qwen3_next.py 50.7 KB
Newer Older
1
2
3
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Inference-only Qwen3Next model."""
4

5
from collections.abc import Iterable
6
from itertools import islice
7
8
9
10
11
12
13
14

import torch
from einops import rearrange
from torch import nn
from transformers.activations import ACT2FN

from vllm.attention import Attention, AttentionBackend, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
from vllm.config import (
    CacheConfig,
    ModelConfig,
    SpeculativeConfig,
    VllmConfig,
    get_current_vllm_config,
)
from vllm.distributed import (
    divide,
    get_ep_group,
    get_pp_group,
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
    tensor_model_parallel_all_gather,
)
30
31
32
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.logger import init_logger
from vllm.model_executor.layers.fla.ops import (
33
34
35
    chunk_gated_delta_rule,
    fused_recurrent_gated_delta_rule,
)
36
from vllm.model_executor.layers.fused_moe import SharedFusedMoE
37
from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
38
39
40
41
from vllm.model_executor.layers.layernorm import (
    GemmaRMSNorm as Qwen3NextRMSNorm,
)
from vllm.model_executor.layers.layernorm import RMSNormGated
42
43
44
45
46
47
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
48
49
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.abstract import MambaBase
50
from vllm.model_executor.layers.mamba.mamba_mixer2 import mamba_v2_sharded_weight_loader
51
from vllm.model_executor.layers.mamba.mamba_utils import (
52
53
54
    MambaStateDtypeCalculator,
    MambaStateShapeCalculator,
)
55
from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
56
57
58
    causal_conv1d_fn,
    causal_conv1d_update,
)
59
60
61
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
62
63
64
65
    DEFAULT_VOCAB_PADDING_SIZE,
    ParallelLMHead,
    VocabParallelEmbedding,
)
66
from vllm.model_executor.model_loader.weight_utils import (
67
68
69
    default_weight_loader,
    sharded_weight_loader,
)
70
from vllm.model_executor.models.qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP
71
from vllm.model_executor.models.utils import sequence_parallel_chunk
72
73
74
75
76
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs import Qwen3NextConfig
from vllm.triton_utils import tl, triton
77
from vllm.utils.torch_utils import direct_register_custom_op
78
79
from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata

80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
from .interfaces import (
    HasInnerState,
    IsHybrid,
    MixtureOfExperts,
    SupportsLoRA,
    SupportsPP,
)
from .utils import (
    AutoWeightsLoader,
    PPMissingLayer,
    extract_layer_index,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)
96
97
98
99
100
101
102

logger = init_logger(__name__)

KVCache = tuple[torch.Tensor, torch.Tensor]


class Qwen3NextSparseMoeBlock(nn.Module):
103
    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
104
        super().__init__()
105
106
107
108
109

        config = vllm_config.model_config.hf_config
        parallel_config = vllm_config.parallel_config
        quant_config = vllm_config.quant_config

110
111
112
        self.tp_size = get_tensor_model_parallel_world_size()

        self.ep_group = get_ep_group().device_group
113
        self.ep_rank = get_ep_group().rank_in_group
114
115
116
        self.ep_size = self.ep_group.size()
        self.n_routed_experts = config.num_experts

117
118
        self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe

119
120
121
        if self.tp_size > config.num_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
122
123
                f"the number of experts {config.num_experts}."
            )
124
125
126
127

        # Load balancing settings.
        vllm_config = get_current_vllm_config()
        eplb_config = vllm_config.parallel_config.eplb_config
128
        self.enable_eplb = parallel_config.enable_eplb
129
130
131

        self.n_logical_experts = self.n_routed_experts
        self.n_redundant_experts = eplb_config.num_redundant_experts
132
        self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
133
134
        self.n_local_physical_experts = self.n_physical_experts // self.ep_size

135
136
137
138
139
140
141
142
143
144
145
146
        self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
        self.physical_expert_end = (
            self.physical_expert_start + self.n_local_physical_experts
        )

        self.gate = ReplicatedLinear(
            config.hidden_size,
            config.num_experts,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate",
        )
147

148
149
        self.shared_expert_gate = torch.nn.Linear(config.hidden_size, 1, bias=False)

150
151
152
153
154
155
        if config.shared_expert_intermediate_size > 0:
            self.shared_expert = Qwen3NextMLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.shared_expert_intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
156
157
                reduce_results=False,
                expert_gate=self.shared_expert_gate,
158
                prefix=f"{prefix}.shared_expert",
159
160
161
            )
        else:
            self.shared_expert = None
162
163
164

        self.experts = SharedFusedMoE(
            shared_experts=self.shared_expert,
165
            gate=self.gate,
166
167
168
169
170
171
172
173
174
175
176
            num_experts=self.n_routed_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.moe_intermediate_size,
            reduce_results=False,
            renormalize=config.norm_topk_prob,
            quant_config=quant_config,
            prefix=f"{prefix}.experts",
            enable_eplb=self.enable_eplb,
            num_redundant_experts=self.n_redundant_experts,
            is_sequence_parallel=self.is_sequence_parallel,
177
            routing_method_type=RoutingMethodType.Renormalize,
178
        )
179
180
181
182

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        # NOTE: hidden_states can have either 1D or 2D shape.
        orig_shape = hidden_states.shape
183
        num_tokens, hidden_dim = hidden_states.shape
184
185
        hidden_states = hidden_states.view(-1, hidden_dim)

186
187
188
        if self.is_sequence_parallel:
            hidden_states = sequence_parallel_chunk(hidden_states)

189
190
191
192
193
194
195
196
197
198
199
        if self.experts.is_internal_router:
            # In this case, the gate/router runs inside the FusedMoE class
            final_hidden_states = self.experts(
                hidden_states=hidden_states, router_logits=hidden_states
            )
        else:
            # 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
            )
200

201
202
        if self.shared_expert is not None:
            final_hidden_states = final_hidden_states[0] + final_hidden_states[1]
203
204
205

        if self.is_sequence_parallel:
            final_hidden_states = tensor_model_parallel_all_gather(
206
207
                final_hidden_states, 0
            )
208
209
            final_hidden_states = final_hidden_states[:num_tokens]
        elif self.tp_size > 1:
210
            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
211
212
                final_hidden_states
            )
213
214
215
216
217
218
219
220
221
222
223

        return final_hidden_states.view(orig_shape)


class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
    @property
    def mamba_type(self) -> str:
        return "linear_attention"

    def get_attn_backend(self) -> type["AttentionBackend"]:
        from vllm.v1.attention.backends.gdn_attn import GDNAttentionBackend
224

225
226
227
228
        return GDNAttentionBackend

    def get_state_dtype(self) -> tuple[torch.dtype, torch.dtype]:
        return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
229
230
            self.model_config.dtype, self.cache_config.mamba_cache_dtype
        )
231
232
233

    def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
        return MambaStateShapeCalculator.gated_delta_net_state_shape(
234
235
236
237
238
239
240
241
            self.tp_size,
            self.num_k_heads,
            self.num_v_heads,
            self.head_k_dim,
            self.head_v_dim,
            self.conv_kernel_size,
            self.num_spec,
        )
242
243
244
245

    def __init__(
        self,
        config: Qwen3NextConfig,
246
247
248
249
        model_config: ModelConfig | None = None,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        speculative_config: SpeculativeConfig | None = None,
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.tp_rank = get_tensor_model_parallel_rank()
        self.hidden_size = config.hidden_size
        self.num_v_heads = config.linear_num_value_heads
        self.num_k_heads = config.linear_num_key_heads
        self.head_k_dim = config.linear_key_head_dim
        self.head_v_dim = config.linear_value_head_dim
        self.key_dim = self.head_k_dim * self.num_k_heads
        self.value_dim = self.head_v_dim * self.num_v_heads

        self.conv_kernel_size = config.linear_conv_kernel_dim
        self.layer_idx = extract_layer_index(prefix)
        self.activation = config.hidden_act
        self.act = ACT2FN[config.hidden_act]
        self.layer_norm_epsilon = config.rms_norm_eps
        self.prefix = prefix

        self.config = config
        self.model_config = model_config
        self.cache_config = cache_config
        self.quant_config = quant_config
        self.speculative_config = speculative_config
275
276
277
278
279
        self.num_spec = (
            self.speculative_config.num_speculative_tokens
            if self.speculative_config
            else 0
        )
280
281
282
283
284
285
286
287
288
289
290
291
292
293

        # QKV
        self.conv_dim = self.key_dim * 2 + self.value_dim
        self.conv1d = ColumnParallelLinear(
            input_size=self.conv_kernel_size,
            output_size=self.conv_dim,
            bias=False,
            prefix=f"{prefix}.conv1d",
        )
        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)

        # projection of the input hidden states
        self.projection_size_qkvz = self.key_dim * 2 + self.value_dim * 2
        self.projection_size_ba = self.num_v_heads * 2
294
        self.in_proj_qkvz = ColumnParallelLinear(
295
            input_size=self.hidden_size,
296
            output_size=self.projection_size_qkvz,
297
298
            bias=False,
            quant_config=quant_config,
299
300
301
302
303
304
305
306
307
            prefix=f"{prefix}.in_proj_qkvz",
        )
        # ba_proj doesn't support blockwise fp8 quantization.
        self.in_proj_ba = ColumnParallelLinear(
            input_size=self.hidden_size,
            output_size=self.projection_size_ba,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.in_proj_ba",
308
309
310
311
312
313
314
        )

        query_key_settings = (self.key_dim, 0, False)
        value_settings = (self.value_dim, 0, False)

        delattr(self.conv1d.weight, "weight_loader")
        set_weight_attrs(
315
316
317
318
319
320
321
322
323
324
325
326
327
            self.conv1d.weight,
            {
                "weight_loader": mamba_v2_sharded_weight_loader(
                    [
                        query_key_settings,
                        query_key_settings,
                        value_settings,
                    ],
                    self.tp_size,
                    self.tp_rank,
                )
            },
        )
328
329
330
331
332
333

        # selective projection used to make dt, B and C input dependant

        # time step projection (discretization)
        # instantiate once and copy inv_dt in init_weights of PretrainedModel
        self.dt_bias = nn.Parameter(
334
335
            torch.ones(self.num_v_heads // self.tp_size),
        )
336
337
338
        self.A_log = nn.Parameter(
            torch.empty(
                divide(self.num_v_heads, self.tp_size),
339
340
            )
        )
341

342
343
        set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(0)})
        set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
344
345
346
347
348
349

        self.norm = RMSNormGated(
            self.head_v_dim,
            eps=self.layer_norm_epsilon,
            group_size=None,
            norm_before_gate=True,
350
            device=current_platform.current_device(),
351
            dtype=config.dtype,
352
353
        )

354
355
356
357
358
359
360
361
        self.out_proj = RowParallelLinear(
            self.value_dim,
            self.hidden_size,
            bias=False,
            input_is_parallel=True,
            quant_config=quant_config,
            prefix=f"{prefix}.out_proj",
        )
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377

        compilation_config = get_current_vllm_config().compilation_config
        if prefix in compilation_config.static_forward_context:
            raise ValueError(f"Duplicate layer name: {prefix}")
        compilation_config.static_forward_context[prefix] = self

    def fix_query_key_value_ordering(
        self,
        mixed_qkvz,
        mixed_ba,
    ):
        """
        Derives `query`, `key` and `value` tensors from `mixed_qkvzba`.
        """
        new_tensor_shape_qkvz = mixed_qkvz.size()[:-1] + (
            self.num_k_heads // self.tp_size,
378
379
380
381
382
383
384
            (
                self.head_k_dim
                + self.head_k_dim
                + (self.head_v_dim + self.head_v_dim)
                * self.num_v_heads
                // self.num_k_heads
            ),
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
        )
        new_tensor_shape_ba = mixed_qkvz.size()[:-1] + (
            self.num_k_heads // self.tp_size,
            2 * self.num_v_heads // self.num_k_heads,
        )

        mixed_qkvz = mixed_qkvz.view(*new_tensor_shape_qkvz)
        mixed_ba = mixed_ba.view(*new_tensor_shape_ba)

        split_arg_list_qkvz = [
            self.head_k_dim,
            self.head_k_dim,
            (self.num_v_heads // self.num_k_heads * self.head_v_dim),
            (self.num_v_heads // self.num_k_heads * self.head_v_dim),
        ]
        split_arg_list_ba = [
            self.num_v_heads // self.num_k_heads,
402
            self.num_v_heads // self.num_k_heads,
403
404
405
406
407
        ]

        # [b, sq, ng, (hn + hn + np/ng * hn + np/ng + np/ng)]
        # --> [b, sq, ng, hn], [b, sq, ng, hn], [b, sq, ng, np/ng * hn],
        #  [b, sq, ng, np/ng * hn], [b, sq, ng, np/ng], [b, sq, ng, np/ng]
408
        (query, key, value, z) = torch.split(mixed_qkvz, split_arg_list_qkvz, dim=2)
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
        (b, a) = torch.split(mixed_ba, split_arg_list_ba, dim=2)

        # [b, sq, ng, np/ng * hn] -> [b, sq, np, hn]
        value = value.reshape(value.size(0), -1, self.head_v_dim)
        z = z.reshape(z.size(0), -1, self.head_v_dim)
        b = b.reshape(b.size(0), self.num_v_heads // self.tp_size)
        a = a.reshape(a.size(0), self.num_v_heads // self.tp_size)

        return query, key, value, z, b, a

    def rearrange_mixed_qkv(self, mixed_qkv):
        if mixed_qkv is None:
            return None, None, None
        query, key, value = torch.split(
            mixed_qkv,
            [
                self.key_dim // self.tp_size,
                self.key_dim // self.tp_size,
                self.value_dim // self.tp_size,
            ],
            dim=-1,
        )
        query, key = map(
432
433
434
435
            lambda x: rearrange(x, "l (h d) -> 1 l h d", d=self.head_k_dim),
            (query, key),
        )
        value = rearrange(value, "l (h d) -> 1 l h d", d=self.head_v_dim)
436
        return query.contiguous(), key.contiguous(), value.contiguous()
437
438
439
440
441
442

    def forward(
        self,
        hidden_states: torch.Tensor,
        output: torch.Tensor,
    ):
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
        """
        Forward pass with three parts:
        1. Input projection
        2. Core attention (custom op)
        3. Output projection
        """
        num_tokens = hidden_states.size(0)

        # ============================================================
        # Part 1: Input Projection
        # ============================================================
        projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states)
        projected_states_ba, _ = self.in_proj_ba(hidden_states)
        query, key, value, z, b, a = self.fix_query_key_value_ordering(
            projected_states_qkvz, projected_states_ba
        )
        query, key, value = map(
            lambda x: rearrange(x, "l p d -> l (p d)"), (query, key, value)
        )
        mixed_qkv = torch.cat((query, key, value), dim=-1)

        # ============================================================
        # Part 2: Core Attention (Custom Op)
        # ============================================================
467
468
        # Note: we should not use torch.empty here like other attention backends,
        # see discussions in https://github.com/vllm-project/vllm/pull/28182
469
470
471
472
473
474
475
476
477
478
479
        core_attn_out = torch.zeros(
            (num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
            dtype=hidden_states.dtype,
            device=hidden_states.device,
        )

        torch.ops.vllm.gdn_attention_core(
            mixed_qkv,
            b,
            a,
            core_attn_out,
480
481
482
            self.prefix,
        )

483
484
485
486
487
488
489
490
491
492
493
494
495
        # ============================================================
        # Part 3: Output Projection
        # ============================================================
        z_shape_og = z.shape
        # Reshape input data into 2D tensor
        core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
        z = z.reshape(-1, z.shape[-1])
        core_attn_out = self.norm(core_attn_out, z)
        core_attn_out = core_attn_out.reshape(z_shape_og)
        core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
        output[:num_tokens], _ = self.out_proj(core_attn_out)

    def _forward_core(
496
        self,
497
498
499
500
        mixed_qkv: torch.Tensor,
        b: torch.Tensor,
        a: torch.Tensor,
        core_attn_out: torch.Tensor,
501
    ):
502
503
504
        """
        Core attention computation (called by custom op).
        """
505
506
507
508
509
510
511
512
513
514
515
516
517
518
        forward_context = get_forward_context()
        attn_metadata: AttentionMetadata = forward_context.attn_metadata

        if attn_metadata is None:
            # V1 profile run
            return

        assert isinstance(attn_metadata, dict)
        attn_metadata = attn_metadata[self.prefix]
        assert isinstance(attn_metadata, GDNAttentionMetadata)
        has_initial_state = attn_metadata.has_initial_state
        spec_query_start_loc = attn_metadata.spec_query_start_loc
        non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
        spec_sequence_masks = attn_metadata.spec_sequence_masks
519
520
        spec_token_indx = attn_metadata.spec_token_indx
        non_spec_token_indx = attn_metadata.non_spec_token_indx
521
522
523
524
525
        spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor  # noqa: E501
        non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor  # noqa: E501
        self_kv_cache = self.kv_cache[forward_context.virtual_engine]
        conv_state = self_kv_cache[0].transpose(-1, -2)
        ssm_state = self_kv_cache[1]
526
        num_actual_tokens = attn_metadata.num_actual_tokens
527
        num_accepted_tokens = attn_metadata.num_accepted_tokens
528

529
530
531
        mixed_qkv = mixed_qkv[:num_actual_tokens]
        b = b[:num_actual_tokens]
        a = a[:num_actual_tokens]
532

533
        # 1. Convolution sequence transformation
534
535
536
        conv_weights = self.conv1d.weight.view(
            self.conv1d.weight.size(0), self.conv1d.weight.size(2)
        )
537
538

        if spec_sequence_masks is not None:
539
            if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
540
541
542
                mixed_qkv_spec = mixed_qkv
                mixed_qkv_non_spec = None
            else:
543
544
                mixed_qkv_spec = mixed_qkv.index_select(0, spec_token_indx)
                mixed_qkv_non_spec = mixed_qkv.index_select(0, non_spec_token_indx)
545
546
547
548
        else:
            mixed_qkv_spec = None
            mixed_qkv_non_spec = mixed_qkv

549
        # 1.1: Process the multi-query part
550
551
552
553
554
555
556
        if spec_sequence_masks is not None:
            mixed_qkv_spec = causal_conv1d_update(
                mixed_qkv_spec,
                conv_state,
                conv_weights,
                self.conv1d.bias,
                self.activation,
557
558
559
                conv_state_indices=spec_state_indices_tensor[:, 0][
                    : attn_metadata.num_spec_decodes
                ],
560
                num_accepted_tokens=num_accepted_tokens,
561
562
                query_start_loc=spec_query_start_loc,
                max_query_len=spec_state_indices_tensor.size(-1),
563
564
565
                validate_data=False,
            )

566
        # 1.2: Process the remaining part
567
        if attn_metadata.num_prefills > 0:
568
            mixed_qkv_non_spec_T = mixed_qkv_non_spec.transpose(0, 1)
569
            # - "cache_indices" updates the conv_state cache in positions
570
            #   pointed to by "state_indices_tensor"
571
            mixed_qkv_non_spec = causal_conv1d_fn(
572
                mixed_qkv_non_spec_T,
573
574
575
576
577
578
579
                conv_weights,
                self.conv1d.bias,
                activation=self.activation,
                conv_states=conv_state,
                has_initial_state=has_initial_state,
                cache_indices=non_spec_state_indices_tensor,
                query_start_loc=non_spec_query_start_loc,
580
                metadata=attn_metadata,
581
582
583
584
585
586
587
588
            ).transpose(0, 1)
        elif attn_metadata.num_decodes > 0:
            mixed_qkv_non_spec = causal_conv1d_update(
                mixed_qkv_non_spec,
                conv_state,
                conv_weights,
                self.conv1d.bias,
                self.activation,
589
                conv_state_indices=non_spec_state_indices_tensor[
590
                    : attn_metadata.num_actual_tokens
591
                ],
592
593
594
595
596
                validate_data=True,
            )
        else:
            mixed_qkv_non_spec = None

597
        query_spec, key_spec, value_spec = self.rearrange_mixed_qkv(mixed_qkv_spec)
598
        query_non_spec, key_non_spec, value_non_spec = self.rearrange_mixed_qkv(
599
600
            mixed_qkv_non_spec
        )
601

602
        g, beta = fused_gdn_gating(self.A_log, a, b, self.dt_bias)
603
604

        if spec_sequence_masks is not None:
605
            if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
606
607
608
609
610
                g_spec = g
                beta_spec = beta
                g_non_spec = None
                beta_non_spec = None
            else:
611
612
613
614
                g_spec = g.index_select(1, spec_token_indx)
                beta_spec = beta.index_select(1, spec_token_indx)
                g_non_spec = g.index_select(1, non_spec_token_indx)
                beta_non_spec = beta.index_select(1, non_spec_token_indx)
615
616
617
618
619
620
        else:
            g_spec = None
            beta_spec = None
            g_non_spec = g
            beta_non_spec = beta

621
        # 2. Recurrent attention
622

623
        # 2.1: Process the multi-query part
624
        if spec_sequence_masks is not None:
625
626
627
628
629
630
631
632
633
634
635
636
637
            core_attn_out_spec, last_recurrent_state = fused_recurrent_gated_delta_rule(
                q=query_spec,
                k=key_spec,
                v=value_spec,
                g=g_spec,
                beta=beta_spec,
                initial_state=ssm_state,
                inplace_final_state=True,
                cu_seqlens=spec_query_start_loc[: attn_metadata.num_spec_decodes + 1],
                ssm_state_indices=spec_state_indices_tensor,
                num_accepted_tokens=num_accepted_tokens,
                use_qk_l2norm_in_kernel=True,
            )
638
639
640
        else:
            core_attn_out_spec, last_recurrent_state = None, None

641
        # 2.2: Process the remaining part
642
        if attn_metadata.num_prefills > 0:
643
            initial_state = ssm_state[non_spec_state_indices_tensor].contiguous()
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
            initial_state[~has_initial_state, ...] = 0
            (
                core_attn_out_non_spec,
                last_recurrent_state,
            ) = chunk_gated_delta_rule(
                q=query_non_spec,
                k=key_non_spec,
                v=value_non_spec,
                g=g_non_spec,
                beta=beta_non_spec,
                initial_state=initial_state,
                output_final_state=True,
                cu_seqlens=non_spec_query_start_loc,
                head_first=False,
                use_qk_l2norm_in_kernel=True,
            )
            # Init cache
            ssm_state[non_spec_state_indices_tensor] = last_recurrent_state.to(
662
663
                ssm_state.dtype
            )
664
665
666
667
668
669
670
671
672
673
        elif attn_metadata.num_decodes > 0:
            core_attn_out_non_spec, last_recurrent_state = (
                fused_recurrent_gated_delta_rule(
                    q=query_non_spec,
                    k=key_non_spec,
                    v=value_non_spec,
                    g=g_non_spec,
                    beta=beta_non_spec,
                    initial_state=ssm_state,
                    inplace_final_state=True,
674
675
676
                    cu_seqlens=non_spec_query_start_loc[
                        : attn_metadata.num_decodes + 1
                    ],
677
678
                    ssm_state_indices=non_spec_state_indices_tensor,
                    use_qk_l2norm_in_kernel=True,
679
680
                )
            )
681
682
683
        else:
            core_attn_out_non_spec, last_recurrent_state = None, None

684
        # 3. Merge core attention output
685
        if spec_sequence_masks is not None and core_attn_out_non_spec is not None:
686
            merged_out = torch.empty(
687
688
689
690
                (1, num_actual_tokens, *core_attn_out_spec.shape[2:]),
                dtype=core_attn_out_non_spec.dtype,
                device=core_attn_out_non_spec.device,
            )
691
692
693
            merged_out.index_copy_(1, spec_token_indx, core_attn_out_spec)
            merged_out.index_copy_(1, non_spec_token_indx, core_attn_out_non_spec)
            core_attn_out[:num_actual_tokens] = merged_out.squeeze(0)
694
        elif spec_sequence_masks is not None:
695
            core_attn_out[:num_actual_tokens] = core_attn_out_spec.squeeze(0)
696
        else:
697
            core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(0)
698
699
700
701
702
703


class Qwen3NextAttention(nn.Module):
    def __init__(
        self,
        config: Qwen3NextConfig,
704
705
706
        model_config: ModelConfig | None = None,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = config.num_attention_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = config.num_key_value_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = config.head_dim or (self.hidden_size // self.num_heads)
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.dual_chunk_attention_config = getattr(
731
732
            config, "dual_chunk_attention_config", None
        )
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
        self.attn_output_gate = getattr(config, "attn_output_gate", True)

        self.qkv_proj = QKVParallelLinear(
            config.hidden_size,
            self.head_dim,
            self.total_num_heads * (1 + self.attn_output_gate),
            self.total_num_kv_heads,
            bias=getattr(config, "qkv_bias", False),
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            config.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        self.rotary_emb = get_rope(
            head_size=self.head_dim,
            rotary_dim=self.head_dim,
            max_position=config.max_position_embeddings,
            base=config.rope_theta,
            rope_scaling=config.rope_scaling,
            partial_rotary_factor=config.partial_rotary_factor,
            dual_chunk_attention_config=self.dual_chunk_attention_config,
        )

        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
            **{
                "layer_idx": extract_layer_index(prefix),
773
774
775
776
                "dual_chunk_attention_config": self.dual_chunk_attention_config,
            }
            if self.dual_chunk_attention_config
            else {},
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
        )

        self.q_norm = Qwen3NextRMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = Qwen3NextRMSNorm(self.head_dim, eps=config.rms_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        output: torch.Tensor,
        hidden_states: torch.Tensor,
    ):
        qkv, _ = self.qkv_proj(hidden_states)

        if self.attn_output_gate:
            q_gate, k, v = qkv.split(
792
793
                [self.q_size * 2, self.kv_size, self.kv_size], dim=-1
            )
794
795
796
797
798
799
            orig_shape = q_gate.shape[:-1]
            q_gate = q_gate.view(*orig_shape, self.num_heads, -1)
            q, gate = torch.chunk(q_gate, 2, dim=-1)
            q = q.reshape(*orig_shape, -1)
            gate = gate.reshape(*orig_shape, -1)
        else:
800
            q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
801
802

        q = self.q_norm(q.view(-1, self.num_heads, self.head_dim)).view(
803
804
            -1, self.num_heads * self.head_dim
        )
805
        k = self.k_norm(k.view(-1, self.num_kv_heads, self.head_dim)).view(
806
807
            -1, self.num_kv_heads * self.head_dim
        )
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822

        q, k = self.rotary_emb(positions, q, k)

        attn_output = self.attn(q, k, v)

        if self.attn_output_gate:
            gate = torch.sigmoid(gate)
            attn_output = attn_output * gate

        output[:], _ = self.o_proj(attn_output)


class Qwen3NextDecoderLayer(nn.Module):
    def __init__(
        self,
823
        vllm_config: VllmConfig,
824
825
826
827
        layer_type: str,
        prefix: str = "",
    ) -> None:
        super().__init__()
828
829
830
831
832
833

        config = vllm_config.model_config.hf_config
        model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        speculative_config = vllm_config.speculative_config
834
835
836
837
838
839
840
841
842
843
844

        self.layer_type = layer_type
        self.layer_idx = extract_layer_index(prefix)

        if self.layer_type == "linear_attention":
            self.linear_attn = Qwen3NextGatedDeltaNet(
                config,
                model_config=model_config,
                cache_config=cache_config,
                quant_config=quant_config,
                speculative_config=speculative_config,
845
846
                prefix=f"{prefix}.linear_attn",
            )
847
848
849
850
851
852
        elif self.layer_type == "full_attention":
            self.self_attn = Qwen3NextAttention(
                config,
                model_config=model_config,
                cache_config=cache_config,
                quant_config=quant_config,
853
                prefix=f"{prefix}.self_attn",
854
855
856
857
            )
        else:
            raise ValueError(f"Invalid layer_type {self.layer_type}")

858
859
860
        mlp_only_layers = (
            [] if not hasattr(config, "mlp_only_layers") else config.mlp_only_layers
        )
861
        if (self.layer_idx not in mlp_only_layers) and (
862
863
864
            config.num_experts > 0
            and (self.layer_idx + 1) % config.decoder_sparse_step == 0
        ):
865
            self.mlp = Qwen3NextSparseMoeBlock(
866
                vllm_config=vllm_config,
867
868
869
870
871
872
873
874
875
876
                prefix=f"{prefix}.mlp",
            )
        else:
            self.mlp = Qwen3NextMLP(
                hidden_size=config.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
            )

877
878
879
        self.input_layernorm = Qwen3NextRMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
880
        self.post_attention_layernorm = Qwen3NextRMSNorm(
881
882
            config.hidden_size, eps=config.rms_norm_eps
        )
883
884
885
886
887
888
889

        self.layer_scale = getattr(config, "layer_scale", False)
        if self.layer_scale:
            self.attn_layer_scale = torch.nn.Parameter(
                torch.zeros(
                    1,
                    1,
890
                    config.hidden_size,
891
                    dtype=config.dtype,
892
893
                ),
            )
894
895
896
897
            self.ffn_layer_scale = torch.nn.Parameter(
                torch.zeros(
                    1,
                    1,
898
                    config.hidden_size,
899
                    dtype=config.dtype,
900
901
                ),
            )
902
903
904
905

    def forward(
        self,
        hidden_states: torch.Tensor,
906
        residual: torch.Tensor | None,
907
908
909
910
911
912
913
        positions: torch.Tensor = None,
        **kwargs: object,
    ):
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
914
            hidden_states, residual = self.input_layernorm(hidden_states, residual)
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934

        self_attention_output = torch.empty_like(hidden_states)
        if self.layer_type == "linear_attention":
            self.linear_attn(
                hidden_states=hidden_states,
                output=self_attention_output,
            )
        elif self.layer_type == "full_attention":
            self.self_attn(
                hidden_states=hidden_states,
                output=self_attention_output,
                positions=positions,
            )
        else:
            raise ValueError("Invalid layer_type")
        hidden_states = self_attention_output

        if self.layer_scale:
            if len(hidden_states.shape) == 2:
                hidden_states = hidden_states * (
935
936
                    self.attn_layer_scale.to(hidden_states.dtype)[0] + 1
                )
937
938
            else:
                hidden_states = hidden_states * (
939
940
                    self.attn_layer_scale.to(hidden_states.dtype) + 1
                )
941
942

        # Fully Connected
943
        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
944
945
946
947
948
        hidden_states = self.mlp(hidden_states)

        if self.layer_scale:
            if len(hidden_states.shape) == 2:
                hidden_states = hidden_states * (
949
950
                    self.ffn_layer_scale.to(hidden_states.dtype)[0] + 1
                )
951
            else:
952
                assert len(hidden_states.shape) == len(self.ffn_layer_scale.shape), (
953
954
955
                    f"shape must be the same {len(hidden_states.shape)}, "
                    f"{len(self.ffn_layer_scale.shape)}"
                )
956
                hidden_states = hidden_states * (
957
958
                    self.ffn_layer_scale.to(hidden_states.dtype) + 1
                )
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974

        return hidden_states, residual


@support_torch_compile
class Qwen3NextModel(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config: Qwen3NextConfig = vllm_config.model_config.hf_config
        parallel_config = vllm_config.parallel_config
        lora_config = vllm_config.lora_config
        eplb_config = parallel_config.eplb_config
        self.num_redundant_experts = eplb_config.num_redundant_experts

        self.config = config
975
976
977
978
979
        lora_vocab = (
            (lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
            if lora_config
            else 0
        )
980
981
982
983
984
985
986
987
988
989
        self.vocab_size = config.vocab_size + lora_vocab

        self.embed_tokens = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
        )

        def get_layer(prefix: str):
            return Qwen3NextDecoderLayer(
990
                vllm_config,
991
992
993
994
995
                layer_type=config.layer_types[extract_layer_index(prefix)],
                prefix=prefix,
            )

        self.start_layer, self.end_layer, self.layers = make_layers(
996
997
998
999
1000
            config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers"
        )
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )
1001

1002
        if get_pp_group().is_last_rank:
1003
            self.norm = Qwen3NextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
1004
1005
        else:
            self.norm = PPMissingLayer()
1006
1007
1008
1009
1010
1011
1012
1013

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

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
1014
1015
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
    ) -> torch.Tensor:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.get_input_embeddings(input_ids)
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

1028
        for layer in islice(self.layers, self.start_layer, self.end_layer):
1029
1030
1031
1032
1033
1034
1035
            hidden_states, residual = layer(
                positions=positions,
                hidden_states=hidden_states,
                residual=residual,
            )

        if not get_pp_group().is_last_rank:
1036
1037
1038
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
1039
1040
1041
1042
1043
1044
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
1045
        return SharedFusedMoE.make_expert_params_mapping(
1046
1047
1048
1049
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            num_experts=self.config.num_experts,
1050
1051
            num_redundant_experts=self.num_redundant_experts,
        )
1052

1053
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        expert_params_mapping = self.get_expert_mapping()
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue

            if name.startswith("mtp."):
                continue

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue

                if "mlp.experts" in name:
                    continue

                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                # Skip layers on other devices.
                if is_pp_missing_parameter(name, self):
                    continue
                # name = apply_attn_prefix(name, params_dict)
                if 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)
                    # Skip layers on other devices.
                    if is_pp_missing_parameter(name, self):
                        continue
                    # Skip loading extra bias for GPTQ models.
1104
1105
1106
                    if (
                        name.endswith(".bias") or name.endswith("_bias")
                    ) and name not in params_dict:
1107
1108
1109
                        continue
                    param = params_dict[name]
                    weight_loader = param.weight_loader
1110
1111
1112
1113
1114
1115
1116
                    weight_loader(
                        param,
                        loaded_weight,
                        name,
                        shard_id=shard_id,
                        expert_id=expert_id,
                    )
1117
1118
1119
1120
1121
1122
1123
1124
                    break
                else:
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    if is_pp_missing_parameter(name, self):
                        continue
                    param = params_dict[name]
1125
1126
1127
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
1128
1129
1130
1131
1132
                    weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


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
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
class QwenNextMixtureOfExperts(MixtureOfExperts):
    def update_physical_experts_metadata(
        self,
        num_physical_experts: int,
        num_local_physical_experts: int,
    ) -> None:
        assert self.num_local_physical_experts == num_local_physical_experts
        self.num_physical_experts = num_physical_experts
        self.num_local_physical_experts = num_local_physical_experts
        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
        for layer in self.model.layers:
            if isinstance(layer.mlp, Qwen3NextSparseMoeBlock):
                moe = layer.mlp
                moe.n_local_physical_experts = num_local_physical_experts
                moe.n_physical_experts = num_physical_experts
                moe.n_redundant_experts = self.num_redundant_experts
                moe.experts.update_expert_map()

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

        self.moe_layers = []
        example_moe = None
        for layer in self.model.layers:
            if isinstance(layer, Qwen3NextDecoderLayer) and isinstance(
                layer.mlp, Qwen3NextSparseMoeBlock
            ):
                example_moe = layer.mlp
                self.moe_layers.append(layer.mlp.experts)

            if example_moe is None:
                raise RuntimeError("No Qwen3Next layer found in the model.layers.")

        # Set MoE hyperparameters
        self.num_moe_layers = len(self.moe_layers)
        self.num_expert_groups = 1
        self.num_shared_experts = 0
        self.num_logical_experts = example_moe.n_logical_experts
        self.num_physical_experts = example_moe.n_physical_experts
        self.num_local_physical_experts = example_moe.n_local_physical_experts
        self.num_routed_experts = example_moe.n_routed_experts
        self.num_redundant_experts = example_moe.n_redundant_experts


1177
class Qwen3NextForCausalLM(
1178
1179
1180
1181
1182
1183
    nn.Module,
    HasInnerState,
    SupportsLoRA,
    SupportsPP,
    QwenNextMixtureOfExperts,
    IsHybrid,
1184
):
1185
1186
1187
1188
1189
1190
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
1191
        "gate_up_proj": ["gate_proj", "up_proj"],
1192
1193
1194
1195
1196
1197
1198
1199
1200
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        config = vllm_config.model_config.hf_config
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
        lora_config = vllm_config.lora_config
        scheduler_config = vllm_config.scheduler_config
1201
        assert not cache_config.enable_prefix_caching, (
1202
            "Qwen3Next currently does not support prefix caching"
1203
        )
1204
1205
1206
1207
1208
        self.quant_config = vllm_config.quant_config

        super().__init__()
        self.config = config
        self.scheduler_config = scheduler_config
1209
1210
1211
        self.model = Qwen3NextModel(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
        self.unpadded_vocab_size = config.vocab_size
        if lora_config:
            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
        self.lm_head = ParallelLMHead(
            self.unpadded_vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
            padding_size=DEFAULT_VOCAB_PADDING_SIZE
            # We need bigger padding if using lora for kernel
            # compatibility
1222
1223
1224
1225
1226
1227
1228
            if not lora_config
            else lora_config.lora_vocab_padding_size,
            prefix=maybe_prefix(prefix, "lm_head"),
        )
        self.logits_processor = LogitsProcessor(
            self.unpadded_vocab_size, config.vocab_size
        )
1229
        self.make_empty_intermediate_tensors = (
1230
1231
            self.model.make_empty_intermediate_tensors
        )
1232
1233

        # Set MoE hyperparameters
1234
        self.set_moe_parameters()
1235
1236
1237
1238
1239
1240
1241
1242

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

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
1243
1244
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
1245
1246
        **kwargs: object,
    ):
1247
1248
1249
        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )
1250
1251
1252
1253
1254
1255
1256
1257
1258

        return hidden_states

    @classmethod
    def get_mamba_state_dtype_from_config(
        cls,
        vllm_config: "VllmConfig",
    ) -> tuple[torch.dtype, torch.dtype]:
        return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
1259
1260
            vllm_config.model_config.dtype, vllm_config.cache_config.mamba_cache_dtype
        )
1261
1262
1263

    @classmethod
    def get_mamba_state_shape_from_config(
1264
        cls, vllm_config: "VllmConfig"
1265
1266
1267
1268
    ) -> tuple[tuple[int, int], tuple[int, int]]:
        parallel_config = vllm_config.parallel_config
        hf_config = vllm_config.model_config.hf_config
        tp_size = parallel_config.tensor_parallel_size
1269
1270
1271
1272
1273
        num_spec = (
            vllm_config.speculative_config.num_speculative_tokens
            if vllm_config.speculative_config
            else 0
        )
1274
        return MambaStateShapeCalculator.gated_delta_net_state_shape(
1275
1276
1277
1278
1279
1280
1281
1282
            tp_size,
            hf_config.linear_num_key_heads,
            hf_config.linear_num_value_heads,
            hf_config.linear_key_head_dim,
            hf_config.linear_value_head_dim,
            hf_config.linear_conv_kernel_dim,
            num_spec,
        )
1283
1284
1285
1286

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
1287
    ) -> torch.Tensor | None:
1288
        return self.logits_processor(self.lm_head, hidden_states)
1289

1290
    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
        loader = AutoWeightsLoader(
            self,
            skip_prefixes=["mtp."],
        )
        return loader.load_weights(weights)

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        return self.model.get_expert_mapping()


1301
1302
1303
1304
1305
def gdn_attention_core(
    mixed_qkv: torch.Tensor,
    b: torch.Tensor,
    a: torch.Tensor,
    core_attn_out: torch.Tensor,
1306
1307
    layer_name: str,
) -> None:
1308
1309
1310
1311
1312
    """
    Custom op for the core attention computation.
    Only handles the convolution + recurrent attention part.
    Input/output projections are handled outside this op.
    """
1313
1314
    forward_context: ForwardContext = get_forward_context()
    self = forward_context.no_compile_layers[layer_name]
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
    self._forward_core(
        mixed_qkv=mixed_qkv,
        b=b,
        a=a,
        core_attn_out=core_attn_out,
    )


def gdn_attention_core_fake(
    mixed_qkv: torch.Tensor,
    b: torch.Tensor,
    a: torch.Tensor,
    core_attn_out: torch.Tensor,
1328
1329
    layer_name: str,
) -> None:
1330
    """Fake implementation for torch.compile."""
1331
1332
1333
1334
    return


direct_register_custom_op(
1335
1336
1337
1338
    op_name="gdn_attention_core",
    op_func=gdn_attention_core,
    mutates_args=["core_attn_out"],
    fake_impl=gdn_attention_core_fake,
1339
1340
1341
1342
1343
1344
)


@triton.jit
def fused_gdn_gating_kernel(
    g,
1345
    beta_output,
1346
1347
    A_log,
    a,
1348
    b,
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
    dt_bias,
    seq_len,
    NUM_HEADS: tl.constexpr,
    beta: tl.constexpr,
    threshold: tl.constexpr,
    BLK_HEADS: tl.constexpr,
):
    i_b, i_s, i_d = tl.program_id(0), tl.program_id(1), tl.program_id(2)
    head_off = i_d * BLK_HEADS + tl.arange(0, BLK_HEADS)
    off = i_b * seq_len * NUM_HEADS + i_s * NUM_HEADS + head_off
    mask = head_off < NUM_HEADS
    blk_A_log = tl.load(A_log + head_off, mask=mask)
    blk_a = tl.load(a + off, mask=mask)
1362
    blk_b = tl.load(b + off, mask=mask)
1363
1364
1365
    blk_bias = tl.load(dt_bias + head_off, mask=mask)
    # If the model is loaded in fp16, without the .float() here, A might be -inf
    x = blk_a.to(tl.float32) + blk_bias.to(tl.float32)
1366
1367
1368
    softplus_x = tl.where(
        beta * x <= threshold, (1 / beta) * tl.log(1 + tl.exp(beta * x)), x
    )
1369
1370
    blk_g = -tl.exp(blk_A_log.to(tl.float32)) * softplus_x
    tl.store(g + off, blk_g.to(g.dtype.element_ty), mask=mask)
1371
    # compute beta_output = sigmoid(b)
1372
1373
1374
1375
    blk_beta_output = tl.sigmoid(blk_b.to(tl.float32))
    tl.store(
        beta_output + off, blk_beta_output.to(beta_output.dtype.element_ty), mask=mask
    )
1376
1377
1378
1379
1380


def fused_gdn_gating(
    A_log: torch.Tensor,
    a: torch.Tensor,
1381
    b: torch.Tensor,
1382
1383
1384
    dt_bias: torch.Tensor,
    beta: float = 1.0,
    threshold: float = 20.0,
1385
1386
1387
1388
1389
1390
1391
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Fused computation of g and beta for Gated Delta Net.
    g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias)
    beta_output = b.sigmoid()
    TODO maybe use torch.compile to replace this triton kernel
    """
1392
1393
1394
    batch, num_heads = a.shape
    seq_len = 1
    grid = (batch, seq_len, triton.cdiv(num_heads, 8))
1395
    g = torch.empty(1, batch, num_heads, dtype=torch.float32, device=a.device)
1396
    beta_output = torch.empty(1, batch, num_heads, dtype=b.dtype, device=b.device)
1397
    fused_gdn_gating_kernel[grid](
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
        g,
        beta_output,
        A_log,
        a,
        b,
        dt_bias,
        seq_len,
        num_heads,
        beta,
        threshold,
        8,
        num_warps=1,
1410
    )
1411
    return g, beta_output