import os import requests import sys # AzureML libraries import azureml.core from azureml.core import Dataset, Environment, Experiment, ScriptRunConfig, Workspace from azureml.core.compute import ComputeTarget, AmlCompute from azureml.core.compute_target import ComputeTargetException from azureml.core.runconfig import PyTorchConfiguration from azureml.core.environment import DockerBuildContext # Check core SDK version number print("SDK version:", azureml.core.VERSION) # For setting up a workspace, refer to: https://github.com/Azure/azureml-examples/tree/main/python-sdk#set-up ws = Workspace.from_config() print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\n') #------------------------------------------------------------------------------- # Prepare Compute Cluster #------------------------------------------------------------------------------- cluster_name = "a100-80gb" # Verify that the cluster doesn't exist already try: compute_target = ComputeTarget(workspace=ws, name=cluster_name) print('Found existing compute target.') except ComputeTargetException: print('Creating a new compute target...') compute_config = AmlCompute.provisioning_configuration(vm_size='Standard_ND96amsr_A100_v4', min_nodes=32, max_nodes=32) # create the cluster compute_target = ComputeTarget.create(ws, cluster_name, compute_config) compute_target.wait_for_completion(show_output=True) #------------------------------------------------------------------------------- # Prepare Data # Megatron-DeepSpeed takes in data_path, vocab_file, and merge_file. # For AML, we are adding a parameter aml_data_download_path which specifies how to deliver the dataset to a compute target. # In the submitted run, files in the datasets will be either mounted or downloaded to local path on the compute target. # # data_path for this example is path to the .bin and .idx file, excluding extension. # e.g. for data/BookCorpusDataset_text_document.bin and data/BookCorpusDataset_text_document.idx, # data_path = "data/BookCorpusDataset_text_document" # # Once the folder is downloaded to the compute target, it will use aml_data_download_path to locate the folder # and data_path to locate .bin and .idx files # # vocab_file and merge_file would also be passed in a similar way. #------------------------------------------------------------------------------- datastore = ws.get_default_datastore() blobstore_datadir = "bookcorpus_data" data_path = f"BookCorpusDataset_text_document" # Load data folder which contains bookcorpus .bin and .idx files train_dataset = Dataset.File.from_files(path=[(datastore, blobstore_datadir)]) aml_data_download_path = train_dataset.as_download(blobstore_datadir) vocab_file_dataset = Dataset.File.from_files("https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json") merge_file_dataset = Dataset.File.from_files("https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt") vocab_file = vocab_file_dataset.as_download() merge_file = merge_file_dataset.as_download() #------------------------------------------------------------------------------- # Setup training environment #------------------------------------------------------------------------------- megatron_ds_env = Environment.from_docker_build_context(name='megatron-ds-curated-acpt', docker_build_context=DockerBuildContext.from_local_directory(workspace = ws, path = '.', dockerfile_path='Dockerfile.dockerfile')) megatron_ds_env.register(ws).build(ws).wait_for_completion() # Comment this out if environment already exists #------------------------------------------------------------------------------- # Training Settings and Arguments #------------------------------------------------------------------------------- node_count = 2 total_processes_count = 16 micro_batch_size = 1 global_batch_size = micro_batch_size * total_processes_count tensorboard_dir = '/tmp/outputs/tensorboard' run_args = ['--tensor-model-parallel-size', 1, '--pipeline-model-parallel-size', 1, '--num-layers', 20, '--hidden-size', 12288, '--num-attention-heads', 96, '--seq-length', 1024, '--loss-scale', 15, '--max-position-embeddings', 1024, '--micro-batch-size', micro_batch_size, '--global-batch-size', global_batch_size, '--train-iters', 100, '--lr', 6.0e-5, '--min-lr', 6.0e-6, '--lr-decay-style', 'cosine', '--log-interval', 1, '--eval-iters', 40, '--eval-interval', 1000, '--aml-data-download-path', aml_data_download_path, '--data-path', data_path, '--vocab-file', vocab_file, '--merge-file', merge_file, '--save-interval', 1000, '--split', '98,2,0', '--clip-grad', 1.0, '--weight-decay', 0.1, '--adam-beta1', 0.9, '--adam-beta2', 0.95, '--init-method-std', 0.006, '--fp16', '--data-impl', 'mmap', '--checkpoint-activations', '--tensorboard-dir', tensorboard_dir, #'--cpu-optimizer', '--deepspeed', '--no-pipeline-parallel', '--deepspeed_config', 'ds_config.json', '--zero-stage', 3, '--deepspeed-activation-checkpointing', '--exit-interval', 5000, ] #------------------------------------------------------------------------------- # DeepSpeed ds_config.json #------------------------------------------------------------------------------- import json ds_config = { "train_batch_size" : global_batch_size, "train_micro_batch_size_per_gpu": micro_batch_size, "steps_per_print": 1, "gradient_accumulation_steps": 1, "zero_optimization": { "stage": 3, "stage3_max_live_parameters": 3e9, "stage3_max_reuse_distance": 3e9, "stage3_param_persistence_threshold": 1e5, "stage3_prefetch_bucket_size": 5e7, "contiguous_gradients": True, "overlap_comm": True, "reduce_bucket_size": 90000000, "sub_group_size": 1e9, "offload_optimizer": { "device": "none", "buffer_count": 4, "pipeline_read": False, "pipeline_write": False, "pin_memory": True } }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "initial_scale_power" : 15, "loss_scale_window": 1000, "hysteresis": 2, "min_loss_scale": 1 }, "wall_clock_breakdown": True, "zero_allow_untested_optimizer": False, "aio": { "block_size": 1048576, "queue_depth": 16, "single_submit": False, "overlap_events": True, "thread_count": 2 } } # Place ds_config.json in the same folder as pretrain_gpt.py (script to run) ds_config_path = '../../ds_config.json' with open(ds_config_path, 'w') as fp: json.dump(ds_config, fp, indent=4) #------------------------------------------------------------------------------- # Create ScriptRunConfig #------------------------------------------------------------------------------- distr_config = PyTorchConfiguration(process_count=total_processes_count, node_count=node_count) megatron_ds_src = ScriptRunConfig(source_directory='../../', script='pretrain_gpt.py', arguments=run_args, compute_target=compute_target, environment=megatron_ds_env, distributed_job_config=distr_config) megatron_ds_src.run_config.environment_variables['NCCL_DEBUG'] = 'WARN' megatron_ds_src.run_config.environment_variables['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' megatron_ds_src.run_config.environment_variables['NCCL_SOCKET_IFNAME'] = 'eth0' megatron_ds_src.run_config.environment_variables['NCCL_IB_PCI_RELAXED_ORDERING']='1' megatron_ds_src.run_config.environment_variables['UCX_TLS']='tcp' megatron_ds_src.run_config.environment_variables['UCX_NET_DEVICES']='eth0' #------------------------------------------------------------------------------- # Submit experiment #------------------------------------------------------------------------------- experiment_name = 'megatron-ds' experiment = Experiment(ws, name=experiment_name) run = experiment.submit(megatron_ds_src, tags={'bs':micro_batch_size, 'gpus':total_processes_count})