deepseek_v2.py 80.1 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
# Copyright 2023-2024 SGLang Team
# 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.
# ==============================================================================
14

Liangsheng Yin's avatar
Liangsheng Yin committed
15
16
17
# Adapted from:
# https://github.com/vllm-project/vllm/blob/fb6af8bc086328ca6659e72d11ffd4309ce4de22/vllm/model_executor/models/deepseek_v2.py
"""Inference-only DeepseekV2 model."""
18

19
import logging
20
import os
21
from enum import IntEnum, auto
Liangsheng Yin's avatar
Liangsheng Yin committed
22
23
24
from typing import Any, Dict, Iterable, Optional, Tuple

import torch
Ke Bao's avatar
Ke Bao committed
25
import torch.nn.functional as F
Liangsheng Yin's avatar
Liangsheng Yin committed
26
from torch import nn
27
from tqdm import tqdm
Liangsheng Yin's avatar
Liangsheng Yin committed
28
from transformers import PretrainedConfig
29
30

from sglang.srt.distributed import (
Liangsheng Yin's avatar
Liangsheng Yin committed
31
    get_tensor_model_parallel_world_size,
32
    parallel_state,
Liangsheng Yin's avatar
Liangsheng Yin committed
33
34
    tensor_model_parallel_all_reduce,
)
35
from sglang.srt.layers.activation import SiluAndMul
36
37
38
39
40
from sglang.srt.layers.communicator import (
    LayerCommunicator,
    LayerScatterModes,
    enable_moe_dense_fully_dp,
)
Lianmin Zheng's avatar
Lianmin Zheng committed
41
42
43
from sglang.srt.layers.dp_attention import (
    get_attention_tp_rank,
    get_attention_tp_size,
44
    get_local_attention_dp_size,
Lianmin Zheng's avatar
Lianmin Zheng committed
45
)
46
from sglang.srt.layers.layernorm import RMSNorm
47
48
49
50
51
52
from sglang.srt.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
Liangsheng Yin's avatar
Liangsheng Yin committed
53
from sglang.srt.layers.logits_processor import LogitsProcessor
fzyzcjy's avatar
fzyzcjy committed
54
from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
Lianmin Zheng's avatar
Lianmin Zheng committed
55
from sglang.srt.layers.moe.ep_moe.token_dispatcher import DeepEPDispatcher
56
from sglang.srt.layers.moe.topk import select_experts
57
from sglang.srt.layers.quantization.base_config import QuantizationConfig
58
from sglang.srt.layers.quantization.deep_gemm import _ENABLE_JIT_DEEPGEMM
59
from sglang.srt.layers.quantization.fp8_kernel import (
60
    is_fp8_fnuz,
61
    per_tensor_quant_mla_fp8,
62
    per_token_group_quant_mla_deep_gemm_masked_fp8,
63
)
HandH1998's avatar
HandH1998 committed
64
from sglang.srt.layers.quantization.fp8_utils import (
65
    block_quant_dequant,
HandH1998's avatar
HandH1998 committed
66
    block_quant_to_tensor_quant,
67
    channel_quant_to_tensor_quant,
68
    normalize_e4m3fn_to_e4m3fnuz,
HandH1998's avatar
HandH1998 committed
69
)
70
71
72
from sglang.srt.layers.quantization.int8_utils import (
    block_dequant as int8_block_dequant,
)
Liangsheng Yin's avatar
Liangsheng Yin committed
73
from sglang.srt.layers.radix_attention import RadixAttention
74
from sglang.srt.layers.rotary_embedding import get_rope
75
76
77
78
from sglang.srt.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
79
80
81
82
from sglang.srt.managers.expert_distribution import (
    get_global_expert_distribution_recorder,
)
from sglang.srt.managers.expert_location import ModelConfigForExpertLocation
83
from sglang.srt.managers.expert_location_dispatch import ExpertLocationDispatchInfo
84
from sglang.srt.managers.schedule_batch import global_server_args_dict
85
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
86
from sglang.srt.model_loader.weight_utils import default_weight_loader
87
88
89
90
from sglang.srt.two_batch_overlap import (
    MaybeTboDeepEPDispatcher,
    model_forward_maybe_tbo,
)
91
92
93
94
95
96
97
98
from sglang.srt.utils import (
    BumpAllocator,
    DeepEPMode,
    add_prefix,
    get_bool_env_var,
    get_int_env_var,
    is_cuda,
    is_hip,
99
    is_non_idle_and_non_empty,
100
    log_info_on_rank0,
101
)
102

103
_is_hip = is_hip()
Yineng Zhang's avatar
Yineng Zhang committed
104
_is_cuda = is_cuda()
105
_is_fp8_fnuz = is_fp8_fnuz()
106

Yineng Zhang's avatar
Yineng Zhang committed
107
if _is_cuda:
108
    from sgl_kernel import awq_dequantize, bmm_fp8, merge_state_v2
109
110
111
112

    from sglang.srt.layers.quantization.deep_gemm import (
        grouped_gemm_nt_f8f8bf16_masked as deep_gemm_grouped_gemm_nt_f8f8bf16_masked,
    )
Yineng Zhang's avatar
Yineng Zhang committed
113
else:
Lianmin Zheng's avatar
Lianmin Zheng committed
114
    from vllm._custom_ops import awq_dequantize
Liangsheng Yin's avatar
Liangsheng Yin committed
115

116
117
118
119
120
if _is_hip:
    from sglang.srt.layers.attention.triton_ops.rocm_mla_decode_rope import (
        decode_attention_fwd_grouped_rope,
    )

121
122
logger = logging.getLogger(__name__)

Liangsheng Yin's avatar
Liangsheng Yin committed
123

124
125
126
127
128
129
130
131
132
133
134
class AttnForwardMethod(IntEnum):
    # Use multi-head attention
    MHA = auto()

    # Use absorbed multi-latent attention
    MLA = auto()

    # Use multi-head attention, but with KV cache chunked.
    # This method can avoid OOM when prefix lengths are long.
    MHA_CHUNKED_KV = auto()

135
136
137
    # Use MLA but with fused RoPE
    MLA_FUSED_ROPE = auto()

138

Liangsheng Yin's avatar
Liangsheng Yin committed
139
140
141
142
143
144
145
146
class DeepseekV2MLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: Optional[QuantizationConfig] = None,
        reduce_results: bool = True,
147
        prefix: str = "",
148
149
        tp_rank: Optional[int] = None,
        tp_size: Optional[int] = None,
Liangsheng Yin's avatar
Liangsheng Yin committed
150
151
    ) -> None:
        super().__init__()
152
153
        self.tp_size = tp_size

Liangsheng Yin's avatar
Liangsheng Yin committed
154
        self.gate_up_proj = MergedColumnParallelLinear(
155
156
157
158
159
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=add_prefix("gate_up_proj", prefix),
160
161
            tp_rank=tp_rank,
            tp_size=tp_size,
Liangsheng Yin's avatar
Liangsheng Yin committed
162
163
164
165
166
167
168
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
169
            prefix=add_prefix("down_proj", prefix),
170
171
            tp_rank=tp_rank,
            tp_size=tp_size,
Liangsheng Yin's avatar
Liangsheng Yin committed
172
173
174
175
176
177
178
179
        )
        if hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {hidden_act}. "
                "Only silu is supported for now."
            )
        self.act_fn = SiluAndMul()

180
181
182
183
    def forward(self, x, forward_batch=None):
        if (self.tp_size == 1) and x.shape[0] == 0:
            return x

Liangsheng Yin's avatar
Liangsheng Yin committed
184
185
186
187
188
189
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


Ke Bao's avatar
Ke Bao committed
190
class MoEGate(nn.Module):
191
192
193
194
195
    def __init__(
        self,
        config,
        prefix: str = "",
    ):
Ke Bao's avatar
Ke Bao committed
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
        super().__init__()
        self.weight = nn.Parameter(
            torch.empty((config.n_routed_experts, config.hidden_size))
        )
        if config.topk_method == "noaux_tc":
            self.e_score_correction_bias = nn.Parameter(
                torch.empty((config.n_routed_experts))
            )
        else:
            self.e_score_correction_bias = None

    def forward(self, hidden_states):
        logits = F.linear(hidden_states, self.weight, None)
        return logits


Liangsheng Yin's avatar
Liangsheng Yin committed
212
213
214
215
216
class DeepseekV2MoE(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
fzyzcjy's avatar
fzyzcjy committed
217
        layer_id: int,
Liangsheng Yin's avatar
Liangsheng Yin committed
218
        quant_config: Optional[QuantizationConfig] = None,
219
        prefix: str = "",
Liangsheng Yin's avatar
Liangsheng Yin committed
220
221
222
223
224
    ):
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.routed_scaling_factor = config.routed_scaling_factor
        self.n_shared_experts = config.n_shared_experts
225
        self.n_share_experts_fusion = global_server_args_dict["n_share_experts_fusion"]
226
        self.config = config
fzyzcjy's avatar
fzyzcjy committed
227
        self.layer_id = layer_id
228

Liangsheng Yin's avatar
Liangsheng Yin committed
229
230
231
232
233
234
235
236
237
238
239
240
        if self.tp_size > config.n_routed_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
                f"the number of experts {config.n_routed_experts}."
            )

        if config.hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {config.hidden_act}. "
                "Only silu is supported for now."
            )

241
        self.gate = MoEGate(config=config, prefix=add_prefix("gate", prefix))
Ke Bao's avatar
Ke Bao committed
242

243
        self.experts = get_moe_impl_class()(
244
245
246
            num_experts=config.n_routed_experts
            + self.n_share_experts_fusion
            + global_server_args_dict["ep_num_redundant_experts"],
247
            top_k=config.num_experts_per_tok + min(self.n_share_experts_fusion, 1),
248
249
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
fzyzcjy's avatar
fzyzcjy committed
250
            layer_id=self.layer_id,
251
252
253
254
255
256
            renormalize=config.norm_topk_prob,
            quant_config=quant_config,
            use_grouped_topk=True,
            num_expert_group=config.n_group,
            topk_group=config.topk_group,
            correction_bias=self.gate.e_score_correction_bias,
257
            routed_scaling_factor=self.routed_scaling_factor,
258
259
260
261
262
263
264
            prefix=add_prefix("experts", prefix),
            **(
                dict(deepep_mode=DeepEPMode[global_server_args_dict["deepep_mode"]])
                if global_server_args_dict["enable_deepep_moe"]
                else {}
            ),
        )
Liangsheng Yin's avatar
Liangsheng Yin committed
265

266
        if config.n_shared_experts is not None and self.n_share_experts_fusion == 0:
Liangsheng Yin's avatar
Liangsheng Yin committed
267
            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
268
            # disable tp for shared experts when enable deepep moe
269
270
271
272
273
274
275
276
277
278
279
280
281
            self.shared_experts = DeepseekV2MLP(
                hidden_size=config.hidden_size,
                intermediate_size=intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                reduce_results=False,
                prefix=add_prefix("shared_experts", prefix),
                **(
                    dict(tp_rank=0, tp_size=1)
                    if global_server_args_dict["enable_deepep_moe"]
                    else {}
                ),
            )
282

283
284
        self.top_k = config.num_experts_per_tok

285
        if global_server_args_dict["enable_deepep_moe"]:
286
287
            # TODO: we will support tp < ep in the future
            self.ep_size = get_tensor_model_parallel_world_size()
288
289
290
291
            self.num_experts = (
                config.n_routed_experts
                + global_server_args_dict["ep_num_redundant_experts"]
            )
292
293
294
295
296
297
298
299
300
            self.renormalize = config.norm_topk_prob
            self.topk_group = config.topk_group
            self.num_expert_group = config.n_group
            self.correction_bias = (
                self.gate.e_score_correction_bias.data
                if self.gate.e_score_correction_bias is not None
                else None
            )

301
            self.deepep_dispatcher = MaybeTboDeepEPDispatcher(
302
303
304
                group=parallel_state.get_tp_group().device_group,
                router_topk=self.top_k,
                permute_fusion=True,
305
                num_experts=self.num_experts,
306
                num_local_experts=config.n_routed_experts // self.tp_size,
Liangsheng Yin's avatar
Liangsheng Yin committed
307
                hidden_size=config.hidden_size,
308
                params_dtype=config.torch_dtype,
309
                deepep_mode=DeepEPMode[global_server_args_dict["deepep_mode"]],
310
                async_finish=True,
311
                return_recv_hook=True,
Liangsheng Yin's avatar
Liangsheng Yin committed
312
313
            )

314
        self._enable_deepep_moe = global_server_args_dict["enable_deepep_moe"]
315

316
317
318
319
320
321
322
    def get_moe_weights(self):
        return [
            x.data
            for name, x in self.experts.named_parameters()
            if name not in ["correction_bias"]
        ]

323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
    def forward(
        self, hidden_states: torch.Tensor, forward_batch: Optional[ForwardBatch] = None
    ) -> torch.Tensor:
        if not self._enable_deepep_moe:
            return self.forward_normal(hidden_states)
        else:
            return self.forward_deepep(hidden_states, forward_batch)

    def forward_normal(self, hidden_states: torch.Tensor) -> torch.Tensor:
        shared_output = self._forward_shared_experts(hidden_states)
        # router_logits: (num_tokens, n_experts)
        router_logits = self.gate(hidden_states)
        final_hidden_states = self.experts(
            hidden_states=hidden_states, router_logits=router_logits
        )
        final_hidden_states *= self.routed_scaling_factor
        if shared_output is not None:
            final_hidden_states = final_hidden_states + shared_output
        if self.tp_size > 1:
            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
        return final_hidden_states

    def forward_deepep(
        self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
    ) -> torch.Tensor:
        forward_mode = forward_batch.forward_mode
        shared_output = None
        if is_non_idle_and_non_empty(forward_mode, hidden_states):
            # router_logits: (num_tokens, n_experts)
            router_logits = self.gate(hidden_states)
            shared_output = self._forward_shared_experts(hidden_states)
            topk_weights, topk_idx = select_experts(
                hidden_states=hidden_states,
                router_logits=router_logits,
                top_k=self.top_k,
                use_grouped_topk=True,
                renormalize=self.renormalize,
                topk_group=self.topk_group,
                num_expert_group=self.num_expert_group,
                correction_bias=self.correction_bias,
                routed_scaling_factor=self.routed_scaling_factor,
                num_token_non_padded=forward_batch.num_token_non_padded,
365
366
367
                expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
                    layer_id=self.layer_id,
                ),
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
            )
        else:
            topk_idx = torch.full(
                (0, self.top_k), -1, dtype=torch.int, device=hidden_states.device
            )
            topk_weights = torch.empty(
                (0, self.top_k), dtype=torch.float32, device=hidden_states.device
            )
        if self.ep_size > 1:
            # TODO(ch-wan): allow users to set num_max_dispatch_tokens_per_rank value
            (
                hidden_states,
                topk_idx,
                topk_weights,
                reorder_topk_ids,
                num_recv_tokens_per_expert,
                seg_indptr,
                masked_m,
                expected_m,
            ) = self.deepep_dispatcher.dispatch(
                hidden_states=hidden_states,
                topk_idx=topk_idx,
                topk_weights=topk_weights,
                forward_mode=forward_mode,
            )
        final_hidden_states = self.experts(
            hidden_states=hidden_states,
            topk_idx=topk_idx,
            topk_weights=topk_weights,
            reorder_topk_ids=reorder_topk_ids,
            seg_indptr=seg_indptr,
            masked_m=masked_m,
            expected_m=expected_m,
            num_recv_tokens_per_expert=num_recv_tokens_per_expert,
            forward_mode=forward_mode,
        )
        if self.ep_size > 1:
            final_hidden_states = self.deepep_dispatcher.combine(
                hidden_states=final_hidden_states,
                topk_idx=topk_idx,
                topk_weights=topk_weights,
                forward_mode=forward_mode,
            )
        final_hidden_states *= self.routed_scaling_factor

        if shared_output is not None:
            final_hidden_states = final_hidden_states + shared_output

        return final_hidden_states

    def _forward_shared_experts(self, hidden_states):
        if self.n_share_experts_fusion == 0:
            return self.shared_experts(hidden_states)
        else:
            return None

424
    def op_gate(self, state):
425
        if is_non_idle_and_non_empty(
426
            state.forward_batch.forward_mode, state.hidden_states_mlp_input
427
        ):
428
            # router_logits: (num_tokens, n_experts)
429
            state.router_logits = self.gate(state.hidden_states_mlp_input)
430
        else:
431
            state.router_logits = None
432

433
    def op_shared_experts(self, state):
434
435
436
        hidden_states_mlp_input = state.pop("hidden_states_mlp_input")
        if (self.n_share_experts_fusion == 0) and is_non_idle_and_non_empty(
            state.forward_batch.forward_mode, hidden_states_mlp_input
437
        ):
438
            state.shared_output = self.shared_experts(hidden_states_mlp_input)
439
        else:
440
            state.shared_output = None
441

442
    def op_select_experts(self, state):
443
        router_logits = state.pop("router_logits")
444
445
        hidden_states = state.hidden_states_mlp_input

446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
        if router_logits is not None:
            state.topk_weights_local, state.topk_idx_local = select_experts(
                hidden_states=hidden_states,
                router_logits=router_logits,
                top_k=self.top_k,
                use_grouped_topk=True,
                renormalize=self.renormalize,
                topk_group=self.topk_group,
                num_expert_group=self.num_expert_group,
                correction_bias=self.correction_bias,
                routed_scaling_factor=self.routed_scaling_factor,
                expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
                    layer_id=self.layer_id,
                ),
            )
        else:
            state.topk_idx_local = torch.full(
                (0, self.top_k), -1, dtype=torch.int, device=hidden_states.device
            )
            state.topk_weights_local = torch.empty(
                (0, self.top_k), dtype=torch.float32, device=hidden_states.device
            )
468

469
    def op_dispatch_a(self, state):
470
        if self.ep_size > 1:
471
            # TODO(ch-wan): allow users to set num_max_dispatch_tokens_per_rank value
472
            self.deepep_dispatcher.dispatch_a(
473
                hidden_states=state.hidden_states_mlp_input,
474
475
476
                topk_idx=state.pop("topk_idx_local"),
                topk_weights=state.pop("topk_weights_local"),
                forward_mode=state.forward_batch.forward_mode,
477
                tbo_subbatch_index=state.get("tbo_subbatch_index"),
478
            )
479

480
    def op_dispatch_b(self, state):
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
        if self.ep_size > 1:
            with get_global_expert_distribution_recorder().with_current_layer(
                self.layer_id
            ):
                (
                    state.hidden_states_experts_input,
                    state.topk_idx_dispatched,
                    state.topk_weights_dispatched,
                    state.reorder_topk_ids,
                    state.num_recv_tokens_per_expert,
                    state.seg_indptr,
                    state.masked_m,
                    state.expected_m,
                ) = self.deepep_dispatcher.dispatch_b(
                    tbo_subbatch_index=state.get("tbo_subbatch_index"),
                )
497
498

    def op_experts(self, state):
499
500
501
502
503
504
505
506
507
508
509
        state.hidden_states_experts_output = self.experts(
            hidden_states=state.pop("hidden_states_experts_input"),
            topk_idx=state.topk_idx_dispatched,
            topk_weights=state.topk_weights_dispatched,
            reorder_topk_ids=state.pop("reorder_topk_ids"),
            seg_indptr=state.pop("seg_indptr"),
            masked_m=state.pop("masked_m"),
            expected_m=state.pop("expected_m"),
            num_recv_tokens_per_expert=state.pop("num_recv_tokens_per_expert"),
            forward_mode=state.forward_batch.forward_mode,
        )
510

511
    def op_combine_a(self, state):
512
        if self.ep_size > 1:
513
            self.deepep_dispatcher.combine_a(
514
                hidden_states=state.pop("hidden_states_experts_output"),
515
516
517
                topk_idx=state.pop("topk_idx_dispatched"),
                topk_weights=state.pop("topk_weights_dispatched"),
                forward_mode=state.forward_batch.forward_mode,
518
                tbo_subbatch_index=state.get("tbo_subbatch_index"),
519
            )
520

521
    def op_combine_b(self, state):
522
523
524
525
        if self.ep_size > 1:
            state.hidden_states_after_combine = self.deepep_dispatcher.combine_b(
                tbo_subbatch_index=state.get("tbo_subbatch_index"),
            )
526
527

    def op_output(self, state):
528
        final_hidden_states = state.pop("hidden_states_after_combine")
529
        final_hidden_states *= self.routed_scaling_factor
530
531
        if (s := state.pop("shared_output")) is not None:
            final_hidden_states = final_hidden_states + s
Liangsheng Yin's avatar
Liangsheng Yin committed
532

533
        state.hidden_states_mlp_output = final_hidden_states
534

Liangsheng Yin's avatar
Liangsheng Yin committed
535
536
537
538
539
540
541
542
543

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

    if scale <= 1:
        return 1.0
    return 0.1 * mscale * math.log(scale) + 1.0


544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
class DeepseekV2AttentionMLA(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        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,
        rope_theta: float = 10000,
        rope_scaling: Optional[Dict[str, Any]] = None,
        max_position_embeddings: int = 8192,
        quant_config: Optional[QuantizationConfig] = None,
Lianmin Zheng's avatar
Lianmin Zheng committed
560
561
        reduce_results: bool = True,
        layer_id: int = None,
562
        prefix: str = "",
563
        alt_stream: Optional[torch.cuda.Stream] = None,
564
565
566
567
568
569
570
571
572
573
    ) -> None:
        super().__init__()
        self.layer_id = layer_id
        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
Lianmin Zheng's avatar
Lianmin Zheng committed
574
575
576
        attn_tp_rank = get_attention_tp_rank()
        attn_tp_size = get_attention_tp_size()

577
        self.num_heads = num_heads
Lianmin Zheng's avatar
Lianmin Zheng committed
578
579
        assert num_heads % attn_tp_size == 0
        self.num_local_heads = num_heads // attn_tp_size
580
581
582
583
        self.scaling = self.qk_head_dim**-0.5
        self.rope_theta = rope_theta
        self.max_position_embeddings = max_position_embeddings

Lianmin Zheng's avatar
Lianmin Zheng committed
584
585
        # For tensor parallel attention
        if self.q_lora_rank is not None:
586
            self.fused_qkv_a_proj_with_mqa = ReplicatedLinear(
Ke Bao's avatar
Ke Bao committed
587
                self.hidden_size,
588
                self.q_lora_rank + self.kv_lora_rank + self.qk_rope_head_dim,
589
590
                bias=False,
                quant_config=quant_config,
591
                prefix=add_prefix("fused_qkv_a_proj_with_mqa", prefix),
592
            )
Lianmin Zheng's avatar
Lianmin Zheng committed
593
594
595
596
            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,
Ke Bao's avatar
Ke Bao committed
597
598
                bias=False,
                quant_config=quant_config,
Lianmin Zheng's avatar
Lianmin Zheng committed
599
600
601
                prefix=add_prefix("q_b_proj", prefix),
                tp_rank=attn_tp_rank,
                tp_size=attn_tp_size,
Ke Bao's avatar
Ke Bao committed
602
            )
Lianmin Zheng's avatar
Lianmin Zheng committed
603
604
        else:
            self.q_proj = ColumnParallelLinear(
605
                self.hidden_size,
Lianmin Zheng's avatar
Lianmin Zheng committed
606
                self.num_heads * self.qk_head_dim,
607
608
                bias=False,
                quant_config=quant_config,
Lianmin Zheng's avatar
Lianmin Zheng committed
609
610
611
                prefix=add_prefix("q_proj", prefix),
                tp_rank=attn_tp_rank,
                tp_size=attn_tp_size,
612
            )
613
614
615
616
617
618
619
620
            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,
                prefix=add_prefix("kv_a_proj_with_mqa", prefix),
            )

Lianmin Zheng's avatar
Lianmin Zheng committed
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
        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,
            prefix=add_prefix("kv_b_proj", prefix),
            tp_rank=attn_tp_rank,
            tp_size=attn_tp_size,
        )
        # O projection.
        self.o_proj = RowParallelLinear(
            self.num_heads * self.v_head_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=add_prefix("o_proj", prefix),
            tp_rank=attn_tp_rank,
            tp_size=attn_tp_size,
        )
641
        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
Ke Bao's avatar
Ke Bao committed
642
643
644
645

        if rope_scaling:
            rope_scaling["rope_type"] = "deepseek_yarn"

646
        self.rotary_emb = get_rope(
647
648
649
650
651
652
653
654
655
656
657
658
659
            qk_rope_head_dim,
            rotary_dim=qk_rope_head_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
            rope_scaling=rope_scaling,
            is_neox_style=False,
        )

        if rope_scaling:
            mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
            scaling_factor = rope_scaling["factor"]
            mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
            self.scaling = self.scaling * mscale * mscale
Ke Bao's avatar
Ke Bao committed
660
661
        else:
            self.rotary_emb.forward = self.rotary_emb.forward_native
662

663
        self.attn_mqa = RadixAttention(
664
665
666
667
668
669
            self.num_local_heads,
            self.kv_lora_rank + self.qk_rope_head_dim,
            self.scaling,
            num_kv_heads=1,
            layer_id=layer_id,
            v_head_dim=self.kv_lora_rank,
670
            quant_config=quant_config,
671
            prefix=add_prefix("attn_mqa", prefix),
672
673
        )

674
675
676
677
678
679
680
        self.attn_mha = RadixAttention(
            self.num_local_heads,
            self.qk_nope_head_dim + self.qk_rope_head_dim,
            self.scaling,
            num_kv_heads=self.num_local_heads,
            layer_id=layer_id,
            v_head_dim=self.v_head_dim,
681
            quant_config=quant_config,
682
            prefix=add_prefix("attn_mha", prefix),
683
684
        )

685
686
        self.alt_stream = alt_stream

Ke Bao's avatar
Ke Bao committed
687
688
        self.w_kc = None
        self.w_vc = None
689
        self.w_scale = 1.0
690

691
692
693
694
        self.w_scale_k = None
        self.w_scale_v = None
        self.use_deep_gemm_bmm = False

Lianmin Zheng's avatar
Lianmin Zheng committed
695
696
697
        self.flashinfer_mla_disable_ragged = global_server_args_dict[
            "flashinfer_mla_disable_ragged"
        ]
698
699
700
        self.disable_chunked_prefix_cache = global_server_args_dict[
            "disable_chunked_prefix_cache"
        ]
701
        self.attention_backend = global_server_args_dict["attention_backend"]
702
703
704
        self.rocm_fused_decode_mla = get_bool_env_var(
            "SGLANG_ROCM_FUSED_DECODE_MLA", "false"
        )
Lianmin Zheng's avatar
Lianmin Zheng committed
705

706
        # TODO: Design a finer way to determine the threshold
707
708
709
        self.chunked_prefix_cache_threshold = get_int_env_var(
            "SGL_CHUNKED_PREFIX_CACHE_THRESHOLD", 8192
        )
710
711
712
713

    def dispatch_attn_forward_method(
        self, forward_batch: ForwardBatch
    ) -> AttnForwardMethod:
714
715
716
717
718
719
720
721
722
723
724
725
        def _dispatch_mla_subtype():
            if _is_hip:
                if (
                    self.rocm_fused_decode_mla
                    and forward_batch.forward_mode.is_decode()
                ):
                    return AttnForwardMethod.MLA_FUSED_ROPE
                else:
                    return AttnForwardMethod.MLA
            else:
                return AttnForwardMethod.MLA

726
        if self.attention_backend == "flashinfer":
Lianmin Zheng's avatar
Lianmin Zheng committed
727
            # Flashinfer MLA: Do not absorb when enabling ragged prefill
728
            if (
Lianmin Zheng's avatar
Lianmin Zheng committed
729
730
731
732
                not self.flashinfer_mla_disable_ragged
                and forward_batch.forward_mode.is_extend()
                and not forward_batch.forward_mode.is_target_verify()
                and not forward_batch.forward_mode.is_draft_extend()
733
                and sum(forward_batch.extend_prefix_lens_cpu) == 0
734
735
736
            ):
                return AttnForwardMethod.MHA
            else:
737
                return _dispatch_mla_subtype()
738
        elif self.attention_backend == "fa3":
739
            # Flash Attention: Use MHA with chunked KV cache when prefilling on long sequences.
740
741
            if forward_batch.extend_prefix_lens_cpu is not None:
                sum_extend_prefix_lens = sum(forward_batch.extend_prefix_lens_cpu)
742
743
744
745
746
            if (
                forward_batch.forward_mode.is_extend()
                and not self.disable_chunked_prefix_cache
                and not forward_batch.forward_mode.is_target_verify()
                and not forward_batch.forward_mode.is_draft_extend()
747
748
749
750
                and (
                    sum_extend_prefix_lens >= self.chunked_prefix_cache_threshold
                    or sum_extend_prefix_lens == 0
                )
751
752
753
            ):
                return AttnForwardMethod.MHA_CHUNKED_KV
            else:
754
                return _dispatch_mla_subtype()
Lianmin Zheng's avatar
Lianmin Zheng committed
755
756
        else:
            # Triton: Use normal computation for prefill and use weight absorption for extend/decode
757
            if (
Lianmin Zheng's avatar
Lianmin Zheng committed
758
759
760
                forward_batch.forward_mode.is_extend()
                and not forward_batch.forward_mode.is_target_verify()
                and not forward_batch.forward_mode.is_draft_extend()
761
                and sum(forward_batch.extend_prefix_lens_cpu) == 0
762
763
764
            ):
                return AttnForwardMethod.MHA
            else:
765
                return _dispatch_mla_subtype()
Lianmin Zheng's avatar
Lianmin Zheng committed
766

767
768
769
770
771
772
773
774
775
776
777
778
779
    def op_prepare(self, state):
        state.attn_intermediate_state = self.forward_prepare(
            positions=state.positions,
            hidden_states=state.pop("hidden_states_after_comm_pre_attn"),
            forward_batch=state.forward_batch,
            zero_allocator=state.zero_allocator,
        )

    def op_core(self, state):
        state.hidden_states_after_attn = self.forward_core(
            state.pop("attn_intermediate_state")
        )

780
781
782
783
    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
784
        forward_batch: ForwardBatch,
785
        zero_allocator: BumpAllocator,
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
    ):
        s = self.forward_prepare(
            positions=positions,
            hidden_states=hidden_states,
            forward_batch=forward_batch,
            zero_allocator=zero_allocator,
        )
        return self.forward_core(s)

    def forward_prepare(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        zero_allocator: BumpAllocator,
    ):
Lianmin Zheng's avatar
Lianmin Zheng committed
802
803
804
805
        if hidden_states.shape[0] == 0:
            assert (
                not self.o_proj.reduce_results
            ), "short-circuiting allreduce will lead to hangs"
806
            return hidden_states, None, forward_batch, None
807

808
809
810
        attn_forward_method = self.dispatch_attn_forward_method(forward_batch)

        if attn_forward_method == AttnForwardMethod.MHA:
811
812
813
            inner_state = self.forward_normal_prepare(
                positions, hidden_states, forward_batch, zero_allocator
            )
814
        elif attn_forward_method == AttnForwardMethod.MHA_CHUNKED_KV:
815
816
            inner_state = self.forward_normal_chunked_kv_prepare(
                positions, hidden_states, forward_batch, zero_allocator
817
            )
818
        elif attn_forward_method == AttnForwardMethod.MLA:
819
            inner_state = self.forward_absorb_prepare(
820
821
822
                positions, hidden_states, forward_batch, zero_allocator
            )
        elif attn_forward_method == AttnForwardMethod.MLA_FUSED_ROPE:
823
824
            inner_state = self.forward_absorb_fused_mla_rope_prepare(
                positions, hidden_states, forward_batch, zero_allocator
825
            )
826
        else:
827
            raise NotImplementedError
828
        return None, attn_forward_method, forward_batch, inner_state
829

830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
    def forward_core(self, intermediate_state):
        hidden_states, attn_forward_method, forward_batch, inner_state = (
            intermediate_state
        )
        if inner_state is None:
            return hidden_states

        if attn_forward_method == AttnForwardMethod.MHA:
            return self.forward_normal_core(*inner_state)
        elif attn_forward_method == AttnForwardMethod.MHA_CHUNKED_KV:
            return self.forward_normal_chunked_kv_core(*inner_state)
        elif attn_forward_method == AttnForwardMethod.MLA:
            return self.forward_absorb_core(*inner_state)
        elif attn_forward_method == AttnForwardMethod.MLA_FUSED_ROPE:
            return self.forward_absorb_fused_mla_rope_core(*inner_state)
        else:
            raise NotImplementedError

    def forward_normal_prepare(
849
850
851
852
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
853
854
        zero_allocator: BumpAllocator,
    ):
855
        if self.q_lora_rank is not None:
856
857
858
            q, latent_cache = self.fused_qkv_a_proj_with_mqa(hidden_states)[0].split(
                [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], dim=-1
            )
859
860
861
862
863
864
            q = self.q_a_layernorm(q)
            q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
        else:
            q = self.q_proj(hidden_states)[0].view(
                -1, self.num_local_heads, self.qk_head_dim
            )
865
866
            latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]

867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
        _, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
        kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
        latent_cache = latent_cache.unsqueeze(1)
        kv_a = self.kv_a_layernorm(kv_a.contiguous())
        kv = self.kv_b_proj(kv_a)[0]
        kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
        k_nope = kv[..., : self.qk_nope_head_dim]
        v = kv[..., self.qk_nope_head_dim :]
        k_pe = latent_cache[:, :, self.kv_lora_rank :]
        q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
        q[..., self.qk_nope_head_dim :] = q_pe
        k = torch.empty_like(q)
        k[..., : self.qk_nope_head_dim] = k_nope
        k[..., self.qk_nope_head_dim :] = k_pe

        latent_cache[:, :, : self.kv_lora_rank] = kv_a.unsqueeze(1)
        latent_cache[:, :, self.kv_lora_rank :] = k_pe

        # Save latent cache
        forward_batch.token_to_kv_pool.set_kv_buffer(
            self.attn_mha, forward_batch.out_cache_loc, latent_cache, None
        )
889
890
891
892

        return q, k, v, forward_batch

    def forward_normal_core(self, q, k, v, forward_batch):
893
894
895
896
897
        attn_output = self.attn_mha(q, k, v, forward_batch, save_kv_cache=False)
        attn_output = attn_output.reshape(-1, self.num_local_heads * self.v_head_dim)
        output, _ = self.o_proj(attn_output)
        return output

898
    def forward_absorb_prepare(
899
900
901
902
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
903
        zero_allocator: BumpAllocator,
904
    ):
905
906
        from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode

907
        if self.q_lora_rank is not None:
908
909
910
            q, latent_cache = self.fused_qkv_a_proj_with_mqa(hidden_states)[0].split(
                [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], dim=-1
            )
911
912
913
            k_nope = latent_cache[..., : self.kv_lora_rank]

            # overlap qk norm
914
            if self.alt_stream is not None and get_is_capture_mode():
915
916
917
918
919
920
921
922
923
924
925
                current_stream = torch.cuda.current_stream()
                self.alt_stream.wait_stream(current_stream)
                q = self.q_a_layernorm(q)
                with torch.cuda.stream(self.alt_stream):
                    k_nope = self.kv_a_layernorm(k_nope)
                current_stream.wait_stream(self.alt_stream)
            else:
                q = self.q_a_layernorm(q)
                k_nope = self.kv_a_layernorm(k_nope)

            k_nope = k_nope.unsqueeze(1)
926
927
928
929
930
            q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
        else:
            q = self.q_proj(hidden_states)[0].view(
                -1, self.num_local_heads, self.qk_head_dim
            )
931
            latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
932
933
934
            k_nope = latent_cache[..., : self.kv_lora_rank]
            k_nope = self.kv_a_layernorm(k_nope).unsqueeze(1)

935
        q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
936
        k_pe = latent_cache[..., self.kv_lora_rank :].unsqueeze(1)
937

938
939
        if self.use_deep_gemm_bmm:
            q_nope_val, q_nope_scale, masked_m, expected_m, aligned_m = (
940
                per_token_group_quant_mla_deep_gemm_masked_fp8(q_nope.transpose(0, 1))
941
942
943
944
            )
            q_nope_out = q_nope.new_empty(
                (self.num_local_heads, aligned_m, self.kv_lora_rank)
            )
945
            deep_gemm_grouped_gemm_nt_f8f8bf16_masked(
946
947
948
949
950
951
952
                (q_nope_val, q_nope_scale),
                (self.w_kc, self.w_scale_k),
                q_nope_out,
                masked_m,
                expected_m,
            )
            q_nope_out = q_nope_out[:, :expected_m, :]
953
954
        elif _is_hip:
            # TODO(haishaw): add bmm_fp8 to ROCm
955
956
957
958
            q_nope_out = torch.bmm(
                q_nope.to(torch.bfloat16).transpose(0, 1),
                self.w_kc.to(torch.bfloat16) * self.w_scale,
            )
959
        elif self.w_kc.dtype == torch.float8_e4m3fn:
960
            q_nope_val, q_nope_scale = per_tensor_quant_mla_fp8(
Lianmin Zheng's avatar
Lianmin Zheng committed
961
                q_nope.transpose(0, 1),
962
                zero_allocator.allocate(1),
963
964
965
966
967
968
            )
            q_nope_out = bmm_fp8(
                q_nope_val, self.w_kc, q_nope_scale, self.w_scale, torch.bfloat16
            )
        else:
            q_nope_out = torch.bmm(q_nope.transpose(0, 1), self.w_kc)
969
970

        q_nope_out = q_nope_out.transpose(0, 1)
971
972
        q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)

973
974
975
976
977
        return q_pe, k_pe, q_nope_out, k_nope, forward_batch, zero_allocator

    def forward_absorb_core(
        self, q_pe, k_pe, q_nope_out, k_nope, forward_batch, zero_allocator
    ):
xu-yfei's avatar
xu-yfei committed
978
        if self.attention_backend == "fa3" or self.attention_backend == "flashinfer":
979
            attn_output = self.attn_mqa(
Ke Bao's avatar
Ke Bao committed
980
                q_nope_out, k_nope, k_nope, forward_batch, q_rope=q_pe, k_rope=k_pe
981
982
983
            )
        else:
            q = torch.cat([q_nope_out, q_pe], dim=-1)
Ke Bao's avatar
Ke Bao committed
984
            k = torch.cat([k_nope, k_pe], dim=-1)
985
            attn_output = self.attn_mqa(q, k, k_nope, forward_batch)
986
987
        attn_output = attn_output.view(-1, self.num_local_heads, self.kv_lora_rank)

988
989
        if self.use_deep_gemm_bmm:
            attn_output_val, attn_output_scale, masked_m, expected_m, aligned_m = (
990
991
                per_token_group_quant_mla_deep_gemm_masked_fp8(
                    attn_output.transpose(0, 1)
992
993
994
995
996
                )
            )
            attn_bmm_output = attn_output.new_empty(
                (self.num_local_heads, aligned_m, self.v_head_dim)
            )
997
            deep_gemm_grouped_gemm_nt_f8f8bf16_masked(
998
999
1000
1001
1002
1003
1004
                (attn_output_val, attn_output_scale),
                (self.w_vc, self.w_scale_v),
                attn_bmm_output,
                masked_m,
                expected_m,
            )
            attn_bmm_output = attn_bmm_output[:, :expected_m, :]
1005
1006
        elif _is_hip:
            # TODO(haishaw): add bmm_fp8 to ROCm
1007
1008
1009
1010
            attn_bmm_output = torch.bmm(
                attn_output.to(torch.bfloat16).transpose(0, 1),
                self.w_vc.to(torch.bfloat16) * self.w_scale,
            )
1011
        elif self.w_vc.dtype == torch.float8_e4m3fn:
1012
            attn_output_val, attn_output_scale = per_tensor_quant_mla_fp8(
Lianmin Zheng's avatar
Lianmin Zheng committed
1013
                attn_output.transpose(0, 1),
1014
                zero_allocator.allocate(1),
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
            )
            attn_bmm_output = bmm_fp8(
                attn_output_val,
                self.w_vc,
                attn_output_scale,
                self.w_scale,
                torch.bfloat16,
            )
        else:
            attn_bmm_output = torch.bmm(attn_output.transpose(0, 1), self.w_vc)
        attn_output = attn_bmm_output.transpose(0, 1).flatten(1, 2)
        output, _ = self.o_proj(attn_output)

        return output

1030
    def forward_absorb_fused_mla_rope_prepare(
1031
1032
1033
1034
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
1035
        zero_allocator: BumpAllocator,
1036
    ):
1037
1038
1039
1040
1041
1042
1043
1044
        enable_rope_fusion = (
            os.getenv("SGLANG_FUSED_MLA_ENABLE_ROPE_FUSION", "1") == "1"
        )
        q_len = hidden_states.shape[0]
        q_input = hidden_states.new_empty(
            q_len, self.num_local_heads, self.kv_lora_rank + self.qk_rope_head_dim
        )
        if self.q_lora_rank is not None:
1045
1046
1047
            q, latent_cache = self.fused_qkv_a_proj_with_mqa(hidden_states)[0].split(
                [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], dim=-1
            )
1048
1049
1050
1051
1052
1053
            q = self.q_a_layernorm(q)
            q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
        else:
            q = self.q_proj(hidden_states)[0].view(
                -1, self.num_local_heads, self.qk_head_dim
            )
1054
            latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
1055
1056
        q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)

1057
1058
        if _is_hip:
            # TODO(haishaw): add bmm_fp8 to ROCm
1059
1060
1061
1062
1063
            q_nope_out = torch.bmm(
                q_nope.to(torch.bfloat16).transpose(0, 1),
                self.w_kc.to(torch.bfloat16) * self.w_scale,
            )
        elif self.w_kc.dtype == torch.float8_e4m3fn:
1064
            q_nope_val, q_nope_scale = per_tensor_quant_mla_fp8(
1065
1066
1067
                q_nope.transpose(0, 1),
                zero_allocator.allocate(1),
                dtype=torch.float8_e4m3fn,
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
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
            )
            q_nope_out = bmm_fp8(
                q_nope_val, self.w_kc, q_nope_scale, self.w_scale, torch.bfloat16
            )
        else:
            q_nope_out = torch.bmm(q_nope.transpose(0, 1), self.w_kc)
        q_input[..., : self.kv_lora_rank] = q_nope_out.transpose(0, 1)
        v_input = latent_cache[..., : self.kv_lora_rank]
        v_input = self.kv_a_layernorm(v_input.contiguous()).unsqueeze(1)
        k_input = latent_cache.unsqueeze(1)
        k_input[..., : self.kv_lora_rank] = v_input

        if not enable_rope_fusion:
            k_pe = k_input[..., self.kv_lora_rank :]
            q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
            q_input[..., self.kv_lora_rank :] = q_pe
            k_input[..., self.kv_lora_rank :] = k_pe
            k_pe_output = None
        else:
            k_pe_output = torch.empty_like(k_input[..., self.kv_lora_rank :])

        q_input[..., self.kv_lora_rank :] = q_pe

        # attn_output = self.attn_mqa(q_input, k_input, v_input, forward_batch)
        # Use Fused ROPE with use_rope=OFF.
        attn_output = torch.empty(
            (q_len, self.num_local_heads, self.kv_lora_rank),
            dtype=q.dtype,
            device=q.device,
        )
        attn_logits, _, kv_indptr, kv_indices, _, _, _ = (
            forward_batch.attn_backend.forward_metadata
        )
        cos_sin_cache = self.rotary_emb.cos_sin_cache
        num_kv_split = forward_batch.attn_backend.num_kv_splits
        sm_scale = self.attn_mqa.scaling
        if attn_logits is None:
            attn_logits = torch.empty(
                (
                    forward_batch.batch_size,
                    self.num_local_heads,
                    num_kv_split,
                    self.kv_lora_rank + 1,
                ),
                dtype=torch.float32,
                device=q.device,
            )

        # save current latent cache.
        forward_batch.token_to_kv_pool.set_kv_buffer(
            self.attn_mqa, forward_batch.out_cache_loc, k_input, None
        )
        key_cache_buf = forward_batch.token_to_kv_pool.get_key_buffer(
            self.attn_mqa.layer_id
        )
        val_cache_buf = key_cache_buf[..., : self.kv_lora_rank]

1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
        return (
            q_input,
            key_cache_buf,
            val_cache_buf,
            attn_output,
            kv_indptr,
            kv_indices,
            k_pe_output,
            cos_sin_cache,
            positions,
            attn_logits,
            num_kv_split,
            sm_scale,
            enable_rope_fusion,
            k_input,
            forward_batch,
            zero_allocator,
        )

    def forward_absorb_fused_mla_rope_core(
        self,
        q_input,
        key_cache_buf,
        val_cache_buf,
        attn_output,
        kv_indptr,
        kv_indices,
        k_pe_output,
        cos_sin_cache,
        positions,
        attn_logits,
        num_kv_split,
        sm_scale,
        enable_rope_fusion,
        k_input,
        forward_batch,
        zero_allocator,
    ):
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
        decode_attention_fwd_grouped_rope(
            q_input,
            key_cache_buf,
            val_cache_buf,
            attn_output,
            kv_indptr,
            kv_indices,
            k_pe_output,
            self.kv_lora_rank,
            self.rotary_emb.rotary_dim,
            cos_sin_cache,
            positions,
            attn_logits,
            num_kv_split,
            sm_scale,
            logit_cap=self.attn_mqa.logit_cap,
            use_rope=enable_rope_fusion,
            is_neox_style=self.rotary_emb.is_neox_style,
        )

        if enable_rope_fusion:
            k_input[..., self.kv_lora_rank :] = k_pe_output
            forward_batch.token_to_kv_pool.set_kv_buffer(
                self.attn_mqa, forward_batch.out_cache_loc, k_input, None
            )

        attn_output = attn_output.view(-1, self.num_local_heads, self.kv_lora_rank)

1191
1192
        if _is_hip:
            # TODO(haishaw): add bmm_fp8 to ROCm
1193
1194
1195
1196
1197
            attn_bmm_output = torch.bmm(
                attn_output.to(torch.bfloat16).transpose(0, 1),
                self.w_vc.to(torch.bfloat16) * self.w_scale,
            )
        elif self.w_vc.dtype == torch.float8_e4m3fn:
1198
            attn_output_val, attn_output_scale = per_tensor_quant_mla_fp8(
1199
1200
1201
                attn_output.transpose(0, 1),
                zero_allocator.allocate(1),
                dtype=torch.float8_e4m3fn,
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
            )
            attn_bmm_output = bmm_fp8(
                attn_output_val,
                self.w_vc,
                attn_output_scale,
                self.w_scale,
                torch.bfloat16,
            )
        else:
            attn_bmm_output = torch.bmm(attn_output.transpose(0, 1), self.w_vc)
        attn_output = attn_bmm_output.transpose(0, 1).flatten(1, 2)
1213
1214
1215
1216
        output, _ = self.o_proj(attn_output)

        return output

1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
    def _chunked_prefix_attn_mha(
        self,
        q: torch.Tensor,
        accum_output: torch.Tensor,
        accum_lse: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:

        assert forward_batch.num_prefix_chunks is not None
        for i in range(forward_batch.num_prefix_chunks):
            forward_batch.set_prefix_chunk_idx(i)

            # Fetch latent cache from memory pool with precomputed chunked kv indices
            latent_cache_buf = forward_batch.token_to_kv_pool.get_key_buffer(
                self.attn_mha.layer_id
            )
            latent_cache = latent_cache_buf[
                forward_batch.prefix_chunk_kv_indices[i]
            ].contiguous()

            kv_a_normed, k_pe = latent_cache.split(
                [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
            )
            kv_a_normed = kv_a_normed.squeeze(1).contiguous()
            kv = self.kv_b_proj(kv_a_normed)[0]
            kv = kv.view(
                -1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim
            )
            v = kv[..., self.qk_nope_head_dim :]
            k_nope = kv[..., : self.qk_nope_head_dim]

            k = torch.empty(
                (
                    k_nope.shape[0],
                    self.num_local_heads,
                    self.qk_nope_head_dim + self.qk_rope_head_dim,
                ),
                dtype=v.dtype,
                device=v.device,
            )
            k[..., : self.qk_nope_head_dim] = k_nope
            k[..., self.qk_nope_head_dim :] = k_pe

            output, lse = self.attn_mha(q, k, v, forward_batch, save_kv_cache=False)
            lse = torch.transpose(lse, 0, 1).contiguous()
            tmp_output = torch.empty_like(accum_output)
            tmp_lse = torch.empty_like(accum_lse)
            merge_state_v2(output, lse, accum_output, accum_lse, tmp_output, tmp_lse)
            accum_output, accum_lse = tmp_output, tmp_lse

        return accum_output

1269
    def forward_normal_chunked_kv_prepare(
1270
1271
1272
1273
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
1274
1275
        zero_allocator: BumpAllocator,
    ):
1276
1277
1278
1279
1280
1281
1282
1283
        # In normal mha, the k and v tensors will become overly large when the prefix length is long.
        # To avoid this, we split the kv cache into chunks and process them one after another.
        # Since mha is compute friendly, the for loop induced here will not introduce significant overhead.
        # The top comments in https://github.com/vllm-project/vllm/blob/main/vllm/v1/attention/backends/mla/common.py
        # will be helpful for understanding the purpose of this function.

        # First do normal mha forward to get output for extended part
        if self.q_lora_rank is not None:
1284
1285
1286
            q, latent_cache = self.fused_qkv_a_proj_with_mqa(hidden_states)[0].split(
                [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], dim=-1
            )
1287
1288
1289
1290
1291
1292
            q = self.q_a_layernorm(q)
            q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
        else:
            q = self.q_proj(hidden_states)[0].view(
                -1, self.num_local_heads, self.qk_head_dim
            )
1293
            latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
        _, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
        kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
        latent_cache = latent_cache.unsqueeze(1)
        kv_a = self.kv_a_layernorm(kv_a.contiguous())
        kv = self.kv_b_proj(kv_a)[0]
        kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
        k_nope = kv[..., : self.qk_nope_head_dim]
        v = kv[..., self.qk_nope_head_dim :]
        k_pe = latent_cache[:, :, self.kv_lora_rank :]

        q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
        q[..., self.qk_nope_head_dim :] = q_pe
        k = torch.empty_like(q)
        k[..., : self.qk_nope_head_dim] = k_nope
        k[..., self.qk_nope_head_dim :] = k_pe

        latent_cache[:, :, : self.kv_lora_rank] = kv_a.unsqueeze(1)
        latent_cache[:, :, self.kv_lora_rank :] = k_pe

        # Save latent cache
        forward_batch.token_to_kv_pool.set_kv_buffer(
            self.attn_mha, forward_batch.out_cache_loc, latent_cache, None
        )

1318
1319
1320
        return q, k, v, forward_batch

    def forward_normal_chunked_kv_core(self, q, k, v, forward_batch):
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
        # Do mha for extended part without prefix
        forward_batch.set_attn_attend_prefix_cache(False)
        attn_output, lse = self.attn_mha(q, k, v, forward_batch, save_kv_cache=False)
        lse = torch.transpose(lse, 0, 1).contiguous()

        # Do mha attention with chunked prefix cache if there are any sequence with prefix
        if any(forward_batch.extend_prefix_lens_cpu):
            # Only initialize the info once
            if forward_batch.num_prefix_chunks is None:
                forward_batch.prepare_chunked_prefix_cache_info(q.device)

            forward_batch.set_attn_attend_prefix_cache(True)
            attn_output = self._chunked_prefix_attn_mha(
                q=q,
                accum_output=attn_output,
                accum_lse=lse,
                forward_batch=forward_batch,
            )

        attn_output = attn_output.reshape(-1, self.num_local_heads * self.v_head_dim)
        output, _ = self.o_proj(attn_output)
        return output

1344

Liangsheng Yin's avatar
Liangsheng Yin committed
1345
1346
1347
1348
1349
1350
1351
class DeepseekV2DecoderLayer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
1352
        is_nextn: bool = False,
1353
        prefix: str = "",
1354
        alt_stream: Optional[torch.cuda.Stream] = None,
Liangsheng Yin's avatar
Liangsheng Yin committed
1355
1356
1357
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
1358
        self.config = config
Liangsheng Yin's avatar
Liangsheng Yin committed
1359
1360
1361
        rope_theta = getattr(config, "rope_theta", 10000)
        rope_scaling = getattr(config, "rope_scaling", None)
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
Lianmin Zheng's avatar
Lianmin Zheng committed
1362
1363
        self.enable_dp_attention = global_server_args_dict["enable_dp_attention"]
        self.layer_id = layer_id
Baizhou Zhang's avatar
Baizhou Zhang committed
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
        self.self_attn = DeepseekV2AttentionMLA(
            config=config,
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            qk_nope_head_dim=config.qk_nope_head_dim,
            qk_rope_head_dim=config.qk_rope_head_dim,
            v_head_dim=config.v_head_dim,
            q_lora_rank=(
                config.q_lora_rank if hasattr(config, "q_lora_rank") else None
            ),
            kv_lora_rank=config.kv_lora_rank,
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            max_position_embeddings=max_position_embeddings,
            quant_config=quant_config,
            layer_id=layer_id,
            reduce_results=False,
            prefix=add_prefix("self_attn", prefix),
1382
            alt_stream=alt_stream,
Baizhou Zhang's avatar
Baizhou Zhang committed
1383
        )
Lianmin Zheng's avatar
Lianmin Zheng committed
1384

1385
1386
1387
1388
1389
1390
1391
1392
        self.is_layer_sparse = self._is_layer_sparse(layer_id, is_nextn=is_nextn)
        is_previous_layer_sparse = self._is_layer_sparse(layer_id - 1, is_nextn=False)

        self.layer_scatter_modes = LayerScatterModes.init_new(
            layer_id=layer_id,
            num_layers=config.num_hidden_layers,
            is_layer_sparse=self.is_layer_sparse,
            is_previous_layer_sparse=is_previous_layer_sparse,
1393
1394
        )

1395
        if self.is_layer_sparse:
1396
1397
1398
1399
            self.mlp = DeepseekV2MoE(
                config=config,
                quant_config=quant_config,
                prefix=add_prefix("mlp", prefix),
fzyzcjy's avatar
fzyzcjy committed
1400
                layer_id=self.layer_id,
1401
            )
Liangsheng Yin's avatar
Liangsheng Yin committed
1402
        else:
1403
            if enable_moe_dense_fully_dp():
1404
1405
1406
                mlp_tp_rank, mlp_tp_size = 0, 1
            else:
                mlp_tp_rank, mlp_tp_size = None, None
Liangsheng Yin's avatar
Liangsheng Yin committed
1407
1408
1409
1410
1411
            self.mlp = DeepseekV2MLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
1412
                prefix=add_prefix("mlp", prefix),
1413
1414
                tp_rank=mlp_tp_rank,
                tp_size=mlp_tp_size,
Liangsheng Yin's avatar
Liangsheng Yin committed
1415
            )
1416

Liangsheng Yin's avatar
Liangsheng Yin committed
1417
1418
1419
1420
1421
        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
        )

1422
1423
1424
1425
        self.layer_communicator = LayerCommunicator(
            layer_scatter_modes=self.layer_scatter_modes,
            input_layernorm=self.input_layernorm,
            post_attention_layernorm=self.post_attention_layernorm,
1426
        )
1427
1428
1429
1430
1431
1432

    def _is_layer_sparse(self, layer_id: int, is_nextn: bool) -> bool:
        return is_nextn or (
            self.config.n_routed_experts is not None
            and layer_id >= self.config.first_k_dense_replace
            and layer_id % self.config.moe_layer_freq == 0
1433
1434
        )

Liangsheng Yin's avatar
Liangsheng Yin committed
1435
1436
1437
1438
    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
1439
        forward_batch: ForwardBatch,
Liangsheng Yin's avatar
Liangsheng Yin committed
1440
        residual: Optional[torch.Tensor],
1441
        zero_allocator: BumpAllocator,
Liangsheng Yin's avatar
Liangsheng Yin committed
1442
    ) -> torch.Tensor:
1443
1444
        hidden_states, residual = self.layer_communicator.prepare_attn(
            hidden_states, residual, forward_batch
1445
1446
        )

1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            forward_batch=forward_batch,
            zero_allocator=zero_allocator,
        )

        hidden_states, residual = self.layer_communicator.prepare_mlp(
            hidden_states, residual, forward_batch
        )

        hidden_states = self.mlp(hidden_states, forward_batch)

        hidden_states, residual = self.layer_communicator.postprocess_layer(
            hidden_states, residual, forward_batch
        )

        return hidden_states, residual

1466
1467
1468
1469
1470
1471
1472
1473
    def op_comm_prepare_attn(
        self,
        state,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        residual: Optional[torch.Tensor],
        zero_allocator: BumpAllocator,
1474
        tbo_subbatch_index: Optional[int] = None,
1475
1476
    ):
        state.hidden_states_after_comm_pre_attn, state.residual_after_input_ln = (
fzyzcjy's avatar
fzyzcjy committed
1477
            self.layer_communicator.prepare_attn(hidden_states, residual, forward_batch)
1478
1479
1480
1481
1482
1483
        )
        state.update(
            dict(
                forward_batch=forward_batch,
                positions=positions,
                zero_allocator=zero_allocator,
1484
                tbo_subbatch_index=tbo_subbatch_index,
1485
            )
1486
        )
1487

1488
1489
1490
1491
1492
1493
1494
    def op_comm_prepare_mlp(self, state):
        state.hidden_states_mlp_input, state.residual_after_comm_pre_mlp = (
            self.layer_communicator.prepare_mlp(
                state.pop("hidden_states_after_attn"),
                state.pop("residual_after_input_ln"),
                state.forward_batch,
            )
1495
        )
1496

1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
    def op_mlp(self, state):
        hidden_states = state.pop("hidden_states_mlp_input")
        if not (
            enable_moe_dense_fully_dp()
            and (not self.is_layer_sparse)
            and hidden_states.shape[0] == 0
        ):
            state.hidden_states_mlp_output = self.mlp(
                hidden_states, state.forward_batch.forward_mode
            )
        else:
            state.hidden_states_mlp_output = hidden_states
1509

1510
    def op_comm_postprocess_layer(self, state):
1511
        hidden_states, residual = self.layer_communicator.postprocess_layer(
1512
1513
1514
            state.pop("hidden_states_mlp_output"),
            state.pop("residual_after_comm_pre_mlp"),
            state.forward_batch,
1515
        )
1516

1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
        output = dict(
            positions=state.positions,
            hidden_states=hidden_states,
            residual=residual,
            forward_batch=state.forward_batch,
            zero_allocator=state.zero_allocator,
            tbo_subbatch_index=state.tbo_subbatch_index,
        )

        state.clear(
            expect_keys={
                "positions",
                "forward_batch",
                "zero_allocator",
                "tbo_subbatch_index",
            }
        )
        return output
1535

Liangsheng Yin's avatar
Liangsheng Yin committed
1536
1537
1538
1539
1540
1541
1542
1543

class DeepseekV2Model(nn.Module):
    fall_back_to_pt_during_load = False

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
1544
        prefix: str = "",
Liangsheng Yin's avatar
Liangsheng Yin committed
1545
1546
1547
1548
    ) -> None:
        super().__init__()
        self.padding_id = config.pad_token_id
        self.vocab_size = config.vocab_size
1549
        self.first_k_dense_replace = config.first_k_dense_replace
Liangsheng Yin's avatar
Liangsheng Yin committed
1550
1551
1552
1553

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
Ke Bao's avatar
Ke Bao committed
1554
            enable_tp=not global_server_args_dict["enable_dp_attention"],
Liangsheng Yin's avatar
Liangsheng Yin committed
1555
        )
1556
        self.alt_stream = torch.cuda.Stream() if _is_cuda else None
Liangsheng Yin's avatar
Liangsheng Yin committed
1557
1558
1559
1560
1561
1562
        self.layers = nn.ModuleList(
            [
                DeepseekV2DecoderLayer(
                    config,
                    layer_id,
                    quant_config=quant_config,
1563
                    prefix=add_prefix(f"layers.{layer_id}", prefix),
1564
                    alt_stream=self.alt_stream,
Liangsheng Yin's avatar
Liangsheng Yin committed
1565
1566
1567
1568
1569
1570
                )
                for layer_id in range(config.num_hidden_layers)
            ]
        )
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

1571
        self.dp_size = get_local_attention_dp_size()
Lianmin Zheng's avatar
Lianmin Zheng committed
1572

1573
1574
1575
    def get_input_embeddings(self) -> torch.Tensor:
        return self.embed_tokens

Liangsheng Yin's avatar
Liangsheng Yin committed
1576
1577
1578
1579
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
1580
        forward_batch: ForwardBatch,
1581
        input_embeds: torch.Tensor = None,
Liangsheng Yin's avatar
Liangsheng Yin committed
1582
    ) -> torch.Tensor:
1583
1584
        total_num_layers = len(self.layers)
        device = input_embeds.device if input_embeds is not None else input_ids.device
1585
        zero_allocator = BumpAllocator(
1586
            buffer_size=total_num_layers * 2 * (2 if forward_batch.can_run_tbo else 1),
1587
            dtype=torch.float32,
1588
            device=device,
1589
        )
Lianmin Zheng's avatar
Lianmin Zheng committed
1590

1591
1592
1593
1594
1595
        if input_embeds is None:
            hidden_states = self.embed_tokens(input_ids)
        else:
            hidden_states = input_embeds

Liangsheng Yin's avatar
Liangsheng Yin committed
1596
        residual = None
1597
1598
1599
1600
1601
1602
1603

        normal_num_layers = (
            self.first_k_dense_replace
            if forward_batch.can_run_tbo
            else total_num_layers
        )
        for i in range(normal_num_layers):
1604
1605
1606
1607
1608
            with get_global_expert_distribution_recorder().with_current_layer(i):
                layer = self.layers[i]
                hidden_states, residual = layer(
                    positions, hidden_states, forward_batch, residual, zero_allocator
                )
1609
1610
1611
1612
1613
1614
1615
1616
1617

        if normal_num_layers != total_num_layers:
            hidden_states, residual = model_forward_maybe_tbo(
                layers=self.layers[normal_num_layers:],
                enable_tbo=True,
                positions=positions,
                forward_batch=forward_batch,
                hidden_states=hidden_states,
                residual=residual,
1618
1619
1620
                input_data_scatter_mode=self.layers[
                    normal_num_layers - 1
                ].layer_scatter_modes.layer_output_mode,
1621
1622
1623
                zero_allocator=zero_allocator,
            )

Ke Bao's avatar
Ke Bao committed
1624
        if not forward_batch.forward_mode.is_idle():
1625
1626
1627
1628
            if residual is None:
                hidden_states = self.norm(hidden_states)
            else:
                hidden_states, _ = self.norm(hidden_states, residual)
Liangsheng Yin's avatar
Liangsheng Yin committed
1629
1630
1631
1632
1633
1634
1635
1636
1637
        return hidden_states


class DeepseekV2ForCausalLM(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
1638
        prefix: str = "",
Liangsheng Yin's avatar
Liangsheng Yin committed
1639
1640
1641
    ) -> None:
        super().__init__()
        self.config = config
1642
        self.tp_size = get_tensor_model_parallel_world_size()
Liangsheng Yin's avatar
Liangsheng Yin committed
1643
        self.quant_config = quant_config
1644
1645
1646
1647
1648
1649
1650
1651
1652
        self.determine_n_share_experts_fusion()
        self.model = DeepseekV2Model(
            config, quant_config, prefix=add_prefix("model", prefix)
        )
        self.lm_head = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
            quant_config=quant_config,
            prefix=add_prefix("lm_head", prefix),
1653
            use_attn_tp_group=global_server_args_dict["enable_dp_lm_head"],
1654
1655
        )
        self.logits_processor = LogitsProcessor(config)
1656
        self.dp_size = get_local_attention_dp_size()
1657
1658
1659
1660

    def determine_n_share_experts_fusion(
        self, architecture: str = "DeepseekV3ForCausalLM"
    ):
1661
        self.n_share_experts_fusion = global_server_args_dict["n_share_experts_fusion"]
1662
1663
1664
        if self.n_share_experts_fusion > 0:
            # Only Deepseek V3/R1 can use shared experts fusion optimization now.
            if (
1665
1666
                not _is_cuda
                or self.config.architectures[0] != architecture
1667
1668
1669
1670
                or self.config.n_routed_experts != 256
            ):
                self.n_share_experts_fusion = 0
                global_server_args_dict["n_share_experts_fusion"] = 0
1671
1672
                log_info_on_rank0(
                    logger,
1673
                    "Only Deepseek V3/R1 on NV-platform can use shared experts fusion optimization. Shared experts fusion optimization is disabled.",
1674
1675
1676
1677
                )
            else:
                assert (
                    self.n_share_experts_fusion == self.tp_size
1678
                ), f"Shared experts fusion optimization is enabled in DeepSeek V3/R1, set it to {self.tp_size} can get best optimized performance."
1679
1680
        elif self.n_share_experts_fusion == 0:
            if (
1681
1682
                _is_cuda
                and torch.cuda.get_device_capability("cuda") >= (9, 0)
1683
                and self.config.architectures[0] == architecture
1684
1685
1686
1687
1688
                and self.config.n_routed_experts == 256
                and (not global_server_args_dict["enable_deepep_moe"])
            ):
                self.n_share_experts_fusion = self.tp_size
                global_server_args_dict["n_share_experts_fusion"] = self.tp_size
1689
1690
1691
                log_info_on_rank0(
                    logger,
                    "Deepseek V3/R1 with fp8 can use shared experts fusion optimization when SM version >=90. Shared experts fusion optimization is enabled.",
1692
                )
1693

Mick's avatar
Mick committed
1694
1695
1696
    def get_input_embeddings(self) -> nn.Embedding:
        return self.model.embed_tokens

1697
    @torch.no_grad()
Liangsheng Yin's avatar
Liangsheng Yin committed
1698
1699
1700
1701
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
1702
        forward_batch: ForwardBatch,
1703
        input_embeds: torch.Tensor = None,
Liangsheng Yin's avatar
Liangsheng Yin committed
1704
    ) -> torch.Tensor:
1705
        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
Lianmin Zheng's avatar
Lianmin Zheng committed
1706

1707
1708
1709
        return self.logits_processor(
            input_ids, hidden_states, self.lm_head, forward_batch
        )
Liangsheng Yin's avatar
Liangsheng Yin committed
1710

1711
    def post_load_weights(self, is_nextn=False):
inkcherry's avatar
inkcherry committed
1712
1713

        # Perform post-processing after loading weights
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
        layer_ids = (
            range(self.config.num_hidden_layers)
            if not is_nextn
            else [self.config.num_hidden_layers]
        )
        for layer_id in layer_ids:
            self_attn = (
                self.model.layers[layer_id].self_attn
                if not is_nextn
                else self.model.decoder.self_attn
            )
Baizhou Zhang's avatar
Baizhou Zhang committed
1725
1726
1727
1728
1729
1730
1731
1732
            if hasattr(self_attn.kv_b_proj, "qweight"):
                # AWQ compatible
                if _is_cuda:
                    w = awq_dequantize(
                        self_attn.kv_b_proj.qweight,
                        self_attn.kv_b_proj.scales,
                        self_attn.kv_b_proj.qzeros,
                    ).T
inkcherry's avatar
inkcherry committed
1733
                else:
Baizhou Zhang's avatar
Baizhou Zhang committed
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
                    w = awq_dequantize(
                        self_attn.kv_b_proj.qweight,
                        self_attn.kv_b_proj.scales,
                        self_attn.kv_b_proj.qzeros,
                        0,
                        0,
                        0,
                    ).T
            else:
                w = self_attn.kv_b_proj.weight
            # NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`.
            # This may affect the accuracy of fp8 model.
1746
1747
1748
1749
            # Fix deepseek v3 blockwise bmm by using deep_gemm
            use_deep_gemm_bmm = False
            model_dtype = torch.get_default_dtype()

Baizhou Zhang's avatar
Baizhou Zhang committed
1750
1751
1752
1753
            if w.dtype in (
                torch.float8_e4m3fn,
                torch.float8_e4m3fnuz,
            ):
1754
1755
1756
1757
                if (
                    hasattr(self.quant_config, "weight_block_size")
                    and self.quant_config.weight_block_size is not None
                ):
Baizhou Zhang's avatar
Baizhou Zhang committed
1758
                    weight_block_size = self.quant_config.weight_block_size
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
                    assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
                    if _is_fp8_fnuz:
                        weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
                            weight=w,
                            weight_scale=self_attn.kv_b_proj.weight_scale_inv,
                            input_scale=None,
                        )
                    else:
                        weight = w
                        weight_scale = self_attn.kv_b_proj.weight_scale_inv

                    if (
                        _is_cuda
                        and weight_block_size[0] == 128
                        and weight_block_size[1] == 128
                        and model_dtype == torch.bfloat16
                    ):
                        if _ENABLE_JIT_DEEPGEMM and get_bool_env_var(
                            "SGL_USE_DEEPGEMM_BMM", "false"
1778
                        ):
1779
1780
                            block_scale = weight_scale
                            use_deep_gemm_bmm = True
1781
                        else:
1782
1783
1784
1785
1786
                            w = block_quant_dequant(
                                weight,
                                weight_scale,
                                weight_block_size,
                                model_dtype,
1787
                            )
1788
1789
1790
1791
1792
                    else:
                        w, scale = block_quant_to_tensor_quant(
                            weight, weight_scale, weight_block_size
                        )
                        self_attn.w_scale = scale
Baizhou Zhang's avatar
Baizhou Zhang committed
1793
                else:
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
                    if _is_fp8_fnuz:
                        weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
                            weight=w,
                            weight_scale=self_attn.kv_b_proj.weight_scale,
                            input_scale=None,
                        )
                    else:
                        weight = w
                        weight_scale = self_attn.kv_b_proj.weight_scale

Baizhou Zhang's avatar
Baizhou Zhang committed
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
                    w, scale = channel_quant_to_tensor_quant(weight, weight_scale)
                    self_attn.w_scale = scale

            if w.dtype == torch.int8:
                if hasattr(self.quant_config, "weight_block_size"):
                    # block-wise int8 need it
                    weight_block_size = self.quant_config.weight_block_size
                    if weight_block_size is not None:
                        assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
                        weight = w
                        weight_scale = self_attn.kv_b_proj.weight_scale_inv
                        w = int8_block_dequant(
                            weight, weight_scale, weight_block_size
                        ).to(torch.bfloat16)
                else:
                    # channel-wise int8 need it
                    w = w.to(torch.bfloat16) * self_attn.kv_b_proj.weight_scale.to(
                        torch.bfloat16
                    )
1823

Baizhou Zhang's avatar
Baizhou Zhang committed
1824
1825
1826
            w_kc, w_vc = w.unflatten(
                0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
            ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
            if not use_deep_gemm_bmm:
                self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
                self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
                if (
                    hasattr(self_attn.kv_b_proj, "weight_scale")
                    and self_attn.w_scale is None
                ):
                    self_attn.w_scale = self_attn.kv_b_proj.weight_scale
                    if _is_hip:
                        self_attn.w_scale *= 2.0
            else:
                num_tiles_k = self_attn.qk_nope_head_dim // weight_block_size[1]
                num_tiles_n = self_attn.v_head_dim // weight_block_size[0]
                ws_kc, ws_vc = block_scale.unflatten(
                    0, (-1, (num_tiles_k + num_tiles_n))
                ).split([num_tiles_k, num_tiles_n], dim=1)
                self_attn.w_scale_k = ws_kc.transpose(1, 2).contiguous()
                self_attn.w_scale_v = ws_vc.contiguous()
                self_attn.w_kc = w_kc.transpose(1, 2).contiguous()
                self_attn.w_vc = w_vc.contiguous()
                self_attn.use_deep_gemm_bmm = True
inkcherry's avatar
inkcherry committed
1848

1849
1850
1851
1852
1853
1854
1855
1856
        # TODO support nextn later
        if not is_nextn:
            self.routed_experts_weights_of_layer = {
                layer_id: layer.mlp.get_moe_weights()
                for layer_id, layer in enumerate(self.model.layers)
                if isinstance(layer.mlp, DeepseekV2MoE)
            }

1857
1858
1859
1860
    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=False):
        if is_nextn:
            if hasattr(self.config, "num_nextn_predict_layers"):
                num_nextn_layers = self.config.num_nextn_predict_layers
1861
                assert num_nextn_layers == 1, "Only 1 nextn layer is supported"
1862
1863
1864
1865
1866
1867
1868
1869
1870
                # compatible with old design
                nextn_layer_id = (
                    0
                    if self.config.num_hidden_layers == 1
                    else self.config.num_hidden_layers
                )
            else:
                raise ValueError("num_nextn_predict_layers is not in the config")

Liangsheng Yin's avatar
Liangsheng Yin committed
1871
1872
1873
1874
1875
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
1876
        if self.n_share_experts_fusion > 0:
1877
1878
            weights_list = list(weights)
            weights_dict = dict(weights_list)
1879
            if self.quant_config is None or self.quant_config.get_name() == "w8a8_int8":
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
                suffix_list = [
                    "down_proj.weight",
                    "down_proj.weight_scale",
                    "gate_proj.weight",
                    "gate_proj.weight_scale",
                    "up_proj.weight",
                    "up_proj.weight_scale",
                ]
            else:
                suffix_list = [
                    "down_proj.weight",
                    "down_proj.weight_scale_inv",
                    "gate_proj.weight",
                    "gate_proj.weight_scale_inv",
                    "up_proj.weight",
                    "up_proj.weight_scale_inv",
                ]
1897
            names_to_remove = []
1898
1899

            moe_layers = (
1900
1901
1902
1903
                range(
                    self.config.first_k_dense_replace,
                    self.config.num_hidden_layers,
                    self.config.moe_layer_freq,
1904
1905
1906
1907
1908
1909
1910
                )
                if not is_nextn
                else [nextn_layer_id]
            )

            for moe_layer in tqdm(
                moe_layers,
1911
1912
1913
                desc=f"Cloning {self.n_share_experts_fusion} "
                "replicas of the shared expert into MoE",
            ):
1914
1915
1916
1917
1918
                for suffix in suffix_list:
                    shared_expert_weight_name = (
                        f"model.layers.{moe_layer}.mlp.shared_experts.{suffix}"
                    )
                    for num_repeat in range(self.n_share_experts_fusion):
1919
1920
1921
1922
1923
1924
                        weights_list.append(
                            (
                                f"model.layers.{moe_layer}."
                                f"mlp.experts."
                                f"{self.config.n_routed_experts + num_repeat}"
                                f".{suffix}",
1925
                                weights_dict[shared_expert_weight_name],
1926
1927
                            )
                        )
1928
                    names_to_remove += [shared_expert_weight_name]
1929
            weights = [w for w in weights_list if w[0] not in names_to_remove]
Liangsheng Yin's avatar
Liangsheng Yin committed
1930
1931
1932

        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
1933
        expert_params_mapping = get_moe_impl_class().make_expert_params_mapping(
Liangsheng Yin's avatar
Liangsheng Yin committed
1934
1935
1936
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
1937
            num_experts=self.config.n_routed_experts + self.n_share_experts_fusion,
Liangsheng Yin's avatar
Liangsheng Yin committed
1938
1939
        )

1940
1941
1942
1943
1944
1945
        # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
        fuse_qkv_a_proj = hasattr(self.config, "q_lora_rank") and (
            self.config.q_lora_rank is not None
        )
        cached_a_proj = {} if fuse_qkv_a_proj else None

1946
1947
1948
1949
1950
1951
1952
1953
1954
        if is_nextn:
            nextn_layer_prefix = f"model.layers.{nextn_layer_id}"
            nextn_spec_weight_names = [
                "shared_head.norm",
                "eh_proj",
                "enorm",
                "hnorm",
            ]

Liangsheng Yin's avatar
Liangsheng Yin committed
1955
1956
        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
            if not is_nextn:
                if hasattr(self.config, "num_nextn_predict_layers"):
                    num_nextn_layers = self.config.num_nextn_predict_layers
                    if num_nextn_layers > 0 and name.startswith("model.layers"):
                        name_list = name.split(".")
                        if (
                            len(name_list) >= 3
                            and int(name_list[2]) >= self.config.num_hidden_layers
                        ):
                            continue
            else:
                if not name.startswith(nextn_layer_prefix):
                    continue

                # Use shared head and embed weights from target model
                if "shared_head.head" in name or "embed_tokens" in name:
                    continue

                is_decoder = True
                # For nextn specific weights
                for weight_name in nextn_spec_weight_names:
                    if weight_name in name:
                        name = name.replace(nextn_layer_prefix, "model")
                        is_decoder = False
                        break
                # For decoder layer weights
                if is_decoder:
                    name = name.replace(nextn_layer_prefix, "model.decoder")

Liangsheng Yin's avatar
Liangsheng Yin committed
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
            if "rotary_emb.inv_freq" in name:
                continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                # Skip non-stacked layers and experts (experts handled below).
                if weight_name not in name:
                    continue
                # 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.
                if ("mlp.experts." in name) and name not in params_dict:
                    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
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue
                    name = name.replace(weight_name, param_name)
                    param = params_dict[name]
                    weight_loader = param.weight_loader
                    weight_loader(
                        param,
                        loaded_weight,
2019
                        name,
Liangsheng Yin's avatar
Liangsheng Yin committed
2020
2021
2022
2023
2024
2025
2026
2027
2028
                        shard_id=shard_id,
                        expert_id=expert_id,
                    )
                    break
                else:
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue

2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
                    if fuse_qkv_a_proj and (
                        "q_a_proj" in name or "kv_a_proj_with_mqa" in name
                    ):
                        cached_a_proj[name] = loaded_weight
                        q_a_proj_name = (
                            name
                            if "q_a_proj" in name
                            else name.replace("kv_a_proj_with_mqa", "q_a_proj")
                        )
                        kv_a_proj_name = (
                            name
                            if "kv_a_proj_with_mqa" in name
                            else name.replace("q_a_proj", "kv_a_proj_with_mqa")
                        )

                        # When both q_a_proj and kv_a_proj_with_mqa has been cached, load the fused weight to parameter
                        if (
                            q_a_proj_name in cached_a_proj
                            and kv_a_proj_name in cached_a_proj
                        ):
                            q_a_proj_weight = cached_a_proj[q_a_proj_name]
                            kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
                            fused_weight = torch.cat(
                                [q_a_proj_weight, kv_a_proj_weight], dim=0
                            )

                            param_name = name.replace(
                                "q_a_proj", "fused_qkv_a_proj_with_mqa"
                            )
                            param = params_dict[param_name]

                            weight_loader = getattr(
                                param, "weight_loader", default_weight_loader
                            )
                            weight_loader(param, fused_weight)
                            cached_a_proj.pop(q_a_proj_name)
                            cached_a_proj.pop(kv_a_proj_name)
                    else:
                        param = params_dict[name]
                        weight_loader = getattr(
                            param, "weight_loader", default_weight_loader
                        )
                        weight_loader(param, loaded_weight)
Liangsheng Yin's avatar
Liangsheng Yin committed
2072

2073
        self.post_load_weights(is_nextn=is_nextn)
Ke Bao's avatar
Ke Bao committed
2074

2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
    def get_embed_and_head(self):
        return self.model.embed_tokens.weight, self.lm_head.weight

    def set_embed_and_head(self, embed, head):
        del self.model.embed_tokens.weight
        del self.lm_head.weight
        self.model.embed_tokens.weight = embed
        self.lm_head.weight = head
        torch.cuda.empty_cache()
        torch.cuda.synchronize()

2086
2087
2088
2089
2090
2091
2092
2093
    @classmethod
    def get_model_config_for_expert_location(cls, config):
        return ModelConfigForExpertLocation(
            num_layers=config.num_hidden_layers,
            num_logical_experts=config.n_routed_experts,
            num_groups=config.n_group,
        )

Liangsheng Yin's avatar
Liangsheng Yin committed
2094

HandH1998's avatar
HandH1998 committed
2095
2096
2097
2098
2099
class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
    pass


EntryClass = [DeepseekV2ForCausalLM, DeepseekV3ForCausalLM]