baichuan.py 23.5 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

codethazine's avatar
codethazine committed
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
Woosuk Kwon's avatar
Woosuk Kwon committed
22
"""Inference-only BaiChuan model compatible with HuggingFace weights."""
23
import math
24
25
from collections.abc import Iterable
from typing import Optional, Union
codethazine's avatar
codethazine committed
26
27

import torch
28
from torch import nn
29
from transformers import PretrainedConfig
codethazine's avatar
codethazine committed
30

zhuwenwen's avatar
zhuwenwen committed
31
import os
zhuwenwen's avatar
zhuwenwen committed
32
import re
33
import vllm.envs as envs
zhuwenwen's avatar
zhuwenwen committed
34

35
from vllm.attention import Attention
36
from vllm.compilation.decorators import support_torch_compile
37
from vllm.config import CacheConfig, VllmConfig
38
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
39
                              get_tensor_model_parallel_world_size)
40
41
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
42
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
43
44
                                               QKVParallelLinear,
                                               RowParallelLinear)
45
from vllm.model_executor.layers.logits_processor import LogitsProcessor
46
from vllm.model_executor.layers.quantization import QuantizationConfig
47
from vllm.model_executor.layers.rotary_embedding import get_rope
48
from vllm.model_executor.layers.vocab_parallel_embedding import (
49
    ParallelLMHead, VocabParallelEmbedding)
50
51
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader, row_parallel_weight_loader)
52
from vllm.model_executor.sampling_metadata import SamplingMetadata
53
from vllm.sequence import IntermediateTensors
codethazine's avatar
codethazine committed
54

55
from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
56
from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
57
                    make_empty_intermediate_tensors_factory, make_layers)
codethazine's avatar
codethazine committed
58

zhuwenwen's avatar
zhuwenwen committed
59
from vllm import _custom_ops as ops
60
from vllm.model_executor.utils import pad_weight, gemm_bank_conf
codethazine's avatar
codethazine committed
61

62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95


def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
    closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
    base = torch.tensor(
        2**(-(2**-(math.log2(closest_power_of_2) - 3))),
        dtype=torch.float32,
    )
    powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
    slopes = torch.pow(base, powers)

    if closest_power_of_2 != total_num_heads:
        extra_base = torch.tensor(
            2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
            dtype=torch.float32,
        )
        num_remaining_heads = min(closest_power_of_2,
                                  total_num_heads - closest_power_of_2)
        extra_powers = torch.arange(start=1,
                                    end=1 + 2 * num_remaining_heads,
                                    step=2,
                                    dtype=torch.int32)
        slopes = torch.cat(
            [slopes, torch.pow(extra_base, extra_powers)], dim=0)
    return slopes


class BaiChuanMLP(nn.Module):

    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
96
        quant_config: Optional[QuantizationConfig] = None,
97
98
99
100
101
    ):
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size, [intermediate_size] * 2,
            bias=False,
102
            quant_config=quant_config)
103
104
105
        self.down_proj = RowParallelLinear(intermediate_size,
                                           hidden_size,
                                           bias=False,
106
                                           quant_config=quant_config)
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
        if hidden_act != "silu":
            raise ValueError(f"Unsupported activation: {hidden_act}. "
                             "Only silu is supported for now.")
        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class BaiChuanAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        position_embedding: str,
        rope_theta: float = 10000,
        max_position_embeddings: int = 8192,
129
        cache_config: Optional[CacheConfig] = None,
130
        quant_config: Optional[QuantizationConfig] = None,
131
        prefix: str = "",
132
133
134
135
136
137
138
139
140
141
    ):
        super().__init__()
        self.hidden_size = hidden_size
        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
        )
        self.total_num_heads = num_heads
        assert self.total_num_heads % tensor_model_parallel_world_size == 0
        self.num_heads = (self.total_num_heads //
                          tensor_model_parallel_world_size)
        self.head_dim = hidden_size // self.total_num_heads
142
        self.position_embedding = position_embedding
143
144
145
146
147
148
149
150
151
152
        self.rope_theta = rope_theta
        self.max_position_embeddings = max_position_embeddings

        # pylint: disable=invalid-name
        self.W_pack = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_heads,
            bias=False,
153
            quant_config=quant_config,
154
155
156
157
158
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
159
            quant_config=quant_config,
160
161
        )
        # Create the alibi slopes and slice them.
162
        if self.position_embedding == "ALIBI":
163
164
165
166
167
168
169
            tp_rank = get_tensor_model_parallel_rank()
            head_start = tp_rank * self.num_heads
            head_end = (tp_rank + 1) * self.num_heads
            alibi_slopes = _get_alibi_slopes(self.total_num_heads)
            alibi_slopes = alibi_slopes[head_start:head_end].tolist()

            scaling = self.head_dim**-0.5
170
171
172
            self.attn = Attention(self.num_heads,
                                  self.head_dim,
                                  scaling,
173
                                  alibi_slopes=alibi_slopes,
174
175
                                  quant_config=quant_config,
                                  prefix=f"{prefix}.attn")
176
177
178
179
180
181
182
183
        else:
            self.rotary_emb = get_rope(
                self.head_dim,
                rotary_dim=self.head_dim,
                max_position=self.max_position_embeddings,
                base=self.rope_theta,
            )
            self.scaling = self.head_dim**-0.5
184
185
186
            self.attn = Attention(self.num_heads,
                                  self.head_dim,
                                  self.scaling,
187
                                  cache_config=cache_config,
188
189
                                  quant_config=quant_config,
                                  prefix=f"{prefix}.attn")
190
            
zhuwenwen's avatar
zhuwenwen committed
191
192
193
194
            self.quant_method = None
            if quant_config is not None:
                self.quant_method=quant_config.get_name()
                self.quant_config=quant_config
195
196
197
198
199
200
201

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.W_pack(hidden_states)
zhuwenwen's avatar
zhuwenwen committed
202
203
        # if os.environ.get('FA_PAD') == '1' and self.quant_method is None:
        #     qkv = qkv[...,:-32]
204
        q, k, v = qkv.chunk(chunks=3, dim=-1)
205
        if self.position_embedding != "ALIBI":
206
            q, k = self.rotary_emb(positions, q, k)
207
        attn_output = self.attn(q, k, v)
208
209
210
211
212
213
214
        output, _ = self.o_proj(attn_output)
        return output


class BaiChuanDecoderLayer(nn.Module):

    def __init__(self,
215
                 config: PretrainedConfig,
216
                 position_embedding: str,
217
                 cache_config: Optional[CacheConfig] = None,
218
219
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = ""):
220
221
222
223
224
225
226
227
228
229
230
        super().__init__()
        self.hidden_size = config.hidden_size
        rope_theta = getattr(config, "rope_theta", 10000)
        max_position_embeddings = getattr(config, "max_position_embeddings",
                                          8192)
        self.self_attn = BaiChuanAttention(
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            position_embedding=position_embedding,
            rope_theta=rope_theta,
            max_position_embeddings=max_position_embeddings,
231
            cache_config=cache_config,
232
            quant_config=quant_config,
233
            prefix=f"{prefix}.self_attn",
234
235
236
237
238
        )
        self.mlp = BaiChuanMLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
239
            quant_config=quant_config,
240
241
242
243
244
245
246
247
248
249
250
        )
        self.input_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                eps=config.rms_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: Optional[torch.Tensor],
251
    ) -> tuple[torch.Tensor, torch.Tensor]:
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
        # Self Attention
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(
                hidden_states, residual)
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
        )

        # Fully Connected
        hidden_states, residual = self.post_attention_layernorm(
            hidden_states, residual)
        hidden_states = self.mlp(hidden_states)
        return hidden_states, residual


271
@support_torch_compile
272
273
class BaiChuanModel(nn.Module):

274
275
276
277
278
279
    def __init__(
        self,
        vllm_config: VllmConfig,
        prefix: str = "",
        position_embedding: str = "ROPE",
    ) -> None:
280
        super().__init__()
281
282
283
284
285

        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config

286
287
288
289
290
291
292
        self.config = config
        self.vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )
293
294
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
295
296
297
298
299
            lambda prefix: BaiChuanDecoderLayer(config,
                                                position_embedding,
                                                cache_config,
                                                quant_config,
                                                prefix=prefix),
300
301
            prefix=f"{prefix}.layers",
        )
302
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
303
304
305
        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(
                ["hidden_states", "residual"], config.hidden_size))
zhuwenwen's avatar
zhuwenwen committed
306
307
308
309
310
311
312
313
314
315
        
        self.quant_method = None
        if quant_config is not None:
            self.quant_method=quant_config.get_name()
            self.quant_config=quant_config

        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
        self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
        self.use_fa_pad = os.environ.get('FA_PAD') == '1'
        self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
316

317
318
    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_tokens(input_ids)
319
320
321
322
323

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
324
        intermediate_tensors: Optional[IntermediateTensors],
325
        inputs_embeds: Optional[torch.Tensor] = None,
326
327
    ) -> Union[torch.Tensor, IntermediateTensors]:
        if get_pp_group().is_first_rank:
328
329
330
331
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.get_input_embeddings(input_ids)
332
333
334
335
336
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]
337
        for layer in self.layers[self.start_layer:self.end_layer]:
338
339
340
341
342
            hidden_states, residual = layer(
                positions,
                hidden_states,
                residual,
            )
343
344
345
346
347
        if not get_pp_group().is_last_rank:
            return IntermediateTensors({
                "hidden_states": hidden_states,
                "residual": residual,
            })
348
349
350
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states

351
352
    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
353
354
355
356
357
358
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
359
        loaded_params: set[str] = set()
360
        for name, loaded_weight in weights:
codethazine's avatar
codethazine committed
361
362
            if "rotary_emb.inv_freq" in name:
                continue
363

364
            for (param_name, weight_name, shard_id) in stacked_params_mapping:
codethazine's avatar
codethazine committed
365
366
                if weight_name not in name:
                    continue
CHU Tianxiang's avatar
CHU Tianxiang committed
367
368
369
370
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
371
372
                if is_pp_missing_parameter(name, self):
                    continue
CHU Tianxiang's avatar
CHU Tianxiang committed
373
                param = params_dict[name]
374
375
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
codethazine's avatar
codethazine committed
376
                break
377
            else:
CHU Tianxiang's avatar
CHU Tianxiang committed
378
379
380
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
381
382
                if is_pp_missing_parameter(name, self):
                    continue
383
384
385
386
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
387
            loaded_params.add(name)
zhuwenwen's avatar
zhuwenwen committed
388
            
389
        if self.use_llama_nn and self.quant_method is None :
zhuwenwen's avatar
zhuwenwen committed
390
391
392
393
            lay_key_words = [
                "self_attn.W_pack.weight",
                "self_attn.o_proj.weight",
                "mlp.gate_up_proj.weight",
394
395
                "mlp.down_proj.weight",
                "lm_head.weight"
zhuwenwen's avatar
zhuwenwen committed
396
397
398
            ]
            combined_words = "|".join(lay_key_words)
            
zhuwenwen's avatar
zhuwenwen committed
399
400
            # lay_qkv_words = ["self_attn.W_pack.weight"]   
            # qkv_words = "|".join(lay_qkv_words)  
zhuwenwen's avatar
zhuwenwen committed
401
            
zhuwenwen's avatar
zhuwenwen committed
402
403
            for layername in loaded_params:
                weight = params_dict[layername]
zhuwenwen's avatar
zhuwenwen committed
404
405
406
407
408
409
                if "lm_head.weight" in layername and weight.shape[1] >= 4096:
                    lay_key_words.append("lm_head.weight")
                    combined_words = "|".join(lay_key_words)
                    os.environ['LM_NN'] = '1'  
                else:
                    os.environ['LM_NN'] = '0' 
zhuwenwen's avatar
zhuwenwen committed
410
                matches = re.findall(combined_words, layername)
411
                if matches:      
zhuwenwen's avatar
zhuwenwen committed
412
413
                    # if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
                    #     weight.data = pad_weight(weight.data, 32)  
414
                        
zhuwenwen's avatar
zhuwenwen committed
415
416
417
                    # if self.use_fa_pad and (re.findall(qkv_words, layername)):
                    #     if not gemm_bank_conf(weight.data.shape[0]):
                    #         weight.data = pad_weight(weight.data, 32)
418
                                    
zhuwenwen's avatar
zhuwenwen committed
419
420
421
422
423
424
425
                    _weight = torch.zeros_like(weight.data)
                    ori_shape =_weight.shape
                    
                    ops.trans_w16_gemm(_weight, weight.data, _weight.shape[0], _weight.shape[1])
                    weight.data.copy_(_weight)
                    
                    weight.data=weight.data.reshape(ori_shape[1], -1)
426
        else:
zhuwenwen's avatar
zhuwenwen committed
427
            os.environ['LM_NN'] = '0'
428
429
            os.environ['LLAMA_NN'] = '0'
            
430
431
432
433
434
435
436
437
        # if self.quant_method == "awq" and not envs.VLLM_USE_TRITON_AWQ:
        #     lay_key_words = [
        #         "self_attn.W_pack.qweight",
        #         "self_attn.o_proj.qweight",
        #         "mlp.gate_up_proj.qweight",
        #         "mlp.down_proj.qweight"
        #     ]
        #     combined_words = "|".join(lay_key_words)
438
            
439
440
        #     for layername in loaded_params:
        #         weight = params_dict[layername]
441
                
442
443
444
445
446
447
        #         matches = re.findall(combined_words, layername)
        #         if matches:
        #             qweight =params_dict[layername]
        #             qzeros=params_dict[layername.replace("qweight", "qzeros")]
        #             scales=params_dict[layername.replace("qweight", "scales")]
        #             zeros_and_scalse =params_dict[layername.replace("qweight", "zeros_and_scales")]
448
                    
449
        #             group_size= self.quant_config.group_size 
450
                   
451
452
453
        #             dim_n = scales.data.shape[1]
        #             dim_k = qweight.data.shape[0]
        #             pad_group=2              
454
                    
455
        #             _qw, _sz=ops.convert_s4(qweight,qzeros,scales,int(group_size)) 
456
                    
457
        #             sz = ops.sz_permute(_sz).reshape(-1,dim_n)       
458
                    
459
460
        #             zeros_and_scalse.data.copy_(sz)
        #             qweight.data.copy_(_qw)
461
                    
462
463
464
        #             #reshape
        #             zeros_and_scalse.data=zeros_and_scalse.reshape(dim_n,-1)    #[k/greop_size,n]------>[n,k/group_size]
        #             qweight.data=qweight.data.reshape(dim_n,-1)                      #[k,n/8]---->[n,k/8]  
465
                
466
467
468
469
470
        #             if dim_k % 4096==0 and self.use_awq_pad:
        #                 zeros_and_scalse_pad= torch.zeros(dim_n,pad_group,dtype=torch.int32).cuda()
        #                 zeros_and_scalse.data=torch.cat((zeros_and_scalse.data,zeros_and_scalse_pad),dim=1).contiguous()
        #                 qweight_pad= torch.zeros(dim_n,int(group_size//4),dtype=torch.int32).cuda()
        #                 qweight.data=torch.cat((qweight.data,qweight_pad),dim=1).contiguous()  
471
        return loaded_params
472

473

474
475
class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
                              SupportsQuant):
476
477
478
479
480
481
482
    packed_modules_mapping = {
        "W_pack": ["W_pack"],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }
483

484
485
    def __init__(
        self,
486
        *,
487
488
489
        vllm_config: VllmConfig,
        prefix: str = "",
        position_embedding: str = "ROPE",
490
    ):
491
        super().__init__()
492
493
494
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config
495
        self.config = config
496
        self.lora_config = lora_config
497
        self.tp_size = get_tensor_model_parallel_world_size()
498
        self.quant_config = quant_config
499
500
501
        self.model = BaiChuanModel(vllm_config=vllm_config,
                                   prefix=prefix,
                                   position_embedding=position_embedding)
502
503
504
        self.lm_head = ParallelLMHead(config.vocab_size,
                                      config.hidden_size,
                                      quant_config=quant_config)
505
        self.lm_head.weight.weight_loader = self.lm_head_weight_loader
506
507
        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.model.embed_tokens.weight
508
        self.logits_processor = LogitsProcessor(config.vocab_size)
509
510
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)
zhuwenwen's avatar
zhuwenwen committed
511
        
512

513
514
515
    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.get_input_embeddings(input_ids)

516
517
518
519
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
520
        intermediate_tensors: Optional[IntermediateTensors] = None,
521
        inputs_embeds: Optional[torch.Tensor] = None,
522
    ) -> Union[torch.Tensor, IntermediateTensors]:
523
        hidden_states = self.model(input_ids, positions, intermediate_tensors,
524
                                   inputs_embeds)
525
526
        return hidden_states

527
528
529
530
531
    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
532
        logits = self.logits_processor(self.lm_head, hidden_states,
533
534
535
                                       sampling_metadata)
        return logits

536
537
    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
538
539
540
541
542
543
544
545
546
547
548
549
550
551
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)

    def lm_head_weight_loader(self, param: nn.Parameter,
                              loaded_weight: torch.Tensor):
        # Unlike Baichuan, Baichuan2 normalizes the head weights.
        # Refer to:
        # https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/84603cde5ebffb6084e476cfaeceaf0b8b91fe54/modeling_baichuan.py#L508
        # Distinguish between Baichuan and Baichuan2 by checking the
        # vocab size. This is suggested by
        # https://github.com/vllm-project/vllm/pull/1022#discussion_r1325652704
        is_baichuan2 = self.config.vocab_size == 125696
        if is_baichuan2:
            loaded_weight = torch.nn.functional.normalize(loaded_weight)
552
553
554
555
        if self.tp_size > 1:
            row_parallel_weight_loader(param, loaded_weight)
        else:
            default_weight_loader(param, loaded_weight)
556
557


558
class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
559
560
561
    """Baichuan 13B and Baichuan2 7B/13B.
    NOTE: the class name has a lower case 'c'.
    """
562

563
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
564
        config = vllm_config.model_config.hf_config
565
        if config.hidden_size == 4096:  # baichuan2 7b
566
567
568
            super().__init__(vllm_config=vllm_config,
                             prefix=prefix,
                             position_embedding="ROPE")
569
        else:  # baichuan 13b, baichuan2 13b
570
571
572
            super().__init__(vllm_config=vllm_config,
                             prefix=prefix,
                             position_embedding="ALIBI")
573
574


575
class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
576
577
578
    """Baichuan 7B.
    NOTE: the class name has an upper case 'C'.
    """
579

580
581
582
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__(vllm_config=vllm_config,
                         prefix=prefix,
zhuwenwen's avatar
zhuwenwen committed
583
                         position_embedding="ROPE")