# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. """ORQA finetuning/evaluation.""" from functools import partial import sys import math import torch import torch.nn.functional as F from megatron import get_args, get_timers, get_tokenizer, print_rank_0 from megatron.core import mpu import megatron.model.biencoder_model from megatron.utils import average_losses_across_data_parallel_group from pretrain_ict import get_group_world_size_rank import tasks.finetune_utils from tasks.orqa.supervised.eval_utils import accuracy_func_provider from tasks.orqa.supervised.eval_utils import process_batch, task_collate_fn from megatron.model import ModelType # input_ is a 2D tensor def check_and_append_tensor_for_gather(group, rank, world_size, input_): # gather the size of the first dimension of the tensor from all ranks current_length = input_.size()[0] first_dim = torch.tensor([[current_length]], device=torch.cuda.current_device()) input_list = [torch.empty_like(first_dim) for _ in range(world_size)] input_list[rank].copy_(first_dim) torch.distributed.all_gather(input_list, first_dim, group=group) all_input_list = torch.cat(input_list, dim=0).contiguous() max_length = torch.max(all_input_list) # if the size are different than the max, extend the tensor # accordingly if max_length > current_length: padding=tuple([0] * (input_.dim() * 2 - 1)) + \ tuple([max_length - current_length]) input_ = F.pad(input=input_, pad=padding) return input_ def orqa(Dataset): def cross_entropy_forward_step(batch, model): """Simple forward step with cross-entropy loss.""" timers = get_timers() tokenizer = get_tokenizer() # Get the batch. timers('batch generator', log_level=2).start() try: batch_ = next(batch) except BaseException: batch_ = batch group, rank, world_size = get_group_world_size_rank() query_tokens, query_mask, query_types, query_pad_mask, \ context_tokens, context_mask, context_types, context_pad_mask, \ neg_context_tokens, neg_context_mask, neg_context_types, \ reference = process_batch(batch_) timers('batch generator').stop() local_batch_size = query_tokens.shape[0] # Text representation of query and context query_list, context_list = [], [] for i in range(local_batch_size): query_list.append(tokenizer.decode(query_tokens[i].tolist())) context_list.append(tokenizer.decode(context_tokens[i].tolist())) if neg_context_tokens is not None: neg_context_tokens = check_and_append_tensor_for_gather(group, rank, world_size, neg_context_tokens) neg_context_mask = check_and_append_tensor_for_gather(group, rank, world_size, neg_context_mask) neg_context_types = check_and_append_tensor_for_gather(group, rank, world_size, neg_context_types) if neg_context_tokens is not None: context_tokens = torch.cat([context_tokens, neg_context_tokens]) context_mask = torch.cat([context_mask, neg_context_mask]) context_types = torch.cat([context_types, neg_context_types]) # Forward model. output_tensor = model(query_tokens, query_mask, query_types, context_tokens, context_mask, context_types) return output_tensor, partial(cross_entropy_loss_func, query_tokens, context_tokens) def cross_entropy_loss_func(query_tokens, context_tokens, output_tensor): args = get_args() local_batch_size = query_tokens.shape[0] group, rank, world_size = get_group_world_size_rank() # recall we assert that model_parallel_size == 1 global_batch_size = world_size * local_batch_size query_logits, context_logits = output_tensor if world_size > 1: input_ = torch.empty_like(context_logits).copy_(\ context_logits).detach_() tensor_list = [torch.empty_like(input_) for _ in range(world_size)] tensor_list[rank].copy_(input_) torch.distributed.all_gather(tensor_list, input_, group=group) # Check if all-gather happens in order assert tensor_list[rank].sum().item() == \ context_logits.sum().item() # Preserves the gradient tensor_list[rank] = context_logits all_context_logits = torch.cat(tensor_list, dim=0).contiguous() # Query tensors input_ = torch.empty_like(query_logits).copy_(\ query_logits).detach_() tensor_list = [torch.empty_like(input_) for _ in range(world_size)] tensor_list[rank].copy_(input_) torch.distributed.all_gather(tensor_list, input_, group=group) # Check if all-gather happens in order assert tensor_list[rank].sum().item() == query_logits.sum().item() # Preserves the gradient tensor_list[rank] = query_logits all_query_logits = torch.cat(tensor_list, dim=0).contiguous() else: all_query_logits = query_logits all_context_logits = context_logits retrieval_scores = torch.matmul(all_query_logits, torch.transpose(all_context_logits, 0, 1)) # Scaling the retrieval scores if args.retriever_score_scaling: retrieval_scores = retrieval_scores / math.sqrt(args.hidden_size) if args.train_with_neg: # if the world size is 3, local batch size is 4, and # local context size is 8, what we want is # labels = [0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19] labels = [] local_context_size = context_tokens.shape[0] for i in range(world_size): j = i * local_context_size labels.extend(list(range(j, j + local_batch_size))) labels = torch.LongTensor(labels).cuda() assert len(labels) == global_batch_size else: labels = torch.arange(global_batch_size).long().cuda() # Cross-entropy loss. softmax_scores = F.log_softmax(retrieval_scores, dim=1) loss = F.nll_loss(softmax_scores, labels, reduction='mean') max_score, max_idxs = torch.max(softmax_scores, 1) correct_predictions_count = (max_idxs == labels).sum().float() # Reduce loss for logging. reduced_loss = average_losses_across_data_parallel_group([loss, \ correct_predictions_count]) # Loss scaling for correct losses in Supervised Retrieval loss = loss * mpu.get_data_parallel_world_size() return loss, {'lm loss': reduced_loss[0], 'correct_prediction_count': reduced_loss[1]} def train_valid_datasets_provider(): """Build train and validation dataset.""" args = get_args() tokenizer = get_tokenizer() train_dataset = Dataset('training', args.train_data, tokenizer, args.retriever_seq_length, evaluate=False) valid_dataset = Dataset('validation', args.valid_data, tokenizer, args.retriever_seq_length, evaluate=True) return train_dataset, valid_dataset def model_provider(pre_process=True, post_process=True): """Build the model.""" args = get_args() print_rank_0('building retriever model for {} ...'.format(args.task)) model_type_orqa = ModelType.encoder_or_decoder model = megatron.model.biencoder_model.biencoder_model_provider(only_context_model=False, only_query_model=False, biencoder_shared_query_context_model=args.biencoder_shared_query_context_model, pre_process=pre_process, post_process=post_process, model_type=model_type_orqa) return model def single_dataset_provider(datapath): args = get_args() tokenizer = get_tokenizer() name = datapath[0].split('/')[-1].split('.')[0] return Dataset(name, datapath, tokenizer, args.retriever_seq_length, evaluate=True) def metrics_func_provider(): """Provide metrics callback function.""" return accuracy_func_provider(single_dataset_provider) """Finetune/evaluate.""" model_type_orqa = ModelType.encoder_or_decoder tasks.finetune_utils.finetune(train_valid_datasets_provider, model_provider, model_type_orqa, forward_step=cross_entropy_forward_step, end_of_epoch_callback_provider=metrics_func_provider, task_collate_fn=task_collate_fn) def main(): args = get_args() if args.task == 'RET-FINETUNE-NQ': from tasks.orqa.supervised.data import NQSupervisedDataset as Dataset else: raise NotImplementedError('ORQA task {} is not implemented.'.format( args.task)) orqa(Dataset)