tpu_model_runner.py 91.3 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import bisect
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import gc
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import time
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from typing import TYPE_CHECKING, Any, cast
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from unittest.mock import patch

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

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import vllm.envs as envs
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from vllm.attention import Attention
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from vllm.attention.backends.abstract import AttentionType
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from vllm.attention.layer import MLAAttention
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from vllm.attention.layers.chunked_local_attention import ChunkedLocalAttention
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from vllm.compilation.wrapper import TorchCompileWithNoGuardsWrapper
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from vllm.config import (
    ParallelConfig,
    VllmConfig,
    get_layers_from_vllm_config,
    update_config,
)
from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
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from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
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from vllm.forward_context import set_forward_context
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from vllm.logger import init_logger
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from vllm.lora.layers import BaseLayerWithLoRA
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
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from vllm.model_executor.model_loader import get_model_loader
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from vllm.model_executor.model_loader.tpu import TPUModelLoader
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from vllm.model_executor.models.interfaces import (
    SupportsMultiModal,
    supports_transcription,
)
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from vllm.model_executor.models.interfaces_base import (
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    is_pooling_model,
    is_text_generation_model,
)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
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from vllm.multimodal.utils import group_mm_kwargs_by_modality
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
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from vllm.utils.math_utils import cdiv, prev_power_of_2
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from vllm.utils.platform_utils import is_pin_memory_available
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from vllm.v1.attention.backends.pallas import (
    TPU_STR_DTYPE_TO_TORCH_DTYPE,
    PallasAttentionBackend,
    PallasMetadata,
    get_page_size_bytes,
)
from vllm.v1.kv_cache_interface import (
    AttentionSpec,
    FullAttentionSpec,
    KVCacheConfig,
    KVCacheSpec,
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    MLAAttentionSpec,
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    SlidingWindowSpec,
)
from vllm.v1.outputs import (
    EMPTY_MODEL_RUNNER_OUTPUT,
    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
)
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from vllm.v1.sample.tpu.metadata import TPUSupportedSamplingMetadata
from vllm.v1.sample.tpu.sampler import Sampler as TPUSampler
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from vllm.v1.worker.kv_connector_model_runner_mixin import (
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    KVConnectorModelRunnerMixin,
    KVConnectorOutput,
)
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from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
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from vllm.v1.worker.tpu_input_batch import CachedRequestState, InputBatch
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from .utils import (
    MultiModalBudget,
    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
    sanity_check_mm_encoder_outputs,
)
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if TYPE_CHECKING:
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    from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
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logger = init_logger(__name__)

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INVALID_TOKEN_ID = -1
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# Smallest output size
MIN_NUM_SEQS = 8
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#########################################################
# 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.
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class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
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    def __init__(
        self,
        vllm_config: VllmConfig,
        device: torch.device,
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        original_parallel_config: ParallelConfig | None = None,
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    ):
        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
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        self.original_parallel_config = original_parallel_config
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        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
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        self.check_recompilation = envs.VLLM_XLA_CHECK_RECOMPILATION
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        # 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))
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            self.mesh = xs.Mesh(device_ids, mesh_shape, ("x", "y"))
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        self.enforce_eager = model_config.enforce_eager
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        self.num_xla_graphs = 0
        self._update_num_xla_graphs("init")

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        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
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        if cache_config.cache_dtype == "auto":
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            model_dtype = self.dtype
            if isinstance(model_dtype, str):
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                self.kv_cache_dtype = TPU_STR_DTYPE_TO_TORCH_DTYPE[model_dtype]
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            else:
                self.kv_cache_dtype = model_dtype
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        else:
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            self.kv_cache_dtype = TPU_STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
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        self._hidden_states_dtype = self.dtype
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        self.sliding_window = model_config.get_sliding_window()
        self.block_size = cache_config.block_size
        self.max_model_len = model_config.max_model_len
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        self.most_model_len = envs.VLLM_TPU_MOST_MODEL_LEN
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        self.max_num_blocks_per_req = cdiv(self.max_model_len, self.block_size)
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        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
        )
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        # InputBatch needs to work with sampling tensors greater than padding
        # to avoid dynamic shapes. Also, avoid suboptimal alignment.
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        self.max_num_reqs = max(scheduler_config.max_num_seqs, MIN_NUM_SEQS)
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        self.num_tokens_paddings = _get_token_paddings(
            min_token_size=16,
            max_token_size=scheduler_config.max_num_batched_tokens,
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            padding_gap=envs.VLLM_TPU_BUCKET_PADDING_GAP,
        )
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        # 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]
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        # Model-related.
        self.num_attn_layers = model_config.get_num_layers_by_block_type(
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            parallel_config, "attention"
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        )
        self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
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        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()
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        self.vocab_size = model_config.get_vocab_size()
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        # Multi-modal data support
        self.mm_registry = MULTIMODAL_REGISTRY
        self.uses_mrope = model_config.uses_mrope
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        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
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            model_config
        )
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        # TODO: Support M-RoPE (e.g, Qwen2-VL)
        assert not self.uses_mrope, "TPU does not support M-RoPE yet."

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        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,
                )
            )
        )
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        # Lazy initialization
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        self.model: nn.Module  # Set after load_model
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        self.kv_caches: list[torch.Tensor] = []
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        # mm_hash -> encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
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        # Request states.
        self.requests: dict[str, CachedRequestState] = {}
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        # 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(),
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            block_sizes=[self.block_size],
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            kernel_block_sizes=[self.cache_config.block_size],
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        )

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        # Cached torch/numpy tensor
        # The pytorch tensor and numpy array share the same buffer.
        # Sometimes the numpy op is faster so we create both.
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        self.input_ids_cpu = torch.zeros(
            self.max_num_tokens, dtype=torch.int32, device="cpu"
        )
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        self.positions_cpu = torch.zeros(
            self.max_num_tokens, dtype=torch.int32, device="cpu"
        )
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        self.positions_np = self.positions_cpu.numpy()
        self.block_table_cpu = torch.zeros(
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            (self.max_num_reqs, self.max_num_blocks_per_req),
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            dtype=torch.int32,
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            device="cpu",
        )
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        # adjust num_reqs to avoid SMEM OOM.
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        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
        )
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        self.num_reqs_max_model_len = min(
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            PallasAttentionBackend.get_max_num_seqs(
                self.max_model_len, self.block_size
            ),
            self.max_num_reqs,
        )
        self.query_start_loc_cpu = torch.zeros(
            self.max_num_tokens + 1,
            dtype=torch.int32,
            device="cpu",
            pin_memory=self.pin_memory,
        )
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        self.query_start_loc_np = self.query_start_loc_cpu.numpy()

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        self.seq_lens_cpu = torch.zeros(
            self.max_num_tokens,
            dtype=torch.int32,
            device="cpu",
            pin_memory=self.pin_memory,
        )
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        self.seq_lens_np = self.seq_lens_cpu.numpy()
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        # Only relevant for multimodal models
        if self.supports_mm_inputs:
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            self.is_mm_embed_cpu = torch.zeros(
                self.max_num_tokens,
                dtype=torch.bool,
                device="cpu",
                pin_memory=self.pin_memory,
            )
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        # Range tensor with values [0 .. self.max_num_tokens - 1].
        # Used to initialize positions / context_lens / seq_lens
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        # Keep in int64 to avoid overflow with long context
        self.arange_np = np.arange(self.max_num_tokens, dtype=np.int64)
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        self.num_reqs_paddings = _get_req_paddings(
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            min_req_size=MIN_NUM_SEQS, max_req_size=self.max_num_reqs
        )
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        # 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] = {}

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        # tensors for structured decoding
        self.grammar_bitmask_cpu = torch.zeros(
            (self.max_num_reqs, cdiv(self.vocab_size, 32)),
            dtype=torch.int32,
            device="cpu",
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            pin_memory=self.pin_memory,
        )
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        self.require_structured_out_cpu = torch.zeros(
            (self.max_num_reqs, 1),
            dtype=torch.bool,
            device="cpu",
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            pin_memory=self.pin_memory,
        )
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        self.structured_decode_arange = torch.arange(
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            0, 32, device="cpu", pin_memory=self.pin_memory
        )
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        self.mm_budget = (
            MultiModalBudget(
                self.model_config,
                self.scheduler_config,
                self.mm_registry,
            )
            if self.supports_mm_inputs
            else None
        )
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        if not self.use_spmd:
            self.sample_from_logits_func = torch.compile(
                self.sample_from_logits,
                backend="openxla",
                fullgraph=True,
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                dynamic=False,
            )
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        else:
            self.sample_from_logits_func = self.sample_from_logits

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        # For passing scheduler_output between successive
        # execute_model() and sample_tokens() calls.
        self.scheduler_output: SchedulerOutput | None = None
        self.mm_embed_inputs: tuple[list[torch.Tensor], torch.Tensor] | None = None

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    def reset_mm_cache(self) -> None:
        if self.mm_budget:
            self.mm_budget.reset_cache()

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    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

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        logger.info(
            "Add new %d compiled XLA graphs due to %s", new_compiled_graphs, case_str
        )
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        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(
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                case_str, self.num_xla_graphs, curr_cached_graph
            )
        )
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    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:
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            True if there is a new/resumed/paused/finished request.
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            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.
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        removed_req_indices: list[int] = []
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        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)

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

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        req_ids_to_add: list[str] = []
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        # Add new requests to the cached states.
        for new_req_data in scheduler_output.scheduled_new_reqs:
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            assert new_req_data.sampling_params is not None, (
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                "Pooling is not supported in TPU yet"
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            )
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            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,
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                prompt_embeds=new_req_data.prompt_embeds,
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                mm_features=new_req_data.mm_features,
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                sampling_params=sampling_params,
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                pooling_params=None,
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                generator=None,
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                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.
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        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
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            req_state = self.requests[req_id]
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            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
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            resumed_from_preemption = req_id in req_data.resumed_req_ids
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            # Update the cached states.
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            req_state.num_computed_tokens = num_computed_tokens
            if not resumed_from_preemption:
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                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
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                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
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                        block_ids.extend(new_ids)
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            else:
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                assert new_block_ids is not None
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                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
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                req_state.block_ids = new_block_ids
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            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.
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            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
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            if new_block_ids is not None:
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                self.input_batch.block_table.append_row(new_block_ids, req_index)
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        # 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]
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            # Fill the empty index or append to the end
            req_index = removed_req_indices.pop() if removed_req_indices else None
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            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)
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        return len(unscheduled_req_ids) > 0 or len(req_ids_to_add) > 0

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

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    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

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

554
        return list(model.pooler.get_supported_tasks())
555

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    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)

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    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
567
        """
568
        Generates the KVCacheSpec by parsing the kv cache format from each
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        Attention module in the static forward context.
        Returns:
571
            KVCacheSpec: A dictionary mapping layer names to their KV cache
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            format. Layers that do not need KV cache are not included.
        """

575
        layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
576
        block_size = self.vllm_config.cache_config.block_size
577
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        cache_dtype_str = self.vllm_config.cache_config.cache_dtype

579
        kv_cache_spec: dict[str, KVCacheSpec] = {}
580
        for layer_name, attn_module in layers.items():
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            # Classic Attention path
            if isinstance(attn_module, Attention):
                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

                if attn_module.attn_type == AttentionType.DECODER:
                    if isinstance(attn_module, ChunkedLocalAttention):
                        logger.warning_once(
                            "Using irope in Pallas is not supported yet, it "
                            "will fall back to global attention for long context."
                        )
                    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=self.kv_cache_dtype,
                            sliding_window=attn_module.sliding_window,
                        )
                    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=self.kv_cache_dtype,
                        )
                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
625
                else:
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                    raise ValueError(f"Unknown attention type: {attn_module.attn_type}")
            # MLAAttention path
            elif isinstance(attn_module, MLAAttention):
                if layer_name in kv_cache_spec:
                    continue
                kv_cache_spec[layer_name] = MLAAttentionSpec(
                    block_size=block_size,
                    num_kv_heads=1,
                    head_size=attn_module.head_size,
                    dtype=self.kv_cache_dtype,
                    cache_dtype_str=cache_dtype_str,
                )
638
            else:
639
                continue
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        return kv_cache_spec

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    def _get_slot_mapping_metadata(
        self, num_reqs, num_scheduled_tokens_per_req
    ) -> np.ndarray:
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        """
        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
658
                to be scheduled for each request.
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        Returns:
            np.ndarray: A 2D array of shape (total_block_len, 3), where each row
662
                contains:
663
                - kv_cache_start_index (int): The starting index in the KV cache
664
                  for the corresponding slice.
665
                - new_kv_start_index (int): The starting index in the new KV
666
                  cache for the corresponding slice.
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669
                - slice_len (int): The length of the slice.
        """
        slices_start = self.input_batch.num_computed_tokens_cpu[:num_reqs]
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        slices_end = (
            self.input_batch.num_computed_tokens_cpu[:num_reqs]
            + num_scheduled_tokens_per_req
        )
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        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 = (
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            no_repeat_req_indices * self.max_num_blocks_per_req + local_block_start_idx
        )
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        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)
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689
        slot_mapping_slices = np.repeat(
            np.array([[0, self.block_size]], dtype=np.int32), total_block_len, axis=0
        )
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        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):
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            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
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        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:])
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        kv_cache_start_indices = slot_mapping_slices[:, 0] + (
            block_numbers * self.block_size
        )
705
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        new_kv_start_indices = cu_slices_lens[:-1]
        slot_mapping_metadata = np.stack(
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            [kv_cache_start_indices, new_kv_start_indices, slice_lens], axis=1
        )
709
710
        return slot_mapping_metadata

711
    def _prepare_inputs(self, scheduler_output: "SchedulerOutput", start_index: int):
712
        assert scheduler_output.total_num_scheduled_tokens > 0
713
714
        num_reqs = self.input_batch.num_reqs
        assert num_reqs > 0
715
        assert start_index < num_reqs
716

717
        # Get the number of scheduled tokens for each request.
718
        use_max_model_len = self.most_model_len is None
719
720
        num_scheduled_tokens_per_req = []
        max_num_scheduled_tokens_all_reqs = 0
721
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723
724
725
        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]
726
            assert req_id is not None
727
            num_tokens = scheduler_output.num_scheduled_tokens[req_id]
728
729
            if not use_max_model_len and num_tokens > self.most_model_len:
                use_max_model_len = True
730
            num_scheduled_tokens_per_req.append(num_tokens)
731
732
        if use_max_model_len:
            if len(num_scheduled_tokens_per_req) > self.num_reqs_max_model_len:
733
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                num_scheduled_tokens_per_req = num_scheduled_tokens_per_req[
                    : self.num_reqs_max_model_len
                ]
736
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739
                end_index = start_index + self.num_reqs_max_model_len
            else:
                end_index = num_reqs
        else:
740
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743
            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
                ]
744
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746
747
                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)
748
749
750
        num_scheduled_tokens_per_req = np.array(
            num_scheduled_tokens_per_req, dtype=np.int32
        )
751
        total_num_scheduled_tokens = sum(num_scheduled_tokens_per_req)
752
753
        assert max_num_scheduled_tokens_all_reqs > 0

754
755
        num_reqs = len(num_scheduled_tokens_per_req)

756
757
758
        # 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.
759
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens_per_req)
760
761
762
763
764

        # 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(
765
766
            [self.arange_np[:n] for n in num_scheduled_tokens_per_req]
        )
767
768
769

        # Get positions.
        positions_np = self.positions_np[:total_num_scheduled_tokens]
770
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773
774
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
775
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777
778
779

        # 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.
780
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782
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
783
784
785
786

        # 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.
787
788
789
790
791
792
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            torch.from_numpy(token_indices),
            out=self.input_ids_cpu[:total_num_scheduled_tokens],
        )
793
794
795

        # Prepare the attention metadata.
        self.query_start_loc_np[0] = 0
796
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798
799
        np.cumsum(
            num_scheduled_tokens_per_req, out=self.query_start_loc_np[1 : num_reqs + 1]
        )
        self.query_start_loc_np[num_reqs + 1 :] = 1
800
801

        self.seq_lens_np[:num_reqs] = (
802
803
804
            self.input_batch.num_computed_tokens_cpu[:num_reqs]
            + num_scheduled_tokens_per_req
        )
805
806

        # Do the padding and copy the tensors to the TPU.
807
        padded_total_num_scheduled_tokens = _get_padded_token_len(
808
809
            self.num_tokens_paddings, total_num_scheduled_tokens
        )
810
811
        # Zero out to avoid spurious values from prev iteration (last cp chunk)
        self.input_ids_cpu[
812
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814
815
816
817
818
819
            total_num_scheduled_tokens:padded_total_num_scheduled_tokens
        ] = 0
        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
        )
820
        if use_max_model_len:
821
822
823
824
825
826
827
828
829
830
            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)
831
        else:
832
833
834
835
836
837
838
839
840
841
842
843
            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)
844
        block_tables = block_tables.to(self.device)
845

846
        # Calculate the slot mapping
847
        slot_mapping_metadata = self._get_slot_mapping_metadata(
848
849
            num_reqs, num_scheduled_tokens_per_req
        )
850
        num_kv_update_slices = slot_mapping_metadata.shape[0]
851
        padded_num_slices = _get_padded_num_kv_cache_update_slices(
852
853
            padded_total_num_scheduled_tokens, self.max_num_reqs, self.block_size
        )
854
855
856
        slot_mapping_metadata = np.pad(
            slot_mapping_metadata,
            [[0, padded_num_slices - len(slot_mapping_metadata)], [0, 0]],
857
858
            constant_values=0,
        )
859
        slot_mapping_metadata = np.transpose(slot_mapping_metadata)
860
        slot_mapping_metadata = torch.tensor(slot_mapping_metadata, device=self.device)
861

862
863
864
865
866
        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
867
            padded_num_scheduled_tokens_per_req[-1] += (
868
                padded_total_num_scheduled_tokens - total_num_scheduled_tokens
869
            )
870

871
            self.set_active_loras(self.input_batch, padded_num_scheduled_tokens_per_req)
872

873
        attn_metadata = PallasMetadata(
874
            slot_mapping=slot_mapping_metadata,
875
            block_tables=block_tables,
876
877
            context_lens=seq_lens,
            query_start_loc=query_start_loc,
878
879
880
881
882
            num_seqs=torch.tensor([num_reqs], dtype=torch.int32, device=self.device),
            num_kv_update_slices=torch.tensor(
                [num_kv_update_slices], dtype=torch.int32, device=self.device
            ),
            num_slices_per_kv_cache_update_block=self._num_slices_per_kv_cache_update_block,
883
        )
884
885
886
887
888
        # 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.
889
        padded_num_reqs = _get_padded_num_reqs_with_upper_limit(
890
891
            num_reqs, self.max_num_reqs
        )
892
893
        # Indices at which we sample (positions of last token in the sequence).
        # Padded to avoid recompiling when `num_reqs` varies.
894
        logits_indices = self.query_start_loc_cpu[1 : padded_num_reqs + 1] - 1
895
        logits_indices = logits_indices.to(self.device)
896

897
898
899
900
901
        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
902
            padded_num_scheduled_tokens_per_req[-1] += (
903
                padded_total_num_scheduled_tokens - total_num_scheduled_tokens
904
            )
905

906
            self.set_active_loras(self.input_batch, padded_num_scheduled_tokens_per_req)
907

908
        layer_names = get_layers_from_vllm_config(self.vllm_config, Attention).keys()
909
        per_layer_attn_metadata = {
910
            layer_name: attn_metadata for layer_name in layer_names
911
        }
912
913
914
915
916
917
918
        return (
            per_layer_attn_metadata,
            logits_indices,
            padded_num_reqs,
            num_reqs,
            end_index,
        )
919

920
    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
921
922
923
924
925
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
            return

        # Batch the multi-modal inputs.
926
        mm_kwargs = list[MultiModalKwargsItem]()
927
928
        # List of tuple (mm_hash, pos_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
929
930
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
931
932

            for mm_input_id in encoder_input_ids:
933
934
935
936
                mm_feature = req_state.mm_features[mm_input_id]
                mm_hash = mm_feature.identifier
                mm_kwargs.append(mm_feature.data)
                mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
937
938
939
940
941
942
943
944

        # 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.
945
        model = cast(SupportsMultiModal, self.model)
946
        encoder_outputs = []
947
        for _, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
948
949
950
951
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
952
            multimodal_cpu_fields=model.multimodal_cpu_fields,
953
        ):
954
955
956
957
958
959
960
            # 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.
961
            torch_xla.sync(wait=False)
962
            curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)
963
            torch_xla.sync(wait=False)
964

965
966
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
967
                expected_num_items=num_items,
968
969
            )

970
971
972
973
974
975
            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)
976
977

        # Cache the encoder outputs.
978
979
980
        # 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.
981
        for (mm_hash, pos_info), output in zip(mm_hashes_pos, encoder_outputs):
982
983
984
            assert pos_info.is_embed is None, (
                "Expected all positions to be contiguous and embeddings."
            )
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            self.encoder_cache[mm_hash] = output
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    def _gather_mm_embeddings(
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        self,
        scheduler_output: "SchedulerOutput",
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    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        padded_total_num_scheduled_tokens = _get_padded_token_len(
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            self.num_tokens_paddings, total_num_scheduled_tokens
        )
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        is_mm_embed = self.is_mm_embed_cpu
        is_mm_embed[:padded_total_num_scheduled_tokens] = False
        mm_embeds = list[torch.Tensor]()
        req_start_idx = 0

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        for req_id in self.input_batch.req_ids:
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            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
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            req_state = self.requests[req_id]
            num_computed_tokens = req_state.num_computed_tokens
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            # 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.
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            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
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                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
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                # 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
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                start_idx = max(num_computed_tokens - start_pos, 0)
                end_idx = min(
                    num_computed_tokens - start_pos + num_scheduled_tokens,
                    num_encoder_tokens,
                )
                assert start_idx < end_idx

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                mm_hash = mm_feature.identifier
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                encoder_output = self.encoder_cache.get(mm_hash, None)
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                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
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                assert pos_info.is_embed is None, (
                    "Expected all positions to be contiguous and embeddings."
                )
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                req_start_pos = req_start_idx + start_pos - num_computed_tokens
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                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = True
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                # Only whole mm items are processed
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                mm_embeds.append(encoder_output)
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            req_start_idx += num_scheduled_tokens

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        is_mm_embed = is_mm_embed[:padded_total_num_scheduled_tokens].to(self.device)
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        return mm_embeds, is_mm_embed

    def _get_model_inputs(
        self,
        input_ids: torch.Tensor,
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        mm_embed_inputs: tuple[list[torch.Tensor], torch.Tensor] | None,
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    ):
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        if self.supports_mm_inputs:
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            mm_embeds, is_mm_embed = mm_embed_inputs or (None, None)

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            # 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.
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            inputs_embeds = self.model.embed_input_ids(
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                input_ids,
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                multimodal_embeddings=mm_embeds,
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                is_multimodal=is_mm_embed,
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            )
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            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

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    @torch.no_grad()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
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        intermediate_tensors: IntermediateTensors | None = None,
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    ) -> ModelRunnerOutput | None:
        if self.scheduler_output is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
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        # Update cached state
        self._update_states(scheduler_output)
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        if not scheduler_output.total_num_scheduled_tokens:
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            if not has_kv_transfer_group():
                # Return empty ModelRunnerOutput if there's no work to do.
                return EMPTY_MODEL_RUNNER_OUTPUT

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            return self.kv_connector_no_forward(scheduler_output, self.vllm_config)
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        mm_embed_inputs = None
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        if self.supports_mm_inputs:
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            # Run the multimodal encoder if any.
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            self._execute_mm_encoder(scheduler_output)
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            mm_embed_inputs = self._gather_mm_embeddings(scheduler_output)

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        torch_xla.sync(wait=False)
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        self.scheduler_output = scheduler_output
        self.mm_embed_inputs = mm_embed_inputs
        return None

    @torch.no_grad()
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput:
        if self.scheduler_output is None:
            # Nothing to do (PP non-final rank case), output isn't used.
            return None  # noqa
        scheduler_output = self.scheduler_output
        mm_embed_inputs = self.mm_embed_inputs
        self.scheduler_output = None
        self.mm_embed_inputs = None

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        # Prepare inputs, the requests might be split into multiple
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        # executions, combine the result of each execution.
        start_index = 0
        combined_selected_tokens: list[torch.Tensor] = []
        combined_logprobs: list[LogprobsLists] = []
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        # 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)

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        while start_index < self.input_batch.num_reqs:
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            attn_metadata, logits_indices, padded_num_reqs, num_reqs, end_index = (
                self._prepare_inputs(scheduler_output, start_index)
            )
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            input_ids, inputs_embeds = self._get_model_inputs(
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                self.input_ids, mm_embed_inputs
            )
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            torch_xla.sync(wait=False)
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            # Run the decoder
            with set_forward_context(
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                attn_metadata,
                self.vllm_config,
                num_tokens=scheduler_output.total_num_scheduled_tokens,
            ):
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                hidden_states = self.model(
                    input_ids=input_ids,
                    positions=self.position_ids,
                    inputs_embeds=inputs_embeds,
                )
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            hidden_states = self.select_hidden_states(hidden_states, logits_indices)
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            logits = self.compute_logits(hidden_states)
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            tpu_sampling_metadata = TPUSupportedSamplingMetadata.from_input_batch(
                self.input_batch, padded_num_reqs, self.device
            )
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            if grammar_output is not None:
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                require_struct_decoding, grammar_bitmask_padded, arange = (
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                    self.prepare_structured_decoding_input(logits, grammar_output)
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                )
                logits = self.structured_decode(
                    require_struct_decoding, grammar_bitmask_padded, logits, arange
                )
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            selected_token_ids = self.sample_from_logits_func(
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                logits, tpu_sampling_metadata
            )
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            # 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.
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            logprobs = (
                self.gather_logprobs(logits, selected_token_ids)
                if tpu_sampling_metadata.logprobs
                else None
            )
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            # 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

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        # 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()
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        finished_sending, finished_recving = self.get_finished_kv_transfers(
            scheduler_output
        )
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        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

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            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]
                ),
            )
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        else:
            logprobs_lists = None
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        # Update the cache state concurrently. Code above will not block until
        # we use `selected_token_ids`. Add mark_step if post-processing changes
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        request_seq_lens: list[tuple[int, CachedRequestState, int]] = []
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        discard_sampled_tokens_req_indices = []
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        num_reqs = self.input_batch.num_reqs
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        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]
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            seq_len = (
                req_state.num_computed_tokens
                + scheduler_output.num_scheduled_tokens[req_id]
            )
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            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)

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                # 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)

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        assert all(
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            req_id is not None for req_id in self.input_batch.req_ids[:num_reqs]
        ), "req_ids contains None"
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        req_ids = cast(list[str], self.input_batch.req_ids[:num_reqs])
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        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
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        for req_id in self.input_batch.req_ids[:num_reqs]:
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            prompt_logprobs_dict[req_id] = None

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        max_gen_len = selected_token_ids.shape[-1]
        if max_gen_len == 1:
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            valid_sampled_token_ids: list[np.ndarray] = [
                row for row in selected_token_ids.numpy()
            ]
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            # 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:
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                valid_sampled_token_ids[i] = np.array([])
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            # Append sampled tokens
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            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
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        else:
            valid_mask = selected_token_ids != INVALID_TOKEN_ID
            gen_lens = valid_mask.sum(dim=1).tolist()
            valid_sampled_token_ids = [
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                seq.numpy() for seq in selected_token_ids[valid_mask].split(gen_lens)
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            ]
            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)
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                self.input_batch.token_ids_cpu[i, target_slice] = (
                    valid_sampled_token_ids[i]
                )
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                req_state.output_token_ids.extend(valid_sampled_token_ids[i])

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        kv_connector_output = (
            None
            if (finished_sending is None and finished_recving is None)
            else KVConnectorOutput(
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                finished_sending=finished_sending,
                finished_recving=finished_recving,
            )
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        )
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        model_runner_output = ModelRunnerOutput(
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            req_ids=req_ids,
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            req_id_to_index=self.input_batch.req_id_to_index,
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            sampled_token_ids=valid_sampled_token_ids,
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            logprobs=logprobs_lists,
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            prompt_logprobs_dict=prompt_logprobs_dict,
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            pooler_output=[],
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            kv_connector_output=kv_connector_output,
        )
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        # 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")

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        return model_runner_output

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    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():
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            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
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                f"Allowed configs: {allowed_config_names}"
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            )
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            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

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    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(
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            "vllm.model_executor.layers.vocab_parallel_embedding."
            "get_tensor_model_parallel_rank",
            return_value=xm_tp_rank,
        ):
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            try:
                if self.use_spmd:
                    tpu_loader = TPUModelLoader(
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                        load_config=self.vllm_config.load_config
                    )
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                    model = tpu_loader.load_model(
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                        vllm_config=self.vllm_config,
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                        model_config=self.vllm_config.model_config,
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                        mesh=self.mesh,
                    )
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                else:
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                    model_loader = get_model_loader(self.load_config)
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                    logger.info("Loading model from scratch...")
                    model = model_loader.load_model(
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                        vllm_config=self.vllm_config, model_config=self.model_config
                    )
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            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. "
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                    f"See the detailed error: {e}"
                ) from e
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        if self.lora_config is not None:
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            model = self.load_lora_model(model, self.vllm_config, self.device)
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            replace_set_lora(model)
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        # Sync all pending XLA execution during model initialization and weight
        # loading.
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        torch_xla.sync(wait=False)
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        xm.wait_device_ops()
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        if not hasattr(self, "model"):
            self.model = model
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        self.sampler = TPUSampler()
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    def reload_weights(self) -> None:
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        assert getattr(self, "model", None) is not None, (
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            "Cannot reload weights before model is loaded."
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        )
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        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
        model_loader.load_weights(self.model, model_config=self.model_config)

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    @torch.no_grad()
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    def _dummy_run(self, num_tokens: int, num_reqs: int, num_blocks: int) -> None:
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        if self.supports_mm_inputs:
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            input_ids = None
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            inputs_embeds = torch.zeros(
                (num_tokens, self.hidden_size), dtype=self.dtype, device=self.device
            )
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        else:
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            input_ids = torch.zeros((num_tokens), dtype=torch.int32).to(self.device)
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            inputs_embeds = None
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        actual_num_reqs = min(num_tokens, num_reqs)
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        position_ids = torch.zeros(num_tokens, dtype=torch.int32).to(self.device)
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        padded_num_slices = _get_padded_num_kv_cache_update_slices(
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            num_tokens, self.max_num_reqs, self.block_size
        )
        num_kv_update_slices = torch.tensor([padded_num_slices], dtype=torch.int32).to(
            self.device
        )
        slot_mapping = torch.zeros((3, padded_num_slices), dtype=torch.int32).to(
            self.device
        )
        block_tables = torch.zeros((num_reqs, num_blocks), dtype=torch.int32).to(
            self.device
        )
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        query_lens = [1] * num_reqs
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        query_start_loc = torch.cumsum(
            torch.tensor([0] + query_lens, dtype=torch.int32), dim=0, dtype=torch.int32
        ).to(self.device)
        context_lens = torch.ones((num_reqs,), dtype=torch.int32).to(self.device)
        num_seqs = torch.tensor([actual_num_reqs], dtype=torch.int32).to(self.device)
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        attn_metadata = PallasMetadata(
            slot_mapping=slot_mapping,
            block_tables=block_tables,
            context_lens=context_lens,
            query_start_loc=query_start_loc,
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            num_seqs=num_seqs,
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            num_kv_update_slices=num_kv_update_slices,
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            num_slices_per_kv_cache_update_block=self._num_slices_per_kv_cache_update_block,
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        )
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        if self.supports_mm_inputs:
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            torch._dynamo.mark_dynamic(inputs_embeds, 0)
        else:
            torch._dynamo.mark_dynamic(input_ids, 0)
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        torch._dynamo.mark_dynamic(position_ids, 0)
        torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 0)
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        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)
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        layer_names = get_layers_from_vllm_config(self.vllm_config, Attention).keys()
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        per_layer_attn_metadata = {
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            layer_name: attn_metadata for layer_name in layer_names
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        }

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        with (
            self.maybe_select_dummy_loras(
                self.lora_config, np.array([num_tokens], dtype=np.int32)
            ),
            set_forward_context(per_layer_attn_metadata, self.vllm_config, 0),
        ):
            out = self.model(
                input_ids=input_ids, positions=position_ids, inputs_embeds=inputs_embeds
            )
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        self._hidden_states_dtype = out.dtype
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    def _set_active_loras(
        self, prompt_lora_mapping, token_lora_mapping, lora_requests
    ) -> None:
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        torch_xla.sync(wait=False)  # Captures input updates
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        super()._set_active_loras(
            prompt_lora_mapping, token_lora_mapping, lora_requests
        )
1458
        torch_xla.sync(wait=False)  # Captures metadata updates
1459

1460
    def _precompile_mm_encoder(self) -> None:
1461
        if not self.supports_mm_inputs:
1462
1463
            return

1464
1465
        # Pre-compile MM encoder for all supported data modalities.
        hf_config = self.vllm_config.model_config.hf_config
1466
1467
1468
1469
1470
1471
1472

        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():
1473
            logger.info(
1474
1475
                "Compiling Multimodal %s Encoder with different input shapes.", mode
            )
1476
1477
            start = time.perf_counter()
            # No padding for MM encoder just yet.
1478
            for num_items in range(1, max_items_per_seq + 1):
1479
1480
                logger.info("  -- mode: %s items: %d", mode, num_items)
                batched_dummy_mm_inputs = self._get_mm_dummy_batch(
1481
1482
1483
                    mode,
                    num_items,
                )
1484
                # Run multimodal encoder.
1485
                torch_xla.sync(wait=False)
1486
                mm_embeds = self.model.embed_multimodal(**batched_dummy_mm_inputs)
1487
                torch_xla.sync(wait=False)
1488
1489
1490
                num_patches = mm_embeds[0].shape[0]
                items_size = num_patches * num_items

1491
                # NOTE (NickLucche) pre-compile `embed_input_ids` when mm
1492
1493
1494
1495
1496
1497
1498
1499
                # 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
1500
1501
1502
                        placeholders_ids = torch.zeros(
                            num_tokens, dtype=torch.int32, device="cpu"
                        )
1503
                        # Align placeholders and actual num mm_embeddings.
1504
                        placeholders_ids[:items_size] = hf_config.image_token_index
1505
1506

                        placeholders_ids = placeholders_ids.to(self.device)
1507
1508
1509
1510

                        mm_mask = torch.tensor([False] * num_tokens)
                        mm_mask[:items_size] = True
                        mm_mask = mm_mask.to(self.device)
1511
                        # Assign outputs or the graph will be cut short.
1512
1513
1514
1515
                        a, b = self._get_model_inputs(
                            placeholders_ids,
                            mm_embed_inputs=([mm_embeds], mm_mask),
                        )
1516
                        assert a is None
1517
                        torch_xla.sync(wait=False)
1518

1519
            # Pre-compile `embed_input_ids` when mm_embeddings are not
1520
1521
            # present. Chunk is only made of text, no mm_placeholders.
            for num_tokens in self.num_tokens_paddings:
1522
1523
1524
                placeholders_ids = torch.zeros(
                    num_tokens, dtype=torch.int32, device="cpu"
                )
1525
                placeholders_ids = placeholders_ids.to(self.device)
1526
1527
1528
1529
                a, b = self._get_model_inputs(
                    placeholders_ids,
                    mm_embed_inputs=None,
                )
1530
                assert a is None
1531
                torch_xla.sync(wait=False)
1532
1533
1534
1535

            xm.wait_device_ops()
            end = time.perf_counter()
            logger.info(
1536
1537
1538
1539
                "Multimodal %s Encoder compilation finished in in %.2f [secs].",
                mode,
                end - start,
            )
1540

1541
    def _precompile_backbone(self) -> None:
1542
1543
        logger.info("Compiling the model with different input shapes.")
        start = time.perf_counter()
1544
        for num_tokens in self.num_tokens_paddings:
1545
            logger.info("  -- num_tokens: %d", num_tokens)
1546
1547
1548
            self._dummy_run(
                num_tokens, self.num_reqs_max_model_len, self.max_num_blocks_per_req
            )
1549
            if self.most_model_len is not None:
1550
1551
1552
1553
1554
                self._dummy_run(
                    num_tokens,
                    self.num_reqs_most_model_len,
                    self.num_blocks_per_most_len_req,
                )
1555
1556
        xm.wait_device_ops()
        end = time.perf_counter()
1557
        logger.info("Compilation finished in %.2f [secs].", end - start)
1558
        self._update_num_xla_graphs("model backbone")
1559

1560
1561
1562
    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.
1563
        logger.info("Compiling select_hidden_states with different input shapes.")
1564
1565
        start = time.perf_counter()
        hsize = self.model_config.get_hidden_size()
1566
        for num_tokens in self.num_tokens_paddings:
1567
1568
1569
            dummy_hidden = torch.zeros(
                (num_tokens, hsize), device=self.device, dtype=self._hidden_states_dtype
            )
1570
1571
            torch._dynamo.mark_dynamic(dummy_hidden, 0)
            for num_reqs in self.num_reqs_paddings:
1572
                indices = torch.zeros(num_reqs, dtype=torch.int32, device=self.device)
1573
1574
                torch._dynamo.mark_dynamic(indices, 0)
                self.select_hidden_states(dummy_hidden, indices)
1575
                logger.info("  -- num_tokens: %d, num_seqs: %d", num_tokens, num_reqs)
1576
1577
1578
1579
                # 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
1580
        xm.wait_device_ops()
1581
        end = time.perf_counter()
1582
        logger.info("Compilation finished in %.2f [secs].", end - start)
1583
        self._update_num_xla_graphs("select_hidden_states")
1584

1585
1586
    def _precompile_compute_logits(self) -> None:
        logger.info("Compiling compute_logits with different input shapes.")
1587
1588
1589
        start = time.perf_counter()
        hsize = self.model_config.get_hidden_size()
        for num_reqs in self.num_reqs_paddings:
1590
1591
1592
            dummy_hidden = torch.zeros(
                (num_reqs, hsize), device=self.device, dtype=self._hidden_states_dtype
            )
1593
1594
1595
1596
1597
1598
1599
1600
1601
            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:
1602
        logger.info("Compiling structured_decoding with different input shapes.")
1603
1604
        start = time.perf_counter()
        for num_reqs in self.num_reqs_paddings:
1605
1606
1607
1608
1609
1610
1611
1612
1613
            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)
1614
1615
1616
1617
            # 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)
1618
1619
1620
1621
1622
1623
            self.structured_decode(
                dummy_require_struct_decoding,
                dummy_grammar_bitmask,
                dummy_logits,
                arange,
            )
1624
1625
1626
1627
1628
1629
1630
            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:
1631
        logger.info("Compiling sample_from_logits with different input shapes.")
1632
1633
        start = time.perf_counter()
        for num_reqs in self.num_reqs_paddings:
1634
1635
1636
1637
1638
            dummy_logits = torch.zeros(
                (num_reqs, self.vocab_size),
                device=self.device,
                dtype=self._hidden_states_dtype,
            )
1639
1640
            # The first dimension of dummy_logits cannot be mark_dynamic
            # because some operations in the sampler require it to be static.
1641
1642
            for all_greedy in [False, True]:
                generate_params_if_all_greedy = not all_greedy
1643
1644
1645
1646
1647
1648
                sampling_metadata = TPUSupportedSamplingMetadata.from_input_batch(
                    self.input_batch,
                    num_reqs,
                    self.device,
                    generate_params_if_all_greedy,
                )
1649
                sampling_metadata.all_greedy = all_greedy
1650
                with self.maybe_select_dummy_loras(
1651
1652
1653
                    self.lora_config, np.array([num_reqs], dtype=np.int32)
                ):
                    self.sample_from_logits_func(dummy_logits, sampling_metadata)
1654
1655
1656
            logger.info("  -- num_seqs: %d", num_reqs)
        xm.wait_device_ops()
        end = time.perf_counter()
1657
1658
        logger.info("Compilation finished in %.2f [secs].", end - start)
        self._update_num_xla_graphs("sample_from_logits")
1659

1660
1661
1662
1663
    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:
1664
1665
1666
1667
1668
1669
            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)
1670
            with self.maybe_select_dummy_loras(
1671
1672
                self.lora_config, np.array([num_reqs], dtype=np.int32)
            ):
1673
                self.gather_logprobs(dummy_logits, dummy_tokens)
1674
1675
1676
1677
1678
1679
            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")

1680
1681
1682
1683
    def capture_model(self) -> None:
        """
        Precompile all the subgraphs with possible input shapes.
        """
1684
1685
1686
1687
1688
1689
1690
1691
        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()
1692

1693
1694
1695
1696
1697
    def profile_run(
        self,
        num_tokens: int,
    ) -> None:
        # Profile with multimodal encoder & encoder cache.
1698
        if self.supports_mm_inputs:
1699
            if self.model_config.multimodal_config.skip_mm_profiling:
1700
                logger.info(
1701
                    "Skipping memory profiling for multimodal encoder and "
1702
1703
                    "encoder cache."
                )
1704
1705
1706
1707
1708
1709
1710
1711
1712
            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.
1713
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
1714
1715
1716
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
1717
1718
1719
1720
1721
1722
1723
1724
1725

                    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,
                    )
1726

1727
1728
1729
1730
1731
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
1732

1733
1734
1735
1736
                    # Run multimodal encoder.
                    # Isolate encoder graph from post-processing to minimize
                    # impact of recompilation until it's fixed.
                    start = time.perf_counter()
1737
                    torch_xla.sync(wait=False)
1738
                    dummy_encoder_outputs = self.model.embed_multimodal(
1739
1740
                        **batched_dummy_mm_inputs
                    )
1741
                    torch_xla.sync(wait=False)
1742
1743
1744
1745
                    xm.wait_device_ops()
                    end = time.perf_counter()
                    logger.info(
                        "Multimodal Encoder profiling finished in %.2f [secs].",
1746
1747
                        end - start,
                    )
1748
1749
1750
1751
1752

                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
1753

1754
                    # Cache the dummy encoder outputs.
1755
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
1756
1757

        # Trigger compilation for general shape.
1758
1759
1760
        self._dummy_run(
            num_tokens, self.num_reqs_max_model_len, self.max_num_blocks_per_req
        )
1761
        if self.most_model_len is not None:
1762
1763
1764
1765
1766
            self._dummy_run(
                num_tokens,
                self.num_reqs_most_model_len,
                self.num_blocks_per_most_len_req,
            )
1767

1768
        torch_xla.sync(wait=False)
1769
1770
1771
1772
        xm.wait_device_ops()
        self.encoder_cache.clear()
        gc.collect()

1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
    def maybe_setup_cross_layer_kv_sharing(
        self,
        kv_caches: dict[str, torch.Tensor],
        kv_cache_config: KVCacheConfig,
    ) -> None:
        """
        Add layers that re-use KV cache to KV cache group of its target layer.
        Mapping of KV cache tensors happens in `initialize_kv_cache_tensors()`
        """
        if not self.shared_kv_cache_layers:
            # No cross-layer KV sharing, return
            return

        add_kv_sharing_layers_to_kv_cache_groups(
            self.shared_kv_cache_layers,
            kv_cache_config.kv_cache_groups,
        )

1791
1792
        for layer_name, target_layer_name in self.shared_kv_cache_layers.items():
            logger.debug("%s reuses KV cache of %s", layer_name, target_layer_name)
1793
1794
            kv_caches[layer_name] = kv_caches[target_layer_name]

1795
1796
1797
1798
    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize KV cache based on `kv_cache_config`.
        Args:
1799
            kv_cache_config: Configuration for the KV cache, including the KV
1800
1801
            cache size of each layer
        """
1802
        if len(kv_cache_config.kv_cache_groups) > 1:
1803
            raise NotImplementedError(
1804
1805
                "Hybrid models with more than one KV cache type are not supported yet."
            )
1806

1807
1808
1809
1810
        if (
            kv_cache_config.kv_cache_groups[0].kv_cache_spec.block_size
            != self.block_size
        ):
1811
1812
1813
1814
1815
1816
1817
            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(),
1818
1819
1820
                block_sizes=[
                    kv_cache_config.kv_cache_groups[0].kv_cache_spec.block_size
                ],
1821
1822
1823
                kernel_block_sizes=[
                    kv_cache_config.kv_cache_groups[0].kv_cache_spec.block_size
                ],
1824
1825
            )
        # Verify dtype compatibility between block_table_cpu and input_batch
1826
1827
1828
1829
        assert (
            self.block_table_cpu.dtype
            == self.input_batch.block_table[0].get_cpu_tensor().dtype
        )
1830

1831
1832
1833
        kv_cache_sizes = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
            assert len(kv_cache_tensor.shared_by) == 1, (
1834
1835
                "KV cache tensor shared by multiple layers is not supported in TPU."
            )
1836
            kv_cache_sizes[kv_cache_tensor.shared_by[0]] = kv_cache_tensor.size
1837

1838
        kv_caches: dict[str, torch.Tensor] = {}
1839
1840
1841
        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:
1842
1843
1844
                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
1845
                if isinstance(kv_cache_spec, AttentionSpec):
1846
1847
1848
                    if self.use_spmd:
                        num_kv_heads = kv_cache_spec.num_kv_heads
                        assert self.original_parallel_config is not None
1849
                        tp_size = self.original_parallel_config.tensor_parallel_size
1850
1851
1852
                        # 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 "
1853
1854
                            f"tp_size {tp_size} under SPMD mode"
                        )
1855
                    kv_cache_shape = PallasAttentionBackend.get_kv_cache_shape(
1856
1857
1858
1859
1860
                        num_blocks,
                        kv_cache_spec.block_size,
                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
                    )
1861
1862
                    dtype = kv_cache_spec.dtype

1863
1864
1865
                    tpu_kv_cache = torch.zeros(kv_cache_shape, dtype=dtype).to(
                        self.device
                    )
1866

1867
                    kv_caches[layer_name] = tpu_kv_cache
1868
1869
                else:
                    raise NotImplementedError
1870

1871
1872
        # Set up cross-layer KV cache sharing if needed
        self.maybe_setup_cross_layer_kv_sharing(kv_caches, kv_cache_config)
1873

1874
1875
1876
        bind_kv_cache(
            kv_caches,
            self.vllm_config.compilation_config.static_forward_context,
1877
1878
            self.kv_caches,
        )
1879

1880
1881
1882
        if self.use_spmd:
            # Shard KV Cache
            for cache in self.kv_caches:
1883
                xs.mark_sharding(cache, self.mesh, (None, "x", None, None))
1884

1885
1886
1887
1888
        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)

1889
    def reset_dynamo_cache(self):
1890
1891
1892
1893
        # 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:
1894
            compiled_model = self.model.get_language_model().model
1895
1896
        else:
            compiled_model = self.model.model
1897
        if isinstance(compiled_model, TorchCompileWithNoGuardsWrapper):
1898
1899
            logger.info("Clear dynamo cache and cached dynamo bytecode.")
            torch._dynamo.eval_frame.remove_from_cache(
1900
                compiled_model.original_code_object()
1901
            )
1902
1903
1904
            # Reset the wrapper to re-initialize.
            compiled_model.compiled = False
            TorchCompileWithNoGuardsWrapper.__init__(compiled_model)
1905

1906
1907
1908
1909
1910
    @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)
1911
    def compute_logits(self, sample_hidden_states: torch.Tensor) -> torch.Tensor:
1912
        return self.model.compute_logits(sample_hidden_states)
1913

1914
1915
1916
    # 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)
1917
    def sample_from_logits(
1918
1919
        self, logits: torch.Tensor, sampling_metadata: TPUSupportedSamplingMetadata
    ) -> torch.Tensor:
1920
        """
1921
        Sample with xla-friendly function. This function is to be traced
1922
1923
        separately from `forward` for lighter compilation overhead.
        """
1924
1925
1926
        if sampling_metadata.all_greedy:
            out_tokens = torch.argmax(logits, dim=-1, keepdim=True)
        else:
1927
            out_tokens = self.sampler(logits, sampling_metadata).sampled_token_ids
1928
1929
        return out_tokens

1930
    @torch.compile(backend="openxla", fullgraph=True, dynamic=False)
1931
1932
1933
    def gather_logprobs(
        self, logits: torch.Tensor, sampled_tokens: torch.Tensor
    ) -> LogprobsTensors:
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        """
        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,
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            token_ids=sampled_tokens.squeeze(-1),
        )
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    @torch.compile(backend="openxla", fullgraph=True, dynamic=False)
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    def structured_decode(
        self,
        require_struct_decoding: torch.Tensor,
        grammar_bitmask: torch.Tensor,
        logits: torch.Tensor,
        arange: torch.Tensor,
    ) -> torch.Tensor:
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        return torch.where(
            require_struct_decoding,
            self.apply_grammar_bitmask(logits, grammar_bitmask, arange),
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            logits,
        )
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    def apply_grammar_bitmask(
        self, logits: torch.Tensor, grammar_bitmask: torch.Tensor, arange: torch.Tensor
    ):
        assert logits.shape[0] == grammar_bitmask.shape[0]
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        logits_cloned = logits.clone()
        for i in range(logits.shape[0]):
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            unpacked_bitmask = (
                torch.bitwise_right_shift(grammar_bitmask[i][:, None], arange[None, :])
                & 1
            ) == 0
            unpacked_bitmask = unpacked_bitmask.reshape(-1)[: self.vocab_size]
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            logits_cloned[i] = logits_cloned[i].masked_fill(
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                unpacked_bitmask, -float("inf")
            )
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        return logits_cloned

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    def embed_multimodal(self, *args, **kwargs):
        return self.model.embed_multimodal(*args, **kwargs)
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    def embed_input_ids(self, *args, **kwargs):
        return self.model.embed_input_ids(*args, **kwargs)
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    def prepare_structured_decoding_input(
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        self, logits: torch.Tensor, grammar_output: "GrammarOutput"
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    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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        grammar_bitmask = grammar_output.grammar_bitmask
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        num_reqs, _ = logits.shape

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

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        cumulative_mask_idx = 0
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        for req_id in grammar_output.structured_output_request_ids:
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            if req_id not in self.input_batch.req_id_to_index:
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                continue
            batch_index = self.input_batch.req_id_to_index[req_id]
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            self.grammar_bitmask_cpu[batch_index] = torch.from_numpy(
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                grammar_bitmask[cumulative_mask_idx]
            )
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            # 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.
            self.require_structured_out_cpu[batch_index] = True
            cumulative_mask_idx += 1

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        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),
        )
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    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
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        assert self.mm_budget is not None

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        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
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            model_config=self.model_config,
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            seq_len=self.max_model_len,
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            mm_counts={modality: 1},
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            cache=self.mm_budget.cache,
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        )
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        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
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        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
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        model = cast(SupportsMultiModal, self.model)
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        return next(
            grouped_mm_kwargs
            for _, _, grouped_mm_kwargs in group_mm_kwargs_by_modality(
                dummy_mm_items,
                device=self.device,
                pin_memory=self.pin_memory,
                merge_by_field_config=model.merge_by_field_config,
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                multimodal_cpu_fields=model.multimodal_cpu_fields,
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            )
        )
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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
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def _get_padded_num_reqs_with_upper_limit(x: int, upper_limit: int) -> int:
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    res = MIN_NUM_SEQS if x <= MIN_NUM_SEQS else 1 << (x - 1).bit_length()
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    return min(res, upper_limit)
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def _get_token_paddings(
    min_token_size: int, max_token_size: int, padding_gap: int
) -> list[int]:
    """Generate a list of padding size, starting from min_token_size,
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    ending with a number that can cover max_token_size
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    If padding_gap == 0 then:
        increase 2X each time (exponential)
    else:
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        first increase the size to twice,
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        then increase the padding size by padding_gap.
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    """
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    # assert min_token_size is power of 2
    assert (min_token_size & (min_token_size - 1) == 0) and min_token_size > 0
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    paddings = []
    num = min_token_size
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    if padding_gap == 0:
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        logger.info("Using exponential token paddings:")
2082
        while True:
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            logger.info("    %d", num)
            paddings.append(num)
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            if num >= max_token_size:
                break
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            num *= 2
    else:
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        logger.info("Using incremental token paddings:")
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        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)

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    return paddings


def _get_padded_token_len(paddings: list[int], x: int) -> int:
2104
    """Return the first element in paddings list greater or equal to x."""
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    index = bisect.bisect_left(paddings, x)
    assert index < len(paddings)
    return paddings[index]
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def _get_padded_num_kv_cache_update_slices(
    num_tokens: int, max_num_reqs: int, page_size: int
) -> int:
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    """Calculates the padded number of KV cache update slices to avoid
    recompilation."""
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    # 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
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    padded_num_slices = 2 * max_num_reqs + num_tokens // page_size
    padded_num_slices = min(padded_num_slices, num_tokens)
    return padded_num_slices


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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.
    """
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    # 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
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    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


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def replace_set_lora(model):
    def _tpu_set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
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        embeddings_tensor: torch.Tensor | None,
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    ):
        # 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.
2158
        self._original_set_lora(index, lora_a, lora_b, embeddings_tensor)
2159
        torch_xla.sync(wait=False)
2160
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    def _tpu_reset_lora(self, index: int):
        self._original_reset_lora(index)
2163
        torch_xla.sync(wait=False)
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2169

    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__)
2170
            module.reset_lora = _tpu_reset_lora.__get__(module, module.__class__)