Add nightly pipeline for MI100 to run convergence and batch size test similar to V100.

orttraining/orttraining/models/bert/main.cc

@@ -33,6 +33,30 @@ using namespace onnxruntime::training;
 using namespace onnxruntime::training::tensorboard;
 using namespace std;
 
+static std::vector<FreeDimensionOverride> overrides = {};
+static SessionOptions session_options = {
+    ExecutionMode::ORT_SEQUENTIAL,     //execution_mode
+    ExecutionOrder::PRIORITY_BASED,    //execution_order
+    false,                             //enable_profiling
+    ORT_TSTR(""),                      //optimized_model_filepath
+    true,                              //enable_mem_pattern
+    true,                              //enable_cpu_mem_arena
+    ORT_TSTR("onnxruntime_profile_"),  //profile_file_prefix
+    "",                                //session_logid
+    -1,                                //session_log_severity_level
+    0,                                 //session_log_verbosity_level
+    5,                                 //max_num_graph_transformation_steps
+    TransformerLevel::Level1,          //graph_optimization_level
+    {},                                //intra_op_param
+    {},                                //inter_op_param
+    overrides,                         //free_dimension_overrides
+    true,                              //use_per_session_threads
+    true,                              //thread_pool_allow_spinning
+    false,                             //use_deterministic_compute
+    {},                                //session_configurations
+    {},                                // initializers_to_share_map
+}; 
+
 struct BertParameters : public TrainingRunner::Parameters {
   int max_sequence_length = 512;
   int max_predictions_per_sequence = 80;
@@ -109,6 +133,7 @@ Status ParseArguments(int argc, char* argv[], BertParameters& params, OrtParamet
       ("iterations_per_loop", "How many steps to make in each estimator call.", cxxopts::value<int>()->default_value("1000"))
       ("max_eval_steps", "Maximum number of eval steps.", cxxopts::value<int>()->default_value("100"))
       ("seed", "Random seed.", cxxopts::value<int64_t>()->default_value("-1"))
+      ("use_deterministic_compute", "Whether to enable deterministic compute.", cxxopts::value<bool>()->default_value("false"))
       ("use_mixed_precision", "Whether to use a mix of fp32 and fp16 arithmetic on GPU.", cxxopts::value<bool>()->default_value("false"))
       ("use_bfloat16", "Whether to use BFloat16 arithmetic on GPU.", cxxopts::value<bool>()->default_value("false"))
       ("enable_adasum", "Whether to use Adasum for allreduction.", cxxopts::value<bool>()->default_value("false"))
@@ -469,6 +494,8 @@ Status ParseArguments(int argc, char* argv[], BertParameters& params, OrtParamet
       std::cout << "Random seed is set to: " << seed << std::endl;
     }
 
+    session_options.use_deterministic_compute = flags["use_deterministic_compute"].as<bool>();
+
     params.enable_gelu_approximation = flags["enable_gelu_approximation"].as<bool>();
     params.attn_dropout_recompute = flags["attn_dropout_recompute"].as<bool>();
     params.gelu_recompute = flags["gelu_recompute"].as<bool>();
@@ -746,7 +773,7 @@ static Status RunPerformanceTest(const BertParameters& params, const Environment
   auto random_perf_data = std::make_shared<RandomDataSet>(num_of_perf_samples, tensor_names, tensor_shapes, tensor_types);
   auto random_perf_data_loader = onnxruntime::make_unique<SingleDataLoader>(random_perf_data, tensor_names);
 
-  TrainingRunner runner{params, env};
+  TrainingRunner runner{params, env, session_options};
   ORT_RETURN_IF_ERROR(runner.Initialize());
   ORT_RETURN_IF_ERROR(runner.Run(random_perf_data_loader.get(), random_perf_data_loader.get()));
 
@@ -756,7 +783,7 @@ static Status RunPerformanceTest(const BertParameters& params, const Environment
 static Status RunTraining(const BertParameters& params, const Environment& env) {
   const size_t max_num_files_preload = 2;
 
-  auto runner = onnxruntime::make_unique<TrainingRunner>(params, env);
+  auto runner = onnxruntime::make_unique<TrainingRunner>(params, env, session_options);
   ORT_RETURN_IF_ERROR(runner->Initialize());
 
   BertParameters params_for_phase;

orttraining/tools/ci_test/results/bert_base.convergence.baseline.mi100.csv

@@ -0,0 +1,11 @@
+step,total_loss,mlm_loss,nsp_loss
+0,11.217,10.5178,0.699256
+5,9.67644,7.52047,2.15598
+10,8.31964,7.54136,0.778281
+15,8.22823,7.54625,0.681978
+20,8.17299,7.49675,0.676236
+25,8.2415,7.5356,0.705902
+30,8.0874,7.39312,0.694279
+35,7.99095,7.25612,0.734829
+40,7.92988,7.25608,0.673804
+45,7.94762,7.27291,0.674713

orttraining/tools/ci_test/run_batch_size_test.py

@@ -13,6 +13,7 @@ def parse_args():
     parser = argparse.ArgumentParser(description="Runs a BERT batch size test.")
     parser.add_argument("--binary_dir", required=True, help="Path to the ORT binary directory.")
     parser.add_argument("--model_root", required=True, help="Path to the model root directory.")
+    parser.add_argument("--gpu_sku", default='V100_16G', required=False, help="GPU model (e.g. V100, MI100).")
     return parser.parse_args()
 
 
@@ -24,7 +25,9 @@ def main():
                                                "max_batch_size", 
                                                "max_predictions_per_seq", 
                                                "additional_options"])
-    configs = [
+
+    configs = {}
+    configs['V100_16G'] = [
         Config(True, 128, 76, 20, ""),
         Config(True, 512, 11, 80, ""),
         Config(False, 128, 39, 20, ""),
@@ -41,8 +44,15 @@ def main():
         Config(True, 512, 50, 80, "--transformer_layer_recompute"),
     ]
 
+    configs['MI100_32G'] = [
+        Config(True, 128, 201, 20, ""),
+        Config(True, 512, 31, 80, ""),
+        Config(False, 128, 109, 20, ""),
+        Config(False, 512, 16, 80, ""),
+    ]
+
     # run BERT training
-    for config in configs:
+    for config in configs[args.gpu_sku]:
         print("##### testing name - {}-{} #####".format("fp16" if config.enable_mixed_precision else "fp32",
                                                         config.sequence_length))
         cmds = [

orttraining/tools/ci_test/run_convergence_test.py

@@ -20,6 +20,8 @@ def parse_args():
                       help="Path to the training data root directory.")
   parser.add_argument("--model_root", required=True,
                       help="Path to the model root directory.")
+  parser.add_argument("--gpu_sku", default='V100', required=False,
+                      help="GPU SKU (e.g. V100, MI100).") 
   return parser.parse_args()
 
 def main():
@@ -49,6 +51,7 @@ def main():
         "--gradient_accumulation_steps", "16",
         "--max_predictions_per_seq=20",
         "--use_mixed_precision",
+        "--use_deterministic_compute",
         "--allreduce_in_fp16",
         "--lambda", "0",
         "--use_nccl",
@@ -57,10 +60,16 @@ def main():
         "--enable_grad_norm_clip=false",
     ]).check_returncode()
 
+    # reference data
+    if args.gpu_sku == 'MI100_32G':
+        reference_csv = "bert_base.convergence.baseline.mi100.csv"
+    else:
+        reference_csv = "bert_base.convergence.baseline.csv"
+
     # verify output
     comparison_result = compare_results_files(
         expected_results_path=os.path.join(
-            SCRIPT_DIR, "results", "bert_base.convergence.baseline.csv"),
+            SCRIPT_DIR, "results", reference_csv),
         actual_results_path=convergence_test_output_path,
         field_comparisons={
             "step": Comparisons.eq(),

tools/ci_build/github/azure-pipelines/orttraining-linux-gpu-amd-e2e-test-ci-pipeline.yml

@@ -0,0 +1,68 @@
+trigger: none
+
+name: 'orttraining_amd_nightly_$(Date:yyyyMMdd)_$(Rev:r)'
+pool: 'AMD-GPU'
+
+jobs:
+- job: Onnxruntime_Linux_GPU_AMD_Training_E2E_Test
+
+  timeoutInMinutes: 60
+
+  steps:
+  - checkout: self
+    clean: true
+    submodules: recursive
+
+  - script: |-
+      echo "##vso[task.prependpath]/home/ciagent/conda/bin/"
+      echo "##vso[task.prependpath]/home/ciagent/pkg/openmpi-4.0.5/bin/"
+      echo '##vso[task.setvariable variable=LD_LIBRARY_PATH]/home/ciagent/pkg/openmpi-4.0.5/lib/'
+      eval "$('/home/ciagent/conda/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
+      echo "Selecting GPU based on HIP_VISIBLE_DEVICES=$HIP_VISIBLE_DEVICES"
+    displayName: 'Initialize environment'
+
+  # update these if the E2E test data changes
+  - script: |-
+      python orttraining/tools/ci_test/download_azure_blob_archive.py \
+        --azure_blob_url https://onnxruntimetestdata.blob.core.windows.net/training/onnxruntime_training_data.zip?snapshot=2020-06-15T23:17:35.8314853Z \
+        --target_dir training_e2e_test_data \
+        --archive_sha256_digest B01C169B6550D1A0A6F1B4E2F34AE2A8714B52DBB70AC04DA85D371F691BDFF9
+    displayName: 'Download onnxruntime_training_data.zip data'
+
+  - script: |-
+      python tools/ci_build/build.py \
+        --config RelWithDebInfo \
+        --enable_training \
+        --mpi_home /home/ciagent/pkg/openmpi-4.0.5 \
+        --use_rocm \
+        --rocm_home /opt/rocm \
+        --nccl_home /opt/rocm \
+        --update \
+        --build_dir ./build \
+        --build \
+        --parallel 8 \
+        --build_wheel \
+        --skip_tests
+    displayName: 'Build onnxruntime'
+
+  - script: |-
+      cd ./build/RelWithDebInfo &&\
+      ../../tools/ci_build/github/pai/pai_test_launcher.sh
+    displayName: 'Run unit tests'
+
+  - script: |-
+     python orttraining/tools/ci_test/run_batch_size_test.py \
+       --binary_dir build/RelWithDebInfo \
+       --model_root training_e2e_test_data/models \
+       --gpu_sku MI100_32G
+    displayName: 'Run batch size test'
+    condition: succeededOrFailed() # ensure all tests are run
+
+  - script: |-
+     python orttraining/tools/ci_test/run_convergence_test.py \
+       --binary_dir build/RelWithDebInfo \
+       --model_root training_e2e_test_data/models \
+       --training_data_root training_e2e_test_data/data \
+       --gpu_sku MI100_32G
+    displayName: 'Run convergence test'
+    condition: succeededOrFailed() # ensure all tests are run