mixtral.py 19.5 KB
Newer Older
1
2
# SPDX-License-Identifier: Apache-2.0

Pierre Stock's avatar
Pierre Stock committed
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
# Copyright 2023 The vLLM team.
# 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.
"""Inference-only Mixtral model."""
25
from typing import Iterable, Optional, Set, Tuple, Union
Pierre Stock's avatar
Pierre Stock committed
26
27
28

import torch
from torch import nn
29
from transformers import MixtralConfig
Pierre Stock's avatar
Pierre Stock committed
30

31
from vllm.attention import Attention
32
from vllm.compilation.decorators import support_torch_compile
33
from vllm.config import CacheConfig, VllmConfig
34
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
35
from vllm.model_executor.layers.fused_moe import FusedMoE
Pierre Stock's avatar
Pierre Stock committed
36
from vllm.model_executor.layers.layernorm import RMSNorm
37
from vllm.model_executor.layers.linear import (QKVParallelLinear,
Philipp Moritz's avatar
Philipp Moritz committed
38
                                               ReplicatedLinear,
Pierre Stock's avatar
Pierre Stock committed
39
                                               RowParallelLinear)
40
from vllm.model_executor.layers.logits_processor import LogitsProcessor
41
from vllm.model_executor.layers.quantization import QuantizationConfig
42
from vllm.model_executor.layers.rotary_embedding import get_rope
Joe Runde's avatar
Joe Runde committed
43
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
Pierre Stock's avatar
Pierre Stock committed
44
from vllm.model_executor.layers.vocab_parallel_embedding import (
45
    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
46
47
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader, maybe_remap_kv_scale_name)
Pierre Stock's avatar
Pierre Stock committed
48
from vllm.model_executor.sampling_metadata import SamplingMetadata
49
from vllm.sequence import IntermediateTensors
Pierre Stock's avatar
Pierre Stock committed
50

51
52
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (is_pp_missing_parameter,
53
54
                    make_empty_intermediate_tensors_factory, make_layers,
                    maybe_prefix)
55

Pierre Stock's avatar
Pierre Stock committed
56

Philipp Moritz's avatar
Philipp Moritz committed
57
58
59
60
61
62
63
64
class MixtralMoE(nn.Module):
    """A tensor-parallel MoE implementation for Mixtral that shards each expert
    across all ranks.

    Each expert's weights are sharded across all ranks and a fused MoE
    kernel is used for the forward pass, and finally we reduce the outputs
    across ranks.
    """
65

66
67
68
69
70
71
72
    def __init__(self,
                 num_experts: int,
                 top_k: int,
                 hidden_size: int,
                 intermediate_size: int,
                 params_dtype: Optional[torch.dtype] = None,
                 quant_config: Optional[QuantizationConfig] = None,
73
74
                 tp_size: Optional[int] = None,
                 prefix: str = ""):
75
        super().__init__()
Philipp Moritz's avatar
Philipp Moritz committed
76
        self.hidden_size = hidden_size
77

78
        # Gate always runs at half / full precision for now.
79

80
81
        self.gate = ReplicatedLinear(hidden_size,
                                     num_experts,
82
                                     bias=False,
83
                                     params_dtype=params_dtype,
84
85
                                     quant_config=None,
                                     prefix=f"{prefix}.gate")
86

87
88
89
90
91
92
93
94
        self.experts = FusedMoE(num_experts=num_experts,
                                top_k=top_k,
                                hidden_size=hidden_size,
                                intermediate_size=intermediate_size,
                                params_dtype=params_dtype,
                                reduce_results=True,
                                renormalize=True,
                                quant_config=quant_config,
95
96
                                tp_size=tp_size,
                                prefix=f"{prefix}.experts")
97

98
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
99
100
        # NOTE: hidden_states can have either 1D or 2D shape.
        orig_shape = hidden_states.shape
Philipp Moritz's avatar
Philipp Moritz committed
101
        hidden_states = hidden_states.view(-1, self.hidden_size)
102
        # router_logits: (num_tokens, n_experts)
103
        router_logits, _ = self.gate(hidden_states)
104
        final_hidden_states = self.experts(hidden_states, router_logits)
105
        return final_hidden_states.view(orig_shape)
Pierre Stock's avatar
Pierre Stock committed
106
107
108
109


class MixtralAttention(nn.Module):

110
111
112
113
114
115
116
117
118
    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        max_position: int = 4096 * 32,
        rope_theta: float = 10000,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
119
        prefix: str = "",
120
    ) -> None:
Pierre Stock's avatar
Pierre Stock committed
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
        super().__init__()
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.rope_theta = rope_theta

143
        self.qkv_proj = QKVParallelLinear(
Pierre Stock's avatar
Pierre Stock committed
144
145
146
147
148
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
149
            quant_config=quant_config,
150
            prefix=f"{prefix}.qkv_proj",
Pierre Stock's avatar
Pierre Stock committed
151
        )
152
        self.o_proj = RowParallelLinear(
Pierre Stock's avatar
Pierre Stock committed
153
154
155
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
156
            quant_config=quant_config,
157
            prefix=f"{prefix}.o_proj",
Pierre Stock's avatar
Pierre Stock committed
158
159
160
161
162
163
        )
        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position,
            base=int(self.rope_theta),
164
            is_neox_style=True,
Pierre Stock's avatar
Pierre Stock committed
165
        )
166
167
168
169
170
        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
                              num_kv_heads=self.num_kv_heads,
                              cache_config=cache_config,
171
172
                              quant_config=quant_config,
                              prefix=f"{prefix}.attn")
Pierre Stock's avatar
Pierre Stock committed
173
174
175
176
177
178

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
179
        qkv, _ = self.qkv_proj(hidden_states)
Pierre Stock's avatar
Pierre Stock committed
180
181
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q, k = self.rotary_emb(positions, q, k)
182
        attn_output = self.attn(q, k, v)
183
        output, _ = self.o_proj(attn_output)
Pierre Stock's avatar
Pierre Stock committed
184
185
186
187
188
189
190
        return output


class MixtralDecoderLayer(nn.Module):

    def __init__(
        self,
191
        config: MixtralConfig,
192
        cache_config: Optional[CacheConfig] = None,
193
        quant_config: Optional[QuantizationConfig] = None,
194
        prefix: str = "",
Pierre Stock's avatar
Pierre Stock committed
195
196
197
198
199
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        # Requires transformers > 4.32.0
        rope_theta = getattr(config, "rope_theta", 10000)
200
        self.self_attn = MixtralAttention(
Pierre Stock's avatar
Pierre Stock committed
201
202
203
204
205
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            max_position=config.max_position_embeddings,
            num_kv_heads=config.num_key_value_heads,
            rope_theta=rope_theta,
206
            cache_config=cache_config,
207
208
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn")
Philipp Moritz's avatar
Philipp Moritz committed
209
210
211
212
        self.block_sparse_moe = MixtralMoE(
            num_experts=config.num_local_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
213
            intermediate_size=config.intermediate_size,
214
215
            quant_config=quant_config,
            prefix=f"{prefix}.block_sparse_moe")
216
217
218
219
        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)
Pierre Stock's avatar
Pierre Stock committed
220
221
222
223

    def forward(
        self,
        positions: torch.Tensor,
224
225
        hidden_states: torch.Tensor,
        residual: Optional[torch.Tensor],
Pierre Stock's avatar
Pierre Stock committed
226
    ) -> torch.Tensor:
227
228
229
230
231
232
233
234
        # 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(
Pierre Stock's avatar
Pierre Stock committed
235
            positions=positions,
236
            hidden_states=hidden_states,
Pierre Stock's avatar
Pierre Stock committed
237
238
        )

239
240
241
242
243
        # Fully Connected
        hidden_states, residual = self.post_attention_layernorm(
            hidden_states, residual)
        hidden_states = self.block_sparse_moe(hidden_states)
        return hidden_states, residual
Pierre Stock's avatar
Pierre Stock committed
244

245

246
@support_torch_compile
247
class MixtralModel(nn.Module):
Pierre Stock's avatar
Pierre Stock committed
248

249
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
Pierre Stock's avatar
Pierre Stock committed
250
        super().__init__()
251
252
253
254
255
256

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

257
258
259
260
        lora_vocab = (lora_config.lora_extra_vocab_size *
                      (lora_config.max_loras or 1)) if lora_config else 0
        self.vocab_size = config.vocab_size + lora_vocab
        self.org_vocab_size = config.vocab_size
261
262

        self.embed_tokens = VocabParallelEmbedding(
263
            self.vocab_size,
Pierre Stock's avatar
Pierre Stock committed
264
            config.hidden_size,
265
            org_num_embeddings=config.vocab_size,
Pierre Stock's avatar
Pierre Stock committed
266
        )
267
268

        self.start_layer, self.end_layer, self.layers = make_layers(
269
270
271
272
273
            config.num_hidden_layers,
            lambda prefix: MixtralDecoderLayer(
                config, cache_config, quant_config=quant_config, prefix=prefix
            ),
            prefix=f"{prefix}.layers")
274

275
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
276
277
278
        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(
                ["hidden_states", "residual"], config.hidden_size))
Pierre Stock's avatar
Pierre Stock committed
279

280
281
282
    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_tokens(input_ids)

Pierre Stock's avatar
Pierre Stock committed
283
284
285
286
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
287
        intermediate_tensors: Optional[IntermediateTensors],
288
        inputs_embeds: Optional[torch.Tensor] = None,
289
    ) -> Union[torch.Tensor, IntermediateTensors]:
290
        if get_pp_group().is_first_rank:
291
292
293
294
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.get_input_embeddings(input_ids)
295
296
297
298
299
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]
300
301
        for layer in self.layers[self.start_layer:self.end_layer]:
            hidden_states, residual = layer(positions, hidden_states, residual)
302
303
304
305
306
        if not get_pp_group().is_last_rank:
            return IntermediateTensors({
                "hidden_states": hidden_states,
                "residual": residual
            })
307
308
309
310
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


311
class MixtralForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
312
313
    fall_back_to_pt_during_load = False

Terry's avatar
Terry committed
314
315
316
317
318
319
320
321
322
323
324
325
326
327
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
    }

    # LoRA specific attributes
    embedding_modules = {
        "embed_tokens": "input_embeddings",
        "lm_head": "output_embeddings",
    }
    embedding_padding_modules = ["lm_head"]
328

329
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
330
        super().__init__()
331
332
333
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config
334
        self.config = config
335
        self.lora_config = lora_config
336
        self.quant_config = quant_config
337

338
339
        self.model = MixtralModel(vllm_config=vllm_config,
                                  prefix=maybe_prefix(prefix, "model"))
Terry's avatar
Terry committed
340
341
342
343
344
345
346
347
348
349
350
        self.unpadded_vocab_size = config.vocab_size
        if lora_config:
            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
        self.lm_head = ParallelLMHead(
            self.unpadded_vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
            padding_size=DEFAULT_VOCAB_PADDING_SIZE
            # We need bigger padding if using lora for kernel
            # compatibility
            if not lora_config else lora_config.lora_vocab_padding_size,
351
            quant_config=quant_config,
Terry's avatar
Terry committed
352
        )
353
354
        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.model.embed_tokens.weight
355
356
        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                config.vocab_size)
Joe Runde's avatar
Joe Runde committed
357
        self.sampler = get_sampler()
358
359
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)
360

361
362
363
    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.get_input_embeddings(input_ids)

364
365
366
367
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
368
        intermediate_tensors: Optional[IntermediateTensors] = None,
369
        inputs_embeds: Optional[torch.Tensor] = None,
370
    ) -> Union[torch.Tensor, IntermediateTensors]:
371
        hidden_states = self.model(input_ids, positions, intermediate_tensors,
372
                                   inputs_embeds)
Pierre Stock's avatar
Pierre Stock committed
373
374
        return hidden_states

375
376
377
378
379
    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
380
        logits = self.logits_processor(self.lm_head, hidden_states,
381
382
383
                                       sampling_metadata)
        return logits

Pierre Stock's avatar
Pierre Stock committed
384
385
    def sample(
        self,
386
        logits: Optional[torch.Tensor],
Pierre Stock's avatar
Pierre Stock committed
387
        sampling_metadata: SamplingMetadata,
388
    ) -> Optional[SamplerOutput]:
389
        next_tokens = self.sampler(logits, sampling_metadata)
Pierre Stock's avatar
Pierre Stock committed
390
391
        return next_tokens

392
393
    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
Pierre Stock's avatar
Pierre Stock committed
394
395
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
396
397
398
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
Pierre Stock's avatar
Pierre Stock committed
399
        ]
400

401
402
403
404
405
406
407
        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        expert_params_mapping = FusedMoE.make_expert_params_mapping(
            ckpt_gate_proj_name="w1",
            ckpt_down_proj_name="w2",
            ckpt_up_proj_name="w3",
            num_experts=self.config.num_local_experts)
Philipp Moritz's avatar
Philipp Moritz committed
408

Pierre Stock's avatar
Pierre Stock committed
409
        params_dict = dict(self.named_parameters())
410
        loaded_params: Set[str] = set()
411
        for name, loaded_weight in weights:
Pierre Stock's avatar
Pierre Stock committed
412
413
            if "rotary_emb.inv_freq" in name:
                continue
Philipp Moritz's avatar
Philipp Moritz committed
414

415
416
            if (self.quant_config is not None and
                (scale_name := self.quant_config.get_cache_scale(name))):
417
                # Loading kv cache quantization scales
418
419
420
421
422
423
424
425
426
                param = params_dict[scale_name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
                                 loaded_weight[0])
                weight_loader(param, loaded_weight)
                loaded_params.add(scale_name)
                continue

Pierre Stock's avatar
Pierre Stock committed
427
428
429
            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
CHU Tianxiang's avatar
CHU Tianxiang committed
430
431
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
432
433
                if ((name.endswith(".bias") or name.endswith("_bias"))
                        and name not in params_dict):
CHU Tianxiang's avatar
CHU Tianxiang committed
434
                    continue
435
436
437
                # Skip layers on other devices.
                if is_pp_missing_parameter(name, self):
                    continue
438
439
440
441
442
                if name.endswith("scale"):
                    # Remapping the name of FP8 kv-scale.
                    name = maybe_remap_kv_scale_name(name, params_dict)
                    if name is None:
                        continue
CHU Tianxiang's avatar
CHU Tianxiang committed
443
                param = params_dict[name]
Pierre Stock's avatar
Pierre Stock committed
444
445
446
447
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
448
449
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
Philipp Moritz's avatar
Philipp Moritz committed
450
451
452
                    if weight_name not in name:
                        continue
                    name = name.replace(weight_name, param_name)
453
454
455
                    # Skip layers on other devices.
                    if is_pp_missing_parameter(name, self):
                        continue
456
457
458
                    if ((name.endswith(".bias") or name.endswith("_bias"))
                            and name not in params_dict):
                        continue
Philipp Moritz's avatar
Philipp Moritz committed
459
460
461
462
                    param = params_dict[name]
                    weight_loader = param.weight_loader
                    weight_loader(param,
                                  loaded_weight,
463
                                  name,
464
                                  shard_id=shard_id,
Philipp Moritz's avatar
Philipp Moritz committed
465
466
467
468
                                  expert_id=expert_id)
                    break
                else:
                    # Skip loading extra bias for GPTQ models.
469
470
                    if ((name.endswith(".bias") or name.endswith("_bias"))
                            and name not in params_dict):
Philipp Moritz's avatar
Philipp Moritz committed
471
                        continue
472
473
474
                    # Skip layers on other devices.
                    if is_pp_missing_parameter(name, self):
                        continue
475
                    # Remapping the name of FP8 kv-scale.
476
477
478
479
                    name = maybe_remap_kv_scale_name(name, params_dict)
                    if name is None:
                        continue

Philipp Moritz's avatar
Philipp Moritz committed
480
481
482
483
                    param = params_dict[name]
                    weight_loader = getattr(param, "weight_loader",
                                            default_weight_loader)
                    weight_loader(param, loaded_weight)
484
485
            loaded_params.add(name)
        return loaded_params