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  1. configuration_mistral.py +181 -0
  2. modeling_llama_yarn.py +1410 -0
configuration_mistral.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """ Mistral model configuration"""
16
+
17
+ from transformers.configuration_utils import PretrainedConfig
18
+ from transformers.utils import logging
19
+
20
+
21
+ logger = logging.get_logger(__name__)
22
+
23
+ MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
24
+ "mistralai/Mistral-7B-v0.1": "https://huggingface.co/mistralai/Mistral-7B-v0.1/resolve/main/config.json",
25
+ "mistralai/Mistral-7B-Instruct-v0.1": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/resolve/main/config.json",
26
+ }
27
+
28
+
29
+ class MistralConfig(PretrainedConfig):
30
+ r"""
31
+ This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
32
+ Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
33
+ with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
34
+
35
+ [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
36
+ [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
37
+
38
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
39
+ documentation from [`PretrainedConfig`] for more information.
40
+
41
+
42
+ Args:
43
+ vocab_size (`int`, *optional*, defaults to 32000):
44
+ Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
45
+ `inputs_ids` passed when calling [`MistralModel`]
46
+ hidden_size (`int`, *optional*, defaults to 4096):
47
+ Dimension of the hidden representations.
48
+ intermediate_size (`int`, *optional*, defaults to 14336):
49
+ Dimension of the MLP representations.
50
+ num_hidden_layers (`int`, *optional*, defaults to 32):
51
+ Number of hidden layers in the Transformer encoder.
52
+ num_attention_heads (`int`, *optional*, defaults to 32):
53
+ Number of attention heads for each attention layer in the Transformer encoder.
54
+ num_key_value_heads (`int`, *optional*, defaults to 8):
55
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
56
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
57
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
58
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
59
+ by meanpooling all the original heads within that group. For more details checkout [this
60
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
61
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
62
+ The non-linear activation function (function or string) in the decoder.
63
+ max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
64
+ The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
65
+ allows sequence of up to 4096*32 tokens.
66
+ initializer_range (`float`, *optional*, defaults to 0.02):
67
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
68
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
69
+ The epsilon used by the rms normalization layers.
70
+ use_cache (`bool`, *optional*, defaults to `True`):
71
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
72
+ relevant if `config.is_decoder=True`.
73
+ pad_token_id (`int`, *optional*):
74
+ The id of the padding token.
75
+ bos_token_id (`int`, *optional*, defaults to 1):
76
+ The id of the "beginning-of-sequence" token.
77
+ eos_token_id (`int`, *optional*, defaults to 2):
78
+ The id of the "end-of-sequence" token.
79
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
80
+ Whether the model's input and output word embeddings should be tied.
81
+ rope_scaling (`Dict`, *optional*):
82
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
83
+ strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
84
+ is `{"type": strategy name, "factor": scaling factor}`.
85
+ rope_theta (`float`, *optional*, defaults to 10000.0):
86
+ The base period of the RoPE embeddings.
87
+ sliding_window (`int`, *optional*, defaults to 4096):
88
+ Sliding window attention window size. If not specified, will default to `4096`.
89
+
90
+
91
+ ```python
92
+ >>> from transformers import MistralModel, MistralConfig
93
+
94
+ >>> # Initializing a Mistral 7B style configuration
95
+ >>> configuration = MistralConfig()
96
+
97
+ >>> # Initializing a model from the Mistral 7B style configuration
98
+ >>> model = MistralModel(configuration)
99
+
100
+ >>> # Accessing the model configuration
101
+ >>> configuration = model.config
102
+ ```"""
103
+
104
+ model_type = "mistral"
105
+ keys_to_ignore_at_inference = ["past_key_values"]
106
+
107
+ def __init__(
108
+ self,
109
+ vocab_size=32000,
110
+ hidden_size=4096,
111
+ intermediate_size=14336,
112
+ num_hidden_layers=32,
113
+ num_attention_heads=32,
114
+ num_key_value_heads=8,
115
+ hidden_act="silu",
116
+ max_position_embeddings=4096 * 32,
117
+ initializer_range=0.02,
118
+ rms_norm_eps=1e-6,
119
+ use_cache=True,
120
+ pad_token_id=None,
121
+ bos_token_id=1,
122
+ eos_token_id=2,
123
+ tie_word_embeddings=False,
124
+ rope_scaling=None,
125
+ rope_theta=10000.0,
126
+ sliding_window=4096,
127
+ **kwargs,
128
+ ):
129
+ self.vocab_size = vocab_size
130
+ self.max_position_embeddings = max_position_embeddings
131
+ self.hidden_size = hidden_size
132
+ self.intermediate_size = intermediate_size
133
+ self.num_hidden_layers = num_hidden_layers
134
+ self.num_attention_heads = num_attention_heads
135
+ self.sliding_window = sliding_window
136
+
137
+ # for backward compatibility
138
+ if num_key_value_heads is None:
139
+ num_key_value_heads = num_attention_heads
140
+
141
+ self.num_key_value_heads = num_key_value_heads
142
+ self.hidden_act = hidden_act
143
+ self.initializer_range = initializer_range
144
+ self.rms_norm_eps = rms_norm_eps
145
+ self.use_cache = use_cache
146
+ self.rope_scaling = rope_scaling
147
+ self._rope_scaling_validation()
148
+ self.rope_theta = rope_theta
149
+
150
+ super().__init__(
151
+ pad_token_id=pad_token_id,
152
+ bos_token_id=bos_token_id,
153
+ eos_token_id=eos_token_id,
154
+ tie_word_embeddings=tie_word_embeddings,
155
+ **kwargs,
156
+ )
157
+
158
+ def _rope_scaling_validation(self):
159
+ """
160
+ Validate the `rope_scaling` configuration.
161
+ """
162
+ if self.rope_scaling is None:
163
+ return
164
+
165
+ if not isinstance(self.rope_scaling, dict):
166
+ raise ValueError(
167
+ "`rope_scaling` must be a dictionary, "
168
+ f"got {self.rope_scaling}"
169
+ )
170
+ rope_scaling_type = self.rope_scaling.get("type", None)
171
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
172
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "yarn", "dynamic-yarn"]:
173
+ raise ValueError(
174
+ f"`rope_scaling`'s name field must be one of ['linear', 'dynamic', 'yarn', 'dynamic-yarn'], got {rope_scaling_type}"
175
+ )
176
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
177
+ raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
178
+ if rope_scaling_type == "yarn" or rope_scaling_type == "dynamic-yarn":
179
+ original_max_position_embeddings = self.rope_scaling.get("original_max_position_embeddings", None)
180
+ if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
181
+ raise ValueError(f"`rope_scaling.original_max_position_embeddings` must be set to an int when using yarn, and dynamic-yarn")
modeling_llama_yarn.py ADDED
@@ -0,0 +1,1410 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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.pytorch_utils import ALL_LAYERNORM_LAYERS
34
+ from transformers.utils import (
35
+ add_start_docstrings,
36
+ add_start_docstrings_to_model_forward,
37
+ is_flash_attn_2_available,
38
+ logging,
39
+ replace_return_docstrings,
40
+ )
41
+ from .configuration_llama import LlamaConfig
42
+
43
+
44
+ if is_flash_attn_2_available():
45
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
46
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
47
+
48
+
49
+ logger = logging.get_logger(__name__)
50
+
51
+ _CONFIG_FOR_DOC = "LlamaConfig"
52
+
53
+
54
+ def _get_unpad_data(padding_mask):
55
+ seqlens_in_batch = padding_mask.sum(dim=-1, dtype=torch.int32)
56
+ indices = torch.nonzero(padding_mask.flatten(), as_tuple=False).flatten()
57
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
58
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
59
+ return (
60
+ indices,
61
+ cu_seqlens,
62
+ max_seqlen_in_batch,
63
+ )
64
+
65
+
66
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
67
+ def _make_causal_mask(
68
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
69
+ ):
70
+ """
71
+ Make causal mask used for bi-directional self-attention.
72
+ """
73
+ bsz, tgt_len = input_ids_shape
74
+ mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
75
+ mask_cond = torch.arange(mask.size(-1), device=device)
76
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
77
+ mask = mask.to(dtype)
78
+
79
+ if past_key_values_length > 0:
80
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
81
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
82
+
83
+
84
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
85
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
86
+ """
87
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
88
+ """
89
+ bsz, src_len = mask.size()
90
+ tgt_len = tgt_len if tgt_len is not None else src_len
91
+
92
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
93
+
94
+ inverted_mask = 1.0 - expanded_mask
95
+
96
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
97
+
98
+ # Inverse dim formula to find dim based on number of rotations
99
+ def _yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
100
+ return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base))
101
+
102
+ # Find dim range bounds based on rotations
103
+ def _yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
104
+ low = math.floor(_yarn_find_correction_dim(
105
+ low_rot, dim, base, max_position_embeddings))
106
+ high = math.ceil(_yarn_find_correction_dim(
107
+ high_rot, dim, base, max_position_embeddings))
108
+ return max(low, 0), min(high, dim-1) # Clamp values just in case
109
+
110
+ def _yarn_linear_ramp_mask(min, max, dim):
111
+ if min == max:
112
+ max += 0.001 # Prevent singularity
113
+
114
+ linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
115
+ ramp_func = torch.clamp(linear_func, 0, 1)
116
+ return ramp_func
117
+
118
+ def _yarn_get_mscale(scale=1):
119
+ if scale <= 1:
120
+ return 1.0
121
+ return 0.1 * math.log(scale) + 1.0
122
+
123
+ class LlamaRMSNorm(nn.Module):
124
+ def __init__(self, hidden_size, eps=1e-6):
125
+ """
126
+ LlamaRMSNorm is equivalent to T5LayerNorm
127
+ """
128
+ super().__init__()
129
+ self.weight = nn.Parameter(torch.ones(hidden_size))
130
+ self.variance_epsilon = eps
131
+
132
+ def forward(self, hidden_states):
133
+ input_dtype = hidden_states.dtype
134
+ hidden_states = hidden_states.to(torch.float32)
135
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
136
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
137
+ return self.weight * hidden_states.to(input_dtype)
138
+
139
+
140
+ ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
141
+
142
+
143
+ class LlamaRotaryEmbedding(nn.Module):
144
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
145
+ super().__init__()
146
+
147
+ self.dim = dim
148
+ self.max_position_embeddings = max_position_embeddings
149
+ self.base = base
150
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
151
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
152
+
153
+ # Build here to make `torch.jit.trace` work.
154
+ self._set_cos_sin_cache(
155
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
156
+ )
157
+
158
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
159
+ self.max_seq_len_cached = seq_len
160
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
161
+
162
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
163
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
164
+ emb = torch.cat((freqs, freqs), dim=-1)
165
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
166
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
167
+
168
+ def forward(self, x, seq_len=None):
169
+ # x: [bs, num_attention_heads, seq_len, head_size]
170
+ if seq_len > self.max_seq_len_cached:
171
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
172
+
173
+ return (
174
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
175
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
176
+ )
177
+
178
+
179
+ class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
180
+ """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
181
+
182
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
183
+ self.scaling_factor = scaling_factor
184
+ super().__init__(dim, max_position_embeddings, base, device)
185
+
186
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
187
+ self.max_seq_len_cached = seq_len
188
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
189
+ t = t / self.scaling_factor
190
+
191
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
192
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
193
+ emb = torch.cat((freqs, freqs), dim=-1)
194
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
195
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
196
+
197
+
198
+ class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
199
+ """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
200
+
201
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
202
+ self.scaling_factor = scaling_factor
203
+ super().__init__(dim, max_position_embeddings, base, device)
204
+
205
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
206
+ self.max_seq_len_cached = seq_len
207
+
208
+ if seq_len > self.max_position_embeddings:
209
+ base = self.base * (
210
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
211
+ ) ** (self.dim / (self.dim - 2))
212
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
213
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
214
+
215
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
216
+
217
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
218
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
219
+ emb = torch.cat((freqs, freqs), dim=-1)
220
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
221
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
222
+
223
+
224
+ class LlamaYaRNScaledRotaryEmbedding(torch.nn.Module):
225
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, scale=1, original_max_position_embeddings=2048, extrapolation_factor=1, attn_factor=1, beta_fast=32, beta_slow=1, finetuned=False, device=None):
226
+ super().__init__()
227
+
228
+ self.dim = dim
229
+ self.max_position_embeddings = max_position_embeddings
230
+ self.base = base
231
+ self.scale = scale
232
+ self.original_max_position_embeddings = original_max_position_embeddings
233
+ self.extrapolation_factor = extrapolation_factor
234
+ self.attn_factor = attn_factor
235
+ self.beta_fast = beta_fast
236
+ self.beta_slow = beta_slow
237
+
238
+ self.yarn(device)
239
+
240
+ # Build here to make `torch.jit.trace` work.
241
+ self.max_seq_len_cached = max_position_embeddings
242
+ t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
243
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
244
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
245
+ emb = torch.cat((freqs, freqs), dim=-1)
246
+ dtype = torch.get_default_dtype()
247
+
248
+ self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(dtype), persistent=False)
249
+ self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(dtype), persistent=False)
250
+
251
+ def forward(self, x, seq_len=None):
252
+ # x: [bs, num_attention_heads, seq_len, head_size]
253
+ # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
254
+ if seq_len > self.max_seq_len_cached:
255
+ self.max_seq_len_cached = seq_len
256
+
257
+ t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
258
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
259
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
260
+ emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
261
+
262
+ self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(x.dtype), persistent=False)
263
+ self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(x.dtype), persistent=False)
264
+ return (
265
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
266
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
267
+ )
268
+
269
+ def yarn(self, device):
270
+ pos_freqs = self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
271
+ inv_freq_extrapolation = 1.0 / pos_freqs
272
+ inv_freq_interpolation = 1.0 / (self.scale * pos_freqs)
273
+
274
+ low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings)
275
+ inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation
276
+ inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
277
+
278
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
279
+ self.mscale = float(_yarn_get_mscale(self.scale) * self.attn_factor) # Get n-d magnitude scaling corrected for interpolation
280
+
281
+
282
+ class LlamaDynamicYaRNScaledRotaryEmbedding(torch.nn.Module):
283
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, original_max_position_embeddings=2048, extrapolation_factor=1, attn_factor=1, beta_fast=32, beta_slow=1, finetuned=False, device=None):
284
+ super().__init__()
285
+
286
+ self.dim = dim
287
+ self.max_position_embeddings = max_position_embeddings
288
+ self.base = base
289
+ self.original_max_position_embeddings = original_max_position_embeddings
290
+ self.extrapolation_factor = extrapolation_factor
291
+ self.attn_factor = attn_factor
292
+ self.beta_fast = beta_fast
293
+ self.beta_slow = beta_slow
294
+
295
+ if finetuned:
296
+ self.yarn(self.max_position_embeddings / self.original_max_position_embeddings, device)
297
+ else:
298
+ inv_freq = 1.0 / \
299
+ (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
300
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
301
+ self.mscale = 1
302
+
303
+ # Build here to make `torch.jit.trace` work.
304
+ self.max_seq_len_cached = max_position_embeddings
305
+ t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
306
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
307
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
308
+ emb = torch.cat((freqs, freqs), dim=-1)
309
+ dtype = torch.get_default_dtype()
310
+
311
+ self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(dtype), persistent=False)
312
+ self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(dtype), persistent=False)
313
+
314
+ def forward(self, x, seq_len=None):
315
+ # x: [bs, num_attention_heads, seq_len, head_size]
316
+ # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
317
+ if seq_len > self.max_seq_len_cached:
318
+ self.max_seq_len_cached = seq_len
319
+
320
+ self.yarn(seq_len / self.max_position_embeddings, x.device)
321
+
322
+ t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
323
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
324
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
325
+ emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
326
+
327
+ self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(x.dtype), persistent=False)
328
+ self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(x.dtype), persistent=False)
329
+ return (
330
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
331
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
332
+ )
333
+
334
+ def yarn(self, scale, device):
335
+ pos_freqs = self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
336
+ inv_freq_extrapolation = 1.0 / pos_freqs
337
+ inv_freq_interpolation = 1.0 / (scale * pos_freqs)
338
+
339
+ low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings)
340
+ inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation
341
+ inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
342
+
343
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
344
+ self.mscale = float(_yarn_get_mscale(scale) * self.attn_factor) # Get n-d magnitude scaling corrected for interpolation
345
+
346
+
347
+ def rotate_half(x):
348
+ """Rotates half the hidden dims of the input."""
349
+ x1 = x[..., : x.shape[-1] // 2]
350
+ x2 = x[..., x.shape[-1] // 2 :]
351
+ return torch.cat((-x2, x1), dim=-1)
352
+
353
+
354
+ # Copied from transformers.models.gpt_neox.modeling_gpt_neox.apply_rotary_pos_emb
355
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
356
+ cos = cos[position_ids].unsqueeze(1) # [seq_len, dim] -> [batch_size, 1, seq_len, head_dim]
357
+ sin = sin[position_ids].unsqueeze(1)
358
+ q_embed = (q * cos) + (rotate_half(q) * sin)
359
+ k_embed = (k * cos) + (rotate_half(k) * sin)
360
+ return q_embed, k_embed
361
+
362
+
363
+ class LlamaMLP(nn.Module):
364
+ def __init__(self, config):
365
+ super().__init__()
366
+ self.config = config
367
+ self.hidden_size = config.hidden_size
368
+ self.intermediate_size = config.intermediate_size
369
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
370
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
371
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
372
+ self.act_fn = ACT2FN[config.hidden_act]
373
+
374
+ def forward(self, x):
375
+ if self.config.pretraining_tp > 1:
376
+ slice = self.intermediate_size // self.config.pretraining_tp
377
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
378
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
379
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
380
+
381
+ gate_proj = torch.cat(
382
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
383
+ )
384
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
385
+
386
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
387
+ down_proj = [
388
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
389
+ ]
390
+ down_proj = sum(down_proj)
391
+ else:
392
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
393
+
394
+ return down_proj
395
+
396
+
397
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
398
+ """
399
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
400
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
401
+ """
402
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
403
+ if n_rep == 1:
404
+ return hidden_states
405
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
406
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
407
+
408
+
409
+ class LlamaAttention(nn.Module):
410
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
411
+
412
+ def __init__(self, config: LlamaConfig):
413
+ super().__init__()
414
+ self.config = config
415
+ self.hidden_size = config.hidden_size
416
+ self.num_heads = config.num_attention_heads
417
+ self.head_dim = self.hidden_size // self.num_heads
418
+ self.num_key_value_heads = config.num_key_value_heads
419
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
420
+ self.max_position_embeddings = config.max_position_embeddings
421
+ self.rope_theta = config.rope_theta
422
+
423
+ if (self.head_dim * self.num_heads) != self.hidden_size:
424
+ raise ValueError(
425
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
426
+ f" and `num_heads`: {self.num_heads})."
427
+ )
428
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
429
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
430
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
431
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
432
+ self._init_rope()
433
+
434
+ def _init_rope(self):
435
+ if self.config.rope_scaling is None:
436
+ self.rotary_emb = LlamaRotaryEmbedding(
437
+ self.head_dim,
438
+ max_position_embeddings=self.max_position_embeddings,
439
+ base=self.rope_theta,
440
+ )
441
+ else:
442
+ scaling_type = self.config.rope_scaling["type"]
443
+ scaling_factor = self.config.rope_scaling["factor"]
444
+ if scaling_type == "linear":
445
+ self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
446
+ self.head_dim,
447
+ max_position_embeddings=self.max_position_embeddings,
448
+ scaling_factor=scaling_factor,
449
+ base=self.rope_theta,
450
+ )
451
+ elif scaling_type == "dynamic":
452
+ self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
453
+ self.head_dim,
454
+ max_position_embeddings=self.max_position_embeddings,
455
+ scaling_factor=scaling_factor,
456
+ base=self.rope_theta,
457
+ )
458
+ elif scaling_type == "yarn":
459
+ original_max_position_embeddings = self.config.rope_scaling["original_max_position_embeddings"]
460
+ self.rotary_emb = LlamaYaRNScaledRotaryEmbedding(
461
+ self.head_dim,
462
+ max_position_embeddings=self.max_position_embeddings,
463
+ scale=scaling_factor,
464
+ original_max_position_embeddings=original_max_position_embeddings
465
+ )
466
+ elif scaling_type == "dynamic-yarn":
467
+ original_max_position_embeddings = self.config.rope_scaling["original_max_position_embeddings"]
468
+ self.rotary_emb = LlamaDynamicYaRNScaledRotaryEmbedding(
469
+ self.head_dim,
470
+ max_position_embeddings=self.max_position_embeddings,
471
+ original_max_position_embeddings=original_max_position_embeddings
472
+ )
473
+ else:
474
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
475
+
476
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
477
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
478
+
479
+ def forward(
480
+ self,
481
+ hidden_states: torch.Tensor,
482
+ attention_mask: Optional[torch.Tensor] = None,
483
+ position_ids: Optional[torch.LongTensor] = None,
484
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
485
+ output_attentions: bool = False,
486
+ use_cache: bool = False,
487
+ padding_mask: Optional[torch.LongTensor] = None,
488
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
489
+ bsz, q_len, _ = hidden_states.size()
490
+
491
+ if self.config.pretraining_tp > 1:
492
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
493
+ query_slices = self.q_proj.weight.split(
494
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
495
+ )
496
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
497
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
498
+
499
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
500
+ query_states = torch.cat(query_states, dim=-1)
501
+
502
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
503
+ key_states = torch.cat(key_states, dim=-1)
504
+
505
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
506
+ value_states = torch.cat(value_states, dim=-1)
507
+
508
+ else:
509
+ query_states = self.q_proj(hidden_states)
510
+ key_states = self.k_proj(hidden_states)
511
+ value_states = self.v_proj(hidden_states)
512
+
513
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
514
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
515
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
516
+
517
+ kv_seq_len = key_states.shape[-2]
518
+ if past_key_value is not None:
519
+ kv_seq_len += past_key_value[0].shape[-2]
520
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
521
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
522
+
523
+ if past_key_value is not None:
524
+ # reuse k, v, self_attention
525
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
526
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
527
+
528
+ past_key_value = (key_states, value_states) if use_cache else None
529
+
530
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
531
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
532
+
533
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
534
+
535
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
536
+ raise ValueError(
537
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
538
+ f" {attn_weights.size()}"
539
+ )
540
+
541
+ if attention_mask is not None:
542
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
543
+ raise ValueError(
544
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
545
+ )
546
+ attn_weights = attn_weights + attention_mask
547
+
548
+ # upcast attention to fp32
549
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
550
+ attn_output = torch.matmul(attn_weights, value_states)
551
+
552
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
553
+ raise ValueError(
554
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
555
+ f" {attn_output.size()}"
556
+ )
557
+
558
+ attn_output = attn_output.transpose(1, 2).contiguous()
559
+
560
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
561
+
562
+ if self.config.pretraining_tp > 1:
563
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
564
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
565
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
566
+ else:
567
+ attn_output = self.o_proj(attn_output)
568
+
569
+ if not output_attentions:
570
+ attn_weights = None
571
+
572
+ return attn_output, attn_weights, past_key_value
573
+
574
+
575
+ class LlamaFlashAttention2(LlamaAttention):
576
+ """
577
+ Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
578
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
579
+ flash attention and deal with padding tokens in case the input contains any of them.
580
+ """
581
+
582
+ def forward(
583
+ self,
584
+ hidden_states: torch.Tensor,
585
+ attention_mask: Optional[torch.Tensor] = None,
586
+ position_ids: Optional[torch.LongTensor] = None,
587
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
588
+ output_attentions: bool = False,
589
+ use_cache: bool = False,
590
+ padding_mask: Optional[torch.LongTensor] = None,
591
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
592
+ # LlamaFlashAttention2 attention does not support output_attentions
593
+ output_attentions = False
594
+
595
+ bsz, q_len, _ = hidden_states.size()
596
+
597
+ query_states = self.q_proj(hidden_states)
598
+ key_states = self.k_proj(hidden_states)
599
+ value_states = self.v_proj(hidden_states)
600
+
601
+ # Flash attention requires the input to have the shape
602
+ # batch_size x seq_length x head_dime x hidden_dim
603
+ # therefore we just need to keep the original shape
604
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
605
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
606
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
607
+
608
+ kv_seq_len = key_states.shape[-2]
609
+ if past_key_value is not None:
610
+ kv_seq_len += past_key_value[0].shape[-2]
611
+
612
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
613
+
614
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
615
+
616
+ if past_key_value is not None:
617
+ # reuse k, v, self_attention
618
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
619
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
620
+
621
+ past_key_value = (key_states, value_states) if use_cache else None
622
+
623
+ query_states = query_states.transpose(1, 2)
624
+ key_states = key_states.transpose(1, 2)
625
+ value_states = value_states.transpose(1, 2)
626
+
627
+ # TODO: llama does not have dropout in the config??
628
+ # It is recommended to use dropout with FA according to the docs
629
+ # when training.
630
+ dropout_rate = 0.0 # if not self.training else self.attn_dropout
631
+
632
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
633
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
634
+ # cast them back in float16 just to be sure everything works as expected.
635
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
636
+ # in fp32. (LlamaRMSNorm handles it correctly)
637
+ input_dtype = query_states.dtype
638
+ if input_dtype == torch.float32:
639
+ logger.warning_once(
640
+ "The input hidden states seems to be silently casted in float32, this might be related to"
641
+ " the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
642
+ " float16."
643
+ )
644
+
645
+ query_states = query_states.to(torch.float16)
646
+ key_states = key_states.to(torch.float16)
647
+ value_states = value_states.to(torch.float16)
648
+
649
+ attn_output = self._flash_attention_forward(
650
+ query_states, key_states, value_states, padding_mask, q_len, dropout=dropout_rate
651
+ )
652
+
653
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
654
+ attn_output = self.o_proj(attn_output)
655
+
656
+ if not output_attentions:
657
+ attn_weights = None
658
+
659
+ return attn_output, attn_weights, past_key_value
660
+
661
+ def _flash_attention_forward(
662
+ self, query_states, key_states, value_states, padding_mask, query_length, dropout=0.0, softmax_scale=None
663
+ ):
664
+ """
665
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
666
+ first unpad the input, then computes the attention scores and pad the final attention scores.
667
+
668
+ Args:
669
+ query_states (`torch.Tensor`):
670
+ Input query states to be passed to Flash Attention API
671
+ key_states (`torch.Tensor`):
672
+ Input key states to be passed to Flash Attention API
673
+ value_states (`torch.Tensor`):
674
+ Input value states to be passed to Flash Attention API
675
+ padding_mask (`torch.Tensor`):
676
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
677
+ position of padding tokens and 1 for the position of non-padding tokens.
678
+ dropout (`int`, *optional*):
679
+ Attention dropout
680
+ softmax_scale (`float`, *optional*):
681
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
682
+ """
683
+ # Contains at least one padding token in the sequence
684
+ if padding_mask is not None:
685
+ batch_size = query_states.shape[0]
686
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
687
+ query_states, key_states, value_states, padding_mask, query_length
688
+ )
689
+
690
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
691
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
692
+
693
+ attn_output_unpad = flash_attn_varlen_func(
694
+ query_states,
695
+ key_states,
696
+ value_states,
697
+ cu_seqlens_q=cu_seqlens_q,
698
+ cu_seqlens_k=cu_seqlens_k,
699
+ max_seqlen_q=max_seqlen_in_batch_q,
700
+ max_seqlen_k=max_seqlen_in_batch_k,
701
+ dropout_p=dropout,
702
+ softmax_scale=softmax_scale,
703
+ causal=True,
704
+ )
705
+
706
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
707
+ else:
708
+ attn_output = flash_attn_func(
709
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=True
710
+ )
711
+
712
+ return attn_output
713
+
714
+ def _upad_input(self, query_layer, key_layer, value_layer, padding_mask, query_length):
715
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(padding_mask)
716
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
717
+
718
+ key_layer = index_first_axis(
719
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
720
+ )
721
+ value_layer = index_first_axis(
722
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
723
+ )
724
+ if query_length == kv_seq_len:
725
+ query_layer = index_first_axis(
726
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
727
+ )
728
+ cu_seqlens_q = cu_seqlens_k
729
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
730
+ indices_q = indices_k
731
+ elif query_length == 1:
732
+ max_seqlen_in_batch_q = 1
733
+ cu_seqlens_q = torch.arange(
734
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
735
+ ) # There is a memcpy here, that is very bad.
736
+ indices_q = cu_seqlens_q[:-1]
737
+ query_layer = query_layer.squeeze(1)
738
+ else:
739
+ # The -q_len: slice assumes left padding.
740
+ padding_mask = padding_mask[:, -query_length:]
741
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, padding_mask)
742
+
743
+ return (
744
+ query_layer,
745
+ key_layer,
746
+ value_layer,
747
+ indices_q,
748
+ (cu_seqlens_q, cu_seqlens_k),
749
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
750
+ )
751
+
752
+
753
+ class LlamaDecoderLayer(nn.Module):
754
+ def __init__(self, config: LlamaConfig):
755
+ super().__init__()
756
+ self.hidden_size = config.hidden_size
757
+ self.self_attn = (
758
+ LlamaAttention(config=config)
759
+ if not getattr(config, "_flash_attn_2_enabled", False)
760
+ else LlamaFlashAttention2(config=config)
761
+ )
762
+ self.mlp = LlamaMLP(config)
763
+ self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
764
+ self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
765
+
766
+ def forward(
767
+ self,
768
+ hidden_states: torch.Tensor,
769
+ attention_mask: Optional[torch.Tensor] = None,
770
+ position_ids: Optional[torch.LongTensor] = None,
771
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
772
+ output_attentions: Optional[bool] = False,
773
+ use_cache: Optional[bool] = False,
774
+ padding_mask: Optional[torch.LongTensor] = None,
775
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
776
+ """
777
+ Args:
778
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
779
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
780
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
781
+ output_attentions (`bool`, *optional*):
782
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
783
+ returned tensors for more detail.
784
+ use_cache (`bool`, *optional*):
785
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
786
+ (see `past_key_values`).
787
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
788
+ """
789
+
790
+ residual = hidden_states
791
+
792
+ hidden_states = self.input_layernorm(hidden_states)
793
+
794
+ # Self Attention
795
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
796
+ hidden_states=hidden_states,
797
+ attention_mask=attention_mask,
798
+ position_ids=position_ids,
799
+ past_key_value=past_key_value,
800
+ output_attentions=output_attentions,
801
+ use_cache=use_cache,
802
+ padding_mask=padding_mask,
803
+ )
804
+ hidden_states = residual + hidden_states
805
+
806
+ # Fully Connected
807
+ residual = hidden_states
808
+ hidden_states = self.post_attention_layernorm(hidden_states)
809
+ hidden_states = self.mlp(hidden_states)
810
+ hidden_states = residual + hidden_states
811
+
812
+ outputs = (hidden_states,)
813
+
814
+ if output_attentions:
815
+ outputs += (self_attn_weights,)
816
+
817
+ if use_cache:
818
+ outputs += (present_key_value,)
819
+
820
+ return outputs
821
+
822
+
823
+ LLAMA_START_DOCSTRING = r"""
824
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
825
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
826
+ etc.)
827
+
828
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
829
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
830
+ and behavior.
831
+
832
+ Parameters:
833
+ config ([`LlamaConfig`]):
834
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
835
+ load the weights associated with the model, only the configuration. Check out the
836
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
837
+ """
838
+
839
+
840
+ @add_start_docstrings(
841
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
842
+ LLAMA_START_DOCSTRING,
843
+ )
844
+ class LlamaPreTrainedModel(PreTrainedModel):
845
+ config_class = LlamaConfig
846
+ base_model_prefix = "model"
847
+ supports_gradient_checkpointing = True
848
+ _no_split_modules = ["LlamaDecoderLayer"]
849
+ _skip_keys_device_placement = "past_key_values"
850
+ _supports_flash_attn_2 = True
851
+
852
+ def _init_weights(self, module):
853
+ std = self.config.initializer_range
854
+ if isinstance(module, nn.Linear):
855
+ module.weight.data.normal_(mean=0.0, std=std)
856
+ if module.bias is not None:
857
+ module.bias.data.zero_()
858
+ elif isinstance(module, nn.Embedding):
859
+ module.weight.data.normal_(mean=0.0, std=std)
860
+ if module.padding_idx is not None:
861
+ module.weight.data[module.padding_idx].zero_()
862
+
863
+ def _set_gradient_checkpointing(self, module, value=False):
864
+ if isinstance(module, LlamaModel):
865
+ module.gradient_checkpointing = value
866
+
867
+
868
+ LLAMA_INPUTS_DOCSTRING = r"""
869
+ Args:
870
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
871
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
872
+ it.
873
+
874
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
875
+ [`PreTrainedTokenizer.__call__`] for details.
876
+
877
+ [What are input IDs?](../glossary#input-ids)
878
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
879
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
880
+
881
+ - 1 for tokens that are **not masked**,
882
+ - 0 for tokens that are **masked**.
883
+
884
+ [What are attention masks?](../glossary#attention-mask)
885
+
886
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
887
+ [`PreTrainedTokenizer.__call__`] for details.
888
+
889
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
890
+ `past_key_values`).
891
+
892
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
893
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
894
+ information on the default strategy.
895
+
896
+ - 1 indicates the head is **not masked**,
897
+ - 0 indicates the head is **masked**.
898
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
899
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
900
+ config.n_positions - 1]`.
901
+
902
+ [What are position IDs?](../glossary#position-ids)
903
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
904
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
905
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
906
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
907
+
908
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
909
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
910
+
911
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
912
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
913
+ of shape `(batch_size, sequence_length)`.
914
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
915
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
916
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
917
+ model's internal embedding lookup matrix.
918
+ use_cache (`bool`, *optional*):
919
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
920
+ `past_key_values`).
921
+ output_attentions (`bool`, *optional*):
922
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
923
+ tensors for more detail.
924
+ output_hidden_states (`bool`, *optional*):
925
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
926
+ more detail.
927
+ return_dict (`bool`, *optional*):
928
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
929
+ """
930
+
931
+
932
+ @add_start_docstrings(
933
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
934
+ LLAMA_START_DOCSTRING,
935
+ )
936
+ class LlamaModel(LlamaPreTrainedModel):
937
+ """
938
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
939
+
940
+ Args:
941
+ config: LlamaConfig
942
+ """
943
+