From 3ad6630f2dc4809c607d2a5b9c32d9d8b01095db Mon Sep 17 00:00:00 2001 From: wenbin Date: Fri, 25 Jun 2021 16:20:06 +0800 Subject: [PATCH] Fix wrong scale length for QkvToContext (#33763) * qkv * ci_test --- .../tensorrt/plugin/qkv_to_context_plugin.cu | 2 +- .../tests/api/trt_dynamic_shape_ernie_test.cc | 62 +++++++++++++------ 2 files changed, 43 insertions(+), 21 deletions(-) diff --git a/paddle/fluid/inference/tensorrt/plugin/qkv_to_context_plugin.cu b/paddle/fluid/inference/tensorrt/plugin/qkv_to_context_plugin.cu index 214e1a81e7d..5f10e5821c4 100644 --- a/paddle/fluid/inference/tensorrt/plugin/qkv_to_context_plugin.cu +++ b/paddle/fluid/inference/tensorrt/plugin/qkv_to_context_plugin.cu @@ -299,7 +299,7 @@ int QkvToContextPluginDynamic::enqueue( platform::DeviceContextPool::Instance().Get( platform::CUDAPlace(device_id))); - int n_q = seq_len * head_number_ * head_size_; + int n_q = seq_len * head_number_ * head_size_ * batch; constexpr int threads = 128; int blocks = (n_q + threads - 1) / threads; diff --git a/paddle/fluid/inference/tests/api/trt_dynamic_shape_ernie_test.cc b/paddle/fluid/inference/tests/api/trt_dynamic_shape_ernie_test.cc index a45b78f05e7..e449fb5096e 100644 --- a/paddle/fluid/inference/tests/api/trt_dynamic_shape_ernie_test.cc +++ b/paddle/fluid/inference/tests/api/trt_dynamic_shape_ernie_test.cc @@ -22,51 +22,60 @@ limitations under the License. */ namespace paddle { namespace inference { -void run(const AnalysisConfig& config, std::vector* out_data) { +void run(const AnalysisConfig& config, std::vector* out_data, int bs) { auto predictor = CreatePaddlePredictor(config); auto input_names = predictor->GetInputNames(); - int run_batch = 1; + int run_batch = bs; const int run_seq_len = 128; + size_t len = run_batch * run_seq_len; - int64_t i0[run_seq_len] = { + int64_t i0_bs1[run_seq_len] = { 1, 3558, 4, 75, 491, 89, 340, 313, 93, 4, 255, 10, 75, 321, 4095, 1902, 4, 134, 49, 75, 311, 14, 44, 178, 543, 15, 12043, 2, 75, 201, 340, 9, 14, 44, 486, 218, 1140, 279, 12043, 2}; - int64_t i1[run_seq_len] = { + int64_t i1_bs1[run_seq_len] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; - int64_t i2[run_seq_len] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, - 30, 31, 32, 33, 34, 35, 36, 37, 38, 39}; - float i3[run_seq_len] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0}; - + int64_t i2_bs1[run_seq_len] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, 35, 36, 37, 38, 39}; + float i3_bs1[run_seq_len] = { + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0}; + std::vector i0_data(len), i1_data(len), i2_data(len); + std::vector i3_data(len); + + for (size_t i = 0; i < len; i++) { + i0_data[i] = i0_bs1[i % run_seq_len]; + i1_data[i] = i1_bs1[i % run_seq_len]; + i2_data[i] = i2_bs1[i % run_seq_len]; + i3_data[i] = i3_bs1[i % run_seq_len]; + } // first input auto input_t = predictor->GetInputTensor(input_names[0]); input_t->Reshape({run_batch, run_seq_len, 1}); - input_t->copy_from_cpu(i0); + input_t->copy_from_cpu(i0_data.data()); // second input auto input_t2 = predictor->GetInputTensor(input_names[1]); input_t2->Reshape({run_batch, run_seq_len, 1}); - input_t2->copy_from_cpu(i1); + input_t2->copy_from_cpu(i1_data.data()); // third input. auto input_t3 = predictor->GetInputTensor(input_names[2]); input_t3->Reshape({run_batch, run_seq_len, 1}); - input_t3->copy_from_cpu(i2); + input_t3->copy_from_cpu(i2_data.data()); auto input_t4 = predictor->GetInputTensor(input_names[3]); input_t4->Reshape({run_batch, run_seq_len, 1}); - input_t4->copy_from_cpu(i3); + input_t4->copy_from_cpu(i3_data.data()); ASSERT_TRUE(predictor->ZeroCopyRun()); @@ -79,8 +88,8 @@ void run(const AnalysisConfig& config, std::vector* out_data) { output_t->copy_to_cpu(out_data->data()); } -void trt_ernie(bool with_fp16, std::vector result, - float near_tolerance) { +void trt_ernie(bool with_fp16, std::vector result, float near_tolerance, + int batch_size = 1) { AnalysisConfig config; std::string model_dir = FLAGS_infer_model; SetConfig(&config, model_dir, true); @@ -120,7 +129,7 @@ void trt_ernie(bool with_fp16, std::vector result, config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape, opt_input_shape); std::vector out_data; - run(config, &out_data); + run(config, &out_data, batch_size); for (size_t i = 0; i < out_data.size(); i++) { EXPECT_NEAR(result[i], out_data[i], near_tolerance); @@ -139,6 +148,19 @@ TEST(AnalysisPredictor, fp16) { #endif } +TEST(AnalysisPredictor, no_fp16_bs2) { + std::vector result = {0.597841, 0.219972, 0.182187, + 0.597841, 0.219972, 0.182187}; + trt_ernie(false, result, 1e-5, 2); +} + +TEST(AnalysisPredictor, fp16_bs2) { +#ifdef TRT_PLUGIN_FP16_AVALIABLE + std::vector result = {0.598, 0.219, 0.182, 0.598, 0.219, 0.182}; + trt_ernie(true, result, 4e-3, 2); +#endif +} + // ernie_varlen std::shared_ptr InitPredictor() { paddle_infer::Config config; -- GitLab