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6d5047c | 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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 | # SPDX-FileCopyrightText: Copyright (c) 2024 McGill NLP
# SPDX-License-Identifier: MIT
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
import torch
from peft import PeftModel
from torch import nn
from transformers import LlamaConfig, LlamaForCausalLM, LlamaModel, LlamaPreTrainedModel
from transformers.cache_utils import Cache, StaticCache
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.models.llama.modeling_llama import (
LlamaAttention,
LlamaDecoderLayer,
# LlamaFlashAttention2,
LlamaMLP,
LlamaRMSNorm,
LlamaRotaryEmbedding,
# LlamaSdpaAttention,
)
from transformers.utils import logging
from .utils import is_transformers_attn_greater_or_equal_4_43_1
logger = logging.get_logger(__name__)
class ModifiedLlamaAttention(LlamaAttention):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_causal = False
# class ModifiedLlamaFlashAttention2(LlamaFlashAttention2):
# def __init__(self, *args, **kwargs):
# super().__init__(*args, **kwargs)
# self.is_causal = False
# class ModifiedLlamaSdpaAttention(LlamaSdpaAttention):
# def __init__(self, *args, **kwargs):
# super().__init__(*args, **kwargs)
# self.is_causal = False
# LLAMA_ATTENTION_CLASSES = {
# "eager": ModifiedLlamaAttention,
# "flash_attention_2": ModifiedLlamaFlashAttention2,
# "sdpa": ModifiedLlamaSdpaAttention,
# }
class ModifiedLlamaDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: LlamaConfig, layer_idx: int):
nn.Module.__init__(self)
self.hidden_size = config.hidden_size
self.self_attn = ModifiedLlamaAttention(config=config, layer_idx=layer_idx)
# self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](
# config=config, layer_idx=layer_idx
# )
self.mlp = LlamaMLP(config)
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
class LlamaBiModel(LlamaModel):
_no_split_modules = ["ModifiedLlamaDecoderLayer"]
def __init__(self, config: LlamaConfig):
if not is_transformers_attn_greater_or_equal_4_43_1():
raise ValueError(
"The current implementation of LlamaEncoderModel follows modeling_llama.py of transformers version >= 4.43.1"
)
LlamaPreTrainedModel.__init__(self, config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[ModifiedLlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = LlamaRotaryEmbedding(config=config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def _update_causal_mask(
self,
attention_mask,
input_tensor,
cache_position,
past_key_values: Cache,
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_static_cache = isinstance(past_key_values, StaticCache)
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
# if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
# if AttentionMaskConverter._ignore_causal_mask_sdpa(
# attention_mask,
# inputs_embeds=input_tensor,
# past_key_values_length=past_seen_tokens,
# is_training=self.training,
# ):
# return None
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_length()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
causal_mask = torch.zeros(
(sequence_length, target_length), dtype=dtype, device=device
) # in original implementation - torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
# Commenting out next 2 lines to disable causal masking
# if sequence_length != 1:
# causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
if attention_mask.dim() == 2:
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
elif attention_mask.dim() == 4:
# backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
# cache. In that case, the 4D attention mask attends to the newest tokens only.
if attention_mask.shape[-2] < cache_position[0] + sequence_length:
offset = cache_position[0]
else:
offset = 0
mask_shape = attention_mask.shape
mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype
causal_mask[
: mask_shape[0],
: mask_shape[1],
offset : mask_shape[2] + offset,
: mask_shape[3],
] = mask_slice
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
class LlamaBiForMNTP(LlamaForCausalLM):
def __init__(self, config):
LlamaPreTrainedModel.__init__(self, config)
self.model = LlamaBiModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
# getter for PEFT model
def get_model_for_peft(self):
return self.model
# setter for PEFT model
def set_model_for_peft(self, model: PeftModel):
self.model = model
# save the PEFT model
def save_peft_model(self, path):
self.model.save_pretrained(path)
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