compilation.py 36 KB
Newer Older
1
2
3
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

4
import enum
5
6
import hashlib
from collections import Counter
7
from collections.abc import Callable
8
from dataclasses import asdict, field
9
from pathlib import Path
10
from typing import TYPE_CHECKING, Any, ClassVar
11

12
from pydantic import TypeAdapter, field_validator
13
14
15
16
17
from pydantic.dataclasses import dataclass

from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass
from vllm.config.utils import config
from vllm.logger import init_logger
18
from vllm.platforms import current_platform
19
20
21
from vllm.utils import is_torch_equal_or_newer, resolve_obj_by_qualname

if TYPE_CHECKING:
22
    from vllm.config import VllmConfig
23
24
25
26
27
28
else:
    VllmConfig = object

logger = init_logger(__name__)


29
30
31
32
33
34
35
36
37
38
39
40
41
42
class CompilationMode:
    """The compilation approach used for torch.compile-based compilation of the
    model."""

    NONE = 0
    """No torch.compile compilation is applied, model runs in fully eager pytorch mode.
    The model runs as-is."""
    STOCK_TORCH_COMPILE = 1
    """The standard `torch.compile` compilation pipeline."""
    DYNAMO_TRACE_ONCE = 2
    """Single Dynamo trace through the model, avoiding recompilation."""
    VLLM_COMPILE = 3
    """Custom vLLM Inductor-based backend with caching, piecewise compilation,
    shape specialization, and custom passes."""
43
44


45
class CUDAGraphMode(enum.Enum):
46
    """Constants for the cudagraph mode in CompilationConfig.
47
48
49
    Meanwhile, the subset enum `NONE`, `PIECEWISE` and `FULL` are also
    treated as concrete runtime mode for cudagraph runtime dispatching.
    """
50

51
52
53
54
55
56
    NONE = 0
    PIECEWISE = 1
    FULL = 2
    FULL_DECODE_ONLY = (FULL, NONE)
    FULL_AND_PIECEWISE = (FULL, PIECEWISE)

57
58
    def decode_mode(self) -> "CUDAGraphMode":
        return CUDAGraphMode(self.value[0]) if self.separate_routine() else self
59

60
61
    def mixed_mode(self) -> "CUDAGraphMode":
        return CUDAGraphMode(self.value[1]) if self.separate_routine() else self
62

63
64
65
66
67
68
    def has_mode(self, mode: "CUDAGraphMode") -> bool:
        assert not mode.separate_routine()
        if self.separate_routine():
            return mode.value in self.value
        return self == mode

69
    def requires_piecewise_compilation(self) -> bool:
70
        return self.has_mode(CUDAGraphMode.PIECEWISE)
71

72
73
    def max_cudagraph_mode(self) -> "CUDAGraphMode":
        return CUDAGraphMode(max(self.value)) if self.separate_routine() else self
74
75
76
77

    def has_full_cudagraphs(self) -> bool:
        return self.max_cudagraph_mode() == CUDAGraphMode.FULL

78
79
80
    def has_piecewise_cudagraphs(self) -> bool:
        return self.requires_piecewise_compilation()

81
82
83
    def separate_routine(self) -> bool:
        return isinstance(self.value, tuple)

84
    def valid_runtime_modes(self) -> bool:
85
        return self in [CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL]
86

87
88
89
    def __str__(self) -> str:
        return self.name

90

91
92
93
94
95
96
97
98
99
@config
@dataclass
class PassConfig:
    """Configuration for custom Inductor passes.

    This is separate from general `CompilationConfig` so that inductor passes
    don't all have access to full configuration - that would create a cycle as
    the `PassManager` is set as a property of config."""

100
    enable_fusion: bool = False
101
102
103
    """Whether to enable the custom fusion (RMSNorm/SiluMul+quant) pass."""
    enable_attn_fusion: bool = False
    """Whether to enable the custom attention+quant fusion pass."""
104
    enable_noop: bool = False
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
    """Whether to enable the custom no-op elimination pass."""
    enable_sequence_parallelism: bool = False
    """Whether to enable sequence parallelism."""
    enable_async_tp: bool = False
    """Whether to enable async TP."""
    enable_fi_allreduce_fusion: bool = False
    """Whether to enable flashinfer allreduce fusion."""
    fi_allreduce_fusion_max_token_num: int = 16384
    """Max number of tokens to used in flashinfer allreduce fusion."""

    # TODO(luka) better pass enabling system.

    def uuid(self):
        """
        Produces a hash unique to the pass configuration.
        Any new fields that affect compilation should be added to the hash.
        Any future fields that don't affect compilation should be excluded.
        """
        return InductorPass.hash_dict(asdict(self))

    def __post_init__(self) -> None:
        if not self.enable_noop:
            if self.enable_fusion:
                logger.warning_once(
                    "Fusion enabled but reshape elimination disabled. "
130
131
                    "RMSNorm/SiluMul + quant (fp8) fusion might not work"
                )
132
133
134
            if self.enable_attn_fusion:
                logger.warning_once(
                    "Fusion enabled but reshape elimination disabled. "
135
136
                    "Attention + quant (fp8) fusion might not work"
                )
137
138
139
140
141
142
143
144


@config
@dataclass
class CompilationConfig:
    """Configuration for compilation. It has three parts:

    - Top-level Compilation control:
145
        - [`mode`][vllm.config.CompilationConfig.mode]
146
147
148
149
150
151
152
        - [`debug_dump_path`][vllm.config.CompilationConfig.debug_dump_path]
        - [`cache_dir`][vllm.config.CompilationConfig.cache_dir]
        - [`backend`][vllm.config.CompilationConfig.backend]
        - [`custom_ops`][vllm.config.CompilationConfig.custom_ops]
        - [`splitting_ops`][vllm.config.CompilationConfig.splitting_ops]
    - CudaGraph capture:
        - [`use_cudagraph`][vllm.config.CompilationConfig.use_cudagraph]
153
        - [`cudagraph_mode`][vllm.config.CompilationConfig.cudagraph_mode]
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
        - [`cudagraph_capture_sizes`]
        [vllm.config.CompilationConfig.cudagraph_capture_sizes]
        - [`cudagraph_num_of_warmups`]
        [vllm.config.CompilationConfig.cudagraph_num_of_warmups]
        - [`cudagraph_copy_inputs`]
        [vllm.config.CompilationConfig.cudagraph_copy_inputs]
        - [`full_cuda_graph`][vllm.config.CompilationConfig.full_cuda_graph]
    - Inductor compilation:
        - [`use_inductor`][vllm.config.CompilationConfig.use_inductor]
        - [`compile_sizes`][vllm.config.CompilationConfig.compile_sizes]
        - [`inductor_compile_config`]
        [vllm.config.CompilationConfig.inductor_compile_config]
        - [`inductor_passes`][vllm.config.CompilationConfig.inductor_passes]
        - custom inductor passes

    Why we have different sizes for cudagraph and inductor:
    - cudagraph: a cudagraph captured for a specific size can only be used
        for the same size. We need to capture all the sizes we want to use.
    - inductor: a graph compiled by inductor for a general shape can be used
        for different sizes. Inductor can also compile for specific sizes,
        where it can have more information to optimize the graph with fully
        static shapes. However, we find the general shape compilation is
        sufficient for most cases. It might be beneficial to compile for
        certain small batchsizes, where inductor is good at optimizing.
    """
179

180
    # Top-level Compilation control
181
    level: int | None = None
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
    """
    Level is deprecated and will be removed in the next release,
    either 0.12.0 or 0.11.2 whichever is soonest.
    Please use mode. Currently all levels are mapped to mode.
    """
    # Top-level Compilation control
    mode: int | None = None
    """The compilation approach used for torch.compile-based compilation of the
    model.

    - None: If None, we will select the default compilation mode.
      For V1 engine this is 3.
    - 0: NONE: No torch.compile compilation is applied, model runs in fully
         eager pytorch mode. The model runs as-is.
    - 1: STOCK_TORCH_COMPILE: The standard `torch.compile` compilation pipeline.
    - 2: DYNAMO_TRACE_ONCE: Single Dynamo trace through the model, avoiding
         recompilation by removing guards.
         Requires no dynamic-shape-dependent control-flow.
    - 3: VLLM_COMPILE: Custom vLLM Inductor-based backend with caching,
         piecewise compilation, shape specialization, and custom passes."""
202
    debug_dump_path: Path | None = None
203
204
205
206
207
    """The path to dump the debug information."""
    cache_dir: str = ""
    """The directory to store the compiled graph, to accelerate Inductor
    compilation. By default, it will use model-related information to generate
    a cache directory."""
208
    backend: str = ""
209
210
    """The backend for compilation. It needs to be a string:

211
212
    - "" (empty string): use the default backend ("inductor" on CUDA-alike
    platforms).
213
214
215
216
217
    - "eager"/"openxla"/...: use the specified backend registered in PyTorch.
    - "full.module.name": a qualified name which can be used to import the

    backend function.
    We use string to avoid serialization issues when using compilation in a
218
    distributed setting. When the compilation mode is 1 or 2, the backend is
219
    used for the compilation directly (it sees the whole graph). When the
220
    compilation mode is 3, the backend is used for the piecewise compilation
221
    (it sees a part of the graph). The backend can not be custom for compilation
222
    mode 3, i.e. the backend must be either eager or inductor. Furthermore,
223
    compilation is only piecewise if splitting ops is set accordingly and
224
    use_inductor_graph_partition is off. Note that the default options for
225
226
    splitting ops are sufficient for piecewise compilation.
    """
227
228
229
230
231
232
233
234
235
236
    custom_ops: list[str] = field(default_factory=list)
    """Fine-grained control over which custom ops to enable/disable. Use 'all'
    to enable all, 'none' to disable all. Also specify a list of custom op
    names to enable (prefixed with a '+'), or disable (prefixed with a '-').
    Examples:

    - 'all,-op1' to enable all except op1
    - 'none,+op1,+op2' to enable only op1 and op2

    By default, all custom ops are enabled when running without Inductor and
237
    disabled when running with Inductor: mode>=VLLM_COMPILE and use_inductor=True.
238
    Inductor generates (fused) Triton kernels for disabled custom ops."""
239
    splitting_ops: list[str] | None = None
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
    """A list of ops to exclude from cudagraphs, used in piecewise compilation.

    The behavior depends on use_inductor_graph_partition:

    - When use_inductor_graph_partition=False (default):
        These ops are used for Dynamo FX-level graph splitting. The graph is
        split at these ops before Inductor compilation, creating separate
        subgraphs for cudagraph capture.

    - When use_inductor_graph_partition=True:
        These ops are used to register Inductor partition rules. The graph
        partitioning happens at Inductor codegen time after all passes and
        fusions are finished, allowing compilation and custom passes to operate
        on the full graph while still excluding these ops from cudagraphs.

    If None, defaults to attention ops for piecewise cudagraphs.
    If empty list [], no ops are excluded (suitable for full cudagraphs)."""
257
258

    # Inductor capture
259
260
261
262
263
264
    use_inductor: bool | None = None
    """
    Whether to use inductor compilation.

    This flag is deprecated and will be removed in the next release 0.12.0.
    Please use the 'backend' option instead.
265
266
267
268
269
270
271

    - False: inductor compilation is not used. graph runs in eager
        (custom_ops enabled by default).
    - True: inductor compilation is used (custom_ops disabled by default).
        One graph for symbolic shape and one graph per size in compile_sizes
        are compiled using configurations in inductor_compile_config.

272
    This setting is ignored if mode<VLLM_COMPILE.
273
274
275
276

    For future compatibility:
    If use_inductor is True, backend="inductor" otherwise backend="eager".
    """
277
    compile_sizes: list[int | str] | None = None
278
279
280
281
282
283
284
285
286
287
288
289
290
291
    """Sizes to compile for inductor. In addition
    to integers, it also supports "cudagraph_capture_sizes" to
    specify the sizes for cudagraph capture."""
    inductor_compile_config: dict = field(default_factory=dict)
    """Additional configurations for inductor.
    - None: use default configurations."""
    inductor_passes: dict[str, str] = field(default_factory=dict)
    """Additional passes for inductor. It is a dictionary
    from pass name to pass function qualified name. We use function
    name because the config uses JSON format. If we pass the config
    from Python, functions can also be passed directly via Python object
    constructor, e.g. `CompilationConfig(inductor_passes={"a": func})`."""

    # CudaGraph compilation
292
    cudagraph_mode: CUDAGraphMode | None = None
293
    """
Harry Mellor's avatar
Harry Mellor committed
294
295
    The mode of the cudagraph:

296
    - NONE, no cudagraph capture.
297
    - PIECEWISE.
298
299
    - FULL.
    - FULL_DECODE_ONLY.
300
    - FULL_AND_PIECEWISE. (v1 default)
301
302

    PIECEWISE mode build piecewise cudagraph only, keeping the cudagraph
co63oc's avatar
co63oc committed
303
    incompatible ops (i.e. some attention ops) outside the cudagraph
304
305
306
307
308
309
310
311
312
313
314
315
316
    for general flexibility.

    FULL mode: Capture full cudagraph for all batches. Can be good for small
    models or workloads with small prompts; not supported by many backends.
    Generally for performance FULL_AND_PIECEWISE is better.
    
    FULL_DECODE_ONLY mode: Capture full cudagraph for decode batches only.
    Mixed prefill-decode batches are run without cudagraphs. Can be good for
    decode instances in a P/D setup where prefill is not as important so we
    can save some memory.
    
    FULL_AND_PIECEWISE mode: Capture full cudagraph for decode batches and
    piecewise cudagraph for prefill and mixed prefill-decode batches.
317
    This is the most performant mode for most models and is the default.
318
319
320
321

    Currently, the cudagraph mode is only used for the v1 engine.
    Note that the cudagraph logic is generally orthogonal to the 
    compilation logic. While piecewise cudagraphs require piecewise 
322
    compilation (mode=VLLM_COMPILE and non-empty splitting_ops), full
323
324
325
326
327
328
    cudagraphs are supported with and without compilation.
    
    Warning: This flag is new and subject to change in addition 
    more modes may be added.
    """
    use_cudagraph: bool = True
329
330
331
332
333
334
    """Whether to use cudagraph inside compilation.
    - False: cudagraph inside compilation is not used.
    - True: cudagraph inside compilation is used. It requires
        that all input buffers have fixed addresses, and all
        splitting ops write their outputs to input buffers.
    In the vLLM V1 Engine, this flag only applies for
335
    CompilationMode.VLLM_COMPILE (aka -O3).
336
337
    Note that this is orthogonal to the cudagraph capture logic
    outside of compilation.
338
    Warning: This flag is deprecated and will be removed in the next major or
339
340
    minor release, i.e. v0.11.0 or v1.0.0. Please use cudagraph_mode=PIECEWISE
    instead.
341
    """
342
343
344
345
346
    cudagraph_num_of_warmups: int = 0
    """Number of warmup runs for cudagraph.
    It means the first several runs will be treated as warmup runs.
    Only after that, the execution will be recorded, and the recorded
    cudagraph will be used for subsequent runs."""
347
    cudagraph_capture_sizes: list[int] | None = None
348
349
350
351
352
353
354
355
    """Sizes to capture cudagraph.
    - None (default): capture sizes are inferred from vllm config.
    - list[int]: capture sizes are specified as given."""
    cudagraph_copy_inputs: bool = False
    """Whether to copy input tensors for
    cudagraph. If the caller can guarantee that the same input buffers
    are always used, it can set this to False. Otherwise, it should
    set this to True, and the compiler will copy the input to an
356
357
358
    internally managed buffer. Default is False. 
    Note that this flag is only effective when cudagraph_mode is PIECEWISE.
    """
359
    full_cuda_graph: bool | None = False
360
361
362
    """whether to use a full cuda graph for the entire forward pass rather than
    splitting certain operations such as attention into subgraphs. Thus this
    flag cannot be used together with splitting_ops. This may provide
363
364
    performance benefits for smaller models.
    Warning: This flag is deprecated and will be removed in the next major or
365
366
    minor release, i.e. v0.11.0 or v1.0.0. Please use cudagraph_mode=
    FULL_AND_PIECEWISE instead.
367
    """
368

369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
    use_inductor_graph_partition: bool = False
    """Use inductor graph partition to split the graph at cudagraph_unsafe ops.
    This partition happens at inductor codegen time after all passes and fusions
    are finished. It generates a single `call` function which wraps
    cudagraph-safe ops into partition functions and leave cudagraph-unsafe ops
    outside the partition functions. For a graph with N cudagraph-unsafe ops
    (e.g., Attention), there would be N+1 partitions. To mark an op as
    cudagraph unsafe, we can add `tags=(torch._C.Tag.cudagraph_unsafe)` when
    register the custom op. 

    This config supports both full cudagraph and piecewise cudagraph without
    compiling twice. For piecewise cudagraph, it applies vLLM CUDAGraph wrapper
    to each partition. For N+1 partitions, there would be N+1
    CUDAGraph wrapper instances.

    For full CUDAGraph, we always apply a single CUDAGraph wrapper outside the
    inductor `call` function in the model runner. The top-level full cudagraph
    capture ignores all partitioning.
    """

389
390
391
392
393
394
395
396
397
    pass_config: PassConfig = field(default_factory=PassConfig)
    """Custom inductor passes, see PassConfig for more details"""

    max_capture_size: int = field(default=None, init=False)  # type: ignore
    """not configurable, computed after init"""
    local_cache_dir: str = field(default=None, init=False)  # type: ignore
    """local cache dir for each rank"""
    bs_to_padded_graph_size: list[int] = field(
        default=None,  # type: ignore
398
399
        init=False,
    )
400
401
402
403
404
405
    """optimization:
    Intuitively, bs_to_padded_graph_size should be dict[int, int].
    since we know all keys are in a range [0, max_capture_size],
    we can optimize it to list[int] for better lookup performance."""

    # keep track of enabled and disabled custom ops
406
    enabled_custom_ops: Counter[str] = field(default_factory=Counter, init=False)
407
    """custom ops that are enabled"""
408
    disabled_custom_ops: Counter[str] = field(default_factory=Counter, init=False)
409
410
411
412
413
414
    """custom ops that are disabled"""
    traced_files: set[str] = field(default_factory=set, init=False)
    """files that are traced for compilation"""
    compilation_time: float = field(default=0.0, init=False)
    """time taken for compilation"""

415
    static_forward_context: dict[str, Any] = field(default_factory=dict, init=False)
416
417
418
419
    """Per-model forward context
    Map from layer name to layer objects that need to be accessed outside
    model code, e.g., Attention, FusedMOE when dp_size>1."""

420
    # Attention ops; used for piecewise cudagraphs
421
    # Use PyTorch operator format: "namespace::name"
422
    _attention_ops: ClassVar[list[str]] = [
423
424
425
426
427
428
429
430
431
432
433
        "vllm::unified_attention",
        "vllm::unified_attention_with_output",
        "vllm::unified_mla_attention",
        "vllm::unified_mla_attention_with_output",
        "vllm::mamba_mixer2",
        "vllm::mamba_mixer",
        "vllm::short_conv",
        "vllm::linear_attention",
        "vllm::plamo2_mamba_mixer",
        "vllm::gdn_attention",
        "vllm::sparse_attn_indexer",
434
435
    ]

436
437
438
439
440
441
442
443
444
445
446
447
448
    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        factors: list[Any] = []
449
        factors.append(self.mode)
450
451
452
453
        factors.append(self.backend)
        factors.append(self.custom_ops)
        factors.append(self.splitting_ops)
        factors.append(self.use_inductor)
454
        factors.append(self.use_inductor_graph_partition)
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
        factors.append(self.inductor_compile_config)
        factors.append(self.inductor_passes)
        factors.append(self.pass_config.uuid())
        return hashlib.sha256(str(factors).encode()).hexdigest()

    def __repr__(self) -> str:
        exclude = {
            "static_forward_context": True,
            "enabled_custom_ops": True,
            "disabled_custom_ops": True,
            "compilation_time": True,
            "bs_to_padded_graph_size": True,
            "traced_files": True,
            "inductor_compile_config": {
                "post_grad_custom_post_pass": True,
            },
        }

        # exclude default attr in pass_config
        pass_config_exclude = {}
        for attr, default_val in vars(PassConfig()).items():
            if getattr(self.pass_config, attr) == default_val:
                pass_config_exclude[attr] = True
        if pass_config_exclude:
            exclude["pass_config"] = pass_config_exclude

481
482
483
        config = TypeAdapter(CompilationConfig).dump_python(
            self, exclude=exclude, exclude_unset=True
        )
484
485

        return str(config)
486
487
488

    __str__ = __repr__

489
490
491
492
493
494
495
496
497
498
    @field_validator("cudagraph_mode", mode="before")
    @classmethod
    def validate_cudagraph_mode_before(cls, value: Any) -> Any:
        """
        enable parse the `cudagraph_mode` enum type from string
        """
        if isinstance(value, str):
            return CUDAGraphMode[value.upper()]
        return value

499
    def __post_init__(self) -> None:
500
501
502
503
504
505
506
507
508
509
510
        if self.level is not None:
            logger.warning(
                "Level is deprecated and will be removed in the next release,"
                "either 0.12.0 or 0.11.2 whichever is soonest."
                "Use mode instead."
                "If both level and mode are given,"
                "only mode will be used."
            )
            if self.mode is None:
                self.mode = self.level

511
512
513
514
515
516
517
518
519
520
521
522
523
        count_none = self.custom_ops.count("none")
        count_all = self.custom_ops.count("all")
        assert count_none + count_all <= 1, "Can only specify 'none' or 'all'"

        # TODO(zou3519/luka): There are 2 issues with auto-functionalization V2:
        # 1. A bug in PyTorch, fixed in 2.7:
        #    https://github.com/pytorch/pytorch/issues/147924
        # 2. Custom passes (fusion) rely on auto-functionalization V1 and don't
        #    work with V2. Addressing this will take extra engineering effort
        #    and it is not yet a priority. RFC here:
        #    https://github.com/vllm-project/vllm/issues/14703

        if is_torch_equal_or_newer("2.6"):
524
            KEY = "enable_auto_functionalized_v2"
525
526
527
528
529
            if KEY not in self.inductor_compile_config:
                self.inductor_compile_config[KEY] = False

        for k, v in self.inductor_passes.items():
            if not isinstance(v, str):
530
531
532
533
                assert callable(v), f"pass {k} should be callable or a qualified name"
                self.inductor_compile_config[k] = (
                    v if isinstance(v, InductorPass) else CallableInductorPass(v)
                )
534
535
536
537
538
539
540
                continue

            # resolve function from qualified name
            names = v.split(".")
            module = ".".join(names[:-1])
            func_name = names[-1]
            func = __import__(module).__dict__[func_name]
541
542
543
            self.inductor_compile_config[k] = (
                func if isinstance(func, InductorPass) else CallableInductorPass(func)
            )
544
545
546
547

        if isinstance(self.pass_config, dict):
            self.pass_config = PassConfig(**self.pass_config)

548
549
550
551
552
553
554
555
556
557
        if (
            is_torch_equal_or_newer("2.9.0.dev")
            and "combo_kernels" not in self.inductor_compile_config
            and "benchmark_combo_kernel" not in self.inductor_compile_config
        ):
            # use horizontal fusion, which is useful for fusing qk-norm and
            # qk-rope when query and key have different shapes.
            self.inductor_compile_config["combo_kernels"] = True
            self.inductor_compile_config["benchmark_combo_kernel"] = True

558
559
        # migrate the deprecated flags
        if not self.use_cudagraph:
560
561
562
563
564
565
566
            logger.warning(
                "use_cudagraph is deprecated, use cudagraph_mode=NONE instead."
            )
            if (
                self.cudagraph_mode is not None
                and self.cudagraph_mode != CUDAGraphMode.NONE
            ):
567
568
569
                raise ValueError(
                    "use_cudagraph and cudagraph_mode are mutually"
                    " exclusive, prefer cudagraph_mode since "
570
571
                    "use_cudagraph is deprecated."
                )
572
573
            self.cudagraph_mode = CUDAGraphMode.NONE
        if self.full_cuda_graph:
574
575
576
577
578
579
580
581
582
583
584
585
            logger.warning(
                "full_cuda_graph is deprecated, use cudagraph_mode=FULL instead."
            )
            if (
                self.cudagraph_mode is not None
                and not self.cudagraph_mode.has_full_cudagraphs()
            ):
                raise ValueError(
                    "full_cuda_graph and cudagraph_mode are "
                    "mutually exclusive, prefer cudagraph_mode "
                    "since full_cuda_graph is deprecated."
                )
586
587
            self.cudagraph_mode = CUDAGraphMode.FULL

588
589
590
591
592
593
594
595
        if self.use_inductor_graph_partition and not is_torch_equal_or_newer(
            "2.9.0.dev"
        ):
            raise ValueError(
                "use_inductor_graph_partition is only "
                "supported with torch>=2.9.0.dev. Set "
                "use_inductor_graph_partition=False instead."
            )
596

597
        for op in self.custom_ops:
598
599
600
601
602
603
            if op[0] not in {"+", "-"} and op not in {"all", "none"}:
                raise ValueError(
                    f"Invalid syntax '{op}' for custom op, "
                    "must be 'all', 'none', '+op' or '-op' "
                    "(where 'op' is the registered op name)"
                )
604

605
606
607
        # Currently only eager and inductor backend are supported.
        # for piecewise compilation. Custom backends are not suppported for
        # piecewise compilation. Update when more backends are supported.
608
        if self.mode == CompilationMode.VLLM_COMPILE and self.backend not in [
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
            "",
            "eager",
            "inductor",
        ]:
            raise ValueError(
                f"Invalid backend for piecewise compilation: {self.backend}"
            )

        if self.use_inductor is not None:
            logger.warning_once(
                "The 'use_inductor' flag is deprecated and will be "
                "removed in the next release (v0.12.0). "
                "Please use the 'backend' option instead.",
            )
            self.backend = "inductor" if self.use_inductor else "eager"

        if self.backend == "":
            self.backend = current_platform.simple_compile_backend

628
    def init_backend(self, vllm_config: "VllmConfig") -> str | Callable:
629
630
631
632
633
634
635
        """
        Initialize the backend for the compilation config from a vllm config.
        Arguments:
            vllm_config: The vllm config to initialize the backend from.
        Returns:
            The backend for the compilation config.
        """
636
        if self.mode is None:
637
            raise ValueError(
638
                "No compilation mode is set. This method should only be \
639
640
641
                called via vllm config where the level is set if none is \
                provided."
            )
642
643
        if self.mode == CompilationMode.NONE:
            raise ValueError("No compilation mode is set.")
644
645

        from torch._dynamo.backends.registry import list_backends
646

647
        torch_backends = list_backends(exclude_tags=tuple())
648
649
650
651
        if self.mode in [
            CompilationMode.STOCK_TORCH_COMPILE,
            CompilationMode.DYNAMO_TRACE_ONCE,
        ]:
652
653
654
655
            if self.backend in torch_backends:
                return self.backend
            return resolve_obj_by_qualname(self.backend)

656
        assert self.mode == CompilationMode.VLLM_COMPILE
657
658
659
660
        if self.backend not in ["eager", "inductor"]:
            raise ValueError(
                f"Invalid backend for piecewise compilation: {self.backend}"
            )
661
662

        from vllm.compilation.backends import VllmBackend
663

664
665
        return VllmBackend(vllm_config)

666
    def init_with_cudagraph_sizes(self, cudagraph_capture_sizes: list[int]) -> None:
667
668
669
670
671
672
673
674
675
        """To complete the initialization of config,
        we need to know the cudagraph sizes."""

        if self.cudagraph_capture_sizes is None:
            self.cudagraph_capture_sizes = cudagraph_capture_sizes
        else:
            # de-duplicate the sizes provided by the config
            dedup_sizes = list(set(self.cudagraph_capture_sizes))
            if len(dedup_sizes) < len(self.cudagraph_capture_sizes):
676
677
678
679
680
681
682
683
                logger.info(
                    (
                        "cudagraph sizes specified by model runner"
                        " %s is overridden by config %s"
                    ),
                    cudagraph_capture_sizes,
                    dedup_sizes,
                )
684
685
686
687
688
689
690
691
            self.cudagraph_capture_sizes = dedup_sizes

        computed_compile_sizes = []
        if self.compile_sizes is not None:
            # de-duplicate the sizes provided by the config
            self.compile_sizes = list(set(self.compile_sizes))
            for x in self.compile_sizes:
                if isinstance(x, str):
692
693
                    assert x == "cudagraph_capture_sizes", (
                        "Unrecognized size type in compile_sizes, "
694
                        f"expect 'cudagraph_capture_sizes', got {x}"
695
                    )
696
697
698
699
700
701
702
703
                    computed_compile_sizes.extend(self.cudagraph_capture_sizes)
                else:
                    assert isinstance(x, int)
                    computed_compile_sizes.append(x)
        self.compile_sizes = computed_compile_sizes  # type: ignore

        # sort to make sure cudagraph capture sizes are in descending order
        self.cudagraph_capture_sizes.sort(reverse=True)
704
705
706
        self.max_capture_size = (
            self.cudagraph_capture_sizes[0] if self.cudagraph_capture_sizes else 0
        )
707
708

        # pre-compute the mapping from batch size to padded graph size
709
710
711
712
        self.bs_to_padded_graph_size = [0 for i in range(self.max_capture_size + 1)]
        for end, start in zip(
            self.cudagraph_capture_sizes, self.cudagraph_capture_sizes[1:] + [0]
        ):
713
714
715
716
717
            for bs in range(start, end):
                if bs == start:
                    self.bs_to_padded_graph_size[bs] = start
                else:
                    self.bs_to_padded_graph_size[bs] = end
718
        self.bs_to_padded_graph_size[self.max_capture_size] = self.max_capture_size
719
720

    def set_splitting_ops_for_v1(self):
721
722
723
        # NOTE: this function needs to be called only when mode is
        # CompilationMode.VLLM_COMPILE
        assert self.mode == CompilationMode.VLLM_COMPILE, (
724
            "set_splitting_ops_for_v1 should only be called when "
725
            "mode is CompilationMode.VLLM_COMPILE"
726
        )
727

728
729
730
731
732
733
734
735
        if self.use_inductor_graph_partition:
            self.set_splitting_ops_for_inductor_graph_partition()
            return

        if self.pass_config.enable_attn_fusion:
            # here use_inductor_graph_partition is False
            self.set_splitting_ops_for_attn_fusion()
            return
736

737
        if self.splitting_ops is None:
738
739
740
741
742
743
744
745
746
            # NOTE: When using full cudagraph, instead of setting an empty
            # list and capture the full cudagraph inside the flattened fx
            # graph, we keep the piecewise fx graph structure but capture
            # the full cudagraph outside the fx graph. This reduces some
            # cpu overhead when the runtime batch_size is not cudagraph
            # captured. see https://github.com/vllm-project/vllm/pull/20059
            # for details. Make a copy to avoid mutating the class-level
            # list via reference.
            self.splitting_ops = list(self._attention_ops)
747
        elif len(self.splitting_ops) == 0:
748
            logger.warning_once("Using piecewise compilation with empty splitting_ops")
749
            if self.cudagraph_mode == CUDAGraphMode.PIECEWISE:
750
                logger.warning_once(
751
                    "Piecewise compilation with empty splitting_ops do not"
752
753
754
755
                    "contains piecewise cudagraph. Setting cudagraph_"
                    "mode to NONE. Hint: If you are using attention backends "
                    "that support cudagraph, consider manually setting "
                    "cudagraph_mode to FULL or FULL_DECODE_ONLY to enable "
756
757
                    "full cudagraphs."
                )
758
759
760
761
762
                self.cudagraph_mode = CUDAGraphMode.NONE
            elif self.cudagraph_mode == CUDAGraphMode.FULL_AND_PIECEWISE:
                logger.warning_once(
                    "Piecewise compilation with empty splitting_ops do not "
                    "contains piecewise cudagraph. Setting cudagraph_mode "
763
764
                    "to FULL."
                )
765
766
                self.cudagraph_mode = CUDAGraphMode.FULL
            self.splitting_ops = []
767
768
769

    def set_splitting_ops_for_inductor_graph_partition(self):
        assert self.use_inductor_graph_partition
770
771
        if self.splitting_ops is None:
            self.splitting_ops = list(self._attention_ops)
772
773
774

    def set_splitting_ops_for_attn_fusion(self):
        assert self.pass_config.enable_attn_fusion
775
776
777
778
779
780
781
782
783
784
785
786
787
788
        # For dynamo-partition (non-inductor) attention fusion,
        # set splitting_ops to empty to avoid splitting at attention ops
        self.splitting_ops = []
        if self.cudagraph_mode.has_piecewise_cudagraphs():
            logger.warning_once(
                "enable_attn_fusion is incompatible with piecewise "
                "cudagraph when use_inductor_graph_partition is off. "
                "In this case, splitting_ops will be set to empty "
                "list, and cudagraph_mode will be set to FULL. "
                "Please ensure you are using attention backends that "
                "support cudagraph or set cudagraph_mode to NONE "
                "explicitly if encountering any problems."
            )
            self.cudagraph_mode = CUDAGraphMode.FULL
789
790
791

        assert not self.splitting_ops_contain_attention(), (
            "attention ops should not be in splitting_ops "
792
793
            "when enable_attn_fusion is True"
        )
794
795
796

    def splitting_ops_contain_attention(self) -> bool:
        return self.splitting_ops is not None and all(
797
798
            op in self.splitting_ops for op in self._attention_ops
        )
799
800

    def is_attention_compiled_piecewise(self) -> bool:
801
802
        if not self.splitting_ops_contain_attention():
            return False
803

804
805
        if not self.use_inductor_graph_partition:
            # Dynamo-level FX split case
806
            return self.mode == CompilationMode.VLLM_COMPILE
807

808
        # Inductor partition case
809
        return self.backend == "inductor" and self.mode > CompilationMode.NONE
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825

    def custom_op_log_check(self):
        """
        This method logs the enabled/disabled custom ops and checks that the
        passed custom_ops field only contains relevant ops.
        It is called at the end of set_current_vllm_config,
        after the custom ops have been instantiated.
        """

        if len(self.enabled_custom_ops) + len(self.disabled_custom_ops) == 0:
            logger.debug("No custom ops found in model.")
            return

        logger.debug("enabled custom ops: %s", self.enabled_custom_ops)
        logger.debug("disabled custom ops: %s", self.disabled_custom_ops)

826
        all_ops_in_model = self.enabled_custom_ops | self.disabled_custom_ops
827
828
829
830
        for op in self.custom_ops:
            if op in {"all", "none"}:
                continue

831
832
833
            assert op[0] in {"+", "-"}, (
                "Invalid custom op syntax (should be checked during init)"
            )
834
835
836
837
838
839
840
841

            # check if op name exists in model
            op_name = op[1:]
            if op_name not in all_ops_in_model:
                from vllm.model_executor.custom_op import CustomOp

                # Does op exist at all or is it just not present in this model?
                # Note: Only imported op classes appear in the registry.
842
843
844
                missing_str = (
                    "doesn't exist (or wasn't imported/registered)"
                    if op_name not in CustomOp.op_registry
845
                    else "not present in model"
846
                )
847

848
849
850
851
852
853
854
855
                enable_str = "enabling" if op[0] == "+" else "disabling"
                logger.warning_once(
                    "Op '%s' %s, %s with '%s' has no effect",
                    op_name,
                    missing_str,
                    enable_str,
                    op,
                )