apertus.py 20.3 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
# Copyright 2025 The Swiss AI Initiative.
# 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 the architectural differences made by
# the Swiss AI Initiative 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 Apertus model compatible with HuggingFace weights."""
27

28
from collections.abc import Iterable
29
from itertools import islice
30
31
32
33
34

import torch
from torch import nn
from transformers import ApertusConfig

35
36
from vllm.attention.backends.abstract import AttentionType
from vllm.attention.layer import Attention
37
38
39
40
41
42
from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import XIELU
from vllm.model_executor.layers.layernorm import RMSNorm
43
44
45
46
47
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
48
49
50
51
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
52
53
54
    ParallelLMHead,
    VocabParallelEmbedding,
)
55
from vllm.model_executor.model_loader.weight_utils import (
56
57
58
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
59
60
61
from vllm.sequence import IntermediateTensors

from .interfaces import SupportsLoRA, SupportsPP
62
63
64
65
66
67
68
69
70
from .utils import (
    AutoWeightsLoader,
    PPMissingLayer,
    extract_layer_index,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)
71
72
73
74
75
76
77
78


class ApertusMLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
79
        quant_config: QuantizationConfig | None = None,
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
        bias: bool = False,
        prefix: str = "",
        reduce_results: bool = True,
    ) -> None:
        super().__init__()
        self.up_proj = ColumnParallelLinear(
            input_size=hidden_size,
            output_size=intermediate_size,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.up_proj",
        )
        self.down_proj = RowParallelLinear(
            input_size=intermediate_size,
            output_size=hidden_size,
            bias=bias,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=f"{prefix}.down_proj",
        )
        if hidden_act != "xielu":
101
102
103
104
            raise ValueError(
                f"Unsupported activation: {hidden_act}. "
                "Only xIELU is supported for now."
            )
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
        self.act_fn = XIELU()

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


class ApertusAttention(nn.Module):
    def __init__(
        self,
        config: ApertusConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        max_position_embeddings: int = 8192,
122
        quant_config: QuantizationConfig | None = None,
123
124
        bias: bool = False,
        bias_o_proj: bool = False,
125
        cache_config: CacheConfig | None = None,
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
        prefix: str = "",
        attn_type: str = AttentionType.DECODER,
    ) -> None:
        super().__init__()
        layer_idx = extract_layer_index(prefix)
        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)
        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
        head_dim = getattr(config, "head_dim", None)
        if head_dim is None:
            head_dim = self.hidden_size // self.total_num_heads
        self.head_dim = head_dim
        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.max_position_embeddings = max_position_embeddings

        self.qkv_proj = QKVParallelLinear(
            hidden_size=hidden_size,
            head_size=self.head_dim,
            total_num_heads=self.total_num_heads,
            total_num_kv_heads=self.total_num_kv_heads,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

        self.o_proj = RowParallelLinear(
            input_size=self.total_num_heads * self.head_dim,
            output_size=hidden_size,
            bias=bias_o_proj,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

174
        self._init_rotary_emb(config, quant_config=quant_config)
175
176
177
178
179
180
181

        sliding_window = None
        if layer_types := getattr(config, "layer_types", None):
            is_sliding = layer_types[layer_idx] == "sliding_attention"
            if is_sliding:
                sliding_window = config.sliding_window

182
183
184
185
186
        attn_cls = (
            EncoderOnlyAttention
            if attn_type == AttentionType.ENCODER_ONLY
            else Attention
        )
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216

        self.attn = attn_cls(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            per_layer_sliding_window=sliding_window,
            attn_type=attn_type,
            prefix=f"{prefix}.attn",
        )

        self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)

    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)
        q = self.q_norm(q.contiguous().view(-1, self.head_dim)).view_as(q)
        k = self.k_norm(k.contiguous().view(-1, self.head_dim)).view_as(k)
        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output

217
218
219
    def _init_rotary_emb(
        self,
        config: ApertusConfig,
220
        quant_config: QuantizationConfig | None,
221
    ) -> None:
222
223
224
225
226
227
228
        is_neox_style = True
        is_gguf = quant_config and quant_config.get_name() == "gguf"
        if is_gguf and config.model_type == "apertus":
            is_neox_style = False

        self.rotary_emb = get_rope(
            self.head_dim,
229
            rotary_dim=self.head_dim,
230
            max_position=self.max_position_embeddings,
231
            rope_parameters=config.rope_parameters,
232
233
234
235
236
237
238
239
            is_neox_style=is_neox_style,
        )


class ApertusDecoderLayer(nn.Module):
    def __init__(
        self,
        config: ApertusConfig,
240
241
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
242
243
244
245
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
246
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
247
248
249
        # Support abacusai/Smaug-72B-v0.1 with attention_bias
        # Support internlm/internlm-7b with bias
        attention_bias = getattr(config, "attention_bias", False) or getattr(
250
251
            config, "bias", False
        )
252
253
        bias_o_proj = attention_bias
        # support internlm/internlm3-8b with qkv_bias
254
        if hasattr(config, "qkv_bias"):
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
            attention_bias = config.qkv_bias

        # Apertus defaults to causal attention as it is a decoder-only model.
        # You can override the HF config with `is_causal=False` to enable
        # bidirectional attention, which is used in some embedding models
        # (e.g. parasail-ai/GritLM-7B-vllm)
        if getattr(config, "is_causal", True):
            attn_type = AttentionType.DECODER
        else:
            attn_type = AttentionType.ENCODER_ONLY

        self.self_attn = ApertusAttention(
            config=config,
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
270
271
272
            num_kv_heads=getattr(
                config, "num_key_value_heads", config.num_attention_heads
            ),
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
            max_position_embeddings=max_position_embeddings,
            quant_config=quant_config,
            bias=attention_bias,
            bias_o_proj=bias_o_proj,
            cache_config=cache_config,
            prefix=f"{prefix}.self_attn",
            attn_type=attn_type,
        )
        self.mlp = ApertusMLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            quant_config=quant_config,
            bias=getattr(config, "mlp_bias", False),
            prefix=f"{prefix}.mlp",
        )
289
290
291
292
        self.attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.feedforward_layernorm = RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
293
294
295
296
297

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
298
        residual: torch.Tensor | None,
299
300
301
302
303
304
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # Self Attention
        if residual is None:
            residual = hidden_states
            hidden_states = self.attention_layernorm(hidden_states)
        else:
305
306
            hidden_states, residual = self.attention_layernorm(hidden_states, residual)
        hidden_states = self.self_attn(positions=positions, hidden_states=hidden_states)
307
308

        # Fully Connected
309
        hidden_states, residual = self.feedforward_layernorm(hidden_states, residual)
310
311
312
313
314
315
        hidden_states = self.mlp(hidden_states)
        return hidden_states, residual


@support_torch_compile
class ApertusModel(nn.Module):
316
317
318
319
320
321
322
    def __init__(
        self,
        *,
        vllm_config: VllmConfig,
        prefix: str = "",
        layer_type: type[nn.Module] = ApertusDecoderLayer,
    ):
323
324
325
326
327
328
329
330
        super().__init__()

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

        self.config = config
        self.quant_config = quant_config
331
332
333

        self.vocab_size = config.vocab_size

334
335
336
        if get_pp_group().is_first_rank or (
            config.tie_word_embeddings and get_pp_group().is_last_rank
        ):
337
338
339
340
341
342
343
344
345
            self.embed_tokens = VocabParallelEmbedding(
                self.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
            )
        else:
            self.embed_tokens = PPMissingLayer()
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
346
347
348
349
350
351
            lambda prefix: layer_type(
                config=config,
                cache_config=cache_config,
                quant_config=quant_config,
                prefix=prefix,
            ),
352
353
354
355
356
357
358
359
360
            prefix=f"{prefix}.layers",
        )
        if get_pp_group().is_last_rank:
            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer()

        self.aux_hidden_state_layers = tuple[int, ...]()

361
362
363
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )
364

365
    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
366
367
368
369
        return self.embed_tokens(input_ids)

    def forward(
        self,
370
        input_ids: torch.Tensor | None,
371
        positions: torch.Tensor,
372
373
374
        intermediate_tensors: IntermediateTensors | None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors | tuple[torch.Tensor, list[torch.Tensor]]:
375
376
377
378
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
379
                hidden_states = self.embed_input_ids(input_ids)
380
381
382
383
384
385
386
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        aux_hidden_states = []
387
388
389
        for idx, layer in enumerate(
            islice(self.layers, self.start_layer, self.end_layer)
        ):
390
391
392
393
394
            if idx in self.aux_hidden_state_layers:
                aux_hidden_states.append(hidden_states + residual)
            hidden_states, residual = layer(positions, hidden_states, residual)

        if not get_pp_group().is_last_rank:
395
396
397
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
398
399
400
401
402
403
404

        hidden_states, _ = self.norm(hidden_states, residual)

        if len(aux_hidden_states) > 0:
            return hidden_states, aux_hidden_states
        return hidden_states

405
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
406
407
408
409
410
411
412
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters())
413
414
415
416
417
418

        # we need to load the buffers for beta and eps (XIELU)
        for name, buffer in self.named_buffers():
            if name.endswith(".beta") or name.endswith(".eps"):
                params_dict[name] = buffer

419
420
421
422
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
423
            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
424
425
426
                # Models trained using ColossalAI may include these tensors in
                # the checkpoint. Skip them.
                continue
427
428
429
            if self.quant_config is not None and (
                scale_name := self.quant_config.get_cache_scale(name)
            ):
430
431
                # Loading kv cache quantization scales
                param = params_dict[scale_name]
432
433
434
435
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                loaded_weight = (
                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
                )
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
                weight_loader(param, loaded_weight)
                loaded_params.add(scale_name)
                continue
            if "scale" in name:
                # Remapping the name of FP8 kv-scale.
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                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

                if is_pp_missing_parameter(name, self):
                    continue

                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue

                if is_pp_missing_parameter(name, self):
                    continue

                param = params_dict[name]
468
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
469
470
471
472
473
474
475
476
477
478
479
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class ApertusForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}

    # LoRA specific attributes
    embedding_modules = {
        "embed_tokens": "input_embeddings",
480
        "lm_head": "output_embeddings",
481
482
    }

483
484
485
486
487
488
489
    def __init__(
        self,
        *,
        vllm_config: VllmConfig,
        prefix: str = "",
        layer_type: type[nn.Module] = ApertusDecoderLayer,
    ):
490
491
492
493
494
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config

495
496
497
498
499
        self.model = self._init_model(
            vllm_config=vllm_config,
            prefix=maybe_prefix(prefix, "model"),
            layer_type=layer_type,
        )
500
501
502

        if get_pp_group().is_last_rank:
            self.lm_head = ParallelLMHead(
503
                config.vocab_size,
504
505
506
507
508
                config.hidden_size,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "lm_head"),
            )
            if config.tie_word_embeddings:
509
                self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
510
511

            logit_scale = getattr(config, "logit_scale", 1.0)
512
            self.logits_processor = LogitsProcessor(
513
                config.vocab_size, scale=logit_scale
514
            )
515
516
517
518
        else:
            self.lm_head = PPMissingLayer()

        self.make_empty_intermediate_tensors = (
519
520
            self.model.make_empty_intermediate_tensors
        )
521
522
523
524
525
526
527
528

    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
        self.model.aux_hidden_state_layers = layers

    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
        num_layers = len(self.model.layers)
        return (2, num_layers // 2, num_layers - 3)

529
530
531
532
533
534
535
536
537
    def _init_model(
        self,
        vllm_config: VllmConfig,
        prefix: str = "",
        layer_type: type[nn.Module] = ApertusDecoderLayer,
    ):
        return ApertusModel(
            vllm_config=vllm_config, prefix=prefix, layer_type=layer_type
        )
538

539
540
    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)
541
542
543
544
545

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
546
547
548
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
549
550
551
        model_output = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )
552
553
554
555
556
        return model_output

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
557
    ) -> torch.Tensor | None:
558
        logits = self.logits_processor(self.lm_head, hidden_states)
559
560
        return logits

561
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
562
563
        loader = AutoWeightsLoader(
            self,
564
            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
565
566
        )
        return loader.load_weights(weights)