From 313926277eaa028f977c4a8b7ab34c057cbc0777 Mon Sep 17 00:00:00 2001 From: ronnywang <524019753@qq.com> Date: Thu, 6 May 2021 14:09:11 +0800 Subject: [PATCH] [ROCM] bugfix for unittest (#32392) * fix test_unpool_op * fix test_inplace_addto_strategy * fix test_conv2d_fusion_op * fix test_imperative_lod_tensor_to_selected_rows, test_imperative_selected_rows_to_lod_tensor * fix test_dot_op * fix test_correlation_op * fix tracer * fix test_memcpy_op --- cmake/operators.cmake | 1 - paddle/fluid/operators/conv_cudnn_op.cu | 49 ++++++++--- paddle/fluid/operators/conv_miopen_helper.h | 70 ++-------------- paddle/fluid/operators/correlation_op.cu | 21 +++-- paddle/fluid/operators/fused/CMakeLists.txt | 3 +- .../fluid/operators/fused/conv_fusion_op.cu | 83 ++++++++++++++++++- paddle/fluid/operators/math/unpooling.cu | 8 ++ paddle/fluid/operators/memcpy_op.cc | 2 +- paddle/fluid/platform/dynload/miopen.h | 1 + .../fluid/tests/unittests/test_dot_op.py | 36 +++++++- ..._imperative_lod_tensor_to_selected_rows.py | 5 +- ..._imperative_selected_rows_to_lod_tensor.py | 5 +- 12 files changed, 193 insertions(+), 91 deletions(-) diff --git a/cmake/operators.cmake b/cmake/operators.cmake index 16288e1fb45..75b1100caa9 100644 --- a/cmake/operators.cmake +++ b/cmake/operators.cmake @@ -180,7 +180,6 @@ function(op_library TARGET) list(REMOVE_ITEM miopen_cu_cc_srcs "affine_grid_cudnn_op.cu.cc") list(REMOVE_ITEM miopen_cu_cc_srcs "grid_sampler_cudnn_op.cu.cc") list(REMOVE_ITEM hip_srcs "cholesky_op.cu") - list(REMOVE_ITEM hip_srcs "correlation_op.cu") list(REMOVE_ITEM hip_srcs "multinomial_op.cu") list(REMOVE_ITEM hip_srcs "decode_jpeg_op.cu") hip_library(${TARGET} SRCS ${cc_srcs} ${hip_cc_srcs} ${miopen_cu_cc_srcs} ${miopen_cu_srcs} ${mkldnn_cc_srcs} ${hip_srcs} DEPS ${op_library_DEPS} diff --git a/paddle/fluid/operators/conv_cudnn_op.cu b/paddle/fluid/operators/conv_cudnn_op.cu index ab535e341f7..7fdb1ccfe96 100644 --- a/paddle/fluid/operators/conv_cudnn_op.cu +++ b/paddle/fluid/operators/conv_cudnn_op.cu @@ -699,24 +699,51 @@ class CUDNNConvGradOpKernel : public framework::OpKernel { // ------------------- cudnn conv backward data --------------------- ScalingParamType alpha = 1.0f; +#ifdef PADDLE_WITH_HIP + // MIOPEN ONLY support beta to be 0.0f + ScalingParamType beta = 0.0f; +#else ScalingParamType beta = ctx.Attr("use_addto") ? 1.0f : 0.0f; +#endif VLOG(4) << "Conv_grad: use_addto = " << ctx.Attr("use_addto"); if (input_grad) { // When beta is 0, it is unnecessary to reset input_grad. // When beta is 1, the output cannot be reset since addt strategy used. #ifdef PADDLE_WITH_HIP - workspace_handle.RunFunc( - [&](void* cudnn_workspace_ptr) { - PADDLE_ENFORCE_CUDA_SUCCESS( - platform::dynload::miopenConvolutionBackwardData( - handle, &alpha, args1.odesc.desc(), output_grad_data, - args1.wdesc.desc(), filter_data, args1.cdesc.desc(), - data_algo, &beta, args1.idesc.desc(), - transformed_input_grad_data, cudnn_workspace_ptr, - workspace_size)); - }, - workspace_size); + if (ctx.Attr("use_addto")) { + Tensor temp_tensor(transformed_input_grad.type()); + temp_tensor.Resize(transformed_input_grad.dims()); + T* temp_tensor_data = temp_tensor.mutable_data(ctx.GetPlace()); + workspace_handle.RunFunc( + [&](void* cudnn_workspace_ptr) { + PADDLE_ENFORCE_CUDA_SUCCESS( + platform::dynload::miopenConvolutionBackwardData( + handle, &alpha, args1.odesc.desc(), output_grad_data, + args1.wdesc.desc(), filter_data, args1.cdesc.desc(), + data_algo, &beta, args1.idesc.desc(), temp_tensor_data, + cudnn_workspace_ptr, workspace_size)); + }, + workspace_size); + PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::miopenOpTensor( + handle, miopenTensorOpAdd, &alpha, args1.idesc.desc(), + transformed_input_grad_data, &alpha, args1.idesc.desc(), + temp_tensor_data, &beta, args1.idesc.desc(), + transformed_input_grad_data)); + } else { + workspace_handle.RunFunc( + [&](void* cudnn_workspace_ptr) { + PADDLE_ENFORCE_CUDA_SUCCESS( + platform::dynload::miopenConvolutionBackwardData( + handle, &alpha, args1.odesc.desc(), output_grad_data, + args1.wdesc.desc(), filter_data, args1.cdesc.desc(), + data_algo, &beta, args1.idesc.desc(), + transformed_input_grad_data, cudnn_workspace_ptr, + workspace_size)); + }, + workspace_size); + } + #else for (int i = 0; i < groups; i++) { workspace_handle.RunFunc( diff --git a/paddle/fluid/operators/conv_miopen_helper.h b/paddle/fluid/operators/conv_miopen_helper.h index 3ab27e1ec4f..befe09c8e6b 100644 --- a/paddle/fluid/operators/conv_miopen_helper.h +++ b/paddle/fluid/operators/conv_miopen_helper.h @@ -146,28 +146,8 @@ struct SearchAlgorithm { cudnn_workspace_ptr, workspace_size, false)); }; - if (!exhaustive_search && !deterministic) { - workspace_handle.RunFuncSync(cudnn_find_func, workspace_size); - algo = find_result.fwd_algo; - } else { - auto& temp = ctx.cuda_device_context(); - AlgorithmsCache& algo_cache = - *(framework::ConvSearchCache::Instance().GetForward()); - - auto x_dims = framework::vectorize(args.x->dims()); - auto w_dims = framework::vectorize(args.w->dims()); - - VLOG(10) << "miopenConvolutionFwdAlgoPerf_t:" - << ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s" - << args.s << ", args.p" << args.p << ", args.d" << args.d; - - algo = algo_cache.GetAlgorithm( - x_dims, w_dims, args.s, args.p, args.d, 0, - static_cast(args.cudnn_dtype), [&]() { - workspace_handle.RunFuncSync(cudnn_find_func, workspace_size); - return find_result.fwd_algo; - }); - } + workspace_handle.RunFuncSync(cudnn_find_func, workspace_size); + algo = find_result.fwd_algo; VLOG(3) << "choose algo " << algo; return algo; } @@ -208,27 +188,8 @@ struct SearchAlgorithm { cudnn_workspace_ptr, workspace_size, false)); }; - if (!exhaustive_search && !deterministic) { - workspace_handle.RunFuncSync(cudnn_find_func, workspace_size); - algo = find_result.bwd_data_algo; - } else { - AlgorithmsCache& algo_cache = - *(framework::ConvSearchCache::Instance().GetBackwardData()); - - auto x_dims = framework::vectorize(args.x->dims()); - auto w_dims = framework::vectorize(args.w->dims()); - - VLOG(10) << "miopenConvolutionFwdAlgoPerf_t" - << ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s" - << args.s << ", args.p" << args.p << ", args.d" << args.d; - - algo = algo_cache.GetAlgorithm( - x_dims, w_dims, args.s, args.p, args.d, 0, - static_cast(args.cudnn_dtype), [&]() { - workspace_handle.RunFuncSync(cudnn_find_func, workspace_size); - return find_result.bwd_data_algo; - }); - } + workspace_handle.RunFuncSync(cudnn_find_func, workspace_size); + algo = find_result.bwd_data_algo; VLOG(3) << "choose algo " << algo; return algo; } @@ -269,27 +230,8 @@ struct SearchAlgorithm { cudnn_workspace_ptr, workspace_size, false)); }; - if (!exhaustive_search && !deterministic) { - workspace_handle.RunFuncSync(cudnn_find_func, workspace_size); - algo = find_result.bwd_weights_algo; - } else { - AlgorithmsCache& algo_cache = - *(framework::ConvSearchCache::Instance().GetBackwardFilter()); - - auto x_dims = framework::vectorize(args.x->dims()); - auto w_dims = framework::vectorize(args.w->dims()); - - VLOG(10) << "miopenConvolutionFwdAlgoPerf_t:" - << ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s" - << args.s << ", args.p" << args.p << ", args.d" << args.d; - - algo = algo_cache.GetAlgorithm( - x_dims, w_dims, args.s, args.p, args.d, 0, - static_cast(args.cudnn_dtype), [&]() { - workspace_handle.RunFuncSync(cudnn_find_func, workspace_size); - return find_result.bwd_weights_algo; - }); - } + workspace_handle.RunFuncSync(cudnn_find_func, workspace_size); + algo = find_result.bwd_weights_algo; VLOG(3) << "choose algo " << algo; return algo; } diff --git a/paddle/fluid/operators/correlation_op.cu b/paddle/fluid/operators/correlation_op.cu index a51fce81324..9b08f875bb6 100644 --- a/paddle/fluid/operators/correlation_op.cu +++ b/paddle/fluid/operators/correlation_op.cu @@ -12,17 +12,22 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifndef PADDLE_WITH_HIP -// HIP not supported yet - #include #include #include "paddle/fluid/framework/op_registry.h" +#ifdef __HIPCC__ +#define __syncwarp() __all(1) +#endif + namespace paddle { namespace operators { +#ifdef __HIPCC__ +#define THREADS_PER_BLOCK 64 +#else #define THREADS_PER_BLOCK 32 +#endif #define FULL_MASK 0xffffffff using framework::Tensor; @@ -30,14 +35,22 @@ using framework::Tensor; template __forceinline__ __device__ T warpReduceSum(T val) { for (int offset = 16; offset > 0; offset /= 2) { +#ifdef __HIPCC__ + val += __shfl_down(val, offset); +#else val += __shfl_down_sync(FULL_MASK, val, offset); +#endif } return val; } template __forceinline__ __device__ T blockReduceSum(T val) { +#ifdef __HIPCC__ + static __shared__ T shared[64]; +#else static __shared__ T shared[32]; +#endif int lane = threadIdx.x % warpSize; int wid = threadIdx.x / warpSize; @@ -483,5 +496,3 @@ REGISTER_OP_CUDA_KERNEL(correlation, ops::CorrelationCUDAKernel, ops::CorrelationCUDAKernel); REGISTER_OP_CUDA_KERNEL(correlation_grad, ops::CorrelationCUDAGradKernel, ops::CorrelationCUDAGradKernel); - -#endif // not PADDLE_WITH_HIP diff --git a/paddle/fluid/operators/fused/CMakeLists.txt b/paddle/fluid/operators/fused/CMakeLists.txt index 287827ced51..104298e0373 100644 --- a/paddle/fluid/operators/fused/CMakeLists.txt +++ b/paddle/fluid/operators/fused/CMakeLists.txt @@ -32,8 +32,7 @@ if (WITH_GPU OR WITH_ROCM) file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(fused_batch_norm_act);\n") endif() # conv_fusion_op needs cudnn 7 above - # HIP not support cudnnConvolutionBiasActivationForward - if ((NOT WITH_ROCM) AND (NOT ${CUDNN_VERSION} VERSION_LESS 7100)) + if (NOT ${CUDNN_VERSION} VERSION_LESS 7100) op_library(conv_fusion_op) file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(conv2d_fusion);\n") endif() diff --git a/paddle/fluid/operators/fused/conv_fusion_op.cu b/paddle/fluid/operators/fused/conv_fusion_op.cu index c9ba7a61e09..f5ee7f55991 100644 --- a/paddle/fluid/operators/fused/conv_fusion_op.cu +++ b/paddle/fluid/operators/fused/conv_fusion_op.cu @@ -18,14 +18,18 @@ limitations under the License. */ #include "paddle/fluid/operators/conv_cudnn_op_cache.h" #include "paddle/fluid/operators/conv_op.h" #include "paddle/fluid/operators/math/padding.h" +#ifdef PADDLE_WITH_HIP +#include "paddle/fluid/platform/miopen_helper.h" +#else #include "paddle/fluid/platform/cudnn_helper.h" +#endif DECLARE_int64(cudnn_exhaustive_search_times); namespace paddle { namespace operators { -#if CUDNN_VERSION >= 7100 +#if PADDLE_WITH_HIP || CUDNN_VERSION >= 7100 using Tensor = framework::Tensor; using ScopedTensorDescriptor = platform::ScopedTensorDescriptor; using ScopedFilterDescriptor = platform::ScopedFilterDescriptor; @@ -162,7 +166,78 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel { if (input->dims().size() == 5) { layout = DataLayout::kNCDHW; } +#ifdef PADDLE_WITH_HIP + miopenConvolutionDescriptor_t cudnn_conv_desc = + conv_desc.descriptor(padding_common, strides, dilations); + PADDLE_ENFORCE_CUDA_SUCCESS( + platform::dynload::miopenSetConvolutionGroupCount(cudnn_conv_desc, + groups)); + // Now only support NCHW + std::vector bias_dim = { + 1, static_cast(transformed_output.dims()[1]), 1, 1}; + miopenTensorDescriptor_t cudnn_input_desc = input_desc.descriptor( + layout, framework::vectorize(transformed_input.dims())); + miopenTensorDescriptor_t cudnn_output_desc = output_desc.descriptor( + layout, framework::vectorize(transformed_output.dims())); + miopenTensorDescriptor_t cudnn_filter_desc = filter_desc.descriptor( + layout, framework::vectorize(filter->dims())); + miopenTensorDescriptor_t cudnn_bias_desc = + bias_desc.descriptor(layout, bias_dim); + miopenActivationDescriptor_t cudnn_act_desc = + act_desc.descriptor(activation); + miopenConvFwdAlgorithm_t algo; + auto handle = dev_ctx.cudnn_handle(); + auto workspace_handle = dev_ctx.cudnn_workspace_handle(); + + auto x_dims = framework::vectorize(transformed_input.dims()); + auto f_dims = framework::vectorize(filter->dims()); + + size_t workspace_size = 0; + PADDLE_ENFORCE_CUDA_SUCCESS( + platform::dynload::miopenConvolutionForwardGetWorkSpaceSize( + handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc, + cudnn_output_desc, &workspace_size)); + int find_count; + miopenConvAlgoPerf_t find_result; + auto cudnn_find_func = [&](void* cudnn_workspace_ptr) { + PADDLE_ENFORCE_CUDA_SUCCESS( + platform::dynload::miopenFindConvolutionForwardAlgorithm( + handle, cudnn_input_desc, input_data, cudnn_filter_desc, + filter_data, cudnn_conv_desc, cudnn_output_desc, output_data, + kNUM_CUDNN_FWD_ALGS, &find_count, &find_result, + cudnn_workspace_ptr, workspace_size, false)); + }; + workspace_handle.RunFuncSync(cudnn_find_func, workspace_size); + algo = find_result.fwd_algo; + VLOG(3) << "cuDNN forward algo " << algo; + + { + ScalingParamType alpha = 1.0f, beta = 0.0f; + auto cudnn_func = [&](void* cudnn_workspace) { + PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::miopenConvolutionForward( + handle, &alpha, cudnn_input_desc, input_data, cudnn_filter_desc, + filter_data, cudnn_conv_desc, algo, &beta, cudnn_output_desc, + output_data, cudnn_workspace, workspace_size)); + }; + workspace_handle.RunFunc(cudnn_func, workspace_size); + PADDLE_ENFORCE_CUDA_SUCCESS( + platform::dynload::miopenConvolutionForwardBias( + handle, &alpha, cudnn_bias_desc, bias_data, &beta, + cudnn_output_desc, output_data)); + if (activation != "identity") { + PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::miopenActivationForward( + handle, cudnn_act_desc, &alpha, cudnn_output_desc, output_data, + &beta, cudnn_output_desc, output_data)); + } + if (residual) { + PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::miopenOpTensor( + handle, miopenTensorOpAdd, &alpha, cudnn_output_desc, output_data, + &alpha, cudnn_output_desc, residual_data, &beta, cudnn_output_desc, + output_data)); + } + } +#else // PADDLE_WITH_HIP cudnnConvolutionDescriptor_t cudnn_conv_desc = conv_desc.descriptor(padding_common, strides, dilations); PADDLE_ENFORCE_CUDA_SUCCESS( @@ -327,6 +402,7 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel { }; workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes); } +#endif std::vector channels = ctx.Attr>("split_channels"); if (channels.size()) { auto outs = ctx.MultiOutput("Outputs"); @@ -358,8 +434,11 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel { } // namespace operators } // namespace paddle -#if CUDNN_VERSION >= 7100 namespace ops = paddle::operators; +#if CUDNN_VERSION >= 7100 REGISTER_OP_CUDA_KERNEL(conv2d_fusion, ops::CUDNNConvFusionOpKernel, ops::CUDNNConvFusionOpKernel); #endif +#ifdef PADDLE_WITH_HIP +REGISTER_OP_CUDA_KERNEL(conv2d_fusion, ops::CUDNNConvFusionOpKernel); +#endif diff --git a/paddle/fluid/operators/math/unpooling.cu b/paddle/fluid/operators/math/unpooling.cu index d78e3385efb..a73f76f53be 100644 --- a/paddle/fluid/operators/math/unpooling.cu +++ b/paddle/fluid/operators/math/unpooling.cu @@ -87,7 +87,11 @@ class Unpool2dMaxFunctor { const T* input_data = input.data(); const int* indices_data = indices.data(); T* output_data = output->mutable_data(context.GetPlace()); +#ifdef __HIPCC__ + int threads = 256; +#else int threads = 1024; +#endif int grid = (input.numel() + threads - 1) / threads; KernelUnpool2dMax<<>>( input.numel(), input_data, indices_data, input_height, input_width, @@ -117,7 +121,11 @@ class Unpool2dMaxGradFunctor { const T* output_data = output.data(); const T* output_grad_data = output_grad.data(); T* input_grad_data = input_grad->mutable_data(context.GetPlace()); +#ifdef __HIPCC__ + int threads = 256; +#else int threads = 1024; +#endif int grid = (input.numel() + threads - 1) / threads; KernelUnpool2dMaxGrad<<>>( input.numel(), input_data, indices_data, input_height, input_width, diff --git a/paddle/fluid/operators/memcpy_op.cc b/paddle/fluid/operators/memcpy_op.cc index 4e10498efa1..ecd2d48dcbd 100644 --- a/paddle/fluid/operators/memcpy_op.cc +++ b/paddle/fluid/operators/memcpy_op.cc @@ -141,7 +141,7 @@ REGISTER_OP_CPU_KERNEL_FUNCTOR(memcpy, float, ops::MemcpyKernel, double, ops::MemcpyKernel, plat::float16, ops::MemcpyKernel); -#ifdef PADDLE_WITH_CUDA +#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_ROCM) REGISTER_OP_CUDA_KERNEL_FUNCTOR(memcpy, float, ops::MemcpyKernel, double, ops::MemcpyKernel, int, ops::MemcpyKernel, int64_t, ops::MemcpyKernel, bool, diff --git a/paddle/fluid/platform/dynload/miopen.h b/paddle/fluid/platform/dynload/miopen.h index 5ff4bff4bff..77ff3f3ccbb 100644 --- a/paddle/fluid/platform/dynload/miopen.h +++ b/paddle/fluid/platform/dynload/miopen.h @@ -110,6 +110,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name); __macro(miopenActivationBackward); \ __macro(miopenConvolutionBackwardWeights); \ __macro(miopenConvolutionForward); \ + __macro(miopenConvolutionForwardBias); \ __macro(miopenConvolutionBackwardBias); \ __macro(miopenConvolutionForwardGetWorkSpaceSize); \ __macro(miopenConvolutionBackwardDataGetWorkSpaceSize); \ diff --git a/python/paddle/fluid/tests/unittests/test_dot_op.py b/python/paddle/fluid/tests/unittests/test_dot_op.py index f65301f2d86..a92104a5a6f 100644 --- a/python/paddle/fluid/tests/unittests/test_dot_op.py +++ b/python/paddle/fluid/tests/unittests/test_dot_op.py @@ -15,6 +15,7 @@ from __future__ import print_function import paddle import paddle.fluid as fluid +import paddle.fluid.core as core import unittest import numpy as np from op_test import OpTest, skip_check_grad_ci @@ -39,13 +40,33 @@ class DotOp(OpTest): self.check_output() def test_check_grad_normal(self): - self.check_grad(['X', 'Y'], 'Out') + if core.is_compiled_with_rocm(): + self.check_grad( + ['X', 'Y'], + 'Out', + user_defined_grads=[self.inputs['Y'], self.inputs['X']]) + else: + self.check_grad(['X', 'Y'], 'Out') def test_check_grad_ingore_x(self): - self.check_grad(['Y'], 'Out', no_grad_set=set("X")) + if core.is_compiled_with_rocm(): + self.check_grad( + ['Y'], + 'Out', + no_grad_set=set("X"), + user_defined_grads=[self.inputs['X']]) + else: + self.check_grad(['Y'], 'Out', no_grad_set=set("X")) def test_check_grad_ingore_y(self): - self.check_grad(['X'], 'Out', no_grad_set=set('Y')) + if core.is_compiled_with_rocm(): + self.check_grad( + ['X'], + 'Out', + no_grad_set=set('Y'), + user_defined_grads=[self.inputs['Y']]) + else: + self.check_grad(['X'], 'Out', no_grad_set=set('Y')) def init_input_output(self): self.x = np.random.uniform(0.1, 1, [121]).astype(self.dtype) @@ -64,6 +85,15 @@ class DotOpBatch(DotOp): [11, 12]) self.out = np.sum(self.x * self.y, axis=1).reshape([11, 1]) + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out') + + def test_check_grad_ingore_x(self): + self.check_grad(['Y'], 'Out', no_grad_set=set("X")) + + def test_check_grad_ingore_y(self): + self.check_grad(['X'], 'Out', no_grad_set=set('Y')) + class TestDotOpError(unittest.TestCase): def test_errors(self): diff --git a/python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py b/python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py index e7af249cf8b..64f1715fc97 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py @@ -76,7 +76,10 @@ class SimpleNet(fluid.Layer): class TestDygraphSimpleNet(unittest.TestCase): def test_simple_net(self): for is_sparse in [True, False]: - for dtype in ["float32", "float64"]: + dtype_list = ["float32"] + if not core.is_compiled_with_rocm(): + dtype_list.append("float64") + for dtype in dtype_list: self.simple_net_float32(is_sparse, dtype) def simple_net_float32(self, is_sparse, dtype): diff --git a/python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py b/python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py index 2f2a3e5de5e..8b2e61f8d2a 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py @@ -82,7 +82,10 @@ class SimpleNet(fluid.Layer): class TestDygraphSimpleNet(unittest.TestCase): def test_simple_net(self): for is_sparse in [True, False]: - for dtype in ["float32", "float64"]: + dtype_list = ["float32"] + if not core.is_compiled_with_rocm(): + dtype_list.append("float64") + for dtype in dtype_list: self.simple_net_float(is_sparse, dtype) def simple_net_float(self, is_sparse, dtype): -- GitLab