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config.json ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "DeepseekV2ForCausalLM"
4
+ ],
5
+ "attention_bias": false,
6
+ "attention_dropout": 0.0,
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_deepseek.DeepseekV2Config",
9
+ "AutoModel": "modeling_deepseek.DeepseekV2Model",
10
+ "AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"
11
+ },
12
+ "aux_loss_alpha": 0.001,
13
+ "bos_token_id": 100000,
14
+ "eos_token_id": 100001,
15
+ "first_k_dense_replace": 1,
16
+ "hidden_act": "silu",
17
+ "hidden_size": 2048,
18
+ "initializer_range": 0.02,
19
+ "intermediate_size": 10944,
20
+ "kv_lora_rank": 512,
21
+ "max_position_embeddings": 163840,
22
+ "model_type": "deepseek_v2",
23
+ "moe_intermediate_size": 1408,
24
+ "moe_layer_freq": 1,
25
+ "n_group": 1,
26
+ "n_routed_experts": 64,
27
+ "n_shared_experts": 2,
28
+ "norm_topk_prob": false,
29
+ "num_attention_heads": 16,
30
+ "num_experts_per_tok": 6,
31
+ "num_hidden_layers": 27,
32
+ "num_key_value_heads": 16,
33
+ "pretraining_tp": 1,
34
+ "q_lora_rank": null,
35
+ "qk_nope_head_dim": 128,
36
+ "qk_rope_head_dim": 64,
37
+ "rms_norm_eps": 1e-06,
38
+ "rope_scaling": {
39
+ "beta_fast": 32,
40
+ "beta_slow": 1,
41
+ "factor": 40,
42
+ "mscale": 0.707,
43
+ "mscale_all_dim": 0.707,
44
+ "original_max_position_embeddings": 4096,
45
+ "type": "yarn"
46
+ },
47
+ "rope_theta": 10000,
48
+ "routed_scaling_factor": 1.0,
49
+ "scoring_func": "softmax",
50
+ "seq_aux": true,
51
+ "tie_word_embeddings": false,
52
+ "topk_group": 1,
53
+ "topk_method": "greedy",
54
+ "torch_dtype": "bfloat16",
55
+ "transformers_version": "4.33.1",
56
+ "use_cache": true,
57
+ "v_head_dim": 128,
58
+ "vocab_size": 102400
59
+ }
configuration_deepseek.py ADDED
@@ -0,0 +1,206 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from transformers.configuration_utils import PretrainedConfig
2
+ from transformers.utils import logging
3
+
4
+ logger = logging.get_logger(__name__)
5
+
6
+ DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
7
+ class DeepseekV2Config(PretrainedConfig):
8
+ r"""
9
+ This is the configuration class to store the configuration of a [`DeepseekV2Model`]. It is used to instantiate an DeepSeek
10
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
11
+ defaults will yield a similar configuration to that of the DeepSeek-V2.
12
+
13
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
14
+ documentation from [`PretrainedConfig`] for more information.
15
+
16
+
17
+ Args:
18
+ vocab_size (`int`, *optional*, defaults to 102400):
19
+ Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
20
+ `inputs_ids` passed when calling [`DeepseekV2Model`]
21
+ hidden_size (`int`, *optional*, defaults to 4096):
22
+ Dimension of the hidden representations.
23
+ intermediate_size (`int`, *optional*, defaults to 11008):
24
+ Dimension of the MLP representations.
25
+ moe_intermediate_size (`int`, *optional*, defaults to 1407):
26
+ Dimension of the MoE representations.
27
+ num_hidden_layers (`int`, *optional*, defaults to 32):
28
+ Number of hidden layers in the Transformer decoder.
29
+ num_attention_heads (`int`, *optional*, defaults to 32):
30
+ Number of attention heads for each attention layer in the Transformer decoder.
31
+ n_shared_experts (`int`, *optional*, defaults to None):
32
+ Number of shared experts, None means dense model.
33
+ n_routed_experts (`int`, *optional*, defaults to None):
34
+ Number of routed experts, None means dense model.
35
+ routed_scaling_factor (`float`, *optional*, defaults to 1.0):
36
+ Scaling factor or routed experts.
37
+ topk_method (`str`, *optional*, defaults to `gready`):
38
+ Topk method used in routed gate.
39
+ n_group (`int`, *optional*, defaults to None):
40
+ Number of groups for routed experts.
41
+ topk_group (`int`, *optional*, defaults to None):
42
+ Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
43
+ num_experts_per_tok (`int`, *optional*, defaults to None):
44
+ Number of selected experts, None means dense model.
45
+ moe_layer_freq (`int`, *optional*, defaults to 1):
46
+ The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
47
+ first_k_dense_replace (`int`, *optional*, defaults to 0):
48
+ Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
49
+ \--k dense layers--/
50
+ norm_topk_prob (`bool`, *optional*, defaults to False):
51
+ Whether to normalize the weights of the routed experts.
52
+ scoring_func (`str`, *optional*, defaults to 'softmax'):
53
+ Method of computing expert weights.
54
+ aux_loss_alpha (`float`, *optional*, defaults to 0.001):
55
+ Auxiliary loss weight coefficient.
56
+ seq_aux = (`bool`, *optional*, defaults to True):
57
+ Whether to compute the auxiliary loss for each individual sample.
58
+ num_key_value_heads (`int`, *optional*):
59
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
60
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
61
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
62
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
63
+ by meanpooling all the original heads within that group. For more details checkout [this
64
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
65
+ `num_attention_heads`.
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.
70
+ initializer_range (`float`, *optional*, defaults to 0.02):
71
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
72
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
73
+ The epsilon used by the rms normalization layers.
74
+ use_cache (`bool`, *optional*, defaults to `True`):
75
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
76
+ relevant if `config.is_decoder=True`.
77
+ pad_token_id (`int`, *optional*):
78
+ Padding token id.
79
+ bos_token_id (`int`, *optional*, defaults to 1):
80
+ Beginning of stream token id.
81
+ eos_token_id (`int`, *optional*, defaults to 2):
82
+ End of stream token id.
83
+ pretraining_tp (`int`, *optional*, defaults to 1):
84
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
85
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
86
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
87
+ issue](https://github.com/pytorch/pytorch/issues/76232).
88
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
89
+ Whether to tie weight embeddings
90
+ rope_theta (`float`, *optional*, defaults to 10000.0):
91
+ The base period of the RoPE embeddings.
92
+ rope_scaling (`Dict`, *optional*):
93
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
94
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
95
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
96
+ `max_position_embeddings` to the expected new maximum.
97
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
98
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
99
+ attention_dropout (`float`, *optional*, defaults to 0.0):
100
+ The dropout ratio for the attention probabilities.
101
+
102
+ ```python
103
+ >>> from transformers import DeepseekV2Model, DeepseekV2Config
104
+
105
+ >>> # Initializing a Deepseek-V2 style configuration
106
+ >>> configuration = DeepseekV2Config()
107
+
108
+ >>> # Accessing the model configuration
109
+ >>> configuration = model.config
110
+ ```"""
111
+
112
+ model_type = "deepseek_v2"
113
+ keys_to_ignore_at_inference = ["past_key_values"]
114
+
115
+ def __init__(
116
+ self,
117
+ vocab_size=102400,
118
+ hidden_size=4096,
119
+ intermediate_size=11008,
120
+ moe_intermediate_size = 1407,
121
+ num_hidden_layers=30,
122
+ num_attention_heads=32,
123
+ num_key_value_heads=32,
124
+ n_shared_experts = None,
125
+ n_routed_experts = None,
126
+ ep_size = 1,
127
+ routed_scaling_factor = 1.0,
128
+ kv_lora_rank = 512,
129
+ q_lora_rank = 1536,
130
+ qk_rope_head_dim = 64,
131
+ v_head_dim = 128,
132
+ qk_nope_head_dim = 128,
133
+ topk_method = 'gready',
134
+ n_group = None,
135
+ topk_group = None,
136
+ num_experts_per_tok = None,
137
+ moe_layer_freq = 1,
138
+ first_k_dense_replace = 0,
139
+ norm_topk_prob = False,
140
+ scoring_func = 'softmax',
141
+ aux_loss_alpha = 0.001,
142
+ seq_aux = True,
143
+ hidden_act="silu",
144
+ max_position_embeddings=2048,
145
+ initializer_range=0.02,
146
+ rms_norm_eps=1e-6,
147
+ use_cache=True,
148
+ pad_token_id=None,
149
+ bos_token_id=100000,
150
+ eos_token_id=100001,
151
+ pretraining_tp=1,
152
+ tie_word_embeddings=False,
153
+ rope_theta=10000.0,
154
+ rope_scaling=None,
155
+ attention_bias=False,
156
+ attention_dropout=0.0,
157
+ **kwargs,
158
+ ):
159
+ self.vocab_size = vocab_size
160
+ self.max_position_embeddings = max_position_embeddings
161
+ self.hidden_size = hidden_size
162
+ self.intermediate_size = intermediate_size
163
+ self.moe_intermediate_size = moe_intermediate_size
164
+ self.num_hidden_layers = num_hidden_layers
165
+ self.num_attention_heads = num_attention_heads
166
+ self.n_shared_experts = n_shared_experts
167
+ self.n_routed_experts = n_routed_experts
168
+ self.ep_size = ep_size
169
+ self.routed_scaling_factor = routed_scaling_factor
170
+ self.kv_lora_rank = kv_lora_rank
171
+ self.q_lora_rank = q_lora_rank
172
+ self.qk_rope_head_dim = qk_rope_head_dim
173
+ self.v_head_dim = v_head_dim
174
+ self.qk_nope_head_dim = qk_nope_head_dim
175
+ self.topk_method = topk_method
176
+ self.n_group = n_group
177
+ self.topk_group = topk_group
178
+ self.num_experts_per_tok = num_experts_per_tok
179
+ self.moe_layer_freq = moe_layer_freq
180
+ self.first_k_dense_replace = first_k_dense_replace
181
+ self.norm_topk_prob = norm_topk_prob
182
+ self.scoring_func = scoring_func
183
+ self.aux_loss_alpha = aux_loss_alpha
184
+ self.seq_aux = seq_aux
185
+ # for backward compatibility
186
+ if num_key_value_heads is None:
187
+ num_key_value_heads = num_attention_heads
188
+
189
+ self.num_key_value_heads = num_key_value_heads
190
+ self.hidden_act = hidden_act
191
+ self.initializer_range = initializer_range
192
+ self.rms_norm_eps = rms_norm_eps
193
+ self.pretraining_tp = pretraining_tp
194
+ self.use_cache = use_cache
195
+ self.rope_theta = rope_theta
196
+ self.rope_scaling = rope_scaling
197
+ self.attention_bias = attention_bias
198
+ self.attention_dropout = attention_dropout
199
+
200
+ super().__init__(
201
+ pad_token_id=pad_token_id,
202
+ bos_token_id=bos_token_id,
203
+ eos_token_id=eos_token_id,
204
+ tie_word_embeddings=tie_word_embeddings,
205
+ **kwargs,
206
+ )
generation_config.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
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3
+ "bos_token_id": 100000,
4
+ "eos_token_id": 100001,
5
+ "do_sample": true,
6
+ "temperature": 0.3,
7
+ "top_p": 0.95,
8
+ "transformers_version": "4.39.3"
9
+ }
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model.safetensors.index.json ADDED
The diff for this file is too large to render. See raw diff
 
modeling_deepseek.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2023 DeepSeek-AI 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 DeepSeek model."""
21
+ import math
22
+ import warnings
23
+ from typing import List, Optional, Tuple, Union
24
+
25
+ import torch
26
+ import torch.nn.functional as F
27
+ import torch.utils.checkpoint
28
+ from torch import nn
29
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
30
+
31
+ from transformers.activations import ACT2FN
32
+ from transformers.cache_utils import Cache, DynamicCache
33
+ from transformers.modeling_attn_mask_utils import (
34
+ AttentionMaskConverter,
35
+ _prepare_4d_attention_mask,
36
+ _prepare_4d_causal_attention_mask,
37
+ )
38
+ from transformers.modeling_outputs import (
39
+ BaseModelOutputWithPast,
40
+ CausalLMOutputWithPast,
41
+ SequenceClassifierOutputWithPast,
42
+ )
43
+ from transformers.modeling_utils import PreTrainedModel
44
+ from transformers.pytorch_utils import (
45
+ ALL_LAYERNORM_LAYERS,
46
+ is_torch_greater_or_equal_than_1_13,
47
+ )
48
+ from transformers.utils import (
49
+ add_start_docstrings,
50
+ add_start_docstrings_to_model_forward,
51
+ is_flash_attn_2_available,
52
+ is_flash_attn_greater_or_equal_2_10,
53
+ logging,
54
+ replace_return_docstrings,
55
+ )
56
+ from transformers.utils.import_utils import is_torch_fx_available
57
+ from .configuration_deepseek import DeepseekV2Config
58
+ import torch.distributed as dist
59
+ import numpy as np
60
+
61
+ if is_flash_attn_2_available():
62
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
63
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
64
+
65
+
66
+ # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
67
+ # It means that the function will not be traced through and simply appear as a node in the graph.
68
+ if is_torch_fx_available():
69
+ if not is_torch_greater_or_equal_than_1_13:
70
+ import torch.fx
71
+
72
+ _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
73
+
74
+
75
+ logger = logging.get_logger(__name__)
76
+
77
+ _CONFIG_FOR_DOC = "DeepseekV2Config"
78
+
79
+
80
+ def _get_unpad_data(attention_mask):
81
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
82
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
83
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
84
+ cu_seqlens = F.pad(
85
+ torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
86
+ )
87
+ return (
88
+ indices,
89
+ cu_seqlens,
90
+ max_seqlen_in_batch,
91
+ )
92
+
93
+
94
+ class DeepseekV2RMSNorm(nn.Module):
95
+ def __init__(self, hidden_size, eps=1e-6):
96
+ """
97
+ DeepseekV2RMSNorm is equivalent to T5LayerNorm
98
+ """
99
+ super().__init__()
100
+ self.weight = nn.Parameter(torch.ones(hidden_size))
101
+ self.variance_epsilon = eps
102
+
103
+ def forward(self, hidden_states):
104
+ input_dtype = hidden_states.dtype
105
+ hidden_states = hidden_states.to(torch.float32)
106
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
107
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
108
+ return self.weight * hidden_states.to(input_dtype)
109
+
110
+
111
+ ALL_LAYERNORM_LAYERS.append(DeepseekV2RMSNorm)
112
+
113
+
114
+ class DeepseekV2RotaryEmbedding(nn.Module):
115
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
116
+ super().__init__()
117
+
118
+ self.dim = dim
119
+ self.max_position_embeddings = max_position_embeddings
120
+ self.base = base
121
+ inv_freq = 1.0 / (
122
+ self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
123
+ )
124
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
125
+
126
+ # Build here to make `torch.jit.trace` work.
127
+ self._set_cos_sin_cache(
128
+ seq_len=max_position_embeddings,
129
+ device=self.inv_freq.device,
130
+ dtype=torch.get_default_dtype(),
131
+ )
132
+ self.max_seq_len_cached = None
133
+
134
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
135
+ self.max_seq_len_cached = seq_len
136
+ t = torch.arange(
137
+ self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
138
+ )
139
+
140
+ freqs = torch.outer(t, self.inv_freq.to(t.device))
141
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
142
+ emb = torch.cat((freqs, freqs), dim=-1)
143
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
144
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
145
+
146
+ def forward(self, x, seq_len=None):
147
+ # x: [bs, num_attention_heads, seq_len, head_size]
148
+ if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached:
149
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
150
+
151
+ return (
152
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
153
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
154
+ )
155
+
156
+
157
+ # Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV2
158
+ class DeepseekV2LinearScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
159
+ """DeepseekV2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
160
+
161
+ def __init__(
162
+ self,
163
+ dim,
164
+ max_position_embeddings=2048,
165
+ base=10000,
166
+ device=None,
167
+ scaling_factor=1.0,
168
+ ):
169
+ self.scaling_factor = scaling_factor
170
+ super().__init__(dim, max_position_embeddings, base, device)
171
+
172
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
173
+ self.max_seq_len_cached = seq_len
174
+ t = torch.arange(
175
+ self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
176
+ )
177
+ t = t / self.scaling_factor
178
+
179
+ freqs = torch.outer(t, self.inv_freq)
180
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
181
+ emb = torch.cat((freqs, freqs), dim=-1)
182
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
183
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
184
+
185
+
186
+ # Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV2
187
+ class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
188
+ """DeepseekV2RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
189
+
190
+ def __init__(
191
+ self,
192
+ dim,
193
+ max_position_embeddings=2048,
194
+ base=10000,
195
+ device=None,
196
+ scaling_factor=1.0,
197
+ ):
198
+ self.scaling_factor = scaling_factor
199
+ super().__init__(dim, max_position_embeddings, base, device)
200
+
201
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
202
+ self.max_seq_len_cached = seq_len
203
+
204
+ if seq_len > self.max_position_embeddings:
205
+ base = self.base * (
206
+ (self.scaling_factor * seq_len / self.max_position_embeddings)
207
+ - (self.scaling_factor - 1)
208
+ ) ** (self.dim / (self.dim - 2))
209
+ inv_freq = 1.0 / (
210
+ base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
211
+ )
212
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
213
+
214
+ t = torch.arange(
215
+ self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
216
+ )
217
+
218
+ freqs = torch.outer(t, self.inv_freq)
219
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
220
+ emb = torch.cat((freqs, freqs), dim=-1)
221
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
222
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
223
+
224
+
225
+ # Inverse dim formula to find dim based on number of rotations
226
+ def yarn_find_correction_dim(
227
+ num_rotations, dim, base=10000, max_position_embeddings=2048
228
+ ):
229
+ return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
230
+ 2 * math.log(base)
231
+ )
232
+
233
+
234
+ # Find dim range bounds based on rotations
235
+ def yarn_find_correction_range(
236
+ low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
237
+ ):
238
+ low = math.floor(
239
+ yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
240
+ )
241
+ high = math.ceil(
242
+ yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
243
+ )
244
+ return max(low, 0), min(high, dim - 1) # Clamp values just in case
245
+
246
+
247
+ def yarn_get_mscale(scale=1, mscale=1):
248
+ if scale <= 1:
249
+ return 1.0
250
+ return 0.1 * mscale * math.log(scale) + 1.0
251
+
252
+
253
+ def yarn_linear_ramp_mask(min, max, dim):
254
+ if min == max:
255
+ max += 0.001 # Prevent singularity
256
+
257
+ linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
258
+ ramp_func = torch.clamp(linear_func, 0, 1)
259
+ return ramp_func
260
+
261
+
262
+ class DeepseekV2YarnRotaryEmbedding(DeepseekV2RotaryEmbedding):
263
+
264
+ def __init__(
265
+ self,
266
+ dim,
267
+ max_position_embeddings=2048,
268
+ base=10000,
269
+ device=None,
270
+ scaling_factor=1.0,
271
+ original_max_position_embeddings=4096,
272
+ beta_fast=32,
273
+ beta_slow=1,
274
+ mscale=1,
275
+ mscale_all_dim=0,
276
+ ):
277
+ self.scaling_factor = scaling_factor
278
+ self.original_max_position_embeddings = original_max_position_embeddings
279
+ self.beta_fast = beta_fast
280
+ self.beta_slow = beta_slow
281
+ self.mscale = mscale
282
+ self.mscale_all_dim = mscale_all_dim
283
+ super().__init__(dim, max_position_embeddings, base, device)
284
+
285
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
286
+ self.max_seq_len_cached = seq_len
287
+ dim = self.dim
288
+
289
+ freq_extra = 1.0 / (
290
+ self.base
291
+ ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
292
+ )
293
+ freq_inter = 1.0 / (
294
+ self.scaling_factor
295
+ * self.base
296
+ ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
297
+ )
298
+
299
+ low, high = yarn_find_correction_range(
300
+ self.beta_fast,
301
+ self.beta_slow,
302
+ dim,
303
+ self.base,
304
+ self.original_max_position_embeddings,
305
+ )
306
+ inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
307
+ device=device, dtype=torch.float32
308
+ )
309
+ inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
310
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
311
+
312
+ t = torch.arange(seq_len, device=device, dtype=torch.float32)
313
+
314
+ freqs = torch.outer(t, inv_freq)
315
+
316
+ _mscale = float(
317
+ yarn_get_mscale(self.scaling_factor, self.mscale)
318
+ / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
319
+ )
320
+
321
+ emb = torch.cat((freqs, freqs), dim=-1)
322
+ self.register_buffer(
323
+ "cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False
324
+ )
325
+ self.register_buffer(
326
+ "sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False
327
+ )
328
+
329
+
330
+ # Copied from transformers.models.llama.modeling_llama.rotate_half
331
+ def rotate_half(x):
332
+ """Rotates half the hidden dims of the input."""
333
+ x1 = x[..., : x.shape[-1] // 2]
334
+ x2 = x[..., x.shape[-1] // 2 :]
335
+ return torch.cat((-x2, x1), dim=-1)
336
+
337
+
338
+ # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
339
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
340
+ """Applies Rotary Position Embedding to the query and key tensors.
341
+
342
+ Args:
343
+ q (`torch.Tensor`): The query tensor.
344
+ k (`torch.Tensor`): The key tensor.
345
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
346
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
347
+ position_ids (`torch.Tensor`):
348
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
349
+ used to pass offsetted position ids when working with a KV-cache.
350
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
351
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
352
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
353
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
354
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
355
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
356
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
357
+ Returns:
358
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
359
+ """
360
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
361
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
362
+
363
+ b, h, s, d = q.shape
364
+ q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
365
+
366
+ b, h, s, d = k.shape
367
+ k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
368
+
369
+ q_embed = (q * cos) + (rotate_half(q) * sin)
370
+ k_embed = (k * cos) + (rotate_half(k) * sin)
371
+ return q_embed, k_embed
372
+
373
+
374
+ class DeepseekV2MLP(nn.Module):
375
+ def __init__(self, config, hidden_size=None, intermediate_size=None):
376
+ super().__init__()
377
+ self.config = config
378
+ self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
379
+ self.intermediate_size = (
380
+ config.intermediate_size if intermediate_size is None else intermediate_size
381
+ )
382
+
383
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
384
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
385
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
386
+ self.act_fn = ACT2FN[config.hidden_act]
387
+
388
+ def forward(self, x):
389
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
390
+ return down_proj
391
+
392
+
393
+ class MoEGate(nn.Module):
394
+ def __init__(self, config):
395
+ super().__init__()
396
+ self.config = config
397
+ self.top_k = config.num_experts_per_tok
398
+ self.n_routed_experts = config.n_routed_experts
399
+ self.routed_scaling_factor = config.routed_scaling_factor
400
+ self.scoring_func = config.scoring_func
401
+ self.alpha = config.aux_loss_alpha
402
+ self.seq_aux = config.seq_aux
403
+ self.topk_method = config.topk_method
404
+ self.n_group = config.n_group
405
+ self.topk_group = config.topk_group
406
+
407
+ # topk selection algorithm
408
+ self.norm_topk_prob = config.norm_topk_prob
409
+ self.gating_dim = config.hidden_size
410
+ self.weight = nn.Parameter(
411
+ torch.empty((self.n_routed_experts, self.gating_dim))
412
+ )
413
+ self.reset_parameters()
414
+
415
+ def reset_parameters(self) -> None:
416
+ import torch.nn.init as init
417
+
418
+ init.kaiming_uniform_(self.weight, a=math.sqrt(5))
419
+
420
+ def forward(self, hidden_states):
421
+ bsz, seq_len, h = hidden_states.shape
422
+ ### compute gating score
423
+ hidden_states = hidden_states.view(-1, h)
424
+ logits = F.linear(
425
+ hidden_states.type(torch.float32), self.weight.type(torch.float32), None
426
+ )
427
+ if self.scoring_func == "softmax":
428
+ scores = logits.softmax(dim=-1, dtype=torch.float32)
429
+ else:
430
+ raise NotImplementedError(
431
+ f"insupportable scoring function for MoE gating: {self.scoring_func}"
432
+ )
433
+
434
+ ### select top-k experts
435
+ if self.topk_method == "greedy":
436
+ topk_weight, topk_idx = torch.topk(
437
+ scores, k=self.top_k, dim=-1, sorted=False
438
+ )
439
+ elif self.topk_method == "group_limited_greedy":
440
+ group_scores = (
441
+ scores.view(bsz * seq_len, self.n_group, -1).max(dim=-1).values
442
+ ) # [n, n_group]
443
+ group_idx = torch.topk(
444
+ group_scores, k=self.topk_group, dim=-1, sorted=False
445
+ )[
446
+ 1
447
+ ] # [n, top_k_group]
448
+ group_mask = torch.zeros_like(group_scores) # [n, n_group]
449
+ group_mask.scatter_(1, group_idx, 1) # [n, n_group]
450
+ score_mask = (
451
+ group_mask.unsqueeze(-1)
452
+ .expand(
453
+ bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
454
+ )
455
+ .reshape(bsz * seq_len, -1)
456
+ ) # [n, e]
457
+ tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0) # [n, e]
458
+ topk_weight, topk_idx = torch.topk(
459
+ tmp_scores, k=self.top_k, dim=-1, sorted=False
460
+ )
461
+
462
+ ### norm gate to sum 1
463
+ if self.top_k > 1 and self.norm_topk_prob:
464
+ denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
465
+ topk_weight = topk_weight / denominator
466
+ else:
467
+ topk_weight = topk_weight * self.routed_scaling_factor
468
+ ### expert-level computation auxiliary loss
469
+ if self.training and self.alpha > 0.0:
470
+ scores_for_aux = scores
471
+ aux_topk = self.top_k
472
+ # always compute aux loss based on the naive greedy topk method
473
+ topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
474
+ if self.seq_aux:
475
+ scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
476
+ ce = torch.zeros(
477
+ bsz, self.n_routed_experts, device=hidden_states.device
478
+ )
479
+ ce.scatter_add_(
480
+ 1,
481
+ topk_idx_for_aux_loss,
482
+ torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device),
483
+ ).div_(seq_len * aux_topk / self.n_routed_experts)
484
+ aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(
485
+ dim=1
486
+ ).mean() * self.alpha
487
+ else:
488
+ mask_ce = F.one_hot(
489
+ topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts
490
+ )
491
+ ce = mask_ce.float().mean(0)
492
+ Pi = scores_for_aux.mean(0)
493
+ fi = ce * self.n_routed_experts
494
+ aux_loss = (Pi * fi).sum() * self.alpha
495
+ else:
496
+ aux_loss = None
497
+ return topk_idx, topk_weight, aux_loss
498
+
499
+
500
+ class AddAuxiliaryLoss(torch.autograd.Function):
501
+ """
502
+ The trick function of adding auxiliary (aux) loss,
503
+ which includes the gradient of the aux loss during backpropagation.
504
+ """
505
+
506
+ @staticmethod
507
+ def forward(ctx, x, loss):
508
+ assert loss.numel() == 1
509
+ ctx.dtype = loss.dtype
510
+ ctx.required_aux_loss = loss.requires_grad
511
+ return x
512
+
513
+ @staticmethod
514
+ def backward(ctx, grad_output):
515
+ grad_loss = None
516
+ if ctx.required_aux_loss:
517
+ grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
518
+ return grad_output, grad_loss
519
+
520
+
521
+ class DeepseekV2MoE(nn.Module):
522
+ """
523
+ A mixed expert module containing shared experts.
524
+ """
525
+
526
+ def __init__(self, config):
527
+ super().__init__()
528
+ self.config = config
529
+ self.num_experts_per_tok = config.num_experts_per_tok
530
+
531
+ if hasattr(config, "ep_size") and config.ep_size > 1:
532
+ assert config.ep_size == dist.get_world_size()
533
+ self.ep_size = config.ep_size
534
+ self.experts_per_rank = config.n_routed_experts // config.ep_size
535
+ self.ep_rank = dist.get_rank()
536
+ self.experts = nn.ModuleList(
537
+ [
538
+ (
539
+ DeepseekV2MLP(
540
+ config, intermediate_size=config.moe_intermediate_size
541
+ )
542
+ if i >= self.ep_rank * self.experts_per_rank
543
+ and i < (self.ep_rank + 1) * self.experts_per_rank
544
+ else None
545
+ )
546
+ for i in range(config.n_routed_experts)
547
+ ]
548
+ )
549
+ else:
550
+ self.ep_size = 1
551
+ self.experts_per_rank = config.n_routed_experts
552
+ self.ep_rank = 0
553
+ self.experts = nn.ModuleList(
554
+ [
555
+ DeepseekV2MLP(config, intermediate_size=config.moe_intermediate_size)
556
+ for i in range(config.n_routed_experts)
557
+ ]
558
+ )
559
+ self.gate = MoEGate(config)
560
+ if config.n_shared_experts is not None:
561
+ intermediate_size = config.moe_intermediate_size * config.n_shared_experts
562
+ self.shared_experts = DeepseekV2MLP(
563
+ config=config, intermediate_size=intermediate_size
564
+ )
565
+
566
+ def forward(self, hidden_states):
567
+ identity = hidden_states
568
+ orig_shape = hidden_states.shape
569
+ topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
570
+ hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
571
+ flat_topk_idx = topk_idx.view(-1)
572
+ if self.training:
573
+ hidden_states = hidden_states.repeat_interleave(
574
+ self.num_experts_per_tok, dim=0
575
+ )
576
+ y = torch.empty_like(hidden_states)
577
+ for i, expert in enumerate(self.experts):
578
+ y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
579
+ y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
580
+ y = y.view(*orig_shape)
581
+ y = AddAuxiliaryLoss.apply(y, aux_loss)
582
+ else:
583
+ y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape)
584
+ if self.config.n_shared_experts is not None:
585
+ y = y + self.shared_experts(identity)
586
+ return y
587
+
588
+ @torch.no_grad()
589
+ def moe_infer(self, x, topk_ids, topk_weight):
590
+ cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
591
+ cnts.scatter_(1, topk_ids, 1)
592
+ tokens_per_expert = cnts.sum(dim=0)
593
+ idxs = topk_ids.view(-1).argsort()
594
+ sorted_tokens = x[idxs // topk_ids.shape[1]]
595
+ sorted_tokens_shape = sorted_tokens.shape
596
+ if self.ep_size > 1:
597
+ tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1)
598
+ tokens_per_expert_group = tokens_per_expert.new_empty(
599
+ tokens_per_expert.shape[0]
600
+ )
601
+ dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert)
602
+ output_splits = (
603
+ tokens_per_expert_group.view(self.ep_size, -1)
604
+ .sum(1)
605
+ .cpu()
606
+ .numpy()
607
+ .tolist()
608
+ )
609
+ gathered_tokens = sorted_tokens.new_empty(
610
+ tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1]
611
+ )
612
+ input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist()
613
+ dist.all_to_all(
614
+ list(gathered_tokens.split(output_splits)),
615
+ list(sorted_tokens.split(input_split_sizes)),
616
+ )
617
+ tokens_per_expert_post_gather = tokens_per_expert_group.view(
618
+ self.ep_size, self.experts_per_rank
619
+ ).sum(dim=0)
620
+ gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32)
621
+ s = 0
622
+ for i, k in enumerate(tokens_per_expert_group.cpu().numpy()):
623
+ gatherd_idxs[s : s + k] = i % self.experts_per_rank
624
+ s += k
625
+ gatherd_idxs = gatherd_idxs.argsort()
626
+ sorted_tokens = gathered_tokens[gatherd_idxs]
627
+ tokens_per_expert = tokens_per_expert_post_gather
628
+ tokens_per_expert = tokens_per_expert.cpu().numpy()
629
+
630
+ outputs = []
631
+ start_idx = 0
632
+ for i, num_tokens in enumerate(tokens_per_expert):
633
+ end_idx = start_idx + num_tokens
634
+ if num_tokens == 0:
635
+ continue
636
+ expert = self.experts[i + self.ep_rank * self.experts_per_rank]
637
+ tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
638
+ expert_out = expert(tokens_for_this_expert)
639
+ outputs.append(expert_out)
640
+ start_idx = end_idx
641
+
642
+ outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
643
+ if self.ep_size > 1:
644
+ new_x = torch.empty_like(outs)
645
+ new_x[gatherd_idxs] = outs
646
+ gathered_tokens = new_x.new_empty(*sorted_tokens_shape)
647
+ dist.all_to_all(
648
+ list(gathered_tokens.split(input_split_sizes)),
649
+ list(new_x.split(output_splits)),
650
+ )
651
+ outs = gathered_tokens
652
+
653
+ new_x = torch.empty_like(outs)
654
+ new_x[idxs] = outs
655
+ final_out = (
656
+ new_x.view(*topk_ids.shape, -1)
657
+ .type(topk_weight.dtype)
658
+ .mul_(topk_weight.unsqueeze(dim=-1))
659
+ .sum(dim=1)
660
+ .type(new_x.dtype)
661
+ )
662
+ return final_out
663
+
664
+
665
+ # Copied from transformers.models.llama.modeling_llama.repeat_kv
666
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
667
+ """
668
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
669
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
670
+ """
671
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
672
+ if n_rep == 1:
673
+ return hidden_states
674
+ hidden_states = hidden_states[:, :, None, :, :].expand(
675
+ batch, num_key_value_heads, n_rep, slen, head_dim
676
+ )
677
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
678
+
679
+
680
+ # Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV2
681
+ class DeepseekV2Attention(nn.Module):
682
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
683
+
684
+ def __init__(self, config: DeepseekV2Config, layer_idx: Optional[int] = None):
685
+ super().__init__()
686
+ self.config = config
687
+ self.layer_idx = layer_idx
688
+ if layer_idx is None:
689
+ logger.warning_once(
690
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
691
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
692
+ "when creating this class."
693
+ )
694
+
695
+ self.attention_dropout = config.attention_dropout
696
+ self.hidden_size = config.hidden_size
697
+ self.num_heads = config.num_attention_heads
698
+
699
+ self.max_position_embeddings = config.max_position_embeddings
700
+ self.rope_theta = config.rope_theta
701
+ self.q_lora_rank = config.q_lora_rank
702
+ self.qk_rope_head_dim = config.qk_rope_head_dim
703
+ self.kv_lora_rank = config.kv_lora_rank
704
+ self.v_head_dim = config.v_head_dim
705
+ self.qk_nope_head_dim = config.qk_nope_head_dim
706
+ self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
707
+
708
+ self.is_causal = True
709
+
710
+ if self.q_lora_rank is None:
711
+ self.q_proj = nn.Linear(
712
+ self.hidden_size, self.num_heads * self.q_head_dim, bias=False
713
+ )
714
+ else:
715
+ self.q_a_proj = nn.Linear(
716
+ self.hidden_size, config.q_lora_rank, bias=config.attention_bias
717
+ )
718
+ self.q_a_layernorm = DeepseekV2RMSNorm(config.q_lora_rank)
719
+ self.q_b_proj = nn.Linear(
720
+ config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
721
+ )
722
+
723
+ self.kv_a_proj_with_mqa = nn.Linear(
724
+ self.hidden_size,
725
+ config.kv_lora_rank + config.qk_rope_head_dim,
726
+ bias=config.attention_bias,
727
+ )
728
+ self.kv_a_layernorm = DeepseekV2RMSNorm(config.kv_lora_rank)
729
+ self.kv_b_proj = nn.Linear(
730
+ config.kv_lora_rank,
731
+ self.num_heads
732
+ * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
733
+ bias=False,
734
+ )
735
+
736
+ self.o_proj = nn.Linear(
737
+ self.num_heads * self.v_head_dim,
738
+ self.hidden_size,
739
+ bias=config.attention_bias,
740
+ )
741
+ self._init_rope()
742
+
743
+ self.softmax_scale = self.q_head_dim ** (-0.5)
744
+ if self.config.rope_scaling is not None:
745
+ mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
746
+ scaling_factor = self.config.rope_scaling["factor"]
747
+ if mscale_all_dim:
748
+ mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
749
+ self.softmax_scale = self.softmax_scale * mscale * mscale
750
+
751
+ def _init_rope(self):
752
+ if self.config.rope_scaling is None:
753
+ self.rotary_emb = DeepseekV2RotaryEmbedding(
754
+ self.qk_rope_head_dim,
755
+ max_position_embeddings=self.max_position_embeddings,
756
+ base=self.rope_theta,
757
+ )
758
+ else:
759
+ scaling_type = self.config.rope_scaling["type"]
760
+ scaling_factor = self.config.rope_scaling["factor"]
761
+ if scaling_type == "linear":
762
+ self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding(
763
+ self.qk_rope_head_dim,
764
+ max_position_embeddings=self.max_position_embeddings,
765
+ scaling_factor=scaling_factor,
766
+ base=self.rope_theta,
767
+ )
768
+ elif scaling_type == "dynamic":
769
+ self.rotary_emb = DeepseekV2DynamicNTKScalingRotaryEmbedding(
770
+ self.qk_rope_head_dim,
771
+ max_position_embeddings=self.max_position_embeddings,
772
+ scaling_factor=scaling_factor,
773
+ base=self.rope_theta,
774
+ )
775
+ elif scaling_type == "yarn":
776
+ kwargs = {
777
+ key: self.config.rope_scaling[key]
778
+ for key in [
779
+ "original_max_position_embeddings",
780
+ "beta_fast",
781
+ "beta_slow",
782
+ "mscale",
783
+ "mscale_all_dim",
784
+ ]
785
+ if key in self.config.rope_scaling
786
+ }
787
+ self.rotary_emb = DeepseekV2YarnRotaryEmbedding(
788
+ self.qk_rope_head_dim,
789
+ max_position_embeddings=self.max_position_embeddings,
790
+ scaling_factor=scaling_factor,
791
+ base=self.rope_theta,
792
+ **kwargs,
793
+ )
794
+ else:
795
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
796
+
797
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
798
+ return (
799
+ tensor.view(bsz, seq_len, self.num_heads, self.v_head_dim)
800
+ .transpose(1, 2)
801
+ .contiguous()
802
+ )
803
+
804
+ def forward(
805
+ self,
806
+ hidden_states: torch.Tensor,
807
+ attention_mask: Optional[torch.Tensor] = None,
808
+ position_ids: Optional[torch.LongTensor] = None,
809
+ past_key_value: Optional[Cache] = None,
810
+ output_attentions: bool = False,
811
+ use_cache: bool = False,
812
+ **kwargs,
813
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
814
+ if "padding_mask" in kwargs:
815
+ warnings.warn(
816
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
817
+ )
818
+ bsz, q_len, _ = hidden_states.size()
819
+
820
+ if self.q_lora_rank is None:
821
+ q = self.q_proj(hidden_states)
822
+ else:
823
+ q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
824
+ q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
825
+ q_nope, q_pe = torch.split(
826
+ q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
827
+ )
828
+
829
+ compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
830
+ compressed_kv, k_pe = torch.split(
831
+ compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
832
+ )
833
+ k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
834
+ kv = (
835
+ self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
836
+ .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
837
+ .transpose(1, 2)
838
+ )
839
+
840
+ k_nope, value_states = torch.split(
841
+ kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
842
+ )
843
+ kv_seq_len = value_states.shape[-2]
844
+ if past_key_value is not None:
845
+ if self.layer_idx is None:
846
+ raise ValueError(
847
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
848
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
849
+ "with a layer index."
850
+ )
851
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
852
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
853
+
854
+ q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
855
+
856
+ query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
857
+ query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
858
+ query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
859
+
860
+ key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
861
+ key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
862
+ key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
863
+ if past_key_value is not None:
864
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
865
+ key_states, value_states = past_key_value.update(
866
+ key_states, value_states, self.layer_idx, cache_kwargs
867
+ )
868
+
869
+ attn_weights = (
870
+ torch.matmul(query_states, key_states.transpose(2, 3)) * self.softmax_scale
871
+ )
872
+
873
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
874
+ raise ValueError(
875
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
876
+ f" {attn_weights.size()}"
877
+ )
878
+ assert attention_mask is not None
879
+ if attention_mask is not None:
880
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
881
+ raise ValueError(
882
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
883
+ )
884
+ attn_weights = attn_weights + attention_mask
885
+
886
+ # upcast attention to fp32
887
+ attn_weights = nn.functional.softmax(
888
+ attn_weights, dim=-1, dtype=torch.float32
889
+ ).to(query_states.dtype)
890
+ attn_weights = nn.functional.dropout(
891
+ attn_weights, p=self.attention_dropout, training=self.training
892
+ )
893
+ attn_output = torch.matmul(attn_weights, value_states)
894
+
895
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim):
896
+ raise ValueError(
897
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_head_dim)}, but is"
898
+ f" {attn_output.size()}"
899
+ )
900
+
901
+ attn_output = attn_output.transpose(1, 2).contiguous()
902
+
903
+ attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
904
+
905
+ attn_output = self.o_proj(attn_output)
906
+
907
+ if not output_attentions:
908
+ attn_weights = None
909
+
910
+ return attn_output, attn_weights, past_key_value
911
+
912
+
913
+ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV2
914
+ class DeepseekV2FlashAttention2(DeepseekV2Attention):
915
+ """
916
+ DeepseekV2 flash attention module. This module inherits from `DeepseekV2Attention` as the weights of the module stays
917
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
918
+ flash attention and deal with padding tokens in case the input contains any of them.
919
+ """
920
+
921
+ def __init__(self, *args, **kwargs):
922
+ super().__init__(*args, **kwargs)
923
+
924
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
925
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
926
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
927
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
928
+
929
+ def forward(
930
+ self,
931
+ hidden_states: torch.Tensor,
932
+ attention_mask: Optional[torch.LongTensor] = None,
933
+ position_ids: Optional[torch.LongTensor] = None,
934
+ past_key_value: Optional[Cache] = None,
935
+ output_attentions: bool = False,
936
+ use_cache: bool = False,
937
+ **kwargs,
938
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
939
+ # DeepseekV2FlashAttention2 attention does not support output_attentions
940
+ if "padding_mask" in kwargs:
941
+ warnings.warn(
942
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
943
+ )
944
+
945
+ # overwrite attention_mask with padding_mask
946
+ attention_mask = kwargs.pop("padding_mask")
947
+
948
+ output_attentions = False
949
+
950
+ bsz, q_len, _ = hidden_states.size()
951
+
952
+ if self.q_lora_rank is None:
953
+ q = self.q_proj(hidden_states)
954
+ else:
955
+ q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
956
+ q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
957
+ q_nope, q_pe = torch.split(
958
+ q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
959
+ )
960
+
961
+ # Flash attention requires the input to have the shape
962
+ # batch_size x seq_length x head_dim x hidden_dim
963
+ # therefore we just need to keep the original shape
964
+ compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
965
+ compressed_kv, k_pe = torch.split(
966
+ compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
967
+ )
968
+ k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
969
+ kv = (
970
+ self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
971
+ .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
972
+ .transpose(1, 2)
973
+ )
974
+
975
+ k_nope, value_states = torch.split(
976
+ kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
977
+ )
978
+ kv_seq_len = value_states.shape[-2]
979
+
980
+ kv_seq_len = value_states.shape[-2]
981
+ if past_key_value is not None:
982
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
983
+
984
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
985
+ q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
986
+
987
+ query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
988
+ query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
989
+ query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
990
+
991
+ key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
992
+ key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
993
+ key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
994
+
995
+ if self.q_head_dim != self.v_head_dim:
996
+ value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim])
997
+
998
+ if past_key_value is not None:
999
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
1000
+ key_states, value_states = past_key_value.update(
1001
+ key_states, value_states, self.layer_idx, cache_kwargs
1002
+ )
1003
+
1004
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
1005
+ # to be able to avoid many of these transpose/reshape/view.
1006
+ query_states = query_states.transpose(1, 2)
1007
+ key_states = key_states.transpose(1, 2)
1008
+ value_states = value_states.transpose(1, 2)
1009
+
1010
+ dropout_rate = self.attention_dropout if self.training else 0.0
1011
+
1012
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
1013
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
1014
+ # cast them back in the correct dtype just to be sure everything works as expected.
1015
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
1016
+ # in fp32. (DeepseekV2RMSNorm handles it correctly)
1017
+
1018
+ input_dtype = query_states.dtype
1019
+ if input_dtype == torch.float32:
1020
+ # Handle the case where the model is quantized
1021
+ if hasattr(self.config, "_pre_quantization_dtype"):
1022
+ target_dtype = self.config._pre_quantization_dtype
1023
+ elif torch.is_autocast_enabled():
1024
+ target_dtype = torch.get_autocast_gpu_dtype()
1025
+ else:
1026
+ target_dtype = self.q_proj.weight.dtype if self.q_lora_rank is None else self.q_a_proj.weight.dtype
1027
+
1028
+ logger.warning_once(
1029
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
1030
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
1031
+ f" {target_dtype}."
1032
+ )
1033
+
1034
+ query_states = query_states.to(target_dtype)
1035
+ key_states = key_states.to(target_dtype)
1036
+ value_states = value_states.to(target_dtype)
1037
+
1038
+ attn_output = self._flash_attention_forward(
1039
+ query_states,
1040
+ key_states,
1041
+ value_states,
1042
+ attention_mask,
1043
+ q_len,
1044
+ dropout=dropout_rate,
1045
+ softmax_scale=self.softmax_scale,
1046
+ )
1047
+ if self.q_head_dim != self.v_head_dim:
1048
+ attn_output = attn_output[:, :, :, : self.v_head_dim]
1049
+
1050
+ attn_output = attn_output.reshape(
1051
+ bsz, q_len, self.num_heads * self.v_head_dim
1052
+ ).contiguous()
1053
+ attn_output = self.o_proj(attn_output)
1054
+
1055
+ if not output_attentions:
1056
+ attn_weights = None
1057
+
1058
+ return attn_output, attn_weights, past_key_value
1059
+
1060
+ def _flash_attention_forward(
1061
+ self,
1062
+ query_states,
1063
+ key_states,
1064
+ value_states,
1065
+ attention_mask,
1066
+ query_length,
1067
+ dropout=0.0,
1068
+ softmax_scale=None,
1069
+ ):
1070
+ """
1071
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
1072
+ first unpad the input, then computes the attention scores and pad the final attention scores.
1073
+
1074
+ Args:
1075
+ query_states (`torch.Tensor`):
1076
+ Input query states to be passed to Flash Attention API
1077
+ key_states (`torch.Tensor`):
1078
+ Input key states to be passed to Flash Attention API
1079
+ value_states (`torch.Tensor`):
1080
+ Input value states to be passed to Flash Attention API
1081
+ attention_mask (`torch.Tensor`):
1082
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
1083
+ position of padding tokens and 1 for the position of non-padding tokens.
1084
+ dropout (`int`, *optional*):
1085
+ Attention dropout
1086
+ softmax_scale (`float`, *optional*):
1087
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
1088
+ """
1089
+ if not self._flash_attn_uses_top_left_mask:
1090
+ causal = self.is_causal
1091
+ else:
1092
+ # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV2FlashAttention2 __init__.
1093
+ causal = self.is_causal and query_length != 1
1094
+
1095
+ # Contains at least one padding token in the sequence
1096
+ if attention_mask is not None:
1097
+ batch_size = query_states.shape[0]
1098
+ (
1099
+ query_states,
1100
+ key_states,
1101
+ value_states,
1102
+ indices_q,
1103
+ cu_seq_lens,
1104
+ max_seq_lens,
1105
+ ) = self._upad_input(
1106
+ query_states, key_states, value_states, attention_mask, query_length
1107
+ )
1108
+
1109
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
1110
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
1111
+
1112
+ attn_output_unpad = flash_attn_varlen_func(
1113
+ query_states,
1114
+ key_states,
1115
+ value_states,
1116
+ cu_seqlens_q=cu_seqlens_q,
1117
+ cu_seqlens_k=cu_seqlens_k,
1118
+ max_seqlen_q=max_seqlen_in_batch_q,
1119
+ max_seqlen_k=max_seqlen_in_batch_k,
1120
+ dropout_p=dropout,
1121
+ softmax_scale=softmax_scale,
1122
+ causal=causal,
1123
+ )
1124
+
1125
+ attn_output = pad_input(
1126
+ attn_output_unpad, indices_q, batch_size, query_length
1127
+ )
1128
+ else:
1129
+ attn_output = flash_attn_func(
1130
+ query_states,
1131
+ key_states,
1132
+ value_states,
1133
+ dropout,
1134
+ softmax_scale=softmax_scale,
1135
+ causal=causal,
1136
+ )
1137
+
1138
+ return attn_output
1139
+
1140
+ def _upad_input(
1141
+ self, query_layer, key_layer, value_layer, attention_mask, query_length
1142
+ ):
1143
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
1144
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
1145
+
1146
+ key_layer = index_first_axis(
1147
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
1148
+ indices_k,
1149
+ )
1150
+ value_layer = index_first_axis(
1151
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
1152
+ indices_k,
1153
+ )
1154
+ if query_length == kv_seq_len:
1155
+ query_layer = index_first_axis(
1156
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
1157
+ indices_k,
1158
+ )
1159
+ cu_seqlens_q = cu_seqlens_k
1160
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
1161
+ indices_q = indices_k
1162
+ elif query_length == 1:
1163
+ max_seqlen_in_batch_q = 1
1164
+ cu_seqlens_q = torch.arange(
1165
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
1166
+ ) # There is a memcpy here, that is very bad.
1167
+ indices_q = cu_seqlens_q[:-1]
1168
+ query_layer = query_layer.squeeze(1)
1169
+ else:
1170
+ # The -q_len: slice assumes left padding.
1171
+ attention_mask = attention_mask[:, -query_length:]
1172
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
1173
+ query_layer, attention_mask
1174
+ )
1175
+
1176
+ return (
1177
+ query_layer,
1178
+ key_layer,
1179
+ value_layer,
1180
+ indices_q,
1181
+ (cu_seqlens_q, cu_seqlens_k),
1182
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
1183
+ )
1184
+
1185
+
1186
+ ATTENTION_CLASSES = {
1187
+ "eager": DeepseekV2Attention,
1188
+ "flash_attention_2": DeepseekV2FlashAttention2,
1189
+ }
1190
+
1191
+
1192
+ class DeepseekV2DecoderLayer(nn.Module):
1193
+ def __init__(self, config: DeepseekV2Config, layer_idx: int):
1194
+ super().__init__()
1195
+ self.hidden_size = config.hidden_size
1196
+
1197
+ self.self_attn = ATTENTION_CLASSES[config._attn_implementation](
1198
+ config=config, layer_idx=layer_idx
1199
+ )
1200
+
1201
+ self.mlp = (
1202
+ DeepseekV2MoE(config)
1203
+ if (
1204
+ config.n_routed_experts is not None
1205
+ and layer_idx >= config.first_k_dense_replace
1206
+ and layer_idx % config.moe_layer_freq == 0
1207
+ )
1208
+ else DeepseekV2MLP(config)
1209
+ )
1210
+ self.input_layernorm = DeepseekV2RMSNorm(
1211
+ config.hidden_size, eps=config.rms_norm_eps
1212
+ )
1213
+ self.post_attention_layernorm = DeepseekV2RMSNorm(
1214
+ config.hidden_size, eps=config.rms_norm_eps
1215
+ )
1216
+
1217
+ def forward(
1218
+ self,
1219
+ hidden_states: torch.Tensor,
1220
+ attention_mask: Optional[torch.Tensor] = None,
1221
+ position_ids: Optional[torch.LongTensor] = None,
1222
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
1223
+ output_attentions: Optional[bool] = False,
1224
+ use_cache: Optional[bool] = False,
1225
+ **kwargs,
1226
+ ) -> Tuple[
1227
+ torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
1228
+ ]:
1229
+ """
1230
+ Args:
1231
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
1232
+ attention_mask (`torch.FloatTensor`, *optional*):
1233
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
1234
+ query_sequence_length, key_sequence_length)` if default attention is used.
1235
+ output_attentions (`bool`, *optional*):
1236
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
1237
+ returned tensors for more detail.
1238
+ use_cache (`bool`, *optional*):
1239
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
1240
+ (see `past_key_values`).
1241
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
1242
+ """
1243
+ if "padding_mask" in kwargs:
1244
+ warnings.warn(
1245
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
1246
+ )
1247
+ residual = hidden_states
1248
+
1249
+ hidden_states = self.input_layernorm(hidden_states)
1250
+
1251
+ # Self Attention
1252
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
1253
+ hidden_states=hidden_states,
1254
+ attention_mask=attention_mask,
1255
+ position_ids=position_ids,
1256
+ past_key_value=past_key_value,
1257
+ output_attentions=output_attentions,
1258
+ use_cache=use_cache,
1259
+ **kwargs,
1260
+ )
1261
+ hidden_states = residual + hidden_states
1262
+
1263
+ # Fully Connected
1264
+ residual = hidden_states
1265
+ hidden_states = self.post_attention_layernorm(hidden_states)
1266
+ hidden_states = self.mlp(hidden_states)
1267
+ hidden_states = residual + hidden_states
1268
+
1269
+ outputs = (hidden_states,)
1270
+
1271
+ if output_attentions:
1272
+ outputs += (self_attn_weights,)
1273
+
1274
+ if use_cache:
1275
+ outputs += (present_key_value,)
1276
+
1277
+ return outputs
1278
+
1279
+
1280
+ DeepseekV2_START_DOCSTRING = r"""
1281
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
1282
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
1283
+ etc.)
1284
+
1285
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
1286
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
1287
+ and behavior.
1288
+
1289
+ Parameters:
1290
+ config ([`DeepseekV2Config`]):
1291
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
1292
+ load the weights associated with the model, only the configuration. Check out the
1293
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
1294
+ """
1295
+
1296
+
1297
+ @add_start_docstrings(
1298
+ "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
1299
+ DeepseekV2_START_DOCSTRING,
1300
+ )
1301
+ class DeepseekV2PreTrainedModel(PreTrainedModel):
1302
+ config_class = DeepseekV2Config
1303
+ base_model_prefix = "model"
1304
+ supports_gradient_checkpointing = True
1305
+ _no_split_modules = ["DeepseekV2DecoderLayer"]
1306
+ _skip_keys_device_placement = "past_key_values"
1307
+ _supports_flash_attn_2 = True
1308
+ _supports_cache_class = True
1309
+
1310
+ def _init_weights(self, module):
1311
+ std = self.config.initializer_range
1312
+ if isinstance(module, nn.Linear):
1313
+ module.weight.data.normal_(mean=0.0, std=std)
1314
+ if module.bias is not None:
1315
+ module.bias.data.zero_()
1316
+ elif isinstance(module, nn.Embedding):
1317
+ module.weight.data.normal_(mean=0.0, std=std)
1318
+ if module.padding_idx is not None:
1319
+ module.weight.data[module.padding_idx].zero_()
1320
+
1321
+
1322
+ DeepseekV2_INPUTS_DOCSTRING = r"""
1323
+ Args:
1324
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
1325
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
1326
+ it.
1327
+
1328
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
1329
+ [`PreTrainedTokenizer.__call__`] for details.
1330
+
1331
+ [What are input IDs?](../glossary#input-ids)
1332
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
1333
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
1334
+
1335
+ - 1 for tokens that are **not masked**,
1336
+ - 0 for tokens that are **masked**.
1337
+
1338
+ [What are attention masks?](../glossary#attention-mask)
1339
+
1340
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
1341
+ [`PreTrainedTokenizer.__call__`] for details.
1342
+
1343
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
1344
+ `past_key_values`).
1345
+
1346
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
1347
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
1348
+ information on the default strategy.
1349
+
1350
+ - 1 indicates the head is **not masked**,
1351
+ - 0 indicates the head is **masked**.
1352
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1353
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
1354
+ config.n_positions - 1]`.
1355
+
1356
+ [What are position IDs?](../glossary#position-ids)
1357
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
1358
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
1359
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
1360
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
1361
+
1362
+ Two formats are allowed:
1363
+ - a [`~cache_utils.Cache`] instance;
1364
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
1365
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
1366
+ cache format.
1367
+
1368
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
1369
+ legacy cache format will be returned.
1370
+
1371
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
1372
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
1373
+ of shape `(batch_size, sequence_length)`.
1374
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
1375
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
1376
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
1377
+ model's internal embedding lookup matrix.
1378
+ use_cache (`bool`, *optional*):
1379
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
1380
+ `past_key_values`).
1381
+ output_attentions (`bool`, *optional*):
1382
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
1383
+ tensors for more detail.
1384
+ output_hidden_states (`bool`, *optional*):
1385
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
1386
+ more detail.
1387
+ return_dict (`bool`, *optional*):
1388
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
1389
+ """
1390
+
1391
+
1392
+ @add_start_docstrings(
1393
+ "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
1394
+ DeepseekV2_START_DOCSTRING,
1395
+ )
1396
+ class DeepseekV2Model(DeepseekV2PreTrainedModel):
1397
+ """
1398
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`]
1399
+
1400
+ Args:
1401
+ config: DeepseekV2Config
1402
+ """
1403
+
1404
+ def __init__(self, config: DeepseekV2Config):
1405
+ super().__init__(config)
1406
+ self.padding_idx = config.pad_token_id
1407
+ self.vocab_size = config.vocab_size
1408
+
1409
+ self.embed_tokens = nn.Embedding(
1410
+ config.vocab_size, config.hidden_size, self.padding_idx
1411
+ )
1412
+ self.layers = nn.ModuleList(
1413
+ [
1414
+ DeepseekV2DecoderLayer(config, layer_idx)
1415
+ for layer_idx in range(config.num_hidden_layers)
1416
+ ]
1417
+ )
1418
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
1419
+ self.norm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
1420
+
1421
+ self.gradient_checkpointing = False
1422
+ # Initialize weights and apply final processing
1423
+ self.post_init()
1424
+
1425
+ def get_input_embeddings(self):
1426
+ return self.embed_tokens
1427
+
1428
+ def set_input_embeddings(self, value):
1429
+ self.embed_tokens = value
1430
+
1431
+ @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
1432
+ def forward(
1433
+ self,
1434
+ input_ids: torch.LongTensor = None,
1435
+ attention_mask: Optional[torch.Tensor] = None,
1436
+ position_ids: Optional[torch.LongTensor] = None,
1437
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1438
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1439
+ use_cache: Optional[bool] = None,
1440
+ output_attentions: Optional[bool] = None,
1441
+ output_hidden_states: Optional[bool] = None,
1442
+ return_dict: Optional[bool] = None,
1443
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
1444
+ output_attentions = (
1445
+ output_attentions
1446
+ if output_attentions is not None
1447
+ else self.config.output_attentions
1448
+ )
1449
+ output_hidden_states = (
1450
+ output_hidden_states
1451
+ if output_hidden_states is not None
1452
+ else self.config.output_hidden_states
1453
+ )
1454
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
1455
+
1456
+ return_dict = (
1457
+ return_dict if return_dict is not None else self.config.use_return_dict
1458
+ )
1459
+
1460
+ # retrieve input_ids and inputs_embeds
1461
+ if input_ids is not None and inputs_embeds is not None:
1462
+ raise ValueError(
1463
+ "You cannot specify both input_ids and inputs_embeds at the same time"
1464
+ )
1465
+ elif input_ids is not None:
1466
+ batch_size, seq_length = input_ids.shape[:2]
1467
+ elif inputs_embeds is not None:
1468
+ batch_size, seq_length = inputs_embeds.shape[:2]
1469
+ else:
1470
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
1471
+
1472
+ if self.gradient_checkpointing and self.training:
1473
+ if use_cache:
1474
+ logger.warning_once(
1475
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`transformers."
1476
+ )
1477
+ use_cache = False
1478
+
1479
+ past_key_values_length = 0
1480
+ if use_cache:
1481
+ use_legacy_cache = not isinstance(past_key_values, Cache)
1482
+ if use_legacy_cache:
1483
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
1484
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
1485
+
1486
+ if position_ids is None:
1487
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
1488
+ position_ids = torch.arange(
1489
+ past_key_values_length,
1490
+ seq_length + past_key_values_length,
1491
+ dtype=torch.long,
1492
+ device=device,
1493
+ )
1494
+ position_ids = position_ids.unsqueeze(0)
1495
+
1496
+ if inputs_embeds is None:
1497
+ inputs_embeds = self.embed_tokens(input_ids)
1498
+
1499
+ if self._use_flash_attention_2:
1500
+ # 2d mask is passed through the layers
1501
+ attention_mask = (
1502
+ attention_mask
1503
+ if (attention_mask is not None and 0 in attention_mask)
1504
+ else None
1505
+ )
1506
+ else:
1507
+ # 4d mask is passed through the layers
1508
+ attention_mask = _prepare_4d_causal_attention_mask(
1509
+ attention_mask,
1510
+ (batch_size, seq_length),
1511
+ inputs_embeds,
1512
+ past_key_values_length,
1513
+ )
1514
+
1515
+ # embed positions
1516
+ hidden_states = inputs_embeds
1517
+
1518
+ # decoder layers
1519
+ all_hidden_states = () if output_hidden_states else None
1520
+ all_self_attns = () if output_attentions else None
1521
+ next_decoder_cache = None
1522
+
1523
+ for decoder_layer in self.layers:
1524
+ if output_hidden_states:
1525
+ all_hidden_states += (hidden_states,)
1526
+
1527
+ if self.gradient_checkpointing and self.training:
1528
+ layer_outputs = self._gradient_checkpointing_func(
1529
+ decoder_layer.__call__,
1530
+ hidden_states,
1531
+ attention_mask,
1532
+ position_ids,
1533
+ past_key_values,
1534
+ output_attentions,
1535
+ use_cache,
1536
+ )
1537
+ else:
1538
+ layer_outputs = decoder_layer(
1539
+ hidden_states,
1540
+ attention_mask=attention_mask,
1541
+ position_ids=position_ids,
1542
+ past_key_value=past_key_values,
1543
+ output_attentions=output_attentions,
1544
+ use_cache=use_cache,
1545
+ )
1546
+
1547
+ hidden_states = layer_outputs[0]
1548
+
1549
+ if use_cache:
1550
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1551
+
1552
+ if output_attentions:
1553
+ all_self_attns += (layer_outputs[1],)
1554
+
1555
+ hidden_states = self.norm(hidden_states)
1556
+
1557
+ # add hidden states from the last decoder layer
1558
+ if output_hidden_states:
1559
+ all_hidden_states += (hidden_states,)
1560
+
1561
+ next_cache = None
1562
+ if use_cache:
1563
+ next_cache = (
1564
+ next_decoder_cache.to_legacy_cache()
1565
+ if use_legacy_cache
1566
+ else next_decoder_cache
1567
+ )
1568
+ if not return_dict:
1569
+ return tuple(
1570
+ v
1571
+ for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
1572
+ if v is not None
1573
+ )
1574
+ return BaseModelOutputWithPast(
1575
+ last_hidden_state=hidden_states,
1576
+ past_key_values=next_cache,
1577
+ hidden_states=all_hidden_states,
1578
+ attentions=all_self_attns,
1579
+ )
1580
+
1581
+
1582
+ class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
1583
+ _tied_weights_keys = ["lm_head.weight"]
1584
+
1585
+ def __init__(self, config):
1586
+ super().__init__(config)
1587
+ self.model = DeepseekV2Model(config)
1588
+ self.vocab_size = config.vocab_size
1589
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1590
+
1591
+ # Initialize weights and apply final processing
1592
+ self.post_init()
1593
+
1594
+ def get_input_embeddings(self):
1595
+ return self.model.embed_tokens
1596
+
1597
+ def set_input_embeddings(self, value):
1598
+ self.model.embed_tokens = value
1599
+
1600
+ def get_output_embeddings(self):
1601
+ return self.lm_head
1602
+
1603
+ def set_output_embeddings(self, new_embeddings):
1604
+ self.lm_head = new_embeddings
1605
+
1606
+ def set_decoder(self, decoder):
1607
+ self.model = decoder
1608
+
1609
+ def get_decoder(self):
1610
+ return self.model
1611
+
1612
+ @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
1613
+ @replace_return_docstrings(
1614
+ output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
1615
+ )
1616
+ def forward(
1617
+ self,
1618
+ input_ids: torch.LongTensor = None,
1619
+ attention_mask: Optional[torch.Tensor] = None,
1620
+ position_ids: Optional[torch.LongTensor] = None,
1621
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1622
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1623
+ labels: Optional[torch.LongTensor] = None,
1624
+ use_cache: Optional[bool] = None,
1625
+ output_attentions: Optional[bool] = None,
1626
+ output_hidden_states: Optional[bool] = None,
1627
+ return_dict: Optional[bool] = None,
1628
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1629
+ r"""
1630
+ Args:
1631
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1632
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
1633
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1634
+ (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
1635
+
1636
+ Returns:
1637
+
1638
+ Example:
1639
+
1640
+ ```python
1641
+ >>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM
1642
+
1643
+ >>> model = DeepseekV2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1644
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1645
+
1646
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1647
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1648
+
1649
+ >>> # Generate
1650
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1651
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1652
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1653
+ ```"""
1654
+ output_attentions = (
1655
+ output_attentions
1656
+ if output_attentions is not None
1657
+ else self.config.output_attentions
1658
+ )
1659
+ output_hidden_states = (
1660
+ output_hidden_states
1661
+ if output_hidden_states is not None
1662
+ else self.config.output_hidden_states
1663
+ )
1664
+ return_dict = (
1665
+ return_dict if return_dict is not None else self.config.use_return_dict
1666
+ )
1667
+
1668
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1669
+ outputs = self.model(
1670
+ input_ids=input_ids,
1671
+ attention_mask=attention_mask,
1672
+ position_ids=position_ids,
1673
+ past_key_values=past_key_values,
1674
+ inputs_embeds=inputs_embeds,
1675
+ use_cache=use_cache,
1676
+ output_attentions=output_attentions,
1677
+ output_hidden_states=output_hidden_states,
1678
+ return_dict=return_dict,
1679
+ )
1680
+
1681
+ hidden_states = outputs[0]
1682
+ logits = self.lm_head(hidden_states)
1683
+ logits = logits.float()
1684
+
1685
+ loss = None
1686
+ if labels is not None:
1687
+ # Shift so that tokens < n predict n
1688
+ shift_logits = logits[..., :-1, :].contiguous()
1689
+ shift_labels = labels[..., 1:].contiguous()
1690
+ # Flatten the tokens
1691
+ loss_fct = CrossEntropyLoss()
1692
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1693
+ shift_labels = shift_labels.view(-1)
1694
+ # Enable model parallelism
1695
+ shift_labels = shift_labels.to(shift_logits.device)
1696
+ loss = loss_fct(shift_logits, shift_labels)
1697
+
1698
+ if not return_dict:
1699
+ output = (logits,) + outputs[1:]
1700
+ return (loss,) + output if loss is not None else output
1701
+
1702
+ return CausalLMOutputWithPast(
1703
+ loss=loss,
1704
+ logits=logits,
1705
+ past_key_values=outputs.past_key_values,
1706
+ hidden_states=outputs.hidden_states,
1707
+ attentions=outputs.attentions,
1708
+ )
1709
+
1710
+ def prepare_inputs_for_generation(
1711
+ self,
1712
+ input_ids,
1713
+ past_key_values=None,
1714
+ attention_mask=None,
1715
+ inputs_embeds=None,
1716
+ **kwargs,
1717
+ ):
1718
+ if past_key_values is not None:
1719
+ if isinstance(past_key_values, Cache):
1720
+ cache_length = past_key_values.get_seq_length()
1721
+ past_length = past_key_values.seen_tokens
1722
+ max_cache_length = past_key_values.get_max_length()
1723
+ else:
1724
+ cache_length = past_length = past_key_values[0][0].shape[2]
1725
+ max_cache_length = None
1726
+
1727
+ # Keep only the unprocessed tokens:
1728
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
1729
+ # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
1730
+ # input)
1731
+ if (
1732
+ attention_mask is not None
1733
+ and attention_mask.shape[1] > input_ids.shape[1]
1734
+ ):
1735
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
1736
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
1737
+ # input_ids based on the past_length.
1738
+ elif past_length < input_ids.shape[1]:
1739
+ input_ids = input_ids[:, past_length:]
1740
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
1741
+
1742
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
1743
+ if (
1744
+ max_cache_length is not None
1745
+ and attention_mask is not None
1746
+ and cache_length + input_ids.shape[1] > max_cache_length
1747
+ ):
1748
+ attention_mask = attention_mask[:, -max_cache_length:]
1749
+
1750
+ position_ids = kwargs.get("position_ids", None)
1751
+ if attention_mask is not None and position_ids is None:
1752
+ # create position_ids on the fly for batch generation
1753
+ position_ids = attention_mask.long().cumsum(-1) - 1
1754
+ position_ids.masked_fill_(attention_mask == 0, 1)
1755
+ if past_key_values:
1756
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1757
+
1758
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1759
+ if inputs_embeds is not None and past_key_values is None:
1760
+ model_inputs = {"inputs_embeds": inputs_embeds}
1761
+ else:
1762
+ model_inputs = {"input_ids": input_ids}
1763
+
1764
+ model_inputs.update(
1765
+ {
1766
+ "position_ids": position_ids,
1767
+ "past_key_values": past_key_values,
1768
+ "use_cache": kwargs.get("use_cache"),
1769
+ "attention_mask": attention_mask,
1770
+ }
1771
+ )
1772
+ return model_inputs
1773
+
1774
+ @staticmethod
1775
+ def _reorder_cache(past_key_values, beam_idx):
1776
+ reordered_past = ()
1777
+ for layer_past in past_key_values:
1778
+ reordered_past += (
1779
+ tuple(
1780
+ past_state.index_select(0, beam_idx.to(past_state.device))
1781
+ for past_state in layer_past
1782
+ ),
1783
+ )
1784
+ return reordered_past
1785
+
1786
+
1787
+ @add_start_docstrings(
1788
+ """
1789
+ The DeepseekV2 Model transformer with a sequence classification head on top (linear layer).
1790
+
1791
+ [`DeepseekV2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1792
+ (e.g. GPT-2) do.
1793
+
1794
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1795
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1796
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1797
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1798
+ each row of the batch).
1799
+ """,
1800
+ DeepseekV2_START_DOCSTRING,
1801
+ )
1802
+ class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
1803
+ def __init__(self, config):
1804
+ super().__init__(config)
1805
+ self.num_labels = config.num_labels
1806
+ self.model = DeepseekV2Model(config)
1807
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1808
+
1809
+ # Initialize weights and apply final processing
1810
+ self.post_init()
1811
+
1812
+ def get_input_embeddings(self):
1813
+ return self.model.embed_tokens
1814
+
1815
+ def set_input_embeddings(self, value):
1816
+ self.model.embed_tokens = value
1817
+
1818
+ @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
1819
+ def forward(
1820
+ self,
1821
+ input_ids: torch.LongTensor = None,
1822
+ attention_mask: Optional[torch.Tensor] = None,
1823
+ position_ids: Optional[torch.LongTensor] = None,
1824
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1825
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1826
+ labels: Optional[torch.LongTensor] = None,
1827
+ use_cache: Optional[bool] = None,
1828
+ output_attentions: Optional[bool] = None,
1829
+ output_hidden_states: Optional[bool] = None,
1830
+ return_dict: Optional[bool] = None,
1831
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1832
+ r"""
1833
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1834
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers.,
1835
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1836
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1837
+ """
1838
+ return_dict = (
1839
+ return_dict if return_dict is not None else self.config.use_return_dict
1840
+ )
1841
+
1842
+ transformer_outputs = self.model(
1843
+ input_ids,
1844
+ attention_mask=attention_mask,
1845
+ position_ids=position_ids,
1846
+ past_key_values=past_key_values,
1847
+ inputs_embeds=inputs_embeds,
1848
+ use_cache=use_cache,
1849
+ output_attentions=output_attentions,
1850
+ output_hidden_states=output_hidden_states,
1851
+ return_dict=return_dict,
1852
+ )
1853
+ hidden_states = transformer_outputs[0]
1854
+ logits = self.score(hidden_states)
1855
+
1856
+ if input_ids is not None:
1857
+ batch_size = input_ids.shape[0]
1858
+ else:
1859
+ batch_size = inputs_embeds.shape[0]
1860
+
1861
+ if self.config.pad_token_id is None and batch_size != 1:
1862
+ raise ValueError(
1863
+ "Cannot handle batch sizes > 1 if no padding token is defined."
1864
+ )
1865
+ if self.config.pad_token_id is None:
1866
+ sequence_lengths = -1
1867
+ else:
1868
+ if input_ids is not None:
1869
+ sequence_lengths = (
1870
+ torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
1871
+ ).to(logits.device)
1872
+ else:
1873
+ sequence_lengths = -1
1874
+
1875
+ pooled_logits = logits[
1876
+ torch.arange(batch_size, device=logits.device), sequence_lengths
1877
+ ]
1878
+
1879
+ loss = None
1880
+ if labels is not None:
1881
+ labels = labels.to(logits.device)
1882
+ if self.config.problem_type is None:
1883
+ if self.num_labels == 1:
1884
+ self.config.problem_type = "regression"
1885
+ elif self.num_labels > 1 and (
1886
+ labels.dtype == torch.long or labels.dtype == torch.int
1887
+ ):
1888
+ self.config.problem_type = "single_label_classification"
1889
+ else:
1890
+ self.config.problem_type = "multi_label_classification"
1891
+
1892
+ if self.config.problem_type == "regression":
1893
+ loss_fct = MSELoss()
1894
+ if self.num_labels == 1:
1895
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1896
+ else:
1897
+ loss = loss_fct(pooled_logits, labels)
1898
+ elif self.config.problem_type == "single_label_classification":
1899
+ loss_fct = CrossEntropyLoss()
1900
+ loss = loss_fct(
1901
+ pooled_logits.view(-1, self.num_labels), labels.view(-1)
1902
+ )
1903
+ elif self.config.problem_type == "multi_label_classification":
1904
+ loss_fct = BCEWithLogitsLoss()
1905
+ loss = loss_fct(pooled_logits, labels)
1906
+ if not return_dict:
1907
+ output = (pooled_logits,) + transformer_outputs[1:]
1908
+ return ((loss,) + output) if loss is not None else output
1909
+
1910
+ return SequenceClassifierOutputWithPast(
1911
+ loss=loss,
1912
+ logits=pooled_logits,
1913
+ past_key_values=transformer_outputs.past_key_values,
1914
+ hidden_states=transformer_outputs.hidden_states,
1915
+ attentions=transformer_outputs.attentions,
1916
+ )
tokenization_deepseek_fast.py ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List, Optional, Union
2
+
3
+
4
+ from transformers.models.llama import LlamaTokenizerFast
5
+
6
+
7
+ class DeepseekTokenizerFast(LlamaTokenizerFast):
8
+
9
+ def convert_ids_to_tokens(
10
+ self, ids: Union[int, List[int]], skip_special_tokens: bool = False
11
+ ) -> Union[str, List[str]]:
12
+ """
13
+ Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
14
+ added tokens.
15
+
16
+ Args:
17
+ ids (`int` or `List[int]`):
18
+ The token id (or token ids) to convert to tokens.
19
+ skip_special_tokens (`bool`, *optional*, defaults to `False`):
20
+ Whether or not to remove special tokens in the decoding.
21
+
22
+ Returns:
23
+ `str` or `List[str]`: The decoded token(s).
24
+ """
25
+ if isinstance(ids, int):
26
+ return self._convert_id_to_token(ids)
27
+ tokens = []
28
+ for index in ids:
29
+ index = int(index)
30
+ if skip_special_tokens and index in self.all_special_ids:
31
+ continue
32
+ token = self._tokenizer.id_to_token(index)
33
+ tokens.append(token if token is not None else "")
34
+ return tokens
35
+
36
+ def _convert_id_to_token(self, index: int) -> Optional[str]:
37
+ token = self._tokenizer.id_to_token(int(index))
38
+ return token if token is not None else ""
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": true,
3
+ "add_eos_token": false,
4
+ "bos_token": {
5
+ "__type": "AddedToken",
6
+ "content": "<|begin▁of▁sentence|>",
7
+ "lstrip": false,
8
+ "normalized": true,
9
+ "rstrip": false,
10
+ "single_word": false
11
+ },
12
+ "clean_up_tokenization_spaces": false,
13
+ "eos_token": {
14
+ "__type": "AddedToken",
15
+ "content": "<|end▁of▁sentence|>",
16
+ "lstrip": false,
17
+ "normalized": true,
18
+ "rstrip": false,
19
+ "single_word": false
20
+ },
21
+ "legacy": true,
22
+ "model_max_length": 16384,
23
+ "pad_token": {
24
+ "__type": "AddedToken",
25
+ "content": "<|end▁of▁sentence|>",
26
+ "lstrip": false,
27
+ "normalized": true,
28
+ "rstrip": false,
29
+ "single_word": false
30
+ },
31
+ "sp_model_kwargs": {},
32
+ "unk_token": null,
33
+ "tokenizer_class": "LlamaTokenizerFast",
34
+ "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}"
35
+ }