deepseek_v2.py 65.8 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

wangding zeng's avatar
wangding zeng committed
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 2023 DeepSeek-AI 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.
25
"""Inference-only DeepseekV2/DeepseekV3 model."""
王敏's avatar
王敏 committed
26
27
import os
import re
28
from vllm import envs
zhuwenwen's avatar
zhuwenwen committed
29

30
31
import typing
from collections.abc import Callable, Iterable
32
from itertools import islice
wangding zeng's avatar
wangding zeng committed
33
34
35

import torch
from torch import nn
36
from transformers import DeepseekV2Config, DeepseekV3Config
wangding zeng's avatar
wangding zeng committed
37

38
from vllm._aiter_ops import rocm_aiter_ops
39
from vllm.attention.layer import Attention
40
from vllm.compilation.decorators import support_torch_compile
41
42
43
44
45
46
47
48
from vllm.config import CacheConfig, ParallelConfig, VllmConfig, get_current_vllm_config
from vllm.distributed import (
    get_ep_group,
    get_pp_group,
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
    tensor_model_parallel_all_gather,
)
49
from vllm.logger import init_logger
wangding zeng's avatar
wangding zeng committed
50
from vllm.model_executor.layers.activation import SiluAndMul
51
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
52
from vllm.model_executor.layers.fused_moe import SharedFusedMoE
53
from vllm.model_executor.layers.layernorm import LayerNorm, RMSNorm
54
55
56
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
57
    QKVParallelLinear,
58
59
60
    ReplicatedLinear,
    RowParallelLinear,
)
wangding zeng's avatar
wangding zeng committed
61
from vllm.model_executor.layers.logits_processor import LogitsProcessor
62
from vllm.model_executor.layers.mla import MLAModules, MultiHeadLatentAttentionWrapper
63
from vllm.model_executor.layers.quantization import QuantizationConfig
64
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
65
66
    per_token_group_quant_fp8,
)
wangding zeng's avatar
wangding zeng committed
67
from vllm.model_executor.layers.rotary_embedding import get_rope
68
from vllm.model_executor.layers.sparse_attn_indexer import SparseAttnIndexer
wangding zeng's avatar
wangding zeng committed
69
from vllm.model_executor.layers.vocab_parallel_embedding import (
70
71
72
    ParallelLMHead,
    VocabParallelEmbedding,
)
73
from vllm.model_executor.model_loader.weight_utils import (
74
75
76
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
77
from vllm.model_executor.models.utils import sequence_parallel_chunk
78
from vllm.platforms import current_platform
79
from vllm.sequence import IntermediateTensors
80
from vllm.v1.attention.backend import AttentionBackend
81
82
83
from vllm.v1.attention.backends.mla.indexer import (
    DeepseekV32IndexerBackend,
)
84
from vllm.v1.kv_cache_interface import KVCacheSpec, MLAAttentionSpec
wangding zeng's avatar
wangding zeng committed
85

86
from .interfaces import MixtureOfExperts, SupportsEagle, SupportsLoRA, SupportsPP
87
88
89
90
91
92
93
from .utils import (
    PPMissingLayer,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)
94
from vllm import _custom_ops as ops
zhuwenwen's avatar
zhuwenwen committed
95
from vllm.utils import W8a8GetCacheJSON
96

97
98
from vllm.model_executor.layers.layernorm import FusedRMSNormQuant

99
100
logger = init_logger(__name__)

wangding zeng's avatar
wangding zeng committed
101

102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
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
class DeepseekAttention(nn.Module):
    """Normal MHA implementation used by Deepseek v1."""

    def __init__(
        self,
        vllm_config: VllmConfig,
        config: DeepseekV2Config | DeepseekV3Config,
        hidden_size: int,
        num_heads: int,
        max_position_embeddings: int = 8192,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
        **kwargs,
    ) -> None:
        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 = 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.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.max_position_embeddings = max_position_embeddings

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
            quant_config=quant_config,
        )

        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
            quant_config=quant_config,
        )

        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=max_position_embeddings,
158
            rope_parameters=config.rope_parameters,
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
        )
        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
174
        *, iqis: tuple[torch.Tensor, torch.Tensor] | None = None
175
176
177
178
179
180
181
182
    ) -> 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, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output

wangding zeng's avatar
wangding zeng committed
183
184
185
186
187
188
189

class DeepseekV2MLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
190
        quant_config: QuantizationConfig | None = None,
wangding zeng's avatar
wangding zeng committed
191
        reduce_results: bool = True,
192
        is_sequence_parallel=False,
193
        prefix: str = "",
wangding zeng's avatar
wangding zeng committed
194
195
    ) -> None:
        super().__init__()
196
197
198
199
200

        # If is_sequence_parallel, the input and output tensors are sharded
        # across the ranks within the tp_group. In this case the weights are
        # replicated and no collective ops are needed.
        # Otherwise we use standard TP with an allreduce at the end.
wangding zeng's avatar
wangding zeng committed
201
        self.gate_up_proj = MergedColumnParallelLinear(
202
203
            hidden_size,
            [intermediate_size] * 2,
wangding zeng's avatar
wangding zeng committed
204
            bias=False,
205
            quant_config=quant_config,
206
            disable_tp=is_sequence_parallel,
207
208
209
210
211
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
wangding zeng's avatar
wangding zeng committed
212
            bias=False,
213
            quant_config=quant_config,
214
            reduce_results=reduce_results,
215
            disable_tp=is_sequence_parallel,
216
217
            prefix=f"{prefix}.down_proj",
        )
wangding zeng's avatar
wangding zeng committed
218
        if hidden_act != "silu":
219
220
221
            raise ValueError(
                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
            )
wangding zeng's avatar
wangding zeng committed
222
223
        self.act_fn = SiluAndMul()

224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
    def forward(self, 
                x,
                *, iqis: tuple[torch.Tensor, torch.Tensor] | None = None
                ):
        if envs.USE_FUSED_RMS_QUANT:
            gate_up, _ = self.gate_up_proj(x, iqis=iqis)
            if envs.USE_FUSED_SILU_MUL_QUANT:
                from lmslim.quantize.quant_ops import lm_fuse_silu_mul_quant
                xq, xs = lm_fuse_silu_mul_quant(gate_up)
                x, _ = self.down_proj(gate_up, iqis=(xq, xs))
            else:
                x = self.act_fn(gate_up)
                x, _ = self.down_proj(x)
        else:
            gate_up, _ = self.gate_up_proj(x)
            x = self.act_fn(gate_up)
            x, _ = self.down_proj(x)
241
        return x
wangding zeng's avatar
wangding zeng committed
242
243
244
245
246


class DeepseekV2MoE(nn.Module):
    def __init__(
        self,
247
        config: DeepseekV2Config | DeepseekV3Config,
248
        parallel_config: ParallelConfig,
249
        quant_config: QuantizationConfig | None = None,
250
        prefix: str = "",
wangding zeng's avatar
wangding zeng committed
251
252
253
    ):
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()
254
255
        self.tp_rank = get_tensor_model_parallel_rank()

256
        self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
257
258

        self.ep_group = get_ep_group().device_group
259
        self.ep_rank = get_ep_group().rank_in_group
260
261
262
        self.ep_size = self.ep_group.size()
        self.n_routed_experts: int = config.n_routed_experts
        self.n_shared_experts: int = config.n_shared_experts
263

264
        self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe
265

266
        if config.hidden_act != "silu":
267
268
269
270
271
272
273
274
275
276
277
278
            raise ValueError(
                f"Unsupported activation: {config.hidden_act}. "
                "Only silu is supported for now."
            )

        self.gate = ReplicatedLinear(
            config.hidden_size,
            config.n_routed_experts,
            bias=False,
            quant_config=None,
            prefix=f"{prefix}.gate",
        )
279
        if getattr(config, "topk_method", None) == "noaux_tc":
zhuwenwen's avatar
zhuwenwen committed
280
281
282
283
284
285
286
287
            if envs.VLLM_ENABLE_MOE_FUSED_GATE:
                # avoid moe_fused_gate precision error
                self.gate.e_score_correction_bias = nn.Parameter(
                torch.empty(config.n_routed_experts))
            else:
                self.gate.e_score_correction_bias = nn.Parameter(
                    torch.empty(config.n_routed_experts, dtype=torch.float32)
                )
288
289
290
        else:
            self.gate.e_score_correction_bias = None

291
        # Load balancing settings.
292
293
        eplb_config = parallel_config.eplb_config
        self.enable_eplb = parallel_config.enable_eplb
294

295
        self.n_redundant_experts = eplb_config.num_redundant_experts
296
        self.n_logical_experts = self.n_routed_experts
297
        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
298
299
        self.n_local_physical_experts = self.n_physical_experts // self.ep_size

300
301
302
303
        self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
        self.physical_expert_end = (
            self.physical_expert_start + self.n_local_physical_experts
        )
304

305
        self.is_rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
306
307
308
309
        self.is_fusion_moe_shared_experts_enabled = (
            rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
        )
        if config.n_shared_experts is None or self.is_fusion_moe_shared_experts_enabled:
310
311
            self.shared_experts = None
        else:
312
            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
313

wangding zeng's avatar
wangding zeng committed
314
315
316
317
318
            self.shared_experts = DeepseekV2MLP(
                hidden_size=config.hidden_size,
                intermediate_size=intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
319
                is_sequence_parallel=self.is_sequence_parallel,
320
                reduce_results=False,
321
                prefix=f"{prefix}.shared_experts",
wangding zeng's avatar
wangding zeng committed
322
323
            )

324
325
        self.experts = SharedFusedMoE(
            shared_experts=self.shared_experts,
326
            gate=self.gate,
327
328
329
330
331
332
333
334
            num_experts=config.n_routed_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            reduce_results=False,
            renormalize=config.norm_topk_prob,
            quant_config=quant_config,
            use_grouped_topk=True,
335
336
            num_expert_group=getattr(config, "n_group", 1),
            topk_group=getattr(config, "topk_group", 1),
337
            prefix=f"{prefix}.experts",
338
            scoring_func=getattr(config, "scoring_func", "softmax"),
339
            # we do scaling outside, set factor to 1.0 to avoid double mul
340
341
            # aiter applies routed_scaling_factor internally
            routed_scaling_factor=1.0
342
            if not self.is_rocm_aiter_moe_enabled
343
            else self.routed_scaling_factor,
344
345
346
347
            e_score_correction_bias=self.gate.e_score_correction_bias,
            enable_eplb=self.enable_eplb,
            num_redundant_experts=self.n_redundant_experts,
            is_sequence_parallel=self.is_sequence_parallel,
348
            n_shared_experts=config.n_shared_experts
349
            if self.is_fusion_moe_shared_experts_enabled
350
            else None,
351
        )
352

353
354
355
    def forward(self, hidden_states: torch.Tensor,
                *, iqis: tuple[torch.Tensor, torch.Tensor] | None = None
                ) -> torch.Tensor:
wangding zeng's avatar
wangding zeng committed
356
357
        num_tokens, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)
358
359
360
361
362
363

        # Chunk the hidden states so they aren't replicated across TP ranks.
        # This avoids duplicate computation in self.experts.
        # TODO: We can replace the all_reduce at the end of attn with a
        # reduce_scatter instead of chunking here.
        if self.is_sequence_parallel:
364
            hidden_states = sequence_parallel_chunk(hidden_states)
365

366
367
368
369
370
        if self.experts.is_internal_router:
            # In this case, the gate/router runs inside the FusedMoE class
            fused_moe_out = self.experts(
                hidden_states=hidden_states, router_logits=hidden_states
            )
371
        else:
372
373
374
375
376
            # router_logits: (num_tokens, n_experts)
            router_logits, _ = self.gate(hidden_states)
            fused_moe_out = self.experts(
                hidden_states=hidden_states, router_logits=router_logits
            )
377

378
379
380
        shared_output, final_hidden_states = fused_moe_out
        if self.shared_experts is None:
            assert shared_output is None
381
382
383

        # Fix FP16 overflow
        # See DeepseekV2DecoderLayer for more details.
zhuwenwen's avatar
zhuwenwen committed
384
        if hidden_states.dtype != torch.float16:
385
            if not self.is_rocm_aiter_moe_enabled:
386
                final_hidden_states *= self.routed_scaling_factor
387
388
        elif self.shared_experts is not None:
            assert shared_output is not None
389
            shared_output *= 1.0 / self.routed_scaling_factor
390
391
392
393

        if self.shared_experts is not None:
            assert shared_output is not None
            final_hidden_states += shared_output
394

395
396
        if self.is_sequence_parallel:
            final_hidden_states = tensor_model_parallel_all_gather(
397
398
                final_hidden_states, 0
            )
399
400
            final_hidden_states = final_hidden_states[:num_tokens]
        elif self.tp_size > 1:
401
402
403
            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
                final_hidden_states
            )
wangding zeng's avatar
wangding zeng committed
404
405
406
407
408
409

        return final_hidden_states.view(num_tokens, hidden_dim)


def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
    import math
410

wangding zeng's avatar
wangding zeng committed
411
412
413
414
415
    if scale <= 1:
        return 1.0
    return 0.1 * mscale * math.log(scale) + 1.0


416
417
418
419
420
421
422
423
424
def _get_llama_4_scaling(
    original_max_position_embeddings: int, scaling_beta: float, positions: torch.Tensor
) -> torch.Tensor:
    scaling = 1 + scaling_beta * torch.log(
        1 + torch.floor(positions / original_max_position_embeddings)
    )
    # Broadcast over num_heads and head_dim
    return scaling[..., None, None]

wangding zeng's avatar
wangding zeng committed
425
426
427
428

class DeepseekV2Attention(nn.Module):
    def __init__(
        self,
429
        vllm_config: VllmConfig,
430
        config: DeepseekV2Config | DeepseekV3Config,
wangding zeng's avatar
wangding zeng committed
431
432
433
434
435
436
437
438
        hidden_size: int,
        num_heads: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        v_head_dim: int,
        q_lora_rank: int,
        kv_lora_rank: int,
        max_position_embeddings: int = 8192,
439
440
441
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        topk_indices_buffer: torch.Tensor | None = None,
442
        prefix: str = "",
wangding zeng's avatar
wangding zeng committed
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_rope_head_dim = qk_rope_head_dim
        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.q_lora_rank = q_lora_rank
        self.kv_lora_rank = kv_lora_rank
        self.num_heads = num_heads
        tp_size = get_tensor_model_parallel_world_size()
        assert num_heads % tp_size == 0
        self.num_local_heads = num_heads // tp_size
        self.scaling = self.qk_head_dim**-0.5
        self.max_position_embeddings = max_position_embeddings
458
459
        assert topk_indices_buffer is None, (
            "topk_indices_buffer is not \
460
        supported for DeepseekV2Attention"
461
        )
wangding zeng's avatar
wangding zeng committed
462
463

        if self.q_lora_rank is not None:
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
            self.q_a_proj = ReplicatedLinear(
                self.hidden_size,
                self.q_lora_rank,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.q_a_proj",
            )
            self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
            self.q_b_proj = ColumnParallelLinear(
                q_lora_rank,
                self.num_heads * self.qk_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.q_b_proj",
            )
wangding zeng's avatar
wangding zeng committed
479
        else:
480
481
482
483
484
485
486
            self.q_proj = ColumnParallelLinear(
                self.hidden_size,
                self.num_heads * self.qk_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.q_proj",
            )
wangding zeng's avatar
wangding zeng committed
487

488
489
490
491
492
        self.kv_a_proj_with_mqa = ReplicatedLinear(
            self.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim,
            bias=False,
            quant_config=quant_config,
493
494
495
            prefix=f"{prefix}.kv_a_proj_with_mqa",
        )
        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
wangding zeng's avatar
wangding zeng committed
496
497
498
499
        self.kv_b_proj = ColumnParallelLinear(
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
            bias=False,
500
            quant_config=quant_config,
501
502
            prefix=f"{prefix}.kv_b_proj",
        )
wangding zeng's avatar
wangding zeng committed
503
        # O projection.
504
505
506
507
508
509
510
        self.o_proj = RowParallelLinear(
            self.num_heads * self.v_head_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )
511
        if config.rope_parameters["rope_type"] != "default":
512
513
514
515
516
            config.rope_parameters["rope_type"] = (
                "deepseek_yarn"
                if config.rope_parameters.get("apply_yarn_scaling", True)
                else "deepseek_llama_scaling"
            )
517

518
519
520
        self.rotary_emb = get_rope(
            qk_rope_head_dim,
            max_position=max_position_embeddings,
521
            rope_parameters=config.rope_parameters,
522
523
            is_neox_style=False,
        )
wangding zeng's avatar
wangding zeng committed
524

525
526
527
528
        if (
            config.rope_parameters["rope_type"] != "default"
            and config.rope_parameters["rope_type"] == "deepseek_yarn"
        ):
529
530
            mscale_all_dim = config.rope_parameters.get("mscale_all_dim", False)
            scaling_factor = config.rope_parameters["factor"]
wangding zeng's avatar
wangding zeng committed
531
532
533
            mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
            self.scaling = self.scaling * mscale * mscale

534
535
536
537
538
539
540
541
542
        self.attn = Attention(
            self.num_local_heads,
            self.qk_head_dim,
            self.scaling,
            num_kv_heads=self.num_local_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
        )
wangding zeng's avatar
wangding zeng committed
543
544
545
546
547

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
548
        llama_4_scaling: torch.Tensor | None,
549
        *, iqis: tuple[torch.Tensor, torch.Tensor] | None = None
wangding zeng's avatar
wangding zeng committed
550
551
552
553
    ) -> torch.Tensor:
        if self.q_lora_rank is not None:
            q = self.q_a_proj(hidden_states)[0]
            q = self.q_a_layernorm(q)
554
            q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
wangding zeng's avatar
wangding zeng committed
555
        else:
556
557
558
559
            q = self.q_proj(hidden_states)[0].view(
                -1, self.num_local_heads, self.qk_head_dim
            )
        q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
wangding zeng's avatar
wangding zeng committed
560
        latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
561
        kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
wangding zeng's avatar
wangding zeng committed
562
        latent_cache = latent_cache.unsqueeze(1)
563
        kv_a = self.kv_a_layernorm(kv_a)
wangding zeng's avatar
wangding zeng committed
564
        kv = self.kv_b_proj(kv_a)[0]
565
        kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
wangding zeng's avatar
wangding zeng committed
566
        k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
567
        k_pe = latent_cache[:, :, self.kv_lora_rank :]
568

wangding zeng's avatar
wangding zeng committed
569
        q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
570

571
        q[..., self.qk_nope_head_dim :] = q_pe
wangding zeng's avatar
wangding zeng committed
572
        k = torch.empty_like(q)
573
574
        k[..., : self.qk_nope_head_dim] = k_nope
        k[..., self.qk_nope_head_dim :] = k_pe
575
576
577
578
579

        # Apply llama 4 scaling if provided
        if llama_4_scaling is not None:
            q *= llama_4_scaling

580
581
        # padding value to qk_head_dim for alignment
        v = torch.nn.functional.pad(
582
583
            v, [0, self.qk_head_dim - self.v_head_dim], value=0
        ).view(-1, self.num_local_heads * self.qk_head_dim)
584
        attn_output = self.attn(q, k, v)
585
586
587
        attn_output = attn_output.view(-1, self.num_local_heads, self.qk_head_dim)[
            ..., : self.v_head_dim
        ].reshape(-1, self.num_local_heads * self.v_head_dim)
wangding zeng's avatar
wangding zeng committed
588
589
590
591
        output, _ = self.o_proj(attn_output)
        return output


592
class DeepseekV32IndexerCache(torch.nn.Module, AttentionLayerBase):
593
594
595
    def __init__(
        self, head_dim: int, dtype: torch.dtype, prefix: str, cache_config: CacheConfig
    ):
596
597
598
599
600
601
602
603
604
605
606
        super().__init__()
        self.kv_cache = [torch.tensor([])]
        self.head_dim = head_dim
        self.prefix = prefix
        self.cache_config = cache_config
        self.dtype = dtype
        compilation_config = get_current_vllm_config().compilation_config
        if prefix in compilation_config.static_forward_context:
            raise ValueError(f"Duplicate layer name: {prefix}")
        compilation_config.static_forward_context[prefix] = self

607
    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
608
609
610
611
612
613
614
        return MLAAttentionSpec(  # Only has one vector instead of K + V
            block_size=self.cache_config.block_size,
            num_kv_heads=1,
            head_size=self.head_dim,
            dtype=self.dtype,
        )

615
    def forward(self): ...
616
617
618
619
620
621

    def get_attn_backend(self) -> AttentionBackend:
        return DeepseekV32IndexerBackend


class Indexer(nn.Module):
622
623
624
    def __init__(
        self,
        vllm_config: VllmConfig,
625
        config: DeepseekV2Config | DeepseekV3Config,
626
627
        hidden_size: int,
        q_lora_rank: int,
628
629
630
        quant_config: QuantizationConfig | None,
        cache_config: CacheConfig | None,
        topk_indices_buffer: torch.Tensor | None,
631
632
        prefix: str = "",
    ):
633
634
635
636
637
638
639
640
641
642
        super().__init__()
        self.vllm_config = vllm_config
        self.config = config
        # self.indexer_cfg = config.attn_module_list_cfg[0]["attn_index"]
        self.topk_tokens = config.index_topk
        self.n_head = config.index_n_heads  # 64
        self.head_dim = config.index_head_dim  # 128
        self.rope_dim = config.qk_rope_head_dim  # 64
        self.q_lora_rank = q_lora_rank  # 1536
        # no tensor parallel, just replicated
643
644
645
646
647
648
649
650
651
652
653
654
655
656
        self.wq_b = ReplicatedLinear(
            self.q_lora_rank,
            self.head_dim * self.n_head,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.wq_b",
        )
        self.wk = ReplicatedLinear(
            hidden_size,
            self.head_dim,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.wk",
        )
657
        self.k_norm = LayerNorm(self.head_dim, eps=1e-6)
658
        self.weights_proj = ReplicatedLinear(
659
660
661
662
663
            hidden_size,
            self.n_head,
            bias=False,
            quant_config=None,
            prefix=f"{prefix}.weights_proj",
664
        )
665
666
667
668
669
670
671
672
673
674
        self.softmax_scale = self.head_dim**-0.5

        self.scale_fmt = "ue8m0"
        self.quant_block_size = 128  # TODO: get from config
        self.topk_indices_buffer = topk_indices_buffer

        # NOTE: (zyongye) we use fp8 naive cache,
        #       where we store value in fp8 and scale in fp32
        #       per self.quant_block_size element
        self.k_cache = DeepseekV32IndexerCache(
675
676
            head_dim=self.head_dim + self.head_dim // self.quant_block_size * 4 if torch.cuda.get_device_properties("cuda").gcnArchName.split(':')[0] == "gfx938" else self.head_dim,
            dtype=torch.uint8  if torch.cuda.get_device_properties("cuda").gcnArchName.split(':')[0] == "gfx938" else torch.bfloat16,
677
            prefix=f"{prefix}.k_cache",
678
679
            cache_config=cache_config,
        )
680
681
        self.max_model_len = vllm_config.model_config.max_model_len
        self.prefix = prefix
682
683
        from vllm.v1.attention.backends.mla.indexer import get_max_prefill_buffer_size

684
        self.max_total_seq_len = get_max_prefill_buffer_size(vllm_config)
685
686
687
688
689
690
691
692
693
694
        self.indexer_op = SparseAttnIndexer(
            self.k_cache,
            self.quant_block_size,
            self.scale_fmt,
            self.topk_tokens,
            self.head_dim,
            self.max_model_len,
            self.max_total_seq_len,
            self.topk_indices_buffer,
        )
695

696
697
698
    def forward(
        self, hidden_states: torch.Tensor, qr: torch.Tensor, positions, rotary_emb
    ) -> torch.Tensor:
699
700
701
        q, _ = self.wq_b(qr)
        q = q.view(-1, self.n_head, self.head_dim)
        q_pe, q_nope = torch.split(
702
703
            q, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
        )
704
705
706
707

        k, _ = self.wk(hidden_states)
        k = self.k_norm(k)
        k_pe, k_nope = torch.split(
708
709
            k, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1
        )
710
711

        q_pe, k_pe = rotary_emb(positions, q_pe, k_pe.unsqueeze(1))
712
713
714
715
716
        # Note: RoPE (NeoX) can introduce extra leading dimensions during compilation
        # so we need to reshape back to token-flattened shapes
        q_pe = q_pe.reshape(-1, self.n_head, self.rope_dim)
        k_pe = k_pe.reshape(-1, 1, self.rope_dim)

717
718
        # `rotary_emb` is shape-preserving; `q_pe` is already
        # [num_tokens, n_head, rope_dim].
719
720
        q = torch.cat([q_pe, q_nope], dim=-1)
        # `k_pe` is [num_tokens, 1, rope_dim] (MQA).
721
        k = torch.cat([k_pe.squeeze(-2), k_nope], dim=-1)
722
723

        # we only quant q here since k quant is fused with cache insertion
724
725
726
727
728
729
730
731
732
733
        if not current_platform.is_rocm() or torch.cuda.get_device_properties("cuda").gcnArchName.split(':')[0] == "gfx938":
            q = q.view(-1, self.head_dim)
            q_fp8, q_scale = per_token_group_quant_fp8(
                q,
                self.quant_block_size,
                column_major_scales=False,
                use_ue8m0=self.scale_fmt is not None,
            )
            q_fp8 = q_fp8.view(-1, self.n_head, self.head_dim)
            q_scale = q_scale.view(-1, self.n_head, 1)
zhuwenwen's avatar
zhuwenwen committed
734
735
        else:
            q_fp8 = q
736
737

        weights, _ = self.weights_proj(hidden_states)
738
739
740
741
742
        if not current_platform.is_rocm() or torch.cuda.get_device_properties("cuda").gcnArchName.split(':')[0] == "gfx938":
            weights = (
                weights.unsqueeze(-1) * q_scale * self.softmax_scale * self.n_head**-0.5
            )
            weights = weights.squeeze(-1)
743

744
        return self.indexer_op(hidden_states, q_fp8, k, weights)
745
746


747
748
749
750
class DeepseekV2MLAAttention(nn.Module):
    """
    Main reference: DeepseekV2 paper, and FlashInfer Implementation
    (https://arxiv.org/abs/2405.04434 and https://github.com/flashinfer-ai/flashinfer/pull/551).
751

752
753
        For more info see MLACommonImpl in:
        vllm/v1/attention/backends/mla/utils.py
754
755
756
757
    """

    def __init__(
        self,
758
        vllm_config: VllmConfig,
759
        config: DeepseekV2Config | DeepseekV3Config,
760
761
762
763
764
        hidden_size: int,
        num_heads: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        v_head_dim: int,
765
        q_lora_rank: int | None,
766
767
        kv_lora_rank: int,
        max_position_embeddings: int = 8192,
768
769
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
770
        prefix: str = "",
771
        topk_indices_buffer: torch.Tensor | None = None,
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_rope_head_dim = qk_rope_head_dim
        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
        self.v_head_dim = v_head_dim

        self.q_lora_rank = q_lora_rank
        self.kv_lora_rank = kv_lora_rank

        self.num_heads = num_heads
        tp_size = get_tensor_model_parallel_world_size()
        assert num_heads % tp_size == 0
        self.num_local_heads = num_heads // tp_size

        self.scaling = self.qk_head_dim**-0.5
        self.max_position_embeddings = max_position_embeddings

        if self.q_lora_rank is not None:
792
            self.fused_qkv_a_proj = MergedColumnParallelLinear(
793
794
795
796
                self.hidden_size,
                [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
                bias=False,
                quant_config=quant_config,
797
                prefix=f"{prefix}.fused_qkv_a_proj",
798
799
                disable_tp=True,
            )
800
801
802
803
804
805
        else:
            self.kv_a_proj_with_mqa = ReplicatedLinear(
                self.hidden_size,
                self.kv_lora_rank + self.qk_rope_head_dim,
                bias=False,
                quant_config=quant_config,
806
807
                prefix=f"{prefix}.kv_a_proj_with_mqa",
            )
808
809

        if self.q_lora_rank is not None:
810
811
812
813
814
815
816
817
            self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
            self.q_b_proj = ColumnParallelLinear(
                self.q_lora_rank,
                self.num_heads * self.qk_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.q_b_proj",
            )
818
        else:
819
820
821
822
823
824
825
826
            self.q_proj = ColumnParallelLinear(
                self.hidden_size,
                self.num_heads * self.qk_head_dim,
                bias=False,
                quant_config=quant_config,
                prefix=f"{prefix}.q_proj",
            )
        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
827
828
829
830
831
        self.kv_b_proj = ColumnParallelLinear(
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
            bias=False,
            quant_config=quant_config,
832
833
834
835
836
837
838
839
840
            prefix=f"{prefix}.kv_b_proj",
        )
        self.o_proj = RowParallelLinear(
            self.num_heads * self.v_head_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )
841

842
        if config.rope_parameters["rope_type"] != "default":
843
844
845
846
847
848
            config.rope_parameters["rope_type"] = (
                "deepseek_yarn"
                if config.rope_parameters.get("apply_yarn_scaling", True)
                else "deepseek_llama_scaling"
            )

849
850
851
        self.rotary_emb = get_rope(
            qk_rope_head_dim,
            max_position=max_position_embeddings,
852
            rope_parameters=config.rope_parameters,
853
854
            is_neox_style=False,
        )
855
856
857
858
859

        if (
            config.rope_parameters["rope_type"] != "default"
            and config.rope_parameters["rope_type"] == "deepseek_yarn"
        ):
860
861
            mscale_all_dim = config.rope_parameters.get("mscale_all_dim", False)
            scaling_factor = config.rope_parameters["factor"]
862
863
            mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
            self.scaling = self.scaling * mscale * mscale
864
865
866
        #添加判断,默认开启DSA
        force_disable_dsa = os.environ.get("VLLM_DISABLE_DSA", "0") == "1"
        self.is_v32 = hasattr(config, "index_topk") and not force_disable_dsa
867
868

        if self.is_v32:
869
870
871
            self.indexer_rope_emb = get_rope(
                qk_rope_head_dim,
                max_position=max_position_embeddings,
872
                rope_parameters=config.rope_parameters,
zhuwenwen's avatar
zhuwenwen committed
873
                is_neox_style=not getattr(config, "indexer_rope_interleave", True),
874
            )
875
876
877
878
879
880
881
882
883
884
            self.indexer = Indexer(
                vllm_config,
                config,
                hidden_size,
                q_lora_rank,
                quant_config,
                cache_config,
                topk_indices_buffer,
                f"{prefix}.indexer",
            )
885
        else:
886
            self.indexer_rope_emb = None
887
888
            self.indexer = None

889
890
        mla_modules = MLAModules(
            kv_a_layernorm=self.kv_a_layernorm,
891
            kv_b_proj=self.kv_b_proj,
892
893
894
            rotary_emb=self.rotary_emb,
            o_proj=self.o_proj,
            fused_qkv_a_proj=self.fused_qkv_a_proj
895
896
            if self.q_lora_rank is not None
            else None,
897
            kv_a_proj_with_mqa=self.kv_a_proj_with_mqa
898
899
900
            if self.q_lora_rank is None
            else None,
            q_a_layernorm=self.q_a_layernorm if self.q_lora_rank is not None else None,
901
902
            q_b_proj=self.q_b_proj if self.q_lora_rank is not None else None,
            q_proj=self.q_proj if self.q_lora_rank is None else None,
903
            indexer=self.indexer,
904
            indexer_rotary_emb=self.indexer_rope_emb,
905
906
            is_sparse=self.is_v32,
            topk_indices_buffer=topk_indices_buffer,
907
        )
908

909
        self.mla_attn = MultiHeadLatentAttentionWrapper(
910
911
912
913
914
915
916
917
918
919
920
921
            self.hidden_size,
            self.num_local_heads,
            self.scaling,
            self.qk_nope_head_dim,
            self.qk_rope_head_dim,
            self.v_head_dim,
            self.q_lora_rank,
            self.kv_lora_rank,
            mla_modules,
            cache_config,
            quant_config,
            prefix,
922
923
924
925
926
927
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
928
        llama_4_scaling: torch.Tensor | None,
929
        *, iqis: tuple[torch.Tensor, torch.Tensor] | None = None
930
    ) -> torch.Tensor:
931
        return self.mla_attn(positions, hidden_states, llama_4_scaling, iqis=iqis)
932
933


wangding zeng's avatar
wangding zeng committed
934
class DeepseekV2DecoderLayer(nn.Module):
935
936
937
938
    def __init__(
        self,
        vllm_config: VllmConfig,
        prefix: str,
939
940
        config: DeepseekV2Config | None = None,
        topk_indices_buffer: torch.Tensor | None = None,
941
    ) -> None:
wangding zeng's avatar
wangding zeng committed
942
        super().__init__()
943

944
945
        if config is None:
            config = vllm_config.model_config.hf_config
946
947
948
949
950
        model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        parallel_config = vllm_config.parallel_config

wangding zeng's avatar
wangding zeng committed
951
        self.hidden_size = config.hidden_size
952
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
953
        moe_layer_freq = getattr(config, "moe_layer_freq", 1)
954
955
        # DecoderLayers are created with `make_layers` which passes the prefix
        # with the layer's index.
956
        layer_idx = int(prefix.split(sep=".")[-1])
957
        self.layer_idx = layer_idx
958
959
960
961
962
963
964
965
966
967

        # verify MLA attention specific fields
        qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 0)
        qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0)
        v_head_dim = getattr(config, "v_head_dim", 0)
        kv_lora_rank = getattr(config, "kv_lora_rank", 0)
        use_mha = config.model_type == "deepseek" or all(
            dim == 0 for dim in (qk_nope_head_dim, qk_rope_head_dim)
        )

968
969
        self.use_mha = use_mha

970
971
972
        if use_mha:
            attn_cls = DeepseekAttention
        elif model_config.use_mla:
973
974
975
976
            attn_cls = DeepseekV2MLAAttention
        else:
            attn_cls = DeepseekV2Attention
        self.self_attn = attn_cls(
977
            vllm_config=vllm_config,
wangding zeng's avatar
wangding zeng committed
978
979
980
            config=config,
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
981
982
983
            qk_nope_head_dim=qk_nope_head_dim,
            qk_rope_head_dim=qk_rope_head_dim,
            v_head_dim=v_head_dim,
984
            q_lora_rank=config.q_lora_rank if hasattr(config, "q_lora_rank") else None,
985
            kv_lora_rank=kv_lora_rank,
wangding zeng's avatar
wangding zeng committed
986
987
988
            max_position_embeddings=max_position_embeddings,
            cache_config=cache_config,
            quant_config=quant_config,
989
            prefix=f"{prefix}.self_attn",
990
            topk_indices_buffer=topk_indices_buffer,
wangding zeng's avatar
wangding zeng committed
991
        )
992

993
994
995
        if (
            config.n_routed_experts is not None
            and layer_idx >= config.first_k_dense_replace
996
            and layer_idx % moe_layer_freq == 0
997
        ):
998
999
            self.mlp = DeepseekV2MoE(
                config=config,
1000
                parallel_config=parallel_config,
1001
1002
1003
                quant_config=quant_config,
                prefix=f"{prefix}.mlp",
            )
wangding zeng's avatar
wangding zeng committed
1004
1005
1006
1007
1008
1009
        else:
            self.mlp = DeepseekV2MLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
1010
                prefix=f"{prefix}.mlp",
wangding zeng's avatar
wangding zeng committed
1011
            )
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
        
        if not envs.USE_FUSED_RMS_QUANT:
            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
            )
        else:
            self.input_layernorm = FusedRMSNormQuant(config.hidden_size, eps=config.rms_norm_eps)
            self.post_attention_layernorm = FusedRMSNormQuant(
                config.hidden_size, eps=config.rms_norm_eps
            )

1024
        self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0)
wangding zeng's avatar
wangding zeng committed
1025

1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
    def forward_RQ(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
        llama_4_scaling: torch.Tensor | None = None,
    ) -> torch.Tensor:
        # Self Attention
        # Fix residual FP16 overflow
        residual_fix_overflow = False
        assert self.input_layernorm.has_weight is True
        if residual is None:
            residual = hidden_states.clone()
            i_q, i_s, _ = self.input_layernorm(x=hidden_states, 
                                               residual=None, 
                                               quant_dtype=torch.int8,
                                               update_input=False
                                               )
            residual_fix_overflow = True
        else:
            i_q, i_s, residual = self.input_layernorm(x=hidden_states, 
                                                      residual=residual, 
                                                      quant_dtype=torch.int8,
                                                      update_input=False
                                                      )
        attn_kwargs = {
            "positions": positions,
            "hidden_states": hidden_states,
            "iqis": (i_q, i_s)
        }
        if not self.use_mha:
            attn_kwargs["llama_4_scaling"] = llama_4_scaling
        hidden_states = self.self_attn(**attn_kwargs)

        if (
            not isinstance(self.self_attn, DeepseekAttention)
            and hidden_states.dtype == torch.float16
        ):
            # Fix FP16 overflow
            # We scale both hidden_states and residual before
            # rmsnorm, and rmsnorm result would not affect by scale.
            hidden_states *= 1.0 / self.routed_scaling_factor
            if self.layer_idx == 0:
                # The residual is shared by all layers, we only scale it on
                # first layer.
                residual *= 1.0 / self.routed_scaling_factor

        # Fully Connected
        update_hs = True if isinstance(self.mlp, DeepseekV2MoE) else False
        assert self.post_attention_layernorm.has_weight is True
        _i_q, _i_s, residual = self.post_attention_layernorm(x=hidden_states, 
                                                             residual=residual, 
                                                             quant_dtype=torch.int8,
                                                             update_input=update_hs
                                                             )
        new_resi = residual
        hidden_states = self.mlp(hidden_states,
                                #  iqis=(_i_q, _i_s) # TODO:wjl
                                 )

        if isinstance(self.mlp, DeepseekV2MLP) and hidden_states.dtype == torch.float16:
            # Fix FP16 overflow
            # Scaling the DeepseekV2MLP output, it is the input of
            # input_layernorm of next decoder layer.
            # The scaling of DeepseekV2MOE output would be done in the forward
            # of DeepseekV2MOE
            hidden_states *= 1.0 / self.routed_scaling_factor

        return hidden_states, new_resi

    def forward_default(
wangding zeng's avatar
wangding zeng committed
1097
1098
1099
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
1100
        residual: torch.Tensor | None,
1101
        llama_4_scaling: torch.Tensor | None = None,
wangding zeng's avatar
wangding zeng committed
1102
    ) -> torch.Tensor:
zhuwenwen's avatar
zhuwenwen committed
1103
1104
1105
1106
1107
1108
1109
        # Self Attention
        # Fix residual FP16 overflow
        residual_fix_overflow = False
        if residual is None:
            residual = hidden_states.clone()
            hidden_states = self.input_layernorm(hidden_states)
            residual_fix_overflow = True
wangding zeng's avatar
wangding zeng committed
1110
        else:
zhuwenwen's avatar
zhuwenwen committed
1111
            hidden_states, residual = self.input_layernorm(hidden_states, residual)
1112
            
1113

1114
1115
1116
1117
1118
1119
1120
        attn_kwargs = {
            "positions": positions,
            "hidden_states": hidden_states,
        }
        if not self.use_mha:
            attn_kwargs["llama_4_scaling"] = llama_4_scaling
        hidden_states = self.self_attn(**attn_kwargs)
wangding zeng's avatar
wangding zeng committed
1121

1122
1123
1124
1125
        if (
            not isinstance(self.self_attn, DeepseekAttention)
            and hidden_states.dtype == torch.float16
        ):
1126
1127
1128
            # Fix FP16 overflow
            # We scale both hidden_states and residual before
            # rmsnorm, and rmsnorm result would not affect by scale.
1129
            hidden_states *= 1.0 / self.routed_scaling_factor
1130
1131
1132
            if self.layer_idx == 0:
                # The residual is shared by all layers, we only scale it on
                # first layer.
1133
                residual *= 1.0 / self.routed_scaling_factor
1134
1135

        # Fully Connected
1136
        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
wangding zeng's avatar
wangding zeng committed
1137
        hidden_states = self.mlp(hidden_states)
1138

1139
        if isinstance(self.mlp, DeepseekV2MLP) and hidden_states.dtype == torch.float16:
1140
1141
1142
1143
1144
            # Fix FP16 overflow
            # Scaling the DeepseekV2MLP output, it is the input of
            # input_layernorm of next decoder layer.
            # The scaling of DeepseekV2MOE output would be done in the forward
            # of DeepseekV2MOE
1145
            hidden_states *= 1.0 / self.routed_scaling_factor
1146

wangding zeng's avatar
wangding zeng committed
1147
1148
        return hidden_states, residual

1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
    def choose_forward(self):
        if envs.USE_FUSED_RMS_QUANT:
            return self.forward_RQ
        else:
            return self.forward_default

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
        llama_4_scaling: torch.Tensor | None = None,
    ) -> torch.Tensor:
        forward_func = self.choose_forward()
        return forward_func(positions=positions,
                            hidden_states=hidden_states,
                            residual=residual,
                            llama_4_scaling=llama_4_scaling)

wangding zeng's avatar
wangding zeng committed
1168

1169
@support_torch_compile
wangding zeng's avatar
wangding zeng committed
1170
1171
1172
class DeepseekV2Model(nn.Module):
    fall_back_to_pt_during_load = False

1173
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
wangding zeng's avatar
wangding zeng committed
1174
        super().__init__()
1175
1176
1177

        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
1178
        self.config = config
1179
        self.device = current_platform.device_type
1180

wangding zeng's avatar
wangding zeng committed
1181
        self.vocab_size = config.vocab_size
1182
1183
1184
1185
        #添加判断,默认开启DSA
        force_disable_dsa = os.environ.get("VLLM_DISABLE_DSA", "0") == "1"
        self.is_v32 = hasattr(config, "index_topk") and not force_disable_dsa        

1186
1187
1188
1189
1190
1191
        if self.is_v32:
            topk_tokens = config.index_topk
            topk_indices_buffer = torch.empty(
                vllm_config.scheduler_config.max_num_batched_tokens,
                topk_tokens,
                dtype=torch.int32,
1192
                device=self.device,
1193
            )
1194
1195
        else:
            topk_indices_buffer = None
wangding zeng's avatar
wangding zeng committed
1196

1197
1198
1199
1200
        if get_pp_group().is_first_rank:
            self.embed_tokens = VocabParallelEmbedding(
                config.vocab_size,
                config.hidden_size,
1201
                quant_config=quant_config,
1202
1203
                prefix=f"{prefix}.embed_tokens",
            )
1204
1205
1206
1207
        else:
            self.embed_tokens = PPMissingLayer()
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
1208
            lambda prefix: DeepseekV2DecoderLayer(
1209
                vllm_config, prefix, topk_indices_buffer=topk_indices_buffer
1210
1211
1212
            ),
            prefix=f"{prefix}.layers",
        )
1213
1214
1215
1216
1217

        if get_pp_group().is_last_rank:
            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer()
1218
1219
1220
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )
wangding zeng's avatar
wangding zeng committed
1221

1222
    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
1223
1224
        return self.embed_tokens(input_ids)

wangding zeng's avatar
wangding zeng committed
1225
1226
    def forward(
        self,
zhuwenwen's avatar
zhuwenwen committed
1227
        input_ids: torch.Tensor,
wangding zeng's avatar
wangding zeng committed
1228
        positions: torch.Tensor,
1229
1230
1231
        intermediate_tensors: IntermediateTensors | None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
1232
        if get_pp_group().is_first_rank:
1233
1234
1235
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
1236
                hidden_states = self.embed_input_ids(input_ids)
1237
1238
1239
1240
1241
1242
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
        # Compute llama 4 scaling once per forward pass if enabled
        llama_4_scaling_config = getattr(self.config, "llama_4_scaling", None)
        llama_4_scaling: torch.Tensor | None
        if llama_4_scaling_config is not None:
            llama_4_scaling = _get_llama_4_scaling(
                original_max_position_embeddings=llama_4_scaling_config[
                    "original_max_position_embeddings"
                ],
                scaling_beta=llama_4_scaling_config["beta"],
                positions=positions,
            )
        else:
            llama_4_scaling = None

1257
        for layer in islice(self.layers, self.start_layer, self.end_layer):
1258
1259
1260
            hidden_states, residual = layer(
                positions, hidden_states, residual, llama_4_scaling
            )
1261
1262

        if not get_pp_group().is_last_rank:
1263
1264
1265
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
1266

wangding zeng's avatar
wangding zeng committed
1267
1268
1269
1270
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
class DeepseekV2MixtureOfExperts(MixtureOfExperts):
    moe_mlp_layers: list[DeepseekV2MoE]
    """
    List of MoE MLP layers in the model.
    """

    def extract_moe_parameters(self, example_moe: DeepseekV2MoE | None):
        if example_moe is None:
            self.num_moe_layers = 0
            self.num_expert_groups = 0
            self.num_logical_experts = 0
            self.num_physical_experts = 0
            self.num_local_physical_experts = 0
            self.num_routed_experts = 0
            self.num_shared_experts = 0
            self.num_redundant_experts = 0
            logger.warning("DeepSeekV2: No DeepseekV2MoE layer found in model.layers.")
        else:
            self.num_logical_experts = example_moe.n_logical_experts
            self.num_physical_experts = example_moe.n_physical_experts
            self.num_local_physical_experts = example_moe.n_local_physical_experts
            self.num_routed_experts = example_moe.n_routed_experts
            self.num_shared_experts = example_moe.n_shared_experts
            self.num_redundant_experts = example_moe.n_redundant_experts

    def update_physical_experts_metadata(
        self,
        num_physical_experts: int,
        num_local_physical_experts: int,
    ) -> None:
        assert self.num_local_physical_experts == num_local_physical_experts
        self.num_physical_experts = num_physical_experts
        self.num_local_physical_experts = num_local_physical_experts
        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
        for moe in self.moe_mlp_layers:
            moe.n_local_physical_experts = num_local_physical_experts
            moe.n_physical_experts = num_physical_experts
            moe.n_redundant_experts = self.num_redundant_experts
            moe.experts.update_expert_map()


class DeepseekV2ForCausalLM(
1313
    nn.Module, SupportsPP, DeepseekV2MixtureOfExperts, SupportsLoRA, SupportsEagle
1314
):
1315
1316
1317
    packed_modules_mapping = {
        "gate_up_proj": ["gate_proj", "up_proj"],
    }
1318
    model_cls = DeepseekV2Model
wangding zeng's avatar
wangding zeng committed
1319

1320
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
wangding zeng's avatar
wangding zeng committed
1321
        super().__init__()
1322
1323
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
1324
1325
1326
1327
1328
1329
1330

        self.quant_method = None
        if quant_config is not None:
            self.quant_method = quant_config.get_name()
            os.environ['LLAMA_NN'] = '0'
            os.environ['LM_NN'] = '0'

wangding zeng's avatar
wangding zeng committed
1331
1332
        self.config = config
        self.quant_config = quant_config
王敏's avatar
王敏 committed
1333

1334
1335
1336
1337
1338
1339
1340
1341
1342
        qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 0)
        qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0)
        self.use_mha = config.model_type == "deepseek" or all(
            dim == 0 for dim in (qk_nope_head_dim, qk_rope_head_dim)
        )

        if self.use_mha:
            self.packed_modules_mapping["qkv_proj"] = ["q_proj", "k_proj", "v_proj"]

1343
1344
1345
1346
        # `packed_modules_mapping` needs to be modified before
        # initializing DeepseekV2Model, as it is passed inplace to
        # quantization config init and may be used to select the
        # quant_method for relevant layers during initialization.
1347
1348
1349
        self.fuse_qkv_a_proj = (
            hasattr(config, "q_lora_rank") and config.q_lora_rank is not None
        )
1350
1351
1352
1353
1354
1355
        if self.fuse_qkv_a_proj:
            self.packed_modules_mapping["fused_qkv_a_proj"] = [
                "q_a_proj",
                "kv_a_proj_with_mqa",
            ]

1356
        self.model = self.model_cls(
1357
1358
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
1359
        if get_pp_group().is_last_rank:
1360
1361
1362
1363
1364
1365
            self.lm_head = ParallelLMHead(
                config.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "lm_head"),
            )
1366
1367
        else:
            self.lm_head = PPMissingLayer()
wangding zeng's avatar
wangding zeng committed
1368
        self.logits_processor = LogitsProcessor(config.vocab_size)
1369
        self.make_empty_intermediate_tensors = (
1370
1371
            self.model.make_empty_intermediate_tensors
        )
1372
1373
1374
1375
1376
1377
1378
        # Set MoE hyperparameters
        self.num_moe_layers = (
            self.config.num_hidden_layers - self.config.first_k_dense_replace
        )
        self.set_moe_parameters()

    def set_moe_parameters(self):
1379
1380
        self.expert_weights = []

1381
        self.num_expert_groups = getattr(self.config, "n_group", 1)
1382

1383
1384
        self.moe_layers = []
        self.moe_mlp_layers = []
1385
        example_moe = None
1386
        for layer in self.model.layers:
1387
1388
1389
            if isinstance(layer, PPMissingLayer):
                continue

1390
1391
            assert isinstance(layer, DeepseekV2DecoderLayer)
            if isinstance(layer.mlp, DeepseekV2MoE):
1392
1393
                # Pick last one layer since the first ones may be dense layers.
                example_moe = layer.mlp
1394
                self.moe_mlp_layers.append(layer.mlp)
1395
1396
                self.moe_layers.append(layer.mlp.experts)

1397
        self.extract_moe_parameters(example_moe)
王敏's avatar
王敏 committed
1398
        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
1399
        self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
1400
        self.tritonsingleton= W8a8GetCacheJSON() 
zhuwenwen's avatar
zhuwenwen committed
1401
        self.tritonsingleton.topk = self.config.num_experts_per_tok
1402
        self.tritonsingleton.quant_method=self.quant_method 
1403

1404
1405
    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)
1406

wangding zeng's avatar
wangding zeng committed
1407
1408
    def forward(
        self,
zhuwenwen's avatar
zhuwenwen committed
1409
        input_ids: torch.Tensor,
wangding zeng's avatar
wangding zeng committed
1410
        positions: torch.Tensor,
1411
1412
1413
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
1414
1415
1416
        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )
wangding zeng's avatar
wangding zeng committed
1417
1418
        return hidden_states

1419
1420
1421
    def compute_logits(
        self,
        hidden_states: torch.Tensor,
1422
    ) -> torch.Tensor | None:
1423
        logits = self.logits_processor(self.lm_head, hidden_states)
wangding zeng's avatar
wangding zeng committed
1424
1425
        return logits

1426
1427
1428
1429
    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        return SharedFusedMoE.make_expert_params_mapping(
1430
            self,
1431
1432
1433
1434
1435
1436
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            num_experts=self.config.n_routed_experts,
            num_redundant_experts=0,
        )
1437

1438
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
1439
1440
1441
        rocm_aiter_moe_shared_expert_enabled = (
            rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
        )
wangding zeng's avatar
wangding zeng committed
1442
1443
1444
1445
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
1446
1447
        ]
        mla_params_mapping = [
1448
1449
            ("fused_qkv_a_proj", "q_a_proj", 0),
            ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
wangding zeng's avatar
wangding zeng committed
1450
        ]
1451
1452
1453
1454
1455
1456
1457
1458
1459
        mha_params_mapping = [
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        if self.use_mha:
            stacked_params_mapping.extend(mha_params_mapping)
        else:
            stacked_params_mapping.extend(mla_params_mapping)
wangding zeng's avatar
wangding zeng committed
1460

1461
1462
        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
1463
        expert_params_mapping = SharedFusedMoE.make_expert_params_mapping(
1464
            self,
王敏's avatar
王敏 committed
1465
1466
1467
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
1468
1469
1470
            num_experts=self.config.n_routed_experts
            + (
                self.config.n_shared_experts
1471
                if rocm_aiter_moe_shared_expert_enabled
1472
1473
                else 0
            ),
1474
1475
            num_redundant_experts=self.num_redundant_experts,
        )
1476

wangding zeng's avatar
wangding zeng committed
1477
        params_dict = dict(self.named_parameters())
1478
        loaded_params: set[str] = set()
1479
1480
1481
        # 判断是否加载"indexer"权重
        model_has_indexer = any("indexer" in param_name for param_name in params_dict.keys())
        
wangding zeng's avatar
wangding zeng committed
1482
1483
1484
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
1485

1486
1487
1488
1489
1490
            #  跳过加载"indexer"权重
            if "indexer" in name and not model_has_indexer:
                logger.info(f"Skipping indexer weight (DSA disabled): {name}")
                continue

1491
1492
1493
            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
            if spec_layer is not None:
                continue  # skip spec decode layers for main model
1494

1495
1496
            is_fusion_moe_shared_experts_layer = (
                rocm_aiter_moe_shared_expert_enabled and ("mlp.shared_experts" in name)
1497
1498
            )

1499
            for param_name, weight_name, shard_id in stacked_params_mapping:
1500
                # Skip non-stacked layers and experts (experts handled below).
wangding zeng's avatar
wangding zeng committed
1501
1502
                if weight_name not in name:
                    continue
1503
1504
1505
1506
1507
1508
                # We have mlp.experts[0].gate_proj in the checkpoint.
                # Since we handle the experts below in expert_params_mapping,
                # we need to skip here BEFORE we update the name, otherwise
                # name will be updated to mlp.experts[0].gate_up_proj, which
                # will then be updated below in expert_params_mapping
                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
1509
                if ("mlp.experts." in name) and name not in params_dict:
1510
                    continue
1511
                if is_fusion_moe_shared_experts_layer:
1512
                    continue
1513
                name_mapped = name.replace(weight_name, param_name)
1514
1515
1516

                # QKV fusion is optional, fall back to normal
                # weight loading if it's not enabled
1517
                # if go with fusion option, then update name
1518
1519
1520
                if (
                    param_name == "fused_qkv_a_proj"
                ) and name_mapped not in params_dict:
1521
                    continue
1522
1523
                else:
                    name = name_mapped
wangding zeng's avatar
wangding zeng committed
1524
1525
1526
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
1527
1528
1529
1530

                if is_pp_missing_parameter(name, self):
                    continue

wangding zeng's avatar
wangding zeng committed
1531
1532
1533
1534
1535
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
1536
1537
                is_expert_weight = False

1538
1539
1540
1541
1542
1543
1544
1545
1546
                # Special handling: when AITER fusion_shared_experts is enabled,
                # checkpoints may provide a single widened shared_experts tensor
                # without explicit expert indices
                # (e.g. ...mlp.shared_experts.gate_proj.weight).
                # For models with multiple shared experts, split that tensor
                # evenly into per-shared-expert slices and load them into
                # appended expert slots mlp.experts.{n_routed_experts + j}.*
                # accordingly.
                num_chunks = 1
1547
                if is_fusion_moe_shared_experts_layer:
1548
1549
1550
1551
                    num_chunks = getattr(self.config, "n_shared_experts", 1) or 1
                    # Determine split axis based on op type
                    # gate/up: ColumnParallel → split along dim 0
                    # down: RowParallel → split along dim 1
1552
1553
1554
1555
1556
                    split_dim = (
                        1
                        if ("down_proj.weight" in name and loaded_weight.ndim > 1)
                        else 0
                    )
1557
1558
1559
1560
                    total = loaded_weight.shape[split_dim]
                    assert total % num_chunks == 0, (
                        f"Shared expert weight dim {total} "
                        f"not divisible by num_chunks {num_chunks}"
1561
                    )
1562
1563
1564
1565
1566
1567
                    chunk_size = total // num_chunks

                for j in range(num_chunks):
                    chunk_name = name
                    weight_to_load = loaded_weight

1568
                    if is_fusion_moe_shared_experts_layer:
1569
1570
1571
1572
1573
                        chunk_slice = slice(j * chunk_size, (j + 1) * chunk_size)
                        if loaded_weight.ndim == 1:
                            weight_to_load = loaded_weight[chunk_slice]
                        elif split_dim == 0:
                            weight_to_load = loaded_weight[chunk_slice, :]
1574
                        else:
1575
                            weight_to_load = loaded_weight[:, chunk_slice]
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
                        # Synthesize an expert-style name so expert mapping
                        # can route it
                        chunk_name = name.replace(
                            "mlp.shared_experts",
                            f"mlp.experts.{self.config.n_routed_experts + j}",
                        )

                    # Use expert_params_mapping to locate the destination
                    # param and delegate to its expert-aware weight_loader
                    # with expert_id.
                    for mapping in expert_params_mapping:
                        param_name, weight_name, expert_id, shard_id = mapping
                        if weight_name not in chunk_name:
                            continue

                        # Anyway, this is an expert weight and should not be
                        # attempted to load as other weights later
                        is_expert_weight = True

                        # Do not modify `name` since the loop may continue here
                        # Instead, create a new variable
                        name_mapped = chunk_name.replace(weight_name, param_name)

                        if is_pp_missing_parameter(name_mapped, self):
                            continue

                        param = params_dict[name_mapped]
                        # We should ask the weight loader to return success or
                        # not here since otherwise we may skip experts with
                        # other available replicas.
                        weight_loader = typing.cast(
                            Callable[..., bool], param.weight_loader
                        )
                        success = weight_loader(
                            param,
                            weight_to_load,
                            name_mapped,
                            shard_id=shard_id,
                            expert_id=expert_id,
                            return_success=True,
                        )
                        if success:
1618
                            if not is_fusion_moe_shared_experts_layer:
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
                                name = name_mapped
                            else:
                                loaded_params.add(name_mapped)
                            break
                    else:
                        if is_expert_weight:
                            # We've checked that this is an expert weight
                            # However it's not mapped locally to this rank
                            # So we simply skip it
                            continue

                        # Skip loading extra bias for GPTQ models.
                        if name.endswith(".bias") and name not in params_dict:
                            continue

                        # Remapping the name of FP8 kv-scale.
                        name = maybe_remap_kv_scale_name(name, params_dict)
                        if name is None:
                            continue

                        if is_pp_missing_parameter(name, self):
                            continue
1641

1642
                        param = params_dict[name]
1643
1644
1645
1646
                        weight_loader = getattr(
                            param, "weight_loader", default_weight_loader
                        )
                        weight_loader(param, loaded_weight)
1647
            if not is_fusion_moe_shared_experts_layer:
1648
                loaded_params.add(name)
1649
                
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
        if self.use_llama_nn and self.quant_method is None:
            lay_key_words = [
                "self_attn.q_proj.weight",
                "self_attn.q_a_proj.weight",
                "self_attn.q_b_proj.weight",
                "self_attn.kv_a_proj_with_mqa.weight",
                "self_attn.kv_b_proj.weight",
                "self_attn.o_proj.weight",
                "mlp.gate_up_proj.weight",
                "mlp.down_proj.weight",
                "mlp.gate.weight",
                "shared_experts.gate_up_proj.weight",
                "shared_experts.down_proj.weight",
                "lm_head.weight",
            ]

            combined_words = "|".join(lay_key_words)
            
            for layername in loaded_params:
                weight = params_dict[layername]
                matches = re.findall(combined_words, layername)
                if matches:
                    _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)
zhuwenwen's avatar
zhuwenwen committed
1679
            
1680
        return loaded_params
1681
1682


1683
1684
1685
1686
class DeepseekForCausalLM(DeepseekV2ForCausalLM):
    pass


1687
1688
class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
    pass
1689
1690


zhuwenwen's avatar
zhuwenwen committed
1691
1692
1693
1694
class GlmMoeDsaForCausalLM(DeepseekV2ForCausalLM):
    pass


1695
1696
# Compatibility with
# https://huggingface.co/deepseek-ai/DeepSeek-V3-Base/blob/main/configuration_deepseek.py
1697
def get_spec_layer_idx_from_weight_name(
1698
1699
    config: DeepseekV2Config | DeepseekV3Config, weight_name: str
) -> int | None:
1700
1701
1702
1703
    if (
        hasattr(config, "num_nextn_predict_layers")
        and config.num_nextn_predict_layers > 0
    ):
1704
1705
        layer_idx = config.num_hidden_layers
        for i in range(config.num_nextn_predict_layers):
1706
            if weight_name.startswith(f"model.layers.{layer_idx + i}."):
1707
1708
                return layer_idx + i
    return None