config.py 46.3 KB
Newer Older
1
import enum
2
import json
3
import os
4
from dataclasses import dataclass, field, fields
5
from typing import TYPE_CHECKING, ClassVar, List, Optional, Union
6
7

import torch
8
from packaging.version import Version
9
from transformers import PretrainedConfig
10

Woosuk Kwon's avatar
Woosuk Kwon committed
11
from vllm.logger import init_logger
12
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
13
from vllm.transformers_utils.config import get_config, get_hf_text_config
14
15
from vllm.utils import (get_cpu_memory, get_nvcc_cuda_version, is_cpu, is_hip,
                        is_neuron)
16

17
18
19
if TYPE_CHECKING:
    from ray.util.placement_group import PlacementGroup

20
    from vllm.model_executor.model_loader.loader import BaseModelLoader
21

22
23
logger = init_logger(__name__)

24
25
26
27
# If true, will load models from ModelScope instead of Hugging Face Hub.
VLLM_USE_MODELSCOPE = os.environ.get("VLLM_USE_MODELSCOPE",
                                     "False").lower() == "true"

28
_GB = 1 << 30
29

30
31

class ModelConfig:
32
33
34
35
    """Configuration for the model.

    Args:
        model: Name or path of the huggingface model to use.
36
        tokenizer: Name or path of the huggingface tokenizer to use.
37
38
        tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
            available, and "slow" will always use the slow tokenizer.
39
40
        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
            downloading the model and tokenizer.
41
42
43
44
        dtype: Data type for model weights and activations. The "auto" option
            will use FP16 precision for FP32 and FP16 models, and BF16 precision
            for BF16 models.
        seed: Random seed for reproducibility.
Jasmond L's avatar
Jasmond L committed
45
46
47
        revision: The specific model version to use. It can be a branch name,
            a tag name, or a commit id. If unspecified, will use the default
            version.
48
        code_revision: The specific revision to use for the model code on
49
            Hugging Face Hub. It can be a branch name, a tag name, or a
50
            commit id. If unspecified, will use the default version.
51
52
53
        tokenizer_revision: The specific tokenizer version to use. It can be a
            branch name, a tag name, or a commit id. If unspecified, will use
            the default version.
54
55
        max_model_len: Maximum length of a sequence (including prompt and
            output). If None, will be derived from the model.
56
57
        quantization: Quantization method that was used to quantize the model
            weights. If None, we assume the model weights are not quantized.
58
59
        quantization_param_path: Path to JSON file containing scaling factors.
            Used to load KV cache scaling factors into the model when KV cache
60
61
            type is FP8_E4M3 on ROCm (AMD GPU). In the future these will also
            be used to load activation and weight scaling factors when the
62
            model dtype is FP8_E4M3 on ROCm.
63
64
65
66
67
68
        enforce_eager: Whether to enforce eager execution. If True, we will
            disable CUDA graph and always execute the model in eager mode.
            If False, we will use CUDA graph and eager execution in hybrid.
        max_context_len_to_capture: Maximum context len covered by CUDA graphs.
            When a sequence has context length larger than this, we fall back
            to eager mode.
69
70
        skip_tokenizer_init: If true, skip initialization of tokenizer and
            detokenizer.
71
    """
72
73
74
75

    def __init__(
        self,
        model: str,
76
77
        tokenizer: str,
        tokenizer_mode: str,
78
        trust_remote_code: bool,
79
        dtype: Union[str, torch.dtype],
80
        seed: int,
81
        revision: Optional[str] = None,
82
        code_revision: Optional[str] = None,
83
        tokenizer_revision: Optional[str] = None,
84
        max_model_len: Optional[int] = None,
85
        quantization: Optional[str] = None,
86
        quantization_param_path: Optional[str] = None,
87
88
        enforce_eager: bool = False,
        max_context_len_to_capture: Optional[int] = None,
89
        max_logprobs: int = 5,
90
        skip_tokenizer_init: bool = False,
91
92
    ) -> None:
        self.model = model
93
        self.tokenizer = tokenizer
94
        self.tokenizer_mode = tokenizer_mode
95
        self.trust_remote_code = trust_remote_code
96
        self.seed = seed
Jasmond L's avatar
Jasmond L committed
97
        self.revision = revision
98
        self.code_revision = code_revision
99
        self.tokenizer_revision = tokenizer_revision
100
        self.quantization = quantization
101
        self.quantization_param_path = quantization_param_path
102
103
        self.enforce_eager = enforce_eager
        self.max_context_len_to_capture = max_context_len_to_capture
104
        self.max_logprobs = max_logprobs
105
        self.skip_tokenizer_init = skip_tokenizer_init
106

107
108
        self.hf_config = get_config(self.model, trust_remote_code, revision,
                                    code_revision)
109
110
111
        self.hf_text_config = get_hf_text_config(self.hf_config)
        self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
        self.max_model_len = _get_and_verify_max_len(self.hf_text_config,
112
                                                     max_model_len)
113
114
        if not self.skip_tokenizer_init:
            self._verify_tokenizer_mode()
115
        self._verify_quantization()
116
        self._verify_cuda_graph()
117
118
119
120
121
122
123
124

    def _verify_tokenizer_mode(self) -> None:
        tokenizer_mode = self.tokenizer_mode.lower()
        if tokenizer_mode not in ["auto", "slow"]:
            raise ValueError(
                f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
                "either 'auto' or 'slow'.")
        self.tokenizer_mode = tokenizer_mode
125

126
    def _verify_quantization(self) -> None:
127
128
        supported_quantization = [*QUANTIZATION_METHODS]
        rocm_supported_quantization = ["gptq", "squeezellm"]
129
130
131
132
        if self.quantization is not None:
            self.quantization = self.quantization.lower()

        # Parse quantization method from the HF model config, if available.
133
134
135
136
137
138
139
140
141
142
        quant_cfg = getattr(self.hf_config, "quantization_config", None)
        if quant_cfg is not None:
            quant_method = quant_cfg.get("quant_method", "").lower()
            # compat: autogptq >=0.8.0 use checkpoint_format: str
            # compat: autogptq <=0.7.1 is_marlin_format: bool
            is_format_marlin = (quant_cfg.get("checkpoint_format") == "marlin"
                                or quant_cfg.get("is_marlin_format", False))

            # Use marlin if the GPTQ model is serialized in marlin format.
            if quant_method == "gptq" and is_format_marlin:
143
144
                logger.info("The model is serialized in Marlin format. "
                            "Using Marlin kernel.")
145
                quant_method = "marlin"
146
                if self.quantization == "gptq":
147
                    self.quantization = quant_method
148

149
            if self.quantization is None:
150
151
                self.quantization = quant_method
            elif self.quantization != quant_method:
152
153
                raise ValueError(
                    "Quantization method specified in the model config "
154
                    f"({quant_method}) does not match the quantization "
155
156
157
158
159
160
161
162
                    f"method specified in the `quantization` argument "
                    f"({self.quantization}).")

        if self.quantization is not None:
            if self.quantization not in supported_quantization:
                raise ValueError(
                    f"Unknown quantization method: {self.quantization}. Must "
                    f"be one of {supported_quantization}.")
163
            if is_hip(
164
            ) and self.quantization not in rocm_supported_quantization:
165
                raise ValueError(
166
167
                    f"{self.quantization} quantization is currently not "
                    f"supported in ROCm.")
168
169
            if self.quantization != "marlin":
                logger.warning(
170
                    "%s quantization is not fully "
171
                    "optimized yet. The speed can be slower than "
172
                    "non-quantized models.", self.quantization)
173

174
175
176
177
178
179
    def _verify_cuda_graph(self) -> None:
        if self.max_context_len_to_capture is None:
            self.max_context_len_to_capture = self.max_model_len
        self.max_context_len_to_capture = min(self.max_context_len_to_capture,
                                              self.max_model_len)

180
181
182
183
    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
184
        total_num_attention_heads = self.hf_text_config.num_attention_heads
185
186
187
188
189
190
191
        tensor_parallel_size = parallel_config.tensor_parallel_size
        if total_num_attention_heads % tensor_parallel_size != 0:
            raise ValueError(
                f"Total number of attention heads ({total_num_attention_heads})"
                " must be divisible by tensor parallel size "
                f"({tensor_parallel_size}).")

192
        total_num_hidden_layers = self.hf_text_config.num_hidden_layers
193
194
195
196
197
198
199
        pipeline_parallel_size = parallel_config.pipeline_parallel_size
        if total_num_hidden_layers % pipeline_parallel_size != 0:
            raise ValueError(
                f"Total number of hidden layers ({total_num_hidden_layers}) "
                "must be divisible by pipeline parallel size "
                f"({pipeline_parallel_size}).")

200
    def get_sliding_window(self) -> Optional[int]:
201
202
203
204
205
206
        """Get the sliding window size, or None if disabled.
        """

        # Some models, like Qwen2 and Qwen1.5, use `use_sliding_window` in
        # addition to sliding window size. We check if that field is present
        # and if it's False, return None.
207
208
        if (hasattr(self.hf_text_config, "use_sliding_window")
                and not self.hf_text_config.use_sliding_window):
209
            return None
210
        return getattr(self.hf_text_config, "sliding_window", None)
211
212

    def get_vocab_size(self) -> int:
213
        return self.hf_text_config.vocab_size
214

215
    def get_hidden_size(self) -> int:
216
        return self.hf_text_config.hidden_size
217
218

    def get_head_size(self) -> int:
219
220
        if hasattr(self.hf_text_config, "head_dim"):
            return self.hf_text_config.head_dim
221
        # FIXME(woosuk): This may not be true for all models.
222
223
        return (self.hf_text_config.hidden_size //
                self.hf_text_config.num_attention_heads)
224

225
226
    def get_total_num_kv_heads(self) -> int:
        """Returns the total number of KV heads."""
Zhuohan Li's avatar
Zhuohan Li committed
227
        # For GPTBigCode & Falcon:
228
        # NOTE: for falcon, when new_decoder_architecture is True, the
Zhuohan Li's avatar
Zhuohan Li committed
229
230
        # multi_query flag is ignored and we use n_head_kv for the number of
        # KV heads.
231
        falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"]
232
        new_decoder_arch_falcon = (
233
            self.hf_config.model_type in falcon_model_types
234
            and getattr(self.hf_config, "new_decoder_architecture", False))
235
        if not new_decoder_arch_falcon and getattr(self.hf_text_config,
236
                                                   "multi_query", False):
Zhuohan Li's avatar
Zhuohan Li committed
237
            # Multi-query attention, only one KV head.
Woosuk Kwon's avatar
Woosuk Kwon committed
238
            # Currently, tensor parallelism is not supported in this case.
Zhuohan Li's avatar
Zhuohan Li committed
239
            return 1
240

241
242
243
244
245
        # For DBRX and MPT
        if self.hf_config.model_type in ["dbrx", "mpt"]:
            return getattr(self.hf_config.attn_config, "kv_n_heads",
                           self.hf_config.num_attention_heads)

246
247
248
249
250
251
252
253
254
255
        attributes = [
            # For Falcon:
            "n_head_kv",
            "num_kv_heads",
            # For LLaMA-2:
            "num_key_value_heads",
            # For ChatGLM:
            "multi_query_group_num",
        ]
        for attr in attributes:
256
            num_kv_heads = getattr(self.hf_text_config, attr, None)
257
258
259
260
261
            if num_kv_heads is not None:
                return num_kv_heads

        # For non-grouped-query attention models, the number of KV heads is
        # equal to the number of attention heads.
262
        return self.hf_text_config.num_attention_heads
263
264
265
266
267
268
269
270
271
272

    def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
        """Returns the number of KV heads per GPU."""
        total_num_kv_heads = self.get_total_num_kv_heads()
        # If tensor parallelism is used, we divide the number of KV heads by
        # the tensor parallel size. We will replicate the KV heads in the
        # case where the number of KV heads is smaller than the tensor
        # parallel size so each GPU has at least one KV head.
        return max(1,
                   total_num_kv_heads // parallel_config.tensor_parallel_size)
273
274

    def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
275
        total_num_hidden_layers = self.hf_text_config.num_hidden_layers
276
277
278
279
        return total_num_hidden_layers // parallel_config.pipeline_parallel_size


class CacheConfig:
280
281
282
283
284
    """Configuration for the KV cache.

    Args:
        block_size: Size of a cache block in number of tokens.
        gpu_memory_utilization: Fraction of GPU memory to use for the
Woosuk Kwon's avatar
Woosuk Kwon committed
285
            vLLM execution.
286
        swap_space: Size of the CPU swap space per GPU (in GiB).
287
        cache_dtype: Data type for kv cache storage.
288
        num_gpu_blocks_override: Number of GPU blocks to use. This overrides the
289
            profiled num_gpu_blocks if specified. Does nothing if None.
290
    """
291

292
293
294
295
296
    def __init__(
        self,
        block_size: int,
        gpu_memory_utilization: float,
        swap_space: int,
297
        cache_dtype: str,
298
        num_gpu_blocks_override: Optional[int] = None,
299
        sliding_window: Optional[int] = None,
300
        enable_prefix_caching: bool = False,
301
302
303
    ) -> None:
        self.block_size = block_size
        self.gpu_memory_utilization = gpu_memory_utilization
304
        self.swap_space_bytes = swap_space * _GB
305
        self.num_gpu_blocks_override = num_gpu_blocks_override
306
        self.cache_dtype = cache_dtype
307
        self.sliding_window = sliding_window
308
        self.enable_prefix_caching = enable_prefix_caching
309
        self._verify_args()
310
        self._verify_cache_dtype()
311
312
313
314
315

        # Will be set after profiling.
        self.num_gpu_blocks = None
        self.num_cpu_blocks = None

316
    def metrics_info(self):
317
318
        # convert cache_config to dict(key: str, value: str) for prometheus
        # metrics info
319
320
        return {key: str(value) for key, value in self.__dict__.items()}

321
322
323
324
325
326
    def _verify_args(self) -> None:
        if self.gpu_memory_utilization > 1.0:
            raise ValueError(
                "GPU memory utilization must be less than 1.0. Got "
                f"{self.gpu_memory_utilization}.")

327
328
329
    def _verify_cache_dtype(self) -> None:
        if self.cache_dtype == "auto":
            pass
330
331
332
333
334
335
336
        elif self.cache_dtype == "fp8":
            if not is_hip():
                nvcc_cuda_version = get_nvcc_cuda_version()
                if nvcc_cuda_version < Version("11.8"):
                    raise ValueError(
                        "FP8 is not supported when cuda version is"
                        "lower than 11.8.")
337
            logger.info(
338
339
340
341
342
343
                "Using fp8 data type to store kv cache. It reduces the GPU "
                "memory footprint and boosts the performance. "
                "But it may cause slight accuracy drop without scaling "
                "factors. FP8_E5M2 (without scaling) is only supported on "
                "cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 "
                "is instead supported for common inference criteria.")
344
345
346
        else:
            raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}")

347
348
349
350
351
352
353
354
355
356
    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
        total_cpu_memory = get_cpu_memory()
        # FIXME(woosuk): Here, it is assumed that the GPUs in a tensor parallel
        # group are in the same node. However, the GPUs may span multiple nodes.
        num_gpus_per_node = parallel_config.tensor_parallel_size
        cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node

357
358
359
        msg = (f"{cpu_memory_usage / _GB:.2f} GiB out of "
               f"the {total_cpu_memory / _GB:.2f} GiB total CPU memory is "
               "allocated for the swap space.")
360
361
362
        if cpu_memory_usage > 0.7 * total_cpu_memory:
            raise ValueError("Too large swap space. " + msg)
        elif cpu_memory_usage > 0.4 * total_cpu_memory:
363
            logger.warning("Possibly too large swap space. %s", msg)
364

365

366
367
368
@dataclass
class TokenizerPoolConfig:
    """Configuration for the tokenizer pool.
369

370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
    Args:
        pool_size: Number of tokenizer workers in the pool.
        pool_type: Type of the pool.
        extra_config: Additional config for the pool.
            The way the config will be used depends on the
            pool type.
    """
    pool_size: int
    pool_type: str
    extra_config: dict

    def __post_init__(self):
        if self.pool_type not in ("ray", ):
            raise ValueError(f"Unknown pool type: {self.pool_type}")
        if not isinstance(self.extra_config, dict):
            raise ValueError("extra_config must be a dictionary.")

    @classmethod
    def create_config(
        cls, tokenizer_pool_size: int, tokenizer_pool_type: str,
        tokenizer_pool_extra_config: Optional[Union[str, dict]]
    ) -> Optional["TokenizerPoolConfig"]:
        """Create a TokenizerPoolConfig from the given parameters.
393

394
        If tokenizer_pool_size is 0, return None.
395

396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
        Args:
            tokenizer_pool_size: Number of tokenizer workers in the pool.
            tokenizer_pool_type: Type of the pool.
            tokenizer_pool_extra_config: Additional config for the pool.
                The way the config will be used depends on the
                pool type. This can be a JSON string (will be parsed).
        """
        if tokenizer_pool_size:
            if isinstance(tokenizer_pool_extra_config, str):
                tokenizer_pool_extra_config_parsed = json.loads(
                    tokenizer_pool_extra_config)
            else:
                tokenizer_pool_extra_config_parsed = (
                    tokenizer_pool_extra_config or {})
            tokenizer_pool_config = cls(tokenizer_pool_size,
                                        tokenizer_pool_type,
                                        tokenizer_pool_extra_config_parsed)
        else:
            tokenizer_pool_config = None
        return tokenizer_pool_config


418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
class LoadFormat(str, enum.Enum):
    AUTO = "auto"
    PT = "pt"
    SAFETENSORS = "safetensors"
    NPCACHE = "npcache"
    DUMMY = "dummy"
    TENSORIZER = "tensorizer"


@dataclass
class LoadConfig:
    """
        download_dir: Directory to download and load the weights, default to the
            default cache directory of huggingface.
        load_format: The format of the model weights to load:
            "auto" will try to load the weights in the safetensors format and
                fall back to the pytorch bin format if safetensors format is
                not available.
            "pt" will load the weights in the pytorch bin format.
            "safetensors" will load the weights in the safetensors format.
            "npcache" will load the weights in pytorch format and store
                a numpy cache to speed up the loading.
            "dummy" will initialize the weights with random values, which is
                mainly for profiling.
            "tensorizer" will use CoreWeave's tensorizer library for
                fast weight loading.
    """

    load_format: Union[str, LoadFormat, "BaseModelLoader"] = LoadFormat.AUTO
    download_dir: Optional[str] = None
    model_loader_extra_config: Optional[Union[str, dict]] = field(
        default_factory=dict)

    def __post_init__(self):
        model_loader_extra_config = self.model_loader_extra_config or {}
        if isinstance(model_loader_extra_config, str):
            self.model_loader_extra_config = json.loads(
                model_loader_extra_config)
        self._verify_load_format()

    def _verify_load_format(self) -> None:
        if not isinstance(self.load_format, str):
            return

        load_format = self.load_format.lower()
        self.load_format = LoadFormat(load_format)

        rocm_not_supported_load_format: List[str] = []
        if is_hip() and load_format in rocm_not_supported_load_format:
            rocm_supported_load_format = [
                f for f in LoadFormat.__members__
                if (f not in rocm_not_supported_load_format)
            ]
            raise ValueError(
                f"load format '{load_format}' is not supported in ROCm. "
                f"Supported load formats are "
                f"{rocm_supported_load_format}")


477
class ParallelConfig:
478
479
480
481
482
483
484
485
    """Configuration for the distributed execution.

    Args:
        pipeline_parallel_size: Number of pipeline parallel groups.
        tensor_parallel_size: Number of tensor parallel groups.
        worker_use_ray: Whether to use Ray for model workers. Will be set to
            True if either pipeline_parallel_size or tensor_parallel_size is
            greater than 1.
zspo's avatar
zspo committed
486
487
488
        max_parallel_loading_workers: Maximum number of multiple batches
            when load model sequentially. To avoid RAM OOM when using tensor
            parallel and large models.
489
490
        disable_custom_all_reduce: Disable the custom all-reduce kernel and
            fall back to NCCL.
491
492
        tokenizer_pool_config: Config for the tokenizer pool.
            If None, will use synchronous tokenization.
493
494
        ray_workers_use_nsight: Whether to profile Ray workers with nsight, see
            https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html#profiling-nsight-profiler.
495
    """
496

497
498
499
500
    def __init__(
        self,
        pipeline_parallel_size: int,
        tensor_parallel_size: int,
501
        worker_use_ray: bool,
502
        max_parallel_loading_workers: Optional[int] = None,
503
        disable_custom_all_reduce: bool = False,
504
        tokenizer_pool_config: Optional[TokenizerPoolConfig] = None,
505
        ray_workers_use_nsight: bool = False,
506
        placement_group: Optional["PlacementGroup"] = None,
507
508
    ) -> None:
        self.pipeline_parallel_size = pipeline_parallel_size
509
        self.tensor_parallel_size = tensor_parallel_size
510
        self.worker_use_ray = worker_use_ray
511
        self.max_parallel_loading_workers = max_parallel_loading_workers
512
        self.disable_custom_all_reduce = disable_custom_all_reduce
513
        self.tokenizer_pool_config = tokenizer_pool_config
514
        self.ray_workers_use_nsight = ray_workers_use_nsight
515
        self.placement_group = placement_group
516

517
        self.world_size = pipeline_parallel_size * self.tensor_parallel_size
518
        if self.world_size > 1:
519
            self.worker_use_ray = True
520
521
522
523
524
525
        self._verify_args()

    def _verify_args(self) -> None:
        if self.pipeline_parallel_size > 1:
            raise NotImplementedError(
                "Pipeline parallelism is not supported yet.")
526
527
528
529
530
531
532
533
534
535
536
        if not self.disable_custom_all_reduce and self.world_size > 1:
            if is_hip():
                self.disable_custom_all_reduce = True
                logger.info(
                    "Disabled the custom all-reduce kernel because it is not "
                    "supported on AMD GPUs.")
            elif self.pipeline_parallel_size > 1:
                self.disable_custom_all_reduce = True
                logger.info(
                    "Disabled the custom all-reduce kernel because it is not "
                    "supported with pipeline parallelism.")
537
538
539
        if self.ray_workers_use_nsight and not self.worker_use_ray:
            raise ValueError("Unable to use nsight profiling unless workers "
                             "run with Ray.")
540

541
542

class SchedulerConfig:
543
544
545
546
547
548
549
    """Scheduler configuration.

    Args:
        max_num_batched_tokens: Maximum number of tokens to be processed in
            a single iteration.
        max_num_seqs: Maximum number of sequences to be processed in a single
            iteration.
Chaofan Lin's avatar
Chaofan Lin committed
550
        max_model_len: Maximum length of a sequence (including prompt
Lily Liu's avatar
Lily Liu committed
551
            and generated text).
552
553
554
555
556
        use_v2_block_manager: Whether to use the BlockSpaceManagerV2 or not.
        num_lookahead_slots: The number of slots to allocate per sequence per
            step, beyond the known token ids. This is used in speculative
            decoding to store KV activations of tokens which may or may not be
            accepted.
557
558
        delay_factor: Apply a delay (of delay factor multiplied by previous
            prompt latency) before scheduling next prompt.
559
560
        enable_chunked_prefill: If True, prefill requests can be chunked based
            on the remaining max_num_batched_tokens.
561
    """
562

563
564
565
566
567
    def __init__(
        self,
        max_num_batched_tokens: Optional[int],
        max_num_seqs: int,
        max_model_len: int,
568
        use_v2_block_manager: bool = False,
569
        num_lookahead_slots: int = 0,
570
        delay_factor: float = 0.0,
571
        enable_chunked_prefill: bool = False,
572
573
574
575
    ) -> None:
        if max_num_batched_tokens is not None:
            self.max_num_batched_tokens = max_num_batched_tokens
        else:
576
577
578
579
580
581
582
583
584
585
            if enable_chunked_prefill:
                # For chunked prefill, choose the well-tuned batch size.
                self.max_num_batched_tokens = 768
            else:
                # If max_model_len is too short, use 2048 as the default value
                # for higher throughput.
                self.max_num_batched_tokens = max(max_model_len, 2048)
        if enable_chunked_prefill:
            logger.info("Chunked prefill is enabled (EXPERIMENTAL).")

586
        self.max_num_seqs = max_num_seqs
Lily Liu's avatar
Lily Liu committed
587
        self.max_model_len = max_model_len
588
        self.use_v2_block_manager = use_v2_block_manager
589
590
        self.num_lookahead_slots = num_lookahead_slots
        self.delay_factor = delay_factor
591
        self.chunked_prefill_enabled = enable_chunked_prefill
592

593
594
595
        self._verify_args()

    def _verify_args(self) -> None:
596
597
        if (self.max_num_batched_tokens < self.max_model_len
                and not self.chunked_prefill_enabled):
598
599
600
601
602
603
604
            raise ValueError(
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) is "
                f"smaller than max_model_len ({self.max_model_len}). "
                "This effectively limits the maximum sequence length to "
                "max_num_batched_tokens and makes vLLM reject longer "
                "sequences. Please increase max_num_batched_tokens or "
                "decrease max_model_len.")
605

606
607
608
609
610
        if self.max_num_batched_tokens < self.max_num_seqs:
            raise ValueError(
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) must "
                "be greater than or equal to max_num_seqs "
                f"({self.max_num_seqs}).")
611

612
613
614
615
616
617
        if self.num_lookahead_slots < 0:
            raise ValueError(
                "num_lookahead_slots "
                f"({self.num_lookahead_slots}) must be greater than or "
                "equal to 0.")

618

619
620
class DeviceConfig:

621
622
623
    def __init__(self, device: str = "auto") -> None:
        if device == "auto":
            # Automated device type detection
624
            if is_neuron():
625
                self.device_type = "neuron"
626
627
            elif is_cpu():
                self.device_type = "cpu"
628
            else:
629
630
631
                # We don't call torch.cuda.is_available() here to
                # avoid initializing CUDA before workers are forked
                self.device_type = "cuda"
632
633
634
635
636
637
638
639
640
641
642
        else:
            # Device type is assigned explicitly
            self.device_type = device

        # Some device types require processing inputs on CPU
        if self.device_type in ["neuron"]:
            self.device = torch.device("cpu")
        else:
            # Set device with device type
            self.device = torch.device(self.device_type)

643

644
645
646
647
648
649
650
651
652
653
654
655
656
657
class SpeculativeConfig:
    """Configuration for speculative decoding.

    The configuration is currently specialized to draft-model speculative
    decoding with top-1 proposals.
    """

    @staticmethod
    def maybe_create_spec_config(
        target_model_config: ModelConfig,
        target_parallel_config: ParallelConfig,
        target_dtype: str,
        speculative_model: Optional[str],
        num_speculative_tokens: Optional[int],
658
659
660
        speculative_max_model_len: Optional[int],
        enable_chunked_prefill: bool,
        use_v2_block_manager: bool,
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
    ) -> Optional["SpeculativeConfig"]:
        """Create a SpeculativeConfig if possible, else return None.

        This function attempts to create a SpeculativeConfig object based on the
        provided parameters. If the necessary conditions are met, it returns an
        instance of SpeculativeConfig. Otherwise, it returns None.

        Args:
            target_model_config (ModelConfig): The configuration of the target
                model.
            target_parallel_config (ParallelConfig): The parallel configuration
                for the target model.
            target_dtype (str): The data type used for the target model.
            speculative_model (Optional[str]): The name of the speculative
                model, if provided.
            num_speculative_tokens (Optional[int]): The number of speculative
                tokens, if provided.
678
679
680
681
682
683
684
685
686
            speculative_max_model_len (Optional[int]): The maximum model len of
                the speculative model. Used when testing the ability to skip
                speculation for some sequences.
            enable_chunked_prefill (bool): Whether vLLM is configured to use
                chunked prefill or not. Used for raising an error since its not
                yet compatible with spec decode.
            use_v2_block_manager (bool): Whether vLLM is configured to use the
                v2 block manager or not. Used for raising an error since the v2
                block manager is required with spec decode.
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701

        Returns:
            Optional["SpeculativeConfig"]: An instance of SpeculativeConfig if
                the necessary conditions are met, else None.
        """

        if (speculative_model is None and num_speculative_tokens is None):
            return None

        if speculative_model is not None and num_speculative_tokens is None:
            raise ValueError(
                "Expected both speculative_model and "
                "num_speculative_tokens to be provided, but found "
                f"{speculative_model=} and {num_speculative_tokens=}.")

702
703
704
        assert (speculative_model is not None
                and num_speculative_tokens is not None)

705
706
707
708
709
710
711
712
713
714
        if enable_chunked_prefill:
            raise ValueError(
                "Speculative decoding and chunked prefill are "
                f"currently mutually exclusive ({enable_chunked_prefill=}).")

        if not use_v2_block_manager:
            raise ValueError(
                "Speculative decoding requires usage of the V2 "
                "block manager. Enable it with --use-v2-block-manager.")

715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
        # TODO: The user should be able to specify revision/quantization/max
        # model len for the draft model. It is not currently supported.
        draft_revision = None
        draft_code_revision = None
        draft_quantization = None

        draft_model_config = ModelConfig(
            model=speculative_model,
            tokenizer=target_model_config.tokenizer,
            tokenizer_mode=target_model_config.tokenizer_mode,
            trust_remote_code=target_model_config.trust_remote_code,
            dtype=target_model_config.dtype,
            seed=target_model_config.seed,
            revision=draft_revision,
            code_revision=draft_code_revision,
            tokenizer_revision=target_model_config.tokenizer_revision,
731
            max_model_len=None,
732
733
734
735
736
737
738
            quantization=draft_quantization,
            enforce_eager=target_model_config.enforce_eager,
            max_context_len_to_capture=target_model_config.
            max_context_len_to_capture,
            max_logprobs=target_model_config.max_logprobs,
        )

739
740
741
742
743
744
745
        draft_model_config.max_model_len = (
            SpeculativeConfig._maybe_override_draft_max_model_len(
                speculative_max_model_len,
                draft_model_config.max_model_len,
                target_model_config.max_model_len,
            ))

746
747
748
749
750
751
752
753
754
755
        draft_parallel_config = (
            SpeculativeConfig.create_draft_parallel_config(
                target_parallel_config))

        return SpeculativeConfig(
            draft_model_config,
            draft_parallel_config,
            num_speculative_tokens,
        )

756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
    @staticmethod
    def _maybe_override_draft_max_model_len(
        speculative_max_model_len: Optional[int],
        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:
                raise ValueError(f"{speculative_max_model_len=} cannot be "
                                 f"larger than {draft_max_model_len=}")

            if speculative_max_model_len > target_max_model_len:
                raise ValueError(f"{speculative_max_model_len=} cannot be "
                                 f"larger than {target_max_model_len=}")

            return speculative_max_model_len

        return min(
            draft_max_model_len,
            target_max_model_len,
        )

791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
    @staticmethod
    def create_draft_parallel_config(
            target_parallel_config: ParallelConfig) -> ParallelConfig:
        """Create a parallel config for use by the draft worker.

        This is mostly a copy of the target parallel config. In the future the
        draft worker can have a different parallel strategy, e.g. TP=1.
        """
        draft_parallel_config = ParallelConfig(
            pipeline_parallel_size=target_parallel_config.
            pipeline_parallel_size,
            tensor_parallel_size=target_parallel_config.tensor_parallel_size,
            worker_use_ray=target_parallel_config.worker_use_ray,
            max_parallel_loading_workers=target_parallel_config.
            max_parallel_loading_workers,
            disable_custom_all_reduce=target_parallel_config.
            disable_custom_all_reduce,
            tokenizer_pool_config=target_parallel_config.tokenizer_pool_config,
            ray_workers_use_nsight=target_parallel_config.
            ray_workers_use_nsight,
            placement_group=target_parallel_config.placement_group,
        )

        return draft_parallel_config

    def __init__(
        self,
        draft_model_config: ModelConfig,
        draft_parallel_config: ParallelConfig,
        num_speculative_tokens: int,
    ):
        """Create a SpeculativeConfig object.

        Args:
            draft_model_config: ModelConfig for the draft model.
            draft_parallel_config: ParallelConfig for the draft model.
            num_speculative_tokens: The number of tokens to sample from the
                draft model before scoring with the target model.
        """
        self.draft_model_config = draft_model_config
        self.draft_parallel_config = draft_parallel_config
        self.num_speculative_tokens = num_speculative_tokens

        self._verify_args()

    def _verify_args(self) -> None:
        if self.num_speculative_tokens <= 0:
            raise ValueError("Expected num_speculative_tokens to be greater "
                             f"than zero ({self.num_speculative_tokens}).")

        if self.draft_model_config:
            self.draft_model_config.verify_with_parallel_config(
                self.draft_parallel_config)

    @property
    def num_lookahead_slots(self) -> int:
        """The number of additional slots the scheduler should allocate per
        step, in addition to the slots allocated for each known token.

        This is equal to the number of speculative tokens, as each speculative
        token must be scored.
        """
        return self.num_speculative_tokens

    def __repr__(self) -> str:
        draft_model = self.draft_model_config.model
        num_spec_tokens = self.num_speculative_tokens
        return f"SpeculativeConfig({draft_model=}, {num_spec_tokens=})"


861
862
863
864
@dataclass
class LoRAConfig:
    max_lora_rank: int
    max_loras: int
865
    fully_sharded_loras: bool = False
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
    max_cpu_loras: Optional[int] = None
    lora_dtype: Optional[torch.dtype] = None
    lora_extra_vocab_size: int = 256
    # This is a constant.
    lora_vocab_padding_size: ClassVar[int] = 256

    def __post_init__(self):
        # Keep this in sync with csrc/punica/bgmv/bgmv_config.h
        possible_max_ranks = (8, 16, 32, 64)
        possible_lora_extra_vocab_size = (0, 256, 512)
        if self.max_lora_rank not in possible_max_ranks:
            raise ValueError(
                f"max_lora_rank ({self.max_lora_rank}) must be one of "
                f"{possible_max_ranks}.")
        if self.lora_extra_vocab_size not in possible_lora_extra_vocab_size:
            raise ValueError(
                f"lora_extra_vocab_size ({self.lora_extra_vocab_size}) "
                f"must be one of {possible_lora_extra_vocab_size}.")
        if self.max_loras < 1:
            raise ValueError(f"max_loras ({self.max_loras}) must be >= 1.")
        if self.max_cpu_loras is None:
            self.max_cpu_loras = self.max_loras
        elif self.max_cpu_loras < self.max_loras:
            raise ValueError(
                f"max_cpu_loras ({self.max_cpu_loras}) must be >= "
zspo's avatar
zspo committed
891
                f"max_loras ({self.max_loras})")
892
893
894
895
896
897

    def verify_with_model_config(self, model_config: ModelConfig):
        if self.lora_dtype in (None, "auto"):
            self.lora_dtype = model_config.dtype
        elif isinstance(self.lora_dtype, str):
            self.lora_dtype = getattr(torch, self.lora_dtype)
898
899
900
901
        if model_config.quantization and model_config.quantization not in [
                "awq", "gptq"
        ]:
            # TODO support marlin and squeezellm
902
903
            logger.warning("%s quantization is not tested with LoRA yet.",
                           model_config.quantization)
904
905
906
907
908
909
910
911
912

    def verify_with_scheduler_config(self, scheduler_config: SchedulerConfig):
        if scheduler_config.max_num_batched_tokens > 65528:
            raise ValueError(
                "Due to limitations of the custom LoRA CUDA kernel, "
                "max_num_batched_tokens must be <= 65528 when "
                "LoRA is enabled.")


913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
@dataclass
class VisionLanguageConfig:
    """Configs the input data format and how models should run for
    vision language models."""

    class ImageInputType(enum.Enum):
        """Image input type into the vision language model.

        An image roughly goes through the following transformation:
        Raw image --> pixel values --> image features --> image embeddings.

        The difference between different image input types is where the
        image encoder (pixel values --> image features) is run.
        Different image input types also correspond to different tensor shapes.

        For example, for Llava, PIXEL_VALUES: (1, 3, 336, 336).
        IMAGE_FEATURES: (1, 576, 1024).
        """
        PIXEL_VALUES = enum.auto()
        IMAGE_FEATURES = enum.auto()

    image_input_type: ImageInputType
    # The input id corresponding to image token.
    image_token_id: int
    # Used for running `run_prefill_max_token`.
    # For models that support varying resolution, this corresponds to
    # worst case scenario (biggest supported resolution).
    image_input_shape: tuple
    image_feature_size: int

    @classmethod
    def get_image_input_enum_type(
            cls, value: str) -> "VisionLanguageConfig.ImageInputType":
        """Get the image input type from a string."""
        try:
            return cls.ImageInputType[value.upper()]
        except KeyError as e:
            raise ValueError(f"{value} is not a valid choice. "
                             f"Expecting to choose from "
                             f"{[x.name for x in cls.ImageInputType]}.") from e


955
956
957
958
959
960
961
962
_STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.float16,
    "float16": torch.float16,
    "float": torch.float32,
    "float32": torch.float32,
    "bfloat16": torch.bfloat16,
}

963
964
_ROCM_NOT_SUPPORTED_DTYPE = ["float", "float32"]

965
966
967

def _get_and_verify_dtype(
    config: PretrainedConfig,
968
    dtype: Union[str, torch.dtype],
969
970
971
972
973
974
975
) -> torch.dtype:
    # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
    # because config.torch_dtype can be None.
    config_dtype = getattr(config, "torch_dtype", None)
    if config_dtype is None:
        config_dtype = torch.float32

976
977
978
979
980
981
982
983
984
    if isinstance(dtype, str):
        dtype = dtype.lower()
        if dtype == "auto":
            if config_dtype == torch.float32:
                # Following the common practice, we use float16 for float32
                # models.
                torch_dtype = torch.float16
            else:
                torch_dtype = config_dtype
985
        else:
986
987
988
989
990
            if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
                raise ValueError(f"Unknown dtype: {dtype}")
            torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
    elif isinstance(dtype, torch.dtype):
        torch_dtype = dtype
991
    else:
992
        raise ValueError(f"Unknown dtype: {dtype}")
993

994
995
996
997
998
    if is_hip() and torch_dtype == torch.float32:
        rocm_supported_dtypes = [
            k for k, v in _STR_DTYPE_TO_TORCH_DTYPE.items()
            if (k not in _ROCM_NOT_SUPPORTED_DTYPE)
        ]
999
        raise ValueError(f"dtype '{dtype}' is not supported in ROCm. "
1000
1001
                         f"Supported dtypes are {rocm_supported_dtypes}")

1002
1003
1004
1005
1006
1007
1008
1009
1010
    # Verify the dtype.
    if torch_dtype != config_dtype:
        if torch_dtype == torch.float32:
            # Upcasting to float32 is allowed.
            pass
        elif config_dtype == torch.float32:
            # Downcasting from float32 to float16 or bfloat16 is allowed.
            pass
        else:
Woosuk Kwon's avatar
Woosuk Kwon committed
1011
            # Casting between float16 and bfloat16 is allowed with a warning.
1012
            logger.warning("Casting %s to %s.", config_dtype, torch_dtype)
1013
1014

    return torch_dtype
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029


def _get_and_verify_max_len(
    hf_config: PretrainedConfig,
    max_model_len: Optional[int],
) -> int:
    """Get and verify the model's maximum length."""
    derived_max_model_len = float("inf")
    possible_keys = [
        # OPT
        "max_position_embeddings",
        # GPT-2
        "n_positions",
        # MPT
        "max_seq_len",
1030
1031
        # ChatGLM2
        "seq_length",
1032
1033
        # Command-R
        "model_max_length",
1034
1035
1036
1037
1038
        # Others
        "max_sequence_length",
        "max_seq_length",
        "seq_len",
    ]
1039
    max_len_key = None
1040
    for key in possible_keys:
1041
1042
1043
1044
1045
        max_len = getattr(hf_config, key, None)
        if max_len is not None:
            max_len_key = key if max_len < derived_max_model_len \
                else max_len_key
            derived_max_model_len = min(derived_max_model_len, max_len)
1046
    if derived_max_model_len == float("inf"):
1047
1048
1049
1050
1051
1052
1053
1054
        if max_model_len is not None:
            # If max_model_len is specified, we use it.
            return max_model_len

        default_max_len = 2048
        logger.warning(
            "The model's config.json does not contain any of the following "
            "keys to determine the original maximum length of the model: "
1055
1056
            "%d. Assuming the model's maximum length is %d.", possible_keys,
            default_max_len)
1057
        derived_max_model_len = default_max_len
1058

1059
    rope_scaling = getattr(hf_config, "rope_scaling", None)
1060
    if rope_scaling is not None and rope_scaling["type"] != "su":
1061
1062
        assert "factor" in rope_scaling
        scaling_factor = rope_scaling["factor"]
Antoni Baum's avatar
Antoni Baum committed
1063
1064
1065
        if rope_scaling["type"] == "yarn":
            derived_max_model_len = rope_scaling[
                "original_max_position_embeddings"]
1066
1067
        derived_max_model_len *= scaling_factor

1068
    if max_model_len is None:
1069
        max_model_len = int(derived_max_model_len)
1070
    elif max_model_len > derived_max_model_len:
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
        # Some models might have a separate key for specifying model_max_length
        # that will be bigger than derived_max_model_len. We compare user input
        # with model_max_length and allow this override when it's smaller.
        model_max_length = getattr(hf_config, "model_max_length", None)
        if model_max_length is not None and max_model_len <= model_max_length:
            pass
        else:
            raise ValueError(
                f"User-specified max_model_len ({max_model_len}) is greater "
                "than the derived max_model_len "
                f"({max_len_key}={derived_max_model_len} or model_max_length="
                f"{model_max_length} in model's config.json). This may lead "
                "to incorrect model outputs or CUDA errors. Make sure the "
                "value is correct and within the model context size.")
1085
    return int(max_model_len)
1086
1087


1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
@dataclass
class DecodingConfig:
    """Dataclass which contains the decoding strategy of the engine"""

    # Which guided decoding algo to use. 'outlines' / 'lm-format-enforcer'
    guided_decoding_backend: str = 'outlines'

    def __post_init__(self):
        valid_guided_backends = ['outlines', 'lm-format-enforcer']
        backend = self.guided_decoding_backend
        if backend not in valid_guided_backends:
            raise ValueError(f"Invalid guided_decoding_backend '{backend},"
                             f"must be one of {valid_guided_backends}")


1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
@dataclass(frozen=True)
class EngineConfig:
    """Dataclass which contains all engine-related configuration. This
    simplifies passing around the distinct configurations in the codebase.
    """

    model_config: ModelConfig
    cache_config: CacheConfig
    parallel_config: ParallelConfig
    scheduler_config: SchedulerConfig
    device_config: DeviceConfig
1114
    load_config: LoadConfig
1115
1116
1117
    lora_config: Optional[LoRAConfig]
    vision_language_config: Optional[VisionLanguageConfig]
    speculative_config: Optional[SpeculativeConfig]
1118
    decoding_config: Optional[DecodingConfig]
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135

    def __post_init__(self):
        """Verify configs are valid & consistent with each other.
        """
        self.model_config.verify_with_parallel_config(self.parallel_config)
        self.cache_config.verify_with_parallel_config(self.parallel_config)

        if self.lora_config:
            self.lora_config.verify_with_model_config(self.model_config)
            self.lora_config.verify_with_scheduler_config(
                self.scheduler_config)

    def to_dict(self):
        """Return the configs as a dictionary, for use in **kwargs.
        """
        return dict(
            (field.name, getattr(self, field.name)) for field in fields(self))