv3d(x, y, padding=5, dilation=3)[..., 1:, 1:, 1:] actual = F.conv3d(x, y, padding="same", dilation=3) self.assertEqual(expect, actual, rtol=rtol, atol=atol) @dtypes(torch.float) def test_conv1d_valid_padding(self, device, dtype): x = torch.rand(1, 1, 10, device=device, dtype=dtype) y = torch.rand(1, 1, 4, device=device, dtype=dtype) expect = F.conv1d(x, y) actual = F.conv1d(x, y, padding="valid") self.assertEqual(expect, actual) @dtypes(torch.float) def test_conv2d_valid_padding(self, device, dtype): x = torch.rand(1, 1, 1, 10, device=device, dtype=dtype) y = torch.rand(1, 1, 1, 4, device=device, dtype=dtype) expect = F.conv2d(x, y) actual = F.conv2d(x, y, padding="valid") self.assertEqual(expect, actual) @dtypes(torch.float) def test_conv3d_valid_padding(self, device, dtype): x = torch.rand(1, 1, 1, 1, 10, dtype=dtype, device=device) y = torch.rand(1, 1, 1, 1, 4, dtype=dtype, device=device) expect = F.conv3d(x, y) actual = F.conv3d(x, y, padding="valid") self.assertEqual(expect, actual) @dtypes(torch.float) def test_conv1d_same_padding_backward(self, device, dtype): x = torch.rand(1, 1, 12, dtype=dtype, device=device, requires_grad=True) y = torch.rand(1, 1, 4, dtype=dtype, device=device, requires_grad=True) z = F.conv1d(x, y, padding=3, dilation=2) z.sum().abs().backward() gx_expect, gy_expect = x.grad, y.grad x.grad, y.grad = None, None z = F.conv1d(x, y, padding="same", dilation=2) z.sum().abs().backward() self.assertEqual(gx_expect, x.grad) self.assertEqual(gy_expect, y.grad) x.grad, y.grad = None, None z = F.conv1d(x, y, padding=2)[..., 1:] z.sum().abs().backward() gx_expect, gy_expect = x.grad, y.grad x.grad, y.grad = None, None z = F.conv1d(x, y, padding="same") z.sum().abs().backward() self.assertEqual(gx_expect, x.grad) self.assertEqual(gy_expect, y.grad) @dtypes(torch.float) def test_conv2d_same_padding_backward(self, device, dtype): x = torch.rand(1, 1, 10, 11, device=device, dtype=dtype, requires_grad=True) y = torch.rand(1, 1, 4, 5, device=device, dtype=dtype, requires_grad=True) z = F.conv2d(x, y, padding=(3, 4), dilation=2) z.sum().abs().backward() gx_expect, gy_expect = x.grad, y.grad x.grad, y.grad = None, None z = F.conv2d(x, y, padding="same", dilation=2) z.sum().abs().backward() self.assertEqual(gx_expect, x.grad) self.assertEqual(gy_expect, y.grad) x.grad, y.grad = None, None y = torch.rand(1, 1, 4, 4, device=device, dtype=dtype, requires_grad=True) z = F.conv2d(x, y, padding=2)[..., 1:, 1:] z.sum().abs().backward() gx_expect, gy_expect = x.grad, y.grad x.grad, y.grad = None, None z = F.conv2d(x, y, padding="same") z.sum().abs().backward() self.assertEqual(gx_expect, x.grad) self.assertEqual(gy_expect, y.grad) @dtypes(torch.double) def test_conv3d_same_padding_backward(self, device, dtype): x = torch.rand(1, 1, 1, 11, 12, dtype=dtype, device=device, requires_grad=True) y = torch.rand(1, 1, 1, 2, 5, dtype=dtype, device=device, requires_grad=True) z = F.conv3d(x, y, padding=(0, 1, 4), dilation=2) z.sum().abs().backward() gx_expect, gy_expect = x.grad, y.grad x.grad, y.grad = None, None z = F.conv3d(x, y, padding="same", dilation=2) z.sum().abs().backward() self.assertEqual(gx_expect, x.grad) self.assertEqual(gy_expect, y.grad) x.grad, y.grad = None, None gradcheck( lambda x, y: F.conv3d(x, y, padding="same", dilation=2), (x, y), check_forward_ad=True, nondet_tol=1e-5, ) gradgradcheck( lambda x, y: F.conv3d(x, y, padding="same", dilation=2), (x, y), check_fwd_over_rev=True, ) y = torch.rand(1, 1, 1, 4, 4, dtype=dtype, device=device, requires_grad=True) z = F.conv3d(x, y, padding=2)[..., 1:, 1:] z.sum().abs().backward() gx_expect, gy_expect = x.grad, y.grad x.grad, y.grad = None, None z = F.conv3d(x, y, padding="same") z.sum().abs().backward() self.assertEqual(gx_expect, x.grad) self.assertEqual(gy_expect, y.grad) gradcheck( lambda x, y: F.conv3d(x, y, padding="same"), (x, y), check_forward_ad=True, nondet_tol=1e-5, ) gradgradcheck( lambda x, y: F.conv3d(x, y, padding="same"), (x, y), check_fwd_over_rev=True, ) @dtypes(torch.float) def test_conv1d_valid_padding_backward(self, device, dtype): x = torch.rand(1, 1, 10, dtype=dtype, device=device, requires_grad=True) y = torch.rand(1, 1, 4, dtype=dtype, device=device, requires_grad=True) F.conv1d(x, y, padding=0).sum().abs().backward() gx_expect, gy_expect = x.grad, y.grad x.grad, y.grad = None, None F.conv1d(x, y, padding="valid").sum().abs().backward() gx_actual, gy_actual = x.grad, y.grad self.assertEqual(gx_expect, gx_actual) self.assertEqual(gy_expect, gy_actual) @unittest.skipIf(not TEST_SCIPY, "Scipy required for the test.") @dtypes(torch.float) @parametrize_test("mode", ("valid", "same")) def test_conv1d_vs_scipy(self, device, dtype, mode): t = make_tensor((1, 10), device=device, dtype=dtype) feat_dim = t.shape[1] weight_even = make_tensor((1, 1, 4), device=device, dtype=dtype) weight_odd = make_tensor((1, 1, 5), device=device, dtype=dtype) def _test(t, weight, mode): t_a = t.view(-1).cpu().numpy() w_a = weight.view(-1).cpu().numpy() expected = scipy.signal.convolve(t_a, w_a, mode=mode) kwargs = {"padding": mode} if mode == "same": p = weight.shape[2] // 2 t = torch.nn.functional.pad(t, (p, p)) kwargs.pop("padding") weight_flipped = torch.flip(weight, (2,)) actual = torch.nn.functional.conv1d(t, weight_flipped, **kwargs).squeeze(0) if mode == "same": actual = actual[:feat_dim] self.assertEqual(actual, expected, atol=2e-5, rtol=2e-5) with set_default_dtype(torch.float): _test(t, weight_even, mode) _test(t, weight_odd, mode) @unittest.skipIf(not TEST_SCIPY, "Scipy required for the test.") @dtypes(torch.float) @parametrize_test("mode", ("valid", "same")) def test_conv2d_vs_scipy(self, device, dtype, mode): t = make_tensor((1, 5, 10), device=device, dtype=dtype) weight_even = make_tensor((1, 1, 2, 4), device=device, dtype=dtype) weight_odd = make_tensor((1, 1, 3, 5), device=device, dtype=dtype) def _test(t, weight, mode): t_a = t.squeeze(0).cpu().numpy() w_a = weight.squeeze(0).squeeze(0).cpu().numpy() expected = scipy.signal.convolve2d(t_a, w_a, mode=mode) kwargs = {"padding": mode} if mode == "same": left_right_pad = weight.shape[3] // 2 top_bottom_pad = weight.shape[2] // 2 p = (left_right_pad, left_right_pad, top_bottom_pad, top_bottom_pad) t = torch.nn.functional.pad(t, p) kwargs.pop("padding") weight_flipped = torch.flip(weight, (2, 3)) actual = torch.nn.functional.conv2d(t, weight_flipped, **kwargs).squeeze(0) if mode == "same": actual = actual[:5, :10] self.assertEqual(actual, expected, rtol=2e-5, atol=5e-6) with set_default_dtype(torch.float): _test(t, weight_even, mode) _test(t, weight_odd, mode) @unittest.skipIf(not TEST_SCIPY, "Scipy required for the test.") @dtypes(torch.float) @parametrize_test("mode", ("valid", "same")) def test_conv3d_vs_scipy(self, device, dtype, mode): t = make_tensor((1, 5, 5, 10), device=device, dtype=dtype) weight_even = make_tensor((1, 1, 2, 2, 4), device=device, dtype=dtype) weight_odd = make_tensor((1, 1, 2, 3, 5), device=device, dtype=dtype) def _test(t, weight, mode): t_a = t.squeeze(0).cpu().numpy() w_a = weight.squeeze(0).squeeze(0).cpu().numpy() expected = scipy.signal.convolve(t_a, w_a, mode=mode) kwargs = {"padding": mode} if mode == "same": left_right_pad = weight.shape[4] // 2 top_bottom_pad = weight.shape[3] // 2 front_back_pad = weight.shape[2] // 2 p = ( left_right_pad, left_right_pad, top_bottom_pad, top_bottom_pad, front_back_pad, front_back_pad, ) t = torch.nn.functional.pad(t, p) kwargs.pop("padding") weight_flipped = torch.flip(weight, (2, 3, 4)) actual = torch.nn.functional.conv3d(t, weight_flipped, **kwargs).squeeze(0) if mode == "same": actual = actual[:5, :5, :10] self.assertEqual(actual, expected, rtol=2e-5, atol=5e-6) with set_default_dtype(torch.float): _test(t, weight_even, mode) _test(t, weight_odd, mode) @dtypes(torch.float) def test_conv2d_valid_padding_backward(self, device, dtype): x = torch.rand(1, 1, 1, 10, device=device, dtype=dtype, requires_grad=True) y = torch.rand(1, 1, 1, 4, device=device, dtype=dtype, requires_grad=True) F.conv2d(x, y, padding=0).sum().abs().backward() gx_expect, gy_expect = x.grad, y.grad x.grad, y.grad = None, None F.conv2d(x, y, padding="valid").sum().abs().backward() gx_actual, gy_actual = x.grad, y.grad self.assertEqual(gx_expect, gx_actual) self.assertEqual(gy_expect, gy_actual) @dtypes(torch.double) def test_conv3d_valid_padding_backward(self, device, dtype): x = torch.rand(1, 1, 1, 1, 10, dtype=dtype, device=device, requires_grad=True) y = torch.rand(1, 1, 1, 1, 4, dtype=dtype, device=device, requires_grad=True) F.conv3d(x, y, padding=0).sum().abs().backward() gx_expect, gy_expect = x.grad, y.grad x.grad, y.grad = None, None F.conv3d(x, y, padding="valid").sum().abs().backward() gx_actual, gy_actual = x.grad, y.grad self.assertEqual(gx_expect, gx_actual) self.assertEqual(gy_expect, gy_actual) gradcheck( lambda x, y: F.conv3d(x, y, padding="valid"), (x, y), check_forward_ad=True, ) gradgradcheck( lambda x, y: F.conv3d(x, y, padding="valid"), (x, y), check_fwd_over_rev=True, ) @parametrize_test("N", range(2, 4), name_fn=lambda N: f"ConvTranspose{N}d") def test_conv_transpose_with_output_size_and_no_batch_dim(self, device, N): inp = torch.randn((1, 15, 13) if N == 2 else (1, 15, 13, 13), device=device) output_size = (1, 240, 200) if N == 2 else (1, 240, 200, 200) ConvTransposeNd = getattr(nn, f"ConvTranspose{N}d") m = ConvTransposeNd( 1, 1, kernel_size=16, stride=16, padding=7, bias=False, device=device ) output = m(inp, output_size=output_size) self.assertEqual(output.shape, output_size) @dtypes(torch.float) def test_conv_empty_channel(self, device, dtype): in_channels = 0 mod = torch.nn.Conv1d(in_channels, 8, 2, stride=2, dtype=dtype).to(device) inp = torch.randn(2, 0, 15, device=device, dtype=dtype) _test_module_empty_input(self, mod, inp, check_size=False) with self.assertRaisesRegex(RuntimeError, "Given groups=1, weight"): inp = torch.randn(2, 1, 0, device=device, dtype=dtype) mod(inp) mod = torch.nn.Conv2d(in_channels, 33, 3, stride=2, dtype=dtype).to(device) inp = torch.randn(2, 0, 50, 100, device=device, dtype=dtype) _test_module_empty_input(self, mod, inp, check_size=False) with self.assertRaisesRegex(RuntimeError, "Given groups=1, weight"): inp = torch.randn(2, 1, 40, 0, device=device, dtype=dtype) mod(inp) mod = torch.nn.Conv3d(in_channels, 33, 3, stride=2, dtype=dtype).to(device) inp = torch.randn(2, 0, 50, 20, 40, device=device, dtype=dtype) _test_module_empty_input(self, mod, inp, check_size=False) with self.assertRaisesRegex(RuntimeError, "Given groups=1, weight"): inp = torch.randn(2, 1, 50, 0, 40, device=device, dtype=dtype) mod(inp) def test_group_conv_empty(self, device): mod = torch.nn.Conv2d(4, 4, stride=2, kernel_size=3, padding=1, groups=4).to( device ) inp = torch.randn(0, 4, 4, 4, device=device) _test_module_empty_input(self, mod, inp, check_size=False) def test_group_convTranspose_empty(self, device): mod = torch.nn.ConvTranspose2d( 4, 4, stride=2, kernel_size=3, padding=1, groups=4 ).to(device) inp = torch.randn(0, 4, 4, 4, device=device) _test_module_empty_input(self, mod, inp, check_size=False) def test_convTranspose_empty(self, device): mod = torch.nn.ConvTranspose2d(4, 4, stride=2, kernel_size=3, padding=1).to( device ) inp = torch.randn(0, 4, 4, 4, device=device) _test_module_empty_input(self, mod, inp, check_size=False) def test_conv_large_nosplit(self, device): dtype = torch.half conv1 = nn.Conv2d(2, 2, 8, 8).to(device).to(dtype) input_large = torch.randn(1, 2, 1024, 1024 * 1024, dtype=dtype, device=device) conv1(input_large) conv2 = torch.nn.Conv2d(1, 1024, 1, 1).to(device).to(dtype) input_large = torch.randn(1, 1, 2048, 1024, dtype=dtype, device=device) conv2(input_large) def test_conv_noncontig_weights(self, device): for dim in (1, 2, 3): for grouped in (False, True): nc = 3 groups = 3 if grouped else 1 w = torch.randn([3] * dim, device=device) w = w.expand([nc, int(nc / groups)] + list(w.shape)) w = w.detach().requires_grad_() x = torch.randn( [1, nc] + ([5] * dim), device=device, requires_grad=True ) y = getattr(F, f"conv{dim}d")(x, w, groups=groups) y.sum().backward() y = getattr(F, f"conv_transpose{dim}d")(x, w, groups=groups) y.sum().backward() def test_conv_noncontig_weights_and_bias(self, device): for bias in [True, False]: conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=bias).to( device, torch.float ) input_nc = torch.randn( (1, 3, 224, 224, 2), device=device, dtype=torch.float )[:, :, :, :, 1] input_c = input_nc.contiguous() weight_nc = torch.randn((64, 3, 7, 7, 2), device=device, dtype=torch.float)[ :, :, :, :, 1 ] conv1.weight = nn.Parameter(weight_nc) weight_c = conv1.weight.contiguous() if bias: bias_nc = torch.randn((64, 2), device=device, dtype=torch.float)[:, 1] conv1.bias = nn.Parameter(bias_nc) bias_c = conv1.bias.contiguous() out1 = conv1(input_nc) conv1.weight = nn.Parameter(weight_c) if bias: conv1.bias = nn.Parameter(bias_c) out2 = conv1(input_c) self.assertEqual(out1, out2) def test_conv_transposed_large(self, device): dtype = torch.half if self.device_type == "cuda" else torch.float conv = nn.ConvTranspose2d(1, 1, 1, 1, bias=False).to(device).to(dtype) input_large = torch.randn(4096, 1, 512, 1024, dtype=dtype, device=device) ret = conv(input_large) maxdiff0 = ( (ret.narrow(0, 0, 1024) - conv(input_large.narrow(0, 0, 1024))) .abs_() .max() .item() ) maxdiff1 = ( (ret.narrow(0, 1024, 1024) - conv(input_large.narrow(0, 1024, 1024))) .abs_() .max() .item() ) maxdiff2 = ( (ret.narrow(0, 2048, 1024) - conv(input_large.narrow(0, 2048, 1024))) .abs_() .max() .item() ) maxdiff3 = ( (ret.narrow(0, 3072, 1024) - conv(input_large.narrow(0, 3072, 1024))) .abs_() .max() .item() ) self.assertEqual(maxdiff0, 0) self.assertEqual(maxdiff1, 0) self.assertEqual(maxdiff2, 0) self.assertEqual(maxdiff3, 0) def test_conv_large(self, device): dtype = torch.half if self.device_type == "cuda" else torch.float conv = nn.Conv2d(2, 2, 8, 8, bias=False).to(device).to(dtype) input_large = torch.randn(4097, 2, 512, 512, dtype=dtype, device=device) ret = conv(input_large) self.assertEqual(ret[:2048], conv(input_large[:2048])) self.assertEqual(ret[2048:4096], conv(input_large[2048:4096])) self.assertEqual(ret[4096:], conv(input_large[4096:])) conv.zero_grad() ret.view(4097, -1).max(dim=1).values.sum().backward() del ret grad1 = conv.weight.grad.detach().clone() conv.zero_grad() conv(input_large[:2048]).view(2048, -1).max(dim=1).values.sum().backward() conv(input_large[2048:4096]).view(2048, -1).max(dim=1).values.sum().backward() conv(input_large[4096:]).view(1, -1).max(dim=1).values.sum().backward() grad2 = conv.weight.grad.detach().clone() scale = 1 / grad2.abs().mean() grad1 = grad1 * scale grad2 = grad2 * scale self.assertEqual(grad1, grad2, atol=5e-2, rtol=5e-3) def test_Conv2d_size_1_kernel(self, device): x_cpu = torch.randn(2, 3, 5, 5) conv_cpu = torch.nn.Conv2d(3, 3, kernel_size=1) y_cpu = conv_cpu(x_cpu) y = torch.rand_like(y_cpu) y_cpu.backward(y) with cudnn.flags(enabled=False): conv_cuda = torch.nn.Conv2d(3, 3, kernel_size=1).to(device) conv_cuda.bias.data.copy_(conv_cpu.bias.data) conv_cuda.weight.data.copy_(conv_cpu.weight.data) y_cuda = conv_cuda(x_cpu.to(device)) y_cuda.backward(y.to(device)) self.assertEqual(y_cpu, y_cuda, atol=1e-5, rtol=0, exact_device=False) self.assertEqual( conv_cpu.bias.grad.data, conv_cuda.bias.grad.data, atol=1e-5, rtol=0, exact_device=False, ) self.assertEqual( conv_cpu.weight.grad.data, conv_cuda.weight.grad.data, atol=1e-5, rtol=0, exact_device=False, ) def test_ConvTranspose2d_size_1_kernel(self, device): x_cpu = torch.randn(2, 3, 5, 5) conv_cpu = torch.nn.ConvTranspose2d(3, 3, kernel_size=1) y_cpu = conv_cpu(x_cpu) y = torch.rand_like(y_cpu) y_cpu.backward(y) conv_cuda = torch.nn.ConvTranspose2d(3, 3, kernel_size=1).to(device) conv_cuda.bias.data.copy_(conv_cpu.bias.data) conv_cuda.weight.data.copy_(conv_cpu.weight.data) y_cuda = conv_cuda(x_cpu.to(device)) y_cuda.backward(y.to(device)) self.assertEqual(y_cpu, y_cuda, atol=1e-5, rtol=0, exact_device=False) self.assertEqual( conv_cpu.bias.grad.data, conv_cuda.bias.grad.data, atol=1e-5, rtol=0, exact_device=False, ) self.assertEqual( conv_cpu.weight.grad.data, conv_cuda.weight.grad.data, atol=1e-5, rtol=0, exact_device=False, ) def test_ConvTranspose3d_size_1_kernel(self, device): with set_default_dtype(torch.double): x_cpu = torch.randn(2, 3, 3, 5, 5) conv_cpu = torch.nn.ConvTranspose3d(3, 3, kernel_size=1) y_cpu = conv_cpu(x_cpu) y = torch.rand_like(y_cpu) y_cpu.backward(y) conv_cuda = torch.nn.ConvTranspose3d(3, 3, kernel_size=1).to(device) conv_cuda.bias.data.copy_(conv_cpu.bias.data) conv_cuda.weight.data.copy_(conv_cpu.weight.data) y_cuda = conv_cuda(x_cpu.to(device)) y_cuda.backward(y.to(device)) self.assertEqual(y_cpu, y_cuda, atol=1e-5, rtol=0, exact_device=False) self.assertEqual( conv_cpu.bias.grad.data, conv_cuda.bias.grad.data, atol=1e-5, rtol=0, exact_device=False, ) self.assertEqual( conv_cpu.weight.grad.data, conv_cuda.weight.grad.data, atol=1e-5, rtol=0, exact_device=False, ) @dtypes(torch.float) def test_Conv2d_naive_groups(self, device, dtype): m = nn.Conv2d(4, 4, kernel_size=3, groups=2).to(device, dtype) i = torch.randn(2, 4, 6, 6, device=device, dtype=dtype, requires_grad=True) output = m(i) grad_output = torch.randn(2, 4, 4, 4, device=device, dtype=dtype) output.backward(grad_output) m1 = nn.Conv2d(2, 2, kernel_size=3).to(device, dtype) m1.weight.data.copy_(m.weight.data[:2]) m1.bias.data.copy_(m.bias.data[:2]) i1 = i.data[:, :2].contiguous().requires_grad_(True) output1 = m1(i1) output1.backward(grad_output[:, :2].contiguous()) m2 = nn.Conv2d(2, 2, kernel_size=3).to(device, dtype) m2.weight.data.copy_(m.weight.data[2:]) m2.bias.data.copy_(m.bias.data[2:]) i2 = i.data[:, 2:].contiguous().requires_grad_(True) output2 = m2(i2) output2.backward(grad_output[:, 2:].contiguous()) self.assertEqual(output, torch.cat([output1, output2], 1)) self.assertEqual( i.grad.data, torch.cat([i1.grad.data, i2.grad.data], 1), atol=dtype2prec_DONTUSE[dtype], rtol=0, ) self.assertEqual( m.bias.grad.data, torch.cat([m1.bias.grad.data, m2.bias.grad.data], 0), atol=dtype2prec_DONTUSE[dtype], rtol=0, ) self.assertEqual( m.weight.grad.data, torch.cat([m1.weight.grad.data, m2.weight.grad.data], 0), atol=dtype2prec_DONTUSE[dtype], rtol=0, ) @dtypes(torch.double) def test_Conv2d_backward_depthwise(self, device, dtype): x = torch.randn(2, 2, 4, 20, device=device, dtype=dtype, requires_grad=True) weight = torch.randn(2, 1, 3, 5, device=device, dtype=dtype, requires_grad=True) def conv2d_depthwise(x, weight): return torch.nn.functional.conv2d( x, weight, bias=None, stride=(1, 10), groups=2 ) torch.autograd.gradcheck(conv2d_depthwise, (x, weight)) @dtypes(torch.half, torch.float) def test_conv_cudnn_nhwc(self, device, dtype): def helper(n, c, h, w, out_channels, kernel_size, groups): input = torch.randint(-3, 3, (n, c, h, w), dtype=dtype, device=device).to( memory_format=torch.channels_last ) input.requires_grad_() conv = nn.Conv2d(c, out_channels, kernel_size, groups=groups).to( device=device, dtype=dtype, memory_format=torch.channels_last ) for p in conv.parameters(): p.data = torch.randint_like(p, -3, 3) ref_input = input.detach().clone().contiguous().double().requires_grad_() ref_conv = nn.Conv2d(c, out_channels, kernel_size, groups=groups) ref_conv.load_state_dict(conv.state_dict()) ref_conv = ref_conv.to( device=device, dtype=torch.double, memory_format=torch.contiguous_format ) out = conv(input) ref_out = ref_conv(ref_input) grad = torch.randint_like(out, -3, 3) ref_grad = grad.detach().clone().double().contiguous() out.backward(grad) ref_out.backward(ref_grad) self.assertTrue(out.is_contiguous(memory_format=torch.channels_last)) self.assertTrue(input.grad.is_contiguous(memory_format=torch.channels_last)) self.assertTrue( conv.weight.grad.is_contiguous(memory_format=torch.channels_last) ) self.assertTrue(ref_out.is_contiguous()) self.assertTrue(ref_input.grad.is_contiguous()) self.assertTrue(ref_conv.weight.grad.is_contiguous()) self.assertEqual(out, ref_out, exact_dtype=False) self.assertEqual(conv.weight.grad, ref_conv.weight.grad, exact_dtype=False) self.assertEqual(conv.bias.grad, ref_conv.bias.grad, exact_dtype=False) self.assertEqual(input.grad, ref_input.grad, exact_dtype=False) helper(2, 8, 4, 4, out_channels=4, kernel_size=3, groups=1) helper(2, 8, 4, 4, out_channels=8, kernel_size=3, groups=8) helper(1, 16, 56, 56, out_channels=16, kernel_size=3, groups=1) helper(1, 16, 56, 56, out_channels=16, kernel_size=3, groups=16) @dtypes(torch.half, torch.float) def test_conv_cudnn_ndhwc(self, device, dtype): def helper(n, c, d, h, w, out_channels, kernel_size, groups): input = torch.randint( -2, 2, (n, c, d, h, w), dtype=dtype, device=device ).to(memory_format=torch.channels_last_3d) input.requires_grad_() conv = nn.Conv3d(c, out_channels, kernel_size, groups=groups).to( device=device, dtype=dtype, memory_format=torch.channels_last_3d ) for p in conv.parameters(): p.data = torch.randint_like(p, -2, 2) ref_input = input.detach().clone().contiguous().double().requires_grad_() ref_conv = nn.Conv3d(c, out_channels, kernel_size, groups=groups) ref_conv.load_state_dict(conv.state_dict()) ref_conv = ref_conv.to( device=device, dtype=torch.double, memory_format=torch.contiguous_format ) out = conv(input) ref_out = ref_conv(ref_input) grad = torch.randint_like(out, -2, 2) ref_grad = grad.detach().clone().double().contiguous() out.backward(grad) ref_out.backward(ref_grad) self.assertTrue(out.is_contiguous(memory_format=torch.channels_last_3d)) self.assertTrue( input.grad.is_contiguous(memory_format=torch.channels_last_3d) ) self.assertTrue( conv.weight.grad.is_contiguous(memory_format=torch.channels_last_3d) ) self.assertTrue(ref_out.is_contiguous()) self.assertTrue(ref_input.grad.is_contiguous()) self.assertTrue(ref_conv.weight.grad.is_contiguous()) self.assertEqual(out, ref_out, exact_dtype=False) self.assertEqual(conv.weight.grad, ref_conv.weight.grad, exact_dtype=False) self.assertEqual(conv.bias.grad, ref_conv.bias.grad, exact_dtype=False) self.assertEqual(input.grad, ref_input.grad, exact_dtype=False) helper(2, 8, 4, 4, 4, out_channels=4, kernel_size=3, groups=1) helper(2, 8, 4, 4, 4, out_channels=8, kernel_size=3, groups=8) helper(1, 16, 18, 18, 18, out_channels=16, kernel_size=3, groups=1) helper(1, 16, 18, 18, 18, out_channels=16, kernel_size=3, groups=16) def _run_conv( self, layer, device, inp, grad, ref_conv, ref_input, ref_out, input_format, weight_format, grad_format, output_format, ): conv = ( layer(inp.size(1), grad.size(1), ref_conv.weight.size(2)).float().to(device) ) conv.load_state_dict(ref_conv.state_dict()) weight_data = ( conv.weight.detach().clone().contiguous(memory_format=weight_format) ) conv.weight.data = weight_data.resize_( weight_data.size(), memory_format=weight_format ) input = inp.clone().contiguous(memory_format=input_format) input.resize_(input.size(), memory_format=input_format) input = input.requires_grad_() grad = grad.contiguous(memory_format=grad_format) grad.resize_(grad.size(), memory_format=grad_format) out = conv(input) out.backward(grad) self.assertTrue(out.is_contiguous(memory_format=output_format)) self.assertEqual(out, ref_out) self.assertEqual(conv.weight.grad, ref_conv.weight.grad) self.assertEqual(conv.bias.grad, ref_conv.bias.grad) self.assertEqual(input.grad, ref_input.grad) def _test_conv_cudnn_nhwc_nchw(self, layer, n, c, h, w, k, filter_size, device): data = torch.randint(1, 10, (n, c, h, w), dtype=torch.float32, device=device) ref_input = data.clone().contiguous().requires_grad_(True) ref_conv = layer(c, k, filter_size).float().to(device) ref_out = ref_conv(ref_input) grad = torch.randint(1, 10, ref_out.size(), dtype=torch.float32, device=device) ref_out.backward(grad) for w_f in [torch.contiguous_format, torch.channels_last]: for g_f in [torch.contiguous_format, torch.channels_last]: for input_format in [torch.contiguous_format, torch.channels_last]: output_format = torch.contiguous_format if input_format == torch.channels_last: output_format = torch.channels_last if w_f == torch.channels_last: output_format = torch.channels_last self._run_conv( layer, device, data, grad, ref_conv, ref_input, ref_out, input_format, w_f, g_f, output_format, ) @dtypes(torch.float, torch.double) def test_conv_cudnn_nhwc_support(self, device, dtype): input = torch.randn( (1, 16, 1, 1), dtype=dtype, device=device, requires_grad=True ) weight = torch.randn( (8, 16, 3, 3), dtype=dtype, device=device, requires_grad=True ) weight = weight.to(memory_format=torch.channels_last) o = torch.conv2d(input, weight, None, (2, 1), (1, 1), (1, 1), 1) self.assertTrue(o.is_contiguous(memory_format=torch.channels_last)) o.sum().backward() @dtypes(torch.float) def test_conv2d_no_grad(self, device, dtype): for batch in [1, 2, 3]: for groups in [1, 2, 4]: input = torch.rand(batch, groups, 8, 8, dtype=dtype, device=device) m = nn.Conv2d( groups, 8, kernel_size=(3, 3), groups=groups, dtype=dtype, device=device, ) with torch.no_grad(): output_ng = m(input) output = m(input) self.assertEqual(output, output_ng, rtol=1e-2, atol=1e-5) def test_conv_double_backward_strided_with_3D_input_and_weight(self, device): input = torch.randn(2, 3, 6, device=device) weight = torch.randn(3, 3, 3, device=device) bias = torch.randn(3, device=device) stride = (2,) padding = (1,) dilation = (1,) transposed = False output_padding = (0,) groups = 1 output = torch.ops.aten.convolution( input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, ) ggI = torch.randn(input.shape, device=device) ggW = torch.randn(weight.shape, device=device) ggB = torch.randn(bias.shape, device=device) gO = torch.randn(output.shape, device=device) output_mask = [True, True, True] ( grad_grad_output, grad_input, grad_weight, ) = torch.ops.aten._convolution_double_backward( ggI, ggW, ggB, gO, weight, input, stride, padding, dilation, transposed, output_padding, groups, output_mask, ) self.assertEqual(grad_grad_output.shape, gO.shape) self.assertEqual(grad_input.shape, input.shape) self.assertEqual(grad_weight.shape, weight.shape) @onlyXPU @dtypes(torch.float16, torch.bfloat16, torch.float32, torch.float64) def test_channels_last_ouput_stride(self, device, dtype): input = torch.randn( (2, 3, 16, 16), device=device, dtype=dtype, requires_grad=True ) weight = torch.randn( (512, 3, 3, 3), device=device, dtype=dtype, requires_grad=True ) input = input.to(memory_format=torch.channels_last) weight = weight.to(memory_format=torch.channels_last) out = torch.conv2d(input, weight, None, (2, 2), (0, 0), (1, 1), 1) if dtype is torch.float64: # Like most conv backend, xpu does not support float64 for chanel last conv. # input NHWC, output NCHW assert_size_stride(out, (2, 512, 7, 7), (25088, 49, 7, 1)) else: # input NHWC, output NHWC assert_size_stride(out, (2, 512, 7, 7), (25088, 1, 3584, 512)) instantiate_device_type_tests( TestConvolutionNNDeviceType, globals(), only_for="xpu", allow_xpu=True ) if __name__ == "__main__": run_tests()