# 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 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))