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 TorchCompileWrapperWithCustomDispatcher
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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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        if self.lora_config is not None:
            self.vocab_size += self.lora_config.lora_extra_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)
531

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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 []

557
        return list(model.pooler.get_supported_tasks())
558

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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)

569
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
570
        """
571
        Generates the KVCacheSpec by parsing the kv cache format from each
572
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        Attention module in the static forward context.
        Returns:
574
            KVCacheSpec: A dictionary mapping layer names to their KV cache
575
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577
            format. Layers that do not need KV cache are not included.
        """

578
        layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
579
        block_size = self.vllm_config.cache_config.block_size
580
581
        cache_dtype_str = self.vllm_config.cache_config.cache_dtype

582
        kv_cache_spec: dict[str, KVCacheSpec] = {}
583
        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
628
                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,
                )
641
            else:
642
                continue
643
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645

        return kv_cache_spec

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648
    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
661
                to be scheduled for each request.
662
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664

        Returns:
            np.ndarray: A 2D array of shape (total_block_len, 3), where each row
665
                contains:
666
                - kv_cache_start_index (int): The starting index in the KV cache
667
                  for the corresponding slice.
668
                - new_kv_start_index (int): The starting index in the new KV
669
                  cache for the corresponding slice.
670
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672
                - slice_len (int): The length of the slice.
        """
        slices_start = self.input_batch.num_computed_tokens_cpu[:num_reqs]
673
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        slices_end = (
            self.input_batch.num_computed_tokens_cpu[:num_reqs]
            + num_scheduled_tokens_per_req
        )
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680
        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 = (
681
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            no_repeat_req_indices * self.max_num_blocks_per_req + local_block_start_idx
        )
683
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689
        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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692
        slot_mapping_slices = np.repeat(
            np.array([[0, self.block_size]], dtype=np.int32), total_block_len, axis=0
        )
693
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695
        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
702
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704
        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:])
705
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707
        kv_cache_start_indices = slot_mapping_slices[:, 0] + (
            block_numbers * self.block_size
        )
708
709
        new_kv_start_indices = cu_slices_lens[:-1]
        slot_mapping_metadata = np.stack(
710
711
            [kv_cache_start_indices, new_kv_start_indices, slice_lens], axis=1
        )
712
713
        return slot_mapping_metadata

714
    def _prepare_inputs(self, scheduler_output: "SchedulerOutput", start_index: int):
715
        assert scheduler_output.total_num_scheduled_tokens > 0
716
717
        num_reqs = self.input_batch.num_reqs
        assert num_reqs > 0
718
        assert start_index < num_reqs
719

720
        # Get the number of scheduled tokens for each request.
721
        use_max_model_len = self.most_model_len is None
722
723
        num_scheduled_tokens_per_req = []
        max_num_scheduled_tokens_all_reqs = 0
724
725
726
727
728
        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]
729
            assert req_id is not None
730
            num_tokens = scheduler_output.num_scheduled_tokens[req_id]
731
732
            if not use_max_model_len and num_tokens > self.most_model_len:
                use_max_model_len = True
733
            num_scheduled_tokens_per_req.append(num_tokens)
734
735
        if use_max_model_len:
            if len(num_scheduled_tokens_per_req) > self.num_reqs_max_model_len:
736
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738
                num_scheduled_tokens_per_req = num_scheduled_tokens_per_req[
                    : self.num_reqs_max_model_len
                ]
739
740
741
742
                end_index = start_index + self.num_reqs_max_model_len
            else:
                end_index = num_reqs
        else:
743
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745
746
            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
                ]
747
748
749
750
                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)
751
752
753
        num_scheduled_tokens_per_req = np.array(
            num_scheduled_tokens_per_req, dtype=np.int32
        )
754
        total_num_scheduled_tokens = sum(num_scheduled_tokens_per_req)
755
756
        assert max_num_scheduled_tokens_all_reqs > 0

757
758
        num_reqs = len(num_scheduled_tokens_per_req)

759
760
761
        # 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.
762
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens_per_req)
763
764
765
766
767

        # 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(
768
769
            [self.arange_np[:n] for n in num_scheduled_tokens_per_req]
        )
770
771
772

        # Get positions.
        positions_np = self.positions_np[:total_num_scheduled_tokens]
773
774
775
776
777
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
778
779
780
781
782

        # 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.
783
784
785
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
786
787
788
789

        # 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.
790
791
792
793
794
795
        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],
        )
796
797
798

        # Prepare the attention metadata.
        self.query_start_loc_np[0] = 0
799
800
801
802
        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
803
804

        self.seq_lens_np[:num_reqs] = (
805
806
807
            self.input_batch.num_computed_tokens_cpu[:num_reqs]
            + num_scheduled_tokens_per_req
        )
808
809

        # Do the padding and copy the tensors to the TPU.
810
        padded_total_num_scheduled_tokens = _get_padded_token_len(
811
812
            self.num_tokens_paddings, total_num_scheduled_tokens
        )
813
814
        # Zero out to avoid spurious values from prev iteration (last cp chunk)
        self.input_ids_cpu[
815
816
817
818
819
820
821
822
            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
        )
823
        if use_max_model_len:
824
825
826
827
828
829
830
831
832
833
            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)
834
        else:
835
836
837
838
839
840
841
842
843
844
845
846
            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)
847
        block_tables = block_tables.to(self.device)
848

849
        # Calculate the slot mapping
850
        slot_mapping_metadata = self._get_slot_mapping_metadata(
851
852
            num_reqs, num_scheduled_tokens_per_req
        )
853
        num_kv_update_slices = slot_mapping_metadata.shape[0]
854
        padded_num_slices = _get_padded_num_kv_cache_update_slices(
855
856
            padded_total_num_scheduled_tokens, self.max_num_reqs, self.block_size
        )
857
858
859
        slot_mapping_metadata = np.pad(
            slot_mapping_metadata,
            [[0, padded_num_slices - len(slot_mapping_metadata)], [0, 0]],
860
861
            constant_values=0,
        )
862
        slot_mapping_metadata = np.transpose(slot_mapping_metadata)
863
        slot_mapping_metadata = torch.tensor(slot_mapping_metadata, device=self.device)
864

865
866
867
868
869
        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
870
            padded_num_scheduled_tokens_per_req[-1] += (
871
                padded_total_num_scheduled_tokens - total_num_scheduled_tokens
872
            )
873

874
            self.set_active_loras(self.input_batch, padded_num_scheduled_tokens_per_req)
875

876
        attn_metadata = PallasMetadata(
877
            slot_mapping=slot_mapping_metadata,
878
            block_tables=block_tables,
879
880
            context_lens=seq_lens,
            query_start_loc=query_start_loc,
881
882
883
884
885
            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,
886
        )
887
888
889
890
891
        # 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.
892
        padded_num_reqs = _get_padded_num_reqs_with_upper_limit(
893
894
            num_reqs, self.max_num_reqs
        )
895
896
        # Indices at which we sample (positions of last token in the sequence).
        # Padded to avoid recompiling when `num_reqs` varies.
897
        logits_indices = self.query_start_loc_cpu[1 : padded_num_reqs + 1] - 1
898
        logits_indices = logits_indices.to(self.device)
899

900
901
902
903
904
        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
905
            padded_num_scheduled_tokens_per_req[-1] += (
906
                padded_total_num_scheduled_tokens - total_num_scheduled_tokens
907
            )
908

909
            self.set_active_loras(self.input_batch, padded_num_scheduled_tokens_per_req)
910

911
        layer_names = get_layers_from_vllm_config(self.vllm_config, Attention).keys()
912
        per_layer_attn_metadata = {
913
            layer_name: attn_metadata for layer_name in layer_names
914
        }
915
916
917
918
919
920
921
        return (
            per_layer_attn_metadata,
            logits_indices,
            padded_num_reqs,
            num_reqs,
            end_index,
        )
922

923
    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
924
925
926
927
928
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
            return

        # Batch the multi-modal inputs.
929
        mm_kwargs = list[MultiModalKwargsItem]()
930
931
        # List of tuple (mm_hash, pos_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
932
933
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
934
935

            for mm_input_id in encoder_input_ids:
936
937
938
939
                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))
940
941
942
943
944
945
946
947

        # 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.
948
        model = cast(SupportsMultiModal, self.model)
949
        encoder_outputs = []
950
        for _, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
951
952
953
954
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
955
            multimodal_cpu_fields=model.multimodal_cpu_fields,
956
        ):
957
958
959
960
961
962
963
            # 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.
964
            torch_xla.sync(wait=False)
965
            curr_group_outputs = model.get_multimodal_embeddings(**mm_kwargs_group)
966
            torch_xla.sync(wait=False)
967

968
969
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
970
                expected_num_items=num_items,
971
972
            )

973
974
975
976
977
978
            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)
979
980

        # Cache the encoder outputs.
981
982
983
        # 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.
984
        for (mm_hash, pos_info), output in zip(mm_hashes_pos, encoder_outputs):
985
986
987
            assert pos_info.is_embed is None, (
                "Expected all positions to be contiguous and embeddings."
            )
988
            self.encoder_cache[mm_hash] = output
989
990

    def _gather_mm_embeddings(
991
992
        self,
        scheduler_output: "SchedulerOutput",
993
994
995
    ) -> 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(
996
997
            self.num_tokens_paddings, total_num_scheduled_tokens
        )
998
999
1000
1001
1002
1003

        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

1004
        for req_id in self.input_batch.req_ids:
1005
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1006
1007
            req_state = self.requests[req_id]
            num_computed_tokens = req_state.num_computed_tokens
1008

1009
1010
1011
1012
            # 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.
1013
1014
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1015
1016
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028

                # 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
1029
1030
1031
1032
1033
1034
1035
1036

                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

1037
                mm_hash = mm_feature.identifier
1038
                encoder_output = self.encoder_cache.get(mm_hash, None)
1039
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
1040

1041
1042
1043
                assert pos_info.is_embed is None, (
                    "Expected all positions to be contiguous and embeddings."
                )
1044
1045

                req_start_pos = req_start_idx + start_pos - num_computed_tokens
1046
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = True
1047
1048

                # Only whole mm items are processed
1049
                mm_embeds.append(encoder_output)
1050

1051
1052
            req_start_idx += num_scheduled_tokens

1053
        is_mm_embed = is_mm_embed[:padded_total_num_scheduled_tokens].to(self.device)
1054
1055
1056
1057
1058
1059

        return mm_embeds, is_mm_embed

    def _get_model_inputs(
        self,
        input_ids: torch.Tensor,
1060
        mm_embed_inputs: tuple[list[torch.Tensor], torch.Tensor] | None,
1061
    ):
1062
        if self.supports_mm_inputs:
1063
1064
            mm_embeds, is_mm_embed = mm_embed_inputs or (None, None)

1065
1066
1067
            # 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.
1068
            inputs_embeds = self.model.get_input_embeddings(
1069
                input_ids,
1070
                multimodal_embeddings=mm_embeds,
1071
                is_multimodal=is_mm_embed,
1072
            )
1073

1074
1075
1076
1077
1078
1079
1080
1081
            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

1082
1083
1084
1085
    @torch.no_grad()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
1086
        intermediate_tensors: IntermediateTensors | None = None,
1087
1088
1089
1090
1091
1092
    ) -> ModelRunnerOutput | None:
        if self.scheduler_output is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
1093
1094
        # Update cached state
        self._update_states(scheduler_output)
1095
        if not scheduler_output.total_num_scheduled_tokens:
1096
1097
1098
1099
            if not has_kv_transfer_group():
                # Return empty ModelRunnerOutput if there's no work to do.
                return EMPTY_MODEL_RUNNER_OUTPUT

1100
            return self.kv_connector_no_forward(scheduler_output, self.vllm_config)
1101

1102
        mm_embed_inputs = None
1103
        if self.supports_mm_inputs:
1104
            # Run the multimodal encoder if any.
1105
            self._execute_mm_encoder(scheduler_output)
1106
1107
            mm_embed_inputs = self._gather_mm_embeddings(scheduler_output)

1108
        torch_xla.sync(wait=False)
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125

        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

1126
        # Prepare inputs, the requests might be split into multiple
1127
1128
1129
1130
        # executions, combine the result of each execution.
        start_index = 0
        combined_selected_tokens: list[torch.Tensor] = []
        combined_logprobs: list[LogprobsLists] = []
1131
1132
1133
1134
1135
1136

        # 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)

1137
        while start_index < self.input_batch.num_reqs:
1138
1139
1140
            attn_metadata, logits_indices, padded_num_reqs, num_reqs, end_index = (
                self._prepare_inputs(scheduler_output, start_index)
            )
1141
            input_ids, inputs_embeds = self._get_model_inputs(
1142
1143
                self.input_ids, mm_embed_inputs
            )
1144
            torch_xla.sync(wait=False)
1145
1146
            # Run the decoder
            with set_forward_context(
1147
1148
1149
1150
                attn_metadata,
                self.vllm_config,
                num_tokens=scheduler_output.total_num_scheduled_tokens,
            ):
1151
1152
1153
1154
1155
                hidden_states = self.model(
                    input_ids=input_ids,
                    positions=self.position_ids,
                    inputs_embeds=inputs_embeds,
                )
1156
            hidden_states = self.select_hidden_states(hidden_states, logits_indices)
1157
            logits = self.compute_logits(hidden_states)
1158
1159
1160
            tpu_sampling_metadata = TPUSupportedSamplingMetadata.from_input_batch(
                self.input_batch, padded_num_reqs, self.device
            )
1161
            if grammar_output is not None:
1162
                require_struct_decoding, grammar_bitmask_padded, arange = (
1163
                    self.prepare_structured_decoding_input(logits, grammar_output)
1164
1165
1166
1167
                )
                logits = self.structured_decode(
                    require_struct_decoding, grammar_bitmask_padded, logits, arange
                )
1168
            selected_token_ids = self.sample_from_logits_func(
1169
1170
                logits, tpu_sampling_metadata
            )
1171
1172
1173
1174
            # 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.
1175
1176
1177
1178
1179
            logprobs = (
                self.gather_logprobs(logits, selected_token_ids)
                if tpu_sampling_metadata.logprobs
                else None
            )
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189

            # 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

1190
1191
1192
1193
1194
        # 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()
1195
1196
1197
        finished_sending, finished_recving = self.get_finished_kv_transfers(
            scheduler_output
        )
1198

1199
1200
1201
1202
1203
1204
1205
1206
1207
        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

1208
1209
1210
1211
1212
1213
1214
1215
1216
            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]
                ),
            )
1217
1218
        else:
            logprobs_lists = None
1219

1220
1221
        # Update the cache state concurrently. Code above will not block until
        # we use `selected_token_ids`. Add mark_step if post-processing changes
1222
        request_seq_lens: list[tuple[int, CachedRequestState, int]] = []
1223
        discard_sampled_tokens_req_indices = []
1224
        num_reqs = self.input_batch.num_reqs
1225
1226
1227
        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]
1228
1229
1230
1231
            seq_len = (
                req_state.num_computed_tokens
                + scheduler_output.num_scheduled_tokens[req_id]
            )
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
            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)

1242
1243
1244
1245
                # 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)

1246
        assert all(
1247
1248
            req_id is not None for req_id in self.input_batch.req_ids[:num_reqs]
        ), "req_ids contains None"
1249
        req_ids = cast(list[str], self.input_batch.req_ids[:num_reqs])
1250

1251
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
1252
        for req_id in self.input_batch.req_ids[:num_reqs]:
1253
1254
            prompt_logprobs_dict[req_id] = None

1255
1256
1257
        max_gen_len = selected_token_ids.shape[-1]
        if max_gen_len == 1:
            valid_sampled_token_ids = selected_token_ids.tolist()
1258

1259
1260
1261
1262
1263
1264
1265
            # Mask out the sampled tokens that should not be sampled.
            # TODO: Keep in sync with gpu_model_runner.py, in particular
            #       the "else" case here
            for i in discard_sampled_tokens_req_indices:
                valid_sampled_token_ids[i].clear()

            # Append sampled tokens
1266
1267
1268
1269
1270
            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
1271

1272
1273
1274
1275
        else:
            valid_mask = selected_token_ids != INVALID_TOKEN_ID
            gen_lens = valid_mask.sum(dim=1).tolist()
            valid_sampled_token_ids = [
1276
                seq.tolist() for seq in selected_token_ids[valid_mask].split(gen_lens)
1277
1278
1279
1280
            ]
            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)
1281
1282
1283
                self.input_batch.token_ids_cpu[i, target_slice] = (
                    valid_sampled_token_ids[i]
                )
1284
1285
                req_state.output_token_ids.extend(valid_sampled_token_ids[i])

1286
1287
1288
1289
        kv_connector_output = (
            None
            if (finished_sending is None and finished_recving is None)
            else KVConnectorOutput(
1290
1291
1292
                finished_sending=finished_sending,
                finished_recving=finished_recving,
            )
1293
        )
1294

1295
        model_runner_output = ModelRunnerOutput(
1296
            req_ids=req_ids,
1297
            req_id_to_index=self.input_batch.req_id_to_index,
1298
            sampled_token_ids=valid_sampled_token_ids,
1299
            logprobs=logprobs_lists,
1300
            prompt_logprobs_dict=prompt_logprobs_dict,
1301
            pooler_output=[],
1302
1303
            kv_connector_output=kv_connector_output,
        )
1304
1305
1306
1307
1308

        # 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")

1309
1310
        return model_runner_output

1311
1312
1313
1314
1315
    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():
1316
1317
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
1318
                f"Allowed configs: {allowed_config_names}"
1319
            )
1320
1321
1322
1323
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
    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(
1338
1339
1340
1341
            "vllm.model_executor.layers.vocab_parallel_embedding."
            "get_tensor_model_parallel_rank",
            return_value=xm_tp_rank,
        ):
1342
1343
1344
            try:
                if self.use_spmd:
                    tpu_loader = TPUModelLoader(
1345
1346
                        load_config=self.vllm_config.load_config
                    )
1347
                    model = tpu_loader.load_model(
1348
                        vllm_config=self.vllm_config,
1349
                        model_config=self.vllm_config.model_config,
1350
1351
                        mesh=self.mesh,
                    )
1352
                else:
1353
                    model_loader = get_model_loader(self.load_config)
1354
1355
                    logger.info("Loading model from scratch...")
                    model = model_loader.load_model(
1356
1357
                        vllm_config=self.vllm_config, model_config=self.model_config
                    )
1358
1359
1360
1361
1362
1363
            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. "
1364
1365
                    f"See the detailed error: {e}"
                ) from e
1366
        if self.lora_config is not None:
1367
            model = self.load_lora_model(model, self.vllm_config, self.device)
1368
            replace_set_lora(model)
1369

1370
1371
        # Sync all pending XLA execution during model initialization and weight
        # loading.
1372
        torch_xla.sync(wait=False)
1373
        xm.wait_device_ops()
1374
1375
        if not hasattr(self, "model"):
            self.model = model
1376
        self.sampler = TPUSampler()
1377

1378
    def reload_weights(self) -> None:
1379
        assert getattr(self, "model", None) is not None, (
1380
            "Cannot reload weights before model is loaded."
1381
        )
1382
1383
1384
1385
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
        model_loader.load_weights(self.model, model_config=self.model_config)

1386
    @torch.no_grad()
1387
    def _dummy_run(self, num_tokens: int, num_reqs: int, num_blocks: int) -> None:
1388
        if self.supports_mm_inputs:
1389
            input_ids = None
1390
1391
1392
            inputs_embeds = torch.zeros(
                (num_tokens, self.hidden_size), dtype=self.dtype, device=self.device
            )
1393
        else:
1394
            input_ids = torch.zeros((num_tokens), dtype=torch.int32).to(self.device)
1395
            inputs_embeds = None
1396
        actual_num_reqs = min(num_tokens, num_reqs)
1397
        position_ids = torch.zeros(num_tokens, dtype=torch.int32).to(self.device)
1398
        padded_num_slices = _get_padded_num_kv_cache_update_slices(
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
            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
        )
1410
        query_lens = [1] * num_reqs
1411
1412
1413
1414
1415
        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)
1416
1417
1418
1419
1420
        attn_metadata = PallasMetadata(
            slot_mapping=slot_mapping,
            block_tables=block_tables,
            context_lens=context_lens,
            query_start_loc=query_start_loc,
1421
            num_seqs=num_seqs,
1422
            num_kv_update_slices=num_kv_update_slices,
1423
            num_slices_per_kv_cache_update_block=self._num_slices_per_kv_cache_update_block,
1424
        )
1425

1426
        if self.supports_mm_inputs:
1427
1428
1429
            torch._dynamo.mark_dynamic(inputs_embeds, 0)
        else:
            torch._dynamo.mark_dynamic(input_ids, 0)
1430
1431
        torch._dynamo.mark_dynamic(position_ids, 0)
        torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 0)
1432
1433
1434
        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)
1435

1436
        layer_names = get_layers_from_vllm_config(self.vllm_config, Attention).keys()
1437
        per_layer_attn_metadata = {
1438
            layer_name: attn_metadata for layer_name in layer_names
1439
1440
        }

1441
1442
1443
1444
1445
1446
1447
1448
1449
        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
            )
1450
        self._hidden_states_dtype = out.dtype
1451

1452
1453
1454
    def _set_active_loras(
        self, prompt_lora_mapping, token_lora_mapping, lora_requests
    ) -> None:
1455
        torch_xla.sync(wait=False)  # Captures input updates
1456
1457
1458
        super()._set_active_loras(
            prompt_lora_mapping, token_lora_mapping, lora_requests
        )
1459
        torch_xla.sync(wait=False)  # Captures metadata updates
1460

1461
    def _precompile_mm_encoder(self) -> None:
1462
        if not self.supports_mm_inputs:
1463
1464
            return

1465
1466
        # Pre-compile MM encoder for all supported data modalities.
        hf_config = self.vllm_config.model_config.hf_config
1467
1468
1469
1470
1471
1472
1473

        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():
1474
            logger.info(
1475
1476
                "Compiling Multimodal %s Encoder with different input shapes.", mode
            )
1477
1478
            start = time.perf_counter()
            # No padding for MM encoder just yet.
1479
            for num_items in range(1, max_items_per_seq + 1):
1480
1481
                logger.info("  -- mode: %s items: %d", mode, num_items)
                batched_dummy_mm_inputs = self._get_mm_dummy_batch(
1482
1483
1484
                    mode,
                    num_items,
                )
1485
                # Run multimodal encoder.
1486
                torch_xla.sync(wait=False)
1487
                mm_embeds = self.model.get_multimodal_embeddings(
1488
1489
                    **batched_dummy_mm_inputs
                )
1490
                torch_xla.sync(wait=False)
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
                num_patches = mm_embeds[0].shape[0]
                items_size = num_patches * num_items

                # NOTE (NickLucche) pre-compile `get_input_embeddings` when mm
                # embeddings are present. We assume `--disable-mm-chunked`,
                # hence only whole items can be scheduled. This implies we just
                # need to compile when `num_items` fit the (padded) `input_ids`
                for num_tokens in self.num_tokens_paddings:
                    if num_tokens >= items_size:
                        # XLA Workaround: if torch.zeros(..device) is used, XLA
                        # compiles a scalar+expansion op, which won't match
                        # the graph generated at runtime. CPU->TPU must be used
1503
1504
1505
                        placeholders_ids = torch.zeros(
                            num_tokens, dtype=torch.int32, device="cpu"
                        )
1506
                        # Align placeholders and actual num mm_embeddings.
1507
                        placeholders_ids[:items_size] = hf_config.image_token_index
1508
1509

                        placeholders_ids = placeholders_ids.to(self.device)
1510
1511
1512
1513

                        mm_mask = torch.tensor([False] * num_tokens)
                        mm_mask[:items_size] = True
                        mm_mask = mm_mask.to(self.device)
1514
                        # Assign outputs or the graph will be cut short.
1515
1516
1517
1518
                        a, b = self._get_model_inputs(
                            placeholders_ids,
                            mm_embed_inputs=([mm_embeds], mm_mask),
                        )
1519
                        assert a is None
1520
                        torch_xla.sync(wait=False)
1521
1522
1523
1524

            # Pre-compile `get_input_embeddings` when mm_embeddings are not
            # present. Chunk is only made of text, no mm_placeholders.
            for num_tokens in self.num_tokens_paddings:
1525
1526
1527
                placeholders_ids = torch.zeros(
                    num_tokens, dtype=torch.int32, device="cpu"
                )
1528
                placeholders_ids = placeholders_ids.to(self.device)
1529
1530
1531
1532
                a, b = self._get_model_inputs(
                    placeholders_ids,
                    mm_embed_inputs=None,
                )
1533
                assert a is None
1534
                torch_xla.sync(wait=False)
1535
1536
1537
1538

            xm.wait_device_ops()
            end = time.perf_counter()
            logger.info(
1539
1540
1541
1542
                "Multimodal %s Encoder compilation finished in in %.2f [secs].",
                mode,
                end - start,
            )
1543

1544
    def _precompile_backbone(self) -> None:
1545
1546
        logger.info("Compiling the model with different input shapes.")
        start = time.perf_counter()
1547
        for num_tokens in self.num_tokens_paddings:
1548
            logger.info("  -- num_tokens: %d", num_tokens)
1549
1550
1551
            self._dummy_run(
                num_tokens, self.num_reqs_max_model_len, self.max_num_blocks_per_req
            )
1552
            if self.most_model_len is not None:
1553
1554
1555
1556
1557
                self._dummy_run(
                    num_tokens,
                    self.num_reqs_most_model_len,
                    self.num_blocks_per_most_len_req,
                )
1558
1559
        xm.wait_device_ops()
        end = time.perf_counter()
1560
        logger.info("Compilation finished in %.2f [secs].", end - start)
1561
        self._update_num_xla_graphs("model backbone")
1562

1563
1564
1565
    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.
1566
        logger.info("Compiling select_hidden_states with different input shapes.")
1567
1568
        start = time.perf_counter()
        hsize = self.model_config.get_hidden_size()
1569
        for num_tokens in self.num_tokens_paddings:
1570
1571
1572
            dummy_hidden = torch.zeros(
                (num_tokens, hsize), device=self.device, dtype=self._hidden_states_dtype
            )
1573
1574
            torch._dynamo.mark_dynamic(dummy_hidden, 0)
            for num_reqs in self.num_reqs_paddings:
1575
                indices = torch.zeros(num_reqs, dtype=torch.int32, device=self.device)
1576
1577
                torch._dynamo.mark_dynamic(indices, 0)
                self.select_hidden_states(dummy_hidden, indices)
1578
                logger.info("  -- num_tokens: %d, num_seqs: %d", num_tokens, num_reqs)
1579
1580
1581
1582
                # 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
1583
        xm.wait_device_ops()
1584
        end = time.perf_counter()
1585
        logger.info("Compilation finished in %.2f [secs].", end - start)
1586
        self._update_num_xla_graphs("select_hidden_states")
1587

1588
1589
    def _precompile_compute_logits(self) -> None:
        logger.info("Compiling compute_logits with different input shapes.")
1590
1591
1592
        start = time.perf_counter()
        hsize = self.model_config.get_hidden_size()
        for num_reqs in self.num_reqs_paddings:
1593
1594
1595
            dummy_hidden = torch.zeros(
                (num_reqs, hsize), device=self.device, dtype=self._hidden_states_dtype
            )
1596
1597
1598
1599
1600
1601
1602
1603
1604
            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:
1605
        logger.info("Compiling structured_decoding with different input shapes.")
1606
1607
        start = time.perf_counter()
        for num_reqs in self.num_reqs_paddings:
1608
1609
1610
1611
1612
1613
1614
1615
1616
            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)
1617
1618
1619
1620
            # 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)
1621
1622
1623
1624
1625
1626
            self.structured_decode(
                dummy_require_struct_decoding,
                dummy_grammar_bitmask,
                dummy_logits,
                arange,
            )
1627
1628
1629
1630
1631
1632
1633
            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:
1634
        logger.info("Compiling sample_from_logits with different input shapes.")
1635
1636
        start = time.perf_counter()
        for num_reqs in self.num_reqs_paddings:
1637
1638
1639
1640
1641
            dummy_logits = torch.zeros(
                (num_reqs, self.vocab_size),
                device=self.device,
                dtype=self._hidden_states_dtype,
            )
1642
1643
            # The first dimension of dummy_logits cannot be mark_dynamic
            # because some operations in the sampler require it to be static.
1644
1645
            for all_greedy in [False, True]:
                generate_params_if_all_greedy = not all_greedy
1646
1647
1648
1649
1650
1651
                sampling_metadata = TPUSupportedSamplingMetadata.from_input_batch(
                    self.input_batch,
                    num_reqs,
                    self.device,
                    generate_params_if_all_greedy,
                )
1652
                sampling_metadata.all_greedy = all_greedy
1653
                with self.maybe_select_dummy_loras(
1654
1655
1656
                    self.lora_config, np.array([num_reqs], dtype=np.int32)
                ):
                    self.sample_from_logits_func(dummy_logits, sampling_metadata)
1657
1658
1659
            logger.info("  -- num_seqs: %d", num_reqs)
        xm.wait_device_ops()
        end = time.perf_counter()
1660
1661
        logger.info("Compilation finished in %.2f [secs].", end - start)
        self._update_num_xla_graphs("sample_from_logits")
1662

1663
1664
1665
1666
    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:
1667
1668
1669
1670
1671
1672
            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)
1673
            with self.maybe_select_dummy_loras(
1674
1675
                self.lora_config, np.array([num_reqs], dtype=np.int32)
            ):
1676
                self.gather_logprobs(dummy_logits, dummy_tokens)
1677
1678
1679
1680
1681
1682
            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")

1683
1684
1685
1686
    def capture_model(self) -> None:
        """
        Precompile all the subgraphs with possible input shapes.
        """
1687
1688
1689
1690
1691
1692
1693
1694
        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()
1695

1696
1697
1698
1699
1700
    def profile_run(
        self,
        num_tokens: int,
    ) -> None:
        # Profile with multimodal encoder & encoder cache.
1701
        if self.supports_mm_inputs:
1702
            if self.model_config.multimodal_config.skip_mm_profiling:
1703
                logger.info(
1704
                    "Skipping memory profiling for multimodal encoder and "
1705
1706
                    "encoder cache."
                )
1707
1708
1709
1710
1711
1712
1713
1714
1715
            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.
1716
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
1717
1718
1719
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
1720
1721
1722
1723
1724
1725
1726
1727
1728

                    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,
                    )
1729

1730
1731
1732
1733
1734
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
1735

1736
1737
1738
1739
                    # Run multimodal encoder.
                    # Isolate encoder graph from post-processing to minimize
                    # impact of recompilation until it's fixed.
                    start = time.perf_counter()
1740
                    torch_xla.sync(wait=False)
1741
1742
1743
                    dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs
                    )
1744
                    torch_xla.sync(wait=False)
1745
1746
1747
1748
                    xm.wait_device_ops()
                    end = time.perf_counter()
                    logger.info(
                        "Multimodal Encoder profiling finished in %.2f [secs].",
1749
1750
                        end - start,
                    )
1751
1752
1753
1754
1755

                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
1756

1757
                    # Cache the dummy encoder outputs.
1758
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
1759
1760

        # Trigger compilation for general shape.
1761
1762
1763
        self._dummy_run(
            num_tokens, self.num_reqs_max_model_len, self.max_num_blocks_per_req
        )
1764
        if self.most_model_len is not None:
1765
1766
1767
1768
1769
            self._dummy_run(
                num_tokens,
                self.num_reqs_most_model_len,
                self.num_blocks_per_most_len_req,
            )
1770

1771
        torch_xla.sync(wait=False)
1772
1773
1774
1775
        xm.wait_device_ops()
        self.encoder_cache.clear()
        gc.collect()

1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
    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,
        )

1794
1795
        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)
1796
1797
            kv_caches[layer_name] = kv_caches[target_layer_name]

1798
1799
1800
1801
    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize KV cache based on `kv_cache_config`.
        Args:
1802
            kv_cache_config: Configuration for the KV cache, including the KV
1803
1804
            cache size of each layer
        """
1805
        if len(kv_cache_config.kv_cache_groups) > 1:
1806
            raise NotImplementedError(
1807
1808
                "Hybrid models with more than one KV cache type are not supported yet."
            )
1809

1810
1811
1812
1813
        if (
            kv_cache_config.kv_cache_groups[0].kv_cache_spec.block_size
            != self.block_size
        ):
1814
1815
1816
1817
1818
1819
1820
            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(),
1821
1822
1823
                block_sizes=[
                    kv_cache_config.kv_cache_groups[0].kv_cache_spec.block_size
                ],
1824
1825
1826
                kernel_block_sizes=[
                    kv_cache_config.kv_cache_groups[0].kv_cache_spec.block_size
                ],
1827
1828
            )
        # Verify dtype compatibility between block_table_cpu and input_batch
1829
1830
1831
1832
        assert (
            self.block_table_cpu.dtype
            == self.input_batch.block_table[0].get_cpu_tensor().dtype
        )
1833

1834
1835
1836
        kv_cache_sizes = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
            assert len(kv_cache_tensor.shared_by) == 1, (
1837
1838
                "KV cache tensor shared by multiple layers is not supported in TPU."
            )
1839
            kv_cache_sizes[kv_cache_tensor.shared_by[0]] = kv_cache_tensor.size
1840

1841
        kv_caches: dict[str, torch.Tensor] = {}
1842
1843
1844
        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:
1845
1846
1847
                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
1848
                if isinstance(kv_cache_spec, AttentionSpec):
1849
1850
1851
                    if self.use_spmd:
                        num_kv_heads = kv_cache_spec.num_kv_heads
                        assert self.original_parallel_config is not None
1852
                        tp_size = self.original_parallel_config.tensor_parallel_size
1853
1854
1855
                        # 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 "
1856
1857
                            f"tp_size {tp_size} under SPMD mode"
                        )
1858
                    kv_cache_shape = PallasAttentionBackend.get_kv_cache_shape(
1859
1860
1861
1862
1863
                        num_blocks,
                        kv_cache_spec.block_size,
                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
                    )
1864
1865
                    dtype = kv_cache_spec.dtype

1866
1867
1868
                    tpu_kv_cache = torch.zeros(kv_cache_shape, dtype=dtype).to(
                        self.device
                    )
1869

1870
                    kv_caches[layer_name] = tpu_kv_cache
1871
1872
                else:
                    raise NotImplementedError
1873

1874
1875
        # Set up cross-layer KV cache sharing if needed
        self.maybe_setup_cross_layer_kv_sharing(kv_caches, kv_cache_config)
1876

1877
1878
1879
        bind_kv_cache(
            kv_caches,
            self.vllm_config.compilation_config.static_forward_context,
1880
1881
            self.kv_caches,
        )
1882

1883
1884
1885
        if self.use_spmd:
            # Shard KV Cache
            for cache in self.kv_caches:
1886
                xs.mark_sharding(cache, self.mesh, (None, "x", None, None))
1887

1888
1889
1890
1891
        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)

1892
    def reset_dynamo_cache(self):
1893
1894
1895
1896
        # 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:
1897
            compiled_model = self.model.get_language_model().model
1898
1899
1900
1901
1902
        else:
            compiled_model = self.model.model
        if isinstance(compiled_model, TorchCompileWrapperWithCustomDispatcher):
            logger.info("Clear dynamo cache and cached dynamo bytecode.")
            torch._dynamo.eval_frame.remove_from_cache(
1903
1904
                compiled_model.original_code_object
            )
1905
            compiled_model.compiled_codes.clear()
1906

1907
1908
1909
1910
1911
    @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)
1912
    def compute_logits(self, sample_hidden_states: torch.Tensor) -> torch.Tensor:
1913
        return self.model.compute_logits(sample_hidden_states)
1914

1915
1916
1917
    # 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)
1918
    def sample_from_logits(
1919
1920
        self, logits: torch.Tensor, sampling_metadata: TPUSupportedSamplingMetadata
    ) -> torch.Tensor:
1921
        """
1922
        Sample with xla-friendly function. This function is to be traced
1923
1924
        separately from `forward` for lighter compilation overhead.
        """
1925
1926
1927
        if sampling_metadata.all_greedy:
            out_tokens = torch.argmax(logits, dim=-1, keepdim=True)
        else:
1928
            out_tokens = self.sampler(logits, sampling_metadata).sampled_token_ids
1929
1930
        return out_tokens

1931
    @torch.compile(backend="openxla", fullgraph=True, dynamic=False)
1932
1933
1934
    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 get_multimodal_embeddings(self, *args, **kwargs):
        return self.model.get_multimodal_embeddings(*args, **kwargs)
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    def get_input_embeddings(self, *args, **kwargs):
        return self.model.get_input_embeddings(*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:")
2083
        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:
2105
    """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,
2154
        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.
2159
        self._original_set_lora(index, lora_a, lora_b, embeddings_tensor)
2160
        torch_xla.sync(wait=False)
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2163

    def _tpu_reset_lora(self, index: int):
        self._original_reset_lora(index)
2164
        torch_xla.sync(wait=False)
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2170

    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__)
2171
            module.reset_lora = _tpu_reset_lora.__get__(module, module.__class__)