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Upload BaichuanForCausalLM

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config.json ADDED
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1
+ {
2
+ "_from_model_config": true,
3
+ "_name_or_path": "../../../Baichuan2-7B-Chat",
4
+ "architectures": [
5
+ "BaichuanForCausalLM"
6
+ ],
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_baichuan.BaichuanConfig",
9
+ "AutoModelForCausalLM": "modeling_baichuan.BaichuanForCausalLM"
10
+ },
11
+ "bos_token_id": 1,
12
+ "eos_token_id": 2,
13
+ "hidden_act": "silu",
14
+ "hidden_size": 4096,
15
+ "initializer_range": 0.02,
16
+ "intermediate_size": 11008,
17
+ "max_position_embeddings": 4096,
18
+ "model_max_length": 4096,
19
+ "model_type": "baichuan",
20
+ "num_attention_heads": 32,
21
+ "num_hidden_layers": 32,
22
+ "pad_token_id": 0,
23
+ "rms_norm_eps": 1e-06,
24
+ "tie_word_embeddings": false,
25
+ "tokenizer_class": "BaichuanTokenizer",
26
+ "torch_dtype": "bfloat16",
27
+ "transformers_version": "4.32.0",
28
+ "use_cache": true,
29
+ "vocab_size": 125696,
30
+ "z_loss_weight": 0
31
+ }
configuration_baichuan.py ADDED
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1
+ # Copyright 2023 Baichuan Inc. All Rights Reserved.
2
+
3
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
4
+ #
5
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
6
+ # and OPT implementations in this library. It has been modified from its
7
+ # original forms to accommodate minor architectural differences compared
8
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
9
+ #
10
+ # Licensed under the Apache License, Version 2.0 (the "License");
11
+ # you may not use this file except in compliance with the License.
12
+ # You may obtain a copy of the License at
13
+ #
14
+ # http://www.apache.org/licenses/LICENSE-2.0
15
+ #
16
+ # Unless required by applicable law or agreed to in writing, software
17
+ # distributed under the License is distributed on an "AS IS" BASIS,
18
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
19
+ # See the License for the specific language governing permissions and
20
+ # limitations under the License.
21
+
22
+ from transformers.configuration_utils import PretrainedConfig
23
+ from transformers.utils import logging
24
+
25
+
26
+ logger = logging.get_logger(__name__)
27
+
28
+
29
+ class BaichuanConfig(PretrainedConfig):
30
+ model_type = "baichuan"
31
+ keys_to_ignore_at_inference = ["past_key_values"]
32
+
33
+ def __init__(
34
+ self,
35
+ vocab_size=125696,
36
+ hidden_size=4096,
37
+ intermediate_size=11008,
38
+ num_hidden_layers=32,
39
+ num_attention_heads=32,
40
+ hidden_act="silu",
41
+ max_position_embeddings=4096,
42
+ initializer_range=0.02,
43
+ rms_norm_eps=1e-6,
44
+ use_cache=True,
45
+ pad_token_id=0,
46
+ bos_token_id=1,
47
+ eos_token_id=2,
48
+ tie_word_embeddings=False,
49
+ z_loss_weight=0,
50
+ **kwargs,
51
+ ):
52
+ self.vocab_size = vocab_size
53
+ self.max_position_embeddings = max_position_embeddings
54
+ self.hidden_size = hidden_size
55
+ self.intermediate_size = intermediate_size
56
+ self.num_hidden_layers = num_hidden_layers
57
+ self.num_attention_heads = num_attention_heads
58
+ self.hidden_act = hidden_act
59
+ self.initializer_range = initializer_range
60
+ self.rms_norm_eps = rms_norm_eps
61
+ self.use_cache = use_cache
62
+ self.z_loss_weight = z_loss_weight
63
+ super().__init__(
64
+ pad_token_id=pad_token_id,
65
+ bos_token_id=bos_token_id,
66
+ eos_token_id=eos_token_id,
67
+ tie_word_embeddings=tie_word_embeddings,
68
+ **kwargs,
69
+ )
generation_config.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "assistant_token_id": 196,
3
+ "bos_token_id": 1,
4
+ "do_sample": true,
5
+ "eos_token_id": 2,
6
+ "max_new_tokens": 2048,
7
+ "pad_token_id": 0,
8
+ "repetition_penalty": 1.05,
9
+ "temperature": 0.3,
10
+ "top_k": 5,
11
+ "top_p": 0.85,
12
+ "transformers_version": "4.32.0",
13
+ "user_token_id": 195
14
+ }
generation_utils.py ADDED
@@ -0,0 +1,83 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List
2
+ from queue import Queue
3
+
4
+ import torch
5
+
6
+
7
+ def build_chat_input(model, tokenizer, messages: List[dict], max_new_tokens: int=0):
8
+ def _parse_messages(messages, split_role="user"):
9
+ system, rounds = "", []
10
+ round = []
11
+ for i, message in enumerate(messages):
12
+ if message["role"] == "system":
13
+ assert i == 0
14
+ system = message["content"]
15
+ continue
16
+ if message["role"] == split_role and round:
17
+ rounds.append(round)
18
+ round = []
19
+ round.append(message)
20
+ if round:
21
+ rounds.append(round)
22
+ return system, rounds
23
+
24
+ max_new_tokens = max_new_tokens or model.generation_config.max_new_tokens
25
+ max_input_tokens = model.config.model_max_length - max_new_tokens
26
+ system, rounds = _parse_messages(messages, split_role="user")
27
+ system_tokens = tokenizer.encode(system)
28
+ max_history_tokens = max_input_tokens - len(system_tokens)
29
+
30
+ history_tokens = []
31
+ for round in rounds[::-1]:
32
+ round_tokens = []
33
+ for message in round:
34
+ if message["role"] == "user":
35
+ round_tokens.append(model.generation_config.user_token_id)
36
+ else:
37
+ round_tokens.append(model.generation_config.assistant_token_id)
38
+ round_tokens.extend(tokenizer.encode(message["content"]))
39
+ if len(history_tokens) == 0 or len(history_tokens) + len(round_tokens) <= max_history_tokens:
40
+ history_tokens = round_tokens + history_tokens # concat left
41
+ if len(history_tokens) < max_history_tokens:
42
+ continue
43
+ break
44
+
45
+ input_tokens = system_tokens + history_tokens
46
+ if messages[-1]["role"] != "assistant":
47
+ input_tokens.append(model.generation_config.assistant_token_id)
48
+ input_tokens = input_tokens[-max_input_tokens:] # truncate left
49
+ return torch.LongTensor([input_tokens]).to(model.device)
50
+
51
+
52
+ class TextIterStreamer:
53
+ def __init__(self, tokenizer, skip_prompt=False, skip_special_tokens=False):
54
+ self.tokenizer = tokenizer
55
+ self.skip_prompt = skip_prompt
56
+ self.skip_special_tokens = skip_special_tokens
57
+ self.tokens = []
58
+ self.text_queue = Queue()
59
+ self.next_tokens_are_prompt = True
60
+
61
+ def put(self, value):
62
+ if self.skip_prompt and self.next_tokens_are_prompt:
63
+ self.next_tokens_are_prompt = False
64
+ else:
65
+ if len(value.shape) > 1:
66
+ value = value[0]
67
+ self.tokens.extend(value.tolist())
68
+ self.text_queue.put(
69
+ self.tokenizer.decode(self.tokens, skip_special_tokens=self.skip_special_tokens))
70
+
71
+ def end(self):
72
+ self.text_queue.put(None)
73
+
74
+ def __iter__(self):
75
+ return self
76
+
77
+ def __next__(self):
78
+ value = self.text_queue.get()
79
+ if value is None:
80
+ raise StopIteration()
81
+ else:
82
+ return value
83
+
modeling_baichuan.py ADDED
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1
+ # Copyright 2023 Baichuan Inc. All Rights Reserved.
2
+
3
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
4
+ #
5
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
6
+ # and OPT implementations in this library. It has been modified from its
7
+ # original forms to accommodate minor architectural differences compared
8
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
9
+ #
10
+ # Licensed under the Apache License, Version 2.0 (the "License");
11
+ # you may not use this file except in compliance with the License.
12
+ # You may obtain a copy of the License at
13
+ #
14
+ # http://www.apache.org/licenses/LICENSE-2.0
15
+ #
16
+ # Unless required by applicable law or agreed to in writing, software
17
+ # distributed under the License is distributed on an "AS IS" BASIS,
18
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
19
+ # See the License for the specific language governing permissions and
20
+ # limitations under the License.
21
+
22
+
23
+ from .configuration_baichuan import BaichuanConfig
24
+ from .generation_utils import build_chat_input, TextIterStreamer
25
+
26
+ import math
27
+ from typing import List, Optional, Tuple, Union
28
+ from threading import Thread
29
+
30
+ import torch
31
+ import torch.utils.checkpoint
32
+ from torch import nn
33
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
34
+ from torch.nn import functional as F
35
+ from transformers import PreTrainedModel, PretrainedConfig
36
+ from transformers.activations import ACT2FN
37
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
38
+ from transformers.generation.utils import GenerationConfig
39
+ from transformers.utils import logging, ContextManagers
40
+
41
+ import os
42
+ from contextlib import contextmanager
43
+ logger = logging.get_logger(__name__)
44
+
45
+ try:
46
+ from xformers import ops as xops
47
+ except ImportError:
48
+ xops = None
49
+ logger.warning(
50
+ "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers."
51
+ )
52
+
53
+
54
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
55
+ def _make_causal_mask(
56
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
57
+ ):
58
+ """
59
+ Make causal mask used for bi-directional self-attention.
60
+ """
61
+ bsz, tgt_len = input_ids_shape
62
+ mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
63
+ mask_cond = torch.arange(mask.size(-1), device=device)
64
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
65
+ mask = mask.to(dtype)
66
+
67
+ if past_key_values_length > 0:
68
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
69
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
70
+
71
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
72
+ """
73
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
74
+ """
75
+ if len(mask.size()) == 3:
76
+ bsz, src_len, _ = mask.size()
77
+ tgt_len = tgt_len if tgt_len is not None else src_len
78
+ expanded_mask = mask[:,None,:,:].expand(bsz, 1, tgt_len, src_len).to(dtype)
79
+ else:
80
+ bsz, src_len = mask.size()
81
+ tgt_len = tgt_len if tgt_len is not None else src_len
82
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
83
+
84
+ inverted_mask = 1.0 - expanded_mask
85
+
86
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
87
+
88
+
89
+ class RMSNorm(nn.Module):
90
+ def __init__(self, hidden_size, eps=1e-6):
91
+ """
92
+ RMSNorm is equivalent to T5LayerNorm
93
+ """
94
+ super().__init__()
95
+ self.weight = nn.Parameter(torch.ones(hidden_size))
96
+ self.variance_epsilon = eps
97
+
98
+ def forward(self, hidden_states):
99
+ variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
100
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
101
+
102
+ # convert into half-precision if necessary
103
+ if self.weight.dtype in [torch.float16, torch.bfloat16]:
104
+ hidden_states = hidden_states.to(self.weight.dtype)
105
+
106
+ return self.weight * hidden_states
107
+
108
+
109
+ class RotaryEmbedding(torch.nn.Module):
110
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
111
+ super().__init__()
112
+ self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
113
+ self.max_seq_len_cached = max_position_embeddings
114
+ t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
115
+ freqs = torch.outer(t, self.inv_freq)
116
+ emb = torch.cat((freqs, freqs), dim=-1)
117
+ self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32)
118
+ self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32)
119
+ def forward(self, x, seq_len=None):
120
+ # x: [bs, num_attention_heads, seq_len, head_size]
121
+ # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
122
+ if seq_len > self.max_seq_len_cached:
123
+ self.max_seq_len_cached = seq_len
124
+ t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
125
+ freqs = torch.outer(t, self.inv_freq)
126
+ emb = torch.cat((freqs, freqs), dim=-1)
127
+ self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32).to(x.device)
128
+ self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32).to(x.device)
129
+ elif self.cos_cached.device != x.device:
130
+ self.cos_cached = self.cos_cached.to(x.device)
131
+ self.sin_cached = self.sin_cached.to(x.device)
132
+ return (
133
+ self.cos_cached[:, :, :seq_len, ...],
134
+ self.sin_cached[:, :, :seq_len, ...],
135
+ )
136
+
137
+
138
+ def rotate_half(x):
139
+ """Rotates half the hidden dims of the input."""
140
+ x1 = x[..., : x.shape[-1] // 2]
141
+ x2 = x[..., x.shape[-1] // 2:]
142
+ return torch.cat((-x2, x1), dim=-1)
143
+
144
+
145
+ def apply_rotary_pos_emb(q, k, cos_, sin_, position_ids):
146
+ cos = cos_.squeeze(1).squeeze(0) # [seq_len, dim]
147
+ sin = sin_.squeeze(1).squeeze(0) # [seq_len, dim]
148
+ cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
149
+ sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
150
+ q_embed = (q.float() * cos) + (rotate_half(q.float()) * sin)
151
+ k_embed = (k.float() * cos) + (rotate_half(k.float()) * sin)
152
+ return q_embed.to(q.dtype), k_embed.to(k.dtype)
153
+
154
+
155
+ class MLP(nn.Module):
156
+ def __init__(
157
+ self,
158
+ hidden_size: int,
159
+ intermediate_size: int,
160
+ hidden_act: str,
161
+ ):
162
+ super().__init__()
163
+ self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
164
+ self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
165
+ self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
166
+ self.act_fn = ACT2FN[hidden_act]
167
+
168
+ def forward(self, x):
169
+ return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
170
+
171
+
172
+ class Attention(nn.Module):
173
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
174
+ def __init__(self, config: BaichuanConfig):
175
+ super().__init__()
176
+ self.config = config
177
+ self.hidden_size = config.hidden_size
178
+ self.num_heads = config.num_attention_heads
179
+ self.head_dim = self.hidden_size // self.num_heads
180
+ self.max_position_embeddings = config.max_position_embeddings
181
+
182
+ if (self.head_dim * self.num_heads) != self.hidden_size:
183
+ raise ValueError(
184
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
185
+ f" and `num_heads`: {self.num_heads})."
186
+ )
187
+ self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
188
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
189
+ self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
190
+
191
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
192
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
193
+
194
+ def forward(
195
+ self,
196
+ hidden_states: torch.Tensor,
197
+ attention_mask: Optional[torch.Tensor] = None,
198
+ position_ids: Optional[torch.LongTensor] = None,
199
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
200
+ output_attentions: bool = False,
201
+ use_cache: bool = False,
202
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
203
+ bsz, q_len, _ = hidden_states.size()
204
+
205
+ proj = self.W_pack(hidden_states)
206
+ proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
207
+ query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
208
+ key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
209
+ value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
210
+
211
+ kv_seq_len = key_states.shape[-2]
212
+ if past_key_value is not None:
213
+ kv_seq_len += past_key_value[0].shape[-2]
214
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
215
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
216
+ # [bsz, nh, t, hd]
217
+
218
+ if past_key_value is not None:
219
+ # reuse k, v, self_attention
220
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
221
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
222
+
223
+ past_key_value = (key_states, value_states) if use_cache else None
224
+ if xops is not None and self.training:
225
+ attn_weights = None
226
+ query_states = query_states.transpose(1, 2)
227
+ key_states = key_states.transpose(1, 2)
228
+ value_states = value_states.transpose(1, 2)
229
+ attn_output = xops.memory_efficient_attention(
230
+ query_states, key_states, value_states, attn_bias=xops.LowerTriangularMask()
231
+ )
232
+ else:
233
+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
234
+ attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask = attention_mask)
235
+ attn_output = attn_output.transpose(1, 2)
236
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
237
+ attn_output = self.o_proj(attn_output)
238
+
239
+ if not output_attentions:
240
+ attn_weights = None
241
+
242
+ return attn_output, attn_weights, past_key_value
243
+
244
+
245
+ class DecoderLayer(nn.Module):
246
+ def __init__(self, config: BaichuanConfig):
247
+ super().__init__()
248
+ self.hidden_size = config.hidden_size
249
+ self.self_attn = Attention(config=config)
250
+ self.mlp = MLP(
251
+ hidden_size=self.hidden_size,
252
+ intermediate_size=config.intermediate_size,
253
+ hidden_act=config.hidden_act,
254
+ )
255
+ self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
256
+ self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
257
+
258
+ def forward(
259
+ self,
260
+ hidden_states: torch.Tensor,
261
+ attention_mask: Optional[torch.Tensor] = None,
262
+ position_ids: Optional[torch.LongTensor] = None,
263
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
264
+ output_attentions: Optional[bool] = False,
265
+ use_cache: Optional[bool] = False,
266
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
267
+
268
+ residual = hidden_states
269
+
270
+ hidden_states = self.input_layernorm(hidden_states)
271
+
272
+ # Self Attention
273
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
274
+ hidden_states=hidden_states,
275
+ attention_mask=attention_mask,
276
+ position_ids=position_ids,
277
+ past_key_value=past_key_value,
278
+ output_attentions=output_attentions,
279
+ use_cache=use_cache,
280
+ )
281
+ hidden_states = residual + hidden_states
282
+
283
+ # Fully Connected
284
+ residual = hidden_states
285
+ hidden_states = self.post_attention_layernorm(hidden_states)
286
+ hidden_states = self.mlp(hidden_states)
287
+ hidden_states = residual + hidden_states
288
+
289
+ outputs = (hidden_states,)
290
+
291
+ if output_attentions:
292
+ outputs += (self_attn_weights,)
293
+
294
+ if use_cache:
295
+ outputs += (present_key_value,)
296
+
297
+ return outputs
298
+
299
+
300
+ class BaichuanPreTrainedModel(PreTrainedModel):
301
+ config_class = BaichuanConfig
302
+ base_model_prefix = "model"
303
+ supports_gradient_checkpointing = True
304
+ _no_split_modules = ["DecoderLayer"]
305
+ _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
306
+
307
+ def _init_weights(self, module):
308
+ std = self.config.initializer_range
309
+ if isinstance(module, nn.Linear):
310
+ module.weight.data.normal_(mean=0.0, std=std)
311
+ if module.bias is not None:
312
+ module.bias.data.zero_()
313
+ elif isinstance(module, nn.Embedding):
314
+ module.weight.data.normal_(mean=0.0, std=std)
315
+ if module.padding_idx is not None:
316
+ module.weight.data[module.padding_idx].zero_()
317
+
318
+ def _set_gradient_checkpointing(self, module, value=False):
319
+ if isinstance(module, BaichuanModel):
320
+ module.gradient_checkpointing = value
321
+
322
+
323
+ class BaichuanModel(BaichuanPreTrainedModel):
324
+ def __init__(self, config: BaichuanConfig):
325
+ super().__init__(config)
326
+ self.padding_idx = config.pad_token_id
327
+ self.vocab_size = config.vocab_size
328
+
329
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
330
+ self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
331
+ self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
332
+
333
+ self.gradient_checkpointing = False
334
+ # Initialize weights and apply final processing
335
+ self.post_init()
336
+
337
+ def get_input_embeddings(self):
338
+ return self.embed_tokens
339
+
340
+ def set_input_embeddings(self, value):
341
+ self.embed_tokens = value
342
+
343
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
344
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
345
+ # create causal mask
346
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
347
+ combined_attention_mask = None
348
+ if input_shape[-1] > 1:
349
+ combined_attention_mask = _make_causal_mask(
350
+ input_shape,
351
+ inputs_embeds.dtype,
352
+ device=inputs_embeds.device,
353
+ past_key_values_length=past_key_values_length,
354
+ )
355
+
356
+ if attention_mask is not None:
357
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
358
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
359
+ inputs_embeds.device
360
+ )
361
+ combined_attention_mask = (
362
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
363
+ )
364
+
365
+ return combined_attention_mask
366
+
367
+ def forward(
368
+ self,
369
+ input_ids: torch.LongTensor = None,
370
+ attention_mask: Optional[torch.Tensor] = None,
371
+ position_ids: Optional[torch.LongTensor] = None,
372
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
373
+ inputs_embeds: Optional[torch.FloatTensor] = None,
374
+ use_cache: Optional[bool] = None,
375
+ output_attentions: Optional[bool] = None,
376
+ output_hidden_states: Optional[bool] = None,
377
+ return_dict: Optional[bool] = None,
378
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
379
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
380
+ output_hidden_states = (
381
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
382
+ )
383
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
384
+
385
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
386
+
387
+ # retrieve input_ids and inputs_embeds
388
+ if input_ids is not None and inputs_embeds is not None:
389
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
390
+ elif input_ids is not None:
391
+ batch_size, seq_length = input_ids.shape
392
+ elif inputs_embeds is not None:
393
+ batch_size, seq_length, _ = inputs_embeds.shape
394
+ else:
395
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
396
+
397
+ seq_length_with_past = seq_length
398
+ past_key_values_length = 0
399
+
400
+ if past_key_values is not None:
401
+ past_key_values_length = past_key_values[0][0].shape[2]
402
+ seq_length_with_past = seq_length_with_past + past_key_values_length
403
+
404
+ if position_ids is None:
405
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
406
+ position_ids = torch.arange(
407
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
408
+ )
409
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
410
+ else:
411
+ position_ids = position_ids.view(-1, seq_length).long()
412
+
413
+ if inputs_embeds is None:
414
+ inputs_embeds = self.embed_tokens(input_ids)
415
+ # embed positions
416
+ if attention_mask is None:
417
+ attention_mask = torch.ones(
418
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
419
+ )
420
+ attention_mask = self._prepare_decoder_attention_mask(
421
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
422
+ )
423
+
424
+ hidden_states = inputs_embeds
425
+
426
+ if self.gradient_checkpointing and self.training:
427
+ if use_cache:
428
+ logger.warning_once(
429
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
430
+ )
431
+ use_cache = False
432
+
433
+ # decoder layers
434
+ all_hidden_states = () if output_hidden_states else None
435
+ all_self_attns = () if output_attentions else None
436
+ next_decoder_cache = () if use_cache else None
437
+
438
+ for idx, decoder_layer in enumerate(self.layers):
439
+ if output_hidden_states:
440
+ all_hidden_states += (hidden_states,)
441
+
442
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
443
+
444
+ if self.gradient_checkpointing and self.training:
445
+
446
+ def create_custom_forward(module):
447
+ def custom_forward(*inputs):
448
+ # None for past_key_value
449
+ return module(*inputs, output_attentions, None)
450
+
451
+ return custom_forward
452
+
453
+ layer_outputs = torch.utils.checkpoint.checkpoint(
454
+ create_custom_forward(decoder_layer),
455
+ hidden_states,
456
+ attention_mask,
457
+ position_ids,
458
+ None,
459
+ )
460
+ else:
461
+ layer_outputs = decoder_layer(
462
+ hidden_states,
463
+ attention_mask=attention_mask,
464
+ position_ids=position_ids,
465
+ past_key_value=past_key_value,
466
+ output_attentions=output_attentions,
467
+ use_cache=use_cache,
468
+ )
469
+
470
+ hidden_states = layer_outputs[0]
471
+
472
+ if use_cache:
473
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
474
+
475
+ if output_attentions:
476
+ all_self_attns += (layer_outputs[1],)
477
+
478
+ hidden_states = self.norm(hidden_states)
479
+
480
+ # add hidden states from the last decoder layer
481
+ if output_hidden_states:
482
+ all_hidden_states += (hidden_states,)
483
+
484
+ next_cache = next_decoder_cache if use_cache else None
485
+ if not return_dict:
486
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
487
+ return BaseModelOutputWithPast(
488
+ last_hidden_state=hidden_states,
489
+ past_key_values=next_cache,
490
+ hidden_states=all_hidden_states,
491
+ attentions=all_self_attns,
492
+ )
493
+
494
+
495
+ class NormHead(nn.Module):
496
+ def __init__(self, hidden_size, vocab_size, bias=False):
497
+ super().__init__()
498
+ self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
499
+ nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
500
+ self.first_flag = True
501
+
502
+ def forward(self, hidden_states):
503
+ if self.training:
504
+ norm_weight = nn.functional.normalize(self.weight)
505
+ self.first_flag = True
506
+ elif self.first_flag:
507
+ self.first_flag = False
508
+ self.weight.data = nn.functional.normalize(self.weight)
509
+ norm_weight = self.weight
510
+ else:
511
+ norm_weight = self.weight
512
+ return nn.functional.linear(hidden_states, norm_weight)
513
+
514
+ _init_weights = True
515
+ @contextmanager
516
+ def no_init_weights(_enable=True):
517
+ global _init_weights
518
+ old_init_weights = _init_weights
519
+ if _enable:
520
+ _init_weights = False
521
+ try:
522
+ yield
523
+ finally:
524
+ _init_weights = old_init_weights
525
+
526
+ class BaichuanForCausalLM(BaichuanPreTrainedModel):
527
+ def __init__(self, config, *model_args, **model_kwargs):
528
+ super().__init__(config, *model_args, **model_kwargs)
529
+ self.model = BaichuanModel(config)
530
+
531
+ self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False)
532
+ if hasattr(config, "quantization_config") and isinstance(config.quantization_config, dict) and config.quantization_config.get('load_in_4bit', False):
533
+ try:
534
+ from .quantizer import quantize_offline, init_model_weight_int4
535
+ except ImportError:
536
+ raise ImportError(f"Needs QLinear to run quantize.")
537
+ quantize_offline(self, 4)
538
+ # Initialize weights and apply final processing
539
+ self.post_init()
540
+
541
+ def get_input_embeddings(self):
542
+ return self.model.embed_tokens
543
+
544
+ def set_input_embeddings(self, value):
545
+ self.model.embed_tokens = value
546
+
547
+ def get_output_embeddings(self):
548
+ return self.lm_head
549
+
550
+ def set_output_embeddings(self, new_embeddings):
551
+ self.lm_head = new_embeddings
552
+
553
+ def set_decoder(self, decoder):
554
+ self.model = decoder
555
+
556
+ def get_decoder(self):
557
+ return self.model
558
+
559
+ @classmethod
560
+ def from_pretrained(
561
+ cls,
562
+ pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
563
+ *model_args,
564
+ config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
565
+ cache_dir: Optional[Union[str, os.PathLike]] = None,
566
+ ignore_mismatched_sizes: bool = False,
567
+ force_download: bool = False,
568
+ local_files_only: bool = False,
569
+ token: Optional[Union[str, bool]] = None,
570
+ revision: str = "main",
571
+ use_safetensors: bool = None,
572
+ **kwargs,
573
+ ):
574
+ # Load config if we don't provide a configuration
575
+ if not isinstance(config, PretrainedConfig):
576
+ config_path = config if config is not None else pretrained_model_name_or_path
577
+ config, model_kwargs = cls.config_class.from_pretrained(
578
+ config_path,
579
+ cache_dir=cache_dir,
580
+ return_unused_kwargs=True,
581
+ force_download=force_download,
582
+ resume_download=False,
583
+ proxies=None,
584
+ local_files_only=local_files_only,
585
+ token=token,
586
+ revision=revision,
587
+ subfolder="",
588
+ _from_auto=False,
589
+ _from_pipeline=None,
590
+ **kwargs,
591
+ )
592
+ else:
593
+ model_kwargs = kwargs
594
+
595
+ if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
596
+ try:
597
+ from .quantizer import init_model_weight_int4
598
+ from accelerate import init_empty_weights, dispatch_model, infer_auto_device_map
599
+ from accelerate.utils import CustomDtype
600
+ from accelerate.utils import get_balanced_memory
601
+ except ImportError:
602
+ raise ImportError(f"Needs import model weight init func to run quantize.")
603
+ # Instantiate model.
604
+ init_contexts = [no_init_weights(_enable=True)]
605
+ init_contexts.append(init_empty_weights())
606
+ with ContextManagers(init_contexts):
607
+ model = cls(config)
608
+
609
+ model_file = os.path.join(pretrained_model_name_or_path, 'pytorch_model.bin')
610
+ state_dict = torch.load(model_file, map_location="cpu")
611
+ model.is_quantized = True
612
+
613
+ device_map = kwargs.pop("device_map", None)
614
+ torch_dtype = kwargs.pop("torch_dtype", None)
615
+
616
+ if device_map is not None:
617
+ kwargs = {"no_split_module_classes": model._no_split_modules}
618
+ target_dtype = CustomDtype.INT4
619
+ max_memory = get_balanced_memory(
620
+ model,
621
+ dtype=target_dtype,
622
+ low_zero=(device_map == "balanced_low_0"),
623
+ max_memory=None,
624
+ **kwargs,
625
+ )
626
+ kwargs["max_memory"] = max_memory
627
+ device_map = infer_auto_device_map(model, dtype=target_dtype, **kwargs)
628
+
629
+ model = init_model_weight_int4(config, model, state_dict)
630
+
631
+ # Set model in evaluation mode to deactivate DropOut modules by default
632
+ model.eval()
633
+ # If it is a model with generation capabilities, attempt to load the generation config
634
+ if model.can_generate():
635
+ try:
636
+ model.generation_config = GenerationConfig.from_pretrained(
637
+ pretrained_model_name_or_path,
638
+ cache_dir=cache_dir,
639
+ force_download=force_download,
640
+ resume_download=False,
641
+ proxies=None,
642
+ local_files_only=local_files_only,
643
+ token=token,
644
+ revision=revision,
645
+ subfolder="",
646
+ _from_auto=False,
647
+ _from_pipeline=None,
648
+ **kwargs,
649
+ )
650
+ except (OSError, TypeError):
651
+ logger.info(
652
+ "Generation config file not found, using a generation config created from the model config."
653
+ )
654
+ pass
655
+
656
+ if device_map is not None:
657
+ dispatch_model(model, device_map=device_map)
658
+
659
+ return model
660
+ return super(BaichuanForCausalLM, cls).from_pretrained(pretrained_model_name_or_path, *model_args,
661
+ config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes,
662
+ force_download=force_download, local_files_only=local_files_only, token=token, revision=revision,
663
+ use_safetensors=use_safetensors, **kwargs)
664
+
665
+ def forward(
666
+ self,
667
+ input_ids: torch.LongTensor = None,
668
+ attention_mask: Optional[torch.Tensor] = None,
669
+ position_ids: Optional[torch.LongTensor] = None,
670
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
671
+ inputs_embeds: Optional[torch.FloatTensor] = None,
672
+ labels: Optional[torch.LongTensor] = None,
673
+ use_cache: Optional[bool] = None,
674
+ output_attentions: Optional[bool] = None,
675
+ output_hidden_states: Optional[bool] = None,
676
+ return_dict: Optional[bool] = None,
677
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
678
+
679
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
680
+ output_hidden_states = (
681
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
682
+ )
683
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
684
+
685
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
686
+ outputs = self.model(
687
+ input_ids=input_ids,
688
+ attention_mask=attention_mask,
689
+ position_ids=position_ids,
690
+ past_key_values=past_key_values,
691
+ inputs_embeds=inputs_embeds,
692
+ use_cache=use_cache,
693
+ output_attentions=output_attentions,
694
+ output_hidden_states=output_hidden_states,
695
+ return_dict=return_dict,
696
+ )
697
+
698
+ hidden_states = outputs[0]
699
+ logits = self.lm_head(hidden_states)
700
+ loss = None
701
+ if labels is not None:
702
+ # Shift so that tokens < n predict n
703
+ shift_logits = logits[..., :-1, :].contiguous()
704
+ shift_labels = labels[..., 1:].contiguous()
705
+ # Flatten the tokens
706
+ loss_fct = CrossEntropyLoss()
707
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
708
+ shift_labels = shift_labels.view(-1)
709
+ softmax_normalizer = shift_logits.max(-1).values ** 2
710
+ z_loss = self.config.z_loss_weight * softmax_normalizer.mean()
711
+ # Enable model parallelism
712
+ shift_labels = shift_labels.to(shift_logits.device)
713
+ loss = loss_fct(shift_logits, shift_labels) + z_loss
714
+
715
+ if not return_dict:
716
+ output = (logits,) + outputs[1:]
717
+ return (loss,) + output if loss is not None else output
718
+
719
+ return CausalLMOutputWithPast(
720
+ loss=loss,
721
+ logits=logits,
722
+ past_key_values=outputs.past_key_values,
723
+ hidden_states=outputs.hidden_states,
724
+ attentions=outputs.attentions,
725
+ )
726
+
727
+ def prepare_inputs_for_generation(
728
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
729
+ ):
730
+ if past_key_values:
731
+ input_ids = input_ids[:, -1:]
732
+
733
+ position_ids = kwargs.get("position_ids", None)
734
+ if attention_mask is not None and position_ids is None:
735
+ # create position_ids on the fly for batch generation
736
+ position_ids = attention_mask.long().cumsum(-1) - 1
737
+ position_ids.masked_fill_(attention_mask == 0, 1)
738
+ if past_key_values:
739
+ position_ids = position_ids[:, -1].unsqueeze(-1)
740
+
741
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
742
+ if inputs_embeds is not None and past_key_values is None:
743
+ model_inputs = {"inputs_embeds": inputs_embeds}
744
+ else:
745
+ model_inputs = {"input_ids": input_ids}
746
+
747
+ model_inputs.update(
748
+ {
749
+ "position_ids": position_ids,
750
+ "past_key_values": past_key_values,
751
+ "use_cache": kwargs.get("use_cache"),
752
+ "attention_mask": attention_mask,
753
+ }
754
+ )
755
+ return model_inputs
756
+
757
+ @staticmethod
758
+ def _reorder_cache(past_key_values, beam_idx):
759
+ reordered_past = ()
760
+ for layer_past in past_key_values:
761
+ reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
762
+ return reordered_past
763
+
764
+ def quantize(self, bits: int):
765
+ try:
766
+ from .quantizer import quantize_online
767
+ except ImportError:
768
+ raise ImportError(f"Needs QLinear to run quantize.")
769
+ return quantize_online(self, bits)
770
+
771
+ def chat(self, tokenizer, messages: List[dict], stream=False,
772
+ generation_config: Optional[GenerationConfig]=None):
773
+ generation_config = generation_config or self.generation_config
774
+ input_ids = build_chat_input(self, tokenizer, messages, generation_config.max_new_tokens)
775
+ if stream:
776
+ streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
777
+ Thread(target=self.generate, kwargs=dict(
778
+ inputs=input_ids, streamer=streamer,
779
+ generation_config=generation_config,
780
+ )).start()
781
+ return streamer
782
+ else:
783
+ outputs = self.generate(input_ids, generation_config=generation_config)
784
+ response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
785
+ return response
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+ }
234
+ }
quantizer.py ADDED
@@ -0,0 +1,210 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import bitsandbytes as bnb
2
+ from bitsandbytes.nn.modules import Params4bit, Int8Params
3
+ import torch
4
+
5
+ def Params4bitCuda(self, device):
6
+ self.data = self.data.cuda(device)
7
+ self.quant_state[0] = self.quant_state[0].cuda(device)
8
+ self.quant_state[4][0] = self.quant_state[4][0].cuda(device)
9
+ self.quant_state[4][1][0] = self.quant_state[4][1][0].cuda(device)
10
+ self.quant_state[4][1][1] = self.quant_state[4][1][1].cuda(device)
11
+
12
+ self.quant_state[6] = self.quant_state[6].cuda(device)
13
+ return self
14
+
15
+ class Linear4bitOnline(torch.nn.Module):
16
+ def __init__(self, weight, bias, quant_type):
17
+ super().__init__()
18
+ self.weight = Params4bit(
19
+ weight.data, requires_grad=False, compress_statistics=True, quant_type=quant_type
20
+ )
21
+ self.compute_dtype = None
22
+ #self.weight.cuda(weight.device)
23
+ self.bias = bias
24
+
25
+ def forward(self, x: torch.Tensor):
26
+ # weights are cast automatically as Int8Params, but the bias has to be cast manually
27
+ if self.bias is not None and self.bias.dtype != x.dtype:
28
+ self.bias.data = self.bias.data.to(x.dtype)
29
+
30
+ if getattr(self.weight, "quant_state", None) is None:
31
+ print(
32
+ "FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first."
33
+ )
34
+ inp_dtype = x.dtype
35
+ if self.compute_dtype is not None:
36
+ x = x.to(self.compute_dtype)
37
+
38
+ bias = None if self.bias is None else self.bias.to(self.compute_dtype)
39
+ out = bnb.matmul_4bit(
40
+ x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state
41
+ )
42
+
43
+ out = out.to(inp_dtype)
44
+
45
+ return out
46
+
47
+ class Linear8bitLtOnline(torch.nn.Module):
48
+ def __init__(
49
+ self,
50
+ weight,
51
+ bias,
52
+ has_fp16_weights=True,
53
+ memory_efficient_backward=False,
54
+ threshold=0.0,
55
+ index=None,
56
+ ):
57
+ super().__init__()
58
+ assert (
59
+ not memory_efficient_backward
60
+ ), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
61
+ self.state = bnb.MatmulLtState()
62
+ self.index = index
63
+
64
+ # Necessary for stacked layers
65
+ self.state.threshold = threshold
66
+ self.state.has_fp16_weights = has_fp16_weights
67
+ self.state.memory_efficient_backward = memory_efficient_backward
68
+ if threshold > 0.0 and not has_fp16_weights:
69
+ self.state.use_pool = True
70
+
71
+ self.weight = Int8Params(
72
+ weight.data,
73
+ has_fp16_weights=has_fp16_weights,
74
+ requires_grad=has_fp16_weights,
75
+ )
76
+ self.bias = bias
77
+
78
+ def init_8bit_state(self):
79
+ self.state.CB = self.weight.CB
80
+ self.state.SCB = self.weight.SCB
81
+ self.weight.CB = None
82
+ self.weight.SCB = None
83
+
84
+ def forward(self, x: torch.Tensor):
85
+ self.state.is_training = self.training
86
+ if self.weight.CB is not None:
87
+ self.init_8bit_state()
88
+
89
+ # weights are cast automatically as Int8Params, but the bias has to be cast manually
90
+ if self.bias is not None and self.bias.dtype != x.dtype:
91
+ self.bias.data = self.bias.data.to(x.dtype)
92
+
93
+ out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
94
+
95
+ if not self.state.has_fp16_weights:
96
+ if self.state.CB is not None and self.state.CxB is not None:
97
+ # we converted 8-bit row major to turing/ampere format in the first inference pass
98
+ # we no longer need the row-major weight
99
+ del self.state.CB
100
+ self.weight.data = self.state.CxB
101
+ return out
102
+
103
+ def quantize_offline(model, bits: int):
104
+ assert (bits == 4), f'bits: {bits} is not supported'
105
+
106
+ for i, layer in enumerate(model.model.layers):
107
+ layer.self_attn.W_pack = bnb.nn.Linear4bit(
108
+ layer.self_attn.W_pack.weight.shape[1],
109
+ layer.self_attn.W_pack.weight.shape[0],
110
+ False,
111
+ torch.float16,
112
+ compress_statistics=True,
113
+ quant_type="nf4",
114
+ )
115
+ layer.self_attn.o_proj = bnb.nn.Linear4bit(
116
+ layer.self_attn.o_proj.weight.shape[1],
117
+ layer.self_attn.o_proj.weight.shape[0],
118
+ False,
119
+ torch.float16,
120
+ compress_statistics=True,
121
+ quant_type="nf4",
122
+ )
123
+
124
+ layer.mlp.gate_proj = bnb.nn.Linear4bit(
125
+ layer.mlp.gate_proj.weight.shape[1],
126
+ layer.mlp.gate_proj.weight.shape[0],
127
+ False,
128
+ torch.float16,
129
+ compress_statistics=True,
130
+ quant_type="nf4",
131
+ )
132
+ layer.mlp.down_proj = bnb.nn.Linear4bit(
133
+ layer.mlp.down_proj.weight.shape[1],
134
+ layer.mlp.down_proj.weight.shape[0],
135
+ False,
136
+ torch.float16,
137
+ compress_statistics=True,
138
+ quant_type="nf4",
139
+ )
140
+ layer.mlp.up_proj = bnb.nn.Linear4bit(
141
+ layer.mlp.up_proj.weight.shape[1],
142
+ layer.mlp.up_proj.weight.shape[0],
143
+ False,
144
+ torch.float16,
145
+ compress_statistics=True,
146
+ quant_type="nf4",
147
+ )
148
+ return model
149
+
150
+ def quantize_online(model, bits: int):
151
+ def quant(weight, bias=None):
152
+ if bits == 8:
153
+ linear = Linear8bitLtOnline(
154
+ weight,
155
+ bias,
156
+ has_fp16_weights=False,
157
+ threshold=6.0,
158
+ )
159
+ if bias is not None:
160
+ linear.bias = torch.nn.Parameter(bias)
161
+ elif bits == 4:
162
+ linear = Linear4bitOnline(
163
+ weight,
164
+ bias,
165
+ quant_type="nf4", #fp4/nf4
166
+ )
167
+ else:
168
+ raise ValueError("quantize only support 4/8 bit")
169
+ return linear
170
+
171
+ for i, layer in enumerate(model.model.layers):
172
+ layer.self_attn.W_pack = quant(layer.self_attn.W_pack.weight)
173
+ layer.self_attn.o_proj = quant(layer.self_attn.o_proj.weight)
174
+ layer.mlp.gate_proj = quant(layer.mlp.gate_proj.weight)
175
+ layer.mlp.down_proj = quant(layer.mlp.down_proj.weight)
176
+ layer.mlp.up_proj = quant(layer.mlp.up_proj.weight)
177
+ return model
178
+
179
+ def init_model_weight_int4(config, model, state_dict):
180
+ #replace Params4bit.cuda with Params4bitCuda
181
+ Params4bit.cuda = Params4bitCuda
182
+
183
+ for i in range(config.num_hidden_layers):
184
+ weight_data = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.data']
185
+ weight_quant_state = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.quant_state']
186
+ model.model.layers[i].self_attn.W_pack.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
187
+
188
+ weight_data = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.data']
189
+ weight_quant_state = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.quant_state']
190
+ model.model.layers[i].self_attn.o_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
191
+
192
+ weight_data = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.data']
193
+ weight_quant_state = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.quant_state']
194
+ model.model.layers[i].mlp.gate_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
195
+
196
+ weight_data = state_dict[f'model.layers.{i}.mlp.up_proj.weight.data']
197
+ weight_quant_state = state_dict[f'model.layers.{i}.mlp.up_proj.weight.quant_state']
198
+ model.model.layers[i].mlp.up_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
199
+
200
+ weight_data = state_dict[f'model.layers.{i}.mlp.down_proj.weight.data']
201
+ weight_quant_state = state_dict[f'model.layers.{i}.mlp.down_proj.weight.quant_state']
202
+ model.model.layers[i].mlp.down_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
203
+
204
+ model.model.layers[i].input_layernorm.weight = state_dict[f'model.layers.{i}.input_layernorm.weight']
205
+ model.model.layers[i].post_attention_layernorm.weight = state_dict[f'model.layers.{i}.post_attention_layernorm.weight']
206
+
207
+ model.model.embed_tokens.weight = state_dict['model.embed_tokens.weight']
208
+ model.model.norm.weight = state_dict['model.norm.weight']
209
+ model.lm_head.weight = state_dict['lm_head.weight']
210
+ return model