docker/bloom13b/Megatron-DeepSpeed/tasks/orqa/supervised/finetune.py

# 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
from megatron.indexer import IndexBuilder
from megatron.model.biencoder_model import biencoder_model_provider
from megatron.utils import average_losses_across_data_parallel_group
from pretrain_ict import get_group_world_size_rank
from tasks.finetune_utils import finetune
from tasks.orqa.supervised.eval_utils import accuracy_func_provider
from tasks.orqa.supervised.eval_utils import process_batch, task_collate_fn
from tasks.orqa.evaluate_utils import ORQAEvaluator

# 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 = 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)

        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."""
    finetune(train_valid_datasets_provider,
             model_provider,
             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)