utils.py

# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Utilities for models."""

import math

import torch

from megatron import get_args, mpu
from megatron.core.dist_checkpointing.mapping import ShardedTensor

def init_method_normal(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(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_


def attention_mask_func(attention_scores, attention_mask):
    attention_scores.masked_fill_(attention_mask, -10000.0)
    return attention_scores


def get_linear_layer(rows, columns, init_method):
    """Simple linear layer with weight initialization."""
    layer = torch.nn.Linear(rows, columns)
    if get_args().perform_initialization:
        init_method(layer.weight)
    with torch.no_grad():
        layer.bias.zero_()
    return layer

@torch.jit.script
def gelu_impl(x):
    """OpenAI's gelu implementation."""
    return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x *
                                       (1.0 + 0.044715 * x * x)))
def openai_gelu(x):
    return gelu_impl(x)

#This is actually Python equivalent of torch.nn.functional.gelu(), also with type hints for ONNX exporter
@torch.jit.script
def erf_gelu(x):
    return x * 0.5 * (torch.erf(x / 1.41421).to(dtype=x.dtype)+torch.ones_like(x).to(dtype=x.dtype))


def make_tp_sharded_tensor_for_checkpoint(tensor, key, tp_axis=0, replica_id=None, **kwargs):
    return ShardedTensor.from_rank_offsets(
        key, tensor,
        (tp_axis, mpu.get_tensor_model_parallel_rank(), mpu.get_tensor_model_parallel_world_size()),
        replica_id=mpu.get_data_parallel_rank() if replica_id is None else replica_id,
        **kwargs
    )

def make_sharded_tensor_for_checkpoint(tensor, key, **kwargs):
    return ShardedTensor.from_rank_offsets(
        key, tensor,
        replica_id=mpu.get_data_parallel_rank() * mpu.get_data_parallel_world_size() + mpu.get_tensor_model_parallel_rank(),
        **kwargs
    )