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Upload LlamaForCausalLM

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config.json ADDED
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+ {
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+ "_name_or_path": "output/storysummarizer-13b-8k-v2/checkpoint-939",
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+ "architectures": [
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+ "LlamaForCausalLM"
5
+ ],
6
+ "auto_map": {
7
+ "AutoConfig": "configuration_llama.LlamaConfig",
8
+ "AutoModel": "modeling_llama.LlamaModel",
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+ "AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM",
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+ "AutoModelForSequenceClassification": "modeling_llama.LlamaForSequenceClassification"
11
+ },
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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": 5120,
16
+ "initializer_range": 0.02,
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+ "intermediate_size": 13824,
18
+ "max_position_embeddings": 8192,
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+ "model_type": "llama",
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+ "num_attention_heads": 40,
21
+ "num_hidden_layers": 40,
22
+ "num_key_value_heads": 40,
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+ "pad_token_id": 0,
24
+ "pretraining_tp": 2,
25
+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": {
27
+ "factor": 2.0,
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+ "type": "linear"
29
+ },
30
+ "tie_word_embeddings": false,
31
+ "torch_dtype": "bfloat16",
32
+ "transformers_version": "4.32.0.dev0",
33
+ "use_cache": false,
34
+ "use_flash_attention": true,
35
+ "vocab_size": 32032
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+ }
configuration_llama.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ LLaMA model configuration"""
21
+
22
+ from transformers.configuration_utils import PretrainedConfig
23
+ from transformers.utils import logging
24
+
25
+
26
+ logger = logging.get_logger(__name__)
27
+
28
+ LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
29
+
30
+
31
+ class LlamaConfig(PretrainedConfig):
32
+ r"""
33
+ This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
34
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
35
+ defaults will yield a similar configuration to that of the LLaMA-7B.
36
+
37
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
38
+ documentation from [`PretrainedConfig`] for more information.
39
+
40
+
41
+ Args:
42
+ vocab_size (`int`, *optional*, defaults to 32000):
43
+ Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
44
+ `inputs_ids` passed when calling [`LlamaModel`]
45
+ hidden_size (`int`, *optional*, defaults to 4096):
46
+ Dimension of the hidden representations.
47
+ intermediate_size (`int`, *optional*, defaults to 11008):
48
+ Dimension of the MLP representations.
49
+ num_hidden_layers (`int`, *optional*, defaults to 32):
50
+ Number of hidden layers in the Transformer encoder.
51
+ num_attention_heads (`int`, *optional*, defaults to 32):
52
+ Number of attention heads for each attention layer in the Transformer encoder.
53
+ num_key_value_heads (`int`, *optional*):
54
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
55
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
56
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
57
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
58
+ by meanpooling all the original heads within that group. For more details checkout [this
59
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
60
+ `num_attention_heads`.
61
+ pretraining_tp (`int`, *optional*, defaults to `1`):
62
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
63
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
64
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
65
+ issue](https://github.com/pytorch/pytorch/issues/76232).
66
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
67
+ The non-linear activation function (function or string) in the decoder.
68
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
69
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
70
+ just in case (e.g., 512 or 1024 or 2048).
71
+ initializer_range (`float`, *optional*, defaults to 0.02):
72
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
73
+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
74
+ The epsilon used by the rms normalization layers.
75
+ use_cache (`bool`, *optional*, defaults to `True`):
76
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
77
+ relevant if `config.is_decoder=True`.
78
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
79
+ Whether to tie weight embeddings
80
+ rope_scaling (`Dict`, *optional*):
81
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
82
+ strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
83
+ is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
84
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
85
+ these scaling strategies behave:
86
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
87
+ experimental feature, subject to breaking API changes in future versions.
88
+
89
+ Example:
90
+
91
+ ```python
92
+ >>> from transformers import LlamaModel, LlamaConfig
93
+
94
+ >>> # Initializing a LLaMA llama-7b style configuration
95
+ >>> configuration = LlamaConfig()
96
+
97
+ >>> # Initializing a model from the llama-7b style configuration
98
+ >>> model = LlamaModel(configuration)
99
+
100
+ >>> # Accessing the model configuration
101
+ >>> configuration = model.config
102
+ ```"""
103
+ model_type = "llama"
104
+ keys_to_ignore_at_inference = ["past_key_values"]
105
+
106
+ def __init__(
107
+ self,
108
+ vocab_size=32000,
109
+ hidden_size=4096,
110
+ intermediate_size=11008,
111
+ num_hidden_layers=32,
112
+ num_attention_heads=32,
113
+ num_key_value_heads=None,
114
+ hidden_act="silu",
115
+ max_position_embeddings=2048,
116
+ initializer_range=0.02,
117
+ rms_norm_eps=1e-6,
118
+ use_cache=True,
119
+ pad_token_id=0,
120
+ bos_token_id=1,
121
+ eos_token_id=2,
122
+ pretraining_tp=1,
123
+ tie_word_embeddings=False,
124
+ rope_scaling=None,
125
+ use_flash_attention=False,
126
+ **kwargs,
127
+ ):
128
+ self.vocab_size = vocab_size
129
+ self.max_position_embeddings = max_position_embeddings
130
+ self.hidden_size = hidden_size
131
+ self.intermediate_size = intermediate_size
132
+ self.num_hidden_layers = num_hidden_layers
133
+ self.num_attention_heads = num_attention_heads
134
+
135
+ # for backward compatibility
136
+ if num_key_value_heads is None:
137
+ num_key_value_heads = num_attention_heads
138
+
139
+ self.num_key_value_heads = num_key_value_heads
140
+ self.hidden_act = hidden_act
141
+ self.initializer_range = initializer_range
142
+ self.rms_norm_eps = rms_norm_eps
143
+ self.pretraining_tp = pretraining_tp
144
+ self.use_cache = use_cache
145
+ self.rope_scaling = rope_scaling
146
+ self._rope_scaling_validation()
147
+ self.use_flash_attention = use_flash_attention
148
+ if self.use_flash_attention:
149
+ try:
150
+ from flash_attn.flash_attn_interface import flash_attn_varlen_func
151
+ from einops import rearrange
152
+ except:
153
+ raise ValueError("`use_flash_attention` requires Flash Attention 2+ and einops.\nTry `pip install einops` and installing Flash Attention from from https://github.com/Dao-AILab/flash-attention")
154
+
155
+ super().__init__(
156
+ pad_token_id=pad_token_id,
157
+ bos_token_id=bos_token_id,
158
+ eos_token_id=eos_token_id,
159
+ tie_word_embeddings=tie_word_embeddings,
160
+ **kwargs,
161
+ )
162
+
163
+ def _rope_scaling_validation(self):
164
+ """
165
+ Validate the `rope_scaling` configuration.
166
+ """
167
+ if self.rope_scaling is None:
168
+ return
169
+
170
+ if not isinstance(self.rope_scaling, dict):
171
+ raise ValueError(
172
+ "`rope_scaling` must be a dictionary, "
173
+ f"got {self.rope_scaling}"
174
+ )
175
+ rope_scaling_type = self.rope_scaling.get("type", None)
176
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
177
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "ntk-by-parts"]:
178
+ raise ValueError(
179
+ f"`rope_scaling`'s name field must be one of ['linear', 'dynamic', 'ntk-by-parts'], got {rope_scaling_type}"
180
+ )
181
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
182
+ raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
183
+ if rope_scaling_type == "ntk-by-parts":
184
+ original_max_position_embeddings = self.rope_scaling.get("original_max_position_embeddings", None)
185
+ if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
186
+ raise ValueError(f"`rope_scaling.original_max_position_embeddings` must be set to an int when using ntk-by-parts")
generation_config.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "eos_token_id": 2,
5
+ "pad_token_id": 0,
6
+ "temperature": 0.9,
7
+ "top_p": 0.6,
8
+ "transformers_version": "4.32.0.dev0"
9
+ }
modeling_llama.py ADDED
@@ -0,0 +1,1182 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ PyTorch LLaMA model."""
21
+ import math
22
+ from typing import List, Optional, Tuple, Union
23
+
24
+ import torch
25
+ import torch.nn.functional as F
26
+ import torch.utils.checkpoint
27
+ from torch import nn
28
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
29
+
30
+ from transformers.activations import ACT2FN
31
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
32
+ from transformers.modeling_utils import PreTrainedModel
33
+ from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
34
+ from .configuration_llama import LlamaConfig
35
+
36
+ try:
37
+ from flash_attn.flash_attn_interface import flash_attn_varlen_func
38
+ from flash_attn.modules.mha import FlashSelfAttention
39
+ from einops import rearrange
40
+ have_flash_attention = True
41
+ except:
42
+ have_flash_attention = False
43
+
44
+ logger = logging.get_logger(__name__)
45
+
46
+ _CONFIG_FOR_DOC = "LlamaConfig"
47
+
48
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
49
+ def _make_causal_mask(
50
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
51
+ ):
52
+ """
53
+ Make causal mask used for bi-directional self-attention.
54
+ """
55
+ bsz, tgt_len = input_ids_shape
56
+ mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
57
+ mask_cond = torch.arange(mask.size(-1), device=device)
58
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
59
+ mask = mask.to(dtype)
60
+
61
+ if past_key_values_length > 0:
62
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
63
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
64
+
65
+
66
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
67
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
68
+ """
69
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
70
+ """
71
+ bsz, src_len = mask.size()
72
+ tgt_len = tgt_len if tgt_len is not None else src_len
73
+
74
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
75
+
76
+ inverted_mask = 1.0 - expanded_mask
77
+
78
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
79
+
80
+ def _ntk_find_correction_factor(num_rotations, dim, base=10000, max_position_embeddings=2048):
81
+ return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base)) #Inverse dim formula to find number of rotations
82
+
83
+ def _ntk_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
84
+ low = math.floor(_ntk_find_correction_factor(low_rot, dim, base, max_position_embeddings))
85
+ high = math.ceil(_ntk_find_correction_factor(high_rot, dim, base, max_position_embeddings))
86
+ return max(low, 0), min(high, dim-1) #Clamp values just in case
87
+
88
+ def _ntk_linear_ramp_mask(min, max, dim):
89
+ if min == max:
90
+ max += 0.001 #Prevent singularity
91
+
92
+ linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
93
+ ramp_func = torch.clamp(linear_func, 0, 1)
94
+ return ramp_func
95
+
96
+ def _ntk_find_newbase_ntk(dim, base=10000, scale=1):
97
+ return base * scale ** (dim / (dim-2))
98
+
99
+ def _ntk_build_inv_freq(dim, base, scaling_factor, ntk_factor, extrapolation_factor, original_max_position_embeddings, device):
100
+ #Interpolation constants found experimentally for LLaMA (might not be totally optimal though)
101
+ #Do not change unless there is a good reason for doing so!
102
+ beta_0 = 1.25
103
+ beta_1 = 0.75
104
+ gamma_0 = 16
105
+ gamma_1 = 2
106
+
107
+ #Three RoPE extrapolation/interpolation methods
108
+ inv_freq_base = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
109
+ inv_freq_linear = 1.0 / (scaling_factor * (base ** (torch.arange(0, dim, 2).float().to(device) / dim)))
110
+ inv_freq_ntk = 1.0 / (_ntk_find_newbase_ntk(dim, base, scaling_factor) ** (torch.arange(0, dim, 2).float().to(device) / dim))
111
+
112
+ current_dtype = inv_freq_ntk.dtype
113
+ current_device = inv_freq_ntk.device
114
+
115
+ #Combine NTK and Linear
116
+ low, high = _ntk_find_correction_range(beta_0, beta_1, dim, base, original_max_position_embeddings)
117
+ inv_freq_mask = (1 - _ntk_linear_ramp_mask(low, high, dim // 2).type(current_dtype).to(current_device)) * ntk_factor
118
+ inv_freq = inv_freq_linear * (1 - inv_freq_mask) + inv_freq_ntk * inv_freq_mask
119
+
120
+ #Combine Extrapolation and NTK and Linear
121
+ low, high = _ntk_find_correction_range(gamma_0, gamma_1, dim, base, original_max_position_embeddings)
122
+ inv_freq_mask = (1 - _ntk_linear_ramp_mask(low, high, dim // 2).type(current_dtype).to(current_device)) * extrapolation_factor
123
+ return inv_freq * (1 - inv_freq_mask) + inv_freq_base * inv_freq_mask
124
+
125
+ def compute_flash_attention_packed(flash_attn, q, k, v, attention_mask=None):
126
+ if attention_mask is not None:
127
+ attention_mask = attention_mask[:, 0, -1]
128
+ q, k, v = (q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2))
129
+
130
+ # q, k, v: [bs, seq_len, num_attention_heads, attn_head_size]
131
+ # attention_mask (float): [bs, seq_len]
132
+ batch_size, max_len = q.size(0), q.size(1)
133
+
134
+ qkv = torch.stack([q, k, v], dim=2).to(
135
+ torch.float16
136
+ ) # need to truncate in case input is fp32
137
+ cu_seqlens, max_seqlen = None, None
138
+
139
+ if attention_mask is None:
140
+ return flash_attn(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
141
+ else:
142
+ # Limitation: non-contiguous attention mask will not be handled correctly
143
+ # model will be able to pay attention between the first and last non-masked token, i.e. left- and right-side padding is supported.
144
+ csums = (attention_mask >= 0).cumsum(dim=1)
145
+ ends = csums.argmax(dim=1) + 1
146
+ starts = ends - csums.max(dim=1).values
147
+ seqlens = ends - starts
148
+
149
+ qkv = torch.cat([qkv[i, starts[i] : ends[i]] for i in range(batch_size)], dim=0)
150
+ zero = torch.zeros_like(
151
+ seqlens[:1]
152
+ ) # torch.tensor([0]) with correct dtype and device
153
+ cu_seqlens = torch.cat([zero, seqlens.cumsum(dim=0)], dim=0).to(torch.int32)
154
+ max_seqlen = seqlens.max().item()
155
+
156
+ out = flash_attn(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
157
+ # out: [num_unmasked_tokens, num_attention_heads, attn_head_size]
158
+
159
+ seqs = [out[start:end] for start, end in zip(cu_seqlens[:-1], cu_seqlens[1:])]
160
+ # stack and pad sequences together
161
+ padded_seqs = [
162
+ F.pad(
163
+ seqs[i],
164
+ (0, 0) * (seqs[i].dim() - 1) + (starts[i], max_len - ends[i]),
165
+ value=0.0,
166
+ )
167
+ for i in range(batch_size)
168
+ ]
169
+
170
+ return torch.stack(padded_seqs).transpose(1, 2)
171
+
172
+ def compute_flash_attention_inference(query_states, key_states, value_states, attention_mask=None, dropout=0.0):
173
+
174
+ scale = query_states.shape[-1] ** (-0.5)
175
+
176
+ batch, _, seq_len_q, _ = query_states.shape
177
+ _, _, seq_len_k, _ = value_states.shape
178
+
179
+ query_states = rearrange(query_states, "b h s d -> b s h d").to(torch.float16)
180
+ key_states = rearrange(key_states, "b h s d -> b s h d").to(torch.float16)
181
+ value_states = rearrange(value_states, "b h s d -> b s h d").to(torch.float16)
182
+
183
+ if attention_mask is not None:
184
+ attention_mask = attention_mask[:, 0, -1]
185
+ csums = (attention_mask >= 0).cumsum(dim=1)
186
+ ends = csums.argmax(dim=1) + 1
187
+ starts = ends - csums.max(dim=1).values
188
+
189
+ query_states = torch.cat([query_states[i, starts[i] : ends[i]] for i in range(batch)], dim=0)
190
+ key_states = torch.cat([key_states[i, starts[i] : ends[i]] for i in range(batch)], dim=0)
191
+ value_states = torch.cat([value_states[i, starts[i] : ends[i]] for i in range(batch)], dim=0)
192
+
193
+ cu_seqlens_q = torch.arange(0, (batch + 1) * seq_len_q, step=seq_len_q, dtype=torch.int32,
194
+ device=query_states.device)
195
+
196
+ cu_seqlens_k = torch.arange(0, (batch + 1) * seq_len_k, step=seq_len_k, dtype=torch.int32,
197
+ device=key_states.device)
198
+
199
+ # No point returning attn_probs since it is not guaranteed to be correct
200
+ if seq_len_q == seq_len_k:
201
+ attn_output = flash_attn_varlen_func(query_states, key_states, value_states,
202
+ cu_seqlens_q, cu_seqlens_k, seq_len_q, seq_len_k,
203
+ dropout, scale, causal=True, return_attn_probs=False)
204
+ else:
205
+ attn_output = flash_attn_varlen_func(query_states, key_states, value_states,
206
+ cu_seqlens_q, cu_seqlens_k, seq_len_q, seq_len_k,
207
+ dropout, scale, causal=False, return_attn_probs=False)
208
+
209
+ return rearrange(attn_output, "(b s) h d-> b h s d", b = batch)
210
+
211
+ class LlamaRMSNorm(nn.Module):
212
+ def __init__(self, hidden_size, eps=1e-6):
213
+ """
214
+ LlamaRMSNorm is equivalent to T5LayerNorm
215
+ """
216
+ super().__init__()
217
+ self.weight = nn.Parameter(torch.ones(hidden_size))
218
+ self.variance_epsilon = eps
219
+
220
+ def forward(self, hidden_states):
221
+ input_dtype = hidden_states.dtype
222
+ hidden_states = hidden_states.to(torch.float32)
223
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
224
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
225
+ return self.weight * hidden_states.to(input_dtype)
226
+
227
+
228
+ class LlamaRotaryEmbedding(torch.nn.Module):
229
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
230
+ super().__init__()
231
+
232
+ self.dim = dim
233
+ self.max_position_embeddings = max_position_embeddings
234
+ self.base = base
235
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
236
+ self.register_buffer("inv_freq", inv_freq)
237
+
238
+ # Build here to make `torch.jit.trace` work.
239
+ self._set_cos_sin_cache(
240
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
241
+ )
242
+
243
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
244
+ self.max_seq_len_cached = seq_len
245
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
246
+
247
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
248
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
249
+ emb = torch.cat((freqs, freqs), dim=-1)
250
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
251
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
252
+
253
+ def forward(self, x, seq_len=None):
254
+ # x: [bs, num_attention_heads, seq_len, head_size]
255
+ if seq_len > self.max_seq_len_cached:
256
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
257
+
258
+ return (
259
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
260
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
261
+ )
262
+
263
+
264
+ class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
265
+ """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
266
+
267
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
268
+ self.scaling_factor = scaling_factor
269
+ super().__init__(dim, max_position_embeddings, base, device)
270
+
271
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
272
+ self.max_seq_len_cached = seq_len
273
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
274
+ t = t / self.scaling_factor
275
+
276
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
277
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
278
+ emb = torch.cat((freqs, freqs), dim=-1)
279
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
280
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
281
+
282
+
283
+ class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
284
+ """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
285
+
286
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
287
+ self.scaling_factor = scaling_factor
288
+ super().__init__(dim, max_position_embeddings, base, device)
289
+
290
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
291
+ self.max_seq_len_cached = seq_len
292
+
293
+ if seq_len > self.max_position_embeddings:
294
+ base = self.base * (
295
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
296
+ ) ** (self.dim / (self.dim - 2))
297
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
298
+ self.register_buffer("inv_freq", inv_freq)
299
+
300
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
301
+
302
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
303
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
304
+ emb = torch.cat((freqs, freqs), dim=-1)
305
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
306
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
307
+
308
+
309
+ class LlamaNTKByPartsRotaryEmbedding(LlamaRotaryEmbedding):
310
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, ntk_factor=1.0, extrapolation_factor=1.0, original_max_position_embeddings=2048):
311
+ super().__init__(dim, max_position_embeddings, base, device)
312
+
313
+ inv_freq = _ntk_build_inv_freq(dim, base, scaling_factor, ntk_factor, extrapolation_factor, original_max_position_embeddings, device)
314
+ self.register_buffer("inv_freq", inv_freq)
315
+
316
+ # Build here to make `torch.jit.trace` work.
317
+ self._set_cos_sin_cache(
318
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
319
+ )
320
+
321
+ def rotate_half(x):
322
+ """Rotates half the hidden dims of the input."""
323
+ x1 = x[..., : x.shape[-1] // 2]
324
+ x2 = x[..., x.shape[-1] // 2 :]
325
+ return torch.cat((-x2, x1), dim=-1)
326
+
327
+
328
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
329
+ # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
330
+ cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
331
+ sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
332
+ cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
333
+ sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
334
+ q_embed = (q * cos) + (rotate_half(q) * sin)
335
+ k_embed = (k * cos) + (rotate_half(k) * sin)
336
+ return q_embed, k_embed
337
+
338
+
339
+ class LlamaMLP(nn.Module):
340
+ def __init__(self, config):
341
+ super().__init__()
342
+ self.config = config
343
+ self.hidden_size = config.hidden_size
344
+ self.intermediate_size = config.intermediate_size
345
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
346
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
347
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
348
+ self.act_fn = ACT2FN[config.hidden_act]
349
+
350
+ def forward(self, x):
351
+ if self.config.pretraining_tp > 1:
352
+ slice = self.intermediate_size // self.config.pretraining_tp
353
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
354
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
355
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
356
+
357
+ gate_proj = torch.cat(
358
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
359
+ )
360
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
361
+
362
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
363
+ down_proj = [
364
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
365
+ ]
366
+ down_proj = sum(down_proj)
367
+ else:
368
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
369
+
370
+ return down_proj
371
+
372
+
373
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
374
+ """
375
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
376
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
377
+ """
378
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
379
+ if n_rep == 1:
380
+ return hidden_states
381
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
382
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
383
+
384
+
385
+ class LlamaAttention(nn.Module):
386
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
387
+
388
+ def __init__(self, config: LlamaConfig):
389
+ super().__init__()
390
+ self.config = config
391
+ self.hidden_size = config.hidden_size
392
+ self.num_heads = config.num_attention_heads
393
+ self.head_dim = self.hidden_size // self.num_heads
394
+ self.num_key_value_heads = config.num_key_value_heads
395
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
396
+ self.max_position_embeddings = config.max_position_embeddings
397
+
398
+ if (self.head_dim * self.num_heads) != self.hidden_size:
399
+ raise ValueError(
400
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
401
+ f" and `num_heads`: {self.num_heads})."
402
+ )
403
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
404
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
405
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
406
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
407
+ self._init_rope()
408
+ self.use_flash_attention = config.use_flash_attention
409
+ if self.use_flash_attention:
410
+ if not have_flash_attention:
411
+ raise RuntimeError("Flash Attention 2 not installed")
412
+ self.flash_attention = FlashSelfAttention(causal=True)
413
+
414
+
415
+ def _init_rope(self):
416
+ if self.config.rope_scaling is None:
417
+ self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
418
+ else:
419
+ scaling_type = self.config.rope_scaling["type"]
420
+ scaling_factor = self.config.rope_scaling["factor"]
421
+ if scaling_type == "linear":
422
+ self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
423
+ self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
424
+ )
425
+ elif scaling_type == "dynamic":
426
+ self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
427
+ self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
428
+ )
429
+ elif scaling_type == "ntk-by-parts":
430
+ original_max_position_embeddings = self.config.rope_scaling["original_max_position_embeddings"]
431
+ self.rotary_emb = LlamaNTKByPartsRotaryEmbedding(
432
+ self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, original_max_position_embeddings=original_max_position_embeddings
433
+ )
434
+ else:
435
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
436
+
437
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
438
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
439
+
440
+ def forward(
441
+ self,
442
+ hidden_states: torch.Tensor,
443
+ attention_mask: Optional[torch.Tensor] = None,
444
+ position_ids: Optional[torch.LongTensor] = None,
445
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
446
+ output_attentions: bool = False,
447
+ use_cache: bool = False,
448
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
449
+ bsz, q_len, _ = hidden_states.size()
450
+
451
+ if self.config.pretraining_tp > 1:
452
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
453
+ query_slices = self.q_proj.weight.split(
454
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
455
+ )
456
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
457
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
458
+
459
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
460
+ query_states = torch.cat(query_states, dim=-1)
461
+
462
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
463
+ key_states = torch.cat(key_states, dim=-1)
464
+
465
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
466
+ value_states = torch.cat(value_states, dim=-1)
467
+
468
+ else:
469
+ query_states = self.q_proj(hidden_states)
470
+ key_states = self.k_proj(hidden_states)
471
+ value_states = self.v_proj(hidden_states)
472
+
473
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
474
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
475
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
476
+
477
+ kv_seq_len = key_states.shape[-2]
478
+ if past_key_value is not None:
479
+ kv_seq_len += past_key_value[0].shape[-2]
480
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
481
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
482
+
483
+ if past_key_value is not None:
484
+ # reuse k, v, self_attention
485
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
486
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
487
+
488
+ past_key_value = (key_states, value_states) if use_cache else None
489
+
490
+ # repeat k/v heads if n_kv_heads < n_heads
491
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
492
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
493
+
494
+ if self.use_flash_attention and not output_attentions:
495
+ out_dtype = value_states.dtype
496
+ if self.training or query_states.shape == key_states.shape:
497
+ self.flash_attention.train(self.training)
498
+ attn_output = compute_flash_attention_packed(self.flash_attention, query_states, key_states, value_states, attention_mask)
499
+ else:
500
+ attn_output = compute_flash_attention_inference(query_states, key_states, value_states, attention_mask)
501
+ attn_output = attn_output.to(out_dtype)
502
+ else:
503
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
504
+
505
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
506
+ raise ValueError(
507
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
508
+ f" {attn_weights.size()}"
509
+ )
510
+
511
+ if attention_mask is not None:
512
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
513
+ raise ValueError(
514
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
515
+ )
516
+ attn_weights = attn_weights + attention_mask
517
+
518
+ # upcast attention to fp32
519
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
520
+ attn_output = torch.matmul(attn_weights, value_states)
521
+
522
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
523
+ raise ValueError(
524
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
525
+ f" {attn_output.size()}"
526
+ )
527
+
528
+ attn_output = attn_output.transpose(1, 2).contiguous()
529
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
530
+
531
+ if self.config.pretraining_tp > 1:
532
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
533
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
534
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
535
+ else:
536
+ attn_output = self.o_proj(attn_output)
537
+
538
+ if not output_attentions:
539
+ attn_weights = None
540
+
541
+ return attn_output, attn_weights, past_key_value
542
+
543
+
544
+ class LlamaDecoderLayer(nn.Module):
545
+ def __init__(self, config: LlamaConfig):
546
+ super().__init__()
547
+ self.hidden_size = config.hidden_size
548
+ self.self_attn = LlamaAttention(config=config)
549
+ self.mlp = LlamaMLP(config)
550
+ self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
551
+ self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
552
+
553
+ def forward(
554
+ self,
555
+ hidden_states: torch.Tensor,
556
+ attention_mask: Optional[torch.Tensor] = None,
557
+ position_ids: Optional[torch.LongTensor] = None,
558
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
559
+ output_attentions: Optional[bool] = False,
560
+ use_cache: Optional[bool] = False,
561
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
562
+ """
563
+ Args:
564
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
565
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
566
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
567
+ output_attentions (`bool`, *optional*):
568
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
569
+ returned tensors for more detail.
570
+ use_cache (`bool`, *optional*):
571
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
572
+ (see `past_key_values`).
573
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
574
+ """
575
+
576
+ residual = hidden_states
577
+
578
+ hidden_states = self.input_layernorm(hidden_states)
579
+
580
+ # Self Attention
581
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
582
+ hidden_states=hidden_states,
583
+ attention_mask=attention_mask,
584
+ position_ids=position_ids,
585
+ past_key_value=past_key_value,
586
+ output_attentions=output_attentions,
587
+ use_cache=use_cache,
588
+ )
589
+ hidden_states = residual + hidden_states
590
+
591
+ # Fully Connected
592
+ residual = hidden_states
593
+ hidden_states = self.post_attention_layernorm(hidden_states)
594
+ hidden_states = self.mlp(hidden_states)
595
+ hidden_states = residual + hidden_states
596
+
597
+ outputs = (hidden_states,)
598
+
599
+ if output_attentions:
600
+ outputs += (self_attn_weights,)
601
+
602
+ if use_cache:
603
+ outputs += (present_key_value,)
604
+
605
+ return outputs
606
+
607
+
608
+ LLAMA_START_DOCSTRING = r"""
609
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
610
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
611
+ etc.)
612
+
613
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
614
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
615
+ and behavior.
616
+
617
+ Parameters:
618
+ config ([`LlamaConfig`]):
619
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
620
+ load the weights associated with the model, only the configuration. Check out the
621
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
622
+ """
623
+
624
+
625
+ @add_start_docstrings(
626
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
627
+ LLAMA_START_DOCSTRING,
628
+ )
629
+ class LlamaPreTrainedModel(PreTrainedModel):
630
+ config_class = LlamaConfig
631
+ base_model_prefix = "model"
632
+ supports_gradient_checkpointing = True
633
+ _no_split_modules = ["LlamaDecoderLayer"]
634
+ _skip_keys_device_placement = "past_key_values"
635
+
636
+ def _init_weights(self, module):
637
+ std = self.config.initializer_range
638
+ if isinstance(module, nn.Linear):
639
+ module.weight.data.normal_(mean=0.0, std=std)
640
+ if module.bias is not None:
641
+ module.bias.data.zero_()
642
+ elif isinstance(module, nn.Embedding):
643
+ module.weight.data.normal_(mean=0.0, std=std)
644
+ if module.padding_idx is not None:
645
+ module.weight.data[module.padding_idx].zero_()
646
+
647
+ def _set_gradient_checkpointing(self, module, value=False):
648
+ if isinstance(module, LlamaModel):
649
+ module.gradient_checkpointing = value
650
+
651
+
652
+ LLAMA_INPUTS_DOCSTRING = r"""
653
+ Args:
654
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
655
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
656
+ it.
657
+
658
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
659
+ [`PreTrainedTokenizer.__call__`] for details.
660
+
661
+ [What are input IDs?](../glossary#input-ids)
662
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
663
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
664
+
665
+ - 1 for tokens that are **not masked**,
666
+ - 0 for tokens that are **masked**.
667
+
668
+ [What are attention masks?](../glossary#attention-mask)
669
+
670
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
671
+ [`PreTrainedTokenizer.__call__`] for details.
672
+
673
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
674
+ `past_key_values`).
675
+
676
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
677
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
678
+ information on the default strategy.
679
+
680
+ - 1 indicates the head is **not masked**,
681
+ - 0 indicates the head is **masked**.
682
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
683
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
684
+ config.n_positions - 1]`.
685
+
686
+ [What are position IDs?](../glossary#position-ids)
687
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
688
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
689
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
690
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
691
+
692
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
693
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
694
+
695
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
696
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
697
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
698
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
699
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
700
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
701
+ model's internal embedding lookup matrix.
702
+ use_cache (`bool`, *optional*):
703
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
704
+ `past_key_values`).
705
+ output_attentions (`bool`, *optional*):
706
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
707
+ tensors for more detail.
708
+ output_hidden_states (`bool`, *optional*):
709
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
710
+ more detail.
711
+ return_dict (`bool`, *optional*):
712
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
713
+ """
714
+
715
+
716
+ @add_start_docstrings(
717
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
718
+ LLAMA_START_DOCSTRING,
719
+ )
720
+ class LlamaModel(LlamaPreTrainedModel):
721
+ """
722
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
723
+
724
+ Args:
725
+ config: LlamaConfig
726
+ """
727
+
728
+ def __init__(self, config: LlamaConfig):
729
+ super().__init__(config)
730
+ self.padding_idx = config.pad_token_id
731
+ self.vocab_size = config.vocab_size
732
+
733
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
734
+ self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
735
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
736
+
737
+ self.gradient_checkpointing = False
738
+ self.use_flash_attention = config.use_flash_attention
739
+ # Initialize weights and apply final processing
740
+ self.post_init()
741
+
742
+ def get_input_embeddings(self):
743
+ return self.embed_tokens
744
+
745
+ def set_input_embeddings(self, value):
746
+ self.embed_tokens = value
747
+
748
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
749
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
750
+ # create causal mask
751
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
752
+ combined_attention_mask = None
753
+ if input_shape[-1] > 1:
754
+ combined_attention_mask = _make_causal_mask(
755
+ input_shape,
756
+ inputs_embeds.dtype,
757
+ device=inputs_embeds.device,
758
+ past_key_values_length=past_key_values_length,
759
+ )
760
+
761
+ if attention_mask is not None:
762
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
763
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
764
+ inputs_embeds.device
765
+ )
766
+ combined_attention_mask = (
767
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
768
+ )
769
+
770
+ return combined_attention_mask
771
+
772
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
773
+ def forward(
774
+ self,
775
+ input_ids: torch.LongTensor = None,
776
+ attention_mask: Optional[torch.Tensor] = None,
777
+ position_ids: Optional[torch.LongTensor] = None,
778
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
779
+ inputs_embeds: Optional[torch.FloatTensor] = None,
780
+ use_cache: Optional[bool] = None,
781
+ output_attentions: Optional[bool] = None,
782
+ output_hidden_states: Optional[bool] = None,
783
+ return_dict: Optional[bool] = None,
784
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
785
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
786
+ output_hidden_states = (
787
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
788
+ )
789
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
790
+
791
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
792
+
793
+ # retrieve input_ids and inputs_embeds
794
+ if input_ids is not None and inputs_embeds is not None:
795
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
796
+ elif input_ids is not None:
797
+ batch_size, seq_length = input_ids.shape
798
+ elif inputs_embeds is not None:
799
+ batch_size, seq_length, _ = inputs_embeds.shape
800
+ else:
801
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
802
+
803
+ seq_length_with_past = seq_length
804
+ past_key_values_length = 0
805
+
806
+ if past_key_values is not None:
807
+ past_key_values_length = past_key_values[0][0].shape[2]
808
+ seq_length_with_past = seq_length_with_past + past_key_values_length
809
+
810
+ if position_ids is None:
811
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
812
+ position_ids = torch.arange(
813
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
814
+ )
815
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
816
+ else:
817
+ position_ids = position_ids.view(-1, seq_length).long()
818
+
819
+ if inputs_embeds is None:
820
+ inputs_embeds = self.embed_tokens(input_ids)
821
+ # embed positions
822
+ if attention_mask is None:
823
+ attention_mask = torch.ones(
824
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
825
+ )
826
+ attention_mask = self._prepare_decoder_attention_mask(
827
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
828
+ )
829
+
830
+ hidden_states = inputs_embeds
831
+
832
+ if self.gradient_checkpointing and self.training:
833
+ if use_cache:
834
+ logger.warning_once(
835
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
836
+ )
837
+ use_cache = False
838
+
839
+ # decoder layers
840
+ all_hidden_states = () if output_hidden_states else None
841
+ all_self_attns = () if output_attentions else None
842
+ next_decoder_cache = () if use_cache else None
843
+
844
+ for idx, decoder_layer in enumerate(self.layers):
845
+ if output_hidden_states:
846
+ all_hidden_states += (hidden_states,)
847
+
848
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
849
+
850
+ if self.gradient_checkpointing and self.training:
851
+
852
+ def create_custom_forward(module):
853
+ def custom_forward(*inputs):
854
+ # None for past_key_value
855
+ return module(*inputs, output_attentions, None)
856
+
857
+ return custom_forward
858
+
859
+ layer_outputs = torch.utils.checkpoint.checkpoint(
860
+ create_custom_forward(decoder_layer),
861
+ hidden_states,
862
+ attention_mask,
863
+ position_ids,
864
+ None,
865
+ )
866
+ else:
867
+ layer_outputs = decoder_layer(
868
+ hidden_states,
869
+ attention_mask=attention_mask,
870
+ position_ids=position_ids,
871
+ past_key_value=past_key_value,
872
+ output_attentions=output_attentions,
873
+ use_cache=use_cache,
874
+ )
875
+
876
+ hidden_states = layer_outputs[0]
877
+
878
+ if use_cache:
879
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
880
+
881
+ if output_attentions:
882
+ all_self_attns += (layer_outputs[1],)
883
+
884
+ hidden_states = self.norm(hidden_states)
885
+
886
+ # add hidden states from the last decoder layer
887
+ if output_hidden_states:
888
+ all_hidden_states += (hidden_states,)
889
+
890
+ next_cache = next_decoder_cache if use_cache else None
891
+ if not return_dict:
892
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
893
+ return BaseModelOutputWithPast(
894
+ last_hidden_state=hidden_states,
895
+ past_key_values=next_cache,
896
+ hidden_states=all_hidden_states,
897
+ attentions=all_self_attns,
898
+ )
899
+
900
+
901
+ class LlamaForCausalLM(LlamaPreTrainedModel):
902
+ _tied_weights_keys = ["lm_head.weight"]
903
+
904
+ def __init__(self, config):
905
+ super().__init__(config)
906
+ self.model = LlamaModel(config)
907
+ self.vocab_size = config.vocab_size
908
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
909
+
910
+ # Initialize weights and apply final processing
911
+ self.post_init()
912
+
913
+ def get_input_embeddings(self):
914
+ return self.model.embed_tokens
915
+
916
+ def set_input_embeddings(self, value):
917
+ self.model.embed_tokens = value
918
+
919
+ def get_output_embeddings(self):
920
+ return self.lm_head
921
+
922
+ def set_output_embeddings(self, new_embeddings):
923
+ self.lm_head = new_embeddings
924
+
925
+ def set_decoder(self, decoder):
926
+ self.model = decoder
927
+
928
+ def get_decoder(self):
929
+ return self.model
930
+
931
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
932
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
933
+ def forward(
934
+ self,
935
+ input_ids: torch.LongTensor = None,
936
+ attention_mask: Optional[torch.Tensor] = None,
937
+ position_ids: Optional[torch.LongTensor] = None,
938
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
939
+ inputs_embeds: Optional[torch.FloatTensor] = None,
940
+ labels: Optional[torch.LongTensor] = None,
941
+ use_cache: Optional[bool] = None,
942
+ output_attentions: Optional[bool] = None,
943
+ output_hidden_states: Optional[bool] = None,
944
+ return_dict: Optional[bool] = None,
945
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
946
+ r"""
947
+ Args:
948
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
949
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
950
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
951
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
952
+
953
+ Returns:
954
+
955
+ Example:
956
+
957
+ ```python
958
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
959
+
960
+ >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
961
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
962
+
963
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
964
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
965
+
966
+ >>> # Generate
967
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
968
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
969
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
970
+ ```"""
971
+
972
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
973
+ output_hidden_states = (
974
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
975
+ )
976
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
977
+
978
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
979
+ outputs = self.model(
980
+ input_ids=input_ids,
981
+ attention_mask=attention_mask,
982
+ position_ids=position_ids,
983
+ past_key_values=past_key_values,
984
+ inputs_embeds=inputs_embeds,
985
+ use_cache=use_cache,
986
+ output_attentions=output_attentions,
987
+ output_hidden_states=output_hidden_states,
988
+ return_dict=return_dict,
989
+ )
990
+
991
+ hidden_states = outputs[0]
992
+ if self.config.pretraining_tp > 1:
993
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
994
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
995
+ logits = torch.cat(logits, dim=-1)
996
+ else:
997
+ logits = self.lm_head(hidden_states)
998
+ logits = logits.float()
999
+
1000
+ loss = None
1001
+ if labels is not None:
1002
+ # Shift so that tokens < n predict n
1003
+ shift_logits = logits[..., :-1, :].contiguous()
1004
+ shift_labels = labels[..., 1:].contiguous()
1005
+ # Flatten the tokens
1006
+ loss_fct = CrossEntropyLoss()
1007
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1008
+ shift_labels = shift_labels.view(-1)
1009
+ # Enable model parallelism
1010
+ shift_labels = shift_labels.to(shift_logits.device)
1011
+ loss = loss_fct(shift_logits, shift_labels)
1012
+
1013
+ if not return_dict:
1014
+ output = (logits,) + outputs[1:]
1015
+ return (loss,) + output if loss is not None else output
1016
+
1017
+ return CausalLMOutputWithPast(
1018
+ loss=loss,
1019
+ logits=logits,
1020
+ past_key_values=outputs.past_key_values,
1021
+ hidden_states=outputs.hidden_states,
1022
+ attentions=outputs.attentions,
1023
+ )
1024
+
1025
+ def prepare_inputs_for_generation(
1026
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1027
+ ):
1028
+ if past_key_values:
1029
+ input_ids = input_ids[:, -1:]
1030
+
1031
+ position_ids = kwargs.get("position_ids", None)
1032
+ if attention_mask is not None and position_ids is None:
1033
+ # create position_ids on the fly for batch generation
1034
+ position_ids = attention_mask.long().cumsum(-1) - 1
1035
+ position_ids.masked_fill_(attention_mask == 0, 1)
1036
+ if past_key_values:
1037
+ position_ids = position_ids[:, -1].unsqueeze(-1)
1038
+
1039
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1040
+ if inputs_embeds is not None and past_key_values is None:
1041
+ model_inputs = {"inputs_embeds": inputs_embeds}
1042
+ else:
1043
+ model_inputs = {"input_ids": input_ids}
1044
+
1045
+ model_inputs.update(
1046
+ {
1047
+ "position_ids": position_ids,
1048
+ "past_key_values": past_key_values,
1049
+ "use_cache": kwargs.get("use_cache"),
1050
+ "attention_mask": attention_mask,
1051
+ }
1052
+ )
1053
+ return model_inputs
1054
+
1055
+ @staticmethod
1056
+ def _reorder_cache(past_key_values, beam_idx):
1057
+ reordered_past = ()
1058
+ for layer_past in past_key_values:
1059
+ reordered_past += (
1060
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1061
+ )
1062
+ return reordered_past
1063
+
1064
+
1065
+ @add_start_docstrings(
1066
+ """
1067
+ The LLaMa Model transformer with a sequence classification head on top (linear layer).
1068
+
1069
+ [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1070
+ (e.g. GPT-2) do.
1071
+
1072
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1073
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1074
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1075
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1076
+ each row of the batch).
1077
+ """,
1078
+ LLAMA_START_DOCSTRING,
1079
+ )
1080
+ class LlamaForSequenceClassification(LlamaPreTrainedModel):
1081
+ def __init__(self, config):
1082
+ super().__init__(config)
1083
+ self.num_labels = config.num_labels
1084
+ self.model = LlamaModel(config)
1085
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1086
+
1087
+ # Initialize weights and apply final processing
1088
+ self.post_init()
1089
+
1090
+ def get_input_embeddings(self):
1091
+ return self.model.embed_tokens
1092
+
1093
+ def set_input_embeddings(self, value):
1094
+ self.model.embed_tokens = value
1095
+
1096
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1097
+ def forward(
1098
+ self,
1099
+ input_ids: torch.LongTensor = None,
1100
+ attention_mask: Optional[torch.Tensor] = None,
1101
+ position_ids: Optional[torch.LongTensor] = None,
1102
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1103
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1104
+ labels: Optional[torch.LongTensor] = None,
1105
+ use_cache: Optional[bool] = None,
1106
+ output_attentions: Optional[bool] = None,
1107
+ output_hidden_states: Optional[bool] = None,
1108
+ return_dict: Optional[bool] = None,
1109
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1110
+ r"""
1111
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1112
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1113
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1114
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1115
+ """
1116
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1117
+
1118
+ transformer_outputs = self.model(
1119
+ input_ids,
1120
+ attention_mask=attention_mask,
1121
+ position_ids=position_ids,
1122
+ past_key_values=past_key_values,
1123
+ inputs_embeds=inputs_embeds,
1124
+ use_cache=use_cache,
1125
+ output_attentions=output_attentions,
1126
+ output_hidden_states=output_hidden_states,
1127
+ return_dict=return_dict,
1128
+ )
1129
+ hidden_states = transformer_outputs[0]
1130
+ logits = self.score(hidden_states)
1131
+
1132
+ if input_ids is not None:
1133
+ batch_size = input_ids.shape[0]
1134
+ else:
1135
+ batch_size = inputs_embeds.shape[0]
1136
+
1137
+ if self.config.pad_token_id is None and batch_size != 1:
1138
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1139
+ if self.config.pad_token_id is None:
1140
+ sequence_lengths = -1
1141
+ else:
1142
+ if input_ids is not None:
1143
+ sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
1144
+ else:
1145
+ sequence_lengths = -1
1146
+
1147
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1148
+
1149
+ loss = None
1150
+ if labels is not None:
1151
+ labels = labels.to(logits.device)
1152
+ if self.config.problem_type is None:
1153
+ if self.num_labels == 1:
1154
+ self.config.problem_type = "regression"
1155
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1156
+ self.config.problem_type = "single_label_classification"
1157
+ else:
1158
+ self.config.problem_type = "multi_label_classification"
1159
+
1160
+ if self.config.problem_type == "regression":
1161
+ loss_fct = MSELoss()
1162
+ if self.num_labels == 1:
1163
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1164
+ else:
1165
+ loss = loss_fct(pooled_logits, labels)
1166
+ elif self.config.problem_type == "single_label_classification":
1167
+ loss_fct = CrossEntropyLoss()
1168
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1169
+ elif self.config.problem_type == "multi_label_classification":
1170
+ loss_fct = BCEWithLogitsLoss()
1171
+ loss = loss_fct(pooled_logits, labels)
1172
+ if not return_dict:
1173
+ output = (pooled_logits,) + transformer_outputs[1:]
1174
+ return ((loss,) + output) if loss is not None else output
1175
+
1176
+ return SequenceClassifierOutputWithPast(
1177
+ loss=loss,
1178
+ logits=pooled_logits,
1179
+ past_key_values=transformer_outputs.past_key_values,
1180
+ hidden_states=transformer_outputs.hidden_states,
1181
+ attentions=transformer_outputs.attentions,
1182
+ )
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