NVIDIA-GPU-Bootcamp-Training/ai/Megatron/English/Python/jupyter_notebook/Megatron-LM/megatron/model/utils.py

# coding=utf-8
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Utilities for models."""

import math

import torch

from megatron import get_args

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)
    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))