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config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "LlamaForCausalLM"
4
+ ],
5
+ "auto_map": {
6
+ "AutoConfig": "configuration_llama.LlamaConfig",
7
+ "AutoModel": "modeling_flash_llama.LlamaModel",
8
+ "AutoModelForCausalLM": "modeling_flash_llama.LlamaForCausalLM",
9
+ "AutoModelForSequenceClassification": "modeling_flash_llama.LlamaForSequenceClassification"
10
+ },
11
+ "bos_token_id": 1,
12
+ "eos_token_id": 2,
13
+ "hidden_act": "silu",
14
+ "hidden_size": 5120,
15
+ "initializer_range": 0.02,
16
+ "intermediate_size": 13824,
17
+ "max_position_embeddings": 16384,
18
+ "model_type": "llama",
19
+ "num_attention_heads": 40,
20
+ "num_hidden_layers": 40,
21
+ "num_key_value_heads": 40,
22
+ "pretraining_tp": 1,
23
+ "rms_norm_eps": 1e-05,
24
+ "rope_scaling": null,
25
+ "rope_theta": 1000000,
26
+ "tie_word_embeddings": false,
27
+ "torch_dtype": "float16",
28
+ "transformers_version": "4.32.0.dev0",
29
+ "use_cache": true,
30
+ "vocab_size": 32016
31
+ }
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 two 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=None,
120
+ bos_token_id=1,
121
+ eos_token_id=2,
122
+ pretraining_tp=1,
123
+ tie_word_embeddings=False,
124
+ rope_scaling=None,
125
+ rope_theta=10000,
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.rope_theta = rope_theta
148
+
149
+ super().__init__(
150
+ pad_token_id=pad_token_id,
151
+ bos_token_id=bos_token_id,
152
+ eos_token_id=eos_token_id,
153
+ tie_word_embeddings=tie_word_embeddings,
154
+ **kwargs,
155
+ )
156
+
157
+ def _rope_scaling_validation(self):
158
+ """
159
+ Validate the `rope_scaling` configuration.
160
+ """
161
+ if self.rope_scaling is None:
162
+ return
163
+
164
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
165
+ raise ValueError(
166
+ "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
167
+ f"got {self.rope_scaling}"
168
+ )
169
+ rope_scaling_type = self.rope_scaling.get("type", None)
170
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
171
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
172
+ raise ValueError(
173
+ f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
174
+ )
175
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
176
+ raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
generation_config.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "eos_token_id": 2,
5
+ "transformers_version": "4.32.0.dev0"
6
+ }
modeling_flash_llama.py ADDED
@@ -0,0 +1,1012 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+
37
+ try:
38
+ from flash_attn.flash_attn_interface import (
39
+ flash_attn_func,
40
+ flash_attn_kvpacked_func,
41
+ flash_attn_qkvpacked_func,
42
+ flash_attn_varlen_kvpacked_func,
43
+ )
44
+ from flash_attn.bert_padding import unpad_input, pad_input
45
+ flash_attn_v2_installed = True
46
+ print('>>>> Flash Attention installed')
47
+ except ImportError:
48
+ flash_attn_v2_installed = False
49
+ raise ImportError('Please install Flash Attention: `pip install flash-attn --no-build-isolation`')
50
+
51
+ try:
52
+ from flash_attn.layers.rotary import apply_rotary_emb_func
53
+ flash_rope_installed = True
54
+ print('>>>> Flash RoPE installed')
55
+ except ImportError:
56
+ flash_rope_installed = False
57
+ raise ImportError('Please install RoPE kernels: `pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary`')
58
+
59
+
60
+ logger = logging.get_logger(__name__)
61
+
62
+ _CONFIG_FOR_DOC = "LlamaConfig"
63
+
64
+
65
+ # @torch.jit.script
66
+ def rmsnorm_func(hidden_states, weight, variance_epsilon):
67
+ input_dtype = hidden_states.dtype
68
+ hidden_states = hidden_states.to(torch.float32)
69
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
70
+ hidden_states = hidden_states * torch.rsqrt(variance + variance_epsilon)
71
+ return (weight * hidden_states).to(input_dtype)
72
+
73
+
74
+ class LlamaRMSNorm(nn.Module):
75
+ def __init__(self, hidden_size, eps=1e-6):
76
+ """
77
+ LlamaRMSNorm is equivalent to T5LayerNorm
78
+ """
79
+ super().__init__()
80
+ self.weight = nn.Parameter(torch.ones(hidden_size))
81
+ self.register_buffer(
82
+ "variance_epsilon",
83
+ torch.tensor(eps),
84
+ persistent=False,
85
+ )
86
+
87
+ def forward(self, hidden_states):
88
+ return rmsnorm_func(hidden_states, self.weight, self.variance_epsilon)
89
+
90
+
91
+ class FlashRotaryEmbedding(torch.nn.Module):
92
+ """
93
+ The rotary position embeddings from RoFormer_ (Su et. al).
94
+ A crucial insight from the method is that the query and keys are
95
+ transformed by rotation matrices which depend on the relative positions.
96
+
97
+ Other implementations are available in the Rotary Transformer repo_ and in
98
+ GPT-NeoX_, GPT-NeoX was an inspiration
99
+
100
+ .. _RoFormer: https://arxiv.org/abs/2104.09864
101
+ .. _repo: https://github.com/ZhuiyiTechnology/roformer
102
+ .. _GPT-NeoX: https://github.com/EleutherAI/gpt-neox
103
+
104
+ If scale_base is not None, this implements XPos (Sun et al., https://arxiv.org/abs/2212.10554).
105
+ A recommended value for scale_base is 512: https://github.com/HazyResearch/flash-attention/issues/96
106
+ Reference: https://github.com/sunyt32/torchscale/blob/main/torchscale/component/xpos_relative_position.py
107
+ """
108
+
109
+ def __init__(self, dim: int, base=10000.0, interleaved=False, scale_base=None,
110
+ scaling_factor=1.0, pos_idx_in_fp32=True, device=None):
111
+ """
112
+ interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
113
+ of 1st half and 2nd half (GPT-NeoX style).
114
+ pos_idx_in_fp32: if True, the position indices [0.0, ..., seqlen - 1] are in fp32,
115
+ otherwise they might be in lower precision.
116
+ This option was added because previously (before 2023-07-02), when we construct
117
+ the position indices, we use the dtype of self.inv_freq. In most cases this would
118
+ be fp32, but if the model is trained in pure bf16 (not mixed precision), then
119
+ self.inv_freq would be bf16, and the position indices are also in bf16.
120
+ Because of the limited precision of bf16 (e.g. 1995.0 is rounded to 2000.0), the
121
+ embeddings for some positions will coincide.
122
+ To maintain compatibility with models previously trained in pure bf16,
123
+ we add this option.
124
+ scaling_factor: RotaryEmbedding extended with linear scaling.
125
+ """
126
+ super().__init__()
127
+ self.dim = dim
128
+ self.base = float(base)
129
+ self.pos_idx_in_fp32 = pos_idx_in_fp32
130
+ # Generate and save the inverse frequency buffer (non trainable)
131
+ inv_freq = self._compute_inv_freq(device)
132
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
133
+ self.interleaved = interleaved
134
+ self.scale_base = scale_base
135
+ self.scaling_factor = scaling_factor
136
+ scale = ((torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim)
137
+ / (1.4 * dim) if scale_base is not None else None)
138
+ self.register_buffer("scale", scale)
139
+
140
+ self._seq_len_cached = 0
141
+ self._cos_cached = None
142
+ self._sin_cached = None
143
+ self._cos_k_cached = None
144
+ self._sin_k_cached = None
145
+
146
+ def _compute_inv_freq(self, device=None):
147
+ return 1 / (self.base ** (torch.arange(0, self.dim, 2, device=device,
148
+ dtype=torch.float32) / self.dim))
149
+
150
+
151
+ def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
152
+ # Reset the tables if the sequence length has changed,
153
+ # if we're on a new device (possibly due to tracing for instance),
154
+ # or if we're switching from inference mode to training
155
+ if (seqlen > self._seq_len_cached or self._cos_cached.device != device
156
+ or self._cos_cached.dtype != dtype
157
+ or (self.training and self._cos_cached.is_inference())):
158
+ self._seq_len_cached = seqlen
159
+ # We want fp32 here, not self.inv_freq.dtype, since the model could be loaded in bf16
160
+ # And the output of arange can be quite large, so bf16 would lose a lot of precision.
161
+ # However, for compatibility reason, we add an option to use the dtype of self.inv_freq.
162
+ if self.pos_idx_in_fp32:
163
+ t = torch.arange(seqlen, device=device, dtype=torch.float32)
164
+ t /= self.scaling_factor
165
+ # We want fp32 here as well since inv_freq will be multiplied with t, and the output
166
+ # will be large. Having it in bf16 will lose a lot of precision and cause the
167
+ # cos & sin output to change significantly.
168
+ # We want to recompute self.inv_freq if it was not loaded in fp32
169
+ if self.inv_freq.dtype != torch.float32:
170
+ inv_freq = self.inv_freq.to(torch.float32)
171
+ else:
172
+ inv_freq = self.inv_freq
173
+ else:
174
+ t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
175
+ t /= self.scaling_factor
176
+ inv_freq = self.inv_freq
177
+ # Don't do einsum, it converts fp32 to fp16 under AMP
178
+ # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
179
+ freqs = torch.outer(t, inv_freq)
180
+ if self.scale is None:
181
+ self._cos_cached = torch.cos(freqs).to(dtype)
182
+ self._sin_cached = torch.sin(freqs).to(dtype)
183
+ else:
184
+ power = ((torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device)
185
+ - seqlen // 2) / self.scale_base)
186
+ scale = self.scale.to(device=power.device) ** power.unsqueeze(-1)
187
+ # We want the multiplication by scale to happen in fp32
188
+ self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
189
+ self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
190
+ self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
191
+ self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
192
+
193
+ def forward(self, q: torch.Tensor, k: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
194
+ """
195
+ q: (batch, seqlen, nheads, headdim)
196
+ k: (batch, seqlen, nheads, headdim)
197
+ seqlen_offset: can be used in generation where the qkv being passed in is only the last
198
+ token in the batch.
199
+ """
200
+ self._update_cos_sin_cache(q.shape[1] + seqlen_offset, device=q.device, dtype=q.dtype)
201
+ if self.scale is None:
202
+ return apply_rotary_emb_func(
203
+ q, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:],
204
+ self.interleaved, True # inplace=True
205
+ ), apply_rotary_emb_func(
206
+ k, self._cos_cached[seqlen_offset:], self._sin_cached[seqlen_offset:],
207
+ self.interleaved, True # inplace=True
208
+ )
209
+ else:
210
+ assert False
211
+
212
+ class LlamaMLP(nn.Module):
213
+ def __init__(self, config):
214
+ super().__init__()
215
+ self.config = config
216
+ self.hidden_size = config.hidden_size
217
+ self.intermediate_size = config.intermediate_size
218
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
219
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
220
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
221
+ self.act_fn = ACT2FN[config.hidden_act]
222
+
223
+ def forward(self, x):
224
+ if self.config.pretraining_tp > 1:
225
+ slice = self.intermediate_size // self.config.pretraining_tp
226
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
227
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
228
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
229
+
230
+ gate_proj = torch.cat(
231
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
232
+ )
233
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
234
+
235
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
236
+ down_proj = [
237
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
238
+ ]
239
+ down_proj = sum(down_proj)
240
+ else:
241
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
242
+
243
+ return down_proj
244
+
245
+ @torch.jit.script
246
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
247
+ """
248
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
249
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
250
+ """
251
+ batch, slen, _, num_key_value_heads, head_dim = hidden_states.shape
252
+ if n_rep == 1:
253
+ return hidden_states
254
+ hidden_states = hidden_states[:, :, :, :, None, :].expand(batch, slen, 2, num_key_value_heads, n_rep, head_dim)
255
+ return hidden_states.reshape(batch, slen, 2, num_key_value_heads * n_rep, head_dim)
256
+
257
+
258
+ class LlamaAttention(nn.Module):
259
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
260
+
261
+ def __init__(self, config: LlamaConfig):
262
+ super().__init__()
263
+ self.config = config
264
+ self.hidden_size = config.hidden_size
265
+ self.num_heads = config.num_attention_heads
266
+ self.head_dim = self.hidden_size // self.num_heads
267
+ self.num_key_value_heads = config.num_key_value_heads
268
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
269
+ self.max_position_embeddings = config.max_position_embeddings
270
+ self.rope_theta = config.rope_theta
271
+
272
+ if (self.head_dim * self.num_heads) != self.hidden_size:
273
+ raise ValueError(
274
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
275
+ f" and `num_heads`: {self.num_heads})."
276
+ )
277
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
278
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
279
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
280
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
281
+
282
+ self.register_buffer(
283
+ "norm_factor",
284
+ torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype()),
285
+ persistent=False,
286
+ )
287
+
288
+ if self.config.rope_scaling is None:
289
+ scaling_factor = 1
290
+ else:
291
+ scaling_type = self.config.rope_scaling["type"]
292
+ scaling_factor = self.config.rope_scaling["factor"]
293
+ assert scaling_type == 'linear'
294
+
295
+ self.rotary_emb = FlashRotaryEmbedding(
296
+ self.head_dim, base=self.rope_theta, interleaved=False, scaling_factor=scaling_factor,
297
+ )
298
+
299
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
300
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
301
+
302
+ def forward(
303
+ self,
304
+ hidden_states: torch.Tensor,
305
+ attention_mask: Optional[torch.Tensor] = None,
306
+ position_ids: Optional[torch.LongTensor] = None,
307
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
308
+ output_attentions: bool = False,
309
+ use_cache: bool = False,
310
+ is_padded_inputs: Optional[bool] = False,
311
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
312
+ bsz, q_len, h_size = hidden_states.size()
313
+
314
+ has_layer_past = past_key_value is not None
315
+
316
+ if has_layer_past:
317
+ past_kv = past_key_value[0]
318
+ past_len = past_key_value[1]
319
+ else:
320
+ past_len = 0
321
+
322
+ if self.config.pretraining_tp > 1:
323
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
324
+ query_slices = self.q_proj.weight.split(
325
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
326
+ )
327
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
328
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
329
+
330
+ q = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
331
+ q = torch.cat(q, dim=-1)
332
+
333
+ k = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
334
+ k = torch.cat(k, dim=-1)
335
+
336
+ v = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
337
+ v = torch.cat(v, dim=-1)
338
+
339
+ else:
340
+ q = self.q_proj(hidden_states)
341
+ k = self.k_proj(hidden_states)
342
+ v = self.v_proj(hidden_states)
343
+
344
+ q = q.view(bsz, q_len, self.num_heads, self.head_dim)
345
+ k = k.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
346
+ v = v.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
347
+
348
+ q, k = self.rotary_emb(q, k, past_len)
349
+
350
+ kv = torch.stack([k, v], 2)
351
+ kv = repeat_kv(kv, self.num_key_value_groups)
352
+
353
+ # Cache QKV values
354
+ if has_layer_past:
355
+ new_len = past_len+q.size(1)
356
+ if new_len > past_kv.size(1):
357
+ past_kv = torch.cat([past_kv, torch.empty(bsz, 256, 2, kv.size(3), kv.size(4), dtype=kv.dtype, device=kv.device)], 1)
358
+ past_kv[:, past_len:new_len] = kv
359
+ kv = past_kv[:, :new_len]
360
+ else:
361
+ past_kv = kv
362
+
363
+ past_key_value = (past_kv, past_len+q.size(1)) if use_cache else None
364
+
365
+ if is_padded_inputs:
366
+
367
+ # varlen, ignore padding tokens, efficient for large batch with many paddings
368
+
369
+ assert attention_mask is not None
370
+
371
+ unpadded_kv, indices_k, cu_seqlens_k, max_seqlen_k = unpad_input(kv, attention_mask)
372
+ unpadded_q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, attention_mask[:, -q.size(1):])
373
+ attn_outputs = flash_attn_varlen_kvpacked_func(
374
+ unpadded_q, unpadded_kv, cu_seqlens_q, cu_seqlens_k,
375
+ max_seqlen_q, max_seqlen_k,
376
+ dropout_p=0.0, softmax_scale=1.0/self.norm_factor,
377
+ causal=(not has_layer_past), return_attn_probs=output_attentions
378
+ )
379
+
380
+ attn_output = attn_outputs[0] if output_attentions else attn_outputs
381
+ attn_output = pad_input(
382
+ attn_output, indices_q, bsz, q_len
383
+ ).reshape(bsz, q_len, h_size)
384
+ attn_weights = attn_outputs[2] if output_attentions else None
385
+
386
+ else:
387
+
388
+ # no padding tokens, more efficient
389
+
390
+ attn_outputs = flash_attn_kvpacked_func(
391
+ q, kv, dropout_p=0.0, softmax_scale=1.0/self.norm_factor, causal=(not has_layer_past), return_attn_probs=output_attentions)
392
+
393
+ attn_output = attn_outputs[0] if output_attentions else attn_outputs
394
+ attn_output = attn_output.reshape(bsz, q_len, h_size)
395
+ attn_weights = attn_outputs[2] if output_attentions else None
396
+
397
+ if self.config.pretraining_tp > 1:
398
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
399
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
400
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
401
+ else:
402
+ attn_output = self.o_proj(attn_output)
403
+
404
+ if not output_attentions:
405
+ attn_weights = None
406
+
407
+ return attn_output, attn_weights, past_key_value
408
+
409
+
410
+ class LlamaDecoderLayer(nn.Module):
411
+ def __init__(self, config: LlamaConfig):
412
+ super().__init__()
413
+ self.hidden_size = config.hidden_size
414
+ self.self_attn = LlamaAttention(config=config)
415
+ self.mlp = LlamaMLP(config)
416
+ self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
417
+ self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
418
+
419
+ def forward(
420
+ self,
421
+ hidden_states: torch.Tensor,
422
+ attention_mask: Optional[torch.Tensor] = None,
423
+ position_ids: Optional[torch.LongTensor] = None,
424
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
425
+ is_padded_inputs: Optional[bool] = False,
426
+ output_attentions: Optional[bool] = False,
427
+ use_cache: Optional[bool] = False,
428
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
429
+ """
430
+ Args:
431
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
432
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
433
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
434
+ output_attentions (`bool`, *optional*):
435
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
436
+ returned tensors for more detail.
437
+ use_cache (`bool`, *optional*):
438
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
439
+ (see `past_key_values`).
440
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
441
+ """
442
+
443
+ residual = hidden_states
444
+
445
+ hidden_states = self.input_layernorm(hidden_states)
446
+
447
+ # Self Attention
448
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
449
+ hidden_states=hidden_states,
450
+ attention_mask=attention_mask,
451
+ position_ids=position_ids,
452
+ past_key_value=past_key_value,
453
+ output_attentions=output_attentions,
454
+ use_cache=use_cache,
455
+ is_padded_inputs=is_padded_inputs,
456
+ )
457
+ hidden_states = residual + hidden_states
458
+
459
+ # Fully Connected
460
+ residual = hidden_states
461
+ hidden_states = self.post_attention_layernorm(hidden_states)
462
+ hidden_states = self.mlp(hidden_states)
463
+ hidden_states = residual + hidden_states
464
+
465
+ outputs = (hidden_states,)
466
+
467
+ if output_attentions:
468
+ outputs += (self_attn_weights,)
469
+
470
+ if use_cache:
471
+ outputs += (present_key_value,)
472
+
473
+ return outputs
474
+
475
+
476
+ LLAMA_START_DOCSTRING = r"""
477
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
478
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
479
+ etc.)
480
+
481
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
482
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
483
+ and behavior.
484
+
485
+ Parameters:
486
+ config ([`LlamaConfig`]):
487
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
488
+ load the weights associated with the model, only the configuration. Check out the
489
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
490
+ """
491
+
492
+
493
+ @add_start_docstrings(
494
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
495
+ LLAMA_START_DOCSTRING,
496
+ )
497
+ class LlamaPreTrainedModel(PreTrainedModel):
498
+ config_class = LlamaConfig
499
+ base_model_prefix = "model"
500
+ supports_gradient_checkpointing = True
501
+ _no_split_modules = ["LlamaDecoderLayer"]
502
+ _skip_keys_device_placement = "past_key_values"
503
+
504
+ def _init_weights(self, module):
505
+ std = self.config.initializer_range
506
+ if isinstance(module, nn.Linear):
507
+ module.weight.data.normal_(mean=0.0, std=std)
508
+ if module.bias is not None:
509
+ module.bias.data.zero_()
510
+ elif isinstance(module, nn.Embedding):
511
+ module.weight.data.normal_(mean=0.0, std=std)
512
+ if module.padding_idx is not None:
513
+ module.weight.data[module.padding_idx].zero_()
514
+
515
+ def _set_gradient_checkpointing(self, module, value=False):
516
+ if isinstance(module, LlamaModel):
517
+ module.gradient_checkpointing = value
518
+
519
+
520
+ LLAMA_INPUTS_DOCSTRING = r"""
521
+ Args:
522
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
523
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
524
+ it.
525
+
526
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
527
+ [`PreTrainedTokenizer.__call__`] for details.
528
+
529
+ [What are input IDs?](../glossary#input-ids)
530
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
531
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
532
+
533
+ - 1 for tokens that are **not masked**,
534
+ - 0 for tokens that are **masked**.
535
+
536
+ [What are attention masks?](../glossary#attention-mask)
537
+
538
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
539
+ [`PreTrainedTokenizer.__call__`] for details.
540
+
541
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
542
+ `past_key_values`).
543
+
544
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
545
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
546
+ information on the default strategy.
547
+
548
+ - 1 indicates the head is **not masked**,
549
+ - 0 indicates the head is **masked**.
550
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
551
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
552
+ config.n_positions - 1]`.
553
+
554
+ [What are position IDs?](../glossary#position-ids)
555
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
556
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
557
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
558
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
559
+
560
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
561
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
562
+
563
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
564
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
565
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
566
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
567
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
568
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
569
+ model's internal embedding lookup matrix.
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 (see
572
+ `past_key_values`).
573
+ output_attentions (`bool`, *optional*):
574
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
575
+ tensors for more detail.
576
+ output_hidden_states (`bool`, *optional*):
577
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
578
+ more detail.
579
+ return_dict (`bool`, *optional*):
580
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
581
+ """
582
+
583
+
584
+ @add_start_docstrings(
585
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
586
+ LLAMA_START_DOCSTRING,
587
+ )
588
+ class LlamaModel(LlamaPreTrainedModel):
589
+ """
590
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
591
+
592
+ Args:
593
+ config: LlamaConfig
594
+ """
595
+
596
+ def __init__(self, config: LlamaConfig):
597
+ super().__init__(config)
598
+ self.padding_idx = config.pad_token_id
599
+ self.vocab_size = config.vocab_size
600
+
601
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
602
+ self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
603
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
604
+
605
+ self.gradient_checkpointing = False
606
+ # Initialize weights and apply final processing
607
+ self.post_init()
608
+
609
+ def get_input_embeddings(self):
610
+ return self.embed_tokens
611
+
612
+ def set_input_embeddings(self, value):
613
+ self.embed_tokens = value
614
+
615
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
616
+ def forward(
617
+ self,
618
+ input_ids: torch.LongTensor = None,
619
+ attention_mask: Optional[torch.Tensor] = None,
620
+ position_ids: Optional[torch.LongTensor] = None,
621
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
622
+ inputs_embeds: Optional[torch.FloatTensor] = None,
623
+ use_cache: Optional[bool] = None,
624
+ output_attentions: Optional[bool] = None,
625
+ output_hidden_states: Optional[bool] = None,
626
+ return_dict: Optional[bool] = None,
627
+ is_padded_inputs: Optional[bool] = False,
628
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
629
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
630
+ output_hidden_states = (
631
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
632
+ )
633
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
634
+
635
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
636
+
637
+ # retrieve input_ids and inputs_embeds
638
+ if input_ids is not None and inputs_embeds is not None:
639
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
640
+ elif input_ids is not None:
641
+ batch_size, seq_length = input_ids.shape
642
+ elif inputs_embeds is not None:
643
+ batch_size, seq_length, _ = inputs_embeds.shape
644
+ else:
645
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
646
+
647
+ seq_length_with_past = seq_length
648
+ past_key_values_length = 0
649
+
650
+ if past_key_values is not None:
651
+ past_key_values_length = past_key_values[0][0].shape[2]
652
+ seq_length_with_past = seq_length_with_past + past_key_values_length
653
+
654
+ position_ids = None
655
+
656
+ if inputs_embeds is None:
657
+ inputs_embeds = self.embed_tokens(input_ids)
658
+
659
+ hidden_states = inputs_embeds
660
+
661
+ if self.gradient_checkpointing and self.training:
662
+ if use_cache:
663
+ logger.warning_once(
664
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
665
+ )
666
+ use_cache = False
667
+
668
+ # decoder layers
669
+ all_hidden_states = () if output_hidden_states else None
670
+ all_self_attns = () if output_attentions else None
671
+ next_decoder_cache = () if use_cache else None
672
+
673
+ for idx, decoder_layer in enumerate(self.layers):
674
+ if output_hidden_states:
675
+ all_hidden_states += (hidden_states,)
676
+
677
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
678
+
679
+ if self.gradient_checkpointing and self.training:
680
+
681
+ def create_custom_forward(module):
682
+ def custom_forward(*inputs):
683
+ # None for past_key_value
684
+ return module(*inputs, output_attentions, None)
685
+
686
+ return custom_forward
687
+
688
+ layer_outputs = torch.utils.checkpoint.checkpoint(
689
+ create_custom_forward(decoder_layer),
690
+ hidden_states,
691
+ attention_mask,
692
+ position_ids,
693
+ None,
694
+ is_padded_inputs
695
+ )
696
+ else:
697
+ layer_outputs = decoder_layer(
698
+ hidden_states,
699
+ attention_mask=attention_mask,
700
+ position_ids=position_ids,
701
+ past_key_value=past_key_value,
702
+ output_attentions=output_attentions,
703
+ use_cache=use_cache,
704
+ is_padded_inputs=is_padded_inputs,
705
+ )
706
+
707
+ hidden_states = layer_outputs[0]
708
+
709
+ if use_cache:
710
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
711
+
712
+ if output_attentions:
713
+ all_self_attns += (layer_outputs[1],)
714
+
715
+ hidden_states = self.norm(hidden_states)
716
+
717
+ # add hidden states from the last decoder layer
718
+ if output_hidden_states:
719
+ all_hidden_states += (hidden_states,)
720
+
721
+ next_cache = next_decoder_cache if use_cache else None
722
+ if not return_dict:
723
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
724
+ return BaseModelOutputWithPast(
725
+ last_hidden_state=hidden_states,
726
+ past_key_values=next_cache,
727
+ hidden_states=all_hidden_states,
728
+ attentions=all_self_attns,
729
+ )
730
+
731
+
732
+ class LlamaForCausalLM(LlamaPreTrainedModel):
733
+ _tied_weights_keys = ["lm_head.weight"]
734
+
735
+ def __init__(self, config):
736
+ super().__init__(config)
737
+ self.model = LlamaModel(config)
738
+ self.vocab_size = config.vocab_size
739
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
740
+
741
+ # Initialize weights and apply final processing
742
+ self.post_init()
743
+
744
+ def get_input_embeddings(self):
745
+ return self.model.embed_tokens
746
+
747
+ def set_input_embeddings(self, value):
748
+ self.model.embed_tokens = value
749
+
750
+ def get_output_embeddings(self):
751
+ return self.lm_head
752
+
753
+ def set_output_embeddings(self, new_embeddings):
754
+ self.lm_head = new_embeddings
755
+
756
+ def set_decoder(self, decoder):
757
+ self.model = decoder
758
+
759
+ def get_decoder(self):
760
+ return self.model
761
+
762
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
763
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
764
+ def forward(
765
+ self,
766
+ input_ids: torch.LongTensor = None,
767
+ attention_mask: Optional[torch.Tensor] = None,
768
+ position_ids: Optional[torch.LongTensor] = None,
769
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
770
+ inputs_embeds: Optional[torch.FloatTensor] = None,
771
+ labels: Optional[torch.LongTensor] = None,
772
+ use_cache: Optional[bool] = None,
773
+ output_attentions: Optional[bool] = None,
774
+ output_hidden_states: Optional[bool] = None,
775
+ return_dict: Optional[bool] = None,
776
+ is_padded_inputs: Optional[bool] = None,
777
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
778
+ r"""
779
+ Args:
780
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
781
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
782
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
783
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
784
+
785
+ Returns:
786
+
787
+ Example:
788
+
789
+ ```python
790
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
791
+
792
+ >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
793
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
794
+
795
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
796
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
797
+
798
+ >>> # Generate
799
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
800
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
801
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
802
+ ```"""
803
+
804
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
805
+ output_hidden_states = (
806
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
807
+ )
808
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
809
+
810
+ is_padded_inputs = ((attention_mask is not None) and (not attention_mask.all().item()))
811
+
812
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
813
+ outputs = self.model(
814
+ input_ids=input_ids,
815
+ attention_mask=attention_mask,
816
+ position_ids=position_ids,
817
+ past_key_values=past_key_values,
818
+ inputs_embeds=inputs_embeds,
819
+ use_cache=use_cache,
820
+ output_attentions=output_attentions,
821
+ output_hidden_states=output_hidden_states,
822
+ return_dict=return_dict,
823
+ is_padded_inputs=is_padded_inputs,
824
+ )
825
+
826
+ hidden_states = outputs[0]
827
+ if self.config.pretraining_tp > 1:
828
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
829
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
830
+ logits = torch.cat(logits, dim=-1)
831
+ else:
832
+ logits = self.lm_head(hidden_states)
833
+ logits = logits.float()
834
+
835
+ loss = None
836
+ if labels is not None:
837
+ # Shift so that tokens < n predict n
838
+ shift_logits = logits[..., :-1, :].contiguous()
839
+ shift_labels = labels[..., 1:].contiguous()
840
+ # Flatten the tokens
841
+ loss_fct = CrossEntropyLoss()
842
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
843
+ shift_labels = shift_labels.view(-1)
844
+ # Enable model parallelism
845
+ shift_labels = shift_labels.to(shift_logits.device)
846
+ loss = loss_fct(shift_logits, shift_labels)
847
+
848
+ if not return_dict:
849
+ output = (logits,) + outputs[1:]
850
+ return (loss,) + output if loss is not None else output
851
+
852
+ return CausalLMOutputWithPast(
853
+ loss=loss,
854
+ logits=logits,
855
+ past_key_values=outputs.past_key_values,
856
+ hidden_states=outputs.hidden_states,
857
+ attentions=outputs.attentions,
858
+ )
859
+
860
+ def prepare_inputs_for_generation(
861
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
862
+ ):
863
+ if past_key_values:
864
+ input_ids = input_ids[:, -1:]
865
+
866
+ position_ids = kwargs.get("position_ids", None)
867
+
868
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
869
+ if inputs_embeds is not None and past_key_values is None:
870
+ model_inputs = {"inputs_embeds": inputs_embeds}
871
+ else:
872
+ model_inputs = {"input_ids": input_ids}
873
+
874
+ model_inputs.update(
875
+ {
876
+ "position_ids": position_ids,
877
+ "past_key_values": past_key_values,
878
+ "use_cache": kwargs.get("use_cache"),
879
+ "attention_mask": attention_mask,
880
+ "is_padded_inputs": ((attention_mask is not None) and (not attention_mask.all().item()))
881
+ }
882
+ )
883
+ return model_inputs
884
+
885
+ @staticmethod
886
+ def _reorder_cache(past_key_values, beam_idx):
887
+ reordered_past = ()
888
+ for layer_past in past_key_values:
889
+ reordered_past += (
890
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
891
+ )
892
+ return reordered_past
893
+
894
+
895
+ @add_start_docstrings(
896
+ """
897
+ The LLaMa Model transformer with a sequence classification head on top (linear layer).
898
+
899
+ [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
900
+ (e.g. GPT-2) do.
901
+
902
+ Since it does classification on the last token, it requires to know the position of the last token. If a
903
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
904
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
905
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
906
+ each row of the batch).
907
+ """,
908
+ LLAMA_START_DOCSTRING,
909
+ )
910
+ class LlamaForSequenceClassification(LlamaPreTrainedModel):
911
+ def __init__(self, config):
912
+ super().__init__(config)
913
+ self.num_labels = config.num_labels
914
+ self.model = LlamaModel(config)
915
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
916
+
917
+ # Initialize weights and apply final processing
918
+ self.post_init()
919
+
920
+ def get_input_embeddings(self):
921
+ return self.model.embed_tokens
922
+
923
+ def set_input_embeddings(self, value):
924
+ self.model.embed_tokens = value
925
+
926
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
927
+ def forward(
928
+ self,
929
+ input_ids: torch.LongTensor = None,
930
+ attention_mask: Optional[torch.Tensor] = None,
931
+ position_ids: Optional[torch.LongTensor] = None,
932
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
933
+ inputs_embeds: Optional[torch.FloatTensor] = None,
934
+ labels: Optional[torch.LongTensor] = None,
935
+ use_cache: Optional[bool] = None,
936
+ output_attentions: Optional[bool] = None,
937
+ output_hidden_states: Optional[bool] = None,
938
+ return_dict: Optional[bool] = None,
939
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
940
+ r"""
941
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
942
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
943
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
944
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
945
+ """
946
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
947
+
948
+ transformer_outputs = self.model(
949
+ input_ids,
950
+ attention_mask=attention_mask,
951
+ position_ids=position_ids,
952
+ past_key_values=past_key_values,
953
+ inputs_embeds=inputs_embeds,
954
+ use_cache=use_cache,
955
+ output_attentions=output_attentions,
956
+ output_hidden_states=output_hidden_states,
957
+ return_dict=return_dict,
958
+ )
959
+ hidden_states = transformer_outputs[0]
960
+ logits = self.score(hidden_states)
961
+
962
+ if input_ids is not None:
963
+ batch_size = input_ids.shape[0]
964
+ else:
965
+ batch_size = inputs_embeds.shape[0]
966
+
967
+ if self.config.pad_token_id is None and batch_size != 1:
968
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
969
+ if self.config.pad_token_id is None:
970
+ sequence_lengths = -1
971
+ else:
972
+ if input_ids is not None:
973
+ sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
974
+ else:
975
+ sequence_lengths = -1
976
+
977
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
978
+
979
+ loss = None
980
+ if labels is not None:
981
+ labels = labels.to(logits.device)
982
+ if self.config.problem_type is None:
983
+ if self.num_labels == 1:
984
+ self.config.problem_type = "regression"
985
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
986
+ self.config.problem_type = "single_label_classification"
987
+ else:
988
+ self.config.problem_type = "multi_label_classification"
989
+
990
+ if self.config.problem_type == "regression":
991
+ loss_fct = MSELoss()
992
+ if self.num_labels == 1:
993
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
994
+ else:
995
+ loss = loss_fct(pooled_logits, labels)
996
+ elif self.config.problem_type == "single_label_classification":
997
+ loss_fct = CrossEntropyLoss()
998
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
999
+ elif self.config.problem_type == "multi_label_classification":
1000
+ loss_fct = BCEWithLogitsLoss()
1001
+ loss = loss_fct(pooled_logits, labels)
1002
+ if not return_dict:
1003
+ output = (pooled_logits,) + transformer_outputs[1:]
1004
+ return ((loss,) + output) if loss is not None else output
1005
+
1006
+ return SequenceClassifierOutputWithPast(
1007
+ loss=loss,
1008
+ logits=pooled_logits,
1009
+ past_key_values=transformer_outputs.past_key_values,
1010
+ hidden_states=transformer_outputs.hidden_states,
1011
+ attentions=transformer_outputs.attentions,
1012
+ )
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