speculative.py 28.2 KB
Newer Older
1
2
3
4
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import ast
5
from typing import TYPE_CHECKING, Any, Literal, get_args
6

7
from pydantic import Field, SkipValidation, model_validator
8
9
10
from pydantic.dataclasses import dataclass
from typing_extensions import Self

11
from vllm.config.model import ModelConfig
12
13
14
from vllm.config.parallel import ParallelConfig
from vllm.config.utils import config
from vllm.logger import init_logger
15
from vllm.utils.hashing import safe_hash
16
from vllm.utils.import_utils import LazyLoader, has_arctic_inference
17
18
19
20
21
22
23
24

if TYPE_CHECKING:
    from transformers import PretrainedConfig

    import vllm.model_executor.layers.quantization as me_quant
else:
    PretrainedConfig = Any

25
26
27
    me_quant = LazyLoader(
        "model_executor", globals(), "vllm.model_executor.layers.quantization"
    )
28
29
30

logger = init_logger(__name__)

31
MTPModelTypes = Literal[
32
33
34
35
36
37
    "deepseek_mtp",
    "mimo_mtp",
    "glm4_moe_mtp",
    "ernie_mtp",
    "qwen3_next_mtp",
    "longcat_flash_mtp",
38
    "mtp",
39
    "pangu_ultra_moe_mtp",
40
41
42
43
44
45
46
47
48
49
]
EagleModelTypes = Literal["eagle", "eagle3", MTPModelTypes]
SpeculativeMethod = Literal[
    "ngram",
    "medusa",
    "mlp_speculator",
    "draft_model",
    "suffix",
    EagleModelTypes,
]
50
51
52
53
54
55


@config
@dataclass
class SpeculativeConfig:
    """Configuration for speculative decoding."""
56

57
    enforce_eager: bool | None = None
58
    """Override the default enforce_eager from model_config"""
59
    # General speculative decoding control
60
    num_speculative_tokens: int = Field(default=None, gt=0)
61
62
    """The number of speculative tokens, if provided. It will default to the
    number in the draft model config if present, otherwise, it is required."""
63
    model: str | None = None
64
65
    """The name of the draft model, eagle head, or additional weights, if
    provided."""
66
    method: SpeculativeMethod | None = None
67
68
69
70
71
72
73
    """The name of the speculative method to use. If users provide and set the
    `model` param, the speculative method type will be detected automatically
    if possible, if `model` param is not provided, the method name must be
    provided.

    If using `ngram` method, the related configuration `prompt_lookup_max` and
    `prompt_lookup_min` should be considered."""
74
    draft_tensor_parallel_size: int | None = Field(default=None, ge=1)
75
76
77
78
    """The degree of the tensor parallelism for the draft model. Can only be 1
    or the same as the target model's tensor parallel size."""

    # Draft model configuration
79
    quantization: me_quant.QuantizationMethods | None = None
80
81
82
    """Quantization method that was used to quantize the draft model weights.
    If `None`, we assume the model weights are not quantized. Note that it only
    takes effect when using the draft model-based speculative method."""
83
    max_model_len: int | None = Field(default=None, ge=1)
84
85
    """The maximum model length of the draft model. Used when testing the
    ability to skip speculation for some sequences."""
86
    revision: str | None = None
87
88
89
    """The specific model version to use for the draft model. It can be a
    branch name, a tag name, or a commit id. If unspecified, will use the
    default version."""
90
    code_revision: str | None = None
91
92
93
94
95
    """The specific revision to use for the draft model code on Hugging Face
    Hub. It can be a branch name, a tag name, or a commit id. If unspecified,
    will use the default version."""

    # Advanced control
96
    disable_by_batch_size: int | None = Field(default=None, ge=2)
97
98
    """Disable speculative decoding for new incoming requests when the number
    of enqueued requests is larger than this value, if provided."""
99
100
101
102
103
    disable_padded_drafter_batch: bool = False
    """Disable input padding for speculative decoding. If set to True,
    speculative input batches can contain sequences of different lengths,
    which may only be supported by certain attention backends. This currently
    only affects the EAGLE method of speculation."""
104
105

    # Ngram proposer configuration
106
    prompt_lookup_max: int | None = Field(default=None, ge=1)
107
108
    """Maximum size of ngram token window when using Ngram proposer, required
    when method is set to ngram."""
109
    prompt_lookup_min: int | None = Field(default=None, ge=1)
110
111
112
    """Minimum size of ngram token window when using Ngram proposer, if
    provided. Defaults to 1."""

113
    speculative_token_tree: str | None = None
114
115
116
117
118
    """Specifies the tree structure for speculative token generation.
    """
    # required configuration params passed from engine
    target_model_config: SkipValidation[ModelConfig] = None  # type: ignore
    """The configuration of the target model."""
119
    target_parallel_config: SkipValidation[ParallelConfig] = None  # type: ignore
120
121
122
123
124
    """The parallel configuration for the target model."""

    # params generated in the post-init stage
    draft_model_config: SkipValidation[ModelConfig] = None  # type: ignore
    """The configuration of the draft model initialized internal."""
125
    draft_parallel_config: SkipValidation[ParallelConfig] = None  # type: ignore
126
127
    """The parallel configuration for the draft model initialized internal."""

128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
    # Suffix decoding configuration
    suffix_decoding_max_tree_depth: int = 24
    """The maximum depth of the suffix decoding global and prompt trees. The
    tree depth limits the sum of the prefix match and speculation lengths."""

    suffix_decoding_max_cached_requests: int = 10000
    """The maximum number of requests to cache in the global suffix tree. If
    exceeded, will trigger eviction in FIFO order. If set to 0, the global
    suffix tree is disabled and past responses are not cached (prompt trees
    are still used)."""

    suffix_decoding_max_spec_factor: float = 1.0
    """The maximum spec factor for suffix decoding. The spec factor controls
    speculation lengths based on the prefix match length: max_spec_tokens =
    max_spec_factor * prefix_match_length."""

    suffix_decoding_min_token_prob: float = 0.1
    """The minimum token probability for suffix decoding. Will only speculate
    tokens with estimated probability (based on frequency counts) greater than
    or equal to this value."""

149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        factors: list[Any] = []
        # Eagle3 affects the computation graph because it returns intermediate
        # hidden states in addition to the final hidden state.
        factors.append(self.method == "eagle3")
165
        hash_str = safe_hash(str(factors).encode(), usedforsecurity=False).hexdigest()
166
167
168
169
        return hash_str

    @staticmethod
    def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
170
        initial_architecture = hf_config.architectures[0]
171
        if hf_config.model_type in ("deepseek_v3", "deepseek_v32"):
172
173
174
            hf_config.model_type = "deepseek_mtp"
        if hf_config.model_type == "deepseek_mtp":
            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
175
176
177
            hf_config.update(
                {"n_predict": n_predict, "architectures": ["DeepSeekMTPModel"]}
            )
178
179
180
181
182
183
184
        if hf_config.model_type in ("pangu_ultra_moe"):
            hf_config.model_type = "pangu_ultra_moe_mtp"
        if hf_config.model_type == "pangu_ultra_moe_mtp":
            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
            hf_config.update(
                {"n_predict": n_predict, "architectures": ["OpenPanguMTPModel"]}
            )
185
186
187
188

        if hf_config.architectures[0] == "MiMoForCausalLM":
            hf_config.model_type = "mimo_mtp"
            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
189
190
191
192
193
194
195
            hf_config.update(
                {
                    "num_hidden_layers": 0,
                    "n_predict": n_predict,
                    "architectures": ["MiMoMTPModel"],
                }
            )
196
197
198
199

        if hf_config.architectures[0] == "Glm4MoeForCausalLM":
            hf_config.model_type = "glm4_moe_mtp"
            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
200
201
202
203
204
205
            hf_config.update(
                {
                    "n_predict": n_predict,
                    "architectures": ["Glm4MoeMTPModel"],
                }
            )
206
207
208
209
210

        if hf_config.model_type == "ernie4_5_moe":
            hf_config.model_type = "ernie_mtp"
        if hf_config.model_type == "ernie_mtp":
            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
211
212
213
            hf_config.update(
                {"n_predict": n_predict, "architectures": ["ErnieMTPModel"]}
            )
214
215
216
217
218

        if hf_config.model_type == "qwen3_next":
            hf_config.model_type = "qwen3_next_mtp"
        if hf_config.model_type == "qwen3_next_mtp":
            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
219
220
221
            hf_config.update(
                {"n_predict": n_predict, "architectures": ["Qwen3NextMTP"]}
            )
XuruiYang's avatar
XuruiYang committed
222
223
224
        if hf_config.model_type == "longcat_flash":
            hf_config.model_type = "longcat_flash_mtp"
            n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
225
226
227
            hf_config.update(
                {"n_predict": n_predict, "architectures": ["LongCatFlashMTPModel"]}
            )
228

229
230
231
        if initial_architecture == "MistralLarge3ForCausalLM":
            hf_config.update({"architectures": ["EagleMistralLarge3ForCausalLM"]})

232
233
234
235
236
237
238
239
240
241
242
        return hf_config

    def __post_init__(self):
        # Note: "method" is a new parameter that helps to extend the
        # configuration of non-model-based proposers, and the "model" parameter
        # will be used to set the draft model, eagle head, or additional weight
        # when needed. If users do not specify "method", the speculative method
        # will be detected automatically if possible. If the speculative method
        # can not be detected, it will be considered as the "draft_model" by
        # default.

243
        if self.method in get_args(MTPModelTypes) and self.method != "mtp":
244
245
246
            logger.warning(
                "method `%s` is deprecated and replaced with mtp.", self.method
            )
247
248
            self.method = "mtp"

249
        if self.model is None and self.num_speculative_tokens is not None:
250
            if self.method == "mtp":
251
252
                if self.target_model_config is None:
                    raise ValueError("target_model_config must be present for mtp")
253
                if self.target_model_config.hf_text_config.model_type == "deepseek_v32":
254
255
256
                    # FIXME(luccafong): cudgraph with v32 MTP is not supported,
                    # remove this when the issue is fixed.
                    self.enforce_eager = True
257
258
259
260
261
262
263
264
                # use the draft model from the same model:
                self.model = self.target_model_config.model
                # Align the quantization of draft model for cases such as
                # --quantization fp8 with a bf16 checkpoint.
                if not self.quantization:
                    self.quantization = self.target_model_config.quantization
            elif self.method in ("ngram", "[ngram]"):
                self.model = "ngram"
265
266
            elif self.method == "suffix":
                self.model = "suffix"
267
            else:
268
                raise ValueError(
269
270
                    "num_speculative_tokens was provided but without speculative model."
                )
271
272
273

        # Automatically configure the method for ngram when "model" is used
        # instead of "method"
274
275
276
        if self.method is None and (
            self.model is not None and self.model in ("ngram", "[ngram]")
        ):
277
278
279
280
281
282
            self.method = "ngram"

        if self.method in ("ngram", "[ngram]"):
            # Unified to "ngram" internally
            self.method = "ngram"
            # Set default values if not provided
283
            if self.prompt_lookup_min is None and self.prompt_lookup_max is None:
284
285
286
287
                # TODO(woosuk): Tune these values. They are arbitrarily chosen.
                self.prompt_lookup_min = 5
                self.prompt_lookup_max = 5
            elif self.prompt_lookup_min is None:
288
289
290
291
292
                if self.prompt_lookup_max is None:
                    raise ValueError(
                        "Either prompt_lookup_max or prompt_lookup_min must be "
                        "provided when using the ngram method."
                    )
293
294
                self.prompt_lookup_min = self.prompt_lookup_max
            elif self.prompt_lookup_max is None:
295
296
297
298
299
                if self.prompt_lookup_min is None:
                    raise ValueError(
                        "Either prompt_lookup_max or prompt_lookup_min must be "
                        "provided when using the ngram method."
                    )
300
301
302
303
304
305
                self.prompt_lookup_max = self.prompt_lookup_min

            # Validate values
            if self.prompt_lookup_min > self.prompt_lookup_max:
                raise ValueError(
                    f"prompt_lookup_min={self.prompt_lookup_min} must "
306
307
                    f"be <= prompt_lookup_max={self.prompt_lookup_max}"
                )
308
309
310
311
312
313

            # TODO: current we still need extract vocab_size from target model
            # config, in future, we may try refactor it out, and set
            # draft related config as None here.
            self.draft_model_config = self.target_model_config
            self.draft_parallel_config = self.target_parallel_config
314
315
        elif self.method == "suffix":
            self._validate_suffix_decoding()
316
317
318
319
320
321
322
323
        else:
            self.prompt_lookup_max = 0
            self.prompt_lookup_min = 0

            if self.model is not None:
                self.draft_model_config = ModelConfig(
                    model=self.model,
                    runner="draft",
324
325
                    tokenizer=self.target_model_config.tokenizer,
                    tokenizer_mode=self.target_model_config.tokenizer_mode,
326
                    trust_remote_code=self.target_model_config.trust_remote_code,
327
328
                    allowed_local_media_path=self.target_model_config.allowed_local_media_path,
                    allowed_media_domains=self.target_model_config.allowed_media_domains,
329
330
331
332
                    dtype=self.target_model_config.dtype,
                    seed=self.target_model_config.seed,
                    revision=self.revision,
                    code_revision=self.code_revision,
333
                    tokenizer_revision=self.target_model_config.tokenizer_revision,
334
                    spec_target_max_model_len=self.target_model_config.max_model_len,
335
336
337
338
                    quantization=self.quantization,
                    enforce_eager=self.target_model_config.enforce_eager,
                    max_logprobs=self.target_model_config.max_logprobs,
                    hf_overrides=SpeculativeConfig.hf_config_override,
339
                    config_format=self.target_model_config.config_format,
340
341
342
                )

                # Automatically detect the method
343
                if self.method in ("eagle", "eagle3"):
344
345
346
347
348
349
350
351
352
353
354
                    pass
                # examples:
                # yuhuili/EAGLE-LLaMA3-Instruct-8B
                # yuhuili/EAGLE3-LLaMA3.1-Instruct-8B
                # AngelSlim/Qwen3-8B_eagle3
                elif "eagle-" in self.draft_model_config.model.lower():
                    self.method = "eagle"
                elif "eagle3" in self.draft_model_config.model.lower():
                    self.method = "eagle3"
                elif self.draft_model_config.hf_config.model_type == "medusa":
                    self.method = "medusa"
355
                elif self.draft_model_config.hf_config.model_type == "mlp_speculator":
356
                    self.method = "mlp_speculator"
357
358
359
                elif self.draft_model_config.hf_config.model_type in get_args(
                    MTPModelTypes
                ):
360
                    self.method = "mtp"
361
362
                    if self.num_speculative_tokens > 1:
                        logger.warning(
363
364
365
366
367
368
369
                            "Enabling num_speculative_tokens > 1 will run"
                            "multiple times of forward on same MTP layer"
                            ",which may result in lower acceptance rate"
                        )
                elif self.draft_model_config.hf_config.model_type in (
                    "longcat_flash_mtp"
                ):
XuruiYang's avatar
XuruiYang committed
370
371
372
                    self.method = "longcat_flash_mtp"
                    if self.num_speculative_tokens > 1:
                        logger.warning(
373
374
375
376
                            "LongCat MTP models only have "
                            "one layer. Might need some code changes "
                            "to support multiple layers."
                        )
377
378
379
380
381
382
                else:
                    self.method = "draft_model"
                    raise NotImplementedError(
                        "Speculative decoding with draft model is not "
                        "supported yet. Please consider using other "
                        "speculative decoding methods such as ngram, medusa, "
383
384
                        "eagle, or mtp."
                    )
385
386
387

                # Replace hf_config for EAGLE draft_model
                if self.method in ("eagle", "eagle3"):
388
389
                    from vllm.transformers_utils.configs import SpeculatorsConfig
                    from vllm.transformers_utils.configs.eagle import EAGLEConfig
390

391
392
393
394
                    if isinstance(
                        self.draft_model_config.hf_config,
                        (EAGLEConfig, SpeculatorsConfig),
                    ):
395
396
397
398
399
                        pass
                    else:
                        eagle_config = EAGLEConfig(
                            self.draft_model_config.hf_config,
                            method=self.method,
400
401
                            model_type="eagle",
                        )
402
                        self.draft_model_config.hf_config = eagle_config
403
404
405
                        self.draft_model_config.model_arch_config = (
                            self.draft_model_config.get_model_arch_config()
                        )
406

407
408
409
410
411
412
                if self.num_speculative_tokens is not None and hasattr(
                    self.draft_model_config.hf_config, "num_lookahead_tokens"
                ):
                    self.draft_model_config.hf_config.num_lookahead_tokens = (
                        self.num_speculative_tokens
                    )
413

414
415
416
                n_predict = getattr(
                    self.draft_model_config.hf_config, "n_predict", None
                )
417
418
419
420
                if n_predict is not None:
                    if self.num_speculative_tokens is None:
                        # Default to max value defined in draft model config.
                        self.num_speculative_tokens = n_predict
421
422
423
424
                    elif (
                        self.num_speculative_tokens > n_predict
                        and self.num_speculative_tokens % n_predict != 0
                    ):
425
426
427
                        # Ensure divisibility for MTP module reuse.
                        raise ValueError(
                            f"num_speculative_tokens:{self.num_speculative_tokens}"
428
429
                            f" must be divisible by {n_predict=}"
                        )
430
431
432

                if self.speculative_token_tree is None:
                    # Generate chain of tokens.
433
434
435
                    self.speculative_token_tree = str(
                        [(i + 1) * (0,) for i in range(self.num_speculative_tokens)]
                    )
436
437
                else:
                    # Sort the token tree breadth-first.
438
                    tree_choices = ast.literal_eval(self.speculative_token_tree)
439
                    self.speculative_token_tree = str(
440
441
                        sorted(tree_choices, key=lambda t: (len(t), t))
                    )
442

443
                self.draft_tensor_parallel_size = (
444
445
446
                    SpeculativeConfig._verify_and_get_draft_tp(
                        self.target_parallel_config,
                        self.draft_tensor_parallel_size,
447
448
                        self.draft_model_config.hf_config,
                    )
449
450
451
452
453
454
455
                )

                self.draft_model_config.max_model_len = (
                    SpeculativeConfig._maybe_override_draft_max_model_len(
                        self.max_model_len,
                        self.draft_model_config.max_model_len,
                        self.target_model_config.max_model_len,
456
457
                    )
                )
458
459
460

                self.draft_parallel_config = (
                    SpeculativeConfig.create_draft_parallel_config(
461
462
463
                        self.target_parallel_config, self.draft_tensor_parallel_size
                    )
                )
464
        return self
465

466
467
468
469
    def _validate_suffix_decoding(self):
        if not has_arctic_inference():
            raise ImportError(
                "Arctic Inference is required for suffix decoding. "
470
                "Install via `pip install arctic-inference==0.1.1`."
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
            )
        if self.num_speculative_tokens is None:
            # Suffix decoding decides the actual number of speculative tokens
            # dynamically and treats num_speculative_tokens as a maximum limit.
            self.num_speculative_tokens = self.suffix_decoding_max_tree_depth
            logger.warning(
                "Defaulted num_speculative_tokens to %s for suffix decoding.",
                self.num_speculative_tokens,
            )
        # Validate values
        if self.suffix_decoding_max_tree_depth < 1:
            raise ValueError(
                f"suffix_decoding_max_tree_depth="
                f"{self.suffix_decoding_max_tree_depth} must be >= 1"
            )
        if self.suffix_decoding_max_cached_requests < 0:
            raise ValueError(
                f"suffix_decoding_max_cached_requests="
                f"{self.suffix_decoding_max_cached_requests} must be >= 0"
            )
        if self.suffix_decoding_max_spec_factor < 0:
            raise ValueError(
                f"suffix_decoding_max_spec_factor="
                f"{self.suffix_decoding_max_spec_factor} must be >= 0"
            )
        if not 0 <= self.suffix_decoding_min_token_prob <= 1:
            raise ValueError(
                f"suffix_decoding_min_token_prob="
                f"{self.suffix_decoding_min_token_prob} must be in [0, 1]"
            )

502
503
    @staticmethod
    def _maybe_override_draft_max_model_len(
504
        speculative_max_model_len: int | None,
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
        draft_max_model_len: int,
        target_max_model_len: int,
    ) -> int:
        """Determine the max sequence len for the draft model. This is usually
        the draft_max_model_len, but may be the target_max_model_len if it is
        less than the draft_max_model_len, or may be speculative_max_model_len
        if it is specified.

        This is necessary so that sequences do not exceed the capacity of the
        draft model or the target model.

        speculative_max_model_len is mainly used for testing that sequences can
        skip speculation.
        """

        if speculative_max_model_len is not None:
            if speculative_max_model_len > draft_max_model_len:
522
523
524
525
                raise ValueError(
                    f"{speculative_max_model_len=} cannot be "
                    f"larger than {draft_max_model_len=}"
                )
526
527

            if speculative_max_model_len > target_max_model_len:
528
529
530
531
                raise ValueError(
                    f"{speculative_max_model_len=} cannot be "
                    f"larger than {target_max_model_len=}"
                )
532
533
534
535
536
537
538
539
540
541

            return speculative_max_model_len

        return min(
            draft_max_model_len,
            target_max_model_len,
        )

    @staticmethod
    def _verify_and_get_draft_tp(
542
        target_parallel_config: ParallelConfig,
543
        speculative_draft_tensor_parallel_size: int | None,
544
545
        draft_hf_config: PretrainedConfig,
    ) -> int:
546
547
548
549
550
551
552
553
554
555
556
557
558
        """
        Verifies and adjusts the tensor parallel size for a draft model
        specified using speculative_draft_tensor_parallel_size.
        """
        # If speculative_draft_tensor_parallel_size is unset then set it
        # appropriately else verify that it is set correctly.
        if speculative_draft_tensor_parallel_size is None:
            if draft_hf_config.model_type == "mlp_speculator":
                speculative_draft_tensor_parallel_size = 1
                if target_parallel_config.tensor_parallel_size > 1:
                    logger.warning(
                        "%s cannot currently be run with tp>1; "
                        "setting speculative_draft_tensor_parallel_size=1",
559
560
                        draft_hf_config.model_type,
                    )
561
            else:
562
                speculative_draft_tensor_parallel_size = (
563
                    target_parallel_config.tensor_parallel_size
564
                )
565
        elif speculative_draft_tensor_parallel_size not in (
566
567
568
            1,
            target_parallel_config.tensor_parallel_size,
        ):
569
570
            raise ValueError(
                f"{speculative_draft_tensor_parallel_size=} cannot be "
571
572
                f"other value than 1 or target model tensor_parallel_size"
            )
573
574
575
576
577
578
579
580
581
582
583
584
        return speculative_draft_tensor_parallel_size

    @staticmethod
    def create_draft_parallel_config(
        target_parallel_config: ParallelConfig,
        speculative_draft_tensor_parallel_size: int,
    ) -> ParallelConfig:
        """Create a parallel config for use by the draft worker.

        This is mostly a copy of the target parallel config, except the tp_size.
        """
        draft_parallel_config = ParallelConfig(
585
            pipeline_parallel_size=target_parallel_config.pipeline_parallel_size,
586
            tensor_parallel_size=speculative_draft_tensor_parallel_size,
587
588
589
590
            distributed_executor_backend=target_parallel_config.distributed_executor_backend,
            max_parallel_loading_workers=target_parallel_config.max_parallel_loading_workers,
            disable_custom_all_reduce=target_parallel_config.disable_custom_all_reduce,
            ray_workers_use_nsight=target_parallel_config.ray_workers_use_nsight,
591
592
593
594
595
            placement_group=target_parallel_config.placement_group,
        )

        return draft_parallel_config

596
    @model_validator(mode="after")
597
598
599
600
601
    def _verify_args(self) -> Self:
        if self.num_speculative_tokens is None:
            raise ValueError(
                "num_speculative_tokens must be provided with "
                "speculative model unless the draft model config contains an "
602
603
                "n_predict parameter."
            )
604
605

        if self.num_speculative_tokens <= 0:
606
607
608
609
            raise ValueError(
                "Expected num_speculative_tokens to be greater "
                f"than zero ({self.num_speculative_tokens})."
            )
610
611
612

        if self.draft_model_config:
            self.draft_model_config.verify_with_parallel_config(
613
614
                self.draft_parallel_config
            )
615

616
617
618
619
620
621
        if self.disable_by_batch_size is not None and self.disable_by_batch_size < 2:
            raise ValueError(
                "Expect the batch size threshold of disabling "
                "speculative decoding is > 1, but got "
                f"{self.disable_by_batch_size=}"
            )
622

623
        eagle3_target_supported = ["llama", "qwen", "minicpm", "gpt_oss"]
624
625
626
627
628
629
630
631
        if (
            self.method == "eagle3"
            and self.target_model_config
            and not any(
                supported_model in self.target_model_config.hf_text_config.model_type
                for supported_model in eagle3_target_supported
            )
        ):
632
633
            raise ValueError(
                f"Eagle3 is only supported for {eagle3_target_supported} models. "  # noqa: E501
634
635
                f"Got {self.target_model_config.hf_text_config.model_type=}"
            )
636
637
638
639

        return self

    def use_eagle(self) -> bool:
640
        return self.method in ("eagle", "eagle3", "mtp")
641
642
643

    def __repr__(self) -> str:
        method = self.method
644
        model = None if method in ("ngram", "suffix") else self.draft_model_config.model
645
646
        num_spec_tokens = self.num_speculative_tokens
        return f"SpeculativeConfig({method=}, {model=}, {num_spec_tokens=})"