config.py 67.1 KB
Newer Older
1
import enum
2
import json
3
from dataclasses import dataclass, field, fields
4
5
from typing import (TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Tuple,
                    Union)
6
7

import torch
8
from transformers import PretrainedConfig, PreTrainedTokenizerBase
9

10
import vllm.envs as envs
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.model_executor.models import ModelRegistry
14
from vllm.tracing import is_otel_installed
15
from vllm.transformers_utils.config import get_config, get_hf_text_config
16
from vllm.utils import (cuda_device_count_stateless, get_cpu_memory, is_cpu,
17
18
                        is_hip, is_neuron, is_openvino, is_tpu, is_xpu,
                        print_warning_once, update_environment_variables)
19

20
21
22
if TYPE_CHECKING:
    from ray.util.placement_group import PlacementGroup

23
    from vllm.model_executor.model_loader.loader import BaseModelLoader
24

25
26
logger = init_logger(__name__)

27
_GB = 1 << 30
28
_EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768
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
37
            It is also used as the content for `model_name` tag in metrics 
            output when `served_model_name` is not specified. 
38
        tokenizer: Name or path of the huggingface tokenizer to use.
39
40
        tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
            available, and "slow" will always use the slow tokenizer.
41
42
        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
            downloading the model and tokenizer.
43
44
45
46
        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
47
48
49
        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.
50
        code_revision: The specific revision to use for the model code on
51
            Hugging Face Hub. It can be a branch name, a tag name, or a
52
            commit id. If unspecified, will use the default version.
53
54
55
        rope_scaling: Dictionary containing the scaling configuration for the
            RoPE embeddings. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum.
56
57
58
        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.
59
60
        max_model_len: Maximum length of a sequence (including prompt and
            output). If None, will be derived from the model.
61
62
        quantization: Quantization method that was used to quantize the model
            weights. If None, we assume the model weights are not quantized.
63
64
        quantization_param_path: Path to JSON file containing scaling factors.
            Used to load KV cache scaling factors into the model when KV cache
65
66
            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
67
            model dtype is FP8_E4M3 on ROCm.
68
69
70
71
72
        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
73
74
75
76
            to eager mode (DEPRECATED. Use max_seq_len_to_capture instead).
        max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs.
            When a sequence has context length larger than this, we fall back
            to eager mode
77
78
79
80
        disable_sliding_window: Whether to disable sliding window. If True,
            we will disable the sliding window functionality of the model.
            If the model does not support sliding window, this argument is
            ignored.
81
82
        skip_tokenizer_init: If true, skip initialization of tokenizer and
            detokenizer.
83
84
85
86
        served_model_name: The model name used in metrics tag `model_name`,
            matches the model name exposed via the APIs. If multiple model 
            names provided, the first name will be used. If not specified, 
            the model name will be the same as `model`.
87
    """
88
89
90
91

    def __init__(
        self,
        model: str,
92
93
        tokenizer: str,
        tokenizer_mode: str,
94
        trust_remote_code: bool,
95
        dtype: Union[str, torch.dtype],
96
        seed: int,
97
        revision: Optional[str] = None,
98
        code_revision: Optional[str] = None,
99
        rope_scaling: Optional[dict] = None,
100
        rope_theta: Optional[float] = None,
101
        tokenizer_revision: Optional[str] = None,
102
        max_model_len: Optional[int] = None,
103
        quantization: Optional[str] = None,
104
        quantization_param_path: Optional[str] = None,
105
106
        enforce_eager: bool = False,
        max_context_len_to_capture: Optional[int] = None,
107
        max_seq_len_to_capture: Optional[int] = None,
108
        max_logprobs: int = 20,
109
        disable_sliding_window: bool = False,
110
        skip_tokenizer_init: bool = False,
111
        served_model_name: Optional[Union[str, List[str]]] = None,
112
        multimodal_config: Optional["VisionLanguageConfig"] = None,
113
114
    ) -> None:
        self.model = model
115
        self.tokenizer = tokenizer
116
        self.tokenizer_mode = tokenizer_mode
117
        self.trust_remote_code = trust_remote_code
118
        self.seed = seed
Jasmond L's avatar
Jasmond L committed
119
        self.revision = revision
120
        self.code_revision = code_revision
121
        self.rope_scaling = rope_scaling
122
        self.rope_theta = rope_theta
123
124
125
126
127
        # The tokenizer version is consistent with the model version by default.
        if tokenizer_revision is None:
            self.tokenizer_revision = revision
        else:
            self.tokenizer_revision = tokenizer_revision
128
        self.quantization = quantization
129
        self.quantization_param_path = quantization_param_path
130
131
        self.enforce_eager = enforce_eager
        self.max_context_len_to_capture = max_context_len_to_capture
132
133
134
135
136
        if self.max_context_len_to_capture is not None:
            raise ValueError("`max_context_len_to_capture` is deprecated. "
                             "Use `max_seq_len_to_capture` instead.")
        self.max_seq_len_to_capture = (max_seq_len_to_capture
                                       or max_context_len_to_capture)
137
        self.max_logprobs = max_logprobs
138
        self.disable_sliding_window = disable_sliding_window
139
        self.skip_tokenizer_init = skip_tokenizer_init
140

141
        self.hf_config = get_config(self.model, trust_remote_code, revision,
142
                                    code_revision, rope_scaling, rope_theta)
143
144
        self.hf_text_config = get_hf_text_config(self.hf_config)
        self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
Woosuk Kwon's avatar
Woosuk Kwon committed
145
146
147
148
149
150
151
152
153
154
155

        if (not self.disable_sliding_window
                and self.hf_text_config.model_type == "gemma2"
                and self.hf_text_config.sliding_window is not None):
            print_warning_once(
                "Gemma 2 uses sliding window attention for every odd layer, "
                "which is currently not supported by vLLM. Disabling sliding "
                "window and capping the max length to the sliding window size "
                f"({self.hf_text_config.sliding_window}).")
            self.disable_sliding_window = True

156
157
158
159
160
        self.max_model_len = _get_and_verify_max_len(
            hf_config=self.hf_text_config,
            max_model_len=max_model_len,
            disable_sliding_window=self.disable_sliding_window,
            sliding_window_len=self.get_hf_config_sliding_window())
161
162
        self.served_model_name = get_served_model_name(model,
                                                       served_model_name)
163
164
        self.multimodal_config = multimodal_config

165
166
        if not self.skip_tokenizer_init:
            self._verify_tokenizer_mode()
167
        self._verify_embedding_mode()
168
        self._verify_quantization()
169
        self._verify_cuda_graph()
170
171
172
173
174
175
176
177

    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
178

179
180
181
182
183
    def _verify_embedding_mode(self) -> None:
        architectures = getattr(self.hf_config, "architectures", [])
        self.embedding_mode = any(
            ModelRegistry.is_embedding_model(arch) for arch in architectures)

184
185
186
    def _parse_quant_hf_config(self):
        quant_cfg = getattr(self.hf_config, "quantization_config", None)
        if quant_cfg is None:
187
188
            # compress-tensors uses a "compression_config" key
            quant_cfg = getattr(self.hf_config, "compression_config", None)
189
190
        return quant_cfg

191
    def _verify_quantization(self) -> None:
192
193
        supported_quantization = [*QUANTIZATION_METHODS]
        rocm_supported_quantization = ["gptq", "squeezellm"]
194
195
196
197
        if self.quantization is not None:
            self.quantization = self.quantization.lower()

        # Parse quantization method from the HF model config, if available.
198
199
        quant_cfg = self._parse_quant_hf_config()

200
201
        if quant_cfg is not None:
            quant_method = quant_cfg.get("quant_method", "").lower()
202
203

            # Detect which checkpoint is it
204
            for _, method in QUANTIZATION_METHODS.items():
205
206
207
208
209
210
                quantization_override = method.override_quantization_method(
                    quant_cfg, self.quantization)
                if quantization_override:
                    quant_method = quantization_override
                    self.quantization = quantization_override
                    break
211

212
            # Verify quantization configurations.
213
            if self.quantization is None:
214
215
                self.quantization = quant_method
            elif self.quantization != quant_method:
216
217
                raise ValueError(
                    "Quantization method specified in the model config "
218
                    f"({quant_method}) does not match the quantization "
219
220
221
222
223
224
225
226
                    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}.")
227
            if is_hip(
228
            ) and self.quantization not in rocm_supported_quantization:
229
                raise ValueError(
230
231
                    f"{self.quantization} quantization is currently not "
                    f"supported in ROCm.")
232
            if (self.quantization
Cody Yu's avatar
Cody Yu committed
233
                    not in ("fp8", "marlin", "gptq_marlin_24", "gptq_marlin")):
234
                logger.warning(
235
                    "%s quantization is not fully "
236
                    "optimized yet. The speed can be slower than "
237
                    "non-quantized models.", self.quantization)
238

239
    def _verify_cuda_graph(self) -> None:
240
241
242
243
        if self.max_seq_len_to_capture is None:
            self.max_seq_len_to_capture = self.max_model_len
        self.max_seq_len_to_capture = min(self.max_seq_len_to_capture,
                                          self.max_model_len)
244

245
246
247
248
    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
249
250
        total_num_attention_heads = getattr(self.hf_text_config,
                                            "num_attention_heads", 0)
251
252
253
254
255
256
257
        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}).")

258
259
        total_num_hidden_layers = getattr(self.hf_text_config,
                                          "num_hidden_layers", 0)
260
261
262
263
264
265
266
        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}).")

267
268
269
270
271
272
        if self.quantization == "bitsandbytes" and (
                parallel_config.tensor_parallel_size > 1
                or parallel_config.pipeline_parallel_size > 1):
            raise ValueError(
                "BitAndBytes quantization with TP or PP is not supported yet.")

273
    def get_hf_config_sliding_window(self) -> Optional[int]:
Woosuk Kwon's avatar
Woosuk Kwon committed
274
        """Get the sliding window size, or None if disabled."""
275
276
277
278

        # 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.
279
280
        if (hasattr(self.hf_text_config, "use_sliding_window")
                and not self.hf_text_config.use_sliding_window):
281
            return None
282
        return getattr(self.hf_text_config, "sliding_window", None)
283

284
285
286
287
288
289
290
291
292
    def get_sliding_window(self) -> Optional[int]:
        """Get the sliding window size, or None if disabled.
        """
        # If user disables sliding window, return None.
        if self.disable_sliding_window:
            return None
        # Otherwise get the value from the hf config.
        return self.get_hf_config_sliding_window()

293
    def get_vocab_size(self) -> int:
294
        return self.hf_text_config.vocab_size
295

296
    def get_hidden_size(self) -> int:
297
        return self.hf_text_config.hidden_size
298
299

    def get_head_size(self) -> int:
wangding zeng's avatar
wangding zeng committed
300
301
302
303
304
305
        # TODO remove hard code
        if hasattr(self.hf_text_config, "model_type"
                   ) and self.hf_text_config.model_type == 'deepseek_v2':
            # FlashAttention supports only head_size 32, 64, 128, 256,
            # we need to pad head_size 192 to 256
            return 256
306
307
        if hasattr(self.hf_text_config, "head_dim"):
            return self.hf_text_config.head_dim
308
        # FIXME(woosuk): This may not be true for all models.
309
310
        return (self.hf_text_config.hidden_size //
                self.hf_text_config.num_attention_heads)
311

312
313
    def get_total_num_kv_heads(self) -> int:
        """Returns the total number of KV heads."""
Zhuohan Li's avatar
Zhuohan Li committed
314
        # For GPTBigCode & Falcon:
315
        # NOTE: for falcon, when new_decoder_architecture is True, the
Zhuohan Li's avatar
Zhuohan Li committed
316
317
        # multi_query flag is ignored and we use n_head_kv for the number of
        # KV heads.
318
        falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"]
319
        new_decoder_arch_falcon = (
320
            self.hf_config.model_type in falcon_model_types
321
            and getattr(self.hf_config, "new_decoder_architecture", False))
322
        if not new_decoder_arch_falcon and getattr(self.hf_text_config,
323
                                                   "multi_query", False):
Zhuohan Li's avatar
Zhuohan Li committed
324
            # Multi-query attention, only one KV head.
Woosuk Kwon's avatar
Woosuk Kwon committed
325
            # Currently, tensor parallelism is not supported in this case.
Zhuohan Li's avatar
Zhuohan Li committed
326
            return 1
327

328
        # For DBRX and MPT
329
330
331
332
333
        if self.hf_config.model_type == "mpt":
            if "kv_n_heads" in self.hf_config.attn_config:
                return self.hf_config.attn_config["kv_n_heads"]
            return self.hf_config.num_attention_heads
        if self.hf_config.model_type == "dbrx":
334
335
336
            return getattr(self.hf_config.attn_config, "kv_n_heads",
                           self.hf_config.num_attention_heads)

337
338
339
340
341
342
343
344
345
346
        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:
347
            num_kv_heads = getattr(self.hf_text_config, attr, None)
348
349
350
351
352
            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.
353
        return self.hf_text_config.num_attention_heads
354
355
356
357
358
359
360
361
362
363

    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)
364

365
366
    def get_num_attention_heads(self,
                                parallel_config: "ParallelConfig") -> int:
367
368
        num_heads = getattr(self.hf_text_config, "num_attention_heads", 0)
        return num_heads // parallel_config.tensor_parallel_size
369

370
    def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
371
        total_num_hidden_layers = self.hf_text_config.num_hidden_layers
372
373
374
375
        return total_num_hidden_layers // parallel_config.pipeline_parallel_size


class CacheConfig:
376
377
378
379
380
    """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
381
            vLLM execution.
382
        swap_space: Size of the CPU swap space per GPU (in GiB).
383
        cache_dtype: Data type for kv cache storage.
384
        num_gpu_blocks_override: Number of GPU blocks to use. This overrides the
385
            profiled num_gpu_blocks if specified. Does nothing if None.
386
    """
387

388
389
390
391
392
    def __init__(
        self,
        block_size: int,
        gpu_memory_utilization: float,
        swap_space: int,
393
        cache_dtype: str,
394
        num_gpu_blocks_override: Optional[int] = None,
395
        sliding_window: Optional[int] = None,
396
        enable_prefix_caching: bool = False,
397
398
399
    ) -> None:
        self.block_size = block_size
        self.gpu_memory_utilization = gpu_memory_utilization
400
        self.swap_space_bytes = swap_space * _GB
401
        self.num_gpu_blocks_override = num_gpu_blocks_override
402
        self.cache_dtype = cache_dtype
403
        self.sliding_window = sliding_window
404
        self.enable_prefix_caching = enable_prefix_caching
405
        self._verify_args()
406
        self._verify_cache_dtype()
407
        self._verify_prefix_caching()
408
409
410
411
412

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

413
    def metrics_info(self):
414
415
        # convert cache_config to dict(key: str, value: str) for prometheus
        # metrics info
416
417
        return {key: str(value) for key, value in self.__dict__.items()}

418
419
420
421
422
423
    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}.")

424
425
426
    def _verify_cache_dtype(self) -> None:
        if self.cache_dtype == "auto":
            pass
427
        elif self.cache_dtype in ("fp8", "fp8_e4m3", "fp8_e5m2"):
428
            logger.info(
429
430
                "Using fp8 data type to store kv cache. It reduces the GPU "
                "memory footprint and boosts the performance. "
431
432
                "Meanwhile, it may cause accuracy drop without a proper "
                "scaling factor")
433
434
435
        else:
            raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}")

436
437
438
439
440
441
442
443
444
445
446
447
448
    def _verify_prefix_caching(self) -> None:
        if not self.enable_prefix_caching:
            return

        if self.sliding_window is not None:
            raise NotImplementedError(
                "Prefix caching is not supported with sliding window. "
                "Run with --disable-sliding-window to use prefix caching.")
        if self.cache_dtype == "fp8":
            raise NotImplementedError(
                "Prefix caching is not supported for fp8 cache_dtype. "
                "Run with --kv-cache-dtype auto to use prefix caching.")

449
450
451
452
453
454
455
456
457
458
    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

459
460
461
        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.")
462
463
464
        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:
465
            logger.warning("Possibly too large swap space. %s", msg)
466

467

468
469
470
@dataclass
class TokenizerPoolConfig:
    """Configuration for the tokenizer pool.
471

472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
    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.
495

496
        If tokenizer_pool_size is 0, return None.
497

498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
        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


520
521
522
523
524
525
526
class LoadFormat(str, enum.Enum):
    AUTO = "auto"
    PT = "pt"
    SAFETENSORS = "safetensors"
    NPCACHE = "npcache"
    DUMMY = "dummy"
    TENSORIZER = "tensorizer"
527
    SHARDED_STATE = "sharded_state"
528
    BITSANDBYTES = "bitsandbytes"
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580


@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}")


581
class ParallelConfig:
582
583
584
585
586
    """Configuration for the distributed execution.

    Args:
        pipeline_parallel_size: Number of pipeline parallel groups.
        tensor_parallel_size: Number of tensor parallel groups.
587
        worker_use_ray: Deprecated, use distributed_executor_backend instead.
zspo's avatar
zspo committed
588
589
590
        max_parallel_loading_workers: Maximum number of multiple batches
            when load model sequentially. To avoid RAM OOM when using tensor
            parallel and large models.
591
592
        disable_custom_all_reduce: Disable the custom all-reduce kernel and
            fall back to NCCL.
593
594
        tokenizer_pool_config: Config for the tokenizer pool.
            If None, will use synchronous tokenization.
595
596
        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.
597
        placement_group: ray distributed model workers placement group.
598
599
600
601
        distributed_executor_backend: Backend to use for distributed model
            workers, either "ray" or "mp" (multiprocessing). If either
            pipeline_parallel_size or tensor_parallel_size is greater than 1,
            will default to "ray" if Ray is installed or "mp" otherwise.
602
    """
603

604
605
606
607
    def __init__(
        self,
        pipeline_parallel_size: int,
        tensor_parallel_size: int,
608
        worker_use_ray: Optional[bool] = None,
609
        max_parallel_loading_workers: Optional[int] = None,
610
        disable_custom_all_reduce: bool = False,
611
        tokenizer_pool_config: Optional[TokenizerPoolConfig] = None,
612
        ray_workers_use_nsight: bool = False,
613
        placement_group: Optional["PlacementGroup"] = None,
614
        distributed_executor_backend: Optional[str] = None,
615
616
    ) -> None:
        self.pipeline_parallel_size = pipeline_parallel_size
617
        self.tensor_parallel_size = tensor_parallel_size
618
        self.distributed_executor_backend = distributed_executor_backend
619
        self.max_parallel_loading_workers = max_parallel_loading_workers
620
        self.disable_custom_all_reduce = disable_custom_all_reduce
621
        self.tokenizer_pool_config = tokenizer_pool_config
622
        self.ray_workers_use_nsight = ray_workers_use_nsight
623
        self.placement_group = placement_group
624

625
        self.world_size = pipeline_parallel_size * self.tensor_parallel_size
626
627
628
629
630
631
632
633
634
        if worker_use_ray:
            if self.distributed_executor_backend is None:
                self.distributed_executor_backend = "ray"
            elif self.distributed_executor_backend != "ray":
                raise ValueError(f"worker-use-ray can't be used with "
                                 f"distributed executor backend "
                                 f"'{self.distributed_executor_backend}'.")

        if self.distributed_executor_backend is None and self.world_size > 1:
635
636
637
            # We use multiprocessing by default if world_size fits on the
            # current node and we aren't in a ray placement group.

638
            from vllm.executor import ray_utils
639
            backend = "mp"
640
            ray_found = ray_utils.ray is not None
641
            if cuda_device_count_stateless() < self.world_size:
642
643
644
645
646
                if not ray_found:
                    raise ValueError("Unable to load Ray which is "
                                     "required for multi-node inference")
                backend = "ray"
            elif ray_found:
647
                if self.placement_group:
648
                    backend = "ray"
649
650
651
652
653
654
                else:
                    from ray import is_initialized as ray_is_initialized
                    if ray_is_initialized():
                        from ray.util import get_current_placement_group
                        if get_current_placement_group():
                            backend = "ray"
655
656
657
            self.distributed_executor_backend = backend
            logger.info("Defaulting to use %s for distributed inference",
                        backend)
658
659
660
661
662
663
        # If CUDA_VISIBLE_DEVICES is set on ROCm prior to vLLM init,
        # propagate changes to HIP_VISIBLE_DEVICES (conversion handled by
        # the update_environment_variables function)
        if is_hip() and envs.CUDA_VISIBLE_DEVICES:
            update_environment_variables(
                {"CUDA_VISIBLE_DEVICES": envs.CUDA_VISIBLE_DEVICES})
664

665
666
667
668
669
670
        self._verify_args()

    def _verify_args(self) -> None:
        if self.pipeline_parallel_size > 1:
            raise NotImplementedError(
                "Pipeline parallelism is not supported yet.")
671
672
673
674
        if self.distributed_executor_backend not in ("ray", "mp", None):
            raise ValueError(
                "Unrecognized distributed executor backend. Supported values "
                "are 'ray' or 'mp'.")
675
676
677
678
679
680
681
682
683
684
685
        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.")
686
687
        if self.ray_workers_use_nsight and (
                not self.distributed_executor_backend == "ray"):
688
689
            raise ValueError("Unable to use nsight profiling unless workers "
                             "run with Ray.")
690

691
692

class SchedulerConfig:
693
694
695
696
697
698
699
    """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
700
        max_model_len: Maximum length of a sequence (including prompt
Lily Liu's avatar
Lily Liu committed
701
            and generated text).
702
703
704
705
706
        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.
707
708
        delay_factor: Apply a delay (of delay factor multiplied by previous
            prompt latency) before scheduling next prompt.
709
710
        enable_chunked_prefill: If True, prefill requests can be chunked based
            on the remaining max_num_batched_tokens.
711
        embedding_mode: Whether the running model is for embedding.
712
713
714
715
716
717
        preemption_mode: Whether to perform preemption by swapping or 
            recomputation. If not specified, we determine the mode as follows:
            We use recomputation by default since it incurs lower overhead than
            swapping. However, when the sequence group has multiple sequences
            (e.g., beam search), recomputation is not currently supported. In
            such a case, we use swapping instead.
718
    """
719

720
721
722
723
724
725
726
727
728
729
    def __init__(self,
                 max_num_batched_tokens: Optional[int],
                 max_num_seqs: int,
                 max_model_len: int,
                 use_v2_block_manager: bool = False,
                 num_lookahead_slots: int = 0,
                 delay_factor: float = 0.0,
                 enable_chunked_prefill: bool = False,
                 embedding_mode: Optional[bool] = False,
                 preemption_mode: Optional[str] = None) -> None:
730
731
732
        if max_num_batched_tokens is not None:
            self.max_num_batched_tokens = max_num_batched_tokens
        else:
733
            if enable_chunked_prefill:
734
735
736
                # It is the values that have the best balance between ITL
                # and TTFT on A100. Note it is not optimized for throughput.
                self.max_num_batched_tokens = 512
737
738
739
740
            elif embedding_mode:
                # For embedding, choose specific value for higher throughput
                self.max_num_batched_tokens = max(
                    max_model_len, _EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS)
741
742
743
744
745
746
747
            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).")

748
        self.max_num_seqs = max_num_seqs
Lily Liu's avatar
Lily Liu committed
749
        self.max_model_len = max_model_len
750
        self.use_v2_block_manager = use_v2_block_manager
751
752
        self.num_lookahead_slots = num_lookahead_slots
        self.delay_factor = delay_factor
753
        self.chunked_prefill_enabled = enable_chunked_prefill
754
        self.embedding_mode = embedding_mode
755
        self.preemption_mode = preemption_mode
756
757
758
        self._verify_args()

    def _verify_args(self) -> None:
759
760
        if (self.max_num_batched_tokens < self.max_model_len
                and not self.chunked_prefill_enabled):
761
762
763
764
765
766
767
            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.")
768

769
770
771
772
773
        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}).")
774

775
776
777
778
779
780
        if self.num_lookahead_slots < 0:
            raise ValueError(
                "num_lookahead_slots "
                f"({self.num_lookahead_slots}) must be greater than or "
                "equal to 0.")

781

782
783
class DeviceConfig:

784
785
786
    def __init__(self, device: str = "auto") -> None:
        if device == "auto":
            # Automated device type detection
787
            if is_neuron():
788
                self.device_type = "neuron"
789
790
            elif is_openvino():
                self.device_type = "openvino"
791
792
            elif is_tpu():
                self.device_type = "tpu"
793
794
            elif is_cpu():
                self.device_type = "cpu"
795
796
            elif is_xpu():
                self.device_type = "xpu"
797
            else:
798
799
800
                # We don't call torch.cuda.is_available() here to
                # avoid initializing CUDA before workers are forked
                self.device_type = "cuda"
801
802
803
804
805
        else:
            # Device type is assigned explicitly
            self.device_type = device

        # Some device types require processing inputs on CPU
806
        if self.device_type in ["neuron", "openvino"]:
807
            self.device = torch.device("cpu")
808
809
        elif self.device_type in ["tpu"]:
            self.device = None
810
811
812
813
        else:
            # Set device with device type
            self.device = torch.device(self.device_type)

814

815
816
817
818
819
820
821
822
823
824
825
826
827
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],
828
        speculative_draft_tensor_parallel_size: Optional[int],
829
        num_speculative_tokens: Optional[int],
830
831
832
        speculative_max_model_len: Optional[int],
        enable_chunked_prefill: bool,
        use_v2_block_manager: bool,
833
        speculative_disable_by_batch_size: Optional[int],
834
835
        ngram_prompt_lookup_max: Optional[int],
        ngram_prompt_lookup_min: Optional[int],
836
837
838
        draft_token_acceptance_method: str,
        typical_acceptance_sampler_posterior_threshold: Optional[float],
        typical_acceptance_sampler_posterior_alpha: Optional[float],
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
    ) -> 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.
854
855
            speculative_draft_tensor_parallel_size (Optional[int]): The degree
                of the tensor parallelism for the draft model.
856
            num_speculative_tokens (Optional[int]): The number of speculative
857
858
                tokens, if provided. Will default to the number in the draft
                model config if present, otherwise is required.
859
860
861
862
863
864
865
866
867
            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.
868
869
870
            speculative_disable_by_batch_size (Optional[int]): Disable
                speculative decoding for new incoming requests when the number
                of enqueue requests  is larger than this value, if provided.
871
872
873
874
            ngram_prompt_lookup_max (Optional[int]): Max size of ngram token
                window, if provided.
            ngram_prompt_lookup_min (Optional[int]): Min size of ngram token
                window, if provided.
875
876
877
878
879
880
881
882
883
884
885
886
887
888
            draft_token_acceptance_method (str): The method to use for
                accepting draft tokens. This can take two possible
                values 'rejection_sampler' and 'typical_acceptance_sampler'
                for RejectionSampler and TypicalAcceptanceSampler
                respectively.
            typical_acceptance_sampler_posterior_threshold (Optional[float]):
                A threshold value that sets a lower bound on the posterior
                probability of a token in the target model for it to be
                accepted. This threshold is used only when we use the 
                TypicalAcceptanceSampler for token acceptance.
            typical_acceptance_sampler_posterior_alpha (Optional[float]):
                A scaling factor for the entropy-based threshold in the
                TypicalAcceptanceSampler.
    
889
890
891
892
893
        Returns:
            Optional["SpeculativeConfig"]: An instance of SpeculativeConfig if
                the necessary conditions are met, else None.
        """

894
895
896
897
        if speculative_model is None:
            if num_speculative_tokens is not None:
                raise ValueError("num_speculative_tokens was provided without "
                                 "speculative_model.")
898
899
            return None

900
901
902
903
904
905
        if (speculative_disable_by_batch_size is not None
                and speculative_disable_by_batch_size < 2):
            raise ValueError("Expect the batch size threshold of disabling "
                             "speculative decoding is > 1, but got "
                             f"{speculative_disable_by_batch_size=}")

906
907
908
909
910
911
912
913
914
915
        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.")

916
917
918
919
920
921
        # 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

922
923
        if speculative_model == "[ngram]":
            if ngram_prompt_lookup_min is None:
924
925
926
927
928
929
930
931
                ngram_prompt_lookup_min = 1
            if ngram_prompt_lookup_max is None or ngram_prompt_lookup_max < 1:
                raise ValueError(f"{ngram_prompt_lookup_max=} must be > 0")
            if ngram_prompt_lookup_min < 1:
                raise ValueError(f"{ngram_prompt_lookup_min=} must be > 0")
            if ngram_prompt_lookup_min > ngram_prompt_lookup_max:
                raise ValueError(f"{ngram_prompt_lookup_min=} cannot be "
                                 f"larger than {ngram_prompt_lookup_max=}")
932

933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
            # 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.
            draft_model_config = target_model_config
            draft_parallel_config = target_parallel_config
        else:
            ngram_prompt_lookup_max = 0
            ngram_prompt_lookup_min = 0
            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,
                max_model_len=None,
                quantization=draft_quantization,
                enforce_eager=target_model_config.enforce_eager,
954
955
                max_seq_len_to_capture=target_model_config.
                max_seq_len_to_capture,
956
957
958
                max_logprobs=target_model_config.max_logprobs,
            )

959
            draft_hf_config = draft_model_config.hf_config
960

961
962
963
964
965
            if (num_speculative_tokens is not None
                    and hasattr(draft_hf_config, "num_lookahead_tokens")):
                draft_hf_config.num_lookahead_tokens = num_speculative_tokens

            n_predict = getattr(draft_hf_config, "n_predict", None)
966
967
968
969
970
971
972
973
            if n_predict is not None:
                if num_speculative_tokens is None:
                    # Default to max value defined in draft model config.
                    num_speculative_tokens = n_predict
                elif num_speculative_tokens > n_predict:
                    # Verify provided value doesn't exceed the maximum
                    # supported by the draft model.
                    raise ValueError(
974
975
976
                        "This speculative model supports a maximum of "
                        f"num_speculative_tokens={n_predict}, but "
                        f"{num_speculative_tokens=} was provided.")
977

978
979
980
981
982
983
984
985
986
            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,
                ))

            draft_parallel_config = (
                SpeculativeConfig.create_draft_parallel_config(
987
988
                    target_parallel_config,
                    speculative_draft_tensor_parallel_size))
989

990
991
992
993
994
995
        if num_speculative_tokens is None:
            raise ValueError(
                "num_speculative_tokens must be provided with "
                "speculative_model unless the draft model config contains an "
                "n_predict parameter.")

996
997
998
999
1000
        if typical_acceptance_sampler_posterior_threshold is None:
            typical_acceptance_sampler_posterior_threshold = 0.09
        if typical_acceptance_sampler_posterior_alpha is None:
            typical_acceptance_sampler_posterior_alpha = 0.3

1001
1002
1003
1004
        return SpeculativeConfig(
            draft_model_config,
            draft_parallel_config,
            num_speculative_tokens,
1005
            speculative_disable_by_batch_size,
1006
1007
            ngram_prompt_lookup_max,
            ngram_prompt_lookup_min,
1008
1009
1010
1011
1012
            draft_token_acceptance_method=draft_token_acceptance_method,
            typical_acceptance_sampler_posterior_threshold=\
                typical_acceptance_sampler_posterior_threshold,
            typical_acceptance_sampler_posterior_alpha=\
                typical_acceptance_sampler_posterior_alpha,
1013
1014
        )

1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
    @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,
        )

1050
1051
    @staticmethod
    def create_draft_parallel_config(
1052
1053
1054
        target_parallel_config: ParallelConfig,
        speculative_draft_tensor_parallel_size: Optional[int]
    ) -> ParallelConfig:
1055
1056
        """Create a parallel config for use by the draft worker.

1057
        This is mostly a copy of the target parallel config, except the tp_size.
1058
        """
1059
1060
1061
1062
1063
1064
1065
1066
1067
        if speculative_draft_tensor_parallel_size is None:
            speculative_draft_tensor_parallel_size = \
                  target_parallel_config.tensor_parallel_size
        elif speculative_draft_tensor_parallel_size != 1:
            # TODO(wooyeon): allow tp values larger than 1
            raise ValueError(
                f"{speculative_draft_tensor_parallel_size=} cannot be"
                f"other value than 1")

1068
1069
1070
        draft_parallel_config = ParallelConfig(
            pipeline_parallel_size=target_parallel_config.
            pipeline_parallel_size,
1071
            tensor_parallel_size=speculative_draft_tensor_parallel_size,
1072
1073
            distributed_executor_backend=target_parallel_config.
            distributed_executor_backend,
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
            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,
1091
1092
1093
        speculative_disable_by_batch_size: Optional[int],
        ngram_prompt_lookup_max: Optional[int],
        ngram_prompt_lookup_min: Optional[int],
1094
1095
1096
        draft_token_acceptance_method: str,
        typical_acceptance_sampler_posterior_threshold: float,
        typical_acceptance_sampler_posterior_alpha: float,
1097
1098
1099
1100
1101
1102
1103
1104
    ):
        """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.
1105
1106
1107
1108
1109
            speculative_disable_by_batch_size: Disable speculative
                decoding for new incoming requests when the number of
                enqueue requests is larger than this value.
            ngram_prompt_lookup_max: Max size of ngram token window.
            ngram_prompt_lookup_min: Min size of ngram token window.
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
            draft_token_acceptance_method (str): The method to use for
                accepting draft tokens. This can take two possible
                values 'rejection_sampler' and 'typical_acceptance_sampler'
                for RejectionSampler and TypicalAcceptanceSampler
                respectively.
            typical_acceptance_sampler_posterior_threshold (Optional[float]):
                A threshold value that sets a lower bound on the posterior
                probability of a token in the target model for it to be
                accepted. This threshold is used only when we use the 
                TypicalAcceptanceSampler for token acceptance.
            typical_acceptance_sampler_posterior_alpha (Optional[float]):
                A scaling factor for the entropy-based threshold in the
                TypicalAcceptanceSampler.
1123
1124
1125
1126
        """
        self.draft_model_config = draft_model_config
        self.draft_parallel_config = draft_parallel_config
        self.num_speculative_tokens = num_speculative_tokens
1127
1128
1129
1130
        self.speculative_disable_by_batch_size = \
            speculative_disable_by_batch_size
        self.ngram_prompt_lookup_max = ngram_prompt_lookup_max or 0
        self.ngram_prompt_lookup_min = ngram_prompt_lookup_min or 0
1131
1132
1133
1134
1135
        self.draft_token_acceptance_method = draft_token_acceptance_method
        self.typical_acceptance_sampler_posterior_threshold = \
            typical_acceptance_sampler_posterior_threshold
        self.typical_acceptance_sampler_posterior_alpha = \
            typical_acceptance_sampler_posterior_alpha
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146

        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)
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
            # Validate and set draft token acceptance related settings.

        if (self.draft_token_acceptance_method is None):
            raise ValueError("draft_token_acceptance_method is not set. "
                             "Expected values are rejection_sampler or "
                             "typical_acceptance_sampler.")

        if (self.draft_token_acceptance_method != 'rejection_sampler'
                and self.draft_token_acceptance_method !=
                'typical_acceptance_sampler'):
            raise ValueError(
                "Expected draft_token_acceptance_method to be either "
                "rejection_sampler or typical_acceptance_sampler. Instead it "
                f"is {self.draft_token_acceptance_method}")

        if (self.typical_acceptance_sampler_posterior_threshold < 0
                or self.typical_acceptance_sampler_posterior_alpha < 0):
            raise ValueError(
                "Expected typical_acceptance_sampler_posterior_threshold "
                "and typical_acceptance_sampler_posterior_alpha to be > 0. "
                "Instead found "
                f"typical_acceptance_sampler_posterior_threshold = "
                f"{self.typical_acceptance_sampler_posterior_threshold} and "
                f"typical_acceptance_sampler_posterior_alpha = "
                f"{self.typical_acceptance_sampler_posterior_alpha}")
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183

    @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:
1184
1185
1186
1187
        if self.ngram_prompt_lookup_max > 0:
            draft_model = "[ngram]"
        else:
            draft_model = self.draft_model_config.model
1188
1189
1190
1191
        num_spec_tokens = self.num_speculative_tokens
        return f"SpeculativeConfig({draft_model=}, {num_spec_tokens=})"


1192
1193
1194
1195
@dataclass
class LoRAConfig:
    max_lora_rank: int
    max_loras: int
1196
    fully_sharded_loras: bool = False
1197
1198
1199
1200
1201
    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
1202
    long_lora_scaling_factors: Optional[Tuple[float]] = None
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222

    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
1223
                f"max_loras ({self.max_loras})")
1224
1225
1226
1227
1228
1229

    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)
1230
1231
1232
1233
        if model_config.quantization and model_config.quantization not in [
                "awq", "gptq"
        ]:
            # TODO support marlin and squeezellm
1234
1235
            logger.warning("%s quantization is not tested with LoRA yet.",
                           model_config.quantization)
1236
1237
1238
1239
1240
1241
1242

    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.")
1243
1244
        if scheduler_config.chunked_prefill_enabled:
            raise ValueError("LoRA is not supported with chunked prefill yet.")
1245
1246


1247
# TODO: To be replaced by MultiModalConfig.
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
@dataclass
class VisionLanguageConfig:
    """Configs the input data format and how models should run for
    vision language models."""
    # 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

1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
    #TODO(ywang96): make this a cached property once we refactor the
    # VisionLanguageConfig class.
    def get_image_token_text(
            self, tokenizer: PreTrainedTokenizerBase) -> Tuple[str, str]:
        """Get the image token placeholder text to be inserted into the 
        text prompt and the string representation of the image token id.
        """
        image_token_str = tokenizer.decode(self.image_token_id)
        return image_token_str * self.image_feature_size, image_token_str

1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
    def as_cli_args_dict(self) -> Dict[str, Any]:
        """Flatten vision language config to pure args.

        Compatible with what llm entrypoint expects.
        """
        result: Dict[str, Any] = {}
        for f in fields(self):
            value = getattr(self, f.name)
            if isinstance(value, enum.Enum):
                result[f.name] = value.name.lower()
            elif isinstance(value, tuple):
                result[f.name] = ",".join([str(item) for item in value])
            else:
                result[f.name] = value

        return result

1287

1288
1289
1290
1291
1292
1293
1294
1295
_STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.float16,
    "float16": torch.float16,
    "float": torch.float32,
    "float32": torch.float32,
    "bfloat16": torch.bfloat16,
}

1296
_ROCM_NOT_SUPPORTED_DTYPE: List[str] = []  #
1297

1298
1299
1300

def _get_and_verify_dtype(
    config: PretrainedConfig,
1301
    dtype: Union[str, torch.dtype],
1302
1303
1304
1305
1306
1307
1308
) -> 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

1309
1310
1311
1312
    if isinstance(dtype, str):
        dtype = dtype.lower()
        if dtype == "auto":
            if config_dtype == torch.float32:
Woosuk Kwon's avatar
Woosuk Kwon committed
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
                if config.model_type == "gemma2":
                    logger.info(
                        "For Gemma 2, we downcast float32 to bfloat16 instead "
                        "of float16 by default. Please specify `dtype` if you "
                        "want to use float16.")
                    torch_dtype = torch.bfloat16
                else:
                    # Following the common practice, we use float16 for float32
                    # models.
                    torch_dtype = torch.float16
1323
1324
            else:
                torch_dtype = config_dtype
1325
        else:
1326
1327
1328
1329
1330
            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
1331
    else:
1332
        raise ValueError(f"Unknown dtype: {dtype}")
1333
1334
1335
1336
1337

    # Verify the dtype.
    if torch_dtype != config_dtype:
        if torch_dtype == torch.float32:
            # Upcasting to float32 is allowed.
1338
            logger.info("Upcasting %s to %s.", config_dtype, torch_dtype)
1339
1340
1341
            pass
        elif config_dtype == torch.float32:
            # Downcasting from float32 to float16 or bfloat16 is allowed.
1342
            logger.info("Downcasting %s to %s.", config_dtype, torch_dtype)
1343
1344
            pass
        else:
Woosuk Kwon's avatar
Woosuk Kwon committed
1345
            # Casting between float16 and bfloat16 is allowed with a warning.
1346
            logger.warning("Casting %s to %s.", config_dtype, torch_dtype)
1347
1348

    return torch_dtype
1349
1350
1351
1352
1353


def _get_and_verify_max_len(
    hf_config: PretrainedConfig,
    max_model_len: Optional[int],
1354
1355
    disable_sliding_window: bool,
    sliding_window_len: Optional[int],
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
) -> 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",
1366
1367
        # ChatGLM2
        "seq_length",
1368
1369
        # Command-R
        "model_max_length",
1370
1371
1372
1373
1374
        # Others
        "max_sequence_length",
        "max_seq_length",
        "seq_len",
    ]
1375
    # Choose the smallest "max_length" from the possible keys.
1376
    max_len_key = None
1377
    for key in possible_keys:
1378
1379
1380
1381
1382
        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)
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392

    # If sliding window is manually disabled, max_length should be less
    # than the sliding window length in the model config.
    if disable_sliding_window and sliding_window_len is not None:
        max_len_key = "sliding_window" \
            if sliding_window_len < derived_max_model_len else max_len_key
        derived_max_model_len = min(derived_max_model_len, sliding_window_len)

    # If none of the keys were found in the config, use a default and
    # log a warning.
1393
    if derived_max_model_len == float("inf"):
1394
1395
1396
1397
1398
1399
1400
1401
        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: "
1402
            "%s. Assuming the model's maximum length is %d.", possible_keys,
1403
            default_max_len)
1404
        derived_max_model_len = default_max_len
1405

1406
    rope_scaling = getattr(hf_config, "rope_scaling", None)
1407
1408
1409
1410
    # The correct one should be "longrope", kept "su" here
    # to be backward compatible
    if rope_scaling is not None and rope_scaling["type"] != "su" \
        and rope_scaling["type"] != "longrope":
1411
1412
1413
1414
1415
1416
1417
        if disable_sliding_window:
            # TODO(robertgshaw): Find a model that supports rope_scaling
            # with sliding window to see if this case should be allowed.
            raise NotImplementedError(
                "Disabling sliding window is not supported for models "
                "with rope_scaling. Please raise an issue so we can "
                "investigate.")
1418
1419
        assert "factor" in rope_scaling
        scaling_factor = rope_scaling["factor"]
Antoni Baum's avatar
Antoni Baum committed
1420
1421
1422
        if rope_scaling["type"] == "yarn":
            derived_max_model_len = rope_scaling[
                "original_max_position_embeddings"]
1423
1424
        derived_max_model_len *= scaling_factor

1425
1426
    # If the user specified a max length, make sure it is smaller than the
    # derived length from the HF model config.
1427
    if max_model_len is None:
1428
        max_model_len = int(derived_max_model_len)
1429
    elif max_model_len > derived_max_model_len:
1430
1431
1432
1433
1434
        # 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:
1435
1436
1437
1438
1439
1440
1441
            if disable_sliding_window:
                # TODO(robertgshaw): Find a model that has model_max_length
                # with sliding window to see if this case should be allowed.
                raise NotImplementedError(
                    "Disabling sliding window is not supported for models "
                    "model_max_length in the config. Please raise an issue "
                    "so we can investigate.")
1442
1443
1444
1445
1446
1447
1448
1449
1450
            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.")
1451
    return int(max_model_len)
1452
1453


1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
def get_served_model_name(model: str,
                          served_model_name: Optional[Union[str, List[str]]]):
    """
    If the input is a non-empty list, the first model_name in 
    `served_model_name` is taken. 
    If the input is a non-empty string, it is used directly. 
    For cases where the input is either an empty string or an 
    empty list, the fallback is to use `self.model`.
    """
    if not served_model_name:
        return model
    if isinstance(served_model_name, list):
        return served_model_name[0]
    return served_model_name


1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
@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}")


1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
@dataclass
class ObservabilityConfig:
    """Configuration for observability."""
    otlp_traces_endpoint: Optional[str] = None

    def __post_init__(self):
        if not is_otel_installed() and self.otlp_traces_endpoint is not None:
            raise ValueError("OpenTelemetry packages must be installed before "
                             "configuring 'otlp_traces_endpoint'")


1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
@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
1507
    load_config: LoadConfig
1508
1509
1510
    lora_config: Optional[LoRAConfig]
    vision_language_config: Optional[VisionLanguageConfig]
    speculative_config: Optional[SpeculativeConfig]
1511
    decoding_config: Optional[DecodingConfig]
1512
    observability_config: Optional[ObservabilityConfig]
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529

    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))