Jamie@TitanML
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Browse files- README.md +60 -0
- config.json +31 -0
- generation_config.json +10 -0
- imgs/benchmarks.png +0 -0
- imgs/training_params.png +0 -0
- modeling_flash_llama.py +1010 -0
- pytorch_model-00001-of-00002.bin +3 -0
- pytorch_model-00002-of-00002.bin +3 -0
- pytorch_model.bin.index.json +298 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +37 -0
README.md
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---
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datasets:
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- oscar-corpus/OSCAR-2301
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- wikipedia
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- bjoernp/tagesschau-2018-2023
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language:
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- en
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- de
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library_name: transformers
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pipeline_tag: text-generation
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---
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# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
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Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
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Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
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Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
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[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
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With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
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Read our [blog post]() or our paper (preprint coming soon) for more details!
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*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
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## Model Details
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- **Finetuned from:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
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- **Model type:** Causal decoder-only transformer language model
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- **Language:** English and German
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- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
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- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
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## Use in 🤗Transformers
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First install direct dependencies:
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```
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pip install transformers torch sentencepiece
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```
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If you want faster inference using flash-attention2, you need to install these dependencies:
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```bash
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pip install packaging ninja
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pip install flash-attn==v2.1.1 --no-build-isolation
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pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary
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```
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Then load the model in transformers:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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model="LeoLM/leo-hessianai-7b",
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device_map="auto",
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torch_dtype=torch.float16,
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trust_remote_code=True # True for flash-attn2 else False
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)
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```
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## Training parameters
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![training_parameters](imgs/training_params.png "Training Hyperparameters")
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## Benchmarks
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![benchmarks](imgs/benchmarks.png "Benchmark Scores")
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"auto_map": {
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"AutoModelForCausalLM": "modeling_flash_llama.LlamaForCausalLM"
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},
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 8192,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 2.0,
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"type": "linear"
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},
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.32.0",
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"use_cache": true,
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"vocab_size": 32000
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}
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generation_config.json
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{
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"bos_token_id": 1,
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"do_sample": true,
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"eos_token_id": 2,
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"max_length": 4096,
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"pad_token_id": 0,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.32.0"
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}
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imgs/benchmarks.png
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imgs/training_params.png
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modeling_flash_llama.py
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|
1 |
+
# coding=utf-8
|
2 |
+
# From https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/blob/main/modeling_flash_llama.py
|
3 |
+
# With fix from Alex Birch: https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/discussions/17
|
4 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
5 |
+
#
|
6 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
7 |
+
# and OPT implementations in this library. It has been modified from its
|
8 |
+
# original forms to accommodate minor architectural differences compared
|
9 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
10 |
+
#
|
11 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
12 |
+
# you may not use this file except in compliance with the License.
|
13 |
+
# You may obtain a copy of the License at
|
14 |
+
#
|
15 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
16 |
+
#
|
17 |
+
# Unless required by applicable law or agreed to in writing, software
|
18 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
19 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
20 |
+
# See the License for the specific language governing permissions and
|
21 |
+
# limitations under the License.
|
22 |
+
""" PyTorch LLaMA model."""
|
23 |
+
from typing import List, Optional, Tuple, Union
|
24 |
+
|
25 |
+
import torch
|
26 |
+
import torch.nn.functional as F
|
27 |
+
import torch.utils.checkpoint
|
28 |
+
from torch import nn
|
29 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
30 |
+
|
31 |
+
from transformers.activations import ACT2FN
|
32 |
+
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
|
33 |
+
from transformers.modeling_utils import PreTrainedModel
|
34 |
+
from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
|
35 |
+
from transformers.models.llama.configuration_llama import LlamaConfig
|
36 |
+
|
37 |
+
|
38 |
+
try:
|
39 |
+
from flash_attn.flash_attn_interface import (
|
40 |
+
flash_attn_kvpacked_func,
|
41 |
+
flash_attn_varlen_kvpacked_func,
|
42 |
+
)
|
43 |
+
from flash_attn.bert_padding import unpad_input, pad_input
|
44 |
+
flash_attn_v2_installed = True
|
45 |
+
print('>>>> Flash Attention installed')
|
46 |
+
except ImportError:
|
47 |
+
flash_attn_v2_installed = False
|
48 |
+
raise ImportError('Please install Flash Attention: `pip install flash-attn --no-build-isolation`')
|
49 |
+
|
50 |
+
try:
|
51 |
+
from flash_attn.layers.rotary import apply_rotary_emb_func
|
52 |
+
flash_rope_installed = True
|
53 |
+
print('>>>> Flash RoPE installed')
|
54 |
+
except ImportError:
|
55 |
+
flash_rope_installed = False
|
56 |
+
raise ImportError('Please install RoPE kernels: `pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary`')
|
57 |
+
|
58 |
+
|
59 |
+
logger = logging.get_logger(__name__)
|
60 |
+
|
61 |
+
_CONFIG_FOR_DOC = "LlamaConfig"
|
62 |
+
|
63 |
+
|
64 |
+
# @torch.jit.script
|
65 |
+
def rmsnorm_func(hidden_states, weight, variance_epsilon):
|
66 |
+
input_dtype = hidden_states.dtype
|
67 |
+
hidden_states = hidden_states.to(torch.float32)
|
68 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
69 |
+
hidden_states = hidden_states * torch.rsqrt(variance + variance_epsilon)
|
70 |
+
return (weight * hidden_states).to(input_dtype)
|
71 |
+
|
72 |
+
|
73 |
+
class LlamaRMSNorm(nn.Module):
|
74 |
+
def __init__(self, hidden_size, eps=1e-6):
|
75 |
+
"""
|
76 |
+
LlamaRMSNorm is equivalent to T5LayerNorm
|
77 |
+
"""
|
78 |
+
super().__init__()
|
79 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
80 |
+
self.register_buffer(
|
81 |
+
"variance_epsilon",
|
82 |
+
torch.tensor(eps),
|
83 |
+
persistent=False,
|
84 |
+
)
|
85 |
+
|
86 |
+
def forward(self, hidden_states):
|
87 |
+
return rmsnorm_func(hidden_states, self.weight, self.variance_epsilon)
|
88 |
+
|
89 |
+
|
90 |
+
class FlashRotaryEmbedding(torch.nn.Module):
|
91 |
+
"""
|
92 |
+
The rotary position embeddings from RoFormer_ (Su et. al).
|
93 |
+
A crucial insight from the method is that the query and keys are
|
94 |
+
transformed by rotation matrices which depend on the relative positions.
|
95 |
+
|
96 |
+
Other implementations are available in the Rotary Transformer repo_ and in
|
97 |
+
GPT-NeoX_, GPT-NeoX was an inspiration
|
98 |
+
|
99 |
+
.. _RoFormer: https://arxiv.org/abs/2104.09864
|
100 |
+
.. _repo: https://github.com/ZhuiyiTechnology/roformer
|
101 |
+
.. _GPT-NeoX: https://github.com/EleutherAI/gpt-neox
|
102 |
+
|
103 |
+
If scale_base is not None, this implements XPos (Sun et al., https://arxiv.org/abs/2212.10554).
|
104 |
+
A recommended value for scale_base is 512: https://github.com/HazyResearch/flash-attention/issues/96
|
105 |
+
Reference: https://github.com/sunyt32/torchscale/blob/main/torchscale/component/xpos_relative_position.py
|
106 |
+
"""
|
107 |
+
|
108 |
+
def __init__(self, dim: int, base=10000.0, interleaved=False, scale_base=None,
|
109 |
+
scaling_factor=1.0, pos_idx_in_fp32=True, device=None):
|
110 |
+
"""
|
111 |
+
interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
|
112 |
+
of 1st half and 2nd half (GPT-NeoX style).
|
113 |
+
pos_idx_in_fp32: if True, the position indices [0.0, ..., seqlen - 1] are in fp32,
|
114 |
+
otherwise they might be in lower precision.
|
115 |
+
This option was added because previously (before 2023-07-02), when we construct
|
116 |
+
the position indices, we use the dtype of self.inv_freq. In most cases this would
|
117 |
+
be fp32, but if the model is trained in pure bf16 (not mixed precision), then
|
118 |
+
self.inv_freq would be bf16, and the position indices are also in bf16.
|
119 |
+
Because of the limited precision of bf16 (e.g. 1995.0 is rounded to 2000.0), the
|
120 |
+
embeddings for some positions will coincide.
|
121 |
+
To maintain compatibility with models previously trained in pure bf16,
|
122 |
+
we add this option.
|
123 |
+
scaling_factor: RotaryEmbedding extended with linear scaling.
|
124 |
+
"""
|
125 |
+
super().__init__()
|
126 |
+
self.dim = dim
|
127 |
+
self.base = float(base)
|
128 |
+
self.pos_idx_in_fp32 = pos_idx_in_fp32
|
129 |
+
# Generate and save the inverse frequency buffer (non trainable)
|
130 |
+
inv_freq = self._compute_inv_freq(device)
|
131 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
132 |
+
self.interleaved = interleaved
|
133 |
+
self.scale_base = scale_base
|
134 |
+
self.scaling_factor = scaling_factor
|
135 |
+
scale = ((torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim)
|
136 |
+
/ (1.4 * dim) if scale_base is not None else None)
|
137 |
+
self.register_buffer("scale", scale)
|
138 |
+
|
139 |
+
self._seq_len_cached = 0
|
140 |
+
self._cos_cached = None
|
141 |
+
self._sin_cached = None
|
142 |
+
self._cos_k_cached = None
|
143 |
+
self._sin_k_cached = None
|
144 |
+
|
145 |
+
def _compute_inv_freq(self, device=None):
|
146 |
+
return 1 / (self.base ** (torch.arange(0, self.dim, 2, device=device,
|
147 |
+
dtype=torch.float32) / self.dim))
|
148 |
+
|
149 |
+
|
150 |
+
def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
|
151 |
+
# Reset the tables if the sequence length has changed,
|
152 |
+
# if we're on a new device (possibly due to tracing for instance),
|
153 |
+
# or if we're switching from inference mode to training
|
154 |
+
if (seqlen > self._seq_len_cached or self._cos_cached.device != device
|
155 |
+
or self._cos_cached.dtype != dtype
|
156 |
+
or (self.training and self._cos_cached.is_inference())):
|
157 |
+
self._seq_len_cached = seqlen
|
158 |
+
# We want fp32 here, not self.inv_freq.dtype, since the model could be loaded in bf16
|
159 |
+
# And the output of arange can be quite large, so bf16 would lose a lot of precision.
|
160 |
+
# However, for compatibility reason, we add an option to use the dtype of self.inv_freq.
|
161 |
+
if self.pos_idx_in_fp32:
|
162 |
+
t = torch.arange(seqlen, device=device, dtype=torch.float32)
|
163 |
+
t /= self.scaling_factor
|
164 |
+
# We want fp32 here as well since inv_freq will be multiplied with t, and the output
|
165 |
+
# will be large. Having it in bf16 will lose a lot of precision and cause the
|
166 |
+
# cos & sin output to change significantly.
|
167 |
+
# We want to recompute self.inv_freq if it was not loaded in fp32
|
168 |
+
if self.inv_freq.dtype != torch.float32:
|
169 |
+
inv_freq = self.inv_freq.to(torch.float32)
|
170 |
+
else:
|
171 |
+
inv_freq = self.inv_freq
|
172 |
+
else:
|
173 |
+
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
|
174 |
+
t /= self.scaling_factor
|
175 |
+
inv_freq = self.inv_freq
|
176 |
+
# Don't do einsum, it converts fp32 to fp16 under AMP
|
177 |
+
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
178 |
+
freqs = torch.outer(t, inv_freq)
|
179 |
+
if self.scale is None:
|
180 |
+
self._cos_cached = torch.cos(freqs).to(dtype)
|
181 |
+
self._sin_cached = torch.sin(freqs).to(dtype)
|
182 |
+
else:
|
183 |
+
power = ((torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device)
|
184 |
+
- seqlen // 2) / self.scale_base)
|
185 |
+
scale = self.scale.to(device=power.device) ** power.unsqueeze(-1)
|
186 |
+
# We want the multiplication by scale to happen in fp32
|
187 |
+
self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
|
188 |
+
self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
|
189 |
+
self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
|
190 |
+
self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
|
191 |
+
|
192 |
+
def forward(self, q: torch.Tensor, k: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
|
193 |
+
"""
|
194 |
+
q: (batch, seqlen, nheads, headdim)
|
195 |
+
k: (batch, seqlen, nheads, headdim)
|
196 |
+
seqlen_offset: can be used in generation where the qkv being passed in is only the last
|
197 |
+
token in the batch.
|
198 |
+
"""
|
199 |
+
self._update_cos_sin_cache(q.shape[1] + seqlen_offset, device=q.device, dtype=q.dtype)
|
200 |
+
if self.scale is None:
|
201 |
+
return apply_rotary_emb_func(
|
202 |
+
q, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:],
|
203 |
+
self.interleaved, True # inplace=True
|
204 |
+
), apply_rotary_emb_func(
|
205 |
+
k, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:],
|
206 |
+
self.interleaved, True # inplace=True
|
207 |
+
)
|
208 |
+
else:
|
209 |
+
assert False
|
210 |
+
|
211 |
+
class LlamaMLP(nn.Module):
|
212 |
+
def __init__(self, config):
|
213 |
+
super().__init__()
|
214 |
+
self.config = config
|
215 |
+
self.hidden_size = config.hidden_size
|
216 |
+
self.intermediate_size = config.intermediate_size
|
217 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
218 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
219 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
220 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
221 |
+
|
222 |
+
def forward(self, x):
|
223 |
+
if self.config.pretraining_tp > 1:
|
224 |
+
slice = self.intermediate_size // self.config.pretraining_tp
|
225 |
+
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
|
226 |
+
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
|
227 |
+
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
|
228 |
+
|
229 |
+
gate_proj = torch.cat(
|
230 |
+
[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
|
231 |
+
)
|
232 |
+
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
|
233 |
+
|
234 |
+
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
|
235 |
+
down_proj = [
|
236 |
+
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
|
237 |
+
]
|
238 |
+
down_proj = sum(down_proj)
|
239 |
+
else:
|
240 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
241 |
+
|
242 |
+
return down_proj
|
243 |
+
|
244 |
+
@torch.jit.script
|
245 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
246 |
+
"""
|
247 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
248 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
249 |
+
"""
|
250 |
+
batch, slen, _, num_key_value_heads, head_dim = hidden_states.shape
|
251 |
+
if n_rep == 1:
|
252 |
+
return hidden_states
|
253 |
+
hidden_states = hidden_states[:, :, :, :, None, :].expand(batch, slen, 2, num_key_value_heads, n_rep, head_dim)
|
254 |
+
return hidden_states.reshape(batch, slen, 2, num_key_value_heads * n_rep, head_dim)
|
255 |
+
|
256 |
+
|
257 |
+
class LlamaAttention(nn.Module):
|
258 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
259 |
+
|
260 |
+
def __init__(self, config: LlamaConfig):
|
261 |
+
super().__init__()
|
262 |
+
self.config = config
|
263 |
+
self.hidden_size = config.hidden_size
|
264 |
+
self.num_heads = config.num_attention_heads
|
265 |
+
self.head_dim = self.hidden_size // self.num_heads
|
266 |
+
self.num_key_value_heads = config.num_key_value_heads
|
267 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
268 |
+
self.max_position_embeddings = config.max_position_embeddings
|
269 |
+
|
270 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
271 |
+
raise ValueError(
|
272 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
273 |
+
f" and `num_heads`: {self.num_heads})."
|
274 |
+
)
|
275 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
276 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
|
277 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
|
278 |
+
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
|
279 |
+
|
280 |
+
self.register_buffer(
|
281 |
+
"norm_factor",
|
282 |
+
torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype()),
|
283 |
+
persistent=False,
|
284 |
+
)
|
285 |
+
|
286 |
+
if self.config.rope_scaling is None:
|
287 |
+
scaling_factor = 1
|
288 |
+
else:
|
289 |
+
scaling_type = self.config.rope_scaling["type"]
|
290 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
291 |
+
assert scaling_type == 'linear'
|
292 |
+
|
293 |
+
self.rotary_emb = FlashRotaryEmbedding(
|
294 |
+
self.head_dim, base=10000, interleaved=False, scaling_factor=scaling_factor,
|
295 |
+
)
|
296 |
+
|
297 |
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
298 |
+
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
299 |
+
|
300 |
+
def forward(
|
301 |
+
self,
|
302 |
+
hidden_states: torch.Tensor,
|
303 |
+
attention_mask: Optional[torch.Tensor] = None,
|
304 |
+
position_ids: Optional[torch.LongTensor] = None,
|
305 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
306 |
+
output_attentions: bool = False,
|
307 |
+
use_cache: bool = False,
|
308 |
+
is_padded_inputs: Optional[bool] = False,
|
309 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
310 |
+
bsz, q_len, h_size = hidden_states.size()
|
311 |
+
|
312 |
+
has_layer_past = past_key_value is not None
|
313 |
+
|
314 |
+
if has_layer_past:
|
315 |
+
past_kv = past_key_value[0]
|
316 |
+
past_len = past_key_value[1]
|
317 |
+
else:
|
318 |
+
past_len = 0
|
319 |
+
|
320 |
+
if self.config.pretraining_tp > 1:
|
321 |
+
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
|
322 |
+
query_slices = self.q_proj.weight.split(
|
323 |
+
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
|
324 |
+
)
|
325 |
+
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
|
326 |
+
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
|
327 |
+
|
328 |
+
q = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
|
329 |
+
q = torch.cat(q, dim=-1)
|
330 |
+
|
331 |
+
k = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
|
332 |
+
k = torch.cat(k, dim=-1)
|
333 |
+
|
334 |
+
v = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
|
335 |
+
v = torch.cat(v, dim=-1)
|
336 |
+
|
337 |
+
else:
|
338 |
+
q = self.q_proj(hidden_states)
|
339 |
+
k = self.k_proj(hidden_states)
|
340 |
+
v = self.v_proj(hidden_states)
|
341 |
+
|
342 |
+
q = q.view(bsz, q_len, self.num_heads, self.head_dim)
|
343 |
+
k = k.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
|
344 |
+
v = v.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
|
345 |
+
|
346 |
+
q, k = self.rotary_emb(q, k, past_len)
|
347 |
+
|
348 |
+
kv = torch.stack([k, v], 2)
|
349 |
+
kv = repeat_kv(kv, self.num_key_value_groups)
|
350 |
+
|
351 |
+
# Cache QKV values
|
352 |
+
if has_layer_past:
|
353 |
+
new_len = past_len+q.size(1)
|
354 |
+
if new_len > past_kv.size(1):
|
355 |
+
past_kv = torch.cat([past_kv, torch.empty(bsz, 256, 2, kv.size(3), kv.size(4), dtype=kv.dtype, device=kv.device)], 1)
|
356 |
+
past_kv[:, past_len:new_len] = kv
|
357 |
+
kv = past_kv[:, :new_len]
|
358 |
+
else:
|
359 |
+
past_kv = kv
|
360 |
+
|
361 |
+
past_key_value = (past_kv, past_len+q.size(1)) if use_cache else None
|
362 |
+
|
363 |
+
if is_padded_inputs:
|
364 |
+
|
365 |
+
# varlen, ignore padding tokens, efficient for large batch with many paddings
|
366 |
+
|
367 |
+
assert attention_mask is not None
|
368 |
+
|
369 |
+
unpadded_kv, indices_k, cu_seqlens_k, max_seqlen_k = unpad_input(kv, attention_mask)
|
370 |
+
unpadded_q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, attention_mask[:, -q.size(1):])
|
371 |
+
attn_outputs = flash_attn_varlen_kvpacked_func(
|
372 |
+
unpadded_q, unpadded_kv, cu_seqlens_q, cu_seqlens_k,
|
373 |
+
max_seqlen_q, max_seqlen_k,
|
374 |
+
dropout_p=0.0, softmax_scale=1.0/self.norm_factor,
|
375 |
+
causal=(not has_layer_past), return_attn_probs=output_attentions
|
376 |
+
)
|
377 |
+
|
378 |
+
attn_output = attn_outputs[0] if output_attentions else attn_outputs
|
379 |
+
attn_output = pad_input(
|
380 |
+
attn_output, indices_q, bsz, q_len
|
381 |
+
).reshape(bsz, q_len, h_size)
|
382 |
+
attn_weights = attn_outputs[2] if output_attentions else None
|
383 |
+
|
384 |
+
else:
|
385 |
+
|
386 |
+
# no padding tokens, more efficient
|
387 |
+
|
388 |
+
attn_outputs = flash_attn_kvpacked_func(
|
389 |
+
q, kv, dropout_p=0.0, softmax_scale=1.0/self.norm_factor, causal=(not has_layer_past), return_attn_probs=output_attentions)
|
390 |
+
|
391 |
+
attn_output = attn_outputs[0] if output_attentions else attn_outputs
|
392 |
+
attn_output = attn_output.reshape(bsz, q_len, h_size)
|
393 |
+
attn_weights = attn_outputs[2] if output_attentions else None
|
394 |
+
|
395 |
+
if self.config.pretraining_tp > 1:
|
396 |
+
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
397 |
+
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
|
398 |
+
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
|
399 |
+
else:
|
400 |
+
attn_output = self.o_proj(attn_output)
|
401 |
+
|
402 |
+
if not output_attentions:
|
403 |
+
attn_weights = None
|
404 |
+
|
405 |
+
return attn_output, attn_weights, past_key_value
|
406 |
+
|
407 |
+
|
408 |
+
class LlamaDecoderLayer(nn.Module):
|
409 |
+
def __init__(self, config: LlamaConfig):
|
410 |
+
super().__init__()
|
411 |
+
self.hidden_size = config.hidden_size
|
412 |
+
self.self_attn = LlamaAttention(config=config)
|
413 |
+
self.mlp = LlamaMLP(config)
|
414 |
+
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
415 |
+
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
416 |
+
|
417 |
+
def forward(
|
418 |
+
self,
|
419 |
+
hidden_states: torch.Tensor,
|
420 |
+
attention_mask: Optional[torch.Tensor] = None,
|
421 |
+
position_ids: Optional[torch.LongTensor] = None,
|
422 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
423 |
+
is_padded_inputs: Optional[bool] = False,
|
424 |
+
output_attentions: Optional[bool] = False,
|
425 |
+
use_cache: Optional[bool] = False,
|
426 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
427 |
+
"""
|
428 |
+
Args:
|
429 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
430 |
+
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
431 |
+
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
432 |
+
output_attentions (`bool`, *optional*):
|
433 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
434 |
+
returned tensors for more detail.
|
435 |
+
use_cache (`bool`, *optional*):
|
436 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
437 |
+
(see `past_key_values`).
|
438 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
439 |
+
"""
|
440 |
+
|
441 |
+
residual = hidden_states
|
442 |
+
|
443 |
+
hidden_states = self.input_layernorm(hidden_states)
|
444 |
+
|
445 |
+
# Self Attention
|
446 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
447 |
+
hidden_states=hidden_states,
|
448 |
+
attention_mask=attention_mask,
|
449 |
+
position_ids=position_ids,
|
450 |
+
past_key_value=past_key_value,
|
451 |
+
output_attentions=output_attentions,
|
452 |
+
use_cache=use_cache,
|
453 |
+
is_padded_inputs=is_padded_inputs,
|
454 |
+
)
|
455 |
+
hidden_states = residual + hidden_states
|
456 |
+
|
457 |
+
# Fully Connected
|
458 |
+
residual = hidden_states
|
459 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
460 |
+
hidden_states = self.mlp(hidden_states)
|
461 |
+
hidden_states = residual + hidden_states
|
462 |
+
|
463 |
+
outputs = (hidden_states,)
|
464 |
+
|
465 |
+
if output_attentions:
|
466 |
+
outputs += (self_attn_weights,)
|
467 |
+
|
468 |
+
if use_cache:
|
469 |
+
outputs += (present_key_value,)
|
470 |
+
|
471 |
+
return outputs
|
472 |
+
|
473 |
+
|
474 |
+
LLAMA_START_DOCSTRING = r"""
|
475 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
476 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
477 |
+
etc.)
|
478 |
+
|
479 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
480 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
481 |
+
and behavior.
|
482 |
+
|
483 |
+
Parameters:
|
484 |
+
config ([`LlamaConfig`]):
|
485 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
486 |
+
load the weights associated with the model, only the configuration. Check out the
|
487 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
488 |
+
"""
|
489 |
+
|
490 |
+
|
491 |
+
@add_start_docstrings(
|
492 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
493 |
+
LLAMA_START_DOCSTRING,
|
494 |
+
)
|
495 |
+
class LlamaPreTrainedModel(PreTrainedModel):
|
496 |
+
config_class = LlamaConfig
|
497 |
+
base_model_prefix = "model"
|
498 |
+
supports_gradient_checkpointing = True
|
499 |
+
_no_split_modules = ["LlamaDecoderLayer"]
|
500 |
+
_skip_keys_device_placement = "past_key_values"
|
501 |
+
|
502 |
+
def _init_weights(self, module):
|
503 |
+
std = self.config.initializer_range
|
504 |
+
if isinstance(module, nn.Linear):
|
505 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
506 |
+
if module.bias is not None:
|
507 |
+
module.bias.data.zero_()
|
508 |
+
elif isinstance(module, nn.Embedding):
|
509 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
510 |
+
if module.padding_idx is not None:
|
511 |
+
module.weight.data[module.padding_idx].zero_()
|
512 |
+
|
513 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
514 |
+
if isinstance(module, LlamaModel):
|
515 |
+
module.gradient_checkpointing = value
|
516 |
+
|
517 |
+
|
518 |
+
LLAMA_INPUTS_DOCSTRING = r"""
|
519 |
+
Args:
|
520 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
521 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
522 |
+
it.
|
523 |
+
|
524 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
525 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
526 |
+
|
527 |
+
[What are input IDs?](../glossary#input-ids)
|
528 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
529 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
530 |
+
|
531 |
+
- 1 for tokens that are **not masked**,
|
532 |
+
- 0 for tokens that are **masked**.
|
533 |
+
|
534 |
+
[What are attention masks?](../glossary#attention-mask)
|
535 |
+
|
536 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
537 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
538 |
+
|
539 |
+
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
540 |
+
`past_key_values`).
|
541 |
+
|
542 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
543 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
544 |
+
information on the default strategy.
|
545 |
+
|
546 |
+
- 1 indicates the head is **not masked**,
|
547 |
+
- 0 indicates the head is **masked**.
|
548 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
549 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
550 |
+
config.n_positions - 1]`.
|
551 |
+
|
552 |
+
[What are position IDs?](../glossary#position-ids)
|
553 |
+
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
554 |
+
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
555 |
+
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
556 |
+
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
557 |
+
|
558 |
+
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
559 |
+
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
560 |
+
|
561 |
+
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
562 |
+
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
563 |
+
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
564 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
565 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
566 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
567 |
+
model's internal embedding lookup matrix.
|
568 |
+
use_cache (`bool`, *optional*):
|
569 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
570 |
+
`past_key_values`).
|
571 |
+
output_attentions (`bool`, *optional*):
|
572 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
573 |
+
tensors for more detail.
|
574 |
+
output_hidden_states (`bool`, *optional*):
|
575 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
576 |
+
more detail.
|
577 |
+
return_dict (`bool`, *optional*):
|
578 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
579 |
+
"""
|
580 |
+
|
581 |
+
|
582 |
+
@add_start_docstrings(
|
583 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
584 |
+
LLAMA_START_DOCSTRING,
|
585 |
+
)
|
586 |
+
class LlamaModel(LlamaPreTrainedModel):
|
587 |
+
"""
|
588 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
|
589 |
+
|
590 |
+
Args:
|
591 |
+
config: LlamaConfig
|
592 |
+
"""
|
593 |
+
|
594 |
+
def __init__(self, config: LlamaConfig):
|
595 |
+
super().__init__(config)
|
596 |
+
self.padding_idx = config.pad_token_id
|
597 |
+
self.vocab_size = config.vocab_size
|
598 |
+
|
599 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
600 |
+
self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
601 |
+
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
602 |
+
|
603 |
+
self.gradient_checkpointing = False
|
604 |
+
# Initialize weights and apply final processing
|
605 |
+
self.post_init()
|
606 |
+
|
607 |
+
def get_input_embeddings(self):
|
608 |
+
return self.embed_tokens
|
609 |
+
|
610 |
+
def set_input_embeddings(self, value):
|
611 |
+
self.embed_tokens = value
|
612 |
+
|
613 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
614 |
+
def forward(
|
615 |
+
self,
|
616 |
+
input_ids: torch.LongTensor = None,
|
617 |
+
attention_mask: Optional[torch.Tensor] = None,
|
618 |
+
position_ids: Optional[torch.LongTensor] = None,
|
619 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
620 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
621 |
+
use_cache: Optional[bool] = None,
|
622 |
+
output_attentions: Optional[bool] = None,
|
623 |
+
output_hidden_states: Optional[bool] = None,
|
624 |
+
return_dict: Optional[bool] = None,
|
625 |
+
is_padded_inputs: Optional[bool] = False,
|
626 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
627 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
628 |
+
output_hidden_states = (
|
629 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
630 |
+
)
|
631 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
632 |
+
|
633 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
634 |
+
|
635 |
+
# retrieve input_ids and inputs_embeds
|
636 |
+
if input_ids is not None and inputs_embeds is not None:
|
637 |
+
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
638 |
+
elif input_ids is not None:
|
639 |
+
batch_size, seq_length = input_ids.shape
|
640 |
+
elif inputs_embeds is not None:
|
641 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
642 |
+
else:
|
643 |
+
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
644 |
+
|
645 |
+
seq_length_with_past = seq_length
|
646 |
+
past_key_values_length = 0
|
647 |
+
|
648 |
+
if past_key_values is not None:
|
649 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
650 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
651 |
+
|
652 |
+
position_ids = None
|
653 |
+
|
654 |
+
if inputs_embeds is None:
|
655 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
656 |
+
|
657 |
+
hidden_states = inputs_embeds
|
658 |
+
|
659 |
+
if self.gradient_checkpointing and self.training:
|
660 |
+
if use_cache:
|
661 |
+
logger.warning_once(
|
662 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
663 |
+
)
|
664 |
+
use_cache = False
|
665 |
+
|
666 |
+
# decoder layers
|
667 |
+
all_hidden_states = () if output_hidden_states else None
|
668 |
+
all_self_attns = () if output_attentions else None
|
669 |
+
next_decoder_cache = () if use_cache else None
|
670 |
+
|
671 |
+
for idx, decoder_layer in enumerate(self.layers):
|
672 |
+
if output_hidden_states:
|
673 |
+
all_hidden_states += (hidden_states,)
|
674 |
+
|
675 |
+
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
676 |
+
|
677 |
+
if self.gradient_checkpointing and self.training:
|
678 |
+
|
679 |
+
def create_custom_forward(module):
|
680 |
+
def custom_forward(*inputs):
|
681 |
+
# None for past_key_value
|
682 |
+
return module(*inputs, output_attentions, None)
|
683 |
+
|
684 |
+
return custom_forward
|
685 |
+
|
686 |
+
layer_outputs = torch.utils.checkpoint.checkpoint(
|
687 |
+
create_custom_forward(decoder_layer),
|
688 |
+
hidden_states,
|
689 |
+
attention_mask,
|
690 |
+
position_ids,
|
691 |
+
None,
|
692 |
+
is_padded_inputs
|
693 |
+
)
|
694 |
+
else:
|
695 |
+
layer_outputs = decoder_layer(
|
696 |
+
hidden_states,
|
697 |
+
attention_mask=attention_mask,
|
698 |
+
position_ids=position_ids,
|
699 |
+
past_key_value=past_key_value,
|
700 |
+
output_attentions=output_attentions,
|
701 |
+
use_cache=use_cache,
|
702 |
+
is_padded_inputs=is_padded_inputs,
|
703 |
+
)
|
704 |
+
|
705 |
+
hidden_states = layer_outputs[0]
|
706 |
+
|
707 |
+
if use_cache:
|
708 |
+
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
709 |
+
|
710 |
+
if output_attentions:
|
711 |
+
all_self_attns += (layer_outputs[1],)
|
712 |
+
|
713 |
+
hidden_states = self.norm(hidden_states)
|
714 |
+
|
715 |
+
# add hidden states from the last decoder layer
|
716 |
+
if output_hidden_states:
|
717 |
+
all_hidden_states += (hidden_states,)
|
718 |
+
|
719 |
+
next_cache = next_decoder_cache if use_cache else None
|
720 |
+
if not return_dict:
|
721 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
722 |
+
return BaseModelOutputWithPast(
|
723 |
+
last_hidden_state=hidden_states,
|
724 |
+
past_key_values=next_cache,
|
725 |
+
hidden_states=all_hidden_states,
|
726 |
+
attentions=all_self_attns,
|
727 |
+
)
|
728 |
+
|
729 |
+
|
730 |
+
class LlamaForCausalLM(LlamaPreTrainedModel):
|
731 |
+
_tied_weights_keys = ["lm_head.weight"]
|
732 |
+
|
733 |
+
def __init__(self, config):
|
734 |
+
super().__init__(config)
|
735 |
+
self.model = LlamaModel(config)
|
736 |
+
self.vocab_size = config.vocab_size
|
737 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
738 |
+
|
739 |
+
# Initialize weights and apply final processing
|
740 |
+
self.post_init()
|
741 |
+
|
742 |
+
def get_input_embeddings(self):
|
743 |
+
return self.model.embed_tokens
|
744 |
+
|
745 |
+
def set_input_embeddings(self, value):
|
746 |
+
self.model.embed_tokens = value
|
747 |
+
|
748 |
+
def get_output_embeddings(self):
|
749 |
+
return self.lm_head
|
750 |
+
|
751 |
+
def set_output_embeddings(self, new_embeddings):
|
752 |
+
self.lm_head = new_embeddings
|
753 |
+
|
754 |
+
def set_decoder(self, decoder):
|
755 |
+
self.model = decoder
|
756 |
+
|
757 |
+
def get_decoder(self):
|
758 |
+
return self.model
|
759 |
+
|
760 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
761 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
762 |
+
def forward(
|
763 |
+
self,
|
764 |
+
input_ids: torch.LongTensor = None,
|
765 |
+
attention_mask: Optional[torch.Tensor] = None,
|
766 |
+
position_ids: Optional[torch.LongTensor] = None,
|
767 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
768 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
769 |
+
labels: Optional[torch.LongTensor] = None,
|
770 |
+
use_cache: Optional[bool] = None,
|
771 |
+
output_attentions: Optional[bool] = None,
|
772 |
+
output_hidden_states: Optional[bool] = None,
|
773 |
+
return_dict: Optional[bool] = None,
|
774 |
+
is_padded_inputs: Optional[bool] = None,
|
775 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
776 |
+
r"""
|
777 |
+
Args:
|
778 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
779 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
780 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
781 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
782 |
+
|
783 |
+
Returns:
|
784 |
+
|
785 |
+
Example:
|
786 |
+
|
787 |
+
```python
|
788 |
+
>>> from transformers import AutoTokenizer, LlamaForCausalLM
|
789 |
+
|
790 |
+
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
791 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
792 |
+
|
793 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
794 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
795 |
+
|
796 |
+
>>> # Generate
|
797 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
798 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
799 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
800 |
+
```"""
|
801 |
+
|
802 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
803 |
+
output_hidden_states = (
|
804 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
805 |
+
)
|
806 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
807 |
+
|
808 |
+
is_padded_inputs = ((attention_mask is not None) and (not attention_mask.all().item()))
|
809 |
+
|
810 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
811 |
+
outputs = self.model(
|
812 |
+
input_ids=input_ids,
|
813 |
+
attention_mask=attention_mask,
|
814 |
+
position_ids=position_ids,
|
815 |
+
past_key_values=past_key_values,
|
816 |
+
inputs_embeds=inputs_embeds,
|
817 |
+
use_cache=use_cache,
|
818 |
+
output_attentions=output_attentions,
|
819 |
+
output_hidden_states=output_hidden_states,
|
820 |
+
return_dict=return_dict,
|
821 |
+
is_padded_inputs=is_padded_inputs,
|
822 |
+
)
|
823 |
+
|
824 |
+
hidden_states = outputs[0]
|
825 |
+
if self.config.pretraining_tp > 1:
|
826 |
+
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
|
827 |
+
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
|
828 |
+
logits = torch.cat(logits, dim=-1)
|
829 |
+
else:
|
830 |
+
logits = self.lm_head(hidden_states)
|
831 |
+
logits = logits.float()
|
832 |
+
|
833 |
+
loss = None
|
834 |
+
if labels is not None:
|
835 |
+
# Shift so that tokens < n predict n
|
836 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
837 |
+
shift_labels = labels[..., 1:].contiguous()
|
838 |
+
# Flatten the tokens
|
839 |
+
loss_fct = CrossEntropyLoss()
|
840 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
841 |
+
shift_labels = shift_labels.view(-1)
|
842 |
+
# Enable model parallelism
|
843 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
844 |
+
loss = loss_fct(shift_logits, shift_labels)
|
845 |
+
|
846 |
+
if not return_dict:
|
847 |
+
output = (logits,) + outputs[1:]
|
848 |
+
return (loss,) + output if loss is not None else output
|
849 |
+
|
850 |
+
return CausalLMOutputWithPast(
|
851 |
+
loss=loss,
|
852 |
+
logits=logits,
|
853 |
+
past_key_values=outputs.past_key_values,
|
854 |
+
hidden_states=outputs.hidden_states,
|
855 |
+
attentions=outputs.attentions,
|
856 |
+
)
|
857 |
+
|
858 |
+
def prepare_inputs_for_generation(
|
859 |
+
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
860 |
+
):
|
861 |
+
if past_key_values:
|
862 |
+
input_ids = input_ids[:, -1:]
|
863 |
+
|
864 |
+
position_ids = kwargs.get("position_ids", None)
|
865 |
+
|
866 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
867 |
+
if inputs_embeds is not None and past_key_values is None:
|
868 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
869 |
+
else:
|
870 |
+
model_inputs = {"input_ids": input_ids}
|
871 |
+
|
872 |
+
model_inputs.update(
|
873 |
+
{
|
874 |
+
"position_ids": position_ids,
|
875 |
+
"past_key_values": past_key_values,
|
876 |
+
"use_cache": kwargs.get("use_cache"),
|
877 |
+
"attention_mask": attention_mask,
|
878 |
+
"is_padded_inputs": ((attention_mask is not None) and (not attention_mask.all().item()))
|
879 |
+
}
|
880 |
+
)
|
881 |
+
return model_inputs
|
882 |
+
|
883 |
+
@staticmethod
|
884 |
+
def _reorder_cache(past_key_values, beam_idx):
|
885 |
+
reordered_past = ()
|
886 |
+
for layer_past in past_key_values:
|
887 |
+
reordered_past += (
|
888 |
+
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
889 |
+
)
|
890 |
+
return reordered_past
|
891 |
+
|
892 |
+
|
893 |
+
@add_start_docstrings(
|
894 |
+
"""
|
895 |
+
The LLaMa Model transformer with a sequence classification head on top (linear layer).
|
896 |
+
|
897 |
+
[`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
898 |
+
(e.g. GPT-2) do.
|
899 |
+
|
900 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
901 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
902 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
903 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
904 |
+
each row of the batch).
|
905 |
+
""",
|
906 |
+
LLAMA_START_DOCSTRING,
|
907 |
+
)
|
908 |
+
class LlamaForSequenceClassification(LlamaPreTrainedModel):
|
909 |
+
def __init__(self, config):
|
910 |
+
super().__init__(config)
|
911 |
+
self.num_labels = config.num_labels
|
912 |
+
self.model = LlamaModel(config)
|
913 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
914 |
+
|
915 |
+
# Initialize weights and apply final processing
|
916 |
+
self.post_init()
|
917 |
+
|
918 |
+
def get_input_embeddings(self):
|
919 |
+
return self.model.embed_tokens
|
920 |
+
|
921 |
+
def set_input_embeddings(self, value):
|
922 |
+
self.model.embed_tokens = value
|
923 |
+
|
924 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
925 |
+
def forward(
|
926 |
+
self,
|
927 |
+
input_ids: torch.LongTensor = None,
|
928 |
+
attention_mask: Optional[torch.Tensor] = None,
|
929 |
+
position_ids: Optional[torch.LongTensor] = None,
|
930 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
931 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
932 |
+
labels: Optional[torch.LongTensor] = None,
|
933 |
+
use_cache: Optional[bool] = None,
|
934 |
+
output_attentions: Optional[bool] = None,
|
935 |
+
output_hidden_states: Optional[bool] = None,
|
936 |
+
return_dict: Optional[bool] = None,
|
937 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
938 |
+
r"""
|
939 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
940 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
941 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
942 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
943 |
+
"""
|
944 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
945 |
+
|
946 |
+
transformer_outputs = self.model(
|
947 |
+
input_ids,
|
948 |
+
attention_mask=attention_mask,
|
949 |
+
position_ids=position_ids,
|
950 |
+
past_key_values=past_key_values,
|
951 |
+
inputs_embeds=inputs_embeds,
|
952 |
+
use_cache=use_cache,
|
953 |
+
output_attentions=output_attentions,
|
954 |
+
output_hidden_states=output_hidden_states,
|
955 |
+
return_dict=return_dict,
|
956 |
+
)
|
957 |
+
hidden_states = transformer_outputs[0]
|
958 |
+
logits = self.score(hidden_states)
|
959 |
+
|
960 |
+
if input_ids is not None:
|
961 |
+
batch_size = input_ids.shape[0]
|
962 |
+
else:
|
963 |
+
batch_size = inputs_embeds.shape[0]
|
964 |
+
|
965 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
966 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
967 |
+
if self.config.pad_token_id is None:
|
968 |
+
sequence_lengths = -1
|
969 |
+
else:
|
970 |
+
if input_ids is not None:
|
971 |
+
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
|
972 |
+
else:
|
973 |
+
sequence_lengths = -1
|
974 |
+
|
975 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
976 |
+
|
977 |
+
loss = None
|
978 |
+
if labels is not None:
|
979 |
+
labels = labels.to(logits.device)
|
980 |
+
if self.config.problem_type is None:
|
981 |
+
if self.num_labels == 1:
|
982 |
+
self.config.problem_type = "regression"
|
983 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
984 |
+
self.config.problem_type = "single_label_classification"
|
985 |
+
else:
|
986 |
+
self.config.problem_type = "multi_label_classification"
|
987 |
+
|
988 |
+
if self.config.problem_type == "regression":
|
989 |
+
loss_fct = MSELoss()
|
990 |
+
if self.num_labels == 1:
|
991 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
992 |
+
else:
|
993 |
+
loss = loss_fct(pooled_logits, labels)
|
994 |
+
elif self.config.problem_type == "single_label_classification":
|
995 |
+
loss_fct = CrossEntropyLoss()
|
996 |
+
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
997 |
+
elif self.config.problem_type == "multi_label_classification":
|
998 |
+
loss_fct = BCEWithLogitsLoss()
|
999 |
+
loss = loss_fct(pooled_logits, labels)
|
1000 |
+
if not return_dict:
|
1001 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
1002 |
+
return ((loss,) + output) if loss is not None else output
|
1003 |
+
|
1004 |
+
return SequenceClassifierOutputWithPast(
|
1005 |
+
loss=loss,
|
1006 |
+
logits=pooled_logits,
|
1007 |
+
past_key_values=transformer_outputs.past_key_values,
|
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tokenizer.json
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tokenizer.model
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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size 499723
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tokenizer_config.json
ADDED
@@ -0,0 +1,37 @@
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{
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"add_bos_token": true,
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"add_eos_token": false,
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"bos_token": {
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"__type": "AddedToken",
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"clean_up_tokenization_spaces": false,
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"eos_token": {
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"__type": "AddedToken",
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"legacy": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": null,
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"padding_side": "right",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": {
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"__type": "AddedToken",
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"use_default_system_prompt": true
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}
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