8 | 
9 |
10 |
11 |
12 | ## Citation
13 |
14 | If you find our code useful for your research, please consider citing:
15 |
16 | @article{liu2024mobilellm,
17 | title={MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases},
18 | author={Liu, Zechun and Zhao, Changsheng and Iandola, Forrest and Lai, Chen and Tian, Yuandong and Fedorov, Igor and Xiong, Yunyang and Chang, Ernie and Shi, Yangyang and Krishnamoorthi, Raghuraman and others},
19 | journal={arXiv preprint arXiv:2402.14905},
20 | year={2024}
21 | }
22 |
23 | ## Run
24 |
25 | ### Step 1. Requirements:
26 | * python 3.9, pytorch >= 2.0
27 | * pip install -r requirement.txt
28 |
29 | ### Step 2. Data preprocessing
30 | Dividing a tokenized dataset or tokenize your own dataset, and even distribute it across the total number of training nodes, where each node comprises 1x8 GPUs. Next, organize the data into the following structure:
31 | - basepath
32 | - 1
33 | - xxx.jsonl
34 | - 2
35 | - xxx.jsonl
36 | - ...
37 | - #nodes
38 | - xxx.jsonl
39 |
40 | Each line of a jsonl file is a key-value pair of tokenized data {"token_ids": [1,2,3,4,...]}.
41 |
42 | Our training code is compatible with the data pre-processing method in https://github.com/LLM360/amber-data-prep.
43 |
44 |
45 | ### Step 3. Training script
46 | The script `pretrain.sh` is provided to initiate training on a 1x8 node setup using torchrun. This script can be modified to adjust the `--nnodes` parameter and other settings to suit different multi-node configurations, such as those using slurm or torchx. The learning rate in the script is for 1x8 node with a batch size of 32. If you increase the number of nodes or the batch size, you need to increase the learning rate linearly.
47 |
48 | Steps to run:
49 | * In `pretrain.sh` file, specify the `--train_data_local_path` to the pre-processed data in Step 2 and `--input_model_filename` to `./configs/{model_size}/`.
50 | * Run `bash pretrain.sh `
51 |
52 | ### Others
53 | The model weights is still under legal review. If you have any questions, feel free to email (zechunliu at meta dot com) and (cszhao at meta dot com)
54 |
55 |
56 | ## Training cost
57 | It takes the following number of days to train MobileLLM on 1T tokens using 32 NVIDIA A100 80G GPUs.
58 | | 125M | 350M | 600M | 1B | 1.5B |
59 | | --- | --- | --- | --- | --- |
60 | | ~3 days| ~6 days| ~8 days | ~12 days | ~18 days |
61 |
62 |
63 | ## Results on Zero-shot Common Sense Reasoning tasks
64 |
65 | ### MobileLLM-125M
66 |
67 | | model | boolq | piqa | siqa | hellaswag | winogrande | arc_easy | arc_challenge | obqa | avg. |
68 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
69 | | OPT-125M | 41.3 | 25.2 | 57.5 | 62.0 | 41.9 | 31.1 | 31.2 | 50.8 | 42.6 |
70 | | GPT-neo-125M | 40.7 | 24.8 | 61.3 | 62.5 | 41.9 | 29.7 | 31.6 | 50.7 | 42.9 |
71 | | Pythia-160M | 40.0 | 25.3 | 59.5 | 62.0 | 41.5 | 29.9 | 31.2 | 50.9 | 42.5 |
72 | | **MobileLLM-125M** | 43.9 | 27.1 | 60.2 | 65.3 | 42.4 | 38.9 | 39.5 | 53.1 | **46.3** |
73 | | **MobileLLM-LS-125M** | 45.8 | 28.7 | 60.4 | 65.7 | 42.9 | 39.5 | 41.1 | 52.1 | **47.0** |
74 |
75 | ### MobileLLM-350M
76 |
77 | | model | boolq | piqa | siqa | hellaswag | winogrande | arc_easy | arc_challenge | obqa | avg. |
78 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
79 | | OPT-350M | 41.9 | 25.7 | 54.0 | 64.8 | 42.6 | 36.2 | 33.3 | 52.4 | 43.9 |
80 | | Pythia-410M | 47.1 | 30.3 | 55.3 | 67.2 | 43.1 | 40.1 | 36.2 | 53.4 | 46.6 |
81 | | **MobileLLM-350M** | 53.8 | 33.5 | 62.4 | 68.6 | 44.7 | 49.6 | 40.0 | 57.6 | **51.3** |
82 | | **MobileLLM-LS-350M** | 54.4 | 32.5 | 62.8 | 69.8 | 44.1 | 50.6 | 45.8 | 57.2 | **52.1** |
83 |
84 | ### MobileLLM-600M
85 |
86 | | model | boolq | piqa | siqa | hellaswag | winogrande | arc_easy | arc_challenge | obqa | avg. |
87 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
88 | | Qwen1.5-500M | 54.7 | 32.1 | 46.9 | 68.9 | 46.0 | 48.8 | 37.7 | 55.0 | 48.8 |
89 | | BLOOM-560M | 43.7 | 27.5 | 53.7 | 65.1 | 42.5 | 36.5 | 32.6 | 52.2 | 44.2 |
90 | | MobiLlama-800M | 52.0 | 31.7 | 54.6 | 73.0 | 43.3 | 52.3 | 42.5 | 56.3 | 50.7 |
91 | | **MobileLLM-600M** | 58.1 | 35.8 | 61.0 | 72.3 | 44.9 | 55.9 | 47.9 | 58.6 | **54.3** |
92 |
93 | ### MobileLLM-1B
94 |
95 | | model | boolq | piqa | siqa | hellaswag | winogrande | arc_easy | arc_challenge | obqa | avg. |
96 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
97 | | Pythia-1B | 49.9 | 30.4 | 58.7 | 69.2 | 43.3 | 47.4 | 38.6 | 52.2 | 48.7 |
98 | | MobiLlama-1B | 59.7 | 38.4 | 59.2 | 74.5 | 44.9 | 62.0 | 43.7 | 59.0 | 55.2 |
99 | | Falcon-1B | 59.5 | 38.4 | 63.9 | 74.6 | 44.6 | 62.9 | 45.6 | 60.9 | 56.3 |
100 | | BLOOM-1.1B | 47.6 | 27.3 | 58.6 | 67.0 | 42.4 | 42.2 | 36.6 | 53.8 | 46.9 |
101 | | TinyLlama-1.1B | 59.2 | 37.1 | 58.1 | 72.9 | 43.9 | 59.1 | 44.7 | 58.8 | 54.2 |
102 | | **MobileLLM-1B** | 63.0 | 39.0 | 66.7 | 74.4 | 45.0 | 61.4 | 46.8 | 62.3 | **57.3** |
103 |
104 | ### MobileLLM-1.5B
105 |
106 | | model | boolq | piqa | siqa | hellaswag | winogrande | arc_easy | arc_challenge | obqa | avg. |
107 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
108 | | GPT-neo-1.3B | 51.3 | 33.0 | 61.8 | 70.9 | 43.7 | 48.6 | 41.2 | 54.5 | 50.6 |
109 | | OPT-1.3B | 54.4 | 31.7 | 58.4 | 71.5 | 44.7 | 53.7 | 44.6 | 59.1 | 52.3 |
110 | | BLOOM-1.7B | 50.9 | 31.2 | 61.7 | 70.0 | 43.2 | 47.2 | 36.2 | 56.1 | 49.6 |
111 | | Qwen1.5-1.8B | 61.1 | 36.5 | 68.3 | 74.1 | 47.2 | 60.4 | 42.9 | 61.2 | 56.5 |
112 | | GPT-neo-2.7B | 55.8 | 34.3 | 62.4 | 72.9 | 43.6 | 55.6 | 40.0 | 57.9 | 52.8 |
113 | | OPT-2.7B | 56.6 | 34.6 | 61.8 | 74.5 | 45.6 | 60.2 | 48.2 | 59.6 | 55.1 |
114 | | Pythia-2.8B | 59.4 | 38.9 | 66.1 | 73.8 | 44.5 | 59.6 | 45.0 | 59.4 | 55.8 |
115 | | BLOOM-3B | 55.1 | 33.6 | 62.1 | 70.5 | 43.2 | 53.9 | 41.6 | 58.2 | 52.3 |
116 | | **MobileLLM-1.5B** | 67.5 | 40.9 | 65.7 | 74.8 | 46.4 | 64.5 | 50.5 | 64.7 | **59.4** |
117 |
118 | ## Acknowledgement
119 |
120 | This code is partially based on HuggingFace transformer repo.
121 |
122 | ## Contact
123 |
124 | Zechun Liu, Meta Inc (zechunliu at meta dot com)
125 |
126 | Changsheng Zhao, Meta Inc (cszhao at meta dot com)
127 |
128 | ## License
129 |
130 | BiT is CC-BY-NC 4.0 licensed as of now.
131 |
132 |
--------------------------------------------------------------------------------
/pretrain.py:
--------------------------------------------------------------------------------
1 | # coding=utf-8
2 | # Copyright (c) Meta Platforms, Inc. and affiliates.
3 | # All rights reserved.
4 | #
5 | # This source code is licensed under the license found in the
6 | # LICENSE file in the root directory of this source tree.
7 |
8 | import json
9 | import logging
10 | import os
11 | from logging import Logger
12 | import re
13 | import sys
14 | from typing import Dict, Iterator, List, Optional
15 | import datetime
16 |
17 | import torch
18 | import transformers
19 |
20 | from utils.modeling_llama import LlamaForCausalLM
21 | from utils.pretrain_trainer import PretrainTrainer
22 | from utils.process_args import process_args
23 | from torch import distributed as dist
24 | from torch.utils.data import Dataset, DataLoader
25 | from transformers import AutoConfig, default_data_collator
26 |
27 | # Define a utility method for setting the logging parameters of a logger
28 | def get_logger(logger_name: Optional[str]) -> logging.Logger:
29 | # Get the logger with the specified name
30 | logger = logging.getLogger(logger_name)
31 |
32 | # Set the logging level of the logger to INFO
33 | logger.setLevel(logging.INFO)
34 |
35 | # Define a formatter for the log messages
36 | formatter = logging.Formatter(
37 | "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
38 | )
39 |
40 | # Create a console handler for outputting log messages to the console
41 | console_handler = logging.StreamHandler()
42 | console_handler.setFormatter(formatter)
43 |
44 | # Add the console handler to the logger
45 | logger.addHandler(console_handler)
46 |
47 | return logger
48 |
49 |
50 | log: Logger = get_logger("mobileLLM")
51 |
52 |
53 | def get_local_rank() -> int:
54 | if os.environ.get("LOCAL_RANK"):
55 | return int(os.environ["LOCAL_RANK"])
56 | else:
57 | logging.warning(
58 | "LOCAL_RANK from os.environ is None, fall back to get rank from torch distributed"
59 | )
60 | return torch.distributed.get_rank()
61 |
62 | def get_global_rank() -> int:
63 | """
64 | Get rank using torch.distributed if available. Otherwise, the RANK env var instead if initialized.
65 | Returns 0 if neither condition is met.
66 | """
67 | if torch.distributed.is_available() and torch.distributed.is_initialized():
68 | return torch.distributed.get_rank()
69 |
70 | environ_rank = os.environ.get("RANK", "")
71 | if environ_rank.isdecimal():
72 | return int(os.environ["RANK"])
73 |
74 | return 0
75 |
76 |
77 | def get_folder_paths(directory: str) -> List[str]:
78 | folder_paths = [
79 | os.path.join(directory, item)
80 | for item in os.listdir(directory)
81 | if os.path.isdir(os.path.join(directory, item))
82 | ]
83 | return folder_paths
84 |
85 | def get_iterable_dataloader(iterator, batch_size):
86 | def custom_collate_fn(batch):
87 | return dict(input_ids=torch.stack(batch), labels=torch.stack(batch))
88 | class IteratorDataset(Dataset):
89 | def __init__(self, iterator):
90 | self.iterator = iterator
91 | def __len__(self):
92 | # Return an arbitrarily large number
93 | return sys.maxsize
94 | def __getitem__(self, index):
95 | try:
96 | ids = next(self.iterator)
97 | return torch.tensor(ids)
98 | except StopIteration:
99 | raise IndexError
100 | # Create dataset
101 | dataset = IteratorDataset(iterator)
102 | # Create DataLoader with custom collate function
103 | dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=custom_collate_fn)
104 | return dataloader
105 |
106 | class JSONLIterator:
107 | def __init__(
108 | self,
109 | fpath: str,
110 | world_size: int,
111 | world_rank: int,
112 | infinite: bool,
113 | ) -> None:
114 | assert 0 <= world_rank < world_size, (world_rank, world_size)
115 | self.f = open(fpath, "r", encoding="utf-8", errors="ignore")
116 | self.fpath = fpath
117 | self.world_size = world_size
118 | self.world_rank = world_rank
119 | self.line_num = 0
120 | self.iter = iter(self.gen(infinite))
121 | self.iter_id = 0
122 |
123 | def __iter__(self) -> "JSONLIterator":
124 | return self
125 |
126 | def __next__(self):
127 | return next(self.iter)
128 |
129 | def gen(self, infinite: bool) -> Iterator[Dict]:
130 | while True:
131 | log.info(f"Starting iteration {self.iter_id} over {self.fpath} ...")
132 | self.iter_id += 1
133 | while True:
134 | try:
135 | line, self.line_num = self.f.readline(), self.line_num + 1
136 | if not line:
137 | break
138 | if (self.line_num - 1) % self.world_size == self.world_rank:
139 | try:
140 | yield json.loads(line)['token_ids']
141 | except json.JSONDecodeError as e:
142 | print("Failed to parse JSON:", e)
143 | except Exception as e:
144 | print(f"Unexpected Jsonl error: {e}")
145 | continue # Skip to the next iteration
146 | except Exception as e:
147 | print(f"Unexpected error while reading line: {e}")
148 | continue
149 | if not infinite:
150 | break
151 | self.f.seek(0)
152 | self.line_num = 0
153 | self.f.close()
154 |
155 | def train() -> None:
156 | dist.init_process_group(
157 | backend="cpu:gloo,cuda:nccl", timeout=datetime.timedelta(hours=8)
158 | )
159 | model_args, data_args, training_args = process_args()
160 |
161 | global_rank = get_global_rank()
162 | local_rank = get_local_rank()
163 |
164 | log.info(f"Global Rank: {global_rank}")
165 | log.info(f"Local Rank: {local_rank}")
166 | config = AutoConfig.from_pretrained(model_args.input_model_filename)
167 | config.share_embedding = model_args.share_embedding
168 | config.layer_sharing = model_args.layer_sharing
169 | model = LlamaForCausalLM(
170 | config=config,
171 | )
172 | log.info(
173 | "model size is "
174 | + str(sum(param.numel() for param in model.model.parameters()) / 1024 / 1024)
175 | )
176 | log.info("Start to load tokenizer...")
177 | tokenizer = transformers.AutoTokenizer.from_pretrained(
178 | pretrained_model_name_or_path=model_args.input_model_filename,
179 | cache_dir=training_args.cache_dir,
180 | model_max_length=training_args.model_max_length,
181 | padding_side="right",
182 | use_fast=False,
183 | )
184 | log.info("Complete tokenizer loading...")
185 |
186 | # go to current node's data rank
187 | local_data_folder = os.path.join(data_args.train_data_local_path, str(global_rank//8+1))
188 |
189 | # Data load locally from shard total data, so world_size is 8 and rank is the current node's local rank
190 | log.info("world_rank for data loader is " + str(local_rank))
191 | log.info("world_size for data loader is " + str(8))
192 | assert os.path.isdir(local_data_folder), local_data_folder
193 | fname_match_re: str = r"\.jsonl$"
194 |
195 | # get the jsonl file name. Currently only support 1 file/folder per node
196 | fnames = [x for x in os.listdir(local_data_folder) if re.search(fname_match_re, x)][0]
197 | data_iter = JSONLIterator(
198 | fpath=os.path.join(local_data_folder,fnames),
199 | world_rank=local_rank,
200 | world_size=8,
201 | infinite=True,
202 | )
203 | trainer = PretrainTrainer(
204 | model=model,
205 | tokenizer=tokenizer,
206 | args=training_args,
207 | train_dataset=get_iterable_dataloader(data_iter, training_args.per_device_train_batch_size) if training_args.do_train else None,
208 | eval_dataset=None,
209 | data_collator=default_data_collator,
210 | )
211 | torch.distributed.barrier(device_ids=[local_rank])
212 |
213 | if training_args.do_train:
214 | _ = trainer.train()
215 | trainer.save_state()
216 |
217 | torch.distributed.barrier(device_ids=[local_rank])
218 |
219 |
220 | if __name__ == "__main__":
221 | train()
222 |
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--------------------------------------------------------------------------------
/utils/modeling_llama.py:
--------------------------------------------------------------------------------
1 | # coding=utf-8
2 | # Copyright (c) Meta Platforms, Inc. and affiliates.
3 | # All rights reserved.
4 | #
5 | # This source code is licensed under the license found in the
6 | # LICENSE file in the root directory of this source tree.
7 |
8 | # coding=utf-8
9 | # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
10 | #
11 | # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
12 | # and OPT implementations in this library. It has been modified from its
13 | # original forms to accommodate minor architectural differences compared
14 | # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
15 | #
16 | # Licensed under the Apache License, Version 2.0 (the "License");
17 | # you may not use this file except in compliance with the License.
18 | # You may obtain a copy of the License at
19 | #
20 | # http://www.apache.org/licenses/LICENSE-2.0
21 | #
22 | # Unless required by applicable law or agreed to in writing, software
23 | # distributed under the License is distributed on an "AS IS" BASIS,
24 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
25 | # See the License for the specific language governing permissions and
26 | # limitations under the License.
27 | import math
28 | from typing import List, Optional, Tuple, Union
29 |
30 | import torch
31 | import torch.nn.functional as F
32 | import torch.utils.checkpoint
33 | from torch import nn
34 | from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
35 |
36 | from transformers.activations import ACT2FN
37 | from transformers.cache_utils import Cache, DynamicCache, StaticCache
38 | from transformers.modeling_attn_mask_utils import AttentionMaskConverter
39 | from transformers.modeling_outputs import (
40 | BaseModelOutputWithPast,
41 | CausalLMOutputWithPast,
42 | QuestionAnsweringModelOutput,
43 | SequenceClassifierOutputWithPast,
44 | TokenClassifierOutput,
45 | )
46 | from transformers.modeling_utils import PreTrainedModel
47 | from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
48 | from transformers.utils import (
49 | add_start_docstrings,
50 | add_start_docstrings_to_model_forward,
51 | is_flash_attn_2_available,
52 | is_flash_attn_greater_or_equal_2_10,
53 | logging,
54 | replace_return_docstrings,
55 | )
56 | from transformers.models.llama.configuration_llama import LlamaConfig
57 |
58 |
59 | if is_flash_attn_2_available():
60 | from flash_attn import flash_attn_func, flash_attn_varlen_func
61 | from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
62 |
63 |
64 | logger = logging.get_logger(__name__)
65 |
66 | _CONFIG_FOR_DOC = "LlamaConfig"
67 |
68 |
69 | def _get_unpad_data(attention_mask):
70 | seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
71 | indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
72 | max_seqlen_in_batch = seqlens_in_batch.max().item()
73 | cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
74 | return (
75 | indices,
76 | cu_seqlens,
77 | max_seqlen_in_batch,
78 | )
79 |
80 |
81 | class LlamaRMSNorm(nn.Module):
82 | def __init__(self, hidden_size, eps=1e-6):
83 | """
84 | LlamaRMSNorm is equivalent to T5LayerNorm
85 | """
86 | super().__init__()
87 | self.weight = nn.Parameter(torch.ones(hidden_size))
88 | self.variance_epsilon = eps
89 |
90 | def forward(self, hidden_states):
91 | input_dtype = hidden_states.dtype
92 | hidden_states = hidden_states.to(torch.float32)
93 | variance = hidden_states.pow(2).mean(-1, keepdim=True)
94 | hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
95 | return self.weight * hidden_states.to(input_dtype)
96 |
97 |
98 | ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
99 |
100 |
101 | class LlamaRotaryEmbedding(nn.Module):
102 | def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
103 | super().__init__()
104 | self.scaling_factor = scaling_factor
105 | self.dim = dim
106 | self.max_position_embeddings = max_position_embeddings
107 | self.base = base
108 | inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
109 | self.register_buffer("inv_freq", inv_freq, persistent=False)
110 | # For BC we register cos and sin cached
111 | self.max_seq_len_cached = max_position_embeddings
112 |
113 | @torch.no_grad()
114 | def forward(self, x, position_ids):
115 | # x: [bs, num_attention_heads, seq_len, head_size]
116 | inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
117 | position_ids_expanded = position_ids[:, None, :].float()
118 | # Force float32 since bfloat16 loses precision on long contexts
119 | # See https://github.com/huggingface/transformers/pull/29285
120 | device_type = x.device.type
121 | device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
122 | with torch.autocast(device_type=device_type, enabled=False):
123 | freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
124 | emb = torch.cat((freqs, freqs), dim=-1)
125 | cos = emb.cos()
126 | sin = emb.sin()
127 | return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
128 |
129 |
130 | class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
131 | """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
132 |
133 | def forward(self, x, position_ids):
134 | # difference to the original RoPE: a scaling factor is aplied to the position ids
135 | position_ids = position_ids.float() / self.scaling_factor
136 | cos, sin = super().forward(x, position_ids)
137 | return cos, sin
138 |
139 |
140 | class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
141 | """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
142 |
143 | def forward(self, x, position_ids):
144 | # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
145 | seq_len = torch.max(position_ids) + 1
146 | if seq_len > self.max_position_embeddings:
147 | base = self.base * (
148 | (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
149 | ) ** (self.dim / (self.dim - 2))
150 | inv_freq = 1.0 / (
151 | base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim)
152 | )
153 | self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: this may break with compilation
154 |
155 | cos, sin = super().forward(x, position_ids)
156 | return cos, sin
157 |
158 |
159 | def rotate_half(x):
160 | """Rotates half the hidden dims of the input."""
161 | x1 = x[..., : x.shape[-1] // 2]
162 | x2 = x[..., x.shape[-1] // 2 :]
163 | return torch.cat((-x2, x1), dim=-1)
164 |
165 |
166 | def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
167 | """Applies Rotary Position Embedding to the query and key tensors.
168 |
169 | Args:
170 | q (`torch.Tensor`): The query tensor.
171 | k (`torch.Tensor`): The key tensor.
172 | cos (`torch.Tensor`): The cosine part of the rotary embedding.
173 | sin (`torch.Tensor`): The sine part of the rotary embedding.
174 | position_ids (`torch.Tensor`, *optional*):
175 | Deprecated and unused.
176 | unsqueeze_dim (`int`, *optional*, defaults to 1):
177 | The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
178 | sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
179 | that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
180 | k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
181 | cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
182 | the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
183 | Returns:
184 | `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
185 | """
186 | cos = cos.unsqueeze(unsqueeze_dim)
187 | sin = sin.unsqueeze(unsqueeze_dim)
188 | q_embed = (q * cos) + (rotate_half(q) * sin)
189 | k_embed = (k * cos) + (rotate_half(k) * sin)
190 | return q_embed, k_embed
191 |
192 |
193 | class LlamaMLP(nn.Module):
194 | def __init__(self, config):
195 | super().__init__()
196 | self.config = config
197 | self.hidden_size = config.hidden_size
198 | self.intermediate_size = config.intermediate_size
199 | self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
200 | self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
201 | self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
202 | self.act_fn = ACT2FN[config.hidden_act]
203 |
204 | def forward(self, x):
205 | if self.config.pretraining_tp > 1:
206 | slice = self.intermediate_size // self.config.pretraining_tp
207 | gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
208 | up_proj_slices = self.up_proj.weight.split(slice, dim=0)
209 | down_proj_slices = self.down_proj.weight.split(slice, dim=1)
210 |
211 | gate_proj = torch.cat(
212 | [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
213 | )
214 | up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
215 |
216 | intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
217 | down_proj = [
218 | F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
219 | ]
220 | down_proj = sum(down_proj)
221 | else:
222 | down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
223 |
224 | return down_proj
225 |
226 |
227 | def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
228 | """
229 | This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
230 | num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
231 | """
232 | batch, num_key_value_heads, slen, head_dim = hidden_states.shape
233 | if n_rep == 1:
234 | return hidden_states
235 | hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
236 | return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
237 |
238 |
239 | class LlamaAttention(nn.Module):
240 | """Multi-headed attention from 'Attention Is All You Need' paper"""
241 |
242 | def __init__(self, config: LlamaConfig, layer_idx: Optional[int] = None):
243 | super().__init__()
244 | self.config = config
245 | self.layer_idx = layer_idx
246 | if layer_idx is None:
247 | logger.warning_once(
248 | f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
249 | "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
250 | "when creating this class."
251 | )
252 |
253 | self.attention_dropout = config.attention_dropout
254 | self.hidden_size = config.hidden_size
255 | self.num_heads = config.num_attention_heads
256 | self.head_dim = self.hidden_size // self.num_heads
257 | self.num_key_value_heads = config.num_key_value_heads
258 | self.num_key_value_groups = self.num_heads // self.num_key_value_heads
259 | self.max_position_embeddings = config.max_position_embeddings
260 | self.rope_theta = config.rope_theta
261 | self.is_causal = True
262 |
263 | if (self.head_dim * self.num_heads) != self.hidden_size:
264 | raise ValueError(
265 | f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
266 | f" and `num_heads`: {self.num_heads})."
267 | )
268 |
269 | self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
270 | self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
271 | self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
272 | self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
273 | self._init_rope()
274 |
275 | def _init_rope(self):
276 | if self.config.rope_scaling is None:
277 | self.rotary_emb = LlamaRotaryEmbedding(
278 | self.head_dim,
279 | max_position_embeddings=self.max_position_embeddings,
280 | base=self.rope_theta,
281 | )
282 | else:
283 | scaling_type = self.config.rope_scaling["type"]
284 | scaling_factor = self.config.rope_scaling["factor"]
285 | if scaling_type == "linear":
286 | self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
287 | self.head_dim,
288 | max_position_embeddings=self.max_position_embeddings,
289 | scaling_factor=scaling_factor,
290 | base=self.rope_theta,
291 | )
292 | elif scaling_type == "dynamic":
293 | self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
294 | self.head_dim,
295 | max_position_embeddings=self.max_position_embeddings,
296 | scaling_factor=scaling_factor,
297 | base=self.rope_theta,
298 | )
299 | else:
300 | raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
301 |
302 | def forward(
303 | self,
304 | hidden_states: torch.Tensor,
305 | attention_mask: Optional[torch.Tensor] = None,
306 | position_ids: Optional[torch.LongTensor] = None,
307 | past_key_value: Optional[Cache] = None,
308 | output_attentions: bool = False,
309 | use_cache: bool = False,
310 | cache_position: Optional[torch.LongTensor] = None,
311 | ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
312 | bsz, q_len, _ = hidden_states.size()
313 |
314 | if self.config.pretraining_tp > 1:
315 | key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
316 | query_slices = self.q_proj.weight.split(
317 | (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
318 | )
319 | key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
320 | value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
321 |
322 | query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
323 | query_states = torch.cat(query_states, dim=-1)
324 |
325 | key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
326 | key_states = torch.cat(key_states, dim=-1)
327 |
328 | value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
329 | value_states = torch.cat(value_states, dim=-1)
330 |
331 | else:
332 | query_states = self.q_proj(hidden_states)
333 | key_states = self.k_proj(hidden_states)
334 | value_states = self.v_proj(hidden_states)
335 |
336 | query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
337 | key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
338 | value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
339 |
340 | cos, sin = self.rotary_emb(value_states, position_ids)
341 | query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
342 |
343 | if past_key_value is not None:
344 | # sin and cos are specific to RoPE models; cache_position needed for the static cache
345 | cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
346 | key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
347 |
348 | key_states = repeat_kv(key_states, self.num_key_value_groups)
349 | value_states = repeat_kv(value_states, self.num_key_value_groups)
350 |
351 | attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
352 |
353 | if attention_mask is not None: # no matter the length, we just slice it
354 | causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
355 | attn_weights = attn_weights + causal_mask
356 |
357 | # upcast attention to fp32
358 | attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
359 | attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
360 | attn_output = torch.matmul(attn_weights, value_states)
361 |
362 | if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
363 | raise ValueError(
364 | f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
365 | f" {attn_output.size()}"
366 | )
367 |
368 | attn_output = attn_output.transpose(1, 2).contiguous()
369 |
370 | attn_output = attn_output.reshape(bsz, q_len, -1)
371 |
372 | if self.config.pretraining_tp > 1:
373 | attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
374 | o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
375 | attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
376 | else:
377 | attn_output = self.o_proj(attn_output)
378 |
379 | if not output_attentions:
380 | attn_weights = None
381 |
382 | return attn_output, attn_weights, past_key_value
383 |
384 |
385 | class LlamaFlashAttention2(LlamaAttention):
386 | """
387 | Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
388 | untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
389 | flash attention and deal with padding tokens in case the input contains any of them.
390 | """
391 |
392 | def __init__(self, *args, **kwargs):
393 | super().__init__(*args, **kwargs)
394 |
395 | # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
396 | # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
397 | # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
398 | self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
399 |
400 | def forward(
401 | self,
402 | hidden_states: torch.Tensor,
403 | attention_mask: Optional[torch.LongTensor] = None,
404 | position_ids: Optional[torch.LongTensor] = None,
405 | past_key_value: Optional[Cache] = None,
406 | output_attentions: bool = False,
407 | use_cache: bool = False,
408 | cache_position: Optional[torch.LongTensor] = None,
409 | ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
410 | if isinstance(past_key_value, StaticCache):
411 | raise ValueError(
412 | "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
413 | "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
414 | )
415 |
416 | output_attentions = False
417 |
418 | bsz, q_len, _ = hidden_states.size()
419 |
420 | query_states = self.q_proj(hidden_states)
421 | key_states = self.k_proj(hidden_states)
422 | value_states = self.v_proj(hidden_states)
423 |
424 | # Flash attention requires the input to have the shape
425 | # batch_size x seq_length x head_dim x hidden_dim
426 | # therefore we just need to keep the original shape
427 | query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
428 | key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
429 | value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
430 |
431 | cos, sin = self.rotary_emb(value_states, position_ids)
432 | query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
433 |
434 | if past_key_value is not None:
435 | # sin and cos are specific to RoPE models; cache_position needed for the static cache
436 | cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
437 | key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
438 |
439 | # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
440 | # to be able to avoid many of these transpose/reshape/view.
441 | query_states = query_states.transpose(1, 2)
442 | key_states = key_states.transpose(1, 2)
443 | value_states = value_states.transpose(1, 2)
444 |
445 | dropout_rate = self.attention_dropout if self.training else 0.0
446 |
447 | # In PEFT, usually we cast the layer norms in float32 for training stability reasons
448 | # therefore the input hidden states gets silently casted in float32. Hence, we need
449 | # cast them back in the correct dtype just to be sure everything works as expected.
450 | # This might slowdown training & inference so it is recommended to not cast the LayerNorms
451 | # in fp32. (LlamaRMSNorm handles it correctly)
452 |
453 | input_dtype = query_states.dtype
454 | if input_dtype == torch.float32:
455 | if torch.is_autocast_enabled():
456 | target_dtype = torch.get_autocast_gpu_dtype()
457 | # Handle the case where the model is quantized
458 | elif hasattr(self.config, "_pre_quantization_dtype"):
459 | target_dtype = self.config._pre_quantization_dtype
460 | else:
461 | target_dtype = self.q_proj.weight.dtype
462 |
463 | logger.warning_once(
464 | f"The input hidden states seems to be silently casted in float32, this might be related to"
465 | f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
466 | f" {target_dtype}."
467 | )
468 |
469 | query_states = query_states.to(target_dtype)
470 | key_states = key_states.to(target_dtype)
471 | value_states = value_states.to(target_dtype)
472 |
473 | attn_output = self._flash_attention_forward(
474 | query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
475 | )
476 |
477 | attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
478 | attn_output = self.o_proj(attn_output)
479 |
480 | if not output_attentions:
481 | attn_weights = None
482 |
483 | return attn_output, attn_weights, past_key_value
484 |
485 | def _flash_attention_forward(
486 | self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
487 | ):
488 | """
489 | Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
490 | first unpad the input, then computes the attention scores and pad the final attention scores.
491 |
492 | Args:
493 | query_states (`torch.Tensor`):
494 | Input query states to be passed to Flash Attention API
495 | key_states (`torch.Tensor`):
496 | Input key states to be passed to Flash Attention API
497 | value_states (`torch.Tensor`):
498 | Input value states to be passed to Flash Attention API
499 | attention_mask (`torch.Tensor`):
500 | The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
501 | position of padding tokens and 1 for the position of non-padding tokens.
502 | dropout (`float`):
503 | Attention dropout
504 | softmax_scale (`float`, *optional*):
505 | The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
506 | """
507 | if not self._flash_attn_uses_top_left_mask:
508 | causal = self.is_causal
509 | else:
510 | # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
511 | causal = self.is_causal and query_length != 1
512 |
513 | # Contains at least one padding token in the sequence
514 | if attention_mask is not None:
515 | batch_size = query_states.shape[0]
516 | query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
517 | query_states, key_states, value_states, attention_mask, query_length
518 | )
519 |
520 | cu_seqlens_q, cu_seqlens_k = cu_seq_lens
521 | max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
522 |
523 | attn_output_unpad = flash_attn_varlen_func(
524 | query_states,
525 | key_states,
526 | value_states,
527 | cu_seqlens_q=cu_seqlens_q,
528 | cu_seqlens_k=cu_seqlens_k,
529 | max_seqlen_q=max_seqlen_in_batch_q,
530 | max_seqlen_k=max_seqlen_in_batch_k,
531 | dropout_p=dropout,
532 | softmax_scale=softmax_scale,
533 | causal=causal,
534 | )
535 |
536 | attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
537 | else:
538 | attn_output = flash_attn_func(
539 | query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
540 | )
541 |
542 | return attn_output
543 |
544 | def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
545 | indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
546 | batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
547 |
548 | key_layer = index_first_axis(
549 | key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
550 | )
551 | value_layer = index_first_axis(
552 | value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
553 | )
554 | if query_length == kv_seq_len:
555 | query_layer = index_first_axis(
556 | query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
557 | )
558 | cu_seqlens_q = cu_seqlens_k
559 | max_seqlen_in_batch_q = max_seqlen_in_batch_k
560 | indices_q = indices_k
561 | elif query_length == 1:
562 | max_seqlen_in_batch_q = 1
563 | cu_seqlens_q = torch.arange(
564 | batch_size + 1, dtype=torch.int32, device=query_layer.device
565 | ) # There is a memcpy here, that is very bad.
566 | indices_q = cu_seqlens_q[:-1]
567 | query_layer = query_layer.squeeze(1)
568 | else:
569 | # The -q_len: slice assumes left padding.
570 | attention_mask = attention_mask[:, -query_length:]
571 | query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
572 |
573 | return (
574 | query_layer,
575 | key_layer,
576 | value_layer,
577 | indices_q,
578 | (cu_seqlens_q, cu_seqlens_k),
579 | (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
580 | )
581 |
582 |
583 | class LlamaSdpaAttention(LlamaAttention):
584 | """
585 | Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
586 | `LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
587 | SDPA API.
588 | """
589 |
590 | # Adapted from LlamaAttention.forward
591 | def forward(
592 | self,
593 | hidden_states: torch.Tensor,
594 | attention_mask: Optional[torch.Tensor] = None,
595 | position_ids: Optional[torch.LongTensor] = None,
596 | past_key_value: Optional[Cache] = None,
597 | output_attentions: bool = False,
598 | use_cache: bool = False,
599 | cache_position: Optional[torch.LongTensor] = None,
600 | ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
601 | if output_attentions:
602 | # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
603 | logger.warning_once(
604 | "LlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
605 | 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
606 | )
607 | return super().forward(
608 | hidden_states=hidden_states,
609 | attention_mask=attention_mask,
610 | position_ids=position_ids,
611 | past_key_value=past_key_value,
612 | output_attentions=output_attentions,
613 | use_cache=use_cache,
614 | cache_position=cache_position,
615 | )
616 |
617 | bsz, q_len, _ = hidden_states.size()
618 |
619 | query_states = self.q_proj(hidden_states)
620 | key_states = self.k_proj(hidden_states)
621 | value_states = self.v_proj(hidden_states)
622 |
623 | query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
624 | key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
625 | value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
626 |
627 | cos, sin = self.rotary_emb(value_states, position_ids)
628 | query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
629 |
630 | if past_key_value is not None:
631 | # sin and cos are specific to RoPE models; cache_position needed for the static cache
632 | cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
633 | key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
634 |
635 | key_states = repeat_kv(key_states, self.num_key_value_groups)
636 | value_states = repeat_kv(value_states, self.num_key_value_groups)
637 |
638 | causal_mask = attention_mask
639 | if attention_mask is not None:
640 | causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
641 |
642 | # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
643 | # Reference: https://github.com/pytorch/pytorch/issues/112577.
644 | if query_states.device.type == "cuda" and causal_mask is not None:
645 | query_states = query_states.contiguous()
646 | key_states = key_states.contiguous()
647 | value_states = value_states.contiguous()
648 |
649 | # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
650 | # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
651 | is_causal = True if causal_mask is None and q_len > 1 else False
652 |
653 | attn_output = torch.nn.functional.scaled_dot_product_attention(
654 | query_states,
655 | key_states,
656 | value_states,
657 | attn_mask=causal_mask,
658 | dropout_p=self.attention_dropout if self.training else 0.0,
659 | is_causal=is_causal,
660 | )
661 |
662 | attn_output = attn_output.transpose(1, 2).contiguous()
663 | attn_output = attn_output.view(bsz, q_len, -1)
664 |
665 | attn_output = self.o_proj(attn_output)
666 |
667 | return attn_output, None, past_key_value
668 |
669 |
670 | LLAMA_ATTENTION_CLASSES = {
671 | "eager": LlamaAttention,
672 | "flash_attention_2": LlamaFlashAttention2,
673 | "sdpa": LlamaSdpaAttention,
674 | }
675 |
676 |
677 | class LlamaDecoderLayer(nn.Module):
678 | def __init__(self, config: LlamaConfig, layer_idx: int):
679 | super().__init__()
680 | self.hidden_size = config.hidden_size
681 |
682 | self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
683 |
684 | self.mlp = LlamaMLP(config)
685 | self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
686 | self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
687 |
688 | def forward(
689 | self,
690 | hidden_states: torch.Tensor,
691 | attention_mask: Optional[torch.Tensor] = None,
692 | position_ids: Optional[torch.LongTensor] = None,
693 | past_key_value: Optional[Cache] = None,
694 | output_attentions: Optional[bool] = False,
695 | use_cache: Optional[bool] = False,
696 | cache_position: Optional[torch.LongTensor] = None,
697 | ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
698 | """
699 | Args:
700 | hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
701 | attention_mask (`torch.FloatTensor`, *optional*):
702 | attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
703 | query_sequence_length, key_sequence_length)` if default attention is used.
704 | output_attentions (`bool`, *optional*):
705 | Whether or not to return the attentions tensors of all attention layers. See `attentions` under
706 | returned tensors for more detail.
707 | use_cache (`bool`, *optional*):
708 | If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
709 | (see `past_key_values`).
710 | past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
711 | """
712 | residual = hidden_states
713 |
714 | hidden_states = self.input_layernorm(hidden_states)
715 |
716 | # Self Attention
717 | hidden_states, self_attn_weights, present_key_value = self.self_attn(
718 | hidden_states=hidden_states,
719 | attention_mask=attention_mask,
720 | position_ids=position_ids,
721 | past_key_value=past_key_value,
722 | output_attentions=output_attentions,
723 | use_cache=use_cache,
724 | cache_position=cache_position,
725 | )
726 | hidden_states = residual + hidden_states
727 |
728 | # Fully Connected
729 | residual = hidden_states
730 | hidden_states = self.post_attention_layernorm(hidden_states)
731 | hidden_states = self.mlp(hidden_states)
732 | hidden_states = residual + hidden_states
733 |
734 | outputs = (hidden_states,)
735 |
736 | if output_attentions:
737 | outputs += (self_attn_weights,)
738 |
739 | if use_cache:
740 | outputs += (present_key_value,)
741 |
742 | return outputs
743 |
744 |
745 | LLAMA_START_DOCSTRING = r"""
746 | This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
747 | library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
748 | etc.)
749 |
750 | This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
751 | Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
752 | and behavior.
753 |
754 | Parameters:
755 | config ([`LlamaConfig`]):
756 | Model configuration class with all the parameters of the model. Initializing with a config file does not
757 | load the weights associated with the model, only the configuration. Check out the
758 | [`~PreTrainedModel.from_pretrained`] method to load the model weights.
759 | """
760 |
761 |
762 | @add_start_docstrings(
763 | "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
764 | LLAMA_START_DOCSTRING,
765 | )
766 | class LlamaPreTrainedModel(PreTrainedModel):
767 | config_class = LlamaConfig
768 | base_model_prefix = "model"
769 | supports_gradient_checkpointing = True
770 | _no_split_modules = ["LlamaDecoderLayer"]
771 | _skip_keys_device_placement = ["past_key_values"]
772 | _supports_flash_attn_2 = True
773 | _supports_sdpa = True
774 | _supports_cache_class = True
775 | _supports_quantized_cache = True
776 | _supports_static_cache = True
777 |
778 | def _init_weights(self, module):
779 | std = self.config.initializer_range
780 | if isinstance(module, nn.Linear):
781 | module.weight.data.normal_(mean=0.0, std=std)
782 | if module.bias is not None:
783 | module.bias.data.zero_()
784 | elif isinstance(module, nn.Embedding):
785 | module.weight.data.normal_(mean=0.0, std=std)
786 | if module.padding_idx is not None:
787 | module.weight.data[module.padding_idx].zero_()
788 |
789 |
790 | LLAMA_INPUTS_DOCSTRING = r"""
791 | Args:
792 | input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
793 | Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
794 | it.
795 |
796 | Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
797 | [`PreTrainedTokenizer.__call__`] for details.
798 |
799 | [What are input IDs?](../glossary#input-ids)
800 | attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
801 | Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
802 |
803 | - 1 for tokens that are **not masked**,
804 | - 0 for tokens that are **masked**.
805 |
806 | [What are attention masks?](../glossary#attention-mask)
807 |
808 | Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
809 | [`PreTrainedTokenizer.__call__`] for details.
810 |
811 | If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
812 | `past_key_values`).
813 |
814 | If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
815 | and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
816 | information on the default strategy.
817 |
818 | - 1 indicates the head is **not masked**,
819 | - 0 indicates the head is **masked**.
820 | position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
821 | Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
822 | config.n_positions - 1]`.
823 |
824 | [What are position IDs?](../glossary#position-ids)
825 | past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
826 | Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
827 | blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
828 | returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
829 |
830 | Two formats are allowed:
831 | - a [`~cache_utils.Cache`] instance;
832 | - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
833 | shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
834 | cache format.
835 |
836 | The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
837 | legacy cache format will be returned.
838 |
839 | If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
840 | have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
841 | of shape `(batch_size, sequence_length)`.
842 | inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
843 | Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
844 | is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
845 | model's internal embedding lookup matrix.
846 | use_cache (`bool`, *optional*):
847 | If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
848 | `past_key_values`).
849 | output_attentions (`bool`, *optional*):
850 | Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
851 | tensors for more detail.
852 | output_hidden_states (`bool`, *optional*):
853 | Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
854 | more detail.
855 | return_dict (`bool`, *optional*):
856 | Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
857 | cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
858 | Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
859 | this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
860 | the complete sequence length.
861 | """
862 |
863 |
864 | @add_start_docstrings(
865 | "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
866 | LLAMA_START_DOCSTRING,
867 | )
868 | class LlamaModel(LlamaPreTrainedModel):
869 | """
870 | Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
871 |
872 | Args:
873 | config: LlamaConfig
874 | """
875 |
876 | def __init__(self, config: LlamaConfig):
877 | super().__init__(config)
878 | self.padding_idx = config.pad_token_id
879 | self.vocab_size = config.vocab_size
880 |
881 | self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
882 | self.layers = nn.ModuleList(
883 | [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
884 | )
885 | self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
886 | self.gradient_checkpointing = False
887 |
888 | self.layer_sharing = config.layer_sharing
889 | # Initialize weights and apply final processing
890 | self.post_init()
891 |
892 | def get_input_embeddings(self):
893 | return self.embed_tokens
894 |
895 | def set_input_embeddings(self, value):
896 | self.embed_tokens = value
897 |
898 | @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
899 | def forward(
900 | self,
901 | input_ids: torch.LongTensor = None,
902 | attention_mask: Optional[torch.Tensor] = None,
903 | position_ids: Optional[torch.LongTensor] = None,
904 | past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
905 | inputs_embeds: Optional[torch.FloatTensor] = None,
906 | use_cache: Optional[bool] = None,
907 | output_attentions: Optional[bool] = None,
908 | output_hidden_states: Optional[bool] = None,
909 | return_dict: Optional[bool] = None,
910 | cache_position: Optional[torch.LongTensor] = None,
911 | ) -> Union[Tuple, BaseModelOutputWithPast]:
912 | output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
913 | output_hidden_states = (
914 | output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
915 | )
916 | use_cache = use_cache if use_cache is not None else self.config.use_cache
917 | return_dict = return_dict if return_dict is not None else self.config.use_return_dict
918 |
919 | if (input_ids is None) ^ (inputs_embeds is not None):
920 | raise ValueError(
921 | "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
922 | )
923 |
924 | if self.gradient_checkpointing and self.training and use_cache:
925 | logger.warning_once(
926 | "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
927 | )
928 | use_cache = False
929 |
930 | if inputs_embeds is None:
931 | inputs_embeds = self.embed_tokens(input_ids)
932 |
933 | return_legacy_cache = False
934 | if use_cache and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs)
935 | return_legacy_cache = True
936 | past_key_values = DynamicCache.from_legacy_cache(past_key_values)
937 |
938 | if cache_position is None:
939 | past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
940 | cache_position = torch.arange(
941 | past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
942 | )
943 | if position_ids is None:
944 | position_ids = cache_position.unsqueeze(0)
945 |
946 | causal_mask = self._update_causal_mask(
947 | attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
948 | )
949 |
950 | # embed positions
951 | hidden_states = inputs_embeds
952 |
953 | # decoder layers
954 | all_hidden_states = () if output_hidden_states else None
955 | all_self_attns = () if output_attentions else None
956 | next_decoder_cache = None
957 |
958 | for decoder_layer in self.layers:
959 | if output_hidden_states:
960 | all_hidden_states += (hidden_states,)
961 |
962 | if self.gradient_checkpointing and self.training:
963 | layer_outputs = self._gradient_checkpointing_func(
964 | decoder_layer.__call__,
965 | hidden_states,
966 | causal_mask,
967 | position_ids,
968 | past_key_values,
969 | output_attentions,
970 | use_cache,
971 | cache_position,
972 | )
973 | else:
974 | layer_outputs = decoder_layer(
975 | hidden_states,
976 | attention_mask=causal_mask,
977 | position_ids=position_ids,
978 | past_key_value=past_key_values,
979 | output_attentions=output_attentions,
980 | use_cache=use_cache,
981 | cache_position=cache_position,
982 | )
983 |
984 | hidden_states = layer_outputs[0]
985 |
986 | if use_cache:
987 | next_decoder_cache = layer_outputs[2 if output_attentions else 1]
988 |
989 | if output_attentions:
990 | all_self_attns += (layer_outputs[1],)
991 |
992 | # Repeat current layer if layer_sharing is enabled
993 | if self.layer_sharing:
994 | if output_hidden_states:
995 | all_hidden_states += (hidden_states,)
996 |
997 | if self.gradient_checkpointing and self.training:
998 | layer_outputs = self._gradient_checkpointing_func(
999 | decoder_layer.__call__,
1000 | hidden_states,
1001 | causal_mask,
1002 | position_ids,
1003 | past_key_values,
1004 | output_attentions,
1005 | use_cache,
1006 | cache_position,
1007 | )
1008 | else:
1009 | layer_outputs = decoder_layer(
1010 | hidden_states,
1011 | attention_mask=causal_mask,
1012 | position_ids=position_ids,
1013 | past_key_value=past_key_values,
1014 | output_attentions=output_attentions,
1015 | use_cache=use_cache,
1016 | cache_position=cache_position,
1017 | )
1018 |
1019 | hidden_states = layer_outputs[0]
1020 |
1021 | if use_cache:
1022 | next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1023 |
1024 | if output_attentions:
1025 | all_self_attns += (layer_outputs[1],)
1026 |
1027 | hidden_states = self.norm(hidden_states)
1028 |
1029 | # add hidden states from the last decoder layer
1030 | if output_hidden_states:
1031 | all_hidden_states += (hidden_states,)
1032 |
1033 | next_cache = next_decoder_cache if use_cache else None
1034 | if return_legacy_cache:
1035 | next_cache = next_cache.to_legacy_cache()
1036 |
1037 | if not return_dict:
1038 | return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
1039 | return BaseModelOutputWithPast(
1040 | last_hidden_state=hidden_states,
1041 | past_key_values=next_cache,
1042 | hidden_states=all_hidden_states,
1043 | attentions=all_self_attns,
1044 | )
1045 |
1046 | def _update_causal_mask(
1047 | self,
1048 | attention_mask: torch.Tensor,
1049 | input_tensor: torch.Tensor,
1050 | cache_position: torch.Tensor,
1051 | past_key_values: Cache,
1052 | output_attentions: bool,
1053 | ):
1054 | # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
1055 | # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
1056 | # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
1057 | # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
1058 |
1059 | if self.config._attn_implementation == "flash_attention_2":
1060 | if attention_mask is not None and 0.0 in attention_mask:
1061 | return attention_mask
1062 | return None
1063 |
1064 | # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
1065 | # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
1066 | # to infer the attention mask.
1067 | past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
1068 | using_static_cache = isinstance(past_key_values, StaticCache)
1069 |
1070 | # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
1071 | if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
1072 | if AttentionMaskConverter._ignore_causal_mask_sdpa(
1073 | attention_mask,
1074 | inputs_embeds=input_tensor,
1075 | past_key_values_length=past_seen_tokens,
1076 | is_training=self.training,
1077 | ):
1078 | return None
1079 |
1080 | dtype, device = input_tensor.dtype, input_tensor.device
1081 | min_dtype = torch.finfo(dtype).min
1082 | sequence_length = input_tensor.shape[1]
1083 | if using_static_cache:
1084 | target_length = past_key_values.get_max_length()
1085 | else:
1086 | target_length = (
1087 | attention_mask.shape[-1]
1088 | if isinstance(attention_mask, torch.Tensor)
1089 | else past_seen_tokens + sequence_length + 1
1090 | )
1091 |
1092 | if attention_mask is not None and attention_mask.dim() == 4:
1093 | # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
1094 | if attention_mask.max() != 0:
1095 | raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
1096 | causal_mask = attention_mask
1097 | else:
1098 | causal_mask = torch.full(
1099 | (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
1100 | )
1101 | if sequence_length != 1:
1102 | causal_mask = torch.triu(causal_mask, diagonal=1)
1103 | causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
1104 | causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
1105 | if attention_mask is not None:
1106 | causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
1107 | mask_length = attention_mask.shape[-1]
1108 | padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
1109 | padding_mask = padding_mask == 0
1110 | causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
1111 | padding_mask, min_dtype
1112 | )
1113 | if (
1114 | self.config._attn_implementation == "sdpa"
1115 | and attention_mask is not None
1116 | and attention_mask.device.type == "cuda"
1117 | and not output_attentions
1118 | ):
1119 | # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
1120 | # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
1121 | # Details: https://github.com/pytorch/pytorch/issues/110213
1122 | causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
1123 |
1124 | return causal_mask
1125 |
1126 |
1127 | class LlamaForCausalLM(LlamaPreTrainedModel):
1128 | _tied_weights_keys = ["lm_head.weight"]
1129 |
1130 | def __init__(self, config):
1131 | super().__init__(config)
1132 | self.model = LlamaModel(config)
1133 | self.vocab_size = config.vocab_size
1134 | if not getattr(self.config, "share_embedding", False):
1135 | self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1136 |
1137 | # Initialize weights and apply final processing
1138 | self.post_init()
1139 |
1140 | def get_input_embeddings(self):
1141 | return self.model.embed_tokens
1142 |
1143 | def set_input_embeddings(self, value):
1144 | self.model.embed_tokens = value
1145 |
1146 | def get_output_embeddings(self):
1147 | return (
1148 | self.lm_head
1149 | if not getattr(self.config, "share_embedding", False)
1150 | else self.get_input_embeddings()
1151 | )
1152 |
1153 | def set_output_embeddings(self, new_embeddings):
1154 | if not getattr(self.config, "share_embedding", False):
1155 | self.lm_head = new_embeddings
1156 | else:
1157 | self.set_input_embeddings(new_embeddings)
1158 |
1159 | def set_decoder(self, decoder):
1160 | self.model = decoder
1161 |
1162 | def get_decoder(self):
1163 | return self.model
1164 |
1165 | @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1166 | @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1167 | def forward(
1168 | self,
1169 | input_ids: torch.LongTensor = None,
1170 | attention_mask: Optional[torch.Tensor] = None,
1171 | position_ids: Optional[torch.LongTensor] = None,
1172 | past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
1173 | inputs_embeds: Optional[torch.FloatTensor] = None,
1174 | labels: Optional[torch.LongTensor] = None,
1175 | use_cache: Optional[bool] = None,
1176 | output_attentions: Optional[bool] = None,
1177 | output_hidden_states: Optional[bool] = None,
1178 | return_dict: Optional[bool] = None,
1179 | cache_position: Optional[torch.LongTensor] = None,
1180 | ) -> Union[Tuple, CausalLMOutputWithPast]:
1181 | r"""
1182 | Args:
1183 | labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1184 | Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1185 | config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1186 | (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1187 |
1188 | Returns:
1189 |
1190 | Example:
1191 |
1192 | ```python
1193 | >>> from transformers import AutoTokenizer, LlamaForCausalLM
1194 |
1195 | >>> model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
1196 | >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
1197 |
1198 | >>> prompt = "Hey, are you conscious? Can you talk to me?"
1199 | >>> inputs = tokenizer(prompt, return_tensors="pt")
1200 |
1201 | >>> # Generate
1202 | >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1203 | >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1204 | "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1205 | ```"""
1206 | output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1207 | output_hidden_states = (
1208 | output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1209 | )
1210 | return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1211 |
1212 | # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1213 | outputs = self.model(
1214 | input_ids=input_ids,
1215 | attention_mask=attention_mask,
1216 | position_ids=position_ids,
1217 | past_key_values=past_key_values,
1218 | inputs_embeds=inputs_embeds,
1219 | use_cache=use_cache,
1220 | output_attentions=output_attentions,
1221 | output_hidden_states=output_hidden_states,
1222 | return_dict=return_dict,
1223 | cache_position=cache_position,
1224 | )
1225 |
1226 | hidden_states = outputs[0]
1227 | if self.config.pretraining_tp > 1:
1228 | lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
1229 | logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
1230 | logits = torch.cat(logits, dim=-1)
1231 | else:
1232 | if not getattr(self.config, "share_embedding", False):
1233 | logits = self.lm_head(hidden_states)
1234 | else:
1235 | logits = F.linear(hidden_states, self.model.embed_tokens.weight)
1236 | logits = logits.float()
1237 |
1238 | loss = None
1239 | if labels is not None:
1240 | # Shift so that tokens < n predict n
1241 | shift_logits = logits[..., :-1, :].contiguous()
1242 | shift_labels = labels[..., 1:].contiguous()
1243 | # Flatten the tokens
1244 | loss_fct = CrossEntropyLoss()
1245 | shift_logits = shift_logits.view(-1, self.config.vocab_size)
1246 | shift_labels = shift_labels.view(-1)
1247 | # Enable model parallelism
1248 | shift_labels = shift_labels.to(shift_logits.device)
1249 | loss = loss_fct(shift_logits, shift_labels)
1250 |
1251 | if not return_dict:
1252 | output = (logits,) + outputs[1:]
1253 | return (loss,) + output if loss is not None else output
1254 |
1255 | return CausalLMOutputWithPast(
1256 | loss=loss,
1257 | logits=logits,
1258 | past_key_values=outputs.past_key_values,
1259 | hidden_states=outputs.hidden_states,
1260 | attentions=outputs.attentions,
1261 | )
1262 |
1263 | def prepare_inputs_for_generation(
1264 | self,
1265 | input_ids,
1266 | past_key_values=None,
1267 | attention_mask=None,
1268 | inputs_embeds=None,
1269 | cache_position=None,
1270 | use_cache=True,
1271 | **kwargs,
1272 | ):
1273 | past_length = 0
1274 | if past_key_values is not None:
1275 | # Past key values are always initialized with a `Cache` object -> no need for if-else anymore
1276 | past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
1277 | max_cache_length = (
1278 | torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
1279 | if past_key_values.get_max_length() is not None
1280 | else None
1281 | )
1282 | cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
1283 |
1284 | # Keep only the unprocessed tokens:
1285 | # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
1286 | # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as input)
1287 | if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
1288 | input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
1289 | # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
1290 | # input_ids based on the past_length.
1291 | elif past_length < input_ids.shape[1]:
1292 | input_ids = input_ids[:, past_length:]
1293 | # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
1294 |
1295 | # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
1296 | if (
1297 | max_cache_length is not None
1298 | and attention_mask is not None
1299 | and cache_length + input_ids.shape[1] > max_cache_length
1300 | ):
1301 | attention_mask = attention_mask[:, -max_cache_length:]
1302 |
1303 | position_ids = kwargs.get("position_ids", None)
1304 | if attention_mask is not None and position_ids is None:
1305 | # create position_ids on the fly for batch generation
1306 | position_ids = attention_mask.long().cumsum(-1) - 1
1307 | position_ids.masked_fill_(attention_mask == 0, 1)
1308 | if past_key_values:
1309 | position_ids = position_ids[:, -input_ids.shape[1] :]
1310 |
1311 | # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1312 | if inputs_embeds is not None and past_length == 0:
1313 | model_inputs = {"inputs_embeds": inputs_embeds}
1314 | else:
1315 | # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
1316 | # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
1317 | # TODO: use `next_tokens` directly instead.
1318 | model_inputs = {"input_ids": input_ids.contiguous()}
1319 |
1320 | input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
1321 | if cache_position is None:
1322 | cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
1323 | elif use_cache:
1324 | cache_position = cache_position[-input_length:]
1325 |
1326 | model_inputs.update(
1327 | {
1328 | "position_ids": position_ids,
1329 | "cache_position": cache_position,
1330 | "past_key_values": past_key_values,
1331 | "use_cache": use_cache,
1332 | "attention_mask": attention_mask,
1333 | }
1334 | )
1335 | return model_inputs
1336 |
1337 | @staticmethod
1338 | def _reorder_cache(past_key_values, beam_idx):
1339 | reordered_past = ()
1340 | for layer_past in past_key_values:
1341 | reordered_past += (
1342 | tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1343 | )
1344 | return reordered_past
1345 |
1346 |
1347 | @add_start_docstrings(
1348 | """
1349 | The LLaMa Model transformer with a sequence classification head on top (linear layer).
1350 |
1351 | [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1352 | (e.g. GPT-2) do.
1353 |
1354 | Since it does classification on the last token, it requires to know the position of the last token. If a
1355 | `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1356 | no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1357 | padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1358 | each row of the batch).
1359 | """,
1360 | LLAMA_START_DOCSTRING,
1361 | )
1362 | class LlamaForSequenceClassification(LlamaPreTrainedModel):
1363 | def __init__(self, config):
1364 | super().__init__(config)
1365 | self.num_labels = config.num_labels
1366 | self.model = LlamaModel(config)
1367 | self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1368 |
1369 | # Initialize weights and apply final processing
1370 | self.post_init()
1371 |
1372 | def get_input_embeddings(self):
1373 | return self.model.embed_tokens
1374 |
1375 | def set_input_embeddings(self, value):
1376 | self.model.embed_tokens = value
1377 |
1378 | @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1379 | def forward(
1380 | self,
1381 | input_ids: torch.LongTensor = None,
1382 | attention_mask: Optional[torch.Tensor] = None,
1383 | position_ids: Optional[torch.LongTensor] = None,
1384 | past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
1385 | inputs_embeds: Optional[torch.FloatTensor] = None,
1386 | labels: Optional[torch.LongTensor] = None,
1387 | use_cache: Optional[bool] = None,
1388 | output_attentions: Optional[bool] = None,
1389 | output_hidden_states: Optional[bool] = None,
1390 | return_dict: Optional[bool] = None,
1391 | ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1392 | r"""
1393 | labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1394 | Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1395 | config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1396 | `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1397 | """
1398 | return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1399 |
1400 | transformer_outputs = self.model(
1401 | input_ids,
1402 | attention_mask=attention_mask,
1403 | position_ids=position_ids,
1404 | past_key_values=past_key_values,
1405 | inputs_embeds=inputs_embeds,
1406 | use_cache=use_cache,
1407 | output_attentions=output_attentions,
1408 | output_hidden_states=output_hidden_states,
1409 | return_dict=return_dict,
1410 | )
1411 | hidden_states = transformer_outputs[0]
1412 | logits = self.score(hidden_states)
1413 |
1414 | if input_ids is not None:
1415 | batch_size = input_ids.shape[0]
1416 | else:
1417 | batch_size = inputs_embeds.shape[0]
1418 |
1419 | if self.config.pad_token_id is None and batch_size != 1:
1420 | raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1421 | if self.config.pad_token_id is None:
1422 | sequence_lengths = -1
1423 | else:
1424 | if input_ids is not None:
1425 | # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
1426 | sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
1427 | sequence_lengths = sequence_lengths % input_ids.shape[-1]
1428 | sequence_lengths = sequence_lengths.to(logits.device)
1429 | else:
1430 | sequence_lengths = -1
1431 |
1432 | pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1433 |
1434 | loss = None
1435 | if labels is not None:
1436 | labels = labels.to(logits.device)
1437 | if self.config.problem_type is None:
1438 | if self.num_labels == 1:
1439 | self.config.problem_type = "regression"
1440 | elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1441 | self.config.problem_type = "single_label_classification"
1442 | else:
1443 | self.config.problem_type = "multi_label_classification"
1444 |
1445 | if self.config.problem_type == "regression":
1446 | loss_fct = MSELoss()
1447 | if self.num_labels == 1:
1448 | loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1449 | else:
1450 | loss = loss_fct(pooled_logits, labels)
1451 | elif self.config.problem_type == "single_label_classification":
1452 | loss_fct = CrossEntropyLoss()
1453 | loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1454 | elif self.config.problem_type == "multi_label_classification":
1455 | loss_fct = BCEWithLogitsLoss()
1456 | loss = loss_fct(pooled_logits, labels)
1457 | if not return_dict:
1458 | output = (pooled_logits,) + transformer_outputs[1:]
1459 | return ((loss,) + output) if loss is not None else output
1460 |
1461 | return SequenceClassifierOutputWithPast(
1462 | loss=loss,
1463 | logits=pooled_logits,
1464 | past_key_values=transformer_outputs.past_key_values,
1465 | hidden_states=transformer_outputs.hidden_states,
1466 | attentions=transformer_outputs.attentions,
1467 | )
1468 |
1469 |
1470 | @add_start_docstrings(
1471 | """
1472 | The Llama Model transformer with a span classification head on top for extractive question-answering tasks like
1473 | SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
1474 | """,
1475 | LLAMA_START_DOCSTRING,
1476 | )
1477 | class LlamaForQuestionAnswering(LlamaPreTrainedModel):
1478 | base_model_prefix = "transformer"
1479 |
1480 | # Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Llama
1481 | def __init__(self, config):
1482 | super().__init__(config)
1483 | self.transformer = LlamaModel(config)
1484 | self.qa_outputs = nn.Linear(config.hidden_size, 2)
1485 |
1486 | # Initialize weights and apply final processing
1487 | self.post_init()
1488 |
1489 | def get_input_embeddings(self):
1490 | return self.transformer.embed_tokens
1491 |
1492 | def set_input_embeddings(self, value):
1493 | self.transformer.embed_tokens = value
1494 |
1495 | @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1496 | def forward(
1497 | self,
1498 | input_ids: Optional[torch.LongTensor] = None,
1499 | attention_mask: Optional[torch.FloatTensor] = None,
1500 | position_ids: Optional[torch.LongTensor] = None,
1501 | past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
1502 | inputs_embeds: Optional[torch.FloatTensor] = None,
1503 | start_positions: Optional[torch.LongTensor] = None,
1504 | end_positions: Optional[torch.LongTensor] = None,
1505 | output_attentions: Optional[bool] = None,
1506 | output_hidden_states: Optional[bool] = None,
1507 | return_dict: Optional[bool] = None,
1508 | ) -> Union[Tuple, QuestionAnsweringModelOutput]:
1509 | r"""
1510 | start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1511 | Labels for position (index) of the start of the labelled span for computing the token classification loss.
1512 | Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1513 | are not taken into account for computing the loss.
1514 | end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1515 | Labels for position (index) of the end of the labelled span for computing the token classification loss.
1516 | Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1517 | are not taken into account for computing the loss.
1518 | """
1519 | return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1520 |
1521 | outputs = self.transformer(
1522 | input_ids,
1523 | attention_mask=attention_mask,
1524 | position_ids=position_ids,
1525 | past_key_values=past_key_values,
1526 | inputs_embeds=inputs_embeds,
1527 | output_attentions=output_attentions,
1528 | output_hidden_states=output_hidden_states,
1529 | return_dict=return_dict,
1530 | )
1531 |
1532 | sequence_output = outputs[0]
1533 |
1534 | logits = self.qa_outputs(sequence_output)
1535 | start_logits, end_logits = logits.split(1, dim=-1)
1536 | start_logits = start_logits.squeeze(-1).contiguous()
1537 | end_logits = end_logits.squeeze(-1).contiguous()
1538 |
1539 | total_loss = None
1540 | if start_positions is not None and end_positions is not None:
1541 | # If we are on multi-GPU, split add a dimension
1542 | if len(start_positions.size()) > 1:
1543 | start_positions = start_positions.squeeze(-1).to(start_logits.device)
1544 | if len(end_positions.size()) > 1:
1545 | end_positions = end_positions.squeeze(-1).to(end_logits.device)
1546 | # sometimes the start/end positions are outside our model inputs, we ignore these terms
1547 | ignored_index = start_logits.size(1)
1548 | start_positions = start_positions.clamp(0, ignored_index)
1549 | end_positions = end_positions.clamp(0, ignored_index)
1550 |
1551 | loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
1552 | start_loss = loss_fct(start_logits, start_positions)
1553 | end_loss = loss_fct(end_logits, end_positions)
1554 | total_loss = (start_loss + end_loss) / 2
1555 |
1556 | if not return_dict:
1557 | output = (start_logits, end_logits) + outputs[2:]
1558 | return ((total_loss,) + output) if total_loss is not None else output
1559 |
1560 | return QuestionAnsweringModelOutput(
1561 | loss=total_loss,
1562 | start_logits=start_logits,
1563 | end_logits=end_logits,
1564 | hidden_states=outputs.hidden_states,
1565 | attentions=outputs.attentions,
1566 | )
1567 |
1568 |
1569 | @add_start_docstrings(
1570 | """
1571 | The Llama Model transformer with a token classification head on top (a linear layer on top of the hidden-states
1572 | output) e.g. for Named-Entity-Recognition (NER) tasks.
1573 | """,
1574 | LLAMA_START_DOCSTRING,
1575 | )
1576 | class LlamaForTokenClassification(LlamaPreTrainedModel):
1577 | def __init__(self, config):
1578 | super().__init__(config)
1579 | self.num_labels = config.num_labels
1580 | self.model = LlamaModel(config)
1581 | if getattr(config, "classifier_dropout", None) is not None:
1582 | classifier_dropout = config.classifier_dropout
1583 | elif getattr(config, "hidden_dropout", None) is not None:
1584 | classifier_dropout = config.hidden_dropout
1585 | else:
1586 | classifier_dropout = 0.1
1587 | self.dropout = nn.Dropout(classifier_dropout)
1588 | self.score = nn.Linear(config.hidden_size, config.num_labels)
1589 |
1590 | # Initialize weights and apply final processing
1591 | self.post_init()
1592 |
1593 | def get_input_embeddings(self):
1594 | return self.model.embed_tokens
1595 |
1596 | def set_input_embeddings(self, value):
1597 | self.model.embed_tokens = value
1598 |
1599 | @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1600 | def forward(
1601 | self,
1602 | input_ids: Optional[torch.LongTensor] = None,
1603 | attention_mask: Optional[torch.Tensor] = None,
1604 | position_ids: Optional[torch.LongTensor] = None,
1605 | past_key_values: Optional[List[torch.FloatTensor]] = None,
1606 | inputs_embeds: Optional[torch.FloatTensor] = None,
1607 | labels: Optional[torch.LongTensor] = None,
1608 | use_cache: Optional[bool] = None,
1609 | output_attentions: Optional[bool] = None,
1610 | output_hidden_states: Optional[bool] = None,
1611 | return_dict: Optional[bool] = None,
1612 | ) -> Union[Tuple, TokenClassifierOutput]:
1613 | r"""
1614 | labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1615 | Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1616 | config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1617 | `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1618 | """
1619 | return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1620 |
1621 | outputs = self.model(
1622 | input_ids,
1623 | attention_mask=attention_mask,
1624 | position_ids=position_ids,
1625 | past_key_values=past_key_values,
1626 | inputs_embeds=inputs_embeds,
1627 | use_cache=use_cache,
1628 | output_attentions=output_attentions,
1629 | output_hidden_states=output_hidden_states,
1630 | return_dict=return_dict,
1631 | )
1632 | sequence_output = outputs[0]
1633 | sequence_output = self.dropout(sequence_output)
1634 | logits = self.score(sequence_output)
1635 |
1636 | loss = None
1637 | if labels is not None:
1638 | loss_fct = CrossEntropyLoss()
1639 | loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
1640 |
1641 | if not return_dict:
1642 | output = (logits,) + outputs[2:]
1643 | return ((loss,) + output) if loss is not None else output
1644 |
1645 | return TokenClassifierOutput(
1646 | loss=loss,
1647 | logits=logits,
1648 | hidden_states=outputs.hidden_states,
1649 | attentions=outputs.attentions,
1650 | )
1651 |
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