tpu_model_runner.py 92.3 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
import bisect
4
import gc
5
import time
6
from typing import TYPE_CHECKING, Any, Literal, Optional, Union, cast
7
8
9
10
11
12
13
from unittest.mock import patch

import numpy as np
import torch
import torch.nn as nn
# TPU XLA related
import torch_xla.core.xla_model as xm
14
import torch_xla.distributed.spmd as xs
15
16
import torch_xla.runtime as xr

17
import vllm.envs as envs
18
from vllm.attention import Attention
19
from vllm.attention.backends.abstract import AttentionType
20
from vllm.attention.layers.chunked_local_attention import ChunkedLocalAttention
21
from vllm.compilation.wrapper import TorchCompileWrapperWithCustomDispatcher
22
23
from vllm.config import (ParallelConfig, VllmConfig,
                         get_layers_from_vllm_config, update_config)
24
25
from vllm.distributed.kv_transfer import (get_kv_transfer_group,
                                          has_kv_transfer_group)
26
from vllm.forward_context import set_forward_context
27
from vllm.logger import init_logger
28
from vllm.lora.layers import BaseLayerWithLoRA
29
from vllm.model_executor.model_loader import get_model_loader
30
from vllm.model_executor.model_loader.tpu import TPUModelLoader
31
32
33
from vllm.model_executor.models.interfaces import supports_transcription
from vllm.model_executor.models.interfaces_base import (
    is_pooling_model, is_text_generation_model)
34
from vllm.multimodal import MULTIMODAL_REGISTRY
35
from vllm.multimodal.inputs import (BatchedTensorInputs, MultiModalKwargsItem,
36
                                    PlaceholderRange)
37
from vllm.multimodal.utils import group_mm_kwargs_by_modality
38
from vllm.sequence import IntermediateTensors
39
from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
40
41
42
43
from vllm.utils import (LayerBlockType, cdiv, is_pin_memory_available,
                        prev_power_of_2)
from vllm.v1.attention.backends.pallas import (TPU_STR_DTYPE_TO_TORCH_DTYPE,
                                               PallasAttentionBackend,
44
45
                                               PallasMetadata,
                                               get_page_size_bytes)
46
47
48
from vllm.v1.kv_cache_interface import (AttentionSpec, FullAttentionSpec,
                                        KVCacheConfig, KVCacheSpec,
                                        SlidingWindowSpec)
49
50
from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, LogprobsLists,
                             LogprobsTensors, ModelRunnerOutput)
51
52
from vllm.v1.sample.tpu.metadata import TPUSupportedSamplingMetadata
from vllm.v1.sample.tpu.sampler import Sampler as TPUSampler
53
from vllm.v1.worker.kv_connector_model_runner_mixin import (
54
    KVConnectorModelRunnerMixin, KVConnectorOutput)
55
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
56
from vllm.v1.worker.tpu_input_batch import CachedRequestState, InputBatch
57

58
59
from .utils import (MultiModalBudget, bind_kv_cache,
                    initialize_kv_cache_for_kv_sharing,
60
                    sanity_check_mm_encoder_outputs)
61

62
if TYPE_CHECKING:
63
    from vllm.v1.core.sched.output import SchedulerOutput
64
65
66

logger = init_logger(__name__)

67
INVALID_TOKEN_ID = -1
68
69
# Smallest output size
MIN_NUM_SEQS = 8
70
71


72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
#########################################################
# Ways to avoid recompilation
#########################################################
#
# The model executor has two primary components:
# 1. preparing the model and sampler inputs
# 2. executing the model and sampler.
# The core idea is to avoid any TPU computation during input preparation. For
# better compilation tracking and increased flexibility, the model execution and
# sampler are divided into several distinct components.
#
# Below are the detailed steps:
#
# Step 1
# It is recommended to avoid TPU operations when preparing the model and sampler
# inputs. CPU tensors can be prepared and transferred to the XLA device using
# cpu_tensor.to(xla_device), which only triggers CPU to TPU transfers and avoids
# compilation.
#
# Step 2
# The TPU execution should be decomposed into subgraphs (4 at the moment):
# 1. the main model
# 2. selecting hidden states for each request
# 3. sampler
# 4. encoder.
# Each subgraph should be decorated in a torch.compile. This is used to make
# sure that we have the same subgraph topology in both dummy_run and
# xecute_model. The results from these subgraphs should either be passed to
# other subgraphs, or transferred from TPU to CPU using xla_tensor.cpu() for
# subsequent processing on the CPU.
#
# Step 3
# The dummy_run should be comprehensive, ensuring all potential input shapes and
# branch predictions are included as subgraph inputs to facilitate
# pre-compilation.
107
class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
108
109
110
111
112

    def __init__(
        self,
        vllm_config: VllmConfig,
        device: torch.device,
113
        original_parallel_config: Optional[ParallelConfig] = None,
114
115
116
117
118
119
120
    ):
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
        self.lora_config = vllm_config.lora_config
        self.load_config = vllm_config.load_config
        self.parallel_config = vllm_config.parallel_config
121
        self.original_parallel_config = original_parallel_config
122
123
124
125
126
127
128
129
130
131
        self.scheduler_config = vllm_config.scheduler_config
        self.speculative_config = vllm_config.speculative_config
        self.observability_config = vllm_config.observability_config
        self.device_config = vllm_config.device_config

        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
        self.device = device
132
        self.check_recompilation = envs.VLLM_XLA_CHECK_RECOMPILATION
133

134
135
136
137
138
139
140
141
        # SPMD Related
        self.use_spmd = envs.VLLM_XLA_USE_SPMD
        if self.use_spmd:
            num_devices = xr.global_runtime_device_count()
            mesh_shape = (num_devices, 1)
            device_ids = np.array(range(num_devices))
            self.mesh = xs.Mesh(device_ids, mesh_shape, ('x', 'y'))

142
        self.enforce_eager = model_config.enforce_eager
143
144
145
146

        self.num_xla_graphs = 0
        self._update_num_xla_graphs("init")

147
148
        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
149
        if cache_config.cache_dtype == "auto":
150
151
            model_dtype = self.dtype
            if isinstance(model_dtype, str):
152
                self.kv_cache_dtype = TPU_STR_DTYPE_TO_TORCH_DTYPE[model_dtype]
153
154
            else:
                self.kv_cache_dtype = model_dtype
155
        else:
156
            self.kv_cache_dtype = TPU_STR_DTYPE_TO_TORCH_DTYPE[
157
                cache_config.cache_dtype]
158
        self._hidden_states_dtype = self.dtype
159
160
161
162

        self.sliding_window = model_config.get_sliding_window()
        self.block_size = cache_config.block_size
        self.max_model_len = model_config.max_model_len
163
        self.most_model_len = envs.VLLM_TPU_MOST_MODEL_LEN
164
        self.max_num_blocks_per_req = cdiv(self.max_model_len, self.block_size)
165
166
167
        self.num_blocks_per_most_len_req = cdiv(
            self.most_model_len,
            self.block_size) if self.most_model_len is not None else None
168
169
        # InputBatch needs to work with sampling tensors greater than padding
        # to avoid dynamic shapes. Also, avoid suboptimal alignment.
170
        self.max_num_reqs = max(scheduler_config.max_num_seqs, MIN_NUM_SEQS)
171
172
173
174
175
176
177
        self.num_tokens_paddings = _get_token_paddings(
            min_token_size=16,
            max_token_size=scheduler_config.max_num_batched_tokens,
            padding_gap=envs.VLLM_TPU_BUCKET_PADDING_GAP)
        # In case `max_num_tokens < max(num_tokens_paddings)` use the actual
        # padded max value to pre-allocate data structures and pre-compile.
        self.max_num_tokens = self.num_tokens_paddings[-1]
178
179
180
181
182
183
184
185
186

        # Model-related.
        self.num_attn_layers = model_config.get_num_layers_by_block_type(
            parallel_config, LayerBlockType.attention)
        self.num_query_heads = model_config.get_num_attention_heads(
            parallel_config)
        self.num_kv_heads = model_config.get_num_kv_heads(parallel_config)
        self.head_size = model_config.get_head_size()
        self.hidden_size = model_config.get_hidden_size()
187
        self.vocab_size = model_config.get_vocab_size()
188

189
190
191
        if self.lora_config is not None:
            self.vocab_size += self.lora_config.lora_extra_vocab_size

192
193
194
        # Multi-modal data support
        self.mm_registry = MULTIMODAL_REGISTRY
        self.uses_mrope = model_config.uses_mrope
195
196
        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
            model_config)
197
198
199
        # TODO: Support M-RoPE (e.g, Qwen2-VL)
        assert not self.uses_mrope, "TPU does not support M-RoPE yet."

200
201
202
203
204
205
206
207
        self._num_slices_per_kv_cache_update_block = \
            _get_num_slices_per_kv_cache_update_block(get_page_size_bytes(
                block_size=self.block_size,
                num_kv_heads=self.num_kv_heads,
                head_size=self.head_size,
                kv_cache_dtype=self.kv_cache_dtype,
            ))

208
        # Lazy initialization
209
        self.model: nn.Module  # Set after load_model
210
        self.kv_caches: list[torch.Tensor] = []
211
212
        # mm_hash -> encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
213
214
215

        # Request states.
        self.requests: dict[str, CachedRequestState] = {}
216

217
218
219
220
221
222
223
224
        # Initialize input batch early to avoid AttributeError in _update_states
        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
            max_model_len=self.max_model_len,
            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
            vocab_size=self.model_config.get_vocab_size(),
225
            block_sizes=[self.block_size],
226
227
        )

228
229
230
231
232
233
234
235
236
237
238
239
        # Cached torch/numpy tensor
        # The pytorch tensor and numpy array share the same buffer.
        # Sometimes the numpy op is faster so we create both.
        self.input_ids_cpu = torch.zeros(self.max_num_tokens,
                                         dtype=torch.int32,
                                         device="cpu")

        self.positions_cpu = torch.zeros(self.max_num_tokens,
                                         dtype=torch.int32,
                                         device="cpu")
        self.positions_np = self.positions_cpu.numpy()
        self.block_table_cpu = torch.zeros(
240
            (self.max_num_reqs, self.max_num_blocks_per_req),
241
            dtype=torch.int32,
242
            device="cpu")
243
244
245
246
247
248
249
250
251
        # adjust num_reqs to avoid SMEM OOM.
        self.num_reqs_most_model_len = min(
            PallasAttentionBackend.get_max_num_seqs(self.most_model_len,
                                                    self.block_size),
            self.max_num_reqs) if self.most_model_len is not None else None
        self.num_reqs_max_model_len = min(
            PallasAttentionBackend.get_max_num_seqs(self.max_model_len,
                                                    self.block_size),
            self.max_num_reqs)
252
253
254
255
256
257
258
259
260
261
262
        self.query_start_loc_cpu = torch.zeros(self.max_num_tokens + 1,
                                               dtype=torch.int32,
                                               device="cpu",
                                               pin_memory=self.pin_memory)
        self.query_start_loc_np = self.query_start_loc_cpu.numpy()

        self.seq_lens_cpu = torch.zeros(self.max_num_tokens,
                                        dtype=torch.int32,
                                        device="cpu",
                                        pin_memory=self.pin_memory)
        self.seq_lens_np = self.seq_lens_cpu.numpy()
263
264
265

        # Range tensor with values [0 .. self.max_num_tokens - 1].
        # Used to initialize positions / context_lens / seq_lens
266
267
        # Keep in int64 to avoid overflow with long context
        self.arange_np = np.arange(self.max_num_tokens, dtype=np.int64)
268
269
        self.num_reqs_paddings = _get_req_paddings(
            min_req_size=MIN_NUM_SEQS, max_req_size=self.max_num_reqs)
270

271
272
273
274
275
276
        # Layer pairings for cross-layer KV sharing.
        # If an Attention layer `layer_name` is in the keys of this dict, it
        # means this layer will perform attention using the keys and values
        # from the KV cache of `shared_kv_cache_layers[layer_name]`.
        self.shared_kv_cache_layers: dict[str, str] = {}

277
278
279
280
281
282
283
284
285
286
287
288
289
290
        # tensors for structured decoding
        self.grammar_bitmask_cpu = torch.zeros(
            (self.max_num_reqs, cdiv(self.vocab_size, 32)),
            dtype=torch.int32,
            device="cpu",
            pin_memory=self.pin_memory)
        self.require_structured_out_cpu = torch.zeros(
            (self.max_num_reqs, 1),
            dtype=torch.bool,
            device="cpu",
            pin_memory=self.pin_memory)
        self.structured_decode_arange = torch.arange(
            0, 32, device="cpu", pin_memory=self.pin_memory)

291
292
293
294
        self.mm_budget = (MultiModalBudget(
            self.model_config,
            self.scheduler_config,
            self.mm_registry,
295
        ) if self.supports_mm_inputs else None)
296

297
298
299
300
301
302
303
304
305
        if not self.use_spmd:
            self.sample_from_logits_func = torch.compile(
                self.sample_from_logits,
                backend="openxla",
                fullgraph=True,
                dynamic=False)
        else:
            self.sample_from_logits_func = self.sample_from_logits

306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
    def _update_num_xla_graphs(self, case_str):
        check_comp = self.check_recompilation and not self.enforce_eager
        if not check_comp:
            return

        total_cached_graphs = xr.get_num_cached_compilation_graph()
        new_compiled_graphs = total_cached_graphs - self.num_xla_graphs
        if new_compiled_graphs == 0:
            return

        logger.info("Add new %d compiled XLA graphs due to %s",
                    new_compiled_graphs, case_str)
        self.num_xla_graphs += new_compiled_graphs

    def _verify_num_xla_graphs(self, case_str):
        check_comp = self.check_recompilation and not self.enforce_eager
        if not check_comp:
            return

        curr_cached_graph = xr.get_num_cached_compilation_graph()
        assert self.num_xla_graphs == curr_cached_graph, (
            "Recompilation after warm up is detected during {}."
            " num_xla_graphs = {} curr_cached_graph = {}".format(
                case_str, self.num_xla_graphs, curr_cached_graph))

331
332
333
334
335
336
337
338
    def _update_states(self, scheduler_output: "SchedulerOutput") -> bool:
        """Update the cached states and the persistent batch with the scheduler
        output.

        The updated states are used by the `_prepare_inputs` function to create
        the input GPU tensors for the model.

        Returns:
339
            True if there is a new/resumed/paused/finished request.
340
341
342
343
344
345
346
347
348
349
350
351
            If False, we can skip copying SamplingMetadata to the GPU.
        """
        # Remove finished requests from the cached states.
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)

        # Remove the finished requests from the persistent batch.
        # NOTE(woosuk): There could be an edge case where finished_req_ids and
        # scheduled_req_ids overlap. This happens when a request is aborted and
        # then resubmitted with the same ID. In this case, we treat them as two
        # distinct requests - clearing the cached states for the first request
        # and handling the second as a new request.
352
        removed_req_indices: list[int] = []
353
354
355
356
357
        for req_id in scheduler_output.finished_req_ids:
            req_index = self.input_batch.remove_request(req_id)
            if req_index is not None:
                removed_req_indices.append(req_index)

358
        # Free the cached encoder outputs.
359
360
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
361

362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
        # Remove the unscheduled requests from the persistent batch.
        # NOTE(woosuk): The unscheduled requests are either preempted requests
        # or running requests that are not scheduled in this step. We remove
        # them from the persistent batch but keep their cached states since
        # they will be scheduled again sometime in the future.
        scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
        cached_req_ids = self.input_batch.req_id_to_index.keys()
        unscheduled_req_ids = cached_req_ids - scheduled_req_ids
        # NOTE(woosuk): The persistent batch optimization assumes that
        # consecutive batches contain mostly the same requests. If batches
        # have low request overlap (e.g., alternating between two distinct
        # sets of requests), this optimization becomes very inefficient.
        for req_id in unscheduled_req_ids:
            req_index = self.input_batch.remove_request(req_id)
            assert req_index is not None
            removed_req_indices.append(req_index)

379
        req_ids_to_add: list[str] = []
380
381
        # Add new requests to the cached states.
        for new_req_data in scheduler_output.scheduled_new_reqs:
382
383
            assert new_req_data.sampling_params is not None,\
                "Pooling is not supported in TPU yet"
384
385
386
387
388
389
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params

            self.requests[req_id] = CachedRequestState(
                req_id=req_id,
                prompt_token_ids=new_req_data.prompt_token_ids,
390
                mm_kwargs=new_req_data.mm_kwargs,
391
                mm_positions=new_req_data.mm_positions,
392
                mm_hashes=new_req_data.mm_hashes,
393
                sampling_params=sampling_params,
394
                pooling_params=None,
395
                generator=None,
396
397
398
399
400
401
402
403
404
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
                output_token_ids=[],
                lora_request=new_req_data.lora_request,
            )

            req_ids_to_add.append(req_id)

        # Update the states of the running/resumed requests.
405
406
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
407
            req_state = self.requests[req_id]
408
409
410
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
            resumed_from_preemption = req_data.resumed_from_preemption[i]
411
412

            # Update the cached states.
413
414
            req_state.num_computed_tokens = num_computed_tokens
            if not resumed_from_preemption:
415
416
417
418
419
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
                    for block_ids, new_ids in zip(req_state.block_ids,
                                                  new_block_ids):
                        block_ids.extend(new_ids)
420
            else:
421
                assert new_block_ids is not None
422
423
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
424
                req_state.block_ids = new_block_ids
425
426
427
428
429
430
431
432
433
434
435

            req_index = self.input_batch.req_id_to_index.get(req_id)
            if req_index is None:
                # The request is not in the persistent batch.
                # The request was either preempted and resumed later, or was not
                # scheduled in the previous step and needs to be added again.
                req_ids_to_add.append(req_id)
                continue

            # Update the persistent batch.
            self.input_batch.num_computed_tokens_cpu[req_index] = (
436
                num_computed_tokens)
437
438
439
            if new_block_ids is not None:
                self.input_batch.block_table.append_row(
                    new_block_ids, req_index)
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456

        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
        removed_req_indices = sorted(removed_req_indices, reverse=True)
        for req_id in req_ids_to_add:
            req_state = self.requests[req_id]
            if removed_req_indices:
                # Fill the empty index.
                req_index = removed_req_indices.pop()
            else:
                # Append to the end.
                req_index = None
            self.input_batch.add_request(req_state, req_index)

        # Condense the batched states if there are empty indices.
        if removed_req_indices:
            self.input_batch.condense(removed_req_indices)
457

458
459
460
461
462
        return len(unscheduled_req_ids) > 0 or len(req_ids_to_add) > 0

    def get_model(self) -> nn.Module:
        return self.model

463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
    def get_supported_generation_tasks(self) -> list[GenerationTask]:
        model = self.get_model()
        supported_tasks = list[GenerationTask]()

        if is_text_generation_model(model):
            supported_tasks.append("generate")

        if supports_transcription(model):
            if model.supports_transcription_only:
                return ["transcription"]

            supported_tasks.append("transcription")

        return supported_tasks

478
479
480
481
482
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

483
        return list(model.pooler.get_supported_tasks())
484

485
486
487
488
489
490
491
492
493
494
    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
        tasks = list[SupportedTask]()

        if self.model_config.runner_type == "generate":
            tasks.extend(self.get_supported_generation_tasks())
        if self.model_config.runner_type == "pooling":
            tasks.extend(self.get_supported_pooling_tasks())

        return tuple(tasks)

495
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
496
        """
497
        Generates the KVCacheSpec by parsing the kv cache format from each
498
499
        Attention module in the static forward context.
        Returns:
500
            KVCacheSpec: A dictionary mapping layer names to their KV cache
501
502
503
            format. Layers that do not need KV cache are not included.
        """

504
        layers = get_layers_from_vllm_config(self.vllm_config, Attention)
505
        block_size = self.vllm_config.cache_config.block_size
506
        kv_cache_spec: dict[str, KVCacheSpec] = {}
507
        for layer_name, attn_module in layers.items():
508
509
510
511
512
513
514
515
516
517
518
519
            if (kv_tgt_layer :=
                    attn_module.kv_sharing_target_layer_name) is not None:
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue

520
            if attn_module.attn_type == AttentionType.DECODER:
521
                if isinstance(attn_module, ChunkedLocalAttention):
522
523
524
                    logger.warning_once(
                        "Using irope in Pallas is not supported yet, it "
                        "will fall back to global attention for long context.")
525
526
527
528
529
                if attn_module.sliding_window is not None:
                    kv_cache_spec[layer_name] = SlidingWindowSpec(
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
530
                        dtype=self.kv_cache_dtype,
531
532
533
534
535
536
537
538
                        sliding_window=attn_module.sliding_window,
                        use_mla=False,
                    )
                else:
                    kv_cache_spec[layer_name] = FullAttentionSpec(
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
539
                        dtype=self.kv_cache_dtype,
540
541
                        use_mla=False,
                    )
542
543
544
545
546
547
548
549
550
551
552
553
            elif attn_module.attn_type in (AttentionType.ENCODER,
                                           AttentionType.ENCODER_ONLY):
                # encoder-only attention does not need KV cache.
                continue
            elif attn_module.attn_type == AttentionType.ENCODER_DECODER:
                raise NotImplementedError
            else:
                raise ValueError(
                    f"Unknown attention type: {attn_module.attn_type}")

        return kv_cache_spec

554
    def _get_slot_mapping_metadata(self, num_reqs,
555
                                   num_scheduled_tokens_per_req) -> np.ndarray:
556
557
558
559
560
561
562
563
564
565
566
567
        """
        Computes metadata for mapping slots to blocks in the key-value (KV)
        cache for a batch of requests.

        This function determines, for each request in the batch, how the
        scheduled tokens are distributed across memory blocks, and generates
        metadata needed to map slices of tokens to their corresponding positions
        in the KV cache.

        Args:
            num_reqs (int): Number of requests in the current batch.
            num_scheduled_tokens_per_req (int or np.ndarray): Number of tokens
568
                to be scheduled for each request.
569
570
571

        Returns:
            np.ndarray: A 2D array of shape (total_block_len, 3), where each row
572
                contains:
573
                - kv_cache_start_index (int): The starting index in the KV cache
574
                  for the corresponding slice.
575
                - new_kv_start_index (int): The starting index in the new KV
576
                  cache for the corresponding slice.
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
                - slice_len (int): The length of the slice.
        """
        slices_start = self.input_batch.num_computed_tokens_cpu[:num_reqs]
        slices_end = self.input_batch.num_computed_tokens_cpu[:num_reqs] + \
            num_scheduled_tokens_per_req
        local_block_start_idx = slices_start // self.block_size
        local_block_end_idx = (slices_end - 1) // self.block_size
        no_repeat_req_indices = self.arange_np[:num_reqs]
        global_block_start_idx = (
            no_repeat_req_indices * self.max_num_blocks_per_req +
            local_block_start_idx)
        block_lens = local_block_end_idx - local_block_start_idx + 1
        global_block_start_idx = np.repeat(global_block_start_idx, block_lens)
        slice_arange = np.concatenate([self.arange_np[:n] for n in block_lens])
        global_block_indices = global_block_start_idx + slice_arange
        block_table_cpu = self.input_batch.block_table[0].get_cpu_tensor()
        block_numbers = block_table_cpu.flatten()[global_block_indices].numpy()
        total_block_len = np.sum(block_lens)
        slot_mapping_slices = np.repeat(np.array([[0, self.block_size]],
                                                 dtype=np.int32),
                                        total_block_len,
                                        axis=0)
        cu_block_lens = np.zeros(len(block_lens) + 1, dtype=np.int32)
        np.cumsum(block_lens, out=cu_block_lens[1:])
        for req_idx in range(num_reqs):
            slot_mapping_slices[cu_block_lens[req_idx]][
                0] = slices_start[req_idx] % self.block_size
            slot_mapping_slices[
                cu_block_lens[req_idx + 1] -
                1][1] = (slices_end[req_idx] - 1) % self.block_size + 1
        slice_lens = slot_mapping_slices[:, 1] - slot_mapping_slices[:, 0]
        cu_slices_lens = np.zeros(len(slice_lens) + 1, dtype=np.int32)
        np.cumsum(slice_lens, out=cu_slices_lens[1:])
        kv_cache_start_indices = slot_mapping_slices[:, 0] + \
            (block_numbers * self.block_size)
        new_kv_start_indices = cu_slices_lens[:-1]
        slot_mapping_metadata = np.stack(
            [kv_cache_start_indices, new_kv_start_indices, slice_lens], axis=1)
        return slot_mapping_metadata

617
618
619
    def _prepare_inputs(self, scheduler_output: "SchedulerOutput",
                        start_index: int):
        assert scheduler_output.total_num_scheduled_tokens > 0
620
621
        num_reqs = self.input_batch.num_reqs
        assert num_reqs > 0
622
        assert start_index < num_reqs
623

624
        # Get the number of scheduled tokens for each request.
625
        use_max_model_len = self.most_model_len is None
626
627
        num_scheduled_tokens_per_req = []
        max_num_scheduled_tokens_all_reqs = 0
628
629
630
631
632
        end_index = start_index

        # Use either most_model_len or max_model_len depending on request size.
        for i in range(start_index, num_reqs):
            req_id = self.input_batch.req_ids[i]
633
            assert req_id is not None
634
            num_tokens = scheduler_output.num_scheduled_tokens[req_id]
635
636
            if not use_max_model_len and num_tokens > self.most_model_len:
                use_max_model_len = True
637
            num_scheduled_tokens_per_req.append(num_tokens)
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
        if use_max_model_len:
            if len(num_scheduled_tokens_per_req) > self.num_reqs_max_model_len:
                num_scheduled_tokens_per_req = \
                    num_scheduled_tokens_per_req[:self.num_reqs_max_model_len]
                end_index = start_index + self.num_reqs_max_model_len
            else:
                end_index = num_reqs
        else:
            if len(num_scheduled_tokens_per_req
                   ) > self.num_reqs_most_model_len:
                num_scheduled_tokens_per_req = \
                    num_scheduled_tokens_per_req[:self.num_reqs_most_model_len]
                end_index = start_index + self.num_reqs_most_model_len
            else:
                end_index = num_reqs
        max_num_scheduled_tokens_all_reqs = max(num_scheduled_tokens_per_req)
654
655
        num_scheduled_tokens_per_req = np.array(num_scheduled_tokens_per_req,
                                                dtype=np.int32)
656
        total_num_scheduled_tokens = sum(num_scheduled_tokens_per_req)
657
658
        assert max_num_scheduled_tokens_all_reqs > 0

659
660
        num_reqs = len(num_scheduled_tokens_per_req)

661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
        # For each scheduled token, what are the corresponding req index.
        req_indices = np.repeat(self.arange_np[:num_reqs],
                                num_scheduled_tokens_per_req)

        # Get batched arange.
        # E.g., [2, 5, 3] -> [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        # For each scheduled token, what is its position in corresponding req.
        arange = np.concatenate(
            [self.arange_np[:n] for n in num_scheduled_tokens_per_req])

        # Get positions.
        positions_np = self.positions_np[:total_num_scheduled_tokens]
        np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
               arange,
               out=positions_np)

        # Get token indices.
        # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        # -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
        # where M is the max_model_len.
        token_indices = (positions_np +
                         req_indices * self.input_batch.token_ids_cpu.shape[1])

        # NOTE(woosuk): We use torch.index_select instead of np.take here
        # because torch.index_select is much faster than np.take for large
        # tensors.
689
690
        torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(),
                           0,
691
692
693
694
695
696
697
                           torch.from_numpy(token_indices),
                           out=self.input_ids_cpu[:total_num_scheduled_tokens])

        # Prepare the attention metadata.
        self.query_start_loc_np[0] = 0
        np.cumsum(num_scheduled_tokens_per_req,
                  out=self.query_start_loc_np[1:num_reqs + 1])
698
        self.query_start_loc_np[num_reqs + 1:] = 1
699
700
701
702
703
704

        self.seq_lens_np[:num_reqs] = (
            self.input_batch.num_computed_tokens_cpu[:num_reqs] +
            num_scheduled_tokens_per_req)

        # Do the padding and copy the tensors to the TPU.
705
        padded_total_num_scheduled_tokens = _get_padded_token_len(
706
            self.num_tokens_paddings, total_num_scheduled_tokens)
707
708
709
        # Zero out to avoid spurious values from prev iteration (last cp chunk)
        self.input_ids_cpu[
            total_num_scheduled_tokens:padded_total_num_scheduled_tokens] = 0
710
711
712
713
714
715
        self.input_ids = self.input_ids_cpu[:
                                            padded_total_num_scheduled_tokens].to(
                                                self.device)
        self.position_ids = self.positions_cpu[:
                                               padded_total_num_scheduled_tokens].to(
                                                   self.device)
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
        if use_max_model_len:
            block_tables = self.block_table_cpu[:self.num_reqs_max_model_len, :
                                                self.max_num_blocks_per_req]
            block_tables[:num_reqs, :self.max_num_blocks_per_req] = (
                self.input_batch.block_table[0].get_cpu_tensor()[:num_reqs])
            query_start_loc = self.query_start_loc_cpu[:self.
                                                       num_reqs_max_model_len +
                                                       1].to(self.device)
            seq_lens = self.seq_lens_cpu[:self.num_reqs_max_model_len].to(
                self.device)
        else:
            block_tables = self.block_table_cpu[:self.
                                                num_reqs_most_model_len, :self.
                                                num_blocks_per_most_len_req]
            block_tables[:num_reqs, :self.num_blocks_per_most_len_req] = (
                self.input_batch.block_table[0].get_cpu_tensor()
                [:num_reqs, :self.num_blocks_per_most_len_req])
            query_start_loc = self.query_start_loc_cpu[:self.
                                                       num_reqs_most_model_len +
                                                       1].to(self.device)
            seq_lens = self.seq_lens_cpu[:self.num_reqs_most_model_len].to(
                self.device)
738
        block_tables = block_tables.to(self.device)
739

740
        # Calculate the slot mapping
741
742
        slot_mapping_metadata = self._get_slot_mapping_metadata(
            num_reqs, num_scheduled_tokens_per_req)
743
        num_kv_update_slices = slot_mapping_metadata.shape[0]
744
745
        padded_num_slices = _get_padded_num_kv_cache_update_slices(
            padded_total_num_scheduled_tokens, self.max_num_reqs,
746
            self.block_size)
747
748
749
750
751
752
753
754
        slot_mapping_metadata = np.pad(
            slot_mapping_metadata,
            [[0, padded_num_slices - len(slot_mapping_metadata)], [0, 0]],
            constant_values=0)
        slot_mapping_metadata = np.transpose(slot_mapping_metadata)
        slot_mapping_metadata = torch.tensor(slot_mapping_metadata,
                                             device=self.device)

755
756
757
758
759
760
761
762
763
764
765
        if self.lora_config is not None:
            # We need to respect padding when activating LoRA adapters
            padded_num_scheduled_tokens_per_req = np.copy(
                num_scheduled_tokens_per_req
            )  # Copying to avoid accidental state corruption bugs
            padded_num_scheduled_tokens_per_req[-1] += \
                padded_total_num_scheduled_tokens - total_num_scheduled_tokens

            self.set_active_loras(self.input_batch,
                                  padded_num_scheduled_tokens_per_req)

766
        attn_metadata = PallasMetadata(
767
            slot_mapping=slot_mapping_metadata,
768
            block_tables=block_tables,
769
770
            context_lens=seq_lens,
            query_start_loc=query_start_loc,
771
772
773
            num_seqs=torch.tensor([num_reqs],
                                  dtype=torch.int32,
                                  device=self.device),
774
775
776
            num_kv_update_slices=torch.tensor([num_kv_update_slices],
                                              dtype=torch.int32,
                                              device=self.device),
777
778
            num_slices_per_kv_cache_update_block=self.
            _num_slices_per_kv_cache_update_block,
779
        )
780
781
782
783
784
        # NOTE(woosuk): Due to chunked prefills, there can be at most 1 partial
        # request in the batch. While we should not sample any token from this
        # partial request, we do so for simplicity. We will ignore the sampled
        # token from the partial request.
        # TODO: Support prompt logprobs.
785
786
        padded_num_reqs = _get_padded_num_reqs_with_upper_limit(
            num_reqs, self.max_num_reqs)
787
788
        # Indices at which we sample (positions of last token in the sequence).
        # Padded to avoid recompiling when `num_reqs` varies.
789
790
        logits_indices = self.query_start_loc_cpu[1:padded_num_reqs + 1] - 1
        logits_indices = logits_indices.to(self.device)
791

792
793
794
795
796
797
798
799
800
801
802
        if self.lora_config is not None:
            # We need to respect padding when activating LoRA adapters
            padded_num_scheduled_tokens_per_req = np.copy(
                num_scheduled_tokens_per_req
            )  # Copying to avoid accidental state corruption bugs
            padded_num_scheduled_tokens_per_req[-1] += \
                padded_total_num_scheduled_tokens - total_num_scheduled_tokens

            self.set_active_loras(self.input_batch,
                                  padded_num_scheduled_tokens_per_req)

803
804
805
806
807
808
        layer_names = get_layers_from_vllm_config(self.vllm_config,
                                                  Attention).keys()
        per_layer_attn_metadata = {
            layer_name: attn_metadata
            for layer_name in layer_names
        }
809
810
        return per_layer_attn_metadata, logits_indices, padded_num_reqs,\
            num_reqs, end_index
811

812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
    def _scatter_placeholders(
        self,
        embeds: torch.Tensor,
        is_embed: Optional[torch.Tensor],
    ) -> torch.Tensor:
        if is_embed is None:
            return embeds

        placeholders = embeds.new_full(
            (is_embed.shape[0], embeds.shape[-1]),
            fill_value=torch.nan,
        )
        placeholders[is_embed] = embeds
        return placeholders

    def _gather_placeholders(
        self,
        placeholders: torch.Tensor,
        is_embed: Optional[torch.Tensor],
    ) -> torch.Tensor:
        if is_embed is None:
            return placeholders

        return placeholders[is_embed]

    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
838
839
840
841
842
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
            return

        # Batch the multi-modal inputs.
843
        mm_kwargs = list[MultiModalKwargsItem]()
844
845
        # List of tuple (mm_hash, pos_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
846
847
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
848
849

            for mm_input_id in encoder_input_ids:
850
                mm_hash = req_state.mm_hashes[mm_input_id]
851
                mm_kwargs.append(req_state.mm_kwargs[mm_input_id])
852
853
                mm_hashes_pos.append(
                    (mm_hash, req_state.mm_positions[mm_input_id]))
854
855
856
857
858
859
860
861
862

        # Batch mm inputs as much as we can: if a request in the batch has
        # multiple modalities or a different modality than the previous one,
        # we process it separately to preserve item order.
        # FIXME(ywang96): This is a hacky way to deal with multiple modalities
        # in the same batch while still being able to benefit from batching
        # multimodal inputs. The proper solution should be reordering the
        # encoder outputs.
        encoder_outputs = []
863
864
        for _, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
                mm_kwargs,
865
                device=self.device,
866
867
                pin_memory=self.pin_memory,
        ):
868
869
870
871
872
873
874
            # Run the encoder.
            # `curr_group_outputs` is either of the following:
            # 1. A tensor of shape (num_items, feature_size, hidden_size)
            # in case feature_size is fixed across all multimodal items.
            # 2. A list or tuple (length: num_items) of tensors, each of shape
            # (feature_size, hidden_size) in case the feature size is dynamic
            # depending on the input multimodal items.
875
            xm.mark_step()
876
            curr_group_outputs = self.model.get_multimodal_embeddings(
877
                **mm_kwargs_group)
878
            xm.mark_step()
879

880
881
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
882
                expected_num_items=num_items,
883
884
            )

885
886
887
888
889
890
            if isinstance(curr_group_outputs, torch.Tensor):
                encoder_outputs.append(curr_group_outputs)
            else:
                assert isinstance(curr_group_outputs, (list, tuple))
                for output in curr_group_outputs:
                    encoder_outputs.append(output)
891
892

        # Cache the encoder outputs.
893
894
895
        # NOTE (NickLucche) here we diverge from logic in other runners, as we
        # assume to only have whole mm items to process. Hence we avoid the
        # intrinsic dynamism that `scatter_mm_placeholders` introduces.
896
897
        for (mm_hash, pos_info), output in zip(
                mm_hashes_pos,
898
899
                encoder_outputs,
        ):
900
901
            if req_id not in self.encoder_cache:
                self.encoder_cache[req_id] = {}
902
903
            assert pos_info.is_embed is None, "Expected all positions to be"\
                " contiguous and embeddings."
904
            self.encoder_cache[mm_hash] = output
905
906

    def _gather_mm_embeddings(
907
908
909
        self,
        scheduler_output: "SchedulerOutput",
    ) -> list[torch.Tensor]:
910
        mm_embeds: list[torch.Tensor] = []
911
912
913
914
915
916
        for req_id in self.input_batch.req_ids:
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
                req_id]
            req_state = self.requests[req_id]
            num_computed_tokens = req_state.num_computed_tokens
            mm_positions = req_state.mm_positions
917
            mm_hashes = req_state.mm_hashes
918
919
920
921
            # TODO unroll loop and assume/enforce --disable_chunked_mm_input
            # NOTE (NickLucche) here we diverge from logic in other runners, as
            # we assume to only have whole mm items to process. Hence we avoid
            # the intrinsic dynamism that `gather_mm_placeholders` introduces.
922
            for i, pos_info in enumerate(mm_positions):
923
924
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
925
926
927
928
929
930
931
932
933
934
935
936
937

                # The encoder output is needed if the two ranges overlap:
                # [num_computed_tokens,
                #  num_computed_tokens + num_scheduled_tokens) and
                # [start_pos, start_pos + num_encoder_tokens)
                if start_pos >= num_computed_tokens + num_scheduled_tokens:
                    # The encoder output is not needed in this step.
                    break
                if start_pos + num_encoder_tokens <= num_computed_tokens:
                    # The encoder output is already processed and stored
                    # in the decoder's KV cache.
                    continue

938
939
940
941
                mm_hash = mm_hashes[i]
                encoder_output = self.encoder_cache.get(mm_hash, None)
                assert encoder_output is not None,\
                      f"Encoder cache miss for {mm_hash}."
942
943
                assert pos_info.is_embed is None, "Expected all positions to"\
                " be contiguous and embeddings."
944
                encoder_output = self.encoder_cache[mm_hash]
945
                mm_embeds.append(encoder_output)
946
        return mm_embeds
947

948
949
    def _get_model_inputs(self, input_ids: torch.Tensor,
                          mm_embeds: list[torch.Tensor]):
950
        if self.supports_mm_inputs:
951
952
953
            # NOTE(woosuk): To unify token ids and soft tokens (vision
            # embeddings), we always use embeddings (rather than token ids)
            # as input to the multimodal model, even when the input is text.
954
955
956
957
            inputs_embeds = self.model.get_input_embeddings(
                input_ids=input_ids,
                multimodal_embeddings=mm_embeds,
            )
958
959
960
961
962
963
964
965
            return None, inputs_embeds
        else:
            # For text-only models, we use token ids as input.
            # While it is possible to use embeddings as input just like the
            # multimodal models, it is not desirable for performance since
            # then the embedding layer is not included in the CUDA graph.
            return input_ids, None

966
967
968
969
    @torch.no_grad()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
970
        intermediate_tensors: Optional[IntermediateTensors] = None,
971
972
973
    ) -> ModelRunnerOutput:
        # Update cached state
        self._update_states(scheduler_output)
974
        if not scheduler_output.total_num_scheduled_tokens:
975
976
977
978
979
980
            if not has_kv_transfer_group():
                # Return empty ModelRunnerOutput if there's no work to do.
                return EMPTY_MODEL_RUNNER_OUTPUT

            return self.kv_connector_no_forward(scheduler_output,
                                                self.vllm_config)
981

982
        if self.supports_mm_inputs:
983
            # Run the multimodal encoder if any.
984
985
            self._execute_mm_encoder(scheduler_output)
            mm_embeds = self._gather_mm_embeddings(scheduler_output)
986
        else:
987
            mm_embeds = []
988
        xm.mark_step()
989
        # Prepare inputs, the requests might be split into multiple
990
991
992
993
        # executions, combine the result of each execution.
        start_index = 0
        combined_selected_tokens: list[torch.Tensor] = []
        combined_logprobs: list[LogprobsLists] = []
994
995
996
997
998
999

        # NOTE: setup current batch's metadata for kv connector.
        # Currently, only verified with NixlConnector
        with set_forward_context(None, self.vllm_config):
            self.maybe_setup_kv_connector(scheduler_output)

1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
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
        while start_index < self.input_batch.num_reqs:
            attn_metadata, logits_indices, padded_num_reqs, num_reqs,\
                end_index = self._prepare_inputs(scheduler_output, start_index)
            input_ids, inputs_embeds = self._get_model_inputs(
                self.input_ids, mm_embeds)
            xm.mark_step()
            # Run the decoder
            with set_forward_context(
                    attn_metadata,
                    self.vllm_config,
                    num_tokens=scheduler_output.total_num_scheduled_tokens):
                hidden_states = self.model(
                    input_ids=input_ids,
                    positions=self.position_ids,
                    inputs_embeds=inputs_embeds,
                )
            hidden_states = self.select_hidden_states(hidden_states,
                                                      logits_indices)
            logits = self.compute_logits(hidden_states)
            tpu_sampling_metadata = TPUSupportedSamplingMetadata.\
                from_input_batch(self.input_batch, padded_num_reqs, self.device)
            if scheduler_output.grammar_bitmask is not None:
                require_struct_decoding, grammar_bitmask_padded, arange = \
                    self.prepare_structured_decoding_input(logits,
                                                           scheduler_output)
                logits = self.structured_decode(require_struct_decoding,
                                                grammar_bitmask_padded, logits,
                                                arange)
            selected_token_ids = self.sample_from_logits_func(
                logits, tpu_sampling_metadata)
            # NOTE (NickLucche) Use the original logits (before any penalties or
            # temperature scaling) for the top-k logprobs. We can't enforce it
            # due to recompilations outside torch.compiled code, so just make
            # sure `sample_from_logits` does not modify the logits in-place.
            logprobs = self.gather_logprobs(logits, selected_token_ids) \
                if tpu_sampling_metadata.logprobs else None

            # Remove padding on cpu and keep dynamic op outside of xla graph.
            selected_token_ids = selected_token_ids.cpu()[:num_reqs]

            combined_selected_tokens.append(selected_token_ids)
            if tpu_sampling_metadata.logprobs:
                combined_logprobs.append(logprobs.tolists())

            start_index = end_index

1046
1047
1048
1049
1050
1051
1052
1053
        # NOTE: current kv load and save get h2d/d2h copies involved.
        # Those copies are blocking. Once they become async., kv_save
        # should be called right after each single forward pass,
        # instead of the forwards of the entire input batch.
        self.maybe_wait_for_kv_save()
        finished_sending, finished_recving = (
            self.get_finished_kv_transfers(scheduler_output))

1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
        selected_token_ids = torch.cat(combined_selected_tokens, dim=0)
        if tpu_sampling_metadata.logprobs:

            def concat_lists(input_lists):
                result = []
                for input_list in input_lists:
                    result.extend(input_list)
                return result

            logprobs_lists = LogprobsLists(logprob_token_ids=concat_lists(
                [lp.logprob_token_ids for lp in combined_logprobs]),
                                           logprobs=concat_lists([
                                               lp.logprobs
                                               for lp in combined_logprobs
                                           ]),
                                           sampled_token_ranks=concat_lists([
                                               lp.sampled_token_ranks
                                               for lp in combined_logprobs
                                           ]))
        else:
            logprobs_lists = None
1075

1076
1077
        # Update the cache state concurrently. Code above will not block until
        # we use `selected_token_ids`. Add mark_step if post-processing changes
1078
        request_seq_lens: list[tuple[int, CachedRequestState, int]] = []
1079
        discard_sampled_tokens_req_indices = []
1080
        num_reqs = self.input_batch.num_reqs
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
        for i, req_id in zip(range(num_reqs), self.input_batch.req_ids):
            assert req_id is not None
            req_state = self.requests[req_id]
            seq_len = (req_state.num_computed_tokens +
                       scheduler_output.num_scheduled_tokens[req_id])
            if seq_len >= req_state.num_tokens:
                request_seq_lens.append((i, req_state, seq_len))
            else:
                # Ignore the sampled token from the partial request.
                # Rewind the generator state as if the token was not sampled.
                generator = self.input_batch.generators.get(i)
                if generator is not None:
                    # This relies on cuda-specific torch-internal impl details
                    generator.set_offset(generator.get_offset() - 4)

1096
1097
1098
1099
                # Record the index of the request that should not be sampled,
                # so that we could clear the sampled tokens before returning.
                discard_sampled_tokens_req_indices.append(i)

1100
1101
1102
        assert all(
            req_id is not None for req_id in
            self.input_batch.req_ids[:num_reqs]), "req_ids contains None"
1103
        req_ids = cast(list[str], self.input_batch.req_ids[:num_reqs])
1104

1105
        prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
1106
        for req_id in self.input_batch.req_ids[:num_reqs]:
1107
1108
            prompt_logprobs_dict[req_id] = None

1109
1110
1111
        max_gen_len = selected_token_ids.shape[-1]
        if max_gen_len == 1:
            valid_sampled_token_ids = selected_token_ids.tolist()
1112

1113
1114
1115
1116
1117
1118
1119
            # Mask out the sampled tokens that should not be sampled.
            # TODO: Keep in sync with gpu_model_runner.py, in particular
            #       the "else" case here
            for i in discard_sampled_tokens_req_indices:
                valid_sampled_token_ids[i].clear()

            # Append sampled tokens
1120
1121
1122
1123
1124
            for i, req_state, seq_len in request_seq_lens:
                token_id = valid_sampled_token_ids[i][0]
                self.input_batch.token_ids_cpu[i, seq_len] = token_id
                req_state.output_token_ids.append(token_id)
                self.input_batch.num_tokens[i] += 1
1125

1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
        else:
            valid_mask = selected_token_ids != INVALID_TOKEN_ID
            gen_lens = valid_mask.sum(dim=1).tolist()
            valid_sampled_token_ids = [
                seq.tolist()
                for seq in selected_token_ids[valid_mask].split(gen_lens)
            ]
            self.input_batch.num_tokens[:num_reqs] += gen_lens
            for i, req_state, seq_len in request_seq_lens:
                target_slice = slice(seq_len - gen_lens[i] + 1, seq_len + 1)
                self.input_batch.token_ids_cpu[
                    i, target_slice] = valid_sampled_token_ids[i]
                req_state.output_token_ids.extend(valid_sampled_token_ids[i])

1140
1141
1142
1143
1144
1145
1146
        kv_connector_output = None if (
            finished_sending is None
            and finished_recving is None) else KVConnectorOutput(
                finished_sending=finished_sending,
                finished_recving=finished_recving,
            )

1147
        model_runner_output = ModelRunnerOutput(
1148
            req_ids=req_ids,
1149
            req_id_to_index=self.input_batch.req_id_to_index,
1150
            sampled_token_ids=valid_sampled_token_ids,
1151
            logprobs=logprobs_lists,
1152
            prompt_logprobs_dict=prompt_logprobs_dict,
1153
            pooler_output=[],
1154
1155
            kv_connector_output=kv_connector_output,
        )
1156
1157
1158
1159
1160

        # Check there are no new graphs compiled - all the graphs should be
        # captured and compiled during warm up.
        self._verify_num_xla_graphs("execute_model")

1161
1162
        return model_runner_output

1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
    def update_config(self, overrides: dict[str, Any]) -> None:
        # TODO: TPU config may need extra validation
        # https://github.com/vllm-project/vllm/pull/20095#discussion_r2201497754
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
            assert config_name in allowed_config_names, \
                f"Config `{config_name}` not supported. " \
                f"Allowed configs: {allowed_config_names}"
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
    def load_model(self) -> None:
        self.device = self.device_config.device

        # NOTE(woosuk): While the executor assigns the TP ranks to the worker
        # process, the ranks can be different from the ranks internally assigned
        # by the xm runtime. Therefore, there is a mismatch in the rank
        # assignment between the gloo (cpu) runtime and the xm (tpu) runtime.
        # This is not a problem in linear layers because all-reduce is
        # rank-agnostic. However, it matters for all-gather as the ranks
        # determine the order of concatenating the output tensors.
        # As a workaround, we use the xm's rank assignment only when loading
        # the embedding weights.
        xm_tp_rank = xr.global_ordinal()
        with patch(
                "vllm.model_executor.layers.vocab_parallel_embedding."
                "get_tensor_model_parallel_rank",
                return_value=xm_tp_rank):
1192
1193
1194
1195
1196
            try:
                if self.use_spmd:
                    tpu_loader = TPUModelLoader(
                        load_config=self.vllm_config.load_config)
                    model = tpu_loader.load_model(
1197
                        vllm_config=self.vllm_config,
1198
1199
                        model_config=self.vllm_config.model_config,
                        mesh=self.mesh)
1200
                else:
1201
                    model_loader = get_model_loader(self.load_config)
1202
1203
1204
1205
                    logger.info("Loading model from scratch...")
                    model = model_loader.load_model(
                        vllm_config=self.vllm_config,
                        model_config=self.model_config)
1206
1207
1208
1209
1210
1211
1212
            except RuntimeError as e:
                raise RuntimeError(
                    f"Unable to load model, a likely reason is the model is "
                    "too large for the current device's HBM memory. "
                    "Consider switching to a smaller model "
                    "or sharding the weights on more chips. "
                    f"See the detailed error: {e}") from e
1213
1214
1215
1216
        if self.lora_config is not None:
            model = self.load_lora_model(model, self.model_config,
                                         self.scheduler_config,
                                         self.lora_config, self.device)
1217
            replace_set_lora(model)
1218

1219
1220
        # Sync all pending XLA execution during model initialization and weight
        # loading.
1221
1222
        xm.mark_step()
        xm.wait_device_ops()
1223
1224
        if not hasattr(self, "model"):
            self.model = model
1225
        self.sampler = TPUSampler()
1226

1227
1228
1229
1230
1231
1232
1233
    def reload_weights(self) -> None:
        assert getattr(self, "model", None) is not None, \
            "Cannot reload weights before model is loaded."
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
        model_loader.load_weights(self.model, model_config=self.model_config)

1234
    @torch.no_grad()
1235
1236
    def _dummy_run(self, num_tokens: int, num_reqs: int,
                   num_blocks: int) -> None:
1237
        if self.supports_mm_inputs:
1238
1239
1240
1241
1242
1243
            input_ids = None
            inputs_embeds = torch.zeros((num_tokens, self.hidden_size),
                                        dtype=self.dtype,
                                        device=self.device)
        else:
            input_ids = torch.zeros((num_tokens),
1244
                                    dtype=torch.int32).to(self.device)
1245
            inputs_embeds = None
1246
        actual_num_reqs = min(num_tokens, num_reqs)
1247
        position_ids = torch.zeros(num_tokens,
1248
                                   dtype=torch.int32).to(self.device)
1249
        padded_num_slices = _get_padded_num_kv_cache_update_slices(
1250
            num_tokens, self.max_num_reqs, self.block_size)
1251
1252
        num_kv_update_slices = torch.tensor([padded_num_slices],
                                            dtype=torch.int32).to(self.device)
1253
1254
        slot_mapping = torch.zeros((3, padded_num_slices),
                                   dtype=torch.int32).to(self.device)
1255
1256
1257
        block_tables = torch.zeros((num_reqs, num_blocks),
                                   dtype=torch.int32).to(self.device)
        query_lens = [1] * num_reqs
1258
1259
1260
1261
        query_start_loc = torch.cumsum(torch.tensor([0] + query_lens,
                                                    dtype=torch.int32),
                                       dim=0,
                                       dtype=torch.int32).to(self.device)
1262
        context_lens = torch.ones((num_reqs, ),
1263
                                  dtype=torch.int32).to(self.device)
1264
        num_seqs = torch.tensor([actual_num_reqs],
1265
                                dtype=torch.int32).to(self.device)
1266
1267
1268
1269
1270
        attn_metadata = PallasMetadata(
            slot_mapping=slot_mapping,
            block_tables=block_tables,
            context_lens=context_lens,
            query_start_loc=query_start_loc,
1271
            num_seqs=num_seqs,
1272
            num_kv_update_slices=num_kv_update_slices,
1273
1274
            num_slices_per_kv_cache_update_block=self.
            _num_slices_per_kv_cache_update_block,
1275
        )
1276

1277
        if self.supports_mm_inputs:
1278
1279
1280
            torch._dynamo.mark_dynamic(inputs_embeds, 0)
        else:
            torch._dynamo.mark_dynamic(input_ids, 0)
1281
1282
        torch._dynamo.mark_dynamic(position_ids, 0)
        torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 0)
1283
1284
1285
        torch._dynamo.mark_dynamic(attn_metadata.block_tables, (0, 1))
        torch._dynamo.mark_dynamic(attn_metadata.context_lens, 0)
        torch._dynamo.mark_dynamic(attn_metadata.query_start_loc, 0)
1286

1287
1288
1289
1290
1291
1292
1293
        layer_names = get_layers_from_vllm_config(self.vllm_config,
                                                  Attention).keys()
        per_layer_attn_metadata = {
            layer_name: attn_metadata
            for layer_name in layer_names
        }

1294
        with self.maybe_select_dummy_loras(
1295
1296
1297
                self.lora_config,
                np.array([num_tokens], dtype=np.int32)), set_forward_context(
                    per_layer_attn_metadata, self.vllm_config, 0):
1298
1299
1300
1301
            out = self.model(input_ids=input_ids,
                             positions=position_ids,
                             inputs_embeds=inputs_embeds)
        self._hidden_states_dtype = out.dtype
1302

1303
1304
1305
1306
1307
1308
1309
    def _set_active_loras(self, prompt_lora_mapping, token_lora_mapping,
                          lora_requests) -> None:
        xm.mark_step()  # Captures input updates
        super()._set_active_loras(prompt_lora_mapping, token_lora_mapping,
                                  lora_requests)
        xm.mark_step()  # Captures metadata updates

1310
    def _precompile_mm_encoder(self) -> None:
1311
        if not self.supports_mm_inputs:
1312
1313
            return

1314
1315
        # Pre-compile MM encoder for all supported data modalities.
        hf_config = self.vllm_config.model_config.hf_config
1316
1317
1318
1319
1320
1321
1322

        mm_budget = self.mm_budget
        assert mm_budget is not None

        max_items_per_seq_by_modality = mm_budget.max_items_per_batch_by_modality  # noqa: E501

        for mode, max_items_per_seq in max_items_per_seq_by_modality.items():
1323
1324
1325
1326
1327
            logger.info(
                "Compiling Multimodal %s Encoder with different input"
                " shapes.", mode)
            start = time.perf_counter()
            # No padding for MM encoder just yet.
1328
            for num_items in range(1, max_items_per_seq + 1):
1329
1330
                logger.info("  -- mode: %s items: %d", mode, num_items)
                batched_dummy_mm_inputs = self._get_mm_dummy_batch(
1331
1332
1333
                    mode,
                    num_items,
                )
1334
1335
                # Run multimodal encoder.
                xm.mark_step()
1336
1337
                mm_embeds = self.model.get_multimodal_embeddings(
                    **batched_dummy_mm_inputs)
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
                xm.mark_step()
                num_patches = mm_embeds[0].shape[0]
                items_size = num_patches * num_items

                # NOTE (NickLucche) pre-compile `get_input_embeddings` when mm
                # embeddings are present. We assume `--disable-mm-chunked`,
                # hence only whole items can be scheduled. This implies we just
                # need to compile when `num_items` fit the (padded) `input_ids`
                for num_tokens in self.num_tokens_paddings:
                    if num_tokens >= items_size:
                        # XLA Workaround: if torch.zeros(..device) is used, XLA
                        # compiles a scalar+expansion op, which won't match
                        # the graph generated at runtime. CPU->TPU must be used
                        placeholders_ids = torch.zeros(num_tokens,
                                                       dtype=torch.int32,
                                                       device="cpu")
                        # Align placeholders and actual num mm_embeddings.
                        placeholders_ids[:items_size] = \
                            hf_config.image_token_index

                        placeholders_ids = placeholders_ids.to(self.device)
                        # Assign outputs or the graph will be cut short.
                        a, b = self._get_model_inputs(placeholders_ids,
                                                      [mm_embeds])
                        assert a is None
                        xm.mark_step()

            # Pre-compile `get_input_embeddings` when mm_embeddings are not
            # present. Chunk is only made of text, no mm_placeholders.
            for num_tokens in self.num_tokens_paddings:
                placeholders_ids = torch.zeros(num_tokens,
                                               dtype=torch.int32,
                                               device="cpu")
                placeholders_ids = placeholders_ids.to(self.device)
                a, b = self._get_model_inputs(placeholders_ids, [])
                assert a is None
                xm.mark_step()

            xm.wait_device_ops()
            end = time.perf_counter()
            logger.info(
                "Multimodal %s Encoder compilation finished in in %.2f "
                "[secs].", mode, end - start)

1382
    def _precompile_backbone(self) -> None:
1383
1384
        logger.info("Compiling the model with different input shapes.")
        start = time.perf_counter()
1385
        for num_tokens in self.num_tokens_paddings:
1386
            logger.info("  -- num_tokens: %d", num_tokens)
1387
1388
1389
1390
1391
            self._dummy_run(num_tokens, self.num_reqs_max_model_len,
                            self.max_num_blocks_per_req)
            if self.most_model_len is not None:
                self._dummy_run(num_tokens, self.num_reqs_most_model_len,
                                self.num_blocks_per_most_len_req)
1392
1393
        xm.wait_device_ops()
        end = time.perf_counter()
1394
        logger.info("Compilation finished in %.2f [secs].", end - start)
1395
        self._update_num_xla_graphs("model backbone")
1396

1397
1398
1399
1400
1401
    def _precompile_select_hidden_states(self) -> None:
        # Compile hidden state selection function for bucketed
        # n_tokens x max_num_reqs. Graph is really small so this is fine.
        logger.info(
            "Compiling select_hidden_states with different input shapes.")
1402
1403
        start = time.perf_counter()
        hsize = self.model_config.get_hidden_size()
1404
        for num_tokens in self.num_tokens_paddings:
1405
1406
            dummy_hidden = torch.zeros((num_tokens, hsize),
                                       device=self.device,
1407
                                       dtype=self._hidden_states_dtype)
1408
1409
1410
1411
1412
1413
1414
            torch._dynamo.mark_dynamic(dummy_hidden, 0)
            for num_reqs in self.num_reqs_paddings:
                indices = torch.zeros(num_reqs,
                                      dtype=torch.int32,
                                      device=self.device)
                torch._dynamo.mark_dynamic(indices, 0)
                self.select_hidden_states(dummy_hidden, indices)
1415
1416
1417
1418
1419
1420
                logger.info("  -- num_tokens: %d, num_seqs: %d", num_tokens,
                            num_reqs)
                # Requests can't be more than tokens. But do compile for the
                # next bigger value in case num_tokens uses bucketed padding.
                if num_reqs >= min(num_tokens, self.max_num_reqs):
                    break
1421
        xm.wait_device_ops()
1422
        end = time.perf_counter()
1423
        logger.info("Compilation finished in %.2f [secs].", end - start)
1424
        self._update_num_xla_graphs("select_hidden_states")
1425

1426
1427
    def _precompile_compute_logits(self) -> None:
        logger.info("Compiling compute_logits with different input shapes.")
1428
1429
1430
1431
1432
1433
        start = time.perf_counter()
        hsize = self.model_config.get_hidden_size()
        for num_reqs in self.num_reqs_paddings:
            dummy_hidden = torch.zeros((num_reqs, hsize),
                                       device=self.device,
                                       dtype=self._hidden_states_dtype)
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
            torch._dynamo.mark_dynamic(dummy_hidden, 0)
            self.compute_logits(dummy_hidden)
            logger.info("  -- num_seqs: %d", num_reqs)
        xm.wait_device_ops()
        end = time.perf_counter()
        logger.info("Compilation finished in %.2f [secs].", end - start)
        self._update_num_xla_graphs("compute_logits")

    def _precompile_structured_decoding(self) -> None:
        logger.info(
            "Compiling structured_decoding with different input shapes.")
        start = time.perf_counter()
        for num_reqs in self.num_reqs_paddings:
            dummy_logits = torch.zeros((num_reqs, self.vocab_size),
                                       device=self.device,
                                       dtype=self._hidden_states_dtype)
            dummy_require_struct_decoding = \
                self.require_structured_out_cpu[:num_reqs].to(self.device)
            dummy_grammar_bitmask = \
                self.grammar_bitmask_cpu[:num_reqs].to(self.device)
            # The first dimension of the above 3 dummy tensors cannot be
            # mark_dynamic because some operations in structured_decode require
            # them to be static.
            arange = self.structured_decode_arange.to(self.device)
            self.structured_decode(dummy_require_struct_decoding,
                                   dummy_grammar_bitmask, dummy_logits, arange)
            logger.info("  -- num_seqs: %d", num_reqs)
        xm.wait_device_ops()
        end = time.perf_counter()
        logger.info("Compilation finished in %.2f [secs].", end - start)
        self._update_num_xla_graphs("structured_decoding")

    def _precompile_sample_from_logits(self) -> None:
        logger.info(
            "Compiling sample_from_logits with different input shapes.")
        start = time.perf_counter()
        for num_reqs in self.num_reqs_paddings:
            dummy_logits = torch.zeros((num_reqs, self.vocab_size),
                                       device=self.device,
                                       dtype=self._hidden_states_dtype)
            # The first dimension of dummy_logits cannot be mark_dynamic
            # because some operations in the sampler require it to be static.
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
            for all_greedy in [False, True]:
                generate_params_if_all_greedy = not all_greedy
                sampling_metadata = (
                    TPUSupportedSamplingMetadata.from_input_batch(
                        self.input_batch,
                        num_reqs,
                        self.device,
                        generate_params_if_all_greedy,
                    ))
                sampling_metadata.all_greedy = all_greedy
1486
1487
1488
                with self.maybe_select_dummy_loras(
                        self.lora_config, np.array([num_reqs],
                                                   dtype=np.int32)):
1489
1490
                    self.sample_from_logits_func(dummy_logits,
                                                 sampling_metadata)
1491
1492
1493
            logger.info("  -- num_seqs: %d", num_reqs)
        xm.wait_device_ops()
        end = time.perf_counter()
1494
1495
        logger.info("Compilation finished in %.2f [secs].", end - start)
        self._update_num_xla_graphs("sample_from_logits")
1496

1497
1498
1499
1500
1501
1502
1503
1504
1505
    def _precompile_gather_logprobs(self) -> None:
        logger.info("Compiling gather_logprobs with different input shapes.")
        start = time.perf_counter()
        for num_reqs in self.num_reqs_paddings:
            dummy_logits = torch.zeros((num_reqs, self.vocab_size),
                                       device=self.device,
                                       dtype=self._hidden_states_dtype)
            dummy_tokens = torch.zeros((num_reqs, 1),
                                       dtype=torch.int64).to(self.device)
1506
1507
1508
            with self.maybe_select_dummy_loras(
                    self.lora_config, np.array([num_reqs], dtype=np.int32)):
                self.gather_logprobs(dummy_logits, dummy_tokens)
1509
1510
1511
1512
1513
1514
            logger.info("  -- num_seqs: %d", num_reqs)
        xm.wait_device_ops()
        end = time.perf_counter()
        logger.info("Compilation finished in %.2f [secs].", end - start)
        self._update_num_xla_graphs("gather_logprobs")

1515
1516
1517
1518
    def capture_model(self) -> None:
        """
        Precompile all the subgraphs with possible input shapes.
        """
1519
1520
1521
1522
1523
1524
1525
1526
        with self.maybe_setup_dummy_loras(self.lora_config):
            self._precompile_mm_encoder()
            self._precompile_backbone()
            self._precompile_select_hidden_states()
            self._precompile_compute_logits()
            self._precompile_structured_decoding()
            self._precompile_sample_from_logits()
            self._precompile_gather_logprobs()
1527

1528
1529
1530
1531
1532
    def profile_run(
        self,
        num_tokens: int,
    ) -> None:
        # Profile with multimodal encoder & encoder cache.
1533
        if self.supports_mm_inputs:
1534
            if self.model_config.multimodal_config.skip_mm_profiling:
1535
                logger.info(
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
                    "Skipping memory profiling for multimodal encoder and "
                    "encoder cache.")
            else:
                mm_budget = self.mm_budget
                assert mm_budget is not None

                # TODO: handle encoder-decoder models once we support them.
                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
                    # NOTE: Currently model is profiled with a single non-text
                    # modality with the max possible input tokens even when
                    # it supports multiple.
1547
1548
1549
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
                    max_mm_items_per_batch = mm_budget \
                        .max_items_per_batch_by_modality[dummy_modality]
1550
1551
1552
1553
1554
1555
1556
1557
1558

                    logger.info(
                        "Encoder cache will be initialized with a budget of "
                        "%s tokens, and profiled with %s %s items of the "
                        "maximum feature size.",
                        encoder_budget,
                        max_mm_items_per_batch,
                        dummy_modality,
                    )
1559

1560
1561
1562
1563
1564
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
1565

1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
                    # Run multimodal encoder.
                    # Isolate encoder graph from post-processing to minimize
                    # impact of recompilation until it's fixed.
                    start = time.perf_counter()
                    xm.mark_step()
                    dummy_encoder_outputs = \
                        self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs)
                    xm.mark_step()
                    xm.wait_device_ops()
                    end = time.perf_counter()
                    logger.info(
                        "Multimodal Encoder profiling finished in %.2f [secs].",
                        end - start)

                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
1585

1586
1587
1588
                    # Cache the dummy encoder outputs.
                    self.encoder_cache["tmp"] = dict(
                        enumerate(dummy_encoder_outputs))
1589
1590

        # Trigger compilation for general shape.
1591
1592
1593
1594
1595
        self._dummy_run(num_tokens, self.num_reqs_max_model_len,
                        self.max_num_blocks_per_req)
        if self.most_model_len is not None:
            self._dummy_run(num_tokens, self.num_reqs_most_model_len,
                            self.num_blocks_per_most_len_req)
1596
1597
1598
1599
1600
1601

        xm.mark_step()
        xm.wait_device_ops()
        self.encoder_cache.clear()
        gc.collect()

1602
1603
1604
1605
    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize KV cache based on `kv_cache_config`.
        Args:
1606
            kv_cache_config: Configuration for the KV cache, including the KV
1607
1608
            cache size of each layer
        """
1609
        if len(kv_cache_config.kv_cache_groups) > 1:
1610
1611
1612
1613
            raise NotImplementedError(
                "Hybrid models with more than one KV cache type are not "
                "supported yet.")

1614
1615
1616
1617
1618
1619
1620
1621
1622
        if kv_cache_config.kv_cache_groups[
                0].kv_cache_spec.block_size != self.block_size:
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
                max_model_len=self.max_model_len,
                max_num_batched_tokens=self.max_num_tokens,
                device=self.device,
                pin_memory=self.pin_memory,
                vocab_size=self.model_config.get_vocab_size(),
1623
1624
1625
                block_sizes=[
                    kv_cache_config.kv_cache_groups[0].kv_cache_spec.block_size
                ],
1626
1627
            )
        # Verify dtype compatibility between block_table_cpu and input_batch
1628
1629
1630
        assert self.block_table_cpu.dtype == self.input_batch.block_table[
            0].get_cpu_tensor().dtype

1631
1632
1633
1634
1635
1636
        kv_cache_sizes = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
            assert len(kv_cache_tensor.shared_by) == 1, (
                "KV cache tensor shared by multiple layers is not supported in "
                "TPU.")
            kv_cache_sizes[kv_cache_tensor.shared_by[0]] = kv_cache_tensor.size
1637

1638
        kv_caches: dict[str, torch.Tensor] = {}
1639
1640
1641
        for kv_cache_group in kv_cache_config.kv_cache_groups:
            kv_cache_spec = kv_cache_group.kv_cache_spec
            for layer_name in kv_cache_group.layer_names:
1642
1643
1644
                tensor_size = kv_cache_sizes[layer_name]
                assert tensor_size % kv_cache_spec.page_size_bytes == 0
                num_blocks = tensor_size // kv_cache_spec.page_size_bytes  # noqa
1645
                if isinstance(kv_cache_spec, AttentionSpec):
1646
1647
1648
1649
1650
1651
1652
1653
1654
                    if self.use_spmd:
                        num_kv_heads = kv_cache_spec.num_kv_heads
                        assert self.original_parallel_config is not None
                        tp_size = \
                            self.original_parallel_config.tensor_parallel_size
                        # TODO: Handle kv cache duplication under SPMD mode.
                        assert num_kv_heads % tp_size == 0, (
                            f"num_kv_heads {num_kv_heads} must be divisible by "
                            f"tp_size {tp_size} under SPMD mode")
1655
1656
1657
1658
1659
                    kv_cache_shape = PallasAttentionBackend.get_kv_cache_shape(
                        num_blocks, kv_cache_spec.block_size,
                        kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
                    dtype = kv_cache_spec.dtype

1660
                    tpu_kv_cache = torch.zeros(kv_cache_shape,
1661
                                               dtype=dtype).to(self.device)
1662

1663
                    kv_caches[layer_name] = tpu_kv_cache
1664
1665
                else:
                    raise NotImplementedError
1666

1667
1668
1669
1670
1671
1672
1673
1674
1675
        # Setup `kv_cache_config` and `kv_caches` for models
        # with cross-layer KV sharing
        if self.shared_kv_cache_layers:
            initialize_kv_cache_for_kv_sharing(
                self.shared_kv_cache_layers,
                kv_cache_config.kv_cache_groups,
                kv_caches,
            )

1676
1677
1678
1679
1680
        bind_kv_cache(
            kv_caches,
            self.vllm_config.compilation_config.static_forward_context,
            self.kv_caches)

1681
1682
1683
1684
1685
        if self.use_spmd:
            # Shard KV Cache
            for cache in self.kv_caches:
                xs.mark_sharding(cache, self.mesh, (None, 'x', None, None))

1686
1687
1688
1689
        if has_kv_transfer_group():
            get_kv_transfer_group().register_kv_caches(kv_caches)
            get_kv_transfer_group().set_host_xfer_buffer_ops(copy_kv_blocks)

1690
    def reset_dynamo_cache(self):
1691
1692
1693
1694
1695

        # NOTE: We check `is_multimodal_model` instead of `supports_mm_inputs`
        # since the compiled model object of the language backbone of a
        # multimodal model needs to be extracted via `get_language_model`.
        if self.model_config.is_multimodal_model:
1696
            compiled_model = self.model.get_language_model().model
1697
1698
1699
1700
1701
1702
1703
        else:
            compiled_model = self.model.model
        if isinstance(compiled_model, TorchCompileWrapperWithCustomDispatcher):
            logger.info("Clear dynamo cache and cached dynamo bytecode.")
            torch._dynamo.eval_frame.remove_from_cache(
                compiled_model.original_code_object)
            compiled_model.compiled_codes.clear()
1704

1705
1706
1707
1708
1709
    @torch.compile(backend="openxla", fullgraph=True, dynamic=False)
    def select_hidden_states(self, hidden_states, indices_do_sample):
        return hidden_states[indices_do_sample]

    @torch.compile(backend="openxla", fullgraph=True, dynamic=False)
1710
1711
1712
1713
    def compute_logits(self,
                       sample_hidden_states: torch.Tensor) -> torch.Tensor:
        return self.model.compute_logits(sample_hidden_states, None)

1714
1715
1716
    # TODO: Under SPMD mode, sample_from_logits has correctness issue.
    #       Re-enable the torch.compile once the issue is fixed in torchxla.
    # @torch.compile(backend="openxla", fullgraph=True, dynamic=False)
1717
1718
1719
    def sample_from_logits(
            self, logits: torch.Tensor,
            sampling_metadata: TPUSupportedSamplingMetadata) -> torch.Tensor:
1720
1721
1722
1723
        """
        Sample with xla-friendly function. This function is to be traced 
        separately from `forward` for lighter compilation overhead.
        """
1724
1725
1726
1727
1728
        if sampling_metadata.all_greedy:
            out_tokens = torch.argmax(logits, dim=-1, keepdim=True)
        else:
            out_tokens = self.sampler(logits,
                                      sampling_metadata).sampled_token_ids
1729
1730
        return out_tokens

1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
    @torch.compile(backend="openxla", fullgraph=True, dynamic=False)
    def gather_logprobs(self, logits: torch.Tensor,
                        sampled_tokens: torch.Tensor) -> LogprobsTensors:
        """
        Gather the top_logprobs with corresponding tokens. Use a fixed number
        of logprobs as an alternative to having multiple pre-compiled graphs.
        Select the number of logprobs actually demanded by each request on CPU.
        """
        logprobs = self.sampler.compute_logprobs(logits)
        return self.sampler.gather_logprobs(
            logprobs,
            self.model_config.max_logprobs,
            token_ids=sampled_tokens.squeeze(-1))

1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
    @torch.compile(backend="openxla", fullgraph=True, dynamic=False)
    def structured_decode(self, require_struct_decoding: torch.Tensor,
                          grammar_bitmask: torch.Tensor, logits: torch.Tensor,
                          arange: torch.Tensor) -> torch.Tensor:
        return torch.where(
            require_struct_decoding,
            self.apply_grammar_bitmask(logits, grammar_bitmask, arange),
            logits)

    def apply_grammar_bitmask(self, logits: torch.Tensor,
                              grammar_bitmask: torch.Tensor,
                              arange: torch.Tensor):
        assert (logits.shape[0] == grammar_bitmask.shape[0])
        logits_cloned = logits.clone()
        for i in range(logits.shape[0]):
            unpacked_bitmask = (torch.bitwise_right_shift(
                grammar_bitmask[i][:, None], arange[None, :]) & 1) == 0
            unpacked_bitmask = unpacked_bitmask.reshape(-1)[:self.vocab_size]
            logits_cloned[i] = logits_cloned[i].masked_fill(
                unpacked_bitmask, -float("inf"))
        return logits_cloned

1767
1768
    def get_multimodal_embeddings(self, *args, **kwargs):
        return self.model.get_multimodal_embeddings(*args, **kwargs)
1769

1770
1771
1772
    def get_input_embeddings(self, *args, **kwargs):
        return self.model.get_input_embeddings(*args, **kwargs)

1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
    def prepare_structured_decoding_input(
        self, logits: torch.Tensor, scheduler_output: "SchedulerOutput"
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        grammar_bitmask = scheduler_output.grammar_bitmask
        assert grammar_bitmask is not None
        num_reqs, _ = logits.shape

        # Reset pre-allocated tensors
        self.grammar_bitmask_cpu.zero_()
        self.require_structured_out_cpu.zero_()

        # We receive the structured output bitmask from the scheduler, but the
        # indices of the requests in the batch may not match the indices of
        # the bitmask since the scheduler doesn't know how the tpu runner is
        # ordering the requests in the batch. We need to match the order of
        # bitmask with the order of requests
        struct_out_indices: list[int] = []
        mask_indices: list[int] = []
        for req_id in self.input_batch.req_ids:
            mask_index = scheduler_output.structured_output_request_ids.get(
                req_id)
            if mask_index is None:
                continue
            batch_index = self.input_batch.req_id_to_index[req_id]
            struct_out_indices.append(batch_index)
            mask_indices.append(mask_index)
        self.grammar_bitmask_cpu[struct_out_indices] = torch.from_numpy(
            grammar_bitmask[mask_indices])
        # It's not guaranteed that all requests in this batch require
        # structured output, so create a bool tensor to represent
        # the requests that need structured output.
        struct_out_indices = torch.tensor(struct_out_indices, dtype=torch.long)
        self.require_structured_out_cpu[struct_out_indices] = True
        return self.require_structured_out_cpu[:num_reqs].to(logits.device), \
            self.grammar_bitmask_cpu[:num_reqs].to(logits.device), \
            self.structured_decode_arange.to(logits.device)

1810
1811
1812
1813
1814
1815
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
1816
1817
        assert self.mm_budget is not None

1818
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
1819
1820
            model_config=self.model_config,
            seq_len=self.max_num_tokens,
1821
            mm_counts={modality: 1},
1822
            cache=self.mm_budget.cache,
1823
        )
1824
1825
1826
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
1827
1828
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
1829

1830
1831
        return next(grouped_mm_kwargs
                    for _, _, grouped_mm_kwargs in group_mm_kwargs_by_modality(
1832
                        dummy_mm_items,
1833
1834
1835
                        device=self.device,
                        pin_memory=self.pin_memory,
                    ))
1836

1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848

def _get_req_paddings(min_req_size: int, max_req_size: int) -> list[int]:
    logger.info("Preparing request paddings:")
    # assert min_req_size is power of 2
    assert (min_req_size & (min_req_size - 1) == 0) and min_req_size > 0
    paddings: list = []
    num = max(MIN_NUM_SEQS, min_req_size)
    while num <= max_req_size and (len(paddings) == 0 or paddings[-1] != num):
        paddings.append(num)
        logger.info("    %d", num)
        num = _get_padded_num_reqs_with_upper_limit(num + 1, max_req_size)
    return paddings
1849
1850


1851
def _get_padded_num_reqs_with_upper_limit(x: int, upper_limit: int) -> int:
1852
    res = MIN_NUM_SEQS if x <= MIN_NUM_SEQS else 1 << (x - 1).bit_length()
1853
    return min(res, upper_limit)
1854
1855


1856
1857
def _get_token_paddings(min_token_size: int, max_token_size: int,
                        padding_gap: int) -> list[int]:
1858
1859
    """Generate a list of padding size, starting from min_token_size, 
    ending with a number that can cover max_token_size
1860

1861
1862
1863
    If padding_gap == 0 then:
        increase 2X each time (exponential)
    else:
1864
        first increase the size to twice,
1865
        then increase the padding size by padding_gap.
1866
    """
1867
1868
    # assert min_token_size is power of 2
    assert (min_token_size & (min_token_size - 1) == 0) and min_token_size > 0
1869
1870
    paddings = []
    num = min_token_size
1871
1872

    if padding_gap == 0:
1873
        logger.info("Using exponential token paddings:")
1874
        while True:
1875
1876
            logger.info("    %d", num)
            paddings.append(num)
1877
1878
            if num >= max_token_size:
                break
1879
1880
            num *= 2
    else:
1881
        logger.info("Using incremental token paddings:")
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
        while num <= padding_gap:
            logger.info("    %d", num)
            paddings.append(num)
            num *= 2
        num //= 2
        while num < max_token_size:
            num += padding_gap
            logger.info("    %d", num)
            paddings.append(num)

1892
1893
1894
1895
1896
1897
1898
1899
1900
    return paddings


def _get_padded_token_len(paddings: list[int], x: int) -> int:
    """Return the first element in paddings list greater or equal to x.
    """
    index = bisect.bisect_left(paddings, x)
    assert index < len(paddings)
    return paddings[index]
1901
1902


1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
def _make_src_and_dst_indices(
    src_block_ids: list[int],
    dst_block_ids: list[int],
    src_device: Union[torch.device, str],
    dst_device: Union[torch.device, str],
) -> tuple[torch.Tensor, torch.Tensor]:
    src_indices = torch.tensor(src_block_ids,
                               device=src_device,
                               dtype=torch.int64)
    dst_indices = torch.tensor(dst_block_ids,
                               device=dst_device,
                               dtype=torch.int64)
    return src_indices, dst_indices


@torch.compile(backend="openxla")
def _insert_blocks_to_tpu(
    cpu_cache: torch.Tensor,
    tpu_cache: torch.Tensor,
    cpu_block_indices: torch.Tensor,
    tpu_block_indices: torch.Tensor,
) -> None:
    torch.ops.xla.dynamo_set_buffer_donor_(tpu_cache, True)
    tpu_cache[tpu_block_indices] = cpu_cache[cpu_block_indices].to(
        tpu_cache.device)


@torch.compile(backend="openxla")
def _swap_out_tpu_blocks(
    tpu_cache: torch.Tensor,
    cpu_cache: torch.Tensor,
    tpu_block_indices: torch.Tensor,
    cpu_block_indices: torch.Tensor,
) -> None:
    """ tpu blocks to cpu blocks"""
    torch.ops.xla.dynamo_set_buffer_donor_(tpu_cache, True)
    cpu_cache[cpu_block_indices] = tpu_cache[tpu_block_indices].cpu()


def copy_kv_blocks(
    src_kv_caches: dict[str, torch.Tensor],
    dst_kv_caches: dict[str, torch.Tensor],
    src_block_ids: list[int],
    dst_block_ids: list[int],
    direction: Literal["h2d", "d2h"],
) -> None:
    """Copy kv blocks between different buffers."""
    if not src_kv_caches or not dst_kv_caches or \
       not src_block_ids or not dst_block_ids or \
       len(src_block_ids) != len(dst_block_ids):
        return

    src_device = next(iter(src_kv_caches.values())).device
    dst_device = next(iter(dst_kv_caches.values())).device

    src_indices, dst_indices = _make_src_and_dst_indices(
        src_block_ids=src_block_ids,
        dst_block_ids=dst_block_ids,
        src_device=src_device,
        dst_device=dst_device)

    _copy_fn = _insert_blocks_to_tpu if direction == "h2d" else \
               _swap_out_tpu_blocks
    for layer_name in src_kv_caches:
        src_tensor = src_kv_caches[layer_name]
        dst_tensor = dst_kv_caches[layer_name]
        _copy_fn(src_tensor, dst_tensor, src_indices, dst_indices)


1972
1973
def _get_padded_num_kv_cache_update_slices(num_tokens: int, max_num_reqs: int,
                                           page_size: int) -> int:
1974
1975
    """Calculates the padded number of KV cache update slices to avoid
    recompilation."""
1976
1977
1978
1979
1980
    # NOTE(chengjiyao): let's say R_i is the token num for i-th request,
    # so it occupies most 2 + R_i // page_size pages. The total maximum
    # possible number of pages needed is sum(2 + R_i // page_size), which
    # is <= 2 * max_num_reqs + sum(R_i) // page_size
    # = 2 * max_num_reqs + num_tokens // page_size
1981
1982
1983
1984
1985
    padded_num_slices = 2 * max_num_reqs + num_tokens // page_size
    padded_num_slices = min(padded_num_slices, num_tokens)
    return padded_num_slices


1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
def _get_num_slices_per_kv_cache_update_block(page_size_bytes: int) -> int:
    """Find the optimum number of slices to copy per Pallas program instance.

    Increasing the number of slices copied in one instance of the kernel program
    will increase HBM bandwidth utilization via more in-flight DMAs.

    However, it will also use more VMEM, and experimentally, we observed
    performance regression at 128 slices on v6e, likely due to running
    out of scalar registers. Thus this function will limit the number of
    slices to 64.
    """
1997
1998
1999
    # The default vmem_limit_bytes of a pallas kernel is 32MB. Here we
    # calculate num_slices_per_block based on 16MB in case any register spills.
    vmem_limit = 16 * 1024 * 1024
2000
2001
2002
2003
2004
2005
2006
2007
    num_slices_per_block = vmem_limit // page_size_bytes
    assert num_slices_per_block > 0, "Number of slices should be positive"
    num_slices_per_block = prev_power_of_2(num_slices_per_block)
    if num_slices_per_block > 64:
        num_slices_per_block = 64
    return num_slices_per_block


2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
def replace_set_lora(model):

    def _tpu_set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
        embeddings_tensor: Optional[torch.Tensor],
        bias: Optional[torch.Tensor] = None,
    ):
        # TODO: The integer index leads to a recompilation, but converting it
        # to a tensor doesn't seem to work anymore. This might be fixed with a
        # later release of torch_xla.
        self._original_set_lora(index, lora_a, lora_b, embeddings_tensor, bias)
        xm.mark_step()

    def _tpu_reset_lora(self, index: int):
        self._original_reset_lora(index)
        xm.mark_step()

    for _, module in model.named_modules():
        if isinstance(module, BaseLayerWithLoRA):
            module._original_set_lora = module.set_lora
            module._original_reset_lora = module.reset_lora
            module.set_lora = _tpu_set_lora.__get__(module, module.__class__)
            module.reset_lora = _tpu_reset_lora.__get__(
                module, module.__class__)