tpu_model_runner.py 74.2 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, Optional, 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
19
from vllm.attention.backends.abstract import AttentionType
from vllm.attention.layer import Attention
20
from vllm.compilation.wrapper import TorchCompileWrapperWithCustomDispatcher
21
from vllm.config import ParallelConfig, VllmConfig, get_layers_from_vllm_config
22
from vllm.forward_context import set_forward_context
23
from vllm.logger import init_logger
24
from vllm.lora.layers import BaseLayerWithLoRA
25
from vllm.model_executor.model_loader import get_model_loader
26
from vllm.model_executor.model_loader.tpu import TPUModelLoader
27
from vllm.multimodal import MULTIMODAL_REGISTRY
28
29
from vllm.multimodal.inputs import (BatchedTensorInputs, MultiModalKwargs,
                                    PlaceholderRange)
30
from vllm.multimodal.utils import group_mm_inputs_by_modality
31
from vllm.sequence import IntermediateTensors
32
from vllm.utils import LayerBlockType, cdiv, is_pin_memory_available
33
from vllm.v1.attention.backends.pallas import (PallasAttentionBackend,
34
                                               PallasMetadata)
35
from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
36
37
38
from vllm.v1.kv_cache_interface import (AttentionSpec, FullAttentionSpec,
                                        KVCacheConfig, KVCacheSpec,
                                        SlidingWindowSpec)
39
from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, LogprobsTensors,
40
                             ModelRunnerOutput)
41
42
from vllm.v1.sample.tpu.metadata import TPUSupportedSamplingMetadata
from vllm.v1.sample.tpu.sampler import Sampler as TPUSampler
43
44
from vllm.v1.utils import bind_kv_cache
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
45
from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
46

47
from .utils import sanity_check_mm_encoder_outputs
48

49
if TYPE_CHECKING:
50
    from vllm.v1.core.sched.output import SchedulerOutput
51
52
53
54
55
56

logger = init_logger(__name__)

# Here we utilize the behavior that out-of-bound index is ignored.
# FIXME(woosuk): Find a more reliable way to prevent possible bugs.
_PAD_SLOT_ID = 1_000_000_000
57
INVALID_TOKEN_ID = -1
58
59
# Smallest output size
MIN_NUM_SEQS = 8
60
61


62
63
64
65
66
67
68
69
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
#########################################################
# 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.
97
class TPUModelRunner(LoRAModelRunnerMixin):
98
99
100
101
102

    def __init__(
        self,
        vllm_config: VllmConfig,
        device: torch.device,
103
        original_parallel_config: Optional[ParallelConfig] = None,
104
105
106
107
108
109
110
    ):
        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
111
        self.original_parallel_config = original_parallel_config
112
113
114
115
116
117
118
119
120
121
122
        self.scheduler_config = vllm_config.scheduler_config
        self.speculative_config = vllm_config.speculative_config
        self.prompt_adapter_config = vllm_config.prompt_adapter_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
123
        self.check_recompilation = envs.VLLM_XLA_CHECK_RECOMPILATION
124

125
126
127
128
129
130
131
132
        # 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'))

133
        self.enforce_eager = model_config.enforce_eager
134
135
136
137

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

138
139
        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
140
        self._hidden_states_dtype = self.dtype
141
142
143
144
145
146

        self.is_multimodal_model = model_config.is_multimodal_model
        self.sliding_window = model_config.get_sliding_window()
        self.block_size = cache_config.block_size
        self.max_model_len = model_config.max_model_len
        self.max_num_blocks_per_req = cdiv(self.max_model_len, self.block_size)
147
148
        # InputBatch needs to work with sampling tensors greater than padding
        # to avoid dynamic shapes. Also, avoid suboptimal alignment.
149
        self.max_num_reqs = max(scheduler_config.max_num_seqs, MIN_NUM_SEQS)
150
151
152
153
154
155
156
        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]
157
158
159
160
161
162
163
164
165

        # 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()
166
        self.vocab_size = model_config.get_vocab_size()
167

168
169
170
        if self.lora_config is not None:
            self.vocab_size += self.lora_config.lora_extra_vocab_size

171
172
173
174
175
176
177
178
179
        # Multi-modal data support
        self.mm_registry = MULTIMODAL_REGISTRY
        self.uses_mrope = model_config.uses_mrope
        # TODO: Support M-RoPE (e.g, Qwen2-VL)
        assert not self.uses_mrope, "TPU does not support M-RoPE yet."

        encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
            model_config=model_config,
            scheduler_config=scheduler_config,
180
            mm_registry=self.mm_registry,
181
182
183
184
185
        )
        self.max_num_encoder_input_tokens = encoder_compute_budget
        self.encoder_cache_size = encoder_cache_size

        # Lazy initialization
186
        self.model: nn.Module  # Set after load_model
187
188
189
190
191
192
        self.kv_caches: list[torch.Tensor] = []
        # req_id -> (input_id -> encoder_output)
        self.encoder_cache: dict[str, dict[int, torch.Tensor]] = {}

        # Request states.
        self.requests: dict[str, CachedRequestState] = {}
193

194
195
196
197
198
199
200
201
202
203
204
        # 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(),
            block_size=self.block_size,
        )

205
206
207
208
209
210
211
212
213
214
215
216
217
        # 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(
218
            (self.max_num_reqs, self.max_num_blocks_per_req),
219
            dtype=torch.int32,
220
221
222
223
224
225
226
227
228
229
230
231
232
            device="cpu")

        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()
233
234
235

        # Range tensor with values [0 .. self.max_num_tokens - 1].
        # Used to initialize positions / context_lens / seq_lens
236
237
        # Keep in int64 to avoid overflow with long context
        self.arange_np = np.arange(self.max_num_tokens, dtype=np.int64)
238
239
        self.num_reqs_paddings = _get_req_paddings(
            min_req_size=MIN_NUM_SEQS, max_req_size=self.max_num_reqs)
240

241
242
243
244
245
246
247
248
249
250
251
252
253
254
        # 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)

255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
        # Get maximum number of mm items per modality (batch size).
        self.max_num_mm_items_by_modality = dict()
        if (self.is_multimodal_model and self.max_num_encoder_input_tokens > 0
                and self.encoder_cache_size > 0):
            max_tokens_by_modality_dict = (
                MULTIMODAL_REGISTRY.
                get_max_tokens_per_item_by_nonzero_modality(self.model_config))
            for modality, max_tokens in max_tokens_by_modality_dict.items():
                # Check how many items of this modality can be supported by
                # the encoder budget.
                encoder_budget = min(self.max_num_encoder_input_tokens,
                                     self.encoder_cache_size)

                max_num_mm_items_encoder_budget = cdiv(encoder_budget,
                                                       max_tokens)

                # Check how many items of this modality can be supported by
                # the decoder budget.
                max_mm_items_per_req = self.mm_registry.\
                    get_mm_limits_per_prompt(self.model_config)[modality]

                # NOTE: We do not consider max_num_batched_tokens on purpose
                # because the multimodal embeddings can be generated in advance
                # and chunked prefilled.
                max_num_mm_items_decoder_budget = self.max_num_reqs * \
                    max_mm_items_per_req

                max_num_mm_items = min(max_num_mm_items_encoder_budget,
                                       max_num_mm_items_decoder_budget)
                self.max_num_mm_items_by_modality[modality] = max_num_mm_items

286
287
288
289
290
291
292
293
294
        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

295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
    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))

320
321
322
323
324
325
326
327
    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:
328
            True if there is a new/resumed/paused/finished request.
329
330
331
332
333
            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)
334
            self.encoder_cache.pop(req_id, None)
335
336
337
338
339
340
341

        # 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.
342
        removed_req_indices: list[int] = []
343
344
345
346
347
        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)

348
349
350
351
352
353
354
355
        # Free the cached encoder outputs.
        for req_id, input_id in scheduler_output.free_encoder_input_ids:
            encoder_outputs = self.encoder_cache.get(req_id)
            if encoder_outputs is not None:
                encoder_outputs.pop(input_id, None)
                if not encoder_outputs:
                    self.encoder_cache.pop(req_id, None)

356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
        # 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)

373
        req_ids_to_add: list[str] = []
374
375
376
377
378
379
380
381
382
383
384
        # Add new requests to the cached states.
        for new_req_data in scheduler_output.scheduled_new_reqs:
            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,
                mm_inputs=new_req_data.mm_inputs,
                mm_positions=new_req_data.mm_positions,
                sampling_params=sampling_params,
385
                generator=None,
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
                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.
        for req_data in scheduler_output.scheduled_cached_reqs:
            req_id = req_data.req_id
            req_state = self.requests[req_id]

            # Update the cached states.
            req_state.num_computed_tokens = req_data.num_computed_tokens
            if not req_data.resumed_from_preemption:
                # Append the new blocks to the existing block IDs.
                req_state.block_ids.extend(req_data.new_block_ids)
            else:
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
                req_state.block_ids = req_data.new_block_ids

            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] = (
                req_data.num_computed_tokens)
420
421
            self.input_batch.block_table.append_row(req_data.new_block_ids,
                                                    req_index)
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438

        # 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)
439

440
441
442
443
444
        return len(unscheduled_req_ids) > 0 or len(req_ids_to_add) > 0

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

445
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
446
        """
447
        Generates the KVCacheSpec by parsing the kv cache format from each
448
449
        Attention module in the static forward context.
        Returns:
450
            KVCacheSpec: A dictionary mapping layer names to their KV cache
451
452
453
            format. Layers that do not need KV cache are not included.
        """

454
        layers = get_layers_from_vllm_config(self.vllm_config, Attention)
455
        block_size = self.vllm_config.cache_config.block_size
456
        kv_cache_spec: dict[str, KVCacheSpec] = {}
457
        for layer_name, attn_module in layers.items():
458
            if attn_module.attn_type == AttentionType.DECODER:
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
                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,
                        dtype=attn_module.dtype,
                        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,
                        dtype=attn_module.dtype,
                        use_mla=False,
                    )
476
477
478
479
480
481
482
483
484
485
486
487
            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

488
    def _prepare_inputs(self, scheduler_output: "SchedulerOutput"):
489
490
491
492
493
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        assert total_num_scheduled_tokens > 0
        num_reqs = self.input_batch.num_reqs
        assert num_reqs > 0

494
495
496
497
        # Get the number of scheduled tokens for each request.
        num_scheduled_tokens_per_req = []
        max_num_scheduled_tokens_all_reqs = 0
        for req_id in self.input_batch.req_ids[:num_reqs]:
498
            assert req_id is not None
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
            num_tokens = scheduler_output.num_scheduled_tokens[req_id]
            num_scheduled_tokens_per_req.append(num_tokens)
            max_num_scheduled_tokens_all_reqs = max(
                max_num_scheduled_tokens_all_reqs, num_tokens)
        num_scheduled_tokens_per_req = np.array(num_scheduled_tokens_per_req,
                                                dtype=np.int32)
        assert max_num_scheduled_tokens_all_reqs > 0

        # 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.
535
536
        torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(),
                           0,
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
                           torch.from_numpy(token_indices),
                           out=self.input_ids_cpu[:total_num_scheduled_tokens])

        # Calculate the slot mapping.
        # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        # -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
        # where K is the max_num_blocks_per_req and the block size is 2.
        # NOTE(woosuk): We can't simply use `token_indices // block_size` here
        # because M (max_model_len) is not necessarily divisible by block_size.
        # req_indices: # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
        block_table_indices = (req_indices * self.max_num_blocks_per_req +
                               positions_np // self.block_size)
        # 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.
552
        block_table_cpu = self.input_batch.block_table[0].get_cpu_tensor()
553
554
555
556
        block_numbers = block_table_cpu.flatten()[block_table_indices].numpy()
        block_offsets = positions_np % self.block_size
        np.add(block_numbers * self.block_size,
               block_offsets,
557
               out=self.input_batch.block_table[0].
558
               slot_mapping_np[:total_num_scheduled_tokens])
559
560
561
562
563

        # 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])
564
        self.query_start_loc_np[num_reqs + 1:] = 1
565
566
567
568
569
570

        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.
571
        padded_total_num_scheduled_tokens = _get_padded_token_len(
572
            self.num_tokens_paddings, total_num_scheduled_tokens)
573
574
575
        # 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
576
577
578
579
580
581
        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)
582
        self.input_batch.block_table[0].slot_mapping_cpu[
583
584
            total_num_scheduled_tokens:] = _PAD_SLOT_ID
        slot_mapping = (
585
            self.input_batch.block_table[0].
586
587
            slot_mapping_cpu[:padded_total_num_scheduled_tokens].to(
                self.device))
588
589
        block_tables = self.block_table_cpu[:self.max_num_reqs]
        block_tables[:num_reqs, :self.max_num_blocks_per_req] = (
590
            self.input_batch.block_table[0].get_cpu_tensor()[:num_reqs])
591
592
        block_tables = block_tables.to(self.device)
        query_start_loc = self.query_start_loc_cpu[:self.max_num_reqs + 1].to(
593
            self.device)
594
        seq_lens = self.seq_lens_cpu[:self.max_num_reqs].to(self.device)
595

596
597
598
599
600
601
602
603
604
605
606
        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)

607
608
        attn_metadata = PallasMetadata(
            slot_mapping=slot_mapping,
609
            block_tables=block_tables,
610
611
            context_lens=seq_lens,
            query_start_loc=query_start_loc,
612
613
614
            num_seqs=torch.tensor([num_reqs],
                                  dtype=torch.int32,
                                  device=self.device),
615
        )
616
617
618
619
620
        # 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.
621
622
        padded_num_reqs = _get_padded_num_reqs_with_upper_limit(
            num_reqs, self.max_num_reqs)
623
624
        # Indices at which we sample (positions of last token in the sequence).
        # Padded to avoid recompiling when `num_reqs` varies.
625
626
        logits_indices = self.query_start_loc_cpu[1:padded_num_reqs + 1] - 1
        logits_indices = logits_indices.to(self.device)
627

628
629
630
631
632
633
634
635
636
637
638
        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)

639
640
641
642
643
644
645
        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
        }
        return per_layer_attn_metadata, logits_indices, padded_num_reqs
646

647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
    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"):
673
674
675
676
677
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
            return

        # Batch the multi-modal inputs.
678
679
        mm_inputs = list[MultiModalKwargs]()
        req_ids_pos = list[tuple[str, int, PlaceholderRange]]()
680
681
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
682
683
684
685
686

            for mm_input_id in encoder_input_ids:
                mm_inputs.append(req_state.mm_inputs[mm_input_id])
                req_ids_pos.append(
                    (req_id, mm_input_id, req_state.mm_positions[mm_input_id]))
687
688
689
690
691
692
693
694
695
696
697
698
699

        # 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.
        grouped_mm_inputs_list = group_mm_inputs_by_modality(mm_inputs)

        encoder_outputs = []
        for grouped_mm_inputs in grouped_mm_inputs_list:
            batched_mm_inputs = MultiModalKwargs.batch(grouped_mm_inputs)
700
701
702
703
704
            batched_mm_inputs = MultiModalKwargs.as_kwargs(
                batched_mm_inputs,
                dtype=self.model_config.dtype,
                device=self.device,
            )
705
706
707
708
709
710
711
712

            # 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.
713
            xm.mark_step()
714
715
            curr_group_outputs = self.model.get_multimodal_embeddings(
                **batched_mm_inputs)
716
            xm.mark_step()
717

718
719
720
721
722
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
                expected_num_items=len(grouped_mm_inputs),
            )

723
724
725
726
727
728
            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)
729
730

        # Cache the encoder outputs.
731
732
733
        # 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.
734
735
736
737
        for (req_id, input_id, pos_info), output in zip(
                req_ids_pos,
                encoder_outputs,
        ):
738
739
            if req_id not in self.encoder_cache:
                self.encoder_cache[req_id] = {}
740
741
742
            assert pos_info.is_embed is None, "Expected all positions to be"\
                " contiguous and embeddings."
            self.encoder_cache[req_id][input_id] = output
743
744

    def _gather_mm_embeddings(
745
746
747
        self,
        scheduler_output: "SchedulerOutput",
    ) -> list[torch.Tensor]:
748
        mm_embeds: list[torch.Tensor] = []
749
750
751
752
753
754
        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
755
756
757
758
            # 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.
759
            for i, pos_info in enumerate(mm_positions):
760
761
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776

                # 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

                assert req_id in self.encoder_cache
                assert i in self.encoder_cache[req_id]
777
778
                assert pos_info.is_embed is None, "Expected all positions to"\
                " be contiguous and embeddings."
779
                encoder_output = self.encoder_cache[req_id][i]
780
                mm_embeds.append(encoder_output)
781
        return mm_embeds
782

783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
    def _get_model_inputs(self, input_ids: torch.Tensor,
                          mm_embeds: list[torch.Tensor]):
        if self.is_multimodal_model:
            # 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.
            if mm_embeds:
                inputs_embeds = self.model.get_input_embeddings(
                    input_ids, mm_embeds)
            else:
                inputs_embeds = self.model.get_input_embeddings(input_ids)
            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

802
803
804
805
    @torch.no_grad()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
806
        intermediate_tensors: Optional[IntermediateTensors] = None,
807
808
809
    ) -> ModelRunnerOutput:
        # Update cached state
        self._update_states(scheduler_output)
810
        if not scheduler_output.total_num_scheduled_tokens:
811
            # Return empty ModelRunnerOutput if there's no work to do.
812
            return EMPTY_MODEL_RUNNER_OUTPUT
813

814
815
        if self.is_multimodal_model:
            # Run the multimodal encoder if any.
816
817
            self._execute_mm_encoder(scheduler_output)
            mm_embeds = self._gather_mm_embeddings(scheduler_output)
818
        else:
819
            mm_embeds = []
820
        xm.mark_step()
821
        # Prepare inputs
822
823
        attn_metadata, logits_indices, padded_num_reqs = self._prepare_inputs(
            scheduler_output)
824
825
826
        input_ids, inputs_embeds = self._get_model_inputs(
            self.input_ids, mm_embeds)
        xm.mark_step()
827
        num_reqs = self.input_batch.num_reqs
828
        # Run the decoder
829
830
831
832
        with set_forward_context(
                attn_metadata,
                self.vllm_config,
                num_tokens=scheduler_output.total_num_scheduled_tokens):
833
            hidden_states = self.model(
834
835
836
                input_ids=input_ids,
                positions=self.position_ids,
                inputs_embeds=inputs_embeds,
837
            )
838
839
        hidden_states = self.select_hidden_states(hidden_states,
                                                  logits_indices)
840
        logits = self.compute_logits(hidden_states)
841
842
        tpu_sampling_metadata = TPUSupportedSamplingMetadata.\
            from_input_batch(self.input_batch, padded_num_reqs, self.device)
843
844
845
846
847
848
        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)
849
850
        selected_token_ids = self.sample_from_logits_func(
            logits, tpu_sampling_metadata)
851
852
853
854
855
856
857
        # 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

858
        # Remove padding on cpu and keep dynamic op outside of xla graph.
859
        selected_token_ids = selected_token_ids.cpu()[:num_reqs]
860
861
        logprobs_lists = logprobs.tolists() \
            if tpu_sampling_metadata.logprobs else None
862

863
864
        # Update the cache state concurrently. Code above will not block until
        # we use `selected_token_ids`. Add mark_step if post-processing changes
865
        request_seq_lens: list[tuple[int, CachedRequestState, int]] = []
866
        discard_sampled_tokens_req_indices = []
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
        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)

882
883
884
885
                # 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)

886
887
888
        assert all(
            req_id is not None for req_id in
            self.input_batch.req_ids[:num_reqs]), "req_ids contains None"
889
        req_ids = cast(list[str], self.input_batch.req_ids[:num_reqs])
890

891
        prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
892
        for req_id in self.input_batch.req_ids[:num_reqs]:
893
894
            prompt_logprobs_dict[req_id] = None

895
896
897
        max_gen_len = selected_token_ids.shape[-1]
        if max_gen_len == 1:
            valid_sampled_token_ids = selected_token_ids.tolist()
898

899
900
901
902
903
904
905
            # 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
906
907
908
909
910
            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
911

912
913
914
915
916
917
918
919
920
921
922
923
924
925
        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])

926
        model_runner_output = ModelRunnerOutput(
927
            req_ids=req_ids,
928
            req_id_to_index=self.input_batch.req_id_to_index,
929
            sampled_token_ids=valid_sampled_token_ids,
930
            spec_token_ids=None,
931
            logprobs=logprobs_lists,
932
            prompt_logprobs_dict=prompt_logprobs_dict,
933
        )
934
935
936
937
938

        # 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")

939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
        return model_runner_output

    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):
958
959
960
961
962
963
964
            if self.use_spmd:
                tpu_loader = TPUModelLoader(
                    load_config=self.vllm_config.load_config)
                model = tpu_loader.load_model(
                    vllm_config=self.vllm_config,
                    model_config=self.vllm_config.model_config,
                    mesh=self.mesh)
965
            else:
966
967
968
969
970
971
972
973
974
975
976
977
                # model = get_model(vllm_config=self.vllm_config)
                model_loader = get_model_loader(self.load_config)
                if not hasattr(self, "model"):
                    logger.info("Loading model from scratch...")
                    model = model_loader.load_model(
                        vllm_config=self.vllm_config,
                        model_config=self.model_config)
                else:
                    logger.info("Model was already initialized. \
                            Loading weights inplace...")
                    model_loader.load_weights(self.model,
                                              model_config=self.model_config)
978
979
980
981
        if self.lora_config is not None:
            model = self.load_lora_model(model, self.model_config,
                                         self.scheduler_config,
                                         self.lora_config, self.device)
982
            replace_set_lora(model)
983

984
985
        # Sync all pending XLA execution during model initialization and weight
        # loading.
986
987
        xm.mark_step()
        xm.wait_device_ops()
988
989
        if not hasattr(self, "model"):
            self.model = model
990
        self.sampler = TPUSampler()
991

992
    @torch.no_grad()
993
    def _dummy_run(self, num_tokens: int) -> None:
994
995
996
997
998
999
1000
        if self.is_multimodal_model:
            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),
1001
                                    dtype=torch.int32).to(self.device)
1002
            inputs_embeds = None
1003
        actual_num_reqs = min(num_tokens, self.max_num_reqs)
1004
        position_ids = torch.zeros(num_tokens,
1005
                                   dtype=torch.int32).to(self.device)
1006
        slot_mapping = torch.zeros(num_tokens,
1007
                                   dtype=torch.int64).to(self.device)
1008
1009
        block_tables = torch.zeros(
            (self.max_num_reqs, self.block_table_cpu.shape[1]),
1010
            dtype=torch.int32).to(self.device)
1011
        query_lens = [1] * self.max_num_reqs
1012
1013
1014
1015
        query_start_loc = torch.cumsum(torch.tensor([0] + query_lens,
                                                    dtype=torch.int32),
                                       dim=0,
                                       dtype=torch.int32).to(self.device)
1016
        context_lens = torch.ones((self.max_num_reqs, ),
1017
                                  dtype=torch.int32).to(self.device)
1018
        num_seqs = torch.tensor([actual_num_reqs],
1019
                                dtype=torch.int32).to(self.device)
1020
1021
1022
1023
1024
        attn_metadata = PallasMetadata(
            slot_mapping=slot_mapping,
            block_tables=block_tables,
            context_lens=context_lens,
            query_start_loc=query_start_loc,
1025
            num_seqs=num_seqs,
1026
        )
1027

1028
1029
1030
1031
        if self.is_multimodal_model:
            torch._dynamo.mark_dynamic(inputs_embeds, 0)
        else:
            torch._dynamo.mark_dynamic(input_ids, 0)
1032
1033
        torch._dynamo.mark_dynamic(position_ids, 0)
        torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 0)
1034

1035
1036
1037
1038
1039
1040
1041
        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
        }

1042
        with self.maybe_select_dummy_loras(
1043
1044
1045
                self.lora_config,
                np.array([num_tokens], dtype=np.int32)), set_forward_context(
                    per_layer_attn_metadata, self.vllm_config, 0):
1046
1047
1048
1049
            out = self.model(input_ids=input_ids,
                             positions=position_ids,
                             inputs_embeds=inputs_embeds)
        self._hidden_states_dtype = out.dtype
1050

1051
1052
1053
1054
1055
1056
1057
    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

1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
    def _precompile_mm_encoder(self) -> None:
        # Pre-compile MM encoder for all supported data modalities.
        hf_config = self.vllm_config.model_config.hf_config
        for mode, max_items_by_mode in \
            self.max_num_mm_items_by_modality.items():
            logger.info(
                "Compiling Multimodal %s Encoder with different input"
                " shapes.", mode)
            start = time.perf_counter()
            # No padding for MM encoder just yet.
            for num_items in range(1, max_items_by_mode + 1):
                logger.info("  -- mode: %s items: %d", mode, num_items)
                batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                    mode, num_items)
                # Run multimodal encoder.
                xm.mark_step()
                mm_embeds = self.model.\
                    get_multimodal_embeddings(**batched_dummy_mm_inputs)
                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)

1120
    def _precompile_backbone(self) -> None:
1121
1122
        logger.info("Compiling the model with different input shapes.")
        start = time.perf_counter()
1123
        for num_tokens in self.num_tokens_paddings:
1124
            logger.info("  -- num_tokens: %d", num_tokens)
1125
            self._dummy_run(num_tokens)
1126
1127
        xm.wait_device_ops()
        end = time.perf_counter()
1128
        logger.info("Compilation finished in %.2f [secs].", end - start)
1129
        self._update_num_xla_graphs("model backbone")
1130

1131
1132
1133
1134
1135
    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.")
1136
1137
        start = time.perf_counter()
        hsize = self.model_config.get_hidden_size()
1138
        for num_tokens in self.num_tokens_paddings:
1139
1140
            dummy_hidden = torch.zeros((num_tokens, hsize),
                                       device=self.device,
1141
                                       dtype=self._hidden_states_dtype)
1142
1143
1144
1145
1146
1147
1148
            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)
1149
1150
1151
1152
1153
1154
                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
1155
        xm.wait_device_ops()
1156
        end = time.perf_counter()
1157
        logger.info("Compilation finished in %.2f [secs].", end - start)
1158
        self._update_num_xla_graphs("select_hidden_states")
1159

1160
1161
    def _precompile_compute_logits(self) -> None:
        logger.info("Compiling compute_logits with different input shapes.")
1162
1163
1164
1165
1166
1167
        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)
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
            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.
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
            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
1220
1221
1222
                with self.maybe_select_dummy_loras(
                        self.lora_config, np.array([num_reqs],
                                                   dtype=np.int32)):
1223
1224
                    self.sample_from_logits_func(dummy_logits,
                                                 sampling_metadata)
1225
1226
1227
            logger.info("  -- num_seqs: %d", num_reqs)
        xm.wait_device_ops()
        end = time.perf_counter()
1228
1229
        logger.info("Compilation finished in %.2f [secs].", end - start)
        self._update_num_xla_graphs("sample_from_logits")
1230

1231
1232
1233
1234
1235
1236
1237
1238
1239
    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)
1240
1241
1242
            with self.maybe_select_dummy_loras(
                    self.lora_config, np.array([num_reqs], dtype=np.int32)):
                self.gather_logprobs(dummy_logits, dummy_tokens)
1243
1244
1245
1246
1247
1248
            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")

1249
1250
1251
1252
    def capture_model(self) -> None:
        """
        Precompile all the subgraphs with possible input shapes.
        """
1253
1254
1255
1256
1257
1258
1259
1260
        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()
1261

1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
    def profile_run(
        self,
        num_tokens: int,
    ) -> None:
        # Profile with multimodal encoder & encoder cache.
        # TODO: handle encoder-decoder models once we support them.
        if (self.is_multimodal_model and self.max_num_encoder_input_tokens > 0
                and self.encoder_cache_size > 0):

            # NOTE: Currently model is profiled with a single non-text
            # modality with the max possible input tokens even when
            # it supports multiple.
            dummy_data_modality, max_num_mm_items = max(
                self.max_num_mm_items_by_modality.items(), key=lambda t: t[1])

            encoder_budget = min(self.max_num_encoder_input_tokens,
                                 self.encoder_cache_size)

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

            # Create dummy batch of multimodal inputs.
            batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                dummy_data_modality, max_num_mm_items)

            # 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 in %.2f [secs].",
                end - start)

            assert len(dummy_encoder_outputs) == max_num_mm_items, (
                "Expected dimension 0 of encoder outputs to match the number "
                f"of multimodal data items: {max_num_mm_items}, got "
                f"{len(dummy_encoder_outputs)=} instead. This is most likely "
                "due to the 'get_multimodal_embeddings' method of the model "
                "not implemented correctly.")

            # Cache the dummy encoder outputs.
            self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))

        # Trigger compilation for general shape.
        self._dummy_run(num_tokens)

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

1321
1322
1323
1324
    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize KV cache based on `kv_cache_config`.
        Args:
1325
            kv_cache_config: Configuration for the KV cache, including the KV
1326
1327
            cache size of each layer
        """
1328
        if len(kv_cache_config.kv_cache_groups) > 1:
1329
1330
1331
1332
            raise NotImplementedError(
                "Hybrid models with more than one KV cache type are not "
                "supported yet.")

1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
        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(),
                block_size=kv_cache_config.kv_cache_groups[0].kv_cache_spec.
                block_size,
            )
        # Verify dtype compatibility between block_table_cpu and input_batch
1346
1347
1348
        assert self.block_table_cpu.dtype == self.input_batch.block_table[
            0].get_cpu_tensor().dtype

1349
        kv_caches: dict[str, torch.Tensor] = {}
1350

1351
1352
1353
1354
1355
1356
        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:
                tensor_config = kv_cache_config.tensors[layer_name]
                assert tensor_config.size % kv_cache_spec.page_size_bytes == 0
                num_blocks = tensor_config.size // kv_cache_spec.page_size_bytes
1357
                if isinstance(kv_cache_spec, AttentionSpec):
1358
1359
1360
1361
1362
1363
1364
1365
1366
                    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")
1367
1368
1369
1370
1371
                    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

1372
                    tpu_kv_cache = torch.zeros(kv_cache_shape,
1373
                                               dtype=dtype).to(self.device)
1374

1375
                    kv_caches[layer_name] = tpu_kv_cache
1376
1377
                else:
                    raise NotImplementedError
1378
1379
1380
1381
1382
1383

        bind_kv_cache(
            kv_caches,
            self.vllm_config.compilation_config.static_forward_context,
            self.kv_caches)

1384
1385
1386
1387
1388
        if self.use_spmd:
            # Shard KV Cache
            for cache in self.kv_caches:
                xs.mark_sharding(cache, self.mesh, (None, 'x', None, None))

1389
1390
    def reset_dynamo_cache(self):
        if self.is_multimodal_model:
1391
            compiled_model = self.model.get_language_model().model
1392
1393
1394
1395
1396
1397
1398
        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()
1399

1400
1401
1402
1403
1404
    @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)
1405
1406
1407
1408
    def compute_logits(self,
                       sample_hidden_states: torch.Tensor) -> torch.Tensor:
        return self.model.compute_logits(sample_hidden_states, None)

1409
1410
1411
    # 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)
1412
1413
1414
    def sample_from_logits(
            self, logits: torch.Tensor,
            sampling_metadata: TPUSupportedSamplingMetadata) -> torch.Tensor:
1415
1416
1417
1418
        """
        Sample with xla-friendly function. This function is to be traced 
        separately from `forward` for lighter compilation overhead.
        """
1419
1420
1421
1422
1423
        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
1424
1425
        return out_tokens

1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
    @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))

1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
    @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

1462
1463
    def get_multimodal_embeddings(self, *args, **kwargs):
        return self.model.get_multimodal_embeddings(*args, **kwargs)
1464

1465
1466
1467
    def get_input_embeddings(self, *args, **kwargs):
        return self.model.get_input_embeddings(*args, **kwargs)

1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
    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)

1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
    def _get_mm_dummy_batch(self, modality: str,
                            batch_size: int) -> BatchedTensorInputs:
        # Dummy data for pre-compiling multimodal models.
        dummy_request_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
            seq_len=self.max_num_tokens,
        )
        dummy_mm_data = dummy_request_data.multi_modal_data

        # Dummy data definition in V0 may contain multiple multimodal items
        # (e.g, multiple images) for a single request, therefore here we
        # always replicate first item by max_num_mm_items times since in V1
        # they are scheduled to be processed separately.
        assert isinstance(dummy_mm_data, MultiModalKwargs), (
            "Expected dummy multimodal data to be of type "
            f"MultiModalKwargs, got {type(dummy_mm_data)=} instead. "
            "This is most likely due to the model not having a merged "
            "processor.")

        # When models have a merged processor, their dummy data is
        # already batched `MultiModalKwargs`, therefore we take the first
        # `MultiModalKwargsItem` from the desired modality to profile on.
        dummy_mm_item = dummy_mm_data.get_item(modality=modality, item_index=0)
        dummy_mm_kwargs = MultiModalKwargs.from_items([dummy_mm_item])

        batched_dummy_mm_inputs = MultiModalKwargs.batch([dummy_mm_kwargs] *
                                                         batch_size)
1532
1533
1534
1535
1536
        return MultiModalKwargs.as_kwargs(
            batched_dummy_mm_inputs,
            dtype=self.model_config.dtype,
            device=self.device,
        )
1537

1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549

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
1550
1551


1552
def _get_padded_num_reqs_with_upper_limit(x: int, upper_limit: int) -> int:
1553
    res = MIN_NUM_SEQS if x <= MIN_NUM_SEQS else 1 << (x - 1).bit_length()
1554
    return min(res, upper_limit)
1555
1556


1557
1558
def _get_token_paddings(min_token_size: int, max_token_size: int,
                        padding_gap: int) -> list[int]:
1559
1560
    """Generate a list of padding size, starting from min_token_size, 
    ending with a number that can cover max_token_size
1561

1562
1563
1564
    If padding_gap == 0 then:
        increase 2X each time (exponential)
    else:
1565
        first increase the size to twice,
1566
        then increase the padding size by padding_gap.
1567
    """
1568
1569
    # assert min_token_size is power of 2
    assert (min_token_size & (min_token_size - 1) == 0) and min_token_size > 0
1570
1571
    paddings = []
    num = min_token_size
1572
1573

    if padding_gap == 0:
1574
        logger.info("Using exponential token paddings:")
1575
        while True:
1576
1577
            logger.info("    %d", num)
            paddings.append(num)
1578
1579
            if num >= max_token_size:
                break
1580
1581
            num *= 2
    else:
1582
        logger.info("Using incremental token paddings:")
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
        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)

1593
1594
1595
1596
1597
1598
1599
1600
1601
    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]
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630


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__)