commandr.py 18.8 KB
Newer Older
1
2
# SPDX-License-Identifier: Apache-2.0

3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
# Copyright 2024 Cohere 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.

# This file is based on the LLama model definition file in transformers
"""PyTorch Cohere model."""
24
from typing import Iterable, Optional, Set, Tuple, Union
25
26
27
28
29

import torch
from torch import nn
from transformers import CohereConfig

30
from vllm.attention import Attention
31
from vllm.compilation.decorators import support_torch_compile
32
from vllm.config import CacheConfig, VllmConfig
33
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
34
from vllm.model_executor.layers.activation import SiluAndMul
35
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
36
37
38
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
39
from vllm.model_executor.layers.quantization import QuantizationConfig
40
from vllm.model_executor.layers.rotary_embedding import get_rope
Joe Runde's avatar
Joe Runde committed
41
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
42
43
from vllm.model_executor.layers.vocab_parallel_embedding import (
    VocabParallelEmbedding)
44
from vllm.model_executor.model_loader.weight_utils import (
45
46
    default_weight_loader, maybe_remap_kv_scale_name,
    row_parallel_weight_loader)
47
from vllm.model_executor.sampling_metadata import SamplingMetadata
48
from vllm.model_executor.utils import set_weight_attrs
49
from vllm.platforms import current_platform
50
from vllm.sequence import IntermediateTensors
51

52
from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
53
from .utils import (extract_layer_index, is_pp_missing_parameter,
54
55
                    make_empty_intermediate_tensors_factory, make_layers,
                    maybe_prefix)
56

57

58
@torch.compile(backend=current_platform.simple_compile_backend)
59
60
61
62
63
64
65
66
67
68
69
def layer_norm_func(hidden_states, weight, variance_epsilon):
    input_dtype = hidden_states.dtype
    hidden_states = hidden_states.to(torch.float32)
    mean = hidden_states.mean(-1, keepdim=True)
    variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
    hidden_states = (hidden_states - mean) * torch.rsqrt(variance +
                                                         variance_epsilon)
    hidden_states = weight.to(torch.float32) * hidden_states
    return hidden_states.to(input_dtype)


70
71
class LayerNorm(nn.Module):

72
    def __init__(self, param_shape=None, eps=1e-5):
73
        super().__init__()
74
        self.weight = nn.Parameter(torch.ones(param_shape))
75
        self.variance_epsilon = eps
76
77
        set_weight_attrs(self.weight,
                         {"weight_loader": row_parallel_weight_loader})
78
79

    def forward(self, hidden_states, residuals=None):
80
81
82
        hidden_states = layer_norm_func(hidden_states, self.weight,
                                        self.variance_epsilon)
        return hidden_states, residuals
83
84
85
86
87
88
89


# Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere
class CohereMLP(nn.Module):

    def __init__(
        self,
90
        config: CohereConfig,
91
        quant_config: Optional[QuantizationConfig] = None,
92
93
94
95
96
97
98
99
100
    ):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_up_proj = MergedColumnParallelLinear(
            self.hidden_size,
            [self.intermediate_size] * 2,
            bias=False,
101
            quant_config=quant_config,
102
103
104
105
106
        )
        self.down_proj = RowParallelLinear(
            self.intermediate_size,
            self.hidden_size,
            bias=False,
107
            quant_config=quant_config,
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
        )
        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 CohereAttention(nn.Module):

    def __init__(
        self,
        config: CohereConfig,
123
        cache_config: Optional[CacheConfig] = None,
124
        quant_config: Optional[QuantizationConfig] = None,
125
        prefix: str = "",
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
    ):
        super().__init__()
        tp_size = get_tensor_model_parallel_world_size()
        self.config = config
        self.attention_dropout = config.attention_dropout
        self.hidden_size = config.hidden_size
        self.total_num_heads = config.num_attention_heads
        self.num_heads = self.total_num_heads // tp_size
        self.head_dim = self.hidden_size // self.total_num_heads
        self.total_num_kv_heads = config.num_key_value_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.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
148
149
150
        self.max_position_embeddings = getattr(
            config, "model_max_length", None) or getattr(
                config, "max_position_embeddings", 8192)
151
152
        self.rope_theta = config.rope_theta
        self.rope_scaling = getattr(config, "rope_scaling", None)
153
        self.use_qk_norm = getattr(config, "use_qk_norm", False)
154
155
156
157
158
159
        self.qkv_proj = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
160
            quant_config=quant_config,
161
162
163
164
165
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            self.hidden_size,
            bias=False,
166
            quant_config=quant_config,
167
168
169
170
171
172
173
174
175
        )
        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=self.max_position_embeddings,
            base=self.rope_theta,
            rope_scaling=self.rope_scaling,
            is_neox_style=False,
        )
176

177
178
179
180
181
        # Model v2 has interleaved sliding windows, v1 does not
        interleaved_sliding_window = getattr(config,
                                             "interleaved_sliding_window",
                                             None)
        self.v1 = interleaved_sliding_window is None
182
183
184
185
186
187

        layer_idx = extract_layer_index(prefix)
        layer_has_sliding_window = (
            getattr(config, "sliding_window_pattern", False)
            and (layer_idx + 1) % self.config.sliding_window_pattern != 0)

188
        self.sliding_window = (interleaved_sliding_window
189
190
                               if layer_has_sliding_window else None)

191
192
193
194
195
        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
                              num_kv_heads=self.num_kv_heads,
                              cache_config=cache_config,
196
                              quant_config=quant_config,
197
                              per_layer_sliding_window=self.sliding_window,
198
                              prefix=f"{prefix}.attn")
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
        if self.use_qk_norm:
            self.q_norm = LayerNorm(param_shape=(self.num_heads,
                                                 self.head_dim),
                                    eps=config.layer_norm_eps)
            self.k_norm = LayerNorm(param_shape=(self.num_kv_heads,
                                                 self.head_dim),
                                    eps=config.layer_norm_eps)

    def _apply_qk_norm(self, q, k):
        q = q.view(*q.shape[:-1], -1, self.head_dim)
        k = k.view(*k.shape[:-1], -1, self.head_dim)
        q, _ = self.q_norm(q)
        k, _ = self.k_norm(k)
        q = q.view(*q.shape[:-2], -1)
        k = k.view(*k.shape[:-2], -1)
        return q, k
215
216
217
218
219
220
221
222

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
223
224
        if self.use_qk_norm:
            q, k = self._apply_qk_norm(q, k)
225
226
        if self.v1 or self.sliding_window:
            q, k = self.rotary_emb(positions, q, k)
227
        attn_output = self.attn(q, k, v)
228
229
230
231
232
233
234
235
        output, _ = self.o_proj(attn_output)
        return output


class CohereDecoderLayer(nn.Module):

    def __init__(self,
                 config: CohereConfig,
236
                 cache_config: Optional[CacheConfig] = None,
237
238
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = ""):
239
240
241
        super().__init__()
        self.hidden_size = config.hidden_size

242
243
        self.self_attn = CohereAttention(config,
                                         cache_config,
244
245
                                         quant_config=quant_config,
                                         prefix=f"{prefix}.self_attn")
246

247
        self.mlp = CohereMLP(config, quant_config=quant_config)
248
        self.input_layernorm = LayerNorm(param_shape=(config.hidden_size),
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
                                         eps=config.layer_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # Self Attention
        residual = hidden_states
        hidden_states, residual = self.input_layernorm(hidden_states, residual)
        hidden_states_attention = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
        )
        hidden_states_mlp = self.mlp(hidden_states)
        # Add everything together
        hidden_states = residual + hidden_states_attention + hidden_states_mlp

        return hidden_states, residual


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

274
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
275
        super().__init__()
276
277
278
279
280
281

        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

282
        self.config = config
283
284
285
286
        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
287
288
        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
                                                   config.hidden_size)
289
290
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
291
292
            lambda prefix: CohereDecoderLayer(
                config, cache_config, quant_config, prefix=prefix),
293
            prefix=f"{prefix}.layers")
294
295
        self.norm = LayerNorm(param_shape=(config.hidden_size),
                              eps=config.layer_norm_eps)
296
297
298
        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(
                ["hidden_states", "residual"], config.hidden_size))
299

300
301
302
    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_tokens(input_ids)

303
304
305
306
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
307
        intermediate_tensors: Optional[IntermediateTensors],
308
        inputs_embeds: Optional[torch.Tensor] = None,
309
310
    ) -> Union[torch.Tensor, IntermediateTensors]:
        if get_pp_group().is_first_rank:
311
312
313
314
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.get_input_embeddings(input_ids)
315
316
317
318
319
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]
320
        for layer in self.layers[self.start_layer:self.end_layer]:
321
322
323
324
325
            hidden_states, residual = layer(
                positions,
                hidden_states,
                residual,
            )
326
327
328
329
330
        if not get_pp_group().is_last_rank:
            return IntermediateTensors({
                "hidden_states": hidden_states,
                "residual": residual
            })
331
332
333
334
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


335
class CohereForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsQuant):
336
337
338
339
340
341
342
343
344
345
346
347
348
349
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }
    # LoRA specific attributes
    embedding_modules = {"embed_tokens": "input_embeddings"}

350
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
351
        super().__init__()
352
353
354
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config
355
        self.config = config
356
357
358
        # currently all existing command R models have `tie_word_embeddings`
        # enabled
        assert config.tie_word_embeddings
359
360
361
        self.unpadded_vocab_size = config.vocab_size
        if lora_config:
            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
362
        self.quant_config = quant_config
363
364
        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                config.vocab_size,
365
                                                scale=config.logit_scale)
366
367
        self.model = CohereModel(vllm_config=vllm_config,
                                 prefix=maybe_prefix(prefix, "model"))
Joe Runde's avatar
Joe Runde committed
368
        self.sampler = get_sampler()
369
370
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)
371

372
373
374
    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.get_input_embeddings(input_ids)

375
376
377
378
379
    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
380
        intermediate_tensors: Optional[IntermediateTensors] = None,
381
        inputs_embeds: Optional[torch.Tensor] = None,
382
    ) -> Union[torch.Tensor, IntermediateTensors]:
383
        hidden_states = self.model(input_ids, positions, intermediate_tensors,
384
                                   inputs_embeds)
385
386
        return hidden_states

387
388
389
390
391
    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
392
393
        is_not_lora = hasattr(self.model.embed_tokens, 'weight')
        if is_not_lora:
394
395
            logits = self.logits_processor(self.model.embed_tokens,
                                           hidden_states, sampling_metadata)
396
        else:
397
398
            logits = self.logits_processor(self.model.embed_tokens.base_layer,
                                           hidden_states, sampling_metadata)
399

400
401
402
403
404
405
406
407
408
409
        return logits

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

410
411
    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
412
413
414
415
416
417
418
419
420
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
421
        loaded_params: Set[str] = set()
422
        for name, loaded_weight in weights:
423
424
425

            if (self.quant_config is not None and
                (scale_name := self.quant_config.get_cache_scale(name))):
426
                # Loading kv cache quantization scales
427
428
429
430
431
432
433
434
435
                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

436
437
438
439
            for param_name, shard_name, shard_id in stacked_params_mapping:
                if shard_name not in name:
                    continue
                name = name.replace(shard_name, param_name)
440
441
442
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
443
444
                if is_pp_missing_parameter(name, self):
                    continue
445
446
447
448
449
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
450
451
452
453
454
455
456
                # lm_head is not used in vllm as it is tied with embed_token.
                # To prevent errors, skip loading lm_head.weight.
                if "lm_head.weight" in name:
                    continue
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
457
458
459
460
461
                # Remapping the name of FP8 kv-scale.
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue

462
463
                if is_pp_missing_parameter(name, self):
                    continue
464
465
466
467
468
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
469
        return loaded_params