File size: 2,892 Bytes
f457f98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Utilities for models."""
import math
import torch
from megatron import get_args
from deepspeed.runtime.zero import GatheredParameters
from deepspeed.accelerator import get_accelerator
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 gather_and_init(param, init_method):
with GatheredParameters(param, modifier_rank=0):
init_method(param)
def perform_masking(attention_scores, attention_mask):
if attention_mask.dtype == torch.bool:
attention_scores.masked_fill_(attention_mask, -10000.0)
else:
attention_scores.add_(attention_mask)
def attention_mask_func(attention_scores, attention_mask):
args = get_args()
if args.curriculum_learning_legacy or args.data_efficiency_curriculum_learning:
attention_mask_ = attention_mask
actual_seqlen = attention_scores.size()[2]
if actual_seqlen != attention_mask_.size()[2]:
# attention_mask has size [1, 1, seqlen, seqlen]
attention_mask_ = attention_mask_[:, :, :actual_seqlen, :actual_seqlen].contiguous()
perform_masking(attention_scores, attention_mask_)
else:
perform_masking(attention_scores, attention_mask)
return attention_scores
def get_linear_layer(rows, columns, init_method, gather_params_on_init=False):
"""Simple linear layer with weight initialization."""
layer = torch.nn.Linear(rows, columns,
device=get_accelerator().current_device_name(),
dtype=get_args().params_dtype)
if get_args().perform_initialization:
with GatheredParameters(layer.weight, modifier_rank=0, enabled=gather_params_on_init):
init_method(layer.weight)
with torch.no_grad():
with GatheredParameters(layer.bias, modifier_rank=0, enabled=gather_params_on_init):
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))
|