megatron_model.py

'''Copyright The Microsoft DeepSpeed Team'''

import torch
import os
import sys
import math

from .common import get_test_path
from deepspeed.pipe import PipelineModule, LayerSpec
from deepspeed.accelerator import get_accelerator


def get_megatron_version():
    p = os.popen("pip list --format=columns | grep megatron-lm")
    pip_list = p.read()
    assert 'megatron-lm' in pip_list, 'Please install Megatron-LM before getting its version'
    ver_str = pip_list.split()[1]
    return float(ver_str[0])


def get_gpt2_model(args_others, mp_size=1):
    from megatron.model import GPT2Model
    from megatron.initialize import initialize_megatron

    args_defaults = {
        'vocab_file': get_test_path('gpt2-vocab.json'),
        'merge_file': get_test_path('gpt2-merges.txt'),
        'tokenizer_type': 'GPT2BPETokenizer',
    }

    args_defaults.update(args_others)

    # setting "make-vocab-size-divisible-by" to avoid word-embedding size change in resizing testing.
    sys.argv.extend([
        '--model-parallel-size',
        str(mp_size),
        '--make-vocab-size-divisible-by',
        str(1)
    ])

    initialize_megatron(args_defaults=args_defaults, ignore_unknown_args=True)
    model = GPT2Model(num_tokentypes=0, parallel_output=False)
    model.to(get_accelerator().device_name())
    from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
    from megatron import mpu
    i = get_accelerator().current_device_name()
    model = torchDDP(model,
                     device_ids=[i],
                     output_device=i,
                     process_group=mpu.get_data_parallel_group())

    return model


class MockGPT2ModelPipe(PipelineModule):
    def __init__(self, num_layers, mp_size, args_others, topo, **kwargs):
        from megatron.initialize import initialize_megatron

        args_defaults = {
            'vocab_file': get_test_path('gpt2-vocab.json'),
            'merge_file': get_test_path('gpt2-merges.txt'),
            'tokenizer_type': 'GPT2BPETokenizer',
        }

        args_defaults.update(args_others)

        # setting "make-vocab-size-divisible-by" to avoid word-embedding size change in resizing testing.
        sys.argv.extend([
            '--model-parallel-size',
            str(mp_size),
            '--make-vocab-size-divisible-by',
            str(1)
        ])

        initialize_megatron(args_defaults=args_defaults, ignore_unknown_args=True)

        from megatron.model.transformer import ParallelTransformerLayer

        class ParallelTransformerLayerPipe(ParallelTransformerLayer):
            def forward(self, args):
                # hardcode attn mask for testing, PP requires the attn_mask to be stashed
                attention_mask = torch.tensor(
                    [[True]],
                    device=get_accelerator().current_device_name())
                return super().forward(args, attention_mask)

        layers = []
        for x in range(num_layers):
            layers.append(
                LayerSpec(ParallelTransformerLayerPipe,
                          self.gpt2_attention_mask_func,
                          self.init_method_normal(0.02),
                          self.scaled_init_method_normal(0.02,
                                                         num_layers),
                          x))
        super().__init__(layers=layers,
                         loss_fn=torch.nn.CrossEntropyLoss(),
                         topology=topo,
                         **kwargs)

    def gpt2_attention_mask_func(self, attention_scores, ltor_mask):
        attention_scores.masked_fill_(ltor_mask, -10000.0)
        return attention_scores

    def init_method_normal(self, sigma):
        """Init method based on N(0, sigma)."""
        def init_(tensor):
            return torch.nn.init.normal_(tensor, mean=0.0, std=sigma)

        return init_

    def scaled_init_method_normal(self, sigma, num_layers):
        """Init method based on N(0, sigma/sqrt(2*num_layers)."""
        std = sigma / math.sqrt(2.0 * num_layers)

        def init_(tensor):
            return torch.nn.init.normal_(tensor, mean=0.0, std=std)

        return init_