diff --git a/paddle/fluid/operators/elementwise/elementwise_op_function.h b/paddle/fluid/operators/elementwise/elementwise_op_function.h index c69baadb3c22e47cacd65d2eefea15da804b7fac..32e49cf3996f120d2e2a8f909883e0c46f7b1352 100644 --- a/paddle/fluid/operators/elementwise/elementwise_op_function.h +++ b/paddle/fluid/operators/elementwise/elementwise_op_function.h @@ -39,7 +39,11 @@ limitations under the License. */ #include "paddle/fluid/platform/cuda_device_function.h" #include "paddle/fluid/platform/cuda_primitives.h" +#ifdef __HIPCC__ +constexpr int ELEMWISE_MAX_BLOCK_DIM = 256; +#else constexpr int ELEMWISE_MAX_BLOCK_DIM = 1024; +#endif #define BLOCK_X 32 #define BLOCK_Y 32 #endif diff --git a/paddle/fluid/operators/group_norm_op.cu b/paddle/fluid/operators/group_norm_op.cu index 45d97723a3e21044daf1609b749a22ae08efad39..18a248f55314f7814c3363fd45181501f0aa8b48 100644 --- a/paddle/fluid/operators/group_norm_op.cu +++ b/paddle/fluid/operators/group_norm_op.cu @@ -174,7 +174,11 @@ class GroupNormKernel int imsize = (data_layout == DataLayout::kNCHW ? x_dims[2] * x_dims[3] : x_dims[1] * x_dims[2]); +#ifdef __HIPCC__ + int block_size = std::max(std::min(256, imsize), 64); +#else int block_size = std::min(1024, imsize); +#endif dim3 grid(group_size, groups, x_dims[0]); dim3 threads(block_size, 1, 1); GroupNormForwardGetMeanAndVar<<>>( @@ -348,7 +352,11 @@ class GroupNormGradKernel int imsize = (data_layout == DataLayout::kNCHW ? x_dims[2] * x_dims[3] : x_dims[1] * x_dims[2]); +#ifdef __HIPCC__ + int block_size = std::max(std::min(256, imsize), 64); +#else int block_size = std::min(1024, imsize); +#endif dim3 grid(group_size, groups, x_dims[0]); dim3 threads(block_size, 1, 1); int flags = diff --git a/paddle/fluid/operators/miopen_lstm_cache.h b/paddle/fluid/operators/miopen_lstm_cache.h index 7c0faa86be0be0c73dcf0d0ad68201c1bfba04cc..a357e6e5af6af0895e07e993bcbba98a33239978 100644 --- a/paddle/fluid/operators/miopen_lstm_cache.h +++ b/paddle/fluid/operators/miopen_lstm_cache.h @@ -75,10 +75,11 @@ class ScopedRNNBase { dropout_state, seed_, state_size); // ------------------- miopen rnn descriptors --------------------- - PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::miopenSetRNNDescriptor( - rnn_desc_.desc(), hidden_size_, num_layers_, miopenRNNlinear, + PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::miopenSetRNNDescriptor_V2( + rnn_desc_.desc(), hidden_size_, num_layers_, dropout_desc_.desc(), + miopenRNNlinear, is_bidirec_ ? miopenRNNbidirection : miopenRNNunidirection, miopenLSTM, - miopenRNNNoBias, miopenRNNdefault, miopen_type)); + miopenRNNwithBias, miopenRNNdefault, miopen_type)); // ------------------- miopen weights_size --------------------- size_t weights_size_; diff --git a/paddle/fluid/platform/miopen_helper.h b/paddle/fluid/platform/miopen_helper.h index 435d28d518df1adf3ed37841324100e0bfbffa88..46c7da839704196952deda6de6d09c7009ee6a78 100644 --- a/paddle/fluid/platform/miopen_helper.h +++ b/paddle/fluid/platform/miopen_helper.h @@ -434,9 +434,10 @@ class ScopedPoolingDescriptor { "The size of kernel and strides should be equal. But " "received size of kernel is %d, size of strides is %d.", kernel.size(), strides.size())); - PADDLE_ENFORCE_CUDA_SUCCESS(dynload::miopenSet2dPoolingDescriptor( - desc_, GetPoolingMode(mode), kernel[0], kernel[1], pads[0], pads[1], - strides[0], strides[1])); + PADDLE_ENFORCE_CUDA_SUCCESS(dynload::miopenSetNdPoolingDescriptor( + desc_, GetPoolingMode(mode), kernel.size(), + const_cast(kernel.data()), const_cast(pads.data()), + const_cast(strides.data()))); return desc_; } diff --git a/python/paddle/fluid/tests/unittests/test_bilinear_tensor_product_op.py b/python/paddle/fluid/tests/unittests/test_bilinear_tensor_product_op.py index ba9db2c104f18c3af969570e4e6c93e2d14c5c60..60e9d0a26b380d78e7b1566b2aa01a8be958fbcf 100644 --- a/python/paddle/fluid/tests/unittests/test_bilinear_tensor_product_op.py +++ b/python/paddle/fluid/tests/unittests/test_bilinear_tensor_product_op.py @@ -42,11 +42,12 @@ class TestBilinearTensorProductOp(OpTest): size0 = 5 size1 = 4 size2 = 5 - a = np.random.random((batch_size, size0)).astype("float64") - b = np.random.random((batch_size, size1)).astype("float64") - w = np.random.random((size2, size0, size1)).astype("float64") - bias = np.random.random((1, size2)).astype("float64") - output = np.zeros((batch_size, size2)).astype("float64") + dtype = "float32" if fluid.core.is_compiled_with_rocm() else "float64" + a = np.random.random((batch_size, size0)).astype(dtype) + b = np.random.random((batch_size, size1)).astype(dtype) + w = np.random.random((size2, size0, size1)).astype(dtype) + bias = np.random.random((1, size2)).astype(dtype) + output = np.zeros((batch_size, size2)).astype(dtype) for i in range(size2): w_i = w[i, :, :] output[:, i] = np.sum(np.matmul(a, w_i) * b, axis=1) diff --git a/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py b/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py index 7aa3d0d16862bd5ac49ab95817ea23d3cc0ef8a7..d5f49919bc951741e2d67e22a76633268ad8582e 100644 --- a/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py +++ b/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py @@ -30,7 +30,7 @@ class TestConvDoubleGradCheck(unittest.TestCase): def func(self, place): shape = [2, 4, 3, 3] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d(x, 2, 1, groups=1, bias_attr=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) @@ -57,7 +57,7 @@ class TestConvDoubleGradCheck(unittest.TestCase): def func(self, place): shape = [2, 4, 3, 3] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d(x, 2, 1, bias_attr=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) @@ -82,7 +82,7 @@ class TestConvDoubleGradCheckTest1(unittest.TestCase): def func(self, place): shape = [2, 3, 3, 3] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d(x, 2, 1, padding=1, bias_attr=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) @@ -107,7 +107,7 @@ class TestConv3DDoubleGradCheck(unittest.TestCase): def func(self, place): shape = [2, 4, 3, 4, 2] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d(x, 2, 1, bias_attr=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) @@ -132,7 +132,7 @@ class TestConv3DDoubleGradCheckTest1(unittest.TestCase): def func(self, place): shape = [2, 4, 5, 3, 2] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d(x, 2, 1, padding=1, bias_attr=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) @@ -157,7 +157,7 @@ class TestConv2DoubleGradCheck_AsyPadding(unittest.TestCase): def func(self, place): shape = [2, 2, 3, 3] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d( input=x, @@ -188,7 +188,7 @@ class TestConv2DoubleGradCheck_PaddingSAME(unittest.TestCase): def func(self, place): shape = [2, 2, 3, 3] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d( input=x, @@ -219,7 +219,7 @@ class TestConv2DoubleGradCheck_PaddingVALID(unittest.TestCase): def func(self, place): shape = [2, 2, 3, 3] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d( input=x, @@ -250,7 +250,7 @@ class TestConv2DoubleGradCheck_ChannelLast(unittest.TestCase): def func(self, place): shape = [2, 2, 3, 3] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d( input=x, @@ -283,7 +283,7 @@ class TestConv2DoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase): def func(self, place): shape = [2, 2, 3, 3] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d( input=x, @@ -316,7 +316,7 @@ class TestConv3DDoubleGradCheck_AsyPadding(unittest.TestCase): def func(self, place): shape = [2, 2, 2, 2, 2] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d( input=x, @@ -347,7 +347,7 @@ class TestConv3DoubleGradCheck_PaddingSAME(unittest.TestCase): def func(self, place): shape = [2, 2, 2, 2, 2] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d( input=x, @@ -379,7 +379,7 @@ class TestConv3DoubleGradCheck_PaddingVALID(unittest.TestCase): def func(self, place): shape = [2, 2, 3, 3, 2] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d( input=x, @@ -410,7 +410,7 @@ class TestConv3DDoubleGradCheck_ChannelLast(unittest.TestCase): def func(self, place): shape = [2, 2, 2, 2, 3] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d( input=x, @@ -443,7 +443,7 @@ class TestConv3DDoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase): def func(self, place): shape = [2, 2, 2, 2, 3] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d( input=x, @@ -476,7 +476,7 @@ class TestDepthWiseConvDoubleGradCheck(unittest.TestCase): def func(self, place): shape = [2, 4, 3, 3] eps = 0.005 - dtype = np.float64 + dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64 x = layers.data('x', shape, False, dtype) # condition of depthwise conv: diff --git a/python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py b/python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py index 1a5e4b28355674010ba8f92b176d5cabca3e1a8d..ea44e23da249caaae2f209d0728d06b1494fbb4d 100644 --- a/python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py +++ b/python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py @@ -191,12 +191,16 @@ def cross_entropy_soft_2d(softmax, class CrossEntropyLoss(unittest.TestCase): + def setUp(self): + self.dtype = 'float32' if fluid.core.is_compiled_with_rocm( + ) else 'float64' ###test for deprecated softmax_with_cross_entropy def test_softmax_with_cross_entropy(self): self.numeric_stable_mode = False self.soft_label = True - self.dtype = np.float64 + self.dtype = 'float32' if fluid.core.is_compiled_with_rocm( + ) else 'float64' self.axis = -1 self.ignore_index = -100 #should not be changed self.N = 4 @@ -248,7 +252,8 @@ class CrossEntropyLoss(unittest.TestCase): def test_cross_entropy_loss_soft_1d(self): self.numeric_stable_mode = False self.soft_label = True - self.dtype = np.float64 + self.dtype = 'float32' if fluid.core.is_compiled_with_rocm( + ) else 'float64' self.axis = -1 self.ignore_index = -100 #should not be changed self.N = 4 @@ -296,9 +301,9 @@ class CrossEntropyLoss(unittest.TestCase): ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data( - name='input', shape=[self.N, self.C], dtype='float64') + name='input', shape=[self.N, self.C], dtype=self.dtype) label = fluid.data( - name='label', shape=[self.N, self.C], dtype='float64') + name='label', shape=[self.N, self.C], dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction=self.reduction, soft_label=True) @@ -321,7 +326,8 @@ class CrossEntropyLoss(unittest.TestCase): def test_cross_entropy_loss_soft_1d_weight(self): self.numeric_stable_mode = False self.soft_label = True - self.dtype = np.float64 + self.dtype = 'float32' if fluid.core.is_compiled_with_rocm( + ) else 'float64' self.axis = -1 self.ignore_index = -100 #should not be changed self.N = 4 @@ -376,10 +382,10 @@ class CrossEntropyLoss(unittest.TestCase): ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data( - name='input', shape=[self.N, self.C], dtype='float64') + name='input', shape=[self.N, self.C], dtype=self.dtype) label = fluid.data( - name='label', shape=[self.N, self.C], dtype='float64') - weight = fluid.data(name='weight', shape=[self.C], dtype='float64') + name='label', shape=[self.N, self.C], dtype=self.dtype) + weight = fluid.data(name='weight', shape=[self.C], dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction=self.reduction, soft_label=True) @@ -403,7 +409,8 @@ class CrossEntropyLoss(unittest.TestCase): def test_cross_entropy_loss_soft_1d_mean(self): self.numeric_stable_mode = False self.soft_label = True - self.dtype = np.float64 + self.dtype = 'float32' if fluid.core.is_compiled_with_rocm( + ) else 'float64' self.axis = -1 self.ignore_index = -100 #should not be changed self.N = 4 @@ -451,9 +458,9 @@ class CrossEntropyLoss(unittest.TestCase): ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data( - name='input', shape=[self.N, self.C], dtype='float64') + name='input', shape=[self.N, self.C], dtype=self.dtype) label = fluid.data( - name='label', shape=[self.N, self.C], dtype='float64') + name='label', shape=[self.N, self.C], dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction=self.reduction, soft_label=True) @@ -475,7 +482,8 @@ class CrossEntropyLoss(unittest.TestCase): def test_cross_entropy_loss_soft_1d_weight_mean(self): self.numeric_stable_mode = False self.soft_label = True - self.dtype = np.float64 + self.dtype = 'float32' if fluid.core.is_compiled_with_rocm( + ) else 'float64' self.axis = -1 self.ignore_index = -100 #should not be changed self.N = 4 @@ -523,10 +531,10 @@ class CrossEntropyLoss(unittest.TestCase): ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data( - name='input', shape=[self.N, self.C], dtype='float64') + name='input', shape=[self.N, self.C], dtype=self.dtype) label = fluid.data( - name='label', shape=[self.N, self.C], dtype='float64') - weight = fluid.data(name='weight', shape=[self.C], dtype='float64') + name='label', shape=[self.N, self.C], dtype=self.dtype) + weight = fluid.data(name='weight', shape=[self.C], dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction=self.reduction, soft_label=True) @@ -549,7 +557,8 @@ class CrossEntropyLoss(unittest.TestCase): def test_cross_entropy_loss_soft_2d(self): self.numeric_stable_mode = False self.soft_label = True - self.dtype = np.float64 + self.dtype = 'float32' if fluid.core.is_compiled_with_rocm( + ) else 'float64' self.axis = -1 self.ignore_index = -100 #should not be changed self.N = 3 @@ -604,11 +613,11 @@ class CrossEntropyLoss(unittest.TestCase): input = fluid.data( name='input', shape=[self.N, self.H, self.W, self.C], - dtype='float64') + dtype=self.dtype) label = fluid.data( name='label', shape=[self.N, self.H, self.W, self.C], - dtype='float64') + dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction=self.reduction, soft_label=True) @@ -631,7 +640,8 @@ class CrossEntropyLoss(unittest.TestCase): def test_cross_entropy_loss_soft_2d_weight_mean(self): self.numeric_stable_mode = False self.soft_label = True - self.dtype = np.float64 + self.dtype = 'float32' if fluid.core.is_compiled_with_rocm( + ) else 'float64' self.axis = -1 self.ignore_index = -100 #should not be changed self.N = 3 @@ -685,12 +695,12 @@ class CrossEntropyLoss(unittest.TestCase): input = fluid.data( name='input', shape=[self.N, self.H, self.W, self.C], - dtype='float64') + dtype=self.dtype) label = fluid.data( name='label', shape=[self.N, self.H, self.W, self.C], - dtype='float64') - weight = fluid.data(name='weight', shape=[self.C], dtype='float64') + dtype=self.dtype) + weight = fluid.data(name='weight', shape=[self.C], dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction=self.reduction, soft_label=True) @@ -713,7 +723,7 @@ class CrossEntropyLoss(unittest.TestCase): ###soft_label test end def test_cross_entropy_loss_1d_with_mean_ignore(self): - input_np = np.random.random([2, 4]).astype(np.float64) + input_np = np.random.random([2, 4]).astype(self.dtype) label_np = np.random.randint(0, 4, size=(2)).astype(np.int64) paddle.enable_static() prog = fluid.Program() @@ -721,7 +731,7 @@ class CrossEntropyLoss(unittest.TestCase): place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): - input = fluid.data(name='input', shape=[2, 4], dtype='float64') + input = fluid.data(name='input', shape=[2, 4], dtype=self.dtype) label = fluid.data(name='label', shape=[2], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(ignore_index=0) ret = cross_entropy_loss(input, label) @@ -752,20 +762,20 @@ class CrossEntropyLoss(unittest.TestCase): def test_cross_entropy_loss_1d_with_weight_mean_ignore(self): N = 100 C = 200 - input_np = np.random.random([N, C]).astype(np.float64) + input_np = np.random.random([N, C]).astype(self.dtype) label_np = np.random.randint(0, C, size=(N)).astype(np.int64) - weight_np = np.random.random([C]).astype(np.float64) + weight_np = np.random.random([C]).astype(self.dtype) paddle.enable_static() prog = fluid.Program() startup_prog = fluid.Program() place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): - input = fluid.data(name='input', shape=[N, C], dtype='float64') + input = fluid.data(name='input', shape=[N, C], dtype=self.dtype) label = fluid.data(name='label', shape=[N], dtype='int64') weight = fluid.data( name='weight', shape=[C], - dtype='float64') #weight for each class + dtype=self.dtype) #weight for each class cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, ignore_index=0) ret = cross_entropy_loss(input, label) @@ -798,20 +808,20 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_with_weight_mean(self): - input_np = np.random.random([2, 4]).astype(np.float64) + input_np = np.random.random([2, 4]).astype(self.dtype) label_np = np.random.randint(0, 4, size=(2)).astype(np.int64) - weight_np = np.random.random([4]).astype(np.float64) #shape:C + weight_np = np.random.random([4]).astype(self.dtype) #shape:C paddle.enable_static() prog = fluid.Program() startup_prog = fluid.Program() place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): - input = fluid.data(name='input', shape=[2, 4], dtype='float64') + input = fluid.data(name='input', shape=[2, 4], dtype=self.dtype) label = fluid.data(name='label', shape=[2], dtype='int64') weight = fluid.data( name='weight', shape=[4], - dtype='float64') #weight for each class + dtype=self.dtype) #weight for each class cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(weight=weight) ret = cross_entropy_loss(input, label) @@ -842,18 +852,18 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_with_weight_sum(self): - input_np = np.random.random([100, 200]).astype(np.float64) #N,C + input_np = np.random.random([100, 200]).astype(self.dtype) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1 - weight_np = np.random.random([200]).astype(np.float64) #C + weight_np = np.random.random([200]).astype(self.dtype) #C paddle.enable_static() prog = fluid.Program() startup_prog = fluid.Program() place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): - input = fluid.data(name='input', shape=[100, 200], dtype='float64') + input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype) label = fluid.data(name='label', shape=[100], dtype='int64') - weight = fluid.data(name='weight', shape=[200], dtype='float64') + weight = fluid.data(name='weight', shape=[200], dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction='sum') ret = cross_entropy_loss(input, label) @@ -882,9 +892,9 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_with_weight_none(self): - input_np = np.random.random([100, 200]).astype(np.float64) #N,C + input_np = np.random.random([100, 200]).astype(self.dtype) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1 - weight_np = np.random.random([200]).astype(np.float64) #C + weight_np = np.random.random([200]).astype(self.dtype) #C paddle.enable_static() prog = fluid.Program() @@ -892,9 +902,9 @@ class CrossEntropyLoss(unittest.TestCase): place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): - input = fluid.data(name='input', shape=[100, 200], dtype='float64') + input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype) label = fluid.data(name='label', shape=[100], dtype='int64') - weight = fluid.data(name='weight', shape=[200], dtype='float64') + weight = fluid.data(name='weight', shape=[200], dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction='none') @@ -926,18 +936,18 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_with_weight_none_func(self): - input_np = np.random.random([100, 200]).astype(np.float64) #N,C + input_np = np.random.random([100, 200]).astype(self.dtype) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N - weight_np = np.random.random([200]).astype(np.float64) #C + weight_np = np.random.random([200]).astype(self.dtype) #C paddle.enable_static() prog = fluid.Program() startup_prog = fluid.Program() place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): - input = fluid.data(name='input', shape=[100, 200], dtype='float64') + input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype) label = fluid.data(name='label', shape=[100], dtype='int64') - weight = fluid.data(name='weight', shape=[200], dtype='float64') + weight = fluid.data(name='weight', shape=[200], dtype=self.dtype) ret = paddle.nn.functional.cross_entropy( input, label, weight=weight, reduction='none') @@ -967,18 +977,18 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_mean(self): - input_np = np.random.random([100, 200]).astype(np.float64) #N,C + input_np = np.random.random([100, 200]).astype(self.dtype) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1 - weight_np = np.random.random([200]).astype(np.float64) #C + weight_np = np.random.random([200]).astype(self.dtype) #C paddle.enable_static() prog = fluid.Program() startup_prog = fluid.Program() place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): - input = fluid.data(name='input', shape=[100, 200], dtype='float64') + input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype) label = fluid.data(name='label', shape=[100], dtype='int64') - weight = fluid.data(name='weight', shape=[100], dtype='float64') + weight = fluid.data(name='weight', shape=[100], dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss() ret = cross_entropy_loss(input, label) exe = fluid.Executor(place) @@ -1000,7 +1010,7 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_sum(self): - input_np = np.random.random([100, 200]).astype(np.float64) #N,C + input_np = np.random.random([100, 200]).astype(self.dtype) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1 paddle.enable_static() prog = fluid.Program() @@ -1008,7 +1018,7 @@ class CrossEntropyLoss(unittest.TestCase): place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): - input = fluid.data(name='input', shape=[100, 200], dtype='float64') + input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype) label = fluid.data(name='label', shape=[100], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='sum') @@ -1033,7 +1043,7 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_1d_none(self): - input_np = np.random.random([100, 200]).astype(np.float64) #N,C + input_np = np.random.random([100, 200]).astype(self.dtype) #N,C label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1 paddle.enable_static() prog = fluid.Program() @@ -1041,7 +1051,7 @@ class CrossEntropyLoss(unittest.TestCase): place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): - input = fluid.data(name='input', shape=[100, 200], dtype='float64') + input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype) label = fluid.data(name='label', shape=[100], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='none') @@ -1068,10 +1078,10 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_with_weight_none(self): - input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC + input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW1 - weight_np = np.random.random(size=(3, )).astype(np.float64) #C + weight_np = np.random.random(size=(3, )).astype(self.dtype) #C paddle.enable_static() prog = fluid.Program() @@ -1080,9 +1090,9 @@ class CrossEntropyLoss(unittest.TestCase): ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data( - name='input', shape=[2, 2, 2, 3], dtype='float64') + name='input', shape=[2, 2, 2, 3], dtype=self.dtype) label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') - weight = fluid.data(name='weight', shape=[3], dtype='float64') + weight = fluid.data(name='weight', shape=[3], dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction='none') ret = cross_entropy_loss(input, label) @@ -1113,10 +1123,10 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_with_weight_mean(self): - input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC + input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW - weight_np = np.random.random(size=(3, )).astype(np.float64) #C + weight_np = np.random.random(size=(3, )).astype(self.dtype) #C paddle.enable_static() prog = fluid.Program() startup_prog = fluid.Program() @@ -1124,9 +1134,9 @@ class CrossEntropyLoss(unittest.TestCase): ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data( - name='input', shape=[2, 2, 2, 3], dtype='float64') + name='input', shape=[2, 2, 2, 3], dtype=self.dtype) label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') - weight = fluid.data(name='weight', shape=[3], dtype='float64') + weight = fluid.data(name='weight', shape=[3], dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction='mean') ret = cross_entropy_loss(input, label) @@ -1155,10 +1165,10 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_with_weight_sum(self): - input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC + input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW - weight_np = np.random.random(size=(3, )).astype(np.float64) #C + weight_np = np.random.random(size=(3, )).astype(self.dtype) #C paddle.enable_static() prog = fluid.Program() @@ -1167,9 +1177,9 @@ class CrossEntropyLoss(unittest.TestCase): ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data( - name='input', shape=[2, 2, 2, 3], dtype='float64') + name='input', shape=[2, 2, 2, 3], dtype=self.dtype) label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') - weight = fluid.data(name='weight', shape=[3], dtype='float64') + weight = fluid.data(name='weight', shape=[3], dtype=self.dtype) cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction='sum') ret = cross_entropy_loss(input, label) @@ -1198,7 +1208,7 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_none(self): - input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC + input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW paddle.enable_static() @@ -1208,7 +1218,7 @@ class CrossEntropyLoss(unittest.TestCase): ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data( - name='input', shape=[2, 2, 2, 3], dtype='float64') + name='input', shape=[2, 2, 2, 3], dtype=self.dtype) label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='none') @@ -1237,7 +1247,7 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_mean(self): - input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC + input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW paddle.enable_static() @@ -1247,7 +1257,7 @@ class CrossEntropyLoss(unittest.TestCase): ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data( - name='input', shape=[2, 2, 2, 3], dtype='float64') + name='input', shape=[2, 2, 2, 3], dtype=self.dtype) label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='mean') @@ -1276,7 +1286,7 @@ class CrossEntropyLoss(unittest.TestCase): self.assertTrue(np.allclose(dy_ret_value, expected)) def test_cross_entropy_loss_2d_sum(self): - input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC + input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC label_np = np.random.randint( 0, 3, size=(2, 2, 2)).astype(np.int64) #NHW paddle.enable_static() @@ -1286,7 +1296,7 @@ class CrossEntropyLoss(unittest.TestCase): ) else fluid.CPUPlace() with fluid.program_guard(prog, startup_prog): input = fluid.data( - name='input', shape=[2, 2, 2, 3], dtype='float64') + name='input', shape=[2, 2, 2, 3], dtype=self.dtype) label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( reduction='sum') diff --git a/python/paddle/fluid/tests/unittests/test_expand_op.py b/python/paddle/fluid/tests/unittests/test_expand_op.py index a325ffe1d0ef4636bad6329099c359bdf79d5dfc..edda6da655ddd9fe7af33fdf574c65f457327ec8 100644 --- a/python/paddle/fluid/tests/unittests/test_expand_op.py +++ b/python/paddle/fluid/tests/unittests/test_expand_op.py @@ -27,8 +27,10 @@ class TestExpandOpRank1(OpTest): def setUp(self): self.op_type = "expand" self.init_data() + self.dtype = "float32" if fluid.core.is_compiled_with_rocm( + ) else "float64" - self.inputs = {'X': np.random.random(self.ori_shape).astype("float64")} + self.inputs = {'X': np.random.random(self.ori_shape).astype(self.dtype)} self.attrs = {'expand_times': self.expand_times} output = np.tile(self.inputs['X'], self.expand_times) self.outputs = {'Out': output} @@ -79,13 +81,16 @@ class TestExpandOpRank1_tensor_attr(OpTest): def setUp(self): self.op_type = "expand" self.init_data() + self.dtype = "float32" if fluid.core.is_compiled_with_rocm( + ) else "float64" + expand_times_tensor = [] for index, ele in enumerate(self.expand_times): expand_times_tensor.append(("x" + str(index), np.ones( (1)).astype('int32') * ele)) self.inputs = { - 'X': np.random.random(self.ori_shape).astype("float64"), + 'X': np.random.random(self.ori_shape).astype(self.dtype), 'expand_times_tensor': expand_times_tensor, } self.attrs = {"expand_times": self.infer_expand_times} @@ -123,9 +128,11 @@ class TestExpandOpRank1_tensor(OpTest): def setUp(self): self.op_type = "expand" self.init_data() + self.dtype = "float32" if fluid.core.is_compiled_with_rocm( + ) else "float64" self.inputs = { - 'X': np.random.random(self.ori_shape).astype("float64"), + 'X': np.random.random(self.ori_shape).astype(self.dtype), 'ExpandTimes': np.array(self.expand_times).astype("int32"), } self.attrs = {} diff --git a/python/paddle/fluid/tests/unittests/test_gpu_package_without_gpu_device.py b/python/paddle/fluid/tests/unittests/test_gpu_package_without_gpu_device.py index d854372bbc6e7d713c823fac540bb2bd425c9f91..2b51bec9cb0e7d7087b1476220148014f9c39cae 100644 --- a/python/paddle/fluid/tests/unittests/test_gpu_package_without_gpu_device.py +++ b/python/paddle/fluid/tests/unittests/test_gpu_package_without_gpu_device.py @@ -26,7 +26,10 @@ from paddle.fluid import core class TestGPUPackagePaddle(unittest.TestCase): def test_import_paddle(self): if core.is_compiled_with_cuda(): - os.environ['CUDA_VISIBLE_DEVICES'] = '' + if core.is_compiled_with_rocm(): + os.environ['HIP_VISIBLE_DEVICES'] = '' + else: + os.environ['CUDA_VISIBLE_DEVICES'] = '' test_file = 'test_no_gpu_run_rand.py' with open(test_file, 'w') as wb: cmd_test = """ diff --git a/python/paddle/fluid/tests/unittests/test_gru_rnn_op.py b/python/paddle/fluid/tests/unittests/test_gru_rnn_op.py index eb1fed81cbee58dac1eba154455e1168861eddce..9f18ec9843d7a40c8e73663e56405ef4b864f25f 100644 --- a/python/paddle/fluid/tests/unittests/test_gru_rnn_op.py +++ b/python/paddle/fluid/tests/unittests/test_gru_rnn_op.py @@ -44,8 +44,9 @@ class TestGRUOp(OpTest): def setUp(self): self.op_type = "rnn" - self.dtype = "float64" - self.sequence_length = np.array( + self.dtype = "float32" if core.is_compiled_with_rocm() else "float64" + self.sequence_length = None if core.is_compiled_with_rocm( + ) else np.array( [12, 11, 10, 9, 8, 7, 6, 5], dtype=np.int32) self.num_layers = 1 self.is_bidirec = False @@ -83,6 +84,24 @@ class TestGRUOp(OpTest): output, last_hidden = rnn1(input, sequence_length=self.sequence_length) + if core.is_compiled_with_rocm(): + + def rocm_rnn_get_place(): + places = [core.CUDAPlace(0)] + return places + + self._get_places = rocm_rnn_get_place + + if self.is_bidirec: + for i in range(0, len(flat_w), 4): + flat_w[i + 1], flat_w[i + 2] = flat_w[i + 2], flat_w[i + 1] + + for i in range(len(flat_w)): + w = np.split(flat_w[i][1], 3, 0) + w = [w[1], w[0], w[2]] + w = np.concatenate(w) + flat_w[i] = (flat_w[i][0], w) + init_h = np.zeros((self.num_layers * self.direction_num, batch_size, self.hidden_size)).astype(self.dtype) diff --git a/python/paddle/fluid/tests/unittests/test_gru_unit_op.py b/python/paddle/fluid/tests/unittests/test_gru_unit_op.py index a570e266072adc167f973cd4b22eda3ecb5e5abb..74afa7db2899b4405e11fc9eb8fbea2f383428cb 100644 --- a/python/paddle/fluid/tests/unittests/test_gru_unit_op.py +++ b/python/paddle/fluid/tests/unittests/test_gru_unit_op.py @@ -121,12 +121,12 @@ class TestGRUUnitOp(OpTest): self.op_type = 'gru_unit' self.inputs = { 'Input': np.random.uniform( - -0.1, 0.1, (batch_size, frame_size * 3)).astype('float64'), + -0.1, 0.1, (batch_size, frame_size * 3)).astype(self.dtype), 'HiddenPrev': np.random.uniform( - -0.1, 0.1, (batch_size, frame_size)).astype('float64'), + -0.1, 0.1, (batch_size, frame_size)).astype(self.dtype), 'Weight': np.random.uniform( -1. / math.sqrt(frame_size), 1. / math.sqrt(frame_size), - (frame_size, frame_size * 3)).astype('float64'), + (frame_size, frame_size * 3)).astype(self.dtype), } self.attrs = { 'activation': GRUActivationType.tanh, @@ -161,12 +161,14 @@ class TestGRUUnitOp(OpTest): else: h = u * c + (1 - u) * h_p self.outputs = { - 'Gate': g.astype('float64'), - 'ResetHiddenPrev': r_h_p.astype('float64'), - 'Hidden': h.astype('float64') + 'Gate': g.astype(self.dtype), + 'ResetHiddenPrev': r_h_p.astype(self.dtype), + 'Hidden': h.astype(self.dtype) } def setUp(self): + self.dtype = 'float32' if fluid.core.is_compiled_with_rocm( + ) else 'float64' self.set_inputs() self.set_outputs() @@ -179,6 +181,8 @@ class TestGRUUnitOp(OpTest): class TestGRUUnitOpOriginMode(TestGRUUnitOp): def setUp(self): + self.dtype = 'float32' if fluid.core.is_compiled_with_rocm( + ) else 'float64' self.set_inputs(origin_mode=True) self.set_outputs(origin_mode=True) @@ -189,7 +193,7 @@ class TestGRUUnitOpWithBias(TestGRUUnitOp): frame_size = self.frame_size super(TestGRUUnitOpWithBias, self).set_inputs() self.inputs['Bias'] = np.random.uniform( - -0.1, 0.1, (1, frame_size * 3)).astype('float64') + -0.1, 0.1, (1, frame_size * 3)).astype(self.dtype) self.attrs = { 'activation': GRUActivationType.identity, 'gate_activation': GRUActivationType.sigmoid, @@ -207,6 +211,8 @@ class TestGRUUnitOpWithBias(TestGRUUnitOp): class TestGRUUnitOpWithBiasOriginMode(TestGRUUnitOpWithBias): def setUp(self): + self.dtype = 'float32' if fluid.core.is_compiled_with_rocm( + ) else 'float64' self.set_inputs(origin_mode=True) self.set_outputs(origin_mode=True) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py index cd019c920756f34e6081da2d98e990d1bb5abee5..36c4d67bf2d813a5910eca7b0d766eee9c2a0824 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py @@ -190,10 +190,18 @@ class TestImperativeOptimizerBase(unittest.TestCase): for key, value in six.iteritems(static_param_init_value): self.assertTrue(np.allclose(value, dy_param_init_value[key])) - self.assertTrue(np.allclose(static_out, dy_out)) + if core.is_compiled_with_rocm(): + self.assertTrue(np.allclose(static_out, dy_out, atol=1e-3)) + else: + self.assertTrue(np.allclose(static_out, dy_out)) for key, value in six.iteritems(static_param_value): - self.assertTrue(np.allclose(value, dy_param_value[key])) + if core.is_compiled_with_rocm(): + self.assertTrue( + np.allclose( + value, dy_param_value[key], atol=1e-3)) + else: + self.assertTrue(np.allclose(value, dy_param_value[key])) class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase): diff --git a/python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py b/python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py index e3d82888f6160d42db6b83562618bcc857b44dc0..eac627d1b5b07656f107dd6e9fa667bb9cbb0249 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py @@ -207,10 +207,18 @@ class TestImperativeOptimizerBase(unittest.TestCase): for key, value in six.iteritems(static_param_init_value): self.assertTrue(np.allclose(value, dy_param_init_value[key])) - self.assertTrue(np.allclose(static_out, dy_out)) + if core.is_compiled_with_rocm(): + self.assertTrue(np.allclose(static_out, dy_out, atol=1e-3)) + else: + self.assertTrue(np.allclose(static_out, dy_out)) for key, value in six.iteritems(static_param_value): - self.assertTrue(np.allclose(value, dy_param_value[key])) + if core.is_compiled_with_rocm(): + self.assertTrue( + np.allclose( + value, dy_param_value[key], atol=1e-3)) + else: + self.assertTrue(np.allclose(value, dy_param_value[key])) class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase): diff --git a/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py b/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py index 82443f8c5493b4d10d039f7cd602e7d49428f24e..372b8d0d4d2766cefb5b1e7aad35ff798d41df51 100644 --- a/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py +++ b/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py @@ -390,8 +390,10 @@ class TestCUDNNLstmOp(OpTest): def setUp(self): self.op_type = "cudnn_lstm" - self.dtype = np.float64 - self.sequence_length = np.array([12, 11, 10, 9, 8], dtype=np.int32) + self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64 + self.sequence_length = None if core.is_compiled_with_rocm( + ) else np.array( + [12, 11, 10, 9, 8], dtype=np.int32) self.num_layers = 1 self.set_attrs() @@ -447,6 +449,13 @@ class TestCUDNNLstmOp(OpTest): hidden_size)).astype(self.dtype) state_out = np.ndarray((300)).astype("uint8") + if core.is_compiled_with_rocm(): + for i in range(len(flat_w)): + w = np.split(flat_w[i][1], 4, 0) + w = [w[0], w[1], w[3], w[2]] + w = np.concatenate(w) + flat_w[i] = (flat_w[i][0], w) + self.inputs = { 'Input': input, 'WeightList': flat_w, @@ -454,6 +463,13 @@ class TestCUDNNLstmOp(OpTest): 'InitC': init_c, 'SequenceLength': self.sequence_length } + if self.sequence_length is None: + self.inputs = { + 'Input': input, + 'WeightList': flat_w, + 'InitH': init_h, + 'InitC': init_c, + } self.attrs = { 'dropout_prob': 0.0, 'is_bidirec': False, @@ -474,8 +490,12 @@ class TestCUDNNLstmOp(OpTest): def test_output_with_place(self): place = core.CUDAPlace(0) - self.check_output_with_place( - place, no_check_set=['Reserve', 'StateOut']) + if core.is_compiled_with_rocm(): + self.check_output_with_place( + place, atol=1e-5, no_check_set=['Reserve', 'StateOut']) + else: + self.check_output_with_place( + place, no_check_set=['Reserve', 'StateOut']) def test_grad_with_place(self): place = core.CUDAPlace(0) @@ -496,14 +516,13 @@ class TestCUDNNlstmAPI(unittest.TestCase): hidden_size = 20 dropout_prob = 0.0 num_layers = 1 + dtype = 'float32' if core.is_compiled_with_rocm() else 'float64' input = fluid.data( - name='input', - shape=[seq_len, batch_size, hidden_size], - dtype='float64') + name='input', shape=[seq_len, batch_size, hidden_size], dtype=dtype) init_h = layers.fill_constant([num_layers, batch_size, hidden_size], - 'float64', 0.0) + dtype, 0.0) init_c = layers.fill_constant([num_layers, batch_size, hidden_size], - 'float64', 0.0) + dtype, 0.0) rnn_out, last_h, last_c = layers.lstm(input, init_h, init_c, seq_len, hidden_size, num_layers, dropout_prob, False) @@ -526,14 +545,13 @@ class TestCUDNNlstmAPI(unittest.TestCase): hidden_size = 20 dropout_prob = 0.0 num_layers = 2 + dtype = 'float32' if core.is_compiled_with_rocm() else 'float64' input = fluid.data( - name='input', - shape=[seq_len, batch_size, hidden_size], - dtype='float64') + name='input', shape=[seq_len, batch_size, hidden_size], dtype=dtype) init_h = layers.fill_constant([num_layers, batch_size, hidden_size], - 'float64', 0.0) + dtype, 0.0) init_c = layers.fill_constant([num_layers, batch_size, hidden_size], - 'float64', 0.0) + dtype, 0.0) rnn_out, last_h, last_c = layers.lstm(input, init_h, init_c, seq_len, hidden_size, num_layers, dropout_prob, False, True) @@ -541,7 +559,7 @@ class TestCUDNNlstmAPI(unittest.TestCase): exe.run(fluid.default_startup_program()) input_i = np.random.uniform( low=-0.1, high=0.1, size=(seq_len, batch_size, - hidden_size)).astype("float64") + hidden_size)).astype(dtype) out = exe.run(fluid.default_main_program(), feed={'input': input_i}, fetch_list=[rnn_out, last_h, last_c, 'cudnn_lstm_0.w_0']) diff --git a/python/paddle/fluid/tests/unittests/test_pool3d_op.py b/python/paddle/fluid/tests/unittests/test_pool3d_op.py index d618875835ffd8cbeba1bccbdfe7829a66c1ba76..40b9be9ee4f9bd69c763422fce850b0a3d6931d3 100644 --- a/python/paddle/fluid/tests/unittests/test_pool3d_op.py +++ b/python/paddle/fluid/tests/unittests/test_pool3d_op.py @@ -224,7 +224,7 @@ class TestPool3D_Op(OpTest): def setUp(self): self.op_type = "pool3d" self.init_kernel_type() - self.dtype = np.float64 + self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64 self.init_test_case() self.padding_algorithm = "EXPLICIT" self.init_paddings() @@ -277,9 +277,16 @@ class TestPool3D_Op(OpTest): return if self.has_cudnn() and self.pool_type != "max": place = core.CUDAPlace(0) - self.check_grad_with_place(place, set(['X']), 'Out') + if core.is_compiled_with_rocm(): + self.check_grad_with_place( + place, set(['X']), 'Out', max_relative_error=1e-2) + else: + self.check_grad_with_place(place, set(['X']), 'Out') elif self.pool_type != "max": - self.check_grad(set(['X']), 'Out') + if core.is_compiled_with_rocm(): + self.check_grad(set(['X']), 'Out', max_relative_error=1e-2) + else: + self.check_grad(set(['X']), 'Out') def init_data_format(self): self.data_format = "NCDHW" @@ -400,7 +407,10 @@ def create_test_cudnn_fp16_class(parent): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) if core.is_float16_supported(place): - self.check_output_with_place(place, atol=1e-3) + if core.is_compiled_with_rocm(): + self.check_output_with_place(place, atol=1e-2) + else: + self.check_output_with_place(place, atol=1e-3) cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16Op") TestCUDNNFp16Case.__name__ = cls_name diff --git a/python/paddle/nn/layer/rnn.py b/python/paddle/nn/layer/rnn.py index a899f18f521e8e7265cba24086da9cb5cc8cc22f..0cefb89340a7c0f847f2c81e48aa473ecfccaa16 100644 --- a/python/paddle/nn/layer/rnn.py +++ b/python/paddle/nn/layer/rnn.py @@ -1053,7 +1053,8 @@ class RNNBase(LayerList): initial_states, paddle.fluid.framework.Variable) else initial_states - if self.could_use_cudnn: + if self.could_use_cudnn and (not fluid.core.is_compiled_with_rocm() or + sequence_length is None): # Add CPU kernel and dispatch in backend later return self._cudnn_impl(inputs, initial_states, sequence_length)