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tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/cpu_worker.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import os
+import platform
+from typing import Callable, Optional
+
+import torch
+
+from vllm import envs
+from vllm.config import VllmConfig
+from vllm.distributed.parallel_state import get_pp_group, get_tp_group
+from vllm.logger import init_logger
+from vllm.model_executor.utils import set_random_seed
+from vllm.platforms import CpuArchEnum, current_platform
+from vllm.platforms.cpu import CpuPlatform, LogicalCPUInfo
+from vllm.sequence import IntermediateTensors
+from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.outputs import ModelRunnerOutput
+from vllm.v1.worker.cpu_model_runner import CPUModelRunner
+from vllm.v1.worker.gpu_worker import (Worker,
+                                       init_worker_distributed_environment)
+
+logger = init_logger(__name__)
+
+
+class CPUWorker(Worker):
+
+    def __init__(self,
+                 vllm_config: VllmConfig,
+                 local_rank: int,
+                 rank: int,
+                 distributed_init_method: str,
+                 is_driver_worker: bool = False):
+        super().__init__(vllm_config,
+                         local_rank,
+                         rank,
+                         distributed_init_method,
+                         is_driver_worker=is_driver_worker)
+
+        self.parallel_config.disable_custom_all_reduce = True
+
+    def init_device(self):
+        # Setup OpenMP threads affinity.
+        omp_cpuids = envs.VLLM_CPU_OMP_THREADS_BIND
+        if omp_cpuids == "auto" and platform.system() == "Linux":
+            if current_platform.get_cpu_architecture() == CpuArchEnum.POWERPC:
+                # For POWERPC SMT-8/4/2
+                self.local_omp_cpuid = self._get_autobind_cpu_ids(
+                    lambda cpus: [cpu for cpu in cpus if cpu.id % 8 < 4])
+            elif current_platform.get_cpu_architecture() == CpuArchEnum.X86:
+                # For x86 SMT-2, use 1 CPU per core
+                self.local_omp_cpuid = self._get_autobind_cpu_ids(
+                    lambda cpus: cpus[-1:])
+            else:
+                self.local_omp_cpuid = "all"
+        else:
+            self.local_omp_cpuid = omp_cpuids.split("|")[self.rank]
+
+        if self.local_omp_cpuid != "all":
+            ret = torch.ops._C_utils.init_cpu_threads_env(self.local_omp_cpuid)
+            if ret:
+                logger.info(ret)
+
+        # Note: unique identifier for creating allreduce shared memory
+        os.environ["VLLM_DIST_IDENT"] = self.distributed_init_method.split(
+            ":")[-1]
+        # Initialize the distributed environment.
+        init_worker_distributed_environment(self.vllm_config, self.rank,
+                                            self.distributed_init_method,
+                                            self.local_rank,
+                                            current_platform.dist_backend)
+        # Set random seed.
+        set_random_seed(self.model_config.seed)
+
+        # Construct the model runner
+        self.model_runner: CPUModelRunner = CPUModelRunner(
+            self.vllm_config, torch.device("cpu"))
+
+    def sleep(self, level: int = 1) -> None:
+        logger.warning("sleep mode is not supported on CPU, ignore it.")
+        pass
+
+    def wake_up(self, tags: Optional[list[str]] = None) -> None:
+        logger.warning("sleep mode is not supported on CPU, ignore it.")
+        pass
+
+    def determine_available_memory(self) -> int:
+        return self.cache_config.cpu_kvcache_space_bytes  # type: ignore
+
+    def compile_or_warm_up_model(self) -> None:
+        # Reset the seed to ensure that the random state is not affected by
+        # the model initialization and profiling.
+        set_random_seed(self.model_config.seed)
+        self.model_runner.warming_up_model()
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        scheduler_output: "SchedulerOutput",
+    ) -> Optional[ModelRunnerOutput]:
+        intermediate_tensors = None
+        if not get_pp_group().is_first_rank:
+            intermediate_tensors = IntermediateTensors(
+                get_pp_group().recv_tensor_dict(
+                    all_gather_group=get_tp_group()))
+
+        output = self.model_runner.execute_model(scheduler_output,
+                                                 intermediate_tensors)
+
+        if not get_pp_group().is_last_rank:
+            assert isinstance(output, IntermediateTensors)
+            get_pp_group().send_tensor_dict(output.tensors,
+                                            all_gather_group=get_tp_group())
+            return None
+
+        assert isinstance(output, ModelRunnerOutput)
+        return output if self.is_driver_worker else None
+
+    def _get_autobind_cpu_ids(
+        self, cpu_selector: Callable[[list[LogicalCPUInfo]],
+                                     list[LogicalCPUInfo]]
+    ) -> str:
+        """
+        Return CPU ids to bind based on NUMA nodes. 
+        Currently for rank N, only CPU ids on the N-th node in available NUMA 
+        node list will be selected.
+        Args:
+            cpu_selector: a callable object to select CPUs from a CPU list 
+            of a physical core. The input is a LogicalCPUInfo list, sorted by
+            the LogicalCPUInfo.id. A selected LogicalCPUInfo list should be 
+            returned.
+        """
+
+        allowed_numa_nodes, logical_cpu_list = \
+            CpuPlatform.get_allowed_cpu_memory_node_list()
+        assert len(allowed_numa_nodes) >= self.parallel_config.world_size, (
+            f"No enough allowed NUMA nodes to bind threads of "
+            f"{self.parallel_config.world_size} CPUWorkers. "
+            f"Allowed NUMA nodes are {allowed_numa_nodes}. "
+            "Please try to bind threads manually.")
+
+        # Get CPUs on NUMA node `allowed_numa_nodes[local_rank]``
+        selected_numa_node = allowed_numa_nodes[
+            self.local_rank]  # type: ignore
+        logical_cpu_list = [
+            x for x in logical_cpu_list if x.numa_node == selected_numa_node
+        ]
+
+        # Select CPUs from each physical core via cpu_selector
+        core_to_cpus: dict[int, list[LogicalCPUInfo]] = {}
+        for cpu_info in logical_cpu_list:
+            if cpu_info.physical_core not in core_to_cpus:
+                core_to_cpus[cpu_info.physical_core] = []
+            core_to_cpus[cpu_info.physical_core].append(cpu_info)
+        logical_cpu_list = []
+        for cpu_list in core_to_cpus.values():
+            cpu_list = sorted(cpu_list, key=lambda x: x.id)
+            logical_cpu_list.extend(cpu_selector(cpu_list))
+        logical_cpu_list = sorted(logical_cpu_list, key=lambda x: x.id)
+
+        # Reserve CPUs for other processes
+        reserve_cpu_num = envs.VLLM_CPU_NUM_OF_RESERVED_CPU
+        if reserve_cpu_num is None:
+            reserve_cpu_num = 1 if self.parallel_config.world_size > 1 else 0
+        assert len(logical_cpu_list) > reserve_cpu_num, (
+            f"VLLM_CPU_NUM_OF_RESERVED_CPU ({reserve_cpu_num}) "
+            f"should less than {len(logical_cpu_list)}.")
+        if reserve_cpu_num != 0:
+            logical_cpu_list = logical_cpu_list[:-reserve_cpu_num]
+
+        logger.info("auto thread-binding list (id, physical core): %s",
+                    [(x.id, x.physical_core) for x in logical_cpu_list])
+        return ",".join([str(x.id) for x in logical_cpu_list])

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/gpu_input_batch.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Datastructures defining a GPU input batch
+
+from dataclasses import dataclass
+from typing import Optional, cast
+
+import numpy as np
+import torch
+from typing_extensions import deprecated
+
+from vllm.lora.request import LoRARequest
+from vllm.multimodal.inputs import (MultiModalKwargs, MultiModalKwargsItem,
+                                    PlaceholderRange)
+from vllm.pooling_params import PoolingParams
+from vllm.sampling_params import SamplingParams, SamplingType
+from vllm.utils import swap_dict_values
+from vllm.v1.outputs import LogprobsTensors
+from vllm.v1.pool.metadata import PoolingMetadata
+from vllm.v1.sample.logits_processor import (BatchUpdateBuilder,
+                                             LogitsProcessors,
+                                             MoveDirectionality)
+from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.spec_decode.utils import is_spec_decode_unsupported
+from vllm.v1.utils import copy_slice
+from vllm.v1.worker.block_table import MultiGroupBlockTable
+
+
+@dataclass
+class CachedRequestState:
+
+    req_id: str
+    prompt_token_ids: list[int]
+    mm_kwargs: list[MultiModalKwargsItem]
+    mm_positions: list[PlaceholderRange]
+    sampling_params: Optional[SamplingParams]
+    pooling_params: Optional[PoolingParams]
+    generator: Optional[torch.Generator]
+
+    block_ids: tuple[list[int], ...]
+    num_computed_tokens: int
+    output_token_ids: list[int]
+
+    mrope_positions: Optional[torch.Tensor] = None
+    mrope_position_delta: Optional[int] = None
+
+    lora_request: Optional[LoRARequest] = None
+
+    def __post_init__(self):
+        self.num_prompt_tokens = len(self.prompt_token_ids)
+
+    @property
+    def num_tokens(self) -> int:
+        return self.num_prompt_tokens + len(self.output_token_ids)
+
+    # Temporary back-compatibility for plugins that define model runner
+    @property
+    @deprecated("`mm_inputs` is superseded by `mm_kwargs` and will be "
+                "removed in v0.13. Please use `mm_kwargs` instead.")
+    def mm_inputs(self) -> list[MultiModalKwargs]:
+        return [MultiModalKwargs([item]) for item in self.mm_kwargs]
+
+    def get_token_id(self, idx: int) -> int:
+        if idx < self.num_prompt_tokens:
+            return self.prompt_token_ids[idx]
+        else:
+            return self.output_token_ids[idx - self.num_prompt_tokens]
+
+
+class InputBatch:
+
+    def __init__(
+        self,
+        max_num_reqs: int,
+        max_model_len: int,
+        max_num_batched_tokens: int,
+        device: torch.device,
+        pin_memory: bool,
+        vocab_size: int,
+        block_sizes: list[int],  # The block_size of each kv cache group
+        logitsprocs: Optional[LogitsProcessors] = None,
+        is_spec_decode: bool = False,
+        is_pooling_model: bool = False,
+    ):
+        self.is_pooling_model = is_pooling_model
+        self.is_spec_decode = is_spec_decode
+        self.max_num_reqs = max_num_reqs
+        self.max_model_len = max_model_len
+        self.max_num_batched_tokens = max_num_batched_tokens
+        self.device = device
+        self.pin_memory = pin_memory
+        self.vocab_size = vocab_size
+
+        self._req_ids: list[Optional[str]] = []
+        self.req_id_to_index: dict[str, int] = {}
+
+        # TODO(woosuk): This buffer could be too large if max_model_len is big.
+        # Find a way to reduce the CPU memory usage.
+        # This buffer is not directly transferred to the GPU, so it does not
+        # need to be pinned.
+        self.token_ids_cpu_tensor = torch.zeros(
+            (max_num_reqs, max_model_len),
+            device="cpu",
+            dtype=torch.int32,
+            pin_memory=False,
+        )
+        self.token_ids_cpu = self.token_ids_cpu_tensor.numpy()
+        self.num_tokens = np.zeros(max_num_reqs, dtype=np.int32)
+        self.num_tokens_no_spec = np.zeros(max_num_reqs, dtype=np.int32)
+        self.num_prompt_tokens = np.zeros(max_num_reqs, dtype=np.int32)
+        self.num_computed_tokens_cpu_tensor = torch.zeros(
+            (max_num_reqs, ),
+            device="cpu",
+            dtype=torch.int32,
+            pin_memory=pin_memory,
+        )
+        self.num_computed_tokens_cpu = \
+            self.num_computed_tokens_cpu_tensor.numpy()
+
+        # Block table.
+        self.block_table = MultiGroupBlockTable(
+            max_num_reqs=max_num_reqs,
+            max_model_len=max_model_len,
+            max_num_batched_tokens=max_num_batched_tokens,
+            pin_memory=pin_memory,
+            device=device,
+            block_sizes=block_sizes,
+        )
+
+        # Sampling-related.
+        self.temperature = torch.empty((max_num_reqs, ),
+                                       dtype=torch.float32,
+                                       device=device)
+        self.temperature_cpu_tensor = torch.empty((max_num_reqs, ),
+                                                  dtype=torch.float32,
+                                                  device="cpu",
+                                                  pin_memory=pin_memory)
+        self.temperature_cpu = self.temperature_cpu_tensor.numpy()
+        self.greedy_reqs: set[str] = set()
+        self.random_reqs: set[str] = set()
+
+        self.top_p = torch.empty((max_num_reqs, ),
+                                 dtype=torch.float32,
+                                 device=device)
+        self.top_p_cpu_tensor = torch.empty((max_num_reqs, ),
+                                            dtype=torch.float32,
+                                            device="cpu",
+                                            pin_memory=pin_memory)
+        self.top_p_cpu = self.top_p_cpu_tensor.numpy()
+        self.top_p_reqs: set[str] = set()
+
+        self.top_k = torch.empty((max_num_reqs, ),
+                                 dtype=torch.int32,
+                                 device=device)
+        self.top_k_cpu_tensor = torch.empty((max_num_reqs, ),
+                                            dtype=torch.int32,
+                                            device="cpu",
+                                            pin_memory=pin_memory)
+        self.top_k_cpu = self.top_k_cpu_tensor.numpy()
+        self.top_k_reqs: set[str] = set()
+
+        # IDs of requests which do not support spec decoding
+        self.spec_decode_unsupported_reqs: set[str] = set()
+
+        # Frequency penalty related data structures
+        self.frequency_penalties = torch.empty((max_num_reqs, ),
+                                               dtype=torch.float,
+                                               device=device)
+        self.frequency_penalties_cpu_tensor = torch.empty(
+            (max_num_reqs, ),
+            dtype=torch.float,
+            device="cpu",
+            pin_memory=pin_memory)
+        self.frequency_penalties_cpu = \
+            self.frequency_penalties_cpu_tensor.numpy()
+        self.frequency_penalties_reqs: set[str] = set()
+
+        # Presence penalty related data structures
+        self.presence_penalties = torch.empty((max_num_reqs, ),
+                                              dtype=torch.float,
+                                              device=device)
+        self.presence_penalties_cpu_tensor = torch.empty((max_num_reqs, ),
+                                                         dtype=torch.float,
+                                                         device="cpu",
+                                                         pin_memory=pin_memory)
+        self.presence_penalties_cpu = self.presence_penalties_cpu_tensor.numpy(
+        )
+        self.presence_penalties_reqs: set[str] = set()
+
+        # Repetition penalty related data structures
+        self.repetition_penalties = torch.empty((max_num_reqs, ),
+                                                dtype=torch.float,
+                                                device=device)
+        self.repetition_penalties_cpu_tensor = torch.empty(
+            (max_num_reqs, ),
+            dtype=torch.float,
+            device="cpu",
+            pin_memory=pin_memory)
+        self.repetition_penalties_cpu = \
+            self.repetition_penalties_cpu_tensor.numpy()
+        self.repetition_penalties_reqs: set[str] = set()
+
+        # lora related
+        self.request_lora_mapping = np.zeros((self.max_num_reqs, ),
+                                             dtype=np.int32)
+        self.lora_id_to_request_ids: dict[int, set[str]] = {}
+        self.lora_id_to_lora_request: dict[int, LoRARequest] = {}
+
+        # req_index -> generator
+        # NOTE(woosuk): The indices of the requests that do not have their own
+        # generator should not be included in the dictionary.
+        self.generators: dict[int, torch.Generator] = {}
+
+        self.num_logprobs: dict[str, int] = {}
+        # NOTE(rob): num_prompt_logprobs only includes reqs
+        # that are currently in the prefill phase.
+        self.num_prompt_logprobs: dict[str, int] = {}
+
+        # To accumulate prompt logprobs tensor chunks across prefill steps.
+        self.in_progress_prompt_logprobs_cpu: dict[str, LogprobsTensors] = {}
+
+        # Internal representation of per-step batch state changes, used for
+        # reordering persistent batch and generating logitsprocs batch state
+        # updates. Should reset each step.
+        self.batch_update_builder = BatchUpdateBuilder()
+
+        # TODO convert this to LogitsProcessor
+        self.has_allowed_token_ids: set[str] = set()
+        # NOTE(lufang): In the mask tensor, if the corresponding token allowed,
+        # the value is False. Since we use masked_fill_ to set -inf.
+        self.allowed_token_ids_mask: Optional[torch.Tensor] = None
+        self.allowed_token_ids_mask_cpu_tensor: Optional[torch.Tensor] = None
+
+        # req_index -> bad_words_token_ids
+        self.bad_words_token_ids: dict[int, list[list[int]]] = {}
+
+        self.logits_processing_needs_token_ids = np.zeros(max_num_reqs,
+                                                          dtype=bool)
+
+        self.req_output_token_ids: list[Optional[list[int]]] = []
+
+        # Store provided logitsprocs. If none are provided, initialize empty
+        # data structure
+        self.logitsprocs = logitsprocs or LogitsProcessors()
+
+        # This is updated each time the batch constituents change.
+        self.sampling_metadata = self._make_sampling_metadata()
+
+        self.pooling_params: dict[str, PoolingParams] = {}
+
+    @property
+    def req_ids(self) -> list[str]:
+        # None elements should only be present transiently
+        # while performing state updates to the batch.
+        return cast(list[str], self._req_ids)
+
+    def _register_add_request(self, request: "CachedRequestState") -> int:
+        """Track add-request operations for logits processors.
+        Not applicable to pooling models.
+        """
+
+        # Detailed added request metadata is only required for non-pooling
+        # models, to support logitsprocs
+        assert request.sampling_params
+
+        # Fill the next empty index if there is one.
+        if (new_req_index := self.batch_update_builder.pop_removed()) is None:
+            # Append to end otherwise.
+            new_req_index = self.num_reqs
+
+        assert new_req_index < self.max_num_reqs
+        self.batch_update_builder.added.append(
+            (new_req_index, request.sampling_params, request.prompt_token_ids,
+             request.output_token_ids))
+        return new_req_index
+
+    def add_request(
+        self,
+        request: "CachedRequestState",
+    ) -> int:
+        if not self.is_pooling_model:
+            # New request index bookkeeping for autoregressive models.
+            req_index = self._register_add_request(request)
+        else:
+            req_index = self.num_reqs
+
+        req_id = request.req_id
+        if req_index == len(self._req_ids):
+            self._req_ids.append(req_id)
+            self.req_output_token_ids.append(request.output_token_ids)
+        else:
+            self._req_ids[req_index] = req_id
+            self.req_output_token_ids[req_index] = request.output_token_ids
+
+        self.req_id_to_index[req_id] = req_index
+
+        # Copy the prompt token ids and output token ids.
+        num_prompt_tokens = len(request.prompt_token_ids)
+        self.num_prompt_tokens[req_index] = num_prompt_tokens
+        self.token_ids_cpu[
+            req_index, :num_prompt_tokens] = request.prompt_token_ids
+        start_idx = num_prompt_tokens
+        end_idx = start_idx + len(request.output_token_ids)
+        self.token_ids_cpu[req_index,
+                           start_idx:end_idx] = request.output_token_ids
+        # Number of token ids in token_ids_cpu.
+        # NOTE(woosuk): This may include spec decode tokens.
+        self.num_tokens[req_index] = request.num_tokens
+        # Number of tokens without spec decode tokens.
+        self.num_tokens_no_spec[req_index] = request.num_tokens
+
+        self.num_computed_tokens_cpu[req_index] = request.num_computed_tokens
+        self.block_table.add_row(request.block_ids, req_index)
+
+        if sampling_params := request.sampling_params:
+            if (self.is_spec_decode
+                    and is_spec_decode_unsupported(sampling_params)):
+                self.spec_decode_unsupported_reqs.add(req_id)
+            if sampling_params.sampling_type == SamplingType.GREEDY:
+                # Avoid later division by zero.
+                self.temperature_cpu[req_index] = -1.0
+                self.greedy_reqs.add(req_id)
+            else:
+                self.temperature_cpu[req_index] = sampling_params.temperature
+                self.random_reqs.add(req_id)
+
+            self.top_p_cpu[req_index] = sampling_params.top_p
+            if sampling_params.top_p < 1:
+                self.top_p_reqs.add(req_id)
+            top_k = sampling_params.top_k
+            if 0 < top_k < self.vocab_size:
+                self.top_k_reqs.add(req_id)
+            else:
+                top_k = self.vocab_size
+            self.top_k_cpu[req_index] = top_k
+            self.frequency_penalties_cpu[
+                req_index] = sampling_params.frequency_penalty
+            if sampling_params.frequency_penalty != 0.0:
+                self.frequency_penalties_reqs.add(req_id)
+            self.presence_penalties_cpu[
+                req_index] = sampling_params.presence_penalty
+            if sampling_params.presence_penalty != 0.0:
+                self.presence_penalties_reqs.add(req_id)
+            self.repetition_penalties_cpu[
+                req_index] = sampling_params.repetition_penalty
+            if sampling_params.repetition_penalty != 1.0:
+                self.repetition_penalties_reqs.add(req_id)
+
+            # NOTE(woosuk): self.generators should not include the requests that
+            # do not have their own generator.
+            if request.generator is not None:
+                self.generators[req_index] = request.generator
+
+            if sampling_params.logprobs is not None:
+                self.num_logprobs[req_id] = (self.vocab_size
+                                             if sampling_params.logprobs == -1
+                                             else sampling_params.logprobs)
+            if sampling_params.prompt_logprobs is not None:
+                self.num_prompt_logprobs[
+                    req_id] = sampling_params.prompt_logprobs
+
+            if sampling_params.allowed_token_ids:
+                self.has_allowed_token_ids.add(req_id)
+                if self.allowed_token_ids_mask_cpu_tensor is None:
+                    # Lazy allocation for this tensor, which can be large.
+                    # False means we don't fill with -inf.
+                    self.allowed_token_ids_mask = torch.zeros(
+                        self.max_num_reqs,
+                        self.vocab_size,
+                        dtype=torch.bool,
+                        device=self.device)
+                    self.allowed_token_ids_mask_cpu_tensor = torch.zeros(
+                        self.max_num_reqs,
+                        self.vocab_size,
+                        dtype=torch.bool,
+                        device="cpu")
+                self.allowed_token_ids_mask_cpu_tensor[req_index] = True
+                # False means we don't fill with -inf.
+                self.allowed_token_ids_mask_cpu_tensor[req_index][
+                    sampling_params.allowed_token_ids] = False
+
+            if sampling_params.bad_words_token_ids:
+                self.bad_words_token_ids[
+                    req_index] = sampling_params.bad_words_token_ids
+        elif pooling_params := request.pooling_params:
+            self.pooling_params[req_id] = pooling_params
+            self.logits_processing_needs_token_ids[req_index] = (
+                pooling_params.requires_token_ids)
+        else:
+            raise NotImplementedError(request)
+
+        # Add request lora ID
+        if request.lora_request:
+            lora_id = request.lora_request.lora_int_id
+            if lora_id not in self.lora_id_to_request_ids:
+                self.lora_id_to_request_ids[lora_id] = set()
+
+            self.request_lora_mapping[req_index] = lora_id
+            self.lora_id_to_request_ids[lora_id].add(request.req_id)
+            self.lora_id_to_lora_request[lora_id] = request.lora_request
+        else:
+            # No LoRA
+            self.request_lora_mapping[req_index] = 0
+
+        return req_index
+
+    def remove_request(self, req_id: str) -> Optional[int]:
+        """This method must always be followed by a call to condense().
+
+        Args:
+          req_id: request to remove
+
+        Returns:
+          Removed request index, or `None` if `req_id` not recognized
+        """
+
+        req_index = self.req_id_to_index.pop(req_id, None)
+        if req_index is None:
+            return None
+        if not self.is_pooling_model:
+            # Autoregressive models require bookkeeping of removed requests to
+            # support logitsprocs.
+            self.batch_update_builder.removed_append(req_index)
+        self._req_ids[req_index] = None
+        self.req_output_token_ids[req_index] = None
+
+        self.greedy_reqs.discard(req_id)
+        self.random_reqs.discard(req_id)
+        self.top_p_reqs.discard(req_id)
+        self.top_k_reqs.discard(req_id)
+        self.spec_decode_unsupported_reqs.discard(req_id)
+        self.frequency_penalties_reqs.discard(req_id)
+        self.presence_penalties_reqs.discard(req_id)
+        self.repetition_penalties_reqs.discard(req_id)
+        self.generators.pop(req_index, None)
+        self.num_logprobs.pop(req_id, None)
+        self.num_prompt_logprobs.pop(req_id, None)
+        self.in_progress_prompt_logprobs_cpu.pop(req_id, None)
+
+        # LoRA
+        lora_id = self.request_lora_mapping[req_index]
+        if lora_id != 0:
+            self.lora_id_to_request_ids[lora_id].discard(req_id)
+            if len(self.lora_id_to_request_ids[lora_id]) == 0:
+                self.lora_id_to_request_ids.pop(lora_id)
+                self.lora_id_to_lora_request.pop(lora_id)
+            self.request_lora_mapping[req_index] = 0
+
+        self.has_allowed_token_ids.discard(req_id)
+        if self.allowed_token_ids_mask_cpu_tensor is not None:
+            # False means we don't fill with -inf.
+            self.allowed_token_ids_mask_cpu_tensor[req_index].fill_(False)
+        self.bad_words_token_ids.pop(req_index, None)
+        self.pooling_params.pop(req_id, None)
+        return req_index
+
+    def swap_states(self, i1: int, i2: int) -> None:
+        # For autoregressive models, track detailed request reordering info
+        # to support logitsprocs
+        self.batch_update_builder.moved.append(
+            (i1, i2, MoveDirectionality.SWAP))
+        old_id_i1 = self._req_ids[i1]
+        old_id_i2 = self._req_ids[i2]
+        self._req_ids[i1], self._req_ids[i2] =\
+            self._req_ids[i2], self._req_ids[i1] # noqa
+        self.req_output_token_ids[i1], self.req_output_token_ids[i2] =\
+            self.req_output_token_ids[i2], self.req_output_token_ids[i1]
+        assert old_id_i1 is not None and old_id_i2 is not None
+        self.req_id_to_index[old_id_i1], self.req_id_to_index[old_id_i2] =\
+            self.req_id_to_index[old_id_i2], self.req_id_to_index[old_id_i1]
+        self.num_tokens[i1], self.num_tokens[i2] =\
+            self.num_tokens[i2], self.num_tokens[i1]
+        self.num_tokens_no_spec[i1], self.num_tokens_no_spec[i2] =\
+            self.num_tokens_no_spec[i2], self.num_tokens_no_spec[i1]
+        self.num_prompt_tokens[i1], self.num_prompt_tokens[i2] =\
+            self.num_prompt_tokens[i2], self.num_prompt_tokens[i1]
+        self.num_computed_tokens_cpu[i1], self.num_computed_tokens_cpu[i2] =\
+            self.num_computed_tokens_cpu[i2], self.num_computed_tokens_cpu[i1]
+        self.temperature_cpu[i1], self.temperature_cpu[i2] =\
+            self.temperature_cpu[i2], self.temperature_cpu[i1]
+        self.top_p_cpu[i1], self.top_p_cpu[i2] =\
+            self.top_p_cpu[i2], self.top_p_cpu[i1]
+        self.top_k_cpu[i1], self.top_k_cpu[i2] =\
+            self.top_k_cpu[i2], self.top_k_cpu[i1]
+        self.frequency_penalties_cpu[i1], self.frequency_penalties_cpu[i2] =\
+            self.frequency_penalties_cpu[i2], self.frequency_penalties_cpu[i1]
+        self.presence_penalties_cpu[i1], self.presence_penalties_cpu[i2] =\
+            self.presence_penalties_cpu[i2], self.presence_penalties_cpu[i1]
+        self.repetition_penalties_cpu[i1], self.repetition_penalties_cpu[i2] =\
+            self.repetition_penalties_cpu[i2], self.repetition_penalties_cpu[i1]
+
+        # NOTE: the following is unsafe
+        # self.token_ids_cpu[i1, ...], self.token_ids_cpu[i2, ...], =\
+        #     self.token_ids_cpu[i2, ...], self.token_ids_cpu[i1, ...]
+        # instead, we need to temporiarily copy the data for one of the indices
+        # TODO(lucas): optimize this by only copying valid indices
+        tmp = self.token_ids_cpu[i1, ...].copy()
+        self.token_ids_cpu[i1, ...] = self.token_ids_cpu[i2, ...]
+        self.token_ids_cpu[i2, ...] = tmp
+
+        swap_dict_values(self.generators, i1, i2)
+        swap_dict_values(self.bad_words_token_ids, i1, i2)
+
+        self.request_lora_mapping[i1], self.request_lora_mapping[i2] =\
+            self.request_lora_mapping[i2], self.request_lora_mapping[i1]
+
+        if self.allowed_token_ids_mask_cpu_tensor is not None:
+            self.allowed_token_ids_mask_cpu_tensor[i1], \
+                self.allowed_token_ids_mask_cpu_tensor[i2] =\
+                self.allowed_token_ids_mask_cpu_tensor[i2], \
+                    self.allowed_token_ids_mask_cpu_tensor[i1]
+        self.block_table.swap_row(i1, i2)
+
+    def condense(self) -> None:
+        """Slide non-empty requests down into lower, empty indices.
+
+        Any consecutive empty indices at the very end of the list are not
+        filled.
+
+        Args:
+          empty_req_indices: empty indices which may be filled.
+
+        Returns:
+          swaps: list of (from,to) swap tuples for moved requests
+          empty_req_indices: indices not filled by condensation
+        """
+        num_reqs = self.num_reqs
+
+        if self.is_pooling_model:
+            # Will be contiguous in pooling case, just trim the lists.
+            del self._req_ids[num_reqs:]
+            del self.req_output_token_ids[num_reqs:]
+            return
+
+        if not (empty_req_indices := self.batch_update_builder.removed):
+            # All removed requests were replaced by added requests, or else no
+            # requests were removed at all. No condense() needed
+            return
+        if num_reqs == 0:
+            # The batched states are empty.
+            self._req_ids.clear()
+            self.req_output_token_ids.clear()
+            return
+
+        # NOTE(woosuk): This function assumes that the empty_req_indices
+        # is sorted in descending order.
+        last_req_index = num_reqs + len(empty_req_indices) - 1
+        while empty_req_indices:
+            # Find the largest non-empty index.
+            while last_req_index in empty_req_indices:
+                last_req_index -= 1
+
+            # Find the smallest empty index.
+            empty_index = self.batch_update_builder.peek_removed()
+            assert empty_index is not None
+            if empty_index >= last_req_index:
+                break
+
+            # Move active request down into empty request
+            # index.
+            self.batch_update_builder.pop_removed()
+            # Autoregressive models require detailed tracking of condense
+            # operations to support logitsprocs
+            self.batch_update_builder.moved.append(
+                (last_req_index, empty_index,
+                 MoveDirectionality.UNIDIRECTIONAL))
+            req_id = self._req_ids[last_req_index]
+            output_token_ids = self.req_output_token_ids[last_req_index]
+            assert req_id is not None
+            self._req_ids[empty_index] = req_id
+            self._req_ids[last_req_index] = None
+            self.req_output_token_ids[empty_index] = output_token_ids
+            self.req_output_token_ids[last_req_index] = None
+            self.req_id_to_index[req_id] = empty_index
+
+            num_tokens = self.num_tokens[last_req_index]
+            self.token_ids_cpu[empty_index, :num_tokens] = self.token_ids_cpu[
+                last_req_index, :num_tokens]
+            self.num_tokens[empty_index] = num_tokens
+            self.num_tokens_no_spec[empty_index] = self.num_tokens_no_spec[
+                last_req_index]
+            self.num_prompt_tokens[empty_index] = self.num_prompt_tokens[
+                last_req_index]
+            self.num_computed_tokens_cpu[
+                empty_index] = self.num_computed_tokens_cpu[last_req_index]
+            self.block_table.move_row(last_req_index, empty_index)
+            self.temperature_cpu[empty_index] = self.temperature_cpu[
+                last_req_index]
+            self.top_p_cpu[empty_index] = self.top_p_cpu[last_req_index]
+            self.top_k_cpu[empty_index] = self.top_k_cpu[last_req_index]
+            self.frequency_penalties_cpu[
+                empty_index] = self.frequency_penalties_cpu[last_req_index]
+            self.presence_penalties_cpu[
+                empty_index] = self.presence_penalties_cpu[last_req_index]
+            self.repetition_penalties_cpu[
+                empty_index] = self.repetition_penalties_cpu[last_req_index]
+            generator = self.generators.pop(last_req_index, None)
+            if generator is not None:
+                self.generators[empty_index] = generator
+
+            self.request_lora_mapping[empty_index] = self.request_lora_mapping[
+                last_req_index]
+
+            # TODO convert these to LogitsProcessors
+            if self.allowed_token_ids_mask_cpu_tensor is not None:
+                self.allowed_token_ids_mask_cpu_tensor[
+                    empty_index] = self.allowed_token_ids_mask_cpu_tensor[
+                        last_req_index]
+
+            bad_words_token_ids = self.bad_words_token_ids.pop(
+                last_req_index, None)
+            if bad_words_token_ids is not None:
+                self.bad_words_token_ids[empty_index] = bad_words_token_ids
+
+            # Decrement last_req_index since it is now empty.
+            last_req_index -= 1
+
+        # Trim lists to the batch size.
+        del self._req_ids[num_reqs:]
+        del self.req_output_token_ids[num_reqs:]
+
+    def refresh_metadata(self):
+        """Apply any batch updates to sampling metadata."""
+
+        if self.is_pooling_model:
+            # Batch changes every step for pooling models.
+            self.sampling_metadata = self._make_sampling_metadata()
+            return
+
+        # For non-pooling models - generate and apply logitsprocs update;
+        # reset batch update tracking.
+        # Update sampling metadata if batch state is changed.
+        batch_update = self.batch_update_builder.get_and_reset(self.num_reqs)
+        for logit_proc in self.logitsprocs.all:
+            logit_proc.update_state(batch_update)
+        if batch_update:
+            self.sampling_metadata = self._make_sampling_metadata()
+
+    def _make_sampling_metadata(self) -> SamplingMetadata:
+        num_reqs = self.num_reqs
+        if not self.all_greedy:
+            temperature = copy_slice(self.temperature_cpu_tensor,
+                                     self.temperature, num_reqs)
+        else:
+            temperature = None
+        if not self.no_top_p:
+            copy_slice(self.top_p_cpu_tensor, self.top_p, num_reqs)
+        if not self.no_top_k:
+            copy_slice(self.top_k_cpu_tensor, self.top_k, num_reqs)
+
+        if not self.no_penalties:
+            # Since syncing these tensors is expensive only copy them
+            # if necessary i.e. if there are requests which require
+            # penalties to be applied during sampling.
+            copy_slice(self.frequency_penalties_cpu_tensor,
+                       self.frequency_penalties, num_reqs)
+            copy_slice(self.presence_penalties_cpu_tensor,
+                       self.presence_penalties, num_reqs)
+            copy_slice(self.repetition_penalties_cpu_tensor,
+                       self.repetition_penalties, num_reqs)
+
+        needs_prompt_token_ids = (
+            not self.no_penalties
+            or self.logits_processing_needs_token_ids[:num_reqs].any())
+        if needs_prompt_token_ids:
+            # The prompt tokens are used only for applying penalties or
+            # step pooling during the sampling/pooling process.
+            # Hence copy these tensors only when there are requests which
+            # need penalties/step_pooler to be applied.
+            prompt_token_ids = self._make_prompt_token_ids_tensor()
+        else:
+            prompt_token_ids = None
+
+        allowed_token_ids_mask: Optional[torch.Tensor] = None
+        if not self.no_allowed_token_ids:
+            assert self.allowed_token_ids_mask is not None
+            copy_slice(self.allowed_token_ids_mask_cpu_tensor,
+                       self.allowed_token_ids_mask, num_reqs)
+            allowed_token_ids_mask = self.allowed_token_ids_mask[:num_reqs]
+
+        return SamplingMetadata(
+            temperature=temperature,
+            all_greedy=self.all_greedy,
+            all_random=self.all_random,
+            top_p=None if self.no_top_p else self.top_p[:num_reqs],
+            top_k=None if self.no_top_k else self.top_k[:num_reqs],
+            generators=self.generators,
+            max_num_logprobs=self.max_num_logprobs,
+            prompt_token_ids=prompt_token_ids,
+            frequency_penalties=self.frequency_penalties[:num_reqs],
+            presence_penalties=self.presence_penalties[:num_reqs],
+            repetition_penalties=self.repetition_penalties[:num_reqs],
+            output_token_ids=cast(list[list[int]], self.req_output_token_ids),
+            no_penalties=self.no_penalties,
+            allowed_token_ids_mask=allowed_token_ids_mask,
+            bad_words_token_ids=self.bad_words_token_ids,
+            logitsprocs=self.logitsprocs,
+        )
+
+    @property
+    def pooling_metadata(self) -> PoolingMetadata:
+        if len(self.pooling_params) == 0:
+            pooling_params = []
+        else:
+            # Note, for now this assumes that all request in the batch
+            # are either sampling or pooling requests
+            assert len(self.req_ids) == len(self.pooling_params)
+            pooling_params = [
+                self.pooling_params[req_id] for req_id in self.req_ids
+            ]
+
+        return PoolingMetadata(
+            prompt_lens=torch.from_numpy(
+                self.num_prompt_tokens[:self.num_reqs]).to(self.device),
+            prompt_token_ids=self.sampling_metadata.prompt_token_ids,
+            pooling_params=pooling_params,
+        )
+
+    def _make_prompt_token_ids_tensor(self) -> torch.Tensor:
+        max_prompt_len = self.num_prompt_tokens[:self.num_reqs].max()
+        prompt_token_ids_cpu_tensor = torch.empty(
+            (self.num_reqs, max_prompt_len),
+            device="cpu",
+            dtype=torch.int64,
+            pin_memory=self.pin_memory,
+        )
+        prompt_token_ids = prompt_token_ids_cpu_tensor.numpy()
+        prompt_token_ids[:] = self.token_ids_cpu[:self.
+                                                 num_reqs, :max_prompt_len]
+        # Use the value of vocab_size as a pad since we don't have a
+        # token_id of this value.
+        for i in range(self.num_reqs):
+            prompt_token_ids[i, self.num_prompt_tokens[i]:] = self.vocab_size
+        return prompt_token_ids_cpu_tensor.to(device=self.device,
+                                              non_blocking=True)
+
+    def make_lora_inputs(
+        self, num_scheduled_tokens: np.ndarray
+    ) -> tuple[tuple[int, ...], tuple[int, ...], set[LoRARequest]]:
+        """
+        Given the num_scheduled_tokens for each request in the batch, return
+        datastructures used to activate the current LoRAs.
+        Returns:
+            1. prompt_lora_mapping: A tuple of size self.num_reqs where,
+               prompt_lora_mapping[i] is the LoRA id to use for the ith prompt.
+            2. token_lora_mapping: A tuple of size np.sum(num_scheduled_tokens)
+               where, token_lora_mapping[i] is the LoRA id to use for ith token.
+            3. lora_requests: Set of relevant LoRA requests.
+        """
+
+        req_lora_mapping = self.request_lora_mapping[:self.num_reqs]
+        prompt_lora_mapping = tuple(req_lora_mapping)
+        token_lora_mapping = tuple(
+            req_lora_mapping.repeat(num_scheduled_tokens))
+        active_lora_requests: set[LoRARequest] = set(
+            self.lora_id_to_lora_request.values())
+
+        return prompt_lora_mapping, token_lora_mapping, active_lora_requests
+
+    @property
+    def num_reqs(self) -> int:
+        return len(self.req_id_to_index)
+
+    @property
+    def all_greedy(self) -> bool:
+        return len(self.random_reqs) == 0
+
+    @property
+    def all_random(self) -> bool:
+        return len(self.greedy_reqs) == 0
+
+    @property
+    def no_top_p(self) -> bool:
+        return len(self.top_p_reqs) == 0
+
+    @property
+    def no_top_k(self) -> bool:
+        return len(self.top_k_reqs) == 0
+
+    @property
+    def no_penalties(self) -> bool:
+        return (len(self.presence_penalties_reqs) == 0
+                and len(self.frequency_penalties_reqs) == 0
+                and len(self.repetition_penalties_reqs) == 0)
+
+    @property
+    def max_num_logprobs(self) -> Optional[int]:
+        return max(self.num_logprobs.values()) if self.num_logprobs else None
+
+    @property
+    def no_prompt_logprob(self) -> bool:
+        return not self.num_prompt_logprobs
+
+    @property
+    def no_allowed_token_ids(self) -> bool:
+        return len(self.has_allowed_token_ids) == 0

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/gpu_worker.py

@@ -0,0 +1,632 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""A GPU worker class."""
+import copy
+import gc
+import os
+from contextlib import AbstractContextManager, nullcontext
+from typing import TYPE_CHECKING, Any, Optional
+
+import torch
+import torch.distributed
+import torch.nn as nn
+
+import vllm.envs as envs
+from vllm.config import VllmConfig
+from vllm.distributed import (ensure_model_parallel_initialized,
+                              init_distributed_environment,
+                              set_custom_all_reduce)
+from vllm.distributed.kv_transfer import ensure_kv_transfer_initialized
+from vllm.distributed.parallel_state import get_pp_group, get_tp_group
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.model_executor import set_random_seed
+from vllm.model_executor.warmup.kernel_warmup import kernel_warmup
+from vllm.platforms import current_platform
+from vllm.sequence import IntermediateTensors
+from vllm.tasks import SupportedTask
+from vllm.utils import GiB_bytes, MemorySnapshot, memory_profiling
+from vllm.v1.engine import ReconfigureDistributedRequest, ReconfigureRankType
+from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
+from vllm.v1.outputs import EMPTY_MODEL_RUNNER_OUTPUT, ModelRunnerOutput
+from vllm.v1.utils import report_usage_stats
+from vllm.v1.worker.gpu_model_runner import GPUModelRunner
+from vllm.v1.worker.worker_base import WorkerBase
+
+logger = init_logger(__name__)
+
+if TYPE_CHECKING:
+    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
+    from vllm.v1.core.sched.output import SchedulerOutput
+
+
+class Worker(WorkerBase):
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+        is_driver_worker: bool = False,
+    ):
+
+        super().__init__(vllm_config=vllm_config,
+                         local_rank=local_rank,
+                         rank=rank,
+                         distributed_init_method=distributed_init_method,
+                         is_driver_worker=is_driver_worker)
+
+        if self.model_config.trust_remote_code:
+            # note: lazy import to avoid importing torch before initializing
+            from vllm.utils import init_cached_hf_modules
+            init_cached_hf_modules()
+
+        # Buffers saved before sleep
+        self._sleep_saved_buffers: dict[str, torch.Tensor] = {}
+
+        # Torch profiler. Enabled and configured through env vars:
+        # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
+        if envs.VLLM_TORCH_PROFILER_DIR:
+            torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
+            logger.info("Profiling enabled. Traces will be saved to: %s",
+                        torch_profiler_trace_dir)
+            logger.debug(
+                "Profiler config: record_shapes=%s,"
+                "profile_memory=%s,with_stack=%s,with_flops=%s",
+                envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
+                envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
+                envs.VLLM_TORCH_PROFILER_WITH_STACK,
+                envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
+            )
+            self.profiler = torch.profiler.profile(
+                activities=[
+                    torch.profiler.ProfilerActivity.CPU,
+                    torch.profiler.ProfilerActivity.CUDA,
+                ],
+                record_shapes=envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
+                profile_memory=envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
+                with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
+                with_flops=envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
+                on_trace_ready=torch.profiler.tensorboard_trace_handler(
+                    torch_profiler_trace_dir, use_gzip=True))
+        else:
+            self.profiler = None
+
+    def sleep(self, level: int = 1) -> None:
+        from vllm.device_allocator.cumem import CuMemAllocator
+
+        free_bytes_before_sleep = torch.cuda.mem_get_info()[0]
+
+        # Save the buffers before level 2 sleep
+        if level == 2:
+            model = self.model_runner.model
+            self._sleep_saved_buffers = {
+                name: buffer.cpu().clone()
+                for name, buffer in model.named_buffers()
+            }
+
+        allocator = CuMemAllocator.get_instance()
+        allocator.sleep(offload_tags=("weights", ) if level == 1 else tuple())
+        free_bytes_after_sleep, total = torch.cuda.mem_get_info()
+        freed_bytes = free_bytes_after_sleep - free_bytes_before_sleep
+        used_bytes = total - free_bytes_after_sleep
+        assert freed_bytes >= 0, "Memory usage increased after sleeping."
+        logger.info(
+            "Sleep mode freed %.2f GiB memory, "
+            "%.2f GiB memory is still in use.", freed_bytes / GiB_bytes,
+            used_bytes / GiB_bytes)
+
+    def wake_up(self, tags: Optional[list[str]] = None) -> None:
+        from vllm.device_allocator.cumem import CuMemAllocator
+
+        allocator = CuMemAllocator.get_instance()
+        allocator.wake_up(tags)
+
+        # Restore the buffers after level 2 sleep
+        if len(self._sleep_saved_buffers):
+            model = self.model_runner.model
+            for name, buffer in model.named_buffers():
+                if name in self._sleep_saved_buffers:
+                    buffer.data.copy_(self._sleep_saved_buffers[name].data)
+            self._sleep_saved_buffers = {}
+
+    def _maybe_get_memory_pool_context(self,
+                                       tag: str) -> AbstractContextManager:
+        if self.vllm_config.model_config.enable_sleep_mode:
+            from vllm.device_allocator.cumem import CuMemAllocator
+
+            allocator = CuMemAllocator.get_instance()
+            if tag == "weights":
+                assert allocator.get_current_usage() == 0, (
+                    "Sleep mode can only be "
+                    "used for one instance per process.")
+            context = allocator.use_memory_pool(tag=tag)
+        else:
+            context = nullcontext()
+        return context
+
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        self.cache_config.num_gpu_blocks = num_gpu_blocks
+        self.cache_config.num_cpu_blocks = num_cpu_blocks
+
+    def init_device(self):
+        if self.device_config.device.type == "cuda":
+            # torch.distributed.all_reduce does not free the input tensor until
+            # the synchronization point. This causes the memory usage to grow
+            # as the number of all_reduce calls increases. This env var disables
+            # this behavior.
+            # Related issue:
+            # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573
+            os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+
+            # This env var set by Ray causes exceptions with graph building.
+            os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
+            self.device = torch.device(f"cuda:{self.local_rank}")
+            current_platform.set_device(self.device)
+
+            _check_if_gpu_supports_dtype(self.model_config.dtype)
+            gc.collect()
+            torch.cuda.empty_cache()
+
+            # take current memory snapshot
+            self.init_snapshot = MemorySnapshot()
+            self.requested_memory = (self.init_snapshot.total_memory *
+                                     self.cache_config.gpu_memory_utilization)
+            if self.init_snapshot.free_memory < self.requested_memory:
+                GiB = lambda b: round(b / GiB_bytes, 2)
+                raise ValueError(
+                    f"Free memory on device "
+                    f"({GiB(self.init_snapshot.free_memory)}/"
+                    f"{GiB(self.init_snapshot.total_memory)} GiB) on startup "
+                    f"is less than desired GPU memory utilization "
+                    f"({self.cache_config.gpu_memory_utilization}, "
+                    f"{GiB(self.requested_memory)} GiB). Decrease GPU memory "
+                    f"utilization or reduce GPU memory used by other processes."
+                )
+        else:
+            raise RuntimeError(
+                f"Not support device type: {self.device_config.device}")
+        # Initialize the distributed environment.
+        init_worker_distributed_environment(self.vllm_config, self.rank,
+                                            self.distributed_init_method,
+                                            self.local_rank,
+                                            current_platform.dist_backend)
+        # Set random seed.
+        set_random_seed(self.model_config.seed)
+
+        # Construct the model runner
+        self.model_runner: GPUModelRunner = GPUModelRunner(
+            self.vllm_config, self.device)
+
+        if self.rank == 0:
+            # If usage stat is enabled, collect relevant info.
+            report_usage_stats(self.vllm_config)
+
+    # FIXME(youkaichao & ywang96): Use TorchDispatchMode instead of memory pool
+    # to hijack tensor allocation.
+    def load_model(self) -> None:
+        eep_scale_up = os.environ.get("VLLM_ELASTIC_EP_SCALE_UP_LAUNCH") == "1"
+        with self._maybe_get_memory_pool_context(tag="weights"):
+            self.model_runner.load_model(eep_scale_up=eep_scale_up)
+
+    def update_config(self, overrides: dict[str, Any]) -> None:
+        self.model_runner.update_config(overrides)
+
+    def reload_weights(self) -> None:
+        with self._maybe_get_memory_pool_context(tag="weights"):
+            self.model_runner.reload_weights()
+
+    @torch.inference_mode()
+    def determine_available_memory(self) -> int:
+        """Profiles the peak memory usage of the model to determine how much
+        memory can be used for KV cache without OOMs.
+
+        The engine will first conduct a profiling of the existing memory usage.
+        Then, it calculate the free memory that can be used for KV cache in
+        bytes.
+
+        Tip:
+            You may limit the usage of GPU memory
+            by adjusting the `gpu_memory_utilization` parameter.
+        """
+        torch.cuda.empty_cache()
+        torch.cuda.reset_peak_memory_stats()
+        GiB = lambda b: b / GiB_bytes
+
+        # Execute a forward pass with dummy inputs to profile the memory usage
+        # of the model.
+        with memory_profiling(
+                self.init_snapshot,
+                weights_memory=int(
+                    self.model_runner.model_memory_usage)) as profile_result:
+            self.model_runner.profile_run()
+
+        free_gpu_memory = profile_result.after_profile.free_memory
+        # NOTE(woosuk): Here we assume that the other processes using the same
+        # GPU did not change their memory usage during the profiling.
+        assert self.init_snapshot.free_memory > free_gpu_memory, (
+            "Error in memory profiling. "
+            f"Initial free memory {GiB(self.init_snapshot.free_memory)} GiB, "
+            f"current free memory {GiB(free_gpu_memory)} GiB. "
+            "This happens when other processes sharing the same container "
+            "release GPU memory while vLLM is profiling during initialization. "
+            "To fix this, ensure consistent GPU memory allocation or "
+            "isolate vLLM in its own container.")
+        available_kv_cache_memory = self.requested_memory \
+            - profile_result.non_kv_cache_memory
+
+        unrequested_memory = self.init_snapshot.free_memory \
+            - self.requested_memory
+        logger.debug(
+            "Initial free memory: %.2f GiB; "
+            "Requested memory: %.2f (util), %.2f GiB",
+            GiB(self.init_snapshot.free_memory),
+            self.cache_config.gpu_memory_utilization,
+            GiB(self.requested_memory),
+        )
+        logger.debug(
+            "Free memory after profiling: %.2f GiB (total), "
+            "%.2f GiB (within requested)",
+            GiB(free_gpu_memory),
+            GiB(free_gpu_memory - unrequested_memory),
+        )
+        logger.debug(profile_result)
+        logger.info("Available KV cache memory: %.2f GiB",
+                    GiB(available_kv_cache_memory))
+        gc.collect()
+
+        return int(available_kv_cache_memory)
+
+    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
+        return self.model_runner.get_kv_cache_spec()
+
+    def initialize_from_config(self, kv_cache_config: KVCacheConfig) -> None:
+        """Allocate GPU KV cache with the specified kv_cache_config."""
+
+        if self.vllm_config.model_config.enable_sleep_mode:
+            from vllm.device_allocator.cumem import CuMemAllocator
+
+            allocator = CuMemAllocator.get_instance()
+            context = allocator.use_memory_pool(tag="kv_cache")
+        else:
+            from contextlib import nullcontext
+            context = nullcontext()
+        with context:
+            self.model_runner.initialize_kv_cache(kv_cache_config)
+
+    def compile_or_warm_up_model(self) -> None:
+        # warm up sizes that are not in cudagraph capture sizes,
+        # but users still want to compile for better performance,
+        # e.g. for the max-num-batched token size in chunked prefill.
+        warmup_sizes = self.vllm_config.compilation_config.compile_sizes.copy()
+        if not self.model_config.enforce_eager:
+            warmup_sizes = [
+                x for x in warmup_sizes if x not in
+                self.vllm_config.compilation_config.cudagraph_capture_sizes
+            ]
+        # We skip EPLB here since we don't want to record dummy metrics
+        for size in sorted(warmup_sizes, reverse=True):
+            logger.info("Compile and warming up model for size %d", size)
+            self.model_runner._dummy_run(size, skip_eplb=True)
+
+        if not self.model_config.enforce_eager:
+            self.model_runner.capture_model()
+
+        # Warm up sampler and preallocate memory buffer for logits and other
+        # sampling related tensors of max possible shape to avoid memory
+        # fragmentation issue.
+        # NOTE: This is called after `capture_model` on purpose to prevent
+        # memory buffers from being cleared by `torch.cuda.empty_cache`.
+        if get_pp_group().is_last_rank:
+            max_num_reqs = min(self.scheduler_config.max_num_seqs,
+                               self.scheduler_config.max_num_batched_tokens)
+
+            # We skip EPLB here since we don't want to record dummy metrics
+            hidden_states, last_hidden_states = \
+                self.model_runner._dummy_run(
+                    num_tokens=max_num_reqs,
+                    skip_eplb=True,
+                )
+            if self.model_runner.is_pooling_model:
+                self.model_runner._dummy_pooler_run(hidden_states)
+            else:
+                self.model_runner._dummy_sampler_run(
+                    hidden_states=last_hidden_states)
+
+        # Warmup kernels used during model execution
+        kernel_warmup(self)
+
+        # Reset the seed to ensure that the random state is not affected by
+        # the model initialization and profiling.
+        set_random_seed(self.model_config.seed)
+
+    def get_model(self) -> nn.Module:
+        return self.model_runner.get_model()
+
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
+        return self.model_runner.get_supported_tasks()
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        scheduler_output: "SchedulerOutput",
+    ) -> Optional[ModelRunnerOutput]:
+        intermediate_tensors = None
+        if not get_pp_group().is_first_rank:
+            intermediate_tensors = IntermediateTensors(
+                get_pp_group().recv_tensor_dict(
+                    all_gather_group=get_tp_group()))
+
+        output = self.model_runner.execute_model(scheduler_output,
+                                                 intermediate_tensors)
+
+        parallel_config = self.vllm_config.parallel_config
+        if parallel_config.distributed_executor_backend != "external_launcher" \
+            and not get_pp_group().is_last_rank:
+            assert isinstance(output, IntermediateTensors)
+            get_pp_group().send_tensor_dict(output.tensors,
+                                            all_gather_group=get_tp_group())
+
+            kv_connector_output = output.kv_connector_output
+            if not kv_connector_output:
+                return None
+
+            # In case of PP with kv transfer, we need to pass through the
+            # kv_connector_output
+            if (not kv_connector_output.finished_sending
+                    and not kv_connector_output.finished_recving):
+                return EMPTY_MODEL_RUNNER_OUTPUT
+
+            output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT)
+            output.kv_connector_output = kv_connector_output
+            return output
+
+        assert isinstance(output, ModelRunnerOutput)
+        return output
+
+    def profile(self, is_start: bool = True):
+        if self.profiler is None:
+            raise RuntimeError("Profiler is not enabled.")
+        if is_start:
+            self.profiler.start()
+        else:
+            self.profiler.stop()
+            print(self.profiler.key_averages().table(
+                sort_by="self_cuda_time_total"))
+
+    def execute_dummy_batch(self) -> None:
+        self.model_runner._dummy_run(1)
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        return self.model_runner.add_lora(lora_request)
+
+    def remove_lora(self, lora_id: int) -> bool:
+        return self.model_runner.remove_lora(lora_id)
+
+    def list_loras(self) -> set[int]:
+        return self.model_runner.list_loras()
+
+    def pin_lora(self, lora_id: int) -> bool:
+        return self.model_runner.pin_lora(lora_id)
+
+    def check_health(self) -> None:
+        # worker will always be healthy as long as it's running.
+        return
+
+    def _eplb_before_scale_down(self, old_ep_size: int,
+                                new_ep_size: int) -> None:
+        from vllm.distributed.parallel_state import get_ep_group
+        if get_ep_group().rank == 0:
+            logger.info("[Elastic EP] Starting expert resharding "
+                        "before scaling down...")
+        rank_mapping = {
+            old_ep_rank: old_ep_rank if old_ep_rank < new_ep_size else -1
+            for old_ep_rank in range(old_ep_size)
+        }
+        assert self.model_runner.eplb_state is not None
+        self.model_runner.eplb_state.rearrange(self.model_runner.model,
+                                               execute_shuffle=True,
+                                               global_expert_load=None,
+                                               rank_mapping=rank_mapping)
+        torch.cuda.synchronize()
+        if get_ep_group().rank == 0:
+            logger.info("[Elastic EP] Expert resharding completed!")
+
+    def _eplb_after_scale_up(
+            self, old_ep_size: int, new_ep_size: int,
+            global_expert_load: Optional[torch.Tensor]) -> None:
+        from vllm.distributed.parallel_state import get_ep_group
+        if get_ep_group().rank == 0:
+            logger.info("[Elastic EP] Starting expert resharding "
+                        "after scaling up...")
+        rank_mapping = {
+            old_ep_rank: old_ep_rank
+            for old_ep_rank in range(old_ep_size)
+        }
+        assert self.model_runner.eplb_state is not None
+        self.model_runner.eplb_state.rearrange(
+            self.model_runner.model,
+            execute_shuffle=True,
+            global_expert_load=global_expert_load,
+            rank_mapping=rank_mapping)
+        if get_ep_group().rank == 0:
+            logger.info("[Elastic EP] Expert resharding completed!")
+
+    def _reconfigure_parallel_config(
+            self, reconfig_request: ReconfigureDistributedRequest) -> None:
+        """
+        Update parallel config with provided reconfig_request
+        """
+        parallel_config = self.vllm_config.parallel_config
+        parallel_config.data_parallel_size = \
+            reconfig_request.new_data_parallel_size
+        if reconfig_request.new_data_parallel_rank != \
+        ReconfigureRankType.KEEP_CURRENT_RANK:
+            parallel_config.data_parallel_rank = \
+                reconfig_request.new_data_parallel_rank
+        if reconfig_request.new_data_parallel_rank_local != \
+        ReconfigureRankType.KEEP_CURRENT_RANK:
+            parallel_config.data_parallel_rank_local = \
+                reconfig_request.new_data_parallel_rank_local
+        parallel_config.data_parallel_master_ip = \
+            reconfig_request.new_data_parallel_master_ip
+        parallel_config.data_parallel_master_port = \
+            reconfig_request.new_data_parallel_master_port
+
+    def _reconfigure_moe(self, old_ep_size: int,
+                         new_ep_size: int) -> Optional[torch.Tensor]:
+        """
+        Reconfigure MoE modules with provided reconfig_request
+
+        Return the global expert load if new_ep_size > old_ep_size,
+        otherwise None
+        """
+        from vllm.distributed.parallel_state import (
+            get_dp_group, get_ep_group, prepare_communication_buffer_for_model)
+        from vllm.model_executor.layers.fused_moe.layer import (
+            FusedMoEParallelConfig)
+
+        parallel_config = self.vllm_config.parallel_config
+        moe_modules = [
+            module for module in self.model_runner.model.modules()
+            if module.__class__.__name__ == "FusedMoE"
+        ]
+        num_local_experts = moe_modules[0].moe_config.num_local_experts
+        assert all(module.moe_config.num_local_experts == num_local_experts
+                   for module in moe_modules), (
+                       "All MoE modules must have the same number of experts")
+        for module in moe_modules:
+            module.moe_config.num_experts = num_local_experts * new_ep_size
+            module.global_num_experts = module.moe_config.num_experts
+            module.moe_parallel_config = FusedMoEParallelConfig.make(
+                tp_size_=get_tp_group().world_size,
+                dp_size_=get_dp_group().world_size,
+                vllm_parallel_config=parallel_config,
+            )
+            module.moe_config.moe_parallel_config = module.moe_parallel_config
+        if new_ep_size < old_ep_size:
+            num_local_physical_experts = num_local_experts
+            assert self.model_runner.eplb_state is not None
+            new_physical_experts = \
+                self.model_runner.eplb_state.physical_to_logical_map.shape[1]
+            parallel_config.num_redundant_experts = (
+                new_physical_experts -
+                self.model_runner.eplb_state.logical_replica_count.shape[1])
+            global_expert_load = None
+        else:
+            num_local_physical_experts = torch.tensor([num_local_experts],
+                                                      dtype=torch.int32,
+                                                      device="cpu")
+            torch.distributed.broadcast(num_local_physical_experts,
+                                        group=get_ep_group().cpu_group,
+                                        group_src=0)
+            num_local_physical_experts = num_local_physical_experts.item()
+            new_physical_experts = num_local_physical_experts * new_ep_size
+            assert self.model_runner.eplb_state is not None
+            global_expert_load = self.model_runner.eplb_state.rearrange(
+                self.model_runner.model, execute_shuffle=False)
+            parallel_config.num_redundant_experts = (
+                new_physical_experts - global_expert_load.shape[1])
+        prepare_communication_buffer_for_model(self.model_runner.model)
+        self.model_runner.model.update_physical_experts_metadata(
+            num_physical_experts=new_physical_experts,
+            num_local_physical_experts=num_local_physical_experts)
+        return global_expert_load
+
+    def reinitialize_distributed(
+            self, reconfig_request: ReconfigureDistributedRequest) -> None:
+        from vllm.config import set_current_vllm_config
+        from vllm.distributed.parallel_state import (
+            cleanup_dist_env_and_memory, get_ep_group)
+
+        old_ep_size = get_ep_group().world_size
+        old_ep_rank = get_ep_group().rank
+        new_ep_size = reconfig_request.new_data_parallel_size * get_tp_group(
+        ).world_size * get_pp_group().world_size
+        if new_ep_size < old_ep_size:
+            self._eplb_before_scale_down(old_ep_size, new_ep_size)
+
+        cleanup_dist_env_and_memory()
+
+        if reconfig_request.new_data_parallel_rank == \
+        ReconfigureRankType.SHUTDOWN_CURRENT_RANK:
+            assert old_ep_rank >= new_ep_size
+            # shutdown
+            return
+
+        self._reconfigure_parallel_config(reconfig_request)
+
+        with set_current_vllm_config(self.vllm_config):
+            init_worker_distributed_environment(self.vllm_config, self.rank,
+                                                self.distributed_init_method,
+                                                self.local_rank)
+
+        global_expert_load = self._reconfigure_moe(old_ep_size, new_ep_size)
+
+        if new_ep_size > old_ep_size:
+            assert global_expert_load is not None
+            self._eplb_after_scale_up(old_ep_size, new_ep_size,
+                                      global_expert_load)
+
+    def save_sharded_state(
+        self,
+        path: str,
+        pattern: Optional[str] = None,
+        max_size: Optional[int] = None,
+    ) -> None:
+        from vllm.model_executor.model_loader import ShardedStateLoader
+        ShardedStateLoader.save_model(
+            self.model_runner.model,
+            path,
+            pattern=pattern,
+            max_size=max_size,
+        )
+
+    def save_tensorized_model(
+        self,
+        tensorizer_config: "TensorizerConfig",
+    ) -> None:
+        self.model_runner.save_tensorized_model(
+            tensorizer_config=tensorizer_config, )
+
+
+def init_worker_distributed_environment(
+    vllm_config: VllmConfig,
+    rank: int,
+    distributed_init_method: Optional[str] = None,
+    local_rank: int = -1,
+    backend: str = "nccl",
+) -> None:
+    """Initialize the distributed environment."""
+    parallel_config = vllm_config.parallel_config
+    set_custom_all_reduce(not parallel_config.disable_custom_all_reduce)
+
+    init_distributed_environment(parallel_config.world_size, rank,
+                                 distributed_init_method, local_rank, backend)
+
+    ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
+                                      parallel_config.pipeline_parallel_size)
+
+    ensure_kv_transfer_initialized(vllm_config)
+
+
+def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
+    # Check if the GPU supports the dtype.
+    if torch_dtype == torch.bfloat16:  # noqa: SIM102
+        if not current_platform.has_device_capability(80):
+            capability = current_platform.get_device_capability()
+            gpu_name = current_platform.get_device_name()
+
+            if capability is None:
+                compute_str = "does not have a compute capability"
+            else:
+                version_str = capability.as_version_str()
+                compute_str = f"has compute capability {version_str}"
+
+            raise ValueError(
+                "Bfloat16 is only supported on GPUs with compute capability "
+                f"of at least 8.0. Your {gpu_name} GPU {compute_str}. "
+                "You can use float16 instead by explicitly setting the "
+                "`dtype` flag in CLI, for example: --dtype=half.")

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/kv_connector_model_runner_mixin.py

@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Define KV connector functionality mixin for model runners.
+"""
+import copy
+from contextlib import AbstractContextManager, contextmanager, nullcontext
+from typing import Generator  # noqa: UP035
+from typing import TYPE_CHECKING, Optional
+
+from vllm.config import VllmConfig
+from vllm.distributed.kv_transfer import (get_kv_transfer_group,
+                                          has_kv_transfer_group)
+from vllm.distributed.kv_transfer.kv_connector.base import KVConnectorBase
+from vllm.forward_context import get_forward_context, set_forward_context
+from vllm.logger import init_logger
+from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, KVConnectorOutput,
+                             ModelRunnerOutput)
+
+if TYPE_CHECKING:
+    from vllm.v1.core.sched.output import SchedulerOutput
+
+logger = init_logger(__name__)
+
+
+# Defined as a kv connector functionality mixin for ModelRunner (GPU, TPU)
+class KVConnectorModelRunnerMixin:
+
+    @staticmethod
+    def maybe_setup_kv_connector(scheduler_output: "SchedulerOutput"):
+        # Update KVConnector with the KVConnector metadata forward().
+        if has_kv_transfer_group():
+            kv_connector = get_kv_transfer_group()
+            assert isinstance(kv_connector, KVConnectorBase)
+            assert scheduler_output.kv_connector_metadata is not None
+            kv_connector.bind_connector_metadata(
+                scheduler_output.kv_connector_metadata)
+
+            # Background KV cache transfers happen here.
+            # These transfers are designed to be async and the requests
+            # involved may be disjoint from the running requests.
+            # Do this here to save a collective_rpc.
+            kv_connector.start_load_kv(get_forward_context())
+
+    @staticmethod
+    def maybe_wait_for_kv_save() -> None:
+        if has_kv_transfer_group():
+            get_kv_transfer_group().wait_for_save()
+
+    @staticmethod
+    def get_finished_kv_transfers(
+        scheduler_output: "SchedulerOutput",
+    ) -> tuple[Optional[set[str]], Optional[set[str]]]:
+        if has_kv_transfer_group():
+            return get_kv_transfer_group().get_finished(
+                scheduler_output.finished_req_ids)
+        return None, None
+
+    @staticmethod
+    def kv_connector_no_forward(scheduler_output: "SchedulerOutput",
+                                vllm_config: VllmConfig) -> ModelRunnerOutput:
+        # KV send/recv even if no work to do.
+        with set_forward_context(
+                None, vllm_config
+        ), KVConnectorModelRunnerMixin._get_kv_connector_output(
+                scheduler_output, wait_for_save=False) as kv_connector_output:
+            pass
+
+        if (not kv_connector_output.finished_sending
+                and not kv_connector_output.finished_recving):
+            return EMPTY_MODEL_RUNNER_OUTPUT
+
+        output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT)
+        output.kv_connector_output = kv_connector_output
+        return output
+
+    @staticmethod
+    def maybe_get_kv_connector_output(
+        scheduler_output: "SchedulerOutput"
+    ) -> AbstractContextManager[Optional[KVConnectorOutput]]:
+        return KVConnectorModelRunnerMixin._get_kv_connector_output(
+            scheduler_output) if has_kv_transfer_group() else nullcontext()
+
+    # This context manager must be used within an active forward context.
+    # It encapsulates the entire KV conector lifecycle within execute_model
+    @staticmethod
+    @contextmanager
+    def _get_kv_connector_output(
+        scheduler_output: "SchedulerOutput",
+        wait_for_save: bool = True
+    ) -> Generator[KVConnectorOutput, None, None]:
+        output = KVConnectorOutput()
+
+        # Update KVConnector with the KVConnector metadata forward().
+        kv_connector = get_kv_transfer_group()
+        assert isinstance(kv_connector, KVConnectorBase)
+        assert scheduler_output.kv_connector_metadata is not None
+        kv_connector.bind_connector_metadata(
+            scheduler_output.kv_connector_metadata)
+
+        # Background KV cache transfers happen here.
+        # These transfers are designed to be async and the requests
+        # involved may be disjoint from the running requests.
+        # Do this here to save a collective_rpc.
+        kv_connector.start_load_kv(get_forward_context())
+        try:
+            yield output
+        finally:
+            if wait_for_save:
+                kv_connector.wait_for_save()
+
+            output.finished_sending, output.finished_recving = (
+                kv_connector.get_finished(scheduler_output.finished_req_ids))
+
+            kv_connector.clear_connector_metadata()

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/lora_model_runner_mixin.py

@@ -0,0 +1,177 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Define LoRA functionality mixin for model runners.
+"""
+
+from contextlib import contextmanager
+from typing import Union
+
+import numpy as np
+import torch.nn as nn
+
+from vllm.config import LoRAConfig, ModelConfig, SchedulerConfig
+from vllm.logger import init_logger
+from vllm.lora.layers import LoRAMapping
+from vllm.lora.request import LoRARequest
+from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
+from vllm.model_executor.models import supports_lora, supports_multimodal
+from vllm.v1.worker.gpu_input_batch import InputBatch as GPUInputBatch
+from vllm.v1.worker.tpu_input_batch import InputBatch as TPUInputBatch
+
+InputBatch = Union[TPUInputBatch, GPUInputBatch]
+
+logger = init_logger(__name__)
+
+
+# Defined as a mixin for GPUModelRunner
+class LoRAModelRunnerMixin:
+
+    LORA_WARMUP_RANK = 8
+
+    def load_lora_model(self, model: nn.Module, model_config: ModelConfig,
+                        scheduler_config: SchedulerConfig,
+                        lora_config: LoRAConfig, device: str) -> nn.Module:
+
+        if not supports_lora(model):
+            raise ValueError(
+                f"{model.__class__.__name__} does not support LoRA yet.")
+
+        if supports_multimodal(model):
+            logger.warning("Regarding multimodal models, vLLM currently "
+                           "only supports adding LoRA to language model.")
+
+        # Use get_text_config() in case of multimodal models
+        text_config = model_config.hf_config.get_text_config()
+
+        # Add LoRA Manager to the Model Runner
+        self.lora_manager = LRUCacheWorkerLoRAManager(
+            scheduler_config.max_num_seqs,
+            scheduler_config.max_num_batched_tokens,
+            model_config.get_vocab_size(),
+            lora_config,
+            device,
+            model.embedding_modules,
+            model.embedding_padding_modules,
+            max_position_embeddings=text_config.max_position_embeddings,
+        )
+        return self.lora_manager.create_lora_manager(model)
+
+    def _set_active_loras(self, prompt_lora_mapping: tuple[int, ...],
+                          token_lora_mapping: tuple[int, ...],
+                          lora_requests: set[LoRARequest]) -> None:
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+
+        # Set is_prefill to True, so we always use the SGMV kernels on
+        # non-cuda platforms.
+        # On cuda platforms we use the same kernels for prefill and
+        # decode and this flag is generally ignored.
+        lora_mapping = LoRAMapping(token_lora_mapping,
+                                   prompt_lora_mapping,
+                                   is_prefill=True)
+        self.lora_manager.set_active_adapters(lora_requests, lora_mapping)
+
+    def set_active_loras(self, input_batch: InputBatch,
+                         num_scheduled_tokens: np.ndarray) -> None:
+
+        prompt_lora_mapping: tuple[int, ...]  # of size input_batch.num_reqs
+        token_lora_mapping: tuple[int,
+                                  ...]  # of size np.sum(num_scheduled_tokens)
+        lora_requests: set[LoRARequest]
+        prompt_lora_mapping, token_lora_mapping, lora_requests = \
+                            input_batch.make_lora_inputs(num_scheduled_tokens)
+        return self._set_active_loras(prompt_lora_mapping, token_lora_mapping,
+                                      lora_requests)
+
+    @contextmanager
+    def maybe_setup_dummy_loras(self, lora_config):
+        if lora_config is None:
+            yield
+        else:
+            # __enter__ code
+            assert self.lora_manager is not None, "LoRA is not enabled"
+
+            num_loras = lora_config.max_loras
+
+            # Make dummy lora requests
+            lora_requests: set[LoRARequest] = {
+                LoRARequest(lora_name=f"warmup_{lora_id}",
+                            lora_int_id=lora_id,
+                            lora_path="/not/a/real/path")
+                for lora_id in range(1, num_loras + 1)
+            }
+
+            with self.lora_manager.dummy_lora_cache():
+                # Add the dummy LoRAs here so _set_active_loras doesn't try to
+                # load from disk.
+                for lr in lora_requests:
+                    self.lora_manager.add_dummy_lora(
+                        lr, rank=self.LORA_WARMUP_RANK)
+
+                yield
+
+            # __exit__ code
+            self.lora_manager.remove_all_adapters()
+
+    @contextmanager
+    def maybe_select_dummy_loras(self, lora_config: LoRAConfig,
+                                 num_scheduled_tokens: np.ndarray):
+        if lora_config is None:
+            yield
+        else:
+            # __enter__ code
+            assert self.lora_manager is not None, "LoRA is not enabled"
+
+            num_reqs = len(num_scheduled_tokens)
+            num_loras = lora_config.max_loras
+
+            # Make prompt lora mapping
+            # Assign LoRA IDs cyclically to simulate a worst-case scenario.
+            prompt_lora_mapping = (np.arange(num_reqs, dtype=np.int32) %
+                                   num_loras) + 1
+
+            # Make token lora mapping
+            token_lora_mapping = np.repeat(prompt_lora_mapping,
+                                           num_scheduled_tokens)
+
+            # Make dummy lora requests
+            lora_requests: set[LoRARequest] = {
+                LoRARequest(lora_name=f"warmup_{lora_id}",
+                            lora_int_id=lora_id,
+                            lora_path="/not/a/real/path")
+                for lora_id in range(1, num_loras + 1)
+            }
+
+            self._set_active_loras(tuple(prompt_lora_mapping),
+                                   tuple(token_lora_mapping), lora_requests)
+
+            yield
+
+    @contextmanager
+    def maybe_dummy_run_with_lora(self, lora_config: LoRAConfig,
+                                  num_scheduled_tokens: np.ndarray):
+        with self.maybe_setup_dummy_loras(
+                lora_config), self.maybe_select_dummy_loras(
+                    lora_config, num_scheduled_tokens):
+            yield
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        return self.lora_manager.add_adapter(lora_request)
+
+    def remove_lora(self, lora_id: int) -> bool:
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        return self.lora_manager.remove_adapter(lora_id)
+
+    def pin_lora(self, lora_id: int) -> bool:
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        return self.lora_manager.pin_adapter(lora_id)
+
+    def list_loras(self) -> set[int]:
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        return self.lora_manager.list_adapters()

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/tpu_input_batch.py

@@ -0,0 +1,585 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Datastructures defining a TPU input batch
+
+from typing import Optional, cast
+
+import numpy as np
+import torch
+
+from vllm.lora.request import LoRARequest
+from vllm.sampling_params import SamplingType
+from vllm.utils import swap_dict_values
+from vllm.v1.outputs import LogprobsTensors
+from vllm.v1.worker.block_table import MultiGroupBlockTable
+from vllm.v1.worker.gpu_input_batch import CachedRequestState
+
+_SAMPLING_EPS = 1e-5
+
+
+class InputBatch:
+
+    def __init__(
+            self,
+            max_num_reqs: int,
+            max_model_len: int,
+            max_num_batched_tokens: int,
+            device: torch.device,
+            pin_memory: bool,
+            vocab_size: int,
+            block_sizes: list[int],  # The block_size of each kv cache group
+    ):
+        self.max_num_reqs = max_num_reqs
+        self.max_model_len = max_model_len
+        self.max_num_batched_tokens = max_num_batched_tokens
+        self.device = device
+        self.pin_memory = pin_memory
+        self.vocab_size = vocab_size
+
+        self._req_ids: list[Optional[str]] = []
+        self.req_id_to_index: dict[str, int] = {}
+
+        # TODO(woosuk): This buffer could be too large if max_model_len is big.
+        # Find a way to reduce the CPU memory usage.
+        # This buffer is not directly transferred to the GPU, so it does not
+        # need to be pinned.
+        self.token_ids_cpu_tensor = torch.zeros(
+            (max_num_reqs, max_model_len),
+            device="cpu",
+            dtype=torch.int32,
+            pin_memory=False,
+        )
+        self.token_ids_cpu = self.token_ids_cpu_tensor.numpy()
+        self.num_tokens = np.zeros(max_num_reqs, dtype=np.int32)
+        self.num_tokens_no_spec = np.zeros(max_num_reqs, dtype=np.int32)
+        self.num_prompt_tokens = np.zeros(max_num_reqs, dtype=np.int32)
+        self.num_computed_tokens_cpu_tensor = torch.zeros(
+            (max_num_reqs, ),
+            device="cpu",
+            dtype=torch.int32,
+            pin_memory=pin_memory,
+        )
+        self.num_computed_tokens_cpu = \
+            self.num_computed_tokens_cpu_tensor.numpy()
+
+        # Block table.
+        self.block_table = MultiGroupBlockTable(
+            max_num_reqs=max_num_reqs,
+            max_model_len=max_model_len,
+            max_num_batched_tokens=max_num_batched_tokens,
+            pin_memory=pin_memory,
+            device=device,
+            block_sizes=block_sizes,
+        )
+
+        # Sampling-related.
+        self.temperature = torch.empty((max_num_reqs, ),
+                                       dtype=torch.float32,
+                                       device=device)
+        self.temperature_cpu_tensor = torch.empty((max_num_reqs, ),
+                                                  dtype=torch.float32,
+                                                  device="cpu",
+                                                  pin_memory=pin_memory)
+        self.temperature_cpu = self.temperature_cpu_tensor.numpy()
+        self.greedy_reqs: set[str] = set()
+        self.random_reqs: set[str] = set()
+
+        self.top_p = torch.empty((max_num_reqs, ),
+                                 dtype=torch.float32,
+                                 device=device)
+        self.top_p_cpu_tensor = torch.empty((max_num_reqs, ),
+                                            dtype=torch.float32,
+                                            device="cpu",
+                                            pin_memory=pin_memory)
+        self.top_p_cpu = self.top_p_cpu_tensor.numpy()
+        self.top_p_reqs: set[str] = set()
+
+        self.top_k = torch.empty((max_num_reqs, ),
+                                 dtype=torch.int32,
+                                 device=device)
+        self.top_k_cpu_tensor = torch.empty((max_num_reqs, ),
+                                            dtype=torch.int32,
+                                            device="cpu",
+                                            pin_memory=pin_memory)
+        self.top_k_cpu = self.top_k_cpu_tensor.numpy()
+        self.top_k_reqs: set[str] = set()
+
+        self.min_p = torch.empty((max_num_reqs, ),
+                                 dtype=torch.float32,
+                                 device=device)
+        self.min_p_cpu_tensor = torch.empty((max_num_reqs, ),
+                                            dtype=torch.float32,
+                                            device="cpu",
+                                            pin_memory=pin_memory)
+        self.min_p_cpu = self.min_p_cpu_tensor.numpy()
+        self.min_p_reqs: set[str] = set()
+
+        # Frequency penalty related data structures
+        self.frequency_penalties = torch.empty((max_num_reqs, ),
+                                               dtype=torch.float,
+                                               device=device)
+        self.frequency_penalties_cpu_tensor = torch.empty(
+            (max_num_reqs, ),
+            dtype=torch.float,
+            device="cpu",
+            pin_memory=pin_memory)
+        self.frequency_penalties_cpu = \
+            self.frequency_penalties_cpu_tensor.numpy()
+        self.frequency_penalties_reqs: set[str] = set()
+
+        # Presence penalty related data structures
+        self.presence_penalties = torch.empty((max_num_reqs, ),
+                                              dtype=torch.float,
+                                              device=device)
+        self.presence_penalties_cpu_tensor = torch.empty((max_num_reqs, ),
+                                                         dtype=torch.float,
+                                                         device="cpu",
+                                                         pin_memory=pin_memory)
+        self.presence_penalties_cpu = self.presence_penalties_cpu_tensor.numpy(
+        )
+        self.presence_penalties_reqs: set[str] = set()
+
+        # Repetition penalty related data structures
+        self.repetition_penalties = torch.empty((max_num_reqs, ),
+                                                dtype=torch.float,
+                                                device=device)
+        self.repetition_penalties_cpu_tensor = torch.empty(
+            (max_num_reqs, ),
+            dtype=torch.float,
+            device="cpu",
+            pin_memory=pin_memory)
+        self.repetition_penalties_cpu = \
+            self.repetition_penalties_cpu_tensor.numpy()
+        self.repetition_penalties_reqs: set[str] = set()
+
+        # req_index -> (min_tokens, stop_token_ids)
+        self.min_tokens: dict[int, tuple[int, set[int]]] = {}
+
+        # lora related
+        self.request_lora_mapping = np.zeros((self.max_num_reqs, ),
+                                             dtype=np.int32)
+        self.lora_id_to_request_ids: dict[int, set[str]] = {}
+        self.lora_id_to_lora_request: dict[int, LoRARequest] = {}
+
+        # req_index -> generator
+        # NOTE(woosuk): The indices of the requests that do not have their own
+        # generator should not be included in the dictionary.
+        self.generators: dict[int, torch.Generator] = {}
+
+        self.num_logprobs: dict[str, int] = {}
+        # NOTE(rob): num_prompt_logprobs only includes reqs
+        # that are currently in the prefill phase.
+        self.num_prompt_logprobs: dict[str, int] = {}
+
+        # To accumulate prompt logprobs tensor chunks across prefill steps.
+        self.in_progress_prompt_logprobs_cpu: dict[str, LogprobsTensors] = {}
+
+        self.logit_bias: list[Optional[dict[int,
+                                            float]]] = [None] * max_num_reqs
+        self.has_allowed_token_ids: set[str] = set()
+        # NOTE(lufang): In the mask tensor, if the corresponding token allowed,
+        # the value is False. Since we use masked_fill_ to set -inf.
+        self.allowed_token_ids_mask: Optional[torch.Tensor] = None
+        self.allowed_token_ids_mask_cpu_tensor: Optional[torch.Tensor] = None
+
+        # req_index -> bad_words_token_ids
+        self.bad_words_token_ids: dict[int, list[list[int]]] = {}
+
+        self.req_output_token_ids: list[Optional[list[int]]] = []
+
+    @property
+    def req_ids(self) -> list[str]:
+        # None elements should only be present transiently
+        # while performing state updates to the batch.
+        return cast(list[str], self._req_ids)
+
+    def add_request(
+        self,
+        request: "CachedRequestState",
+        req_index: Optional[int] = None,
+    ) -> None:
+        if req_index is None:
+            req_index = self.num_reqs
+        assert req_index < self.max_num_reqs
+
+        req_id = request.req_id
+        if req_index == len(self._req_ids):
+            self._req_ids.append(req_id)
+            self.req_output_token_ids.append(request.output_token_ids)
+        else:
+            self._req_ids[req_index] = req_id
+            self.req_output_token_ids[req_index] = request.output_token_ids
+
+        self.req_id_to_index[req_id] = req_index
+
+        # Copy the prompt token ids and output token ids.
+        num_prompt_tokens = len(request.prompt_token_ids)
+        self.num_prompt_tokens[req_index] = num_prompt_tokens
+        self.token_ids_cpu[
+            req_index, :num_prompt_tokens] = request.prompt_token_ids
+        start_idx = num_prompt_tokens
+        end_idx = start_idx + len(request.output_token_ids)
+        self.token_ids_cpu[req_index,
+                           start_idx:end_idx] = request.output_token_ids
+        # Number of token ids in token_ids_cpu.
+        # NOTE(woosuk): This may include spec decode tokens.
+        self.num_tokens[req_index] = request.num_tokens
+        # Number of tokens without spec decode tokens.
+        self.num_tokens_no_spec[req_index] = request.num_tokens
+
+        self.num_computed_tokens_cpu[req_index] = request.num_computed_tokens
+        self.block_table.add_row(request.block_ids, req_index)
+
+        sampling_params = request.sampling_params
+        assert sampling_params is not None, "pooling requests not supported yet"
+        if sampling_params.sampling_type == SamplingType.GREEDY:
+            # Avoid later division by zero.
+            self.temperature_cpu[req_index] = -1.0
+            self.greedy_reqs.add(req_id)
+        else:
+            self.temperature_cpu[req_index] = sampling_params.temperature
+            self.random_reqs.add(req_id)
+
+        self.top_p_cpu[req_index] = sampling_params.top_p
+        if sampling_params.top_p < 1:
+            self.top_p_reqs.add(req_id)
+        top_k = sampling_params.top_k
+        if 0 < top_k < self.vocab_size:
+            self.top_k_reqs.add(req_id)
+        else:
+            top_k = self.vocab_size
+        self.top_k_cpu[req_index] = top_k
+        self.min_p_cpu[req_index] = sampling_params.min_p
+        self.frequency_penalties_cpu[
+            req_index] = sampling_params.frequency_penalty
+        if sampling_params.min_p > _SAMPLING_EPS:
+            self.min_p_reqs.add(req_id)
+        if sampling_params.frequency_penalty != 0.0:
+            self.frequency_penalties_reqs.add(req_id)
+        self.presence_penalties_cpu[
+            req_index] = sampling_params.presence_penalty
+        if sampling_params.presence_penalty != 0.0:
+            self.presence_penalties_reqs.add(req_id)
+        self.repetition_penalties_cpu[
+            req_index] = sampling_params.repetition_penalty
+        if sampling_params.repetition_penalty != 1.0:
+            self.repetition_penalties_reqs.add(req_id)
+        if sampling_params.min_tokens:
+            self.min_tokens[req_index] = (sampling_params.min_tokens,
+                                          sampling_params.all_stop_token_ids)
+
+        # NOTE(woosuk): self.generators should not include the requests that
+        # do not have their own generator.
+        if request.generator is not None:
+            self.generators[req_index] = request.generator
+
+        if sampling_params.logprobs is not None:
+            self.num_logprobs[req_id] = sampling_params.logprobs
+        if sampling_params.prompt_logprobs is not None:
+            self.num_prompt_logprobs[req_id] = sampling_params.prompt_logprobs
+        if sampling_params.logit_bias is not None:
+            self.logit_bias[req_index] = sampling_params.logit_bias
+
+        if sampling_params.allowed_token_ids:
+            self.has_allowed_token_ids.add(req_id)
+            if self.allowed_token_ids_mask_cpu_tensor is None:
+                # Lazy allocation for this tensor, which can be large.
+                # False means we don't fill with -inf.
+                self.allowed_token_ids_mask = torch.zeros(self.max_num_reqs,
+                                                          self.vocab_size,
+                                                          dtype=torch.bool,
+                                                          device=self.device)
+                self.allowed_token_ids_mask_cpu_tensor = torch.zeros(
+                    self.max_num_reqs,
+                    self.vocab_size,
+                    dtype=torch.bool,
+                    device="cpu")
+            self.allowed_token_ids_mask_cpu_tensor[req_index] = True
+            # False means we don't fill with -inf.
+            self.allowed_token_ids_mask_cpu_tensor[req_index][
+                sampling_params.allowed_token_ids] = False
+
+        if sampling_params.bad_words_token_ids:
+            self.bad_words_token_ids[
+                req_index] = sampling_params.bad_words_token_ids
+
+        # Add request lora ID
+        if request.lora_request:
+            lora_id = request.lora_request.lora_int_id
+            if lora_id not in self.lora_id_to_request_ids:
+                self.lora_id_to_request_ids[lora_id] = set()
+
+            self.request_lora_mapping[req_index] = lora_id
+            self.lora_id_to_request_ids[lora_id].add(request.req_id)
+            self.lora_id_to_lora_request[lora_id] = request.lora_request
+        else:
+            # No LoRA
+            self.request_lora_mapping[req_index] = 0
+
+    def remove_request(self, req_id: str) -> Optional[int]:
+        """This method must always be followed by a call to condense()."""
+
+        req_index = self.req_id_to_index.pop(req_id, None)
+        if req_index is None:
+            return None
+        self._req_ids[req_index] = None
+        self.req_output_token_ids[req_index] = None
+
+        self.greedy_reqs.discard(req_id)
+        self.random_reqs.discard(req_id)
+        self.top_p_reqs.discard(req_id)
+        self.top_k_reqs.discard(req_id)
+        self.min_p_reqs.discard(req_id)
+        self.min_tokens.pop(req_index, None)
+        self.frequency_penalties_reqs.discard(req_id)
+        self.presence_penalties_reqs.discard(req_id)
+        self.repetition_penalties_reqs.discard(req_id)
+        self.generators.pop(req_index, None)
+        self.num_logprobs.pop(req_id, None)
+        self.num_prompt_logprobs.pop(req_id, None)
+        self.in_progress_prompt_logprobs_cpu.pop(req_id, None)
+
+        # LoRA
+        lora_id = self.request_lora_mapping[req_index]
+        if lora_id != 0:
+            self.lora_id_to_request_ids[lora_id].discard(req_id)
+            if len(self.lora_id_to_request_ids[lora_id]) == 0:
+                self.lora_id_to_request_ids.pop(lora_id)
+                self.lora_id_to_lora_request.pop(lora_id)
+            self.request_lora_mapping[req_index] = 0
+
+        self.logit_bias[req_index] = None
+        self.has_allowed_token_ids.discard(req_id)
+        if self.allowed_token_ids_mask_cpu_tensor is not None:
+            # False means we don't fill with -inf.
+            self.allowed_token_ids_mask_cpu_tensor[req_index].fill_(False)
+        self.bad_words_token_ids.pop(req_index, None)
+        return req_index
+
+    def swap_states(self, i1: int, i2: int) -> None:
+        old_id_i1 = self._req_ids[i1]
+        old_id_i2 = self._req_ids[i2]
+        self._req_ids[i1], self._req_ids[i2] =\
+            self._req_ids[i2], self._req_ids[i1] # noqa
+        self.req_output_token_ids[i1], self.req_output_token_ids[i2] =\
+            self.req_output_token_ids[i2], self.req_output_token_ids[i1]
+        assert old_id_i1 is not None and old_id_i2 is not None
+        self.req_id_to_index[old_id_i1], self.req_id_to_index[old_id_i2] =\
+            self.req_id_to_index[old_id_i2], self.req_id_to_index[old_id_i1]
+        self.num_tokens[i1], self.num_tokens[i2] =\
+            self.num_tokens[i2], self.num_tokens[i1]
+        self.num_tokens_no_spec[i1], self.num_tokens_no_spec[i2] =\
+            self.num_tokens_no_spec[i2], self.num_tokens_no_spec[i1]
+        self.num_prompt_tokens[i1], self.num_prompt_tokens[i2] =\
+            self.num_prompt_tokens[i2], self.num_prompt_tokens[i1]
+        self.num_computed_tokens_cpu[i1], self.num_computed_tokens_cpu[i2] =\
+            self.num_computed_tokens_cpu[i2], self.num_computed_tokens_cpu[i1]
+        self.temperature_cpu[i1], self.temperature_cpu[i2] =\
+            self.temperature_cpu[i2], self.temperature_cpu[i1]
+        self.top_p_cpu[i1], self.top_p_cpu[i2] =\
+            self.top_p_cpu[i2], self.top_p_cpu[i1]
+        self.top_k_cpu[i1], self.top_k_cpu[i2] =\
+            self.top_k_cpu[i2], self.top_k_cpu[i1]
+        self.frequency_penalties_cpu[i1], self.frequency_penalties_cpu[i2] =\
+            self.frequency_penalties_cpu[i2], self.frequency_penalties_cpu[i1]
+        self.presence_penalties_cpu[i1], self.presence_penalties_cpu[i2] =\
+            self.presence_penalties_cpu[i2], self.presence_penalties_cpu[i1]
+        self.repetition_penalties_cpu[i1], self.repetition_penalties_cpu[i2] =\
+            self.repetition_penalties_cpu[i2], self.repetition_penalties_cpu[i1]
+        self.min_p_cpu[i1], self.min_p_cpu[i2] =\
+            self.min_p_cpu[i2], self.min_p_cpu[i1]
+
+        # NOTE: the following is unsafe
+        # self.token_ids_cpu[i1, ...], self.token_ids_cpu[i2, ...], =\
+        #     self.token_ids_cpu[i2, ...], self.token_ids_cpu[i1, ...]
+        # instead, we need to temporiarily copy the data for one of the indices
+        # TODO(lucas): optimize this by only copying valid indices
+        tmp = self.token_ids_cpu[i1, ...].copy()
+        self.token_ids_cpu[i1, ...] = self.token_ids_cpu[i2, ...]
+        self.token_ids_cpu[i2, ...] = tmp
+
+        swap_dict_values(self.generators, i1, i2)
+        swap_dict_values(self.min_tokens, i1, i2)
+        swap_dict_values(self.bad_words_token_ids, i1, i2)
+
+        self.request_lora_mapping[i1], self.request_lora_mapping[i2] =\
+            self.request_lora_mapping[i2], self.request_lora_mapping[i1]
+        self.logit_bias[i1], self.logit_bias[i2] =\
+            self.logit_bias[i2], self.logit_bias[i1]
+
+        if self.allowed_token_ids_mask_cpu_tensor is not None:
+            self.allowed_token_ids_mask_cpu_tensor[i1], \
+                self.allowed_token_ids_mask_cpu_tensor[i2] =\
+                self.allowed_token_ids_mask_cpu_tensor[i2], \
+                    self.allowed_token_ids_mask_cpu_tensor[i1]
+        self.block_table.swap_row(i1, i2)
+
+    def condense(self, empty_req_indices: list[int]) -> None:
+        """Move non-empty requests down into lower, empty indices.
+        
+        Args:
+          empty_req_indices: empty batch indices, sorted descending.
+        """
+        num_reqs = self.num_reqs
+        if num_reqs == 0:
+            # The batched states are empty.
+            self._req_ids.clear()
+            self.req_output_token_ids.clear()
+            return
+
+        # NOTE(woosuk): This function assumes that the empty_req_indices
+        # is sorted in descending order.
+        last_req_index = num_reqs + len(empty_req_indices) - 1
+        while empty_req_indices:
+            # Find the largest non-empty index.
+            while last_req_index in empty_req_indices:
+                last_req_index -= 1
+
+            # Find the smallest empty index.
+            empty_index = empty_req_indices.pop()
+            if empty_index >= last_req_index:
+                break
+
+            # Swap the states.
+            req_id = self._req_ids[last_req_index]
+            output_token_ids = self.req_output_token_ids[last_req_index]
+            assert req_id is not None
+            self._req_ids[empty_index] = req_id
+            self._req_ids[last_req_index] = None
+            self.req_output_token_ids[empty_index] = output_token_ids
+            self.req_output_token_ids[last_req_index] = None
+            self.req_id_to_index[req_id] = empty_index
+
+            num_tokens = self.num_tokens[last_req_index]
+            self.token_ids_cpu[empty_index, :num_tokens] = self.token_ids_cpu[
+                last_req_index, :num_tokens]
+            self.num_tokens[empty_index] = num_tokens
+            self.num_tokens_no_spec[empty_index] = self.num_tokens_no_spec[
+                last_req_index]
+            self.num_prompt_tokens[empty_index] = self.num_prompt_tokens[
+                last_req_index]
+            self.num_computed_tokens_cpu[
+                empty_index] = self.num_computed_tokens_cpu[last_req_index]
+            self.block_table.move_row(last_req_index, empty_index)
+            self.temperature_cpu[empty_index] = self.temperature_cpu[
+                last_req_index]
+            self.top_p_cpu[empty_index] = self.top_p_cpu[last_req_index]
+            self.top_k_cpu[empty_index] = self.top_k_cpu[last_req_index]
+            self.frequency_penalties_cpu[
+                empty_index] = self.frequency_penalties_cpu[last_req_index]
+            self.presence_penalties_cpu[
+                empty_index] = self.presence_penalties_cpu[last_req_index]
+            self.repetition_penalties_cpu[
+                empty_index] = self.repetition_penalties_cpu[last_req_index]
+            self.min_p_cpu[empty_index] = self.min_p_cpu[last_req_index]
+            generator = self.generators.pop(last_req_index, None)
+            if generator is not None:
+                self.generators[empty_index] = generator
+
+            min_token = self.min_tokens.pop(last_req_index, None)
+            if min_token is not None:
+                self.min_tokens[empty_index] = min_token
+
+            self.request_lora_mapping[empty_index] = self.request_lora_mapping[
+                last_req_index]
+
+            self.logit_bias[empty_index] = self.logit_bias[last_req_index]
+
+            if self.allowed_token_ids_mask_cpu_tensor is not None:
+                self.allowed_token_ids_mask_cpu_tensor[
+                    empty_index] = self.allowed_token_ids_mask_cpu_tensor[
+                        last_req_index]
+
+            bad_words_token_ids = self.bad_words_token_ids.pop(
+                last_req_index, None)
+            if bad_words_token_ids is not None:
+                self.bad_words_token_ids[empty_index] = bad_words_token_ids
+            # Decrement last_req_index since it is now empty.
+            last_req_index -= 1
+
+        # Trim lists to the batch size.
+        del self._req_ids[self.num_reqs:]
+        del self.req_output_token_ids[self.num_reqs:]
+
+    def _make_prompt_token_ids_tensor(self) -> torch.Tensor:
+        max_prompt_len = self.num_prompt_tokens[:self.num_reqs].max()
+        prompt_token_ids_cpu_tensor = torch.empty(
+            (self.num_reqs, max_prompt_len),
+            device="cpu",
+            dtype=torch.int64,
+            pin_memory=self.pin_memory,
+        )
+        prompt_token_ids = prompt_token_ids_cpu_tensor.numpy()
+        prompt_token_ids[:] = self.token_ids_cpu[:self.
+                                                 num_reqs, :max_prompt_len]
+        # Use the value of vocab_size as a pad since we don't have a
+        # token_id of this value.
+        for i in range(self.num_reqs):
+            prompt_token_ids[i, self.num_prompt_tokens[i]:] = self.vocab_size
+        return prompt_token_ids_cpu_tensor.to(device=self.device,
+                                              non_blocking=True)
+
+    def make_lora_inputs(
+        self, num_scheduled_tokens: np.ndarray
+    ) -> tuple[tuple[int, ...], tuple[int, ...], set[LoRARequest]]:
+        """
+        Given the num_scheduled_tokens for each request in the batch, return
+        datastructures used to activate the current LoRAs.
+        Returns:
+            1. prompt_lora_mapping: A tuple of size self.num_reqs where,
+               prompt_lora_mapping[i] is the LoRA id to use for the ith prompt.
+            2. token_lora_mapping: A tuple of size np.sum(num_scheduled_tokens)
+               where, token_lora_mapping[i] is the LoRA id to use for ith token.
+            3. lora_requests: Set of relevant LoRA requests.
+        """
+
+        req_lora_mapping = self.request_lora_mapping[:self.num_reqs]
+        prompt_lora_mapping = tuple(req_lora_mapping)
+        token_lora_mapping = tuple(
+            req_lora_mapping.repeat(num_scheduled_tokens))
+        active_lora_requests: set[LoRARequest] = set(
+            self.lora_id_to_lora_request.values())
+
+        return prompt_lora_mapping, token_lora_mapping, active_lora_requests
+
+    @property
+    def num_reqs(self) -> int:
+        return len(self.req_id_to_index)
+
+    @property
+    def all_greedy(self) -> bool:
+        return len(self.random_reqs) == 0
+
+    @property
+    def all_random(self) -> bool:
+        return len(self.greedy_reqs) == 0
+
+    @property
+    def no_top_p(self) -> bool:
+        return len(self.top_p_reqs) == 0
+
+    @property
+    def no_top_k(self) -> bool:
+        return len(self.top_k_reqs) == 0
+
+    @property
+    def no_min_p(self) -> bool:
+        return len(self.min_p_reqs) == 0
+
+    @property
+    def no_penalties(self) -> bool:
+        return (len(self.presence_penalties_reqs) == 0
+                and len(self.frequency_penalties_reqs) == 0
+                and len(self.repetition_penalties_reqs) == 0)
+
+    @property
+    def max_num_logprobs(self) -> Optional[int]:
+        return max(self.num_logprobs.values()) if self.num_logprobs else None
+
+    @property
+    def no_prompt_logprob(self) -> bool:
+        return not self.num_prompt_logprobs
+
+    @property
+    def no_allowed_token_ids(self) -> bool:
+        return len(self.has_allowed_token_ids) == 0

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/tpu_model_runner.py

@@ -0,0 +1,2033 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import bisect
+import gc
+import time
+from typing import TYPE_CHECKING, Any, Literal, Optional, Union, cast
+from unittest.mock import patch
+
+import numpy as np
+import torch
+import torch.nn as nn
+# TPU XLA related
+import torch_xla.core.xla_model as xm
+import torch_xla.distributed.spmd as xs
+import torch_xla.runtime as xr
+
+import vllm.envs as envs
+from vllm.attention import Attention
+from vllm.attention.backends.abstract import AttentionType
+from vllm.attention.layers.chunked_local_attention import ChunkedLocalAttention
+from vllm.compilation.wrapper import TorchCompileWrapperWithCustomDispatcher
+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)
+from vllm.forward_context import set_forward_context
+from vllm.logger import init_logger
+from vllm.lora.layers import BaseLayerWithLoRA
+from vllm.model_executor.model_loader import get_model_loader
+from vllm.model_executor.model_loader.tpu import TPUModelLoader
+from vllm.model_executor.models.interfaces import supports_transcription
+from vllm.model_executor.models.interfaces_base import (
+    is_pooling_model, is_text_generation_model)
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (BatchedTensorInputs, MultiModalKwargsItem,
+                                    PlaceholderRange)
+from vllm.multimodal.utils import group_mm_kwargs_by_modality
+from vllm.sequence import IntermediateTensors
+from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
+from vllm.utils import (LayerBlockType, cdiv, is_pin_memory_available,
+                        prev_power_of_2)
+from vllm.v1.attention.backends.pallas import (TPU_STR_DTYPE_TO_TORCH_DTYPE,
+                                               PallasAttentionBackend,
+                                               PallasMetadata,
+                                               get_page_size_bytes)
+from vllm.v1.kv_cache_interface import (AttentionSpec, FullAttentionSpec,
+                                        KVCacheConfig, KVCacheSpec,
+                                        SlidingWindowSpec)
+from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, LogprobsLists,
+                             LogprobsTensors, ModelRunnerOutput)
+from vllm.v1.sample.tpu.metadata import TPUSupportedSamplingMetadata
+from vllm.v1.sample.tpu.sampler import Sampler as TPUSampler
+from vllm.v1.worker.kv_connector_model_runner_mixin import (
+    KVConnectorModelRunnerMixin, KVConnectorOutput)
+from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
+from vllm.v1.worker.tpu_input_batch import CachedRequestState, InputBatch
+
+from .utils import (MultiModalBudget, bind_kv_cache,
+                    initialize_kv_cache_for_kv_sharing,
+                    sanity_check_mm_encoder_outputs)
+
+if TYPE_CHECKING:
+    from vllm.v1.core.sched.output import SchedulerOutput
+
+logger = init_logger(__name__)
+
+INVALID_TOKEN_ID = -1
+# Smallest output size
+MIN_NUM_SEQS = 8
+
+
+#########################################################
+# 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.
+class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        device: torch.device,
+        original_parallel_config: Optional[ParallelConfig] = None,
+    ):
+        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
+        self.original_parallel_config = original_parallel_config
+        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
+        self.check_recompilation = envs.VLLM_XLA_CHECK_RECOMPILATION
+
+        # SPMD Related
+        self.use_spmd = envs.VLLM_XLA_USE_SPMD
+        if self.use_spmd:
+            num_devices = xr.global_runtime_device_count()
+            mesh_shape = (num_devices, 1)
+            device_ids = np.array(range(num_devices))
+            self.mesh = xs.Mesh(device_ids, mesh_shape, ('x', 'y'))
+
+        self.enforce_eager = model_config.enforce_eager
+
+        self.num_xla_graphs = 0
+        self._update_num_xla_graphs("init")
+
+        self.pin_memory = is_pin_memory_available()
+        self.dtype = self.model_config.dtype
+        if cache_config.cache_dtype == "auto":
+            model_dtype = self.dtype
+            if isinstance(model_dtype, str):
+                self.kv_cache_dtype = TPU_STR_DTYPE_TO_TORCH_DTYPE[model_dtype]
+            else:
+                self.kv_cache_dtype = model_dtype
+        else:
+            self.kv_cache_dtype = TPU_STR_DTYPE_TO_TORCH_DTYPE[
+                cache_config.cache_dtype]
+        self._hidden_states_dtype = self.dtype
+
+        self.sliding_window = model_config.get_sliding_window()
+        self.block_size = cache_config.block_size
+        self.max_model_len = model_config.max_model_len
+        self.most_model_len = envs.VLLM_TPU_MOST_MODEL_LEN
+        self.max_num_blocks_per_req = cdiv(self.max_model_len, self.block_size)
+        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
+        # InputBatch needs to work with sampling tensors greater than padding
+        # to avoid dynamic shapes. Also, avoid suboptimal alignment.
+        self.max_num_reqs = max(scheduler_config.max_num_seqs, MIN_NUM_SEQS)
+        self.num_tokens_paddings = _get_token_paddings(
+            min_token_size=16,
+            max_token_size=scheduler_config.max_num_batched_tokens,
+            padding_gap=envs.VLLM_TPU_BUCKET_PADDING_GAP)
+        # In case `max_num_tokens < max(num_tokens_paddings)` use the actual
+        # padded max value to pre-allocate data structures and pre-compile.
+        self.max_num_tokens = self.num_tokens_paddings[-1]
+
+        # Model-related.
+        self.num_attn_layers = model_config.get_num_layers_by_block_type(
+            parallel_config, LayerBlockType.attention)
+        self.num_query_heads = model_config.get_num_attention_heads(
+            parallel_config)
+        self.num_kv_heads = model_config.get_num_kv_heads(parallel_config)
+        self.head_size = model_config.get_head_size()
+        self.hidden_size = model_config.get_hidden_size()
+        self.vocab_size = model_config.get_vocab_size()
+
+        if self.lora_config is not None:
+            self.vocab_size += self.lora_config.lora_extra_vocab_size
+
+        # Multi-modal data support
+        self.mm_registry = MULTIMODAL_REGISTRY
+        self.uses_mrope = model_config.uses_mrope
+        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
+            model_config)
+        # TODO: Support M-RoPE (e.g, Qwen2-VL)
+        assert not self.uses_mrope, "TPU does not support M-RoPE yet."
+
+        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,
+            ))
+
+        # Lazy initialization
+        self.model: nn.Module  # Set after load_model
+        self.kv_caches: list[torch.Tensor] = []
+        # req_id -> (input_id -> encoder_output)
+        self.encoder_cache: dict[str, dict[int, torch.Tensor]] = {}
+
+        # Request states.
+        self.requests: dict[str, CachedRequestState] = {}
+
+        # Initialize input batch early to avoid AttributeError in _update_states
+        self.input_batch = InputBatch(
+            max_num_reqs=self.max_num_reqs,
+            max_model_len=self.max_model_len,
+            max_num_batched_tokens=self.max_num_tokens,
+            device=self.device,
+            pin_memory=self.pin_memory,
+            vocab_size=self.model_config.get_vocab_size(),
+            block_sizes=[self.block_size],
+        )
+
+        # Cached torch/numpy tensor
+        # The pytorch tensor and numpy array share the same buffer.
+        # Sometimes the numpy op is faster so we create both.
+        self.input_ids_cpu = torch.zeros(self.max_num_tokens,
+                                         dtype=torch.int32,
+                                         device="cpu")
+
+        self.positions_cpu = torch.zeros(self.max_num_tokens,
+                                         dtype=torch.int32,
+                                         device="cpu")
+        self.positions_np = self.positions_cpu.numpy()
+        self.block_table_cpu = torch.zeros(
+            (self.max_num_reqs, self.max_num_blocks_per_req),
+            dtype=torch.int32,
+            device="cpu")
+        # adjust num_reqs to avoid SMEM OOM.
+        self.num_reqs_most_model_len = min(
+            PallasAttentionBackend.get_max_num_seqs(self.most_model_len,
+                                                    self.block_size),
+            self.max_num_reqs) if self.most_model_len is not None else None
+        self.num_reqs_max_model_len = min(
+            PallasAttentionBackend.get_max_num_seqs(self.max_model_len,
+                                                    self.block_size),
+            self.max_num_reqs)
+        self.query_start_loc_cpu = torch.zeros(self.max_num_tokens + 1,
+                                               dtype=torch.int32,
+                                               device="cpu",
+                                               pin_memory=self.pin_memory)
+        self.query_start_loc_np = self.query_start_loc_cpu.numpy()
+
+        self.seq_lens_cpu = torch.zeros(self.max_num_tokens,
+                                        dtype=torch.int32,
+                                        device="cpu",
+                                        pin_memory=self.pin_memory)
+        self.seq_lens_np = self.seq_lens_cpu.numpy()
+
+        # Range tensor with values [0 .. self.max_num_tokens - 1].
+        # Used to initialize positions / context_lens / seq_lens
+        # Keep in int64 to avoid overflow with long context
+        self.arange_np = np.arange(self.max_num_tokens, dtype=np.int64)
+        self.num_reqs_paddings = _get_req_paddings(
+            min_req_size=MIN_NUM_SEQS, max_req_size=self.max_num_reqs)
+
+        # 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] = {}
+
+        # tensors for structured decoding
+        self.grammar_bitmask_cpu = torch.zeros(
+            (self.max_num_reqs, cdiv(self.vocab_size, 32)),
+            dtype=torch.int32,
+            device="cpu",
+            pin_memory=self.pin_memory)
+        self.require_structured_out_cpu = torch.zeros(
+            (self.max_num_reqs, 1),
+            dtype=torch.bool,
+            device="cpu",
+            pin_memory=self.pin_memory)
+        self.structured_decode_arange = torch.arange(
+            0, 32, device="cpu", pin_memory=self.pin_memory)
+
+        self.mm_budget = (MultiModalBudget(
+            self.model_config,
+            self.scheduler_config,
+            self.mm_registry,
+            max_model_len=self.max_model_len,
+            max_num_reqs=self.max_num_reqs,
+        ) if self.supports_mm_inputs else None)
+
+        if not self.use_spmd:
+            self.sample_from_logits_func = torch.compile(
+                self.sample_from_logits,
+                backend="openxla",
+                fullgraph=True,
+                dynamic=False)
+        else:
+            self.sample_from_logits_func = self.sample_from_logits
+
+    def _update_num_xla_graphs(self, case_str):
+        check_comp = self.check_recompilation and not self.enforce_eager
+        if not check_comp:
+            return
+
+        total_cached_graphs = xr.get_num_cached_compilation_graph()
+        new_compiled_graphs = total_cached_graphs - self.num_xla_graphs
+        if new_compiled_graphs == 0:
+            return
+
+        logger.info("Add new %d compiled XLA graphs due to %s",
+                    new_compiled_graphs, case_str)
+        self.num_xla_graphs += new_compiled_graphs
+
+    def _verify_num_xla_graphs(self, case_str):
+        check_comp = self.check_recompilation and not self.enforce_eager
+        if not check_comp:
+            return
+
+        curr_cached_graph = xr.get_num_cached_compilation_graph()
+        assert self.num_xla_graphs == curr_cached_graph, (
+            "Recompilation after warm up is detected during {}."
+            " num_xla_graphs = {} curr_cached_graph = {}".format(
+                case_str, self.num_xla_graphs, curr_cached_graph))
+
+    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:
+            True if there is a new/resumed/paused/finished request.
+            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)
+            self.encoder_cache.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.
+        removed_req_indices: list[int] = []
+        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)
+
+        # Free the cached encoder outputs.
+        for req_id, input_id in scheduler_output.free_encoder_input_ids:
+            encoder_outputs = self.encoder_cache.get(req_id)
+            if encoder_outputs is not None:
+                encoder_outputs.pop(input_id, None)
+                if not encoder_outputs:
+                    self.encoder_cache.pop(req_id, None)
+
+        # 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)
+
+        req_ids_to_add: list[str] = []
+        # Add new requests to the cached states.
+        for new_req_data in scheduler_output.scheduled_new_reqs:
+            assert new_req_data.sampling_params is not None,\
+                "Pooling is not supported in TPU yet"
+            req_id = new_req_data.req_id
+            sampling_params = new_req_data.sampling_params
+
+            self.requests[req_id] = CachedRequestState(
+                req_id=req_id,
+                prompt_token_ids=new_req_data.prompt_token_ids,
+                mm_kwargs=new_req_data.mm_kwargs,
+                mm_positions=new_req_data.mm_positions,
+                sampling_params=sampling_params,
+                pooling_params=None,
+                generator=None,
+                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.
+        req_data = scheduler_output.scheduled_cached_reqs
+        for i, req_id in enumerate(req_data.req_ids):
+            req_state = self.requests[req_id]
+            num_computed_tokens = req_data.num_computed_tokens[i]
+            new_block_ids = req_data.new_block_ids[i]
+            resumed_from_preemption = req_data.resumed_from_preemption[i]
+
+            # Update the cached states.
+            req_state.num_computed_tokens = num_computed_tokens
+            if not resumed_from_preemption:
+                # Append the new blocks to the existing block IDs.
+                for block_ids, new_ids in zip(req_state.block_ids,
+                                              new_block_ids):
+                    block_ids.extend(new_ids)
+            else:
+                # The request is resumed from preemption.
+                # Replace the existing block IDs with the new ones.
+                req_state.block_ids = new_block_ids
+
+            req_index = self.input_batch.req_id_to_index.get(req_id)
+            if req_index is None:
+                # The request is not in the persistent batch.
+                # The request was either preempted and resumed later, or was not
+                # scheduled in the previous step and needs to be added again.
+                req_ids_to_add.append(req_id)
+                continue
+
+            # Update the persistent batch.
+            self.input_batch.num_computed_tokens_cpu[req_index] = (
+                num_computed_tokens)
+            self.input_batch.block_table.append_row(new_block_ids, req_index)
+
+        # Add the new or resumed requests to the persistent batch.
+        # The smaller empty indices are filled first.
+        removed_req_indices = sorted(removed_req_indices, reverse=True)
+        for req_id in req_ids_to_add:
+            req_state = self.requests[req_id]
+            if removed_req_indices:
+                # Fill the empty index.
+                req_index = removed_req_indices.pop()
+            else:
+                # Append to the end.
+                req_index = None
+            self.input_batch.add_request(req_state, req_index)
+
+        # Condense the batched states if there are empty indices.
+        if removed_req_indices:
+            self.input_batch.condense(removed_req_indices)
+
+        return len(unscheduled_req_ids) > 0 or len(req_ids_to_add) > 0
+
+    def get_model(self) -> nn.Module:
+        return self.model
+
+    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
+
+    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
+        model = self.get_model()
+        if not is_pooling_model(model):
+            return []
+
+        return list(model.pooler.get_supported_tasks())
+
+    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)
+
+    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
+        """
+        Generates the KVCacheSpec by parsing the kv cache format from each
+        Attention module in the static forward context.
+        Returns:
+            KVCacheSpec: A dictionary mapping layer names to their KV cache
+            format. Layers that do not need KV cache are not included.
+        """
+
+        layers = get_layers_from_vllm_config(self.vllm_config, Attention)
+        block_size = self.vllm_config.cache_config.block_size
+        kv_cache_spec: dict[str, KVCacheSpec] = {}
+        for layer_name, attn_module in layers.items():
+            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,
+                        use_mla=False,
+                    )
+                else:
+                    kv_cache_spec[layer_name] = FullAttentionSpec(
+                        block_size=block_size,
+                        num_kv_heads=attn_module.num_kv_heads,
+                        head_size=attn_module.head_size,
+                        dtype=self.kv_cache_dtype,
+                        use_mla=False,
+                    )
+            elif attn_module.attn_type in (AttentionType.ENCODER,
+                                           AttentionType.ENCODER_ONLY):
+                # encoder-only attention does not need KV cache.
+                continue
+            elif attn_module.attn_type == AttentionType.ENCODER_DECODER:
+                raise NotImplementedError
+            else:
+                raise ValueError(
+                    f"Unknown attention type: {attn_module.attn_type}")
+
+        return kv_cache_spec
+
+    def _get_slot_mapping_metadata(self, num_reqs,
+                                   num_scheduled_tokens_per_req):
+        """
+        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
+            to be scheduled for each request.
+
+        Returns:
+            np.ndarray: A 2D array of shape (total_block_len, 3), where each row
+            contains:
+                - kv_cache_start_index (int): The starting index in the KV cache
+                    for the corresponding slice.
+                - new_kv_start_index (int): The starting index in the new KV
+                    cache for the corresponding slice.
+                - slice_len (int): The length of the slice.
+        """
+        slices_start = self.input_batch.num_computed_tokens_cpu[:num_reqs]
+        slices_end = self.input_batch.num_computed_tokens_cpu[:num_reqs] + \
+            num_scheduled_tokens_per_req
+        local_block_start_idx = slices_start // self.block_size
+        local_block_end_idx = (slices_end - 1) // self.block_size
+        no_repeat_req_indices = self.arange_np[:num_reqs]
+        global_block_start_idx = (
+            no_repeat_req_indices * self.max_num_blocks_per_req +
+            local_block_start_idx)
+        block_lens = local_block_end_idx - local_block_start_idx + 1
+        global_block_start_idx = np.repeat(global_block_start_idx, block_lens)
+        slice_arange = np.concatenate([self.arange_np[:n] for n in block_lens])
+        global_block_indices = global_block_start_idx + slice_arange
+        block_table_cpu = self.input_batch.block_table[0].get_cpu_tensor()
+        block_numbers = block_table_cpu.flatten()[global_block_indices].numpy()
+        total_block_len = np.sum(block_lens)
+        slot_mapping_slices = np.repeat(np.array([[0, self.block_size]],
+                                                 dtype=np.int32),
+                                        total_block_len,
+                                        axis=0)
+        cu_block_lens = np.zeros(len(block_lens) + 1, dtype=np.int32)
+        np.cumsum(block_lens, out=cu_block_lens[1:])
+        for req_idx in range(num_reqs):
+            slot_mapping_slices[cu_block_lens[req_idx]][
+                0] = slices_start[req_idx] % self.block_size
+            slot_mapping_slices[
+                cu_block_lens[req_idx + 1] -
+                1][1] = (slices_end[req_idx] - 1) % self.block_size + 1
+        slice_lens = slot_mapping_slices[:, 1] - slot_mapping_slices[:, 0]
+        cu_slices_lens = np.zeros(len(slice_lens) + 1, dtype=np.int32)
+        np.cumsum(slice_lens, out=cu_slices_lens[1:])
+        kv_cache_start_indices = slot_mapping_slices[:, 0] + \
+            (block_numbers * self.block_size)
+        new_kv_start_indices = cu_slices_lens[:-1]
+        slot_mapping_metadata = np.stack(
+            [kv_cache_start_indices, new_kv_start_indices, slice_lens], axis=1)
+        return slot_mapping_metadata
+
+    def _prepare_inputs(self, scheduler_output: "SchedulerOutput",
+                        start_index: int):
+        assert scheduler_output.total_num_scheduled_tokens > 0
+        num_reqs = self.input_batch.num_reqs
+        assert num_reqs > 0
+        assert start_index < num_reqs
+
+        # Get the number of scheduled tokens for each request.
+        use_max_model_len = self.most_model_len is None
+        num_scheduled_tokens_per_req = []
+        max_num_scheduled_tokens_all_reqs = 0
+        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]
+            assert req_id is not None
+            num_tokens = scheduler_output.num_scheduled_tokens[req_id]
+            if not use_max_model_len and num_tokens > self.most_model_len:
+                use_max_model_len = True
+            num_scheduled_tokens_per_req.append(num_tokens)
+        if use_max_model_len:
+            if len(num_scheduled_tokens_per_req) > self.num_reqs_max_model_len:
+                num_scheduled_tokens_per_req = \
+                    num_scheduled_tokens_per_req[:self.num_reqs_max_model_len]
+                end_index = start_index + self.num_reqs_max_model_len
+            else:
+                end_index = num_reqs
+        else:
+            if len(num_scheduled_tokens_per_req
+                   ) > self.num_reqs_most_model_len:
+                num_scheduled_tokens_per_req = \
+                    num_scheduled_tokens_per_req[:self.num_reqs_most_model_len]
+                end_index = start_index + self.num_reqs_most_model_len
+            else:
+                end_index = num_reqs
+        max_num_scheduled_tokens_all_reqs = max(num_scheduled_tokens_per_req)
+        num_scheduled_tokens_per_req = np.array(num_scheduled_tokens_per_req,
+                                                dtype=np.int32)
+        total_num_scheduled_tokens = sum(num_scheduled_tokens_per_req)
+        assert max_num_scheduled_tokens_all_reqs > 0
+
+        num_reqs = len(num_scheduled_tokens_per_req)
+
+        # Get request indices.
+        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
+        # For each scheduled token, what are the corresponding req index.
+        req_indices = np.repeat(self.arange_np[:num_reqs],
+                                num_scheduled_tokens_per_req)
+
+        # Get batched arange.
+        # E.g., [2, 5, 3] -> [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
+        # For each scheduled token, what is its position in corresponding req.
+        arange = np.concatenate(
+            [self.arange_np[:n] for n in num_scheduled_tokens_per_req])
+
+        # Get positions.
+        positions_np = self.positions_np[:total_num_scheduled_tokens]
+        np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
+               arange,
+               out=positions_np)
+
+        # Get token indices.
+        # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
+        # -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
+        # where M is the max_model_len.
+        token_indices = (positions_np +
+                         req_indices * self.input_batch.token_ids_cpu.shape[1])
+
+        # NOTE(woosuk): We use torch.index_select instead of np.take here
+        # because torch.index_select is much faster than np.take for large
+        # tensors.
+        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])
+
+        # Prepare the attention metadata.
+        self.query_start_loc_np[0] = 0
+        np.cumsum(num_scheduled_tokens_per_req,
+                  out=self.query_start_loc_np[1:num_reqs + 1])
+        self.query_start_loc_np[num_reqs + 1:] = 1
+
+        self.seq_lens_np[:num_reqs] = (
+            self.input_batch.num_computed_tokens_cpu[:num_reqs] +
+            num_scheduled_tokens_per_req)
+
+        # Do the padding and copy the tensors to the TPU.
+        padded_total_num_scheduled_tokens = _get_padded_token_len(
+            self.num_tokens_paddings, total_num_scheduled_tokens)
+        # Zero out to avoid spurious values from prev iteration (last cp chunk)
+        self.input_ids_cpu[
+            total_num_scheduled_tokens:padded_total_num_scheduled_tokens] = 0
+        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)
+        if use_max_model_len:
+            block_tables = self.block_table_cpu[:self.num_reqs_max_model_len, :
+                                                self.max_num_blocks_per_req]
+            block_tables[:num_reqs, :self.max_num_blocks_per_req] = (
+                self.input_batch.block_table[0].get_cpu_tensor()[:num_reqs])
+            query_start_loc = self.query_start_loc_cpu[:self.
+                                                       num_reqs_max_model_len +
+                                                       1].to(self.device)
+            seq_lens = self.seq_lens_cpu[:self.num_reqs_max_model_len].to(
+                self.device)
+        else:
+            block_tables = self.block_table_cpu[:self.
+                                                num_reqs_most_model_len, :self.
+                                                num_blocks_per_most_len_req]
+            block_tables[:num_reqs, :self.num_blocks_per_most_len_req] = (
+                self.input_batch.block_table[0].get_cpu_tensor()
+                [:num_reqs, :self.num_blocks_per_most_len_req])
+            query_start_loc = self.query_start_loc_cpu[:self.
+                                                       num_reqs_most_model_len +
+                                                       1].to(self.device)
+            seq_lens = self.seq_lens_cpu[:self.num_reqs_most_model_len].to(
+                self.device)
+        block_tables = block_tables.to(self.device)
+
+        # Calculate the slot mapping
+        slot_mapping_metadata = self._get_slot_mapping_metadata(
+            num_reqs, num_scheduled_tokens_per_req)
+        num_kv_update_slices = slot_mapping_metadata.shape[0]
+        padded_num_slices = _get_padded_num_kv_cache_update_slices(
+            padded_total_num_scheduled_tokens, self.max_num_reqs,
+            self.block_size)
+        slot_mapping_metadata = np.pad(
+            slot_mapping_metadata,
+            [[0, padded_num_slices - len(slot_mapping_metadata)], [0, 0]],
+            constant_values=0)
+        slot_mapping_metadata = np.transpose(slot_mapping_metadata)
+        slot_mapping_metadata = torch.tensor(slot_mapping_metadata,
+                                             device=self.device)
+
+        if self.lora_config is not None:
+            # We need to respect padding when activating LoRA adapters
+            padded_num_scheduled_tokens_per_req = np.copy(
+                num_scheduled_tokens_per_req
+            )  # Copying to avoid accidental state corruption bugs
+            padded_num_scheduled_tokens_per_req[-1] += \
+                padded_total_num_scheduled_tokens - total_num_scheduled_tokens
+
+            self.set_active_loras(self.input_batch,
+                                  padded_num_scheduled_tokens_per_req)
+
+        attn_metadata = PallasMetadata(
+            slot_mapping=slot_mapping_metadata,
+            block_tables=block_tables,
+            context_lens=seq_lens,
+            query_start_loc=query_start_loc,
+            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,
+        )
+        # 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.
+        padded_num_reqs = _get_padded_num_reqs_with_upper_limit(
+            num_reqs, self.max_num_reqs)
+        # Indices at which we sample (positions of last token in the sequence).
+        # Padded to avoid recompiling when `num_reqs` varies.
+        logits_indices = self.query_start_loc_cpu[1:padded_num_reqs + 1] - 1
+        logits_indices = logits_indices.to(self.device)
+
+        if self.lora_config is not None:
+            # We need to respect padding when activating LoRA adapters
+            padded_num_scheduled_tokens_per_req = np.copy(
+                num_scheduled_tokens_per_req
+            )  # Copying to avoid accidental state corruption bugs
+            padded_num_scheduled_tokens_per_req[-1] += \
+                padded_total_num_scheduled_tokens - total_num_scheduled_tokens
+
+            self.set_active_loras(self.input_batch,
+                                  padded_num_scheduled_tokens_per_req)
+
+        layer_names = get_layers_from_vllm_config(self.vllm_config,
+                                                  Attention).keys()
+        per_layer_attn_metadata = {
+            layer_name: attn_metadata
+            for layer_name in layer_names
+        }
+        return per_layer_attn_metadata, logits_indices, padded_num_reqs,\
+            num_reqs, end_index
+
+    def _scatter_placeholders(
+        self,
+        embeds: torch.Tensor,
+        is_embed: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        if is_embed is None:
+            return embeds
+
+        placeholders = embeds.new_full(
+            (is_embed.shape[0], embeds.shape[-1]),
+            fill_value=torch.nan,
+        )
+        placeholders[is_embed] = embeds
+        return placeholders
+
+    def _gather_placeholders(
+        self,
+        placeholders: torch.Tensor,
+        is_embed: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        if is_embed is None:
+            return placeholders
+
+        return placeholders[is_embed]
+
+    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
+        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
+        if not scheduled_encoder_inputs:
+            return
+
+        # Batch the multi-modal inputs.
+        mm_kwargs = list[MultiModalKwargsItem]()
+        req_ids_pos = list[tuple[str, int, PlaceholderRange]]()
+        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
+            req_state = self.requests[req_id]
+
+            for mm_input_id in encoder_input_ids:
+                mm_kwargs.append(req_state.mm_kwargs[mm_input_id])
+                req_ids_pos.append(
+                    (req_id, mm_input_id, req_state.mm_positions[mm_input_id]))
+
+        # Batch mm inputs as much as we can: if a request in the batch has
+        # multiple modalities or a different modality than the previous one,
+        # we process it separately to preserve item order.
+        # FIXME(ywang96): This is a hacky way to deal with multiple modalities
+        # in the same batch while still being able to benefit from batching
+        # multimodal inputs. The proper solution should be reordering the
+        # encoder outputs.
+        encoder_outputs = []
+        for _, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
+                mm_kwargs,
+                device=self.device,
+                pin_memory=self.pin_memory,
+        ):
+            # 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.
+            xm.mark_step()
+            curr_group_outputs = self.model.get_multimodal_embeddings(
+                **mm_kwargs_group)
+            xm.mark_step()
+
+            sanity_check_mm_encoder_outputs(
+                curr_group_outputs,
+                expected_num_items=num_items,
+            )
+
+            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)
+
+        # Cache the encoder outputs.
+        # 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.
+        for (req_id, input_id, pos_info), output in zip(
+                req_ids_pos,
+                encoder_outputs,
+        ):
+            if req_id not in self.encoder_cache:
+                self.encoder_cache[req_id] = {}
+            assert pos_info.is_embed is None, "Expected all positions to be"\
+                " contiguous and embeddings."
+            self.encoder_cache[req_id][input_id] = output
+
+    def _gather_mm_embeddings(
+        self,
+        scheduler_output: "SchedulerOutput",
+    ) -> list[torch.Tensor]:
+        mm_embeds: list[torch.Tensor] = []
+        for req_id in self.input_batch.req_ids:
+            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
+                req_id]
+            req_state = self.requests[req_id]
+            num_computed_tokens = req_state.num_computed_tokens
+            mm_positions = req_state.mm_positions
+            # 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.
+            for i, pos_info in enumerate(mm_positions):
+                start_pos = pos_info.offset
+                num_encoder_tokens = pos_info.length
+
+                # The encoder output is needed if the two ranges overlap:
+                # [num_computed_tokens,
+                #  num_computed_tokens + num_scheduled_tokens) and
+                # [start_pos, start_pos + num_encoder_tokens)
+                if start_pos >= num_computed_tokens + num_scheduled_tokens:
+                    # The encoder output is not needed in this step.
+                    break
+                if start_pos + num_encoder_tokens <= num_computed_tokens:
+                    # The encoder output is already processed and stored
+                    # in the decoder's KV cache.
+                    continue
+
+                assert req_id in self.encoder_cache
+                assert i in self.encoder_cache[req_id]
+                assert pos_info.is_embed is None, "Expected all positions to"\
+                " be contiguous and embeddings."
+                encoder_output = self.encoder_cache[req_id][i]
+                mm_embeds.append(encoder_output)
+        return mm_embeds
+
+    def _get_model_inputs(self, input_ids: torch.Tensor,
+                          mm_embeds: list[torch.Tensor]):
+        if self.supports_mm_inputs:
+            # 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.
+            inputs_embeds = self.model.get_input_embeddings(
+                input_ids=input_ids,
+                multimodal_embeddings=mm_embeds,
+            )
+            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
+
+    @torch.no_grad()
+    def execute_model(
+        self,
+        scheduler_output: "SchedulerOutput",
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+    ) -> ModelRunnerOutput:
+        # Update cached state
+        self._update_states(scheduler_output)
+        if not scheduler_output.total_num_scheduled_tokens:
+            if not has_kv_transfer_group():
+                # Return empty ModelRunnerOutput if there's no work to do.
+                return EMPTY_MODEL_RUNNER_OUTPUT
+
+            return self.kv_connector_no_forward(scheduler_output,
+                                                self.vllm_config)
+
+        if self.supports_mm_inputs:
+            # Run the multimodal encoder if any.
+            self._execute_mm_encoder(scheduler_output)
+            mm_embeds = self._gather_mm_embeddings(scheduler_output)
+        else:
+            mm_embeds = []
+        xm.mark_step()
+        # Prepare inputs, the requests might be split into multiple
+        # executions, combine the result of each execution.
+        start_index = 0
+        combined_selected_tokens: list[torch.Tensor] = []
+        combined_logprobs: list[LogprobsLists] = []
+
+        # 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)
+
+        while start_index < self.input_batch.num_reqs:
+            attn_metadata, logits_indices, padded_num_reqs, num_reqs,\
+                end_index = self._prepare_inputs(scheduler_output, start_index)
+            input_ids, inputs_embeds = self._get_model_inputs(
+                self.input_ids, mm_embeds)
+            xm.mark_step()
+            # Run the decoder
+            with set_forward_context(
+                    attn_metadata,
+                    self.vllm_config,
+                    num_tokens=scheduler_output.total_num_scheduled_tokens):
+                hidden_states = self.model(
+                    input_ids=input_ids,
+                    positions=self.position_ids,
+                    inputs_embeds=inputs_embeds,
+                )
+            hidden_states = self.select_hidden_states(hidden_states,
+                                                      logits_indices)
+            logits = self.compute_logits(hidden_states)
+            tpu_sampling_metadata = TPUSupportedSamplingMetadata.\
+                from_input_batch(self.input_batch, padded_num_reqs, self.device)
+            if scheduler_output.grammar_bitmask is not None:
+                require_struct_decoding, grammar_bitmask_padded, arange = \
+                    self.prepare_structured_decoding_input(logits,
+                                                           scheduler_output)
+                logits = self.structured_decode(require_struct_decoding,
+                                                grammar_bitmask_padded, logits,
+                                                arange)
+            selected_token_ids = self.sample_from_logits_func(
+                logits, tpu_sampling_metadata)
+            # NOTE (NickLucche) Use the original logits (before any penalties or
+            # temperature scaling) for the top-k logprobs. We can't enforce it
+            # due to recompilations outside torch.compiled code, so just make
+            # sure `sample_from_logits` does not modify the logits in-place.
+            logprobs = self.gather_logprobs(logits, selected_token_ids) \
+                if tpu_sampling_metadata.logprobs else None
+
+            # Remove padding on cpu and keep dynamic op outside of xla graph.
+            selected_token_ids = selected_token_ids.cpu()[:num_reqs]
+
+            combined_selected_tokens.append(selected_token_ids)
+            if tpu_sampling_metadata.logprobs:
+                combined_logprobs.append(logprobs.tolists())
+
+            start_index = end_index
+
+        # NOTE: current kv load and save get h2d/d2h copies involved.
+        # Those copies are blocking. Once they become async., kv_save
+        # should be called right after each single forward pass,
+        # instead of the forwards of the entire input batch.
+        self.maybe_wait_for_kv_save()
+        finished_sending, finished_recving = (
+            self.get_finished_kv_transfers(scheduler_output))
+
+        selected_token_ids = torch.cat(combined_selected_tokens, dim=0)
+        if tpu_sampling_metadata.logprobs:
+
+            def concat_lists(input_lists):
+                result = []
+                for input_list in input_lists:
+                    result.extend(input_list)
+                return result
+
+            logprobs_lists = LogprobsLists(logprob_token_ids=concat_lists(
+                [lp.logprob_token_ids for lp in combined_logprobs]),
+                                           logprobs=concat_lists([
+                                               lp.logprobs
+                                               for lp in combined_logprobs
+                                           ]),
+                                           sampled_token_ranks=concat_lists([
+                                               lp.sampled_token_ranks
+                                               for lp in combined_logprobs
+                                           ]))
+        else:
+            logprobs_lists = None
+
+        # Update the cache state concurrently. Code above will not block until
+        # we use `selected_token_ids`. Add mark_step if post-processing changes
+        request_seq_lens: list[tuple[int, CachedRequestState, int]] = []
+        discard_sampled_tokens_req_indices = []
+        num_reqs = self.input_batch.num_reqs
+        for i, req_id in zip(range(num_reqs), self.input_batch.req_ids):
+            assert req_id is not None
+            req_state = self.requests[req_id]
+            seq_len = (req_state.num_computed_tokens +
+                       scheduler_output.num_scheduled_tokens[req_id])
+            if seq_len >= req_state.num_tokens:
+                request_seq_lens.append((i, req_state, seq_len))
+            else:
+                # Ignore the sampled token from the partial request.
+                # Rewind the generator state as if the token was not sampled.
+                generator = self.input_batch.generators.get(i)
+                if generator is not None:
+                    # This relies on cuda-specific torch-internal impl details
+                    generator.set_offset(generator.get_offset() - 4)
+
+                # 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)
+
+        assert all(
+            req_id is not None for req_id in
+            self.input_batch.req_ids[:num_reqs]), "req_ids contains None"
+        req_ids = cast(list[str], self.input_batch.req_ids[:num_reqs])
+
+        prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
+        for req_id in self.input_batch.req_ids[:num_reqs]:
+            prompt_logprobs_dict[req_id] = None
+
+        max_gen_len = selected_token_ids.shape[-1]
+        if max_gen_len == 1:
+            valid_sampled_token_ids = selected_token_ids.tolist()
+
+            # 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
+            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
+
+        else:
+            valid_mask = selected_token_ids != INVALID_TOKEN_ID
+            gen_lens = valid_mask.sum(dim=1).tolist()
+            valid_sampled_token_ids = [
+                seq.tolist()
+                for seq in selected_token_ids[valid_mask].split(gen_lens)
+            ]
+            self.input_batch.num_tokens[:num_reqs] += gen_lens
+            for i, req_state, seq_len in request_seq_lens:
+                target_slice = slice(seq_len - gen_lens[i] + 1, seq_len + 1)
+                self.input_batch.token_ids_cpu[
+                    i, target_slice] = valid_sampled_token_ids[i]
+                req_state.output_token_ids.extend(valid_sampled_token_ids[i])
+
+        kv_connector_output = None if (
+            finished_sending is None
+            and finished_recving is None) else KVConnectorOutput(
+                finished_sending=finished_sending,
+                finished_recving=finished_recving,
+            )
+
+        model_runner_output = ModelRunnerOutput(
+            req_ids=req_ids,
+            req_id_to_index=self.input_batch.req_id_to_index,
+            sampled_token_ids=valid_sampled_token_ids,
+            spec_token_ids=None,
+            logprobs=logprobs_lists,
+            prompt_logprobs_dict=prompt_logprobs_dict,
+            pooler_output=[],
+            kv_connector_output=kv_connector_output,
+        )
+
+        # 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")
+
+        return model_runner_output
+
+    def update_config(self, overrides: dict[str, Any]) -> None:
+        # TODO: TPU config may need extra validation
+        # https://github.com/vllm-project/vllm/pull/20095#discussion_r2201497754
+        allowed_config_names = {"load_config", "model_config"}
+        for config_name, config_overrides in overrides.items():
+            assert config_name in allowed_config_names, \
+                f"Config `{config_name}` not supported. " \
+                f"Allowed configs: {allowed_config_names}"
+            config = getattr(self, config_name)
+            new_config = update_config(config, config_overrides)
+            setattr(self, config_name, new_config)
+
+    def load_model(self) -> None:
+        self.device = self.device_config.device
+
+        # NOTE(woosuk): While the executor assigns the TP ranks to the worker
+        # process, the ranks can be different from the ranks internally assigned
+        # by the xm runtime. Therefore, there is a mismatch in the rank
+        # assignment between the gloo (cpu) runtime and the xm (tpu) runtime.
+        # This is not a problem in linear layers because all-reduce is
+        # rank-agnostic. However, it matters for all-gather as the ranks
+        # determine the order of concatenating the output tensors.
+        # As a workaround, we use the xm's rank assignment only when loading
+        # the embedding weights.
+        xm_tp_rank = xr.global_ordinal()
+        with patch(
+                "vllm.model_executor.layers.vocab_parallel_embedding."
+                "get_tensor_model_parallel_rank",
+                return_value=xm_tp_rank):
+            try:
+                if self.use_spmd:
+                    tpu_loader = TPUModelLoader(
+                        load_config=self.vllm_config.load_config)
+                    model = tpu_loader.load_model(
+                        vllm_config=self.vllm_config,
+                        model_config=self.vllm_config.model_config,
+                        mesh=self.mesh)
+                else:
+                    model_loader = get_model_loader(self.load_config)
+                    logger.info("Loading model from scratch...")
+                    model = model_loader.load_model(
+                        vllm_config=self.vllm_config,
+                        model_config=self.model_config)
+            except RuntimeError as e:
+                raise RuntimeError(
+                    f"Unable to load model, a likely reason is the model is "
+                    "too large for the current device's HBM memory. "
+                    "Consider switching to a smaller model "
+                    "or sharding the weights on more chips. "
+                    f"See the detailed error: {e}") from e
+        if self.lora_config is not None:
+            model = self.load_lora_model(model, self.model_config,
+                                         self.scheduler_config,
+                                         self.lora_config, self.device)
+            replace_set_lora(model)
+
+        # Sync all pending XLA execution during model initialization and weight
+        # loading.
+        xm.mark_step()
+        xm.wait_device_ops()
+        if not hasattr(self, "model"):
+            self.model = model
+        self.sampler = TPUSampler()
+
+    def reload_weights(self) -> None:
+        assert getattr(self, "model", None) is not None, \
+            "Cannot reload weights before model is loaded."
+        model_loader = get_model_loader(self.load_config)
+        logger.info("Reloading weights inplace...")
+        model_loader.load_weights(self.model, model_config=self.model_config)
+
+    @torch.no_grad()
+    def _dummy_run(self, num_tokens: int, num_reqs: int,
+                   num_blocks: int) -> None:
+        if self.supports_mm_inputs:
+            input_ids = None
+            inputs_embeds = torch.zeros((num_tokens, self.hidden_size),
+                                        dtype=self.dtype,
+                                        device=self.device)
+        else:
+            input_ids = torch.zeros((num_tokens),
+                                    dtype=torch.int32).to(self.device)
+            inputs_embeds = None
+        actual_num_reqs = min(num_tokens, num_reqs)
+        position_ids = torch.zeros(num_tokens,
+                                   dtype=torch.int32).to(self.device)
+        padded_num_slices = _get_padded_num_kv_cache_update_slices(
+            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)
+        query_lens = [1] * num_reqs
+        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)
+        attn_metadata = PallasMetadata(
+            slot_mapping=slot_mapping,
+            block_tables=block_tables,
+            context_lens=context_lens,
+            query_start_loc=query_start_loc,
+            num_seqs=num_seqs,
+            num_kv_update_slices=num_kv_update_slices,
+            num_slices_per_kv_cache_update_block=self.
+            _num_slices_per_kv_cache_update_block,
+        )
+
+        if self.supports_mm_inputs:
+            torch._dynamo.mark_dynamic(inputs_embeds, 0)
+        else:
+            torch._dynamo.mark_dynamic(input_ids, 0)
+        torch._dynamo.mark_dynamic(position_ids, 0)
+        torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 0)
+        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)
+
+        layer_names = get_layers_from_vllm_config(self.vllm_config,
+                                                  Attention).keys()
+        per_layer_attn_metadata = {
+            layer_name: attn_metadata
+            for layer_name in layer_names
+        }
+
+        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)
+        self._hidden_states_dtype = out.dtype
+
+    def _set_active_loras(self, prompt_lora_mapping, token_lora_mapping,
+                          lora_requests) -> None:
+        xm.mark_step()  # Captures input updates
+        super()._set_active_loras(prompt_lora_mapping, token_lora_mapping,
+                                  lora_requests)
+        xm.mark_step()  # Captures metadata updates
+
+    def _precompile_mm_encoder(self) -> None:
+        if not self.supports_mm_inputs:
+            return
+
+        # Pre-compile MM encoder for all supported data modalities.
+        hf_config = self.vllm_config.model_config.hf_config
+
+        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():
+            logger.info(
+                "Compiling Multimodal %s Encoder with different input"
+                " shapes.", mode)
+            start = time.perf_counter()
+            # No padding for MM encoder just yet.
+            for num_items in range(1, max_items_per_seq + 1):
+                logger.info("  -- mode: %s items: %d", mode, num_items)
+                batched_dummy_mm_inputs = self._get_mm_dummy_batch(
+                    mode,
+                    num_items,
+                )
+                # Run multimodal encoder.
+                xm.mark_step()
+                mm_embeds = self.model.get_multimodal_embeddings(
+                    **batched_dummy_mm_inputs)
+                xm.mark_step()
+                num_patches = mm_embeds[0].shape[0]
+                items_size = num_patches * num_items
+
+                # NOTE (NickLucche) pre-compile `get_input_embeddings` when mm
+                # embeddings are present. We assume `--disable-mm-chunked`,
+                # hence only whole items can be scheduled. This implies we just
+                # need to compile when `num_items` fit the (padded) `input_ids`
+                for num_tokens in self.num_tokens_paddings:
+                    if num_tokens >= items_size:
+                        # XLA Workaround: if torch.zeros(..device) is used, XLA
+                        # compiles a scalar+expansion op, which won't match
+                        # the graph generated at runtime. CPU->TPU must be used
+                        placeholders_ids = torch.zeros(num_tokens,
+                                                       dtype=torch.int32,
+                                                       device="cpu")
+                        # Align placeholders and actual num mm_embeddings.
+                        placeholders_ids[:items_size] = \
+                            hf_config.image_token_index
+
+                        placeholders_ids = placeholders_ids.to(self.device)
+                        # Assign outputs or the graph will be cut short.
+                        a, b = self._get_model_inputs(placeholders_ids,
+                                                      [mm_embeds])
+                        assert a is None
+                        xm.mark_step()
+
+            # Pre-compile `get_input_embeddings` when mm_embeddings are not
+            # present. Chunk is only made of text, no mm_placeholders.
+            for num_tokens in self.num_tokens_paddings:
+                placeholders_ids = torch.zeros(num_tokens,
+                                               dtype=torch.int32,
+                                               device="cpu")
+                placeholders_ids = placeholders_ids.to(self.device)
+                a, b = self._get_model_inputs(placeholders_ids, [])
+                assert a is None
+                xm.mark_step()
+
+            xm.wait_device_ops()
+            end = time.perf_counter()
+            logger.info(
+                "Multimodal %s Encoder compilation finished in in %.2f "
+                "[secs].", mode, end - start)
+
+    def _precompile_backbone(self) -> None:
+        logger.info("Compiling the model with different input shapes.")
+        start = time.perf_counter()
+        for num_tokens in self.num_tokens_paddings:
+            logger.info("  -- num_tokens: %d", num_tokens)
+            self._dummy_run(num_tokens, self.num_reqs_max_model_len,
+                            self.max_num_blocks_per_req)
+            if self.most_model_len is not None:
+                self._dummy_run(num_tokens, self.num_reqs_most_model_len,
+                                self.num_blocks_per_most_len_req)
+        xm.wait_device_ops()
+        end = time.perf_counter()
+        logger.info("Compilation finished in %.2f [secs].", end - start)
+        self._update_num_xla_graphs("model backbone")
+
+    def _precompile_select_hidden_states(self) -> None:
+        # Compile hidden state selection function for bucketed
+        # n_tokens x max_num_reqs. Graph is really small so this is fine.
+        logger.info(
+            "Compiling select_hidden_states with different input shapes.")
+        start = time.perf_counter()
+        hsize = self.model_config.get_hidden_size()
+        for num_tokens in self.num_tokens_paddings:
+            dummy_hidden = torch.zeros((num_tokens, hsize),
+                                       device=self.device,
+                                       dtype=self._hidden_states_dtype)
+            torch._dynamo.mark_dynamic(dummy_hidden, 0)
+            for num_reqs in self.num_reqs_paddings:
+                indices = torch.zeros(num_reqs,
+                                      dtype=torch.int32,
+                                      device=self.device)
+                torch._dynamo.mark_dynamic(indices, 0)
+                self.select_hidden_states(dummy_hidden, indices)
+                logger.info("  -- num_tokens: %d, num_seqs: %d", num_tokens,
+                            num_reqs)
+                # Requests can't be more than tokens. But do compile for the
+                # next bigger value in case num_tokens uses bucketed padding.
+                if num_reqs >= min(num_tokens, self.max_num_reqs):
+                    break
+        xm.wait_device_ops()
+        end = time.perf_counter()
+        logger.info("Compilation finished in %.2f [secs].", end - start)
+        self._update_num_xla_graphs("select_hidden_states")
+
+    def _precompile_compute_logits(self) -> None:
+        logger.info("Compiling compute_logits with different input shapes.")
+        start = time.perf_counter()
+        hsize = self.model_config.get_hidden_size()
+        for num_reqs in self.num_reqs_paddings:
+            dummy_hidden = torch.zeros((num_reqs, hsize),
+                                       device=self.device,
+                                       dtype=self._hidden_states_dtype)
+            torch._dynamo.mark_dynamic(dummy_hidden, 0)
+            self.compute_logits(dummy_hidden)
+            logger.info("  -- num_seqs: %d", num_reqs)
+        xm.wait_device_ops()
+        end = time.perf_counter()
+        logger.info("Compilation finished in %.2f [secs].", end - start)
+        self._update_num_xla_graphs("compute_logits")
+
+    def _precompile_structured_decoding(self) -> None:
+        logger.info(
+            "Compiling structured_decoding with different input shapes.")
+        start = time.perf_counter()
+        for num_reqs in self.num_reqs_paddings:
+            dummy_logits = torch.zeros((num_reqs, self.vocab_size),
+                                       device=self.device,
+                                       dtype=self._hidden_states_dtype)
+            dummy_require_struct_decoding = \
+                self.require_structured_out_cpu[:num_reqs].to(self.device)
+            dummy_grammar_bitmask = \
+                self.grammar_bitmask_cpu[:num_reqs].to(self.device)
+            # The first dimension of the above 3 dummy tensors cannot be
+            # mark_dynamic because some operations in structured_decode require
+            # them to be static.
+            arange = self.structured_decode_arange.to(self.device)
+            self.structured_decode(dummy_require_struct_decoding,
+                                   dummy_grammar_bitmask, dummy_logits, arange)
+            logger.info("  -- num_seqs: %d", num_reqs)
+        xm.wait_device_ops()
+        end = time.perf_counter()
+        logger.info("Compilation finished in %.2f [secs].", end - start)
+        self._update_num_xla_graphs("structured_decoding")
+
+    def _precompile_sample_from_logits(self) -> None:
+        logger.info(
+            "Compiling sample_from_logits with different input shapes.")
+        start = time.perf_counter()
+        for num_reqs in self.num_reqs_paddings:
+            dummy_logits = torch.zeros((num_reqs, self.vocab_size),
+                                       device=self.device,
+                                       dtype=self._hidden_states_dtype)
+            # The first dimension of dummy_logits cannot be mark_dynamic
+            # because some operations in the sampler require it to be static.
+            for all_greedy in [False, True]:
+                generate_params_if_all_greedy = not all_greedy
+                sampling_metadata = (
+                    TPUSupportedSamplingMetadata.from_input_batch(
+                        self.input_batch,
+                        num_reqs,
+                        self.device,
+                        generate_params_if_all_greedy,
+                    ))
+                sampling_metadata.all_greedy = all_greedy
+                with self.maybe_select_dummy_loras(
+                        self.lora_config, np.array([num_reqs],
+                                                   dtype=np.int32)):
+                    self.sample_from_logits_func(dummy_logits,
+                                                 sampling_metadata)
+            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("sample_from_logits")
+
+    def _precompile_gather_logprobs(self) -> None:
+        logger.info("Compiling gather_logprobs with different input shapes.")
+        start = time.perf_counter()
+        for num_reqs in self.num_reqs_paddings:
+            dummy_logits = torch.zeros((num_reqs, self.vocab_size),
+                                       device=self.device,
+                                       dtype=self._hidden_states_dtype)
+            dummy_tokens = torch.zeros((num_reqs, 1),
+                                       dtype=torch.int64).to(self.device)
+            with self.maybe_select_dummy_loras(
+                    self.lora_config, np.array([num_reqs], dtype=np.int32)):
+                self.gather_logprobs(dummy_logits, dummy_tokens)
+            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")
+
+    def capture_model(self) -> None:
+        """
+        Precompile all the subgraphs with possible input shapes.
+        """
+        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()
+
+    def profile_run(
+        self,
+        num_tokens: int,
+    ) -> None:
+        # Profile with multimodal encoder & encoder cache.
+        if self.supports_mm_inputs:
+            if self.model_config.multimodal_config.skip_mm_profiling:
+                logger.info(
+                    "Skipping memory profiling for multimodal encoder and "
+                    "encoder cache.")
+            else:
+                mm_budget = self.mm_budget
+                assert mm_budget is not None
+
+                # TODO: handle encoder-decoder models once we support them.
+                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
+                    # NOTE: Currently model is profiled with a single non-text
+                    # modality with the max possible input tokens even when
+                    # it supports multiple.
+                    (
+                        dummy_modality,
+                        max_tokens,
+                    ) = mm_budget.get_modality_with_max_tokens()
+                    (
+                        max_mm_items_per_prompt,
+                        max_mm_items_per_batch,
+                    ) = mm_budget.get_max_items(dummy_modality, max_tokens)
+
+                    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,
+                    )
+
+                    # Create dummy batch of multimodal inputs.
+                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
+                        dummy_modality,
+                        max_mm_items_per_batch,
+                    )
+
+                    # Run multimodal encoder.
+                    # Isolate encoder graph from post-processing to minimize
+                    # impact of recompilation until it's fixed.
+                    start = time.perf_counter()
+                    xm.mark_step()
+                    dummy_encoder_outputs = \
+                        self.model.get_multimodal_embeddings(
+                        **batched_dummy_mm_inputs)
+                    xm.mark_step()
+                    xm.wait_device_ops()
+                    end = time.perf_counter()
+                    logger.info(
+                        "Multimodal Encoder profiling finished in %.2f [secs].",
+                        end - start)
+
+                    sanity_check_mm_encoder_outputs(
+                        dummy_encoder_outputs,
+                        expected_num_items=max_mm_items_per_batch,
+                    )
+
+                    # Cache the dummy encoder outputs.
+                    self.encoder_cache["tmp"] = dict(
+                        enumerate(dummy_encoder_outputs))
+
+        # Trigger compilation for general shape.
+        self._dummy_run(num_tokens, self.num_reqs_max_model_len,
+                        self.max_num_blocks_per_req)
+        if self.most_model_len is not None:
+            self._dummy_run(num_tokens, self.num_reqs_most_model_len,
+                            self.num_blocks_per_most_len_req)
+
+        xm.mark_step()
+        xm.wait_device_ops()
+        self.encoder_cache.clear()
+        gc.collect()
+
+    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
+        """
+        Initialize KV cache based on `kv_cache_config`.
+        Args:
+            kv_cache_config: Configuration for the KV cache, including the KV
+            cache size of each layer
+        """
+        if len(kv_cache_config.kv_cache_groups) > 1:
+            raise NotImplementedError(
+                "Hybrid models with more than one KV cache type are not "
+                "supported yet.")
+
+        if kv_cache_config.kv_cache_groups[
+                0].kv_cache_spec.block_size != self.block_size:
+            self.input_batch = InputBatch(
+                max_num_reqs=self.max_num_reqs,
+                max_model_len=self.max_model_len,
+                max_num_batched_tokens=self.max_num_tokens,
+                device=self.device,
+                pin_memory=self.pin_memory,
+                vocab_size=self.model_config.get_vocab_size(),
+                block_sizes=[
+                    kv_cache_config.kv_cache_groups[0].kv_cache_spec.block_size
+                ],
+            )
+        # Verify dtype compatibility between block_table_cpu and input_batch
+        assert self.block_table_cpu.dtype == self.input_batch.block_table[
+            0].get_cpu_tensor().dtype
+
+        kv_cache_sizes = {}
+        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
+            assert len(kv_cache_tensor.shared_by) == 1, (
+                "KV cache tensor shared by multiple layers is not supported in "
+                "TPU.")
+            kv_cache_sizes[kv_cache_tensor.shared_by[0]] = kv_cache_tensor.size
+
+        kv_caches: dict[str, torch.Tensor] = {}
+        for kv_cache_group in kv_cache_config.kv_cache_groups:
+            kv_cache_spec = kv_cache_group.kv_cache_spec
+            for layer_name in kv_cache_group.layer_names:
+                tensor_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
+                if isinstance(kv_cache_spec, AttentionSpec):
+                    if self.use_spmd:
+                        num_kv_heads = kv_cache_spec.num_kv_heads
+                        assert self.original_parallel_config is not None
+                        tp_size = \
+                            self.original_parallel_config.tensor_parallel_size
+                        # TODO: Handle kv cache duplication under SPMD mode.
+                        assert num_kv_heads % tp_size == 0, (
+                            f"num_kv_heads {num_kv_heads} must be divisible by "
+                            f"tp_size {tp_size} under SPMD mode")
+                    kv_cache_shape = PallasAttentionBackend.get_kv_cache_shape(
+                        num_blocks, kv_cache_spec.block_size,
+                        kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
+                    dtype = kv_cache_spec.dtype
+
+                    tpu_kv_cache = torch.zeros(kv_cache_shape,
+                                               dtype=dtype).to(self.device)
+
+                    kv_caches[layer_name] = tpu_kv_cache
+                else:
+                    raise NotImplementedError
+
+        # Setup `kv_cache_config` and `kv_caches` for models
+        # with cross-layer KV sharing
+        if self.shared_kv_cache_layers:
+            initialize_kv_cache_for_kv_sharing(
+                self.shared_kv_cache_layers,
+                kv_cache_config.kv_cache_groups,
+                kv_caches,
+            )
+
+        bind_kv_cache(
+            kv_caches,
+            self.vllm_config.compilation_config.static_forward_context,
+            self.kv_caches)
+
+        if self.use_spmd:
+            # Shard KV Cache
+            for cache in self.kv_caches:
+                xs.mark_sharding(cache, self.mesh, (None, 'x', None, None))
+
+        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)
+
+    def reset_dynamo_cache(self):
+
+        # 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:
+            compiled_model = self.model.get_language_model().model
+        else:
+            compiled_model = self.model.model
+        if isinstance(compiled_model, TorchCompileWrapperWithCustomDispatcher):
+            logger.info("Clear dynamo cache and cached dynamo bytecode.")
+            torch._dynamo.eval_frame.remove_from_cache(
+                compiled_model.original_code_object)
+            compiled_model.compiled_codes.clear()
+
+    @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)
+    def compute_logits(self,
+                       sample_hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.model.compute_logits(sample_hidden_states, None)
+
+    # 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)
+    def sample_from_logits(
+            self, logits: torch.Tensor,
+            sampling_metadata: TPUSupportedSamplingMetadata) -> torch.Tensor:
+        """
+        Sample with xla-friendly function. This function is to be traced 
+        separately from `forward` for lighter compilation overhead.
+        """
+        if sampling_metadata.all_greedy:
+            out_tokens = torch.argmax(logits, dim=-1, keepdim=True)
+        else:
+            out_tokens = self.sampler(logits,
+                                      sampling_metadata).sampled_token_ids
+        return out_tokens
+
+    @torch.compile(backend="openxla", fullgraph=True, dynamic=False)
+    def gather_logprobs(self, logits: torch.Tensor,
+                        sampled_tokens: torch.Tensor) -> LogprobsTensors:
+        """
+        Gather the top_logprobs with corresponding tokens. Use a fixed number
+        of logprobs as an alternative to having multiple pre-compiled graphs.
+        Select the number of logprobs actually demanded by each request on CPU.
+        """
+        logprobs = self.sampler.compute_logprobs(logits)
+        return self.sampler.gather_logprobs(
+            logprobs,
+            self.model_config.max_logprobs,
+            token_ids=sampled_tokens.squeeze(-1))
+
+    @torch.compile(backend="openxla", fullgraph=True, dynamic=False)
+    def structured_decode(self, require_struct_decoding: torch.Tensor,
+                          grammar_bitmask: torch.Tensor, logits: torch.Tensor,
+                          arange: torch.Tensor) -> torch.Tensor:
+        return torch.where(
+            require_struct_decoding,
+            self.apply_grammar_bitmask(logits, grammar_bitmask, arange),
+            logits)
+
+    def apply_grammar_bitmask(self, logits: torch.Tensor,
+                              grammar_bitmask: torch.Tensor,
+                              arange: torch.Tensor):
+        assert (logits.shape[0] == grammar_bitmask.shape[0])
+        logits_cloned = logits.clone()
+        for i in range(logits.shape[0]):
+            unpacked_bitmask = (torch.bitwise_right_shift(
+                grammar_bitmask[i][:, None], arange[None, :]) & 1) == 0
+            unpacked_bitmask = unpacked_bitmask.reshape(-1)[:self.vocab_size]
+            logits_cloned[i] = logits_cloned[i].masked_fill(
+                unpacked_bitmask, -float("inf"))
+        return logits_cloned
+
+    def get_multimodal_embeddings(self, *args, **kwargs):
+        return self.model.get_multimodal_embeddings(*args, **kwargs)
+
+    def get_input_embeddings(self, *args, **kwargs):
+        return self.model.get_input_embeddings(*args, **kwargs)
+
+    def prepare_structured_decoding_input(
+        self, logits: torch.Tensor, scheduler_output: "SchedulerOutput"
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        grammar_bitmask = scheduler_output.grammar_bitmask
+        assert grammar_bitmask is not None
+        num_reqs, _ = logits.shape
+
+        # Reset pre-allocated tensors
+        self.grammar_bitmask_cpu.zero_()
+        self.require_structured_out_cpu.zero_()
+
+        # We receive the structured output bitmask from the scheduler, but the
+        # indices of the requests in the batch may not match the indices of
+        # the bitmask since the scheduler doesn't know how the tpu runner is
+        # ordering the requests in the batch. We need to match the order of
+        # bitmask with the order of requests
+        struct_out_indices: list[int] = []
+        mask_indices: list[int] = []
+        for req_id in self.input_batch.req_ids:
+            mask_index = scheduler_output.structured_output_request_ids.get(
+                req_id)
+            if mask_index is None:
+                continue
+            batch_index = self.input_batch.req_id_to_index[req_id]
+            struct_out_indices.append(batch_index)
+            mask_indices.append(mask_index)
+        self.grammar_bitmask_cpu[struct_out_indices] = torch.from_numpy(
+            grammar_bitmask[mask_indices])
+        # It's not guaranteed that all requests in this batch require
+        # structured output, so create a bool tensor to represent
+        # the requests that need structured output.
+        struct_out_indices = torch.tensor(struct_out_indices, dtype=torch.long)
+        self.require_structured_out_cpu[struct_out_indices] = True
+        return self.require_structured_out_cpu[:num_reqs].to(logits.device), \
+            self.grammar_bitmask_cpu[:num_reqs].to(logits.device), \
+            self.structured_decode_arange.to(logits.device)
+
+    def _get_mm_dummy_batch(
+        self,
+        modality: str,
+        max_items_per_batch: int,
+    ) -> BatchedTensorInputs:
+        """Dummy data for profiling and precompiling multimodal models."""
+        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
+            model_config=self.model_config,
+            seq_len=self.max_num_tokens,
+            mm_counts={modality: 1},
+        )
+        dummy_mm_data = dummy_decoder_data.multi_modal_data
+
+        # Result in the maximum GPU consumption of the model
+        dummy_mm_item = dummy_mm_data.get_item(modality=modality, item_index=0)
+
+        return next(grouped_mm_kwargs
+                    for _, _, grouped_mm_kwargs in group_mm_kwargs_by_modality(
+                        [dummy_mm_item] * max_items_per_batch,
+                        device=self.device,
+                        pin_memory=self.pin_memory,
+                    ))
+
+
+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
+
+
+def _get_padded_num_reqs_with_upper_limit(x: int, upper_limit: int) -> int:
+    res = MIN_NUM_SEQS if x <= MIN_NUM_SEQS else 1 << (x - 1).bit_length()
+    return min(res, upper_limit)
+
+
+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, 
+    ending with a number that can cover max_token_size
+
+    If padding_gap == 0 then:
+        increase 2X each time (exponential)
+    else:
+        first increase the size to twice,
+        then increase the padding size by padding_gap.
+    """
+    # assert min_token_size is power of 2
+    assert (min_token_size & (min_token_size - 1) == 0) and min_token_size > 0
+    paddings = []
+    num = min_token_size
+
+    if padding_gap == 0:
+        logger.info("Using exponential token paddings:")
+        while True:
+            logger.info("    %d", num)
+            paddings.append(num)
+            if num >= max_token_size:
+                break
+            num *= 2
+    else:
+        logger.info("Using incremental token paddings:")
+        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)
+
+    return paddings
+
+
+def _get_padded_token_len(paddings: list[int], x: int) -> int:
+    """Return the first element in paddings list greater or equal to x.
+    """
+    index = bisect.bisect_left(paddings, x)
+    assert index < len(paddings)
+    return paddings[index]
+
+
+def _make_src_and_dst_indices(
+    src_block_ids: list[int],
+    dst_block_ids: list[int],
+    src_device: Union[torch.device, str],
+    dst_device: Union[torch.device, str],
+) -> tuple[torch.Tensor, torch.Tensor]:
+    src_indices = torch.tensor(src_block_ids,
+                               device=src_device,
+                               dtype=torch.int64)
+    dst_indices = torch.tensor(dst_block_ids,
+                               device=dst_device,
+                               dtype=torch.int64)
+    return src_indices, dst_indices
+
+
+@torch.compile(backend="openxla")
+def _insert_blocks_to_tpu(
+    cpu_cache: torch.Tensor,
+    tpu_cache: torch.Tensor,
+    cpu_block_indices: torch.Tensor,
+    tpu_block_indices: torch.Tensor,
+) -> None:
+    torch.ops.xla.dynamo_set_buffer_donor_(tpu_cache, True)
+    tpu_cache[tpu_block_indices] = cpu_cache[cpu_block_indices].to(
+        tpu_cache.device)
+
+
+@torch.compile(backend="openxla")
+def _swap_out_tpu_blocks(
+    tpu_cache: torch.Tensor,
+    cpu_cache: torch.Tensor,
+    tpu_block_indices: torch.Tensor,
+    cpu_block_indices: torch.Tensor,
+) -> None:
+    """ tpu blocks to cpu blocks"""
+    torch.ops.xla.dynamo_set_buffer_donor_(tpu_cache, True)
+    cpu_cache[cpu_block_indices] = tpu_cache[tpu_block_indices].cpu()
+
+
+def copy_kv_blocks(
+    src_kv_caches: dict[str, torch.Tensor],
+    dst_kv_caches: dict[str, torch.Tensor],
+    src_block_ids: list[int],
+    dst_block_ids: list[int],
+    direction: Literal["h2d", "d2h"],
+) -> None:
+    """Copy kv blocks between different buffers."""
+    if not src_kv_caches or not dst_kv_caches or \
+       not src_block_ids or not dst_block_ids or \
+       len(src_block_ids) != len(dst_block_ids):
+        return
+
+    src_device = next(iter(src_kv_caches.values())).device
+    dst_device = next(iter(dst_kv_caches.values())).device
+
+    src_indices, dst_indices = _make_src_and_dst_indices(
+        src_block_ids=src_block_ids,
+        dst_block_ids=dst_block_ids,
+        src_device=src_device,
+        dst_device=dst_device)
+
+    _copy_fn = _insert_blocks_to_tpu if direction == "h2d" else \
+               _swap_out_tpu_blocks
+    for layer_name in src_kv_caches:
+        src_tensor = src_kv_caches[layer_name]
+        dst_tensor = dst_kv_caches[layer_name]
+        _copy_fn(src_tensor, dst_tensor, src_indices, dst_indices)
+
+
+def _get_padded_num_kv_cache_update_slices(num_tokens: int, max_num_reqs: int,
+                                           page_size: int) -> int:
+    """Calculates the padded number of KV cache update slices to avoid
+    recompilation."""
+    # 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
+    padded_num_slices = 2 * max_num_reqs + num_tokens // page_size
+    padded_num_slices = min(padded_num_slices, num_tokens)
+    return padded_num_slices
+
+
+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.
+    """
+    # 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
+    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
+
+
+def replace_set_lora(model):
+
+    def _tpu_set_lora(
+        self,
+        index: int,
+        lora_a: torch.Tensor,
+        lora_b: torch.Tensor,
+        embeddings_tensor: Optional[torch.Tensor],
+        bias: Optional[torch.Tensor] = None,
+    ):
+        # TODO: The integer index leads to a recompilation, but converting it
+        # to a tensor doesn't seem to work anymore. This might be fixed with a
+        # later release of torch_xla.
+        self._original_set_lora(index, lora_a, lora_b, embeddings_tensor, bias)
+        xm.mark_step()
+
+    def _tpu_reset_lora(self, index: int):
+        self._original_reset_lora(index)
+        xm.mark_step()
+
+    for _, module in model.named_modules():
+        if isinstance(module, BaseLayerWithLoRA):
+            module._original_set_lora = module.set_lora
+            module._original_reset_lora = module.reset_lora
+            module.set_lora = _tpu_set_lora.__get__(module, module.__class__)
+            module.reset_lora = _tpu_reset_lora.__get__(
+                module, module.__class__)

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/tpu_worker.py

@@ -0,0 +1,333 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""A TPU worker class."""
+import os
+from typing import Any, Optional
+
+import torch
+import torch.distributed
+import torch.nn as nn
+import torch_xla.core.xla_model as xm
+import torch_xla.debug.profiler as xp
+import torch_xla.runtime as xr
+
+import vllm.envs as envs
+from vllm.config import VllmConfig
+from vllm.distributed import (ensure_model_parallel_initialized,
+                              init_distributed_environment)
+from vllm.distributed.kv_transfer import (ensure_kv_transfer_initialized,
+                                          has_kv_transfer_group)
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.model_executor import set_random_seed
+from vllm.platforms import current_platform
+from vllm.tasks import SupportedTask
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, cdiv
+from vllm.v1.attention.backends.pallas import TPU_HEAD_SIZE_ALIGNMENT
+from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.kv_cache_interface import (AttentionSpec, KVCacheConfig,
+                                        KVCacheSpec)
+from vllm.v1.outputs import ModelRunnerOutput
+from vllm.v1.utils import report_usage_stats
+from vllm.v1.worker.tpu_model_runner import TPUModelRunner
+from vllm.v1.worker.utils import bind_kv_cache
+
+logger = init_logger(__name__)
+
+
+class TPUWorker:
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+        is_driver_worker: bool = False,
+    ):
+        self.is_driver_worker = is_driver_worker
+        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
+        self.use_spmd = envs.VLLM_XLA_USE_SPMD
+        self.original_parallel_config = None
+        if self.use_spmd:
+            # Under SPMD mode, distributed env is initialized as if there is
+            # only one worker/device.
+            self.original_parallel_config = self.parallel_config
+            self.parallel_config.tensor_parallel_size = 1
+            self.parallel_config.pipeline_parallel_size = 1
+            self.parallel_config.world_size = 1
+        self.scheduler_config = vllm_config.scheduler_config
+        self.device_config = vllm_config.device_config
+        self.speculative_config = vllm_config.speculative_config
+        self.observability_config = vllm_config.observability_config
+
+        self.parallel_config.rank = rank
+        self.local_rank = local_rank
+        self.rank = rank
+        self.distributed_init_method = distributed_init_method
+
+        if self.cache_config.cache_dtype == "auto":
+            self.cache_dtype = self.model_config.dtype
+        else:
+            self.cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
+                self.cache_config.cache_dtype]
+
+        if self.model_config.trust_remote_code:
+            # note: lazy import to avoid importing torch before initializing
+            from vllm.utils import init_cached_hf_modules
+            init_cached_hf_modules()
+
+        # Delay profiler initialization to the start of the profiling.
+        # This is because in vLLM V1, MP runtime is initialized before the
+        # TPU Worker is initialized. The profiler server needs to start after
+        # MP runtime is initialized.
+        self.profiler = None
+        self.profile_dir = None
+        if envs.VLLM_TORCH_PROFILER_DIR and self.rank < 1:
+            # For TPU, we can only have 1 active profiler session for 1 profiler
+            # server. So we only profile on rank0.
+            self.profile_dir = envs.VLLM_TORCH_PROFILER_DIR
+            logger.info("Profiling enabled. Traces will be saved to: %s",
+                        self.profile_dir)
+
+        if self.model_config.seed is None:
+            self.model_config.seed = 0
+
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        self.cache_config.num_gpu_blocks = num_gpu_blocks
+        self.cache_config.num_cpu_blocks = num_cpu_blocks
+
+    def init_device(self):
+        os.environ["PJRT_DEVICE"] = "TPU"
+        # Note: Currently the XLA compiler wrongly uses 2D ring strategy on 1D
+        # ring, the xla tpu compiler flag
+        # `xla_tpu_force_1d_allreduce_at_chunk_count` is a temporary solution to
+        # fix this. It will be removed after the bug in XLA compiler is fixed.
+        os.environ["LIBTPU_INIT_ARGS"] = (
+            os.environ.get("LIBTPU_INIT_ARGS", "") +
+            " --xla_tpu_force_1d_allreduce_at_chunk_count=1"
+            " --xla_jf_conv_input_fusion=False")
+        # --xla_jf_conv_input_fusion=False is used to improve the perf of
+        # quantized matmul.
+        torch.set_grad_enabled(False)
+        torch.set_default_dtype(self.model_config.dtype)
+
+        # Initialize the distributed environment.
+        self._init_tpu_worker_distributed_environment(
+            self.vllm_config, self.rank, self.distributed_init_method,
+            self.local_rank)
+
+        # Device initialization should happen after initializing
+        # the distributed runtime.
+        self.device = xm.xla_device()
+        self.device_config.device = self.device
+
+        # Set random seed.
+        set_random_seed(self.model_config.seed)
+        if self.model_config.seed is not None:
+            xm.set_rng_state(self.model_config.seed, self.device)
+
+        # Increase the cache size limit, which is the maximum number of
+        # dynamo graphs that can be compiled.
+        # TODO (NickLucche) On gsm we compile 80+ graphs.
+        # Re-evaluate limit, with MM we may get close to this limit.
+        torch._dynamo.config.cache_size_limit = 128
+        # Use persistent cache to avoid XLA recompilation.
+        # NOTE(woosuk): Set per-rank cache path since different ranks
+        # can have slightly different XLA graphs.
+        world_size = self.parallel_config.world_size
+        rank = xr.global_ordinal()
+        # The PyTorch/XLA compilation cache uses the Torch IR to generate keys.
+        # Consequently, changes in optimization flags, which affect compilation
+        # results, don't change the cache key. This can result in the wrong
+        # compilation being used. To prevent this, disabling the XLA compilation
+        # cache during development is recommended.We can disable it by
+        # `export VLLM_XLA_CACHE_PATH=`
+        if envs.VLLM_XLA_CACHE_PATH:
+            per_rank_path = os.path.join(envs.VLLM_XLA_CACHE_PATH,
+                                         f"tp{world_size}_rank{rank}")
+            xr.initialize_cache(per_rank_path, readonly=False)
+
+        # Init ModelRunner here, so that we have access to self.device.
+        self.model_runner = \
+            TPUModelRunner(self.vllm_config, self.device,
+                           self.original_parallel_config)
+
+        if rank == 0:
+            # If usage stat is enabled, collect relevant info.
+            report_usage_stats(self.vllm_config)
+
+    def determine_available_memory(self) -> int:
+        kv_caches: dict[str, torch.Tensor] = {}
+        kv_cache_spec = self.model_runner.get_kv_cache_spec()
+        for layer_name, layer_spec in kv_cache_spec.items():
+            if isinstance(layer_spec, AttentionSpec):
+                dtype = layer_spec.dtype
+
+                # Use an empty tensor instead of `None`` to force Dynamo to pass
+                # it by reference, rather by specializing on the value ``None``.
+                tpu_kv_cache = torch.tensor([], dtype=dtype).to(self.device)
+                kv_caches[layer_name] = tpu_kv_cache
+            else:
+                raise NotImplementedError(
+                    f"Unsupported KV cache spec '{type(layer_spec)}'")
+
+        runner_kv_caches: list[torch.Tensor] = []
+        bind_kv_cache(
+            kv_caches,
+            self.vllm_config.compilation_config.static_forward_context,
+            runner_kv_caches)
+
+        # `max_num_tokens >= max_num_batched_tokens` due to padding.
+        with self.model_runner.maybe_setup_dummy_loras(self.lora_config):
+            self.model_runner.profile_run(self.model_runner.max_num_tokens)
+
+        # Synchronize before measuring the memory usage.
+        xm.wait_device_ops()
+
+        # During the profiling run, the model runs without KV cache. After
+        # the profiling run, the model always runs with KV cache. Here we clear
+        # the dynamo cache and cached bytecode to ensure the model always has
+        # one compiled bytecode. Having one FX graph/cached bytecode per
+        # compiled model is required for `support_torch_compile` decorator to
+        # skip dynamo guard.
+        self.model_runner.reset_dynamo_cache()
+
+        # Get the maximum amount of memory used by the model weights and
+        # intermediate activations.
+        if self.use_spmd:
+            # This is a workaround for the TPU SPMD mode. The get_memory_info
+            # API doesn't work with SPMD mode in PyTorch/XLA.
+            # TODO: use xm.get_memory_info for SPMD once it's supported in
+            # PyTorch/XLA.
+            import tpu_info
+            chip_type, _ = tpu_info.device.get_local_chips()
+            device_usage = tpu_info.metrics.get_chip_usage(chip_type)
+            total_memory_size = device_usage[0].total_memory
+            current_mem = device_usage[0].memory_usage
+        else:
+            m = xm.get_memory_info(self.device)
+            total_memory_size = m["bytes_limit"]
+            current_mem = m["bytes_used"]
+        # Ideally we would use profiled = m["peak_bytes_used"] to
+        # get weights + activations. But there is memory used during
+        # compilation / weight loading that impacts the peak and
+        # there is no way to reset peak memory in XLA, So we
+        # use the heuristic of 2% of weights.
+        profiled = current_mem * 1.02
+
+        # Calculate the TPU KV cache size based on profiling.
+        usable_memory_size = int(total_memory_size *
+                                 self.cache_config.gpu_memory_utilization)
+        tpu_kv_cache_bytes = max(usable_memory_size - profiled, 0)
+        head_size = self.model_config.get_head_size()
+        if head_size > 0:
+            padded_head_size = cdiv(
+                head_size, TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT
+            if padded_head_size != head_size:
+                logger.warning_once("head size is padded to %d",
+                                    padded_head_size)
+            # We adjust the usable memory size for the KV cache to prevent OOM
+            # errors, even after padding the head_size.
+            tpu_kv_cache_bytes = (tpu_kv_cache_bytes * head_size //
+                                  padded_head_size)
+        return int(tpu_kv_cache_bytes)
+
+    def execute_model(
+        self,
+        scheduler_output: "SchedulerOutput",
+    ) -> Optional[ModelRunnerOutput]:
+        output = self.model_runner.execute_model(scheduler_output)
+        # every worker's output is needed when kv_transfer_group is setup
+        return output if self.is_driver_worker or has_kv_transfer_group(
+        ) else None
+
+    def profile(self, is_start: bool = True):
+        if self.rank < 1:
+            if self.profile_dir is None:
+                raise RuntimeError("Profiler is not enabled.")
+            if is_start:
+                if self.profiler is None:
+                    self.profiler = xp.start_server(9012)
+                xp.start_trace(self.profile_dir)
+            else:
+                xp.stop_trace()
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        return self.model_runner.add_lora(lora_request)
+
+    def load_model(self) -> None:
+        self.model_runner.load_model()
+
+    def update_config(self, overrides: dict[str, Any]) -> None:
+        self.model_runner.update_config(overrides)
+
+    def reload_weights(self) -> None:
+        self.model_runner.reload_weights()
+
+    def compile_or_warm_up_model(self) -> None:
+        if not self.model_config.enforce_eager:
+            self.model_runner.capture_model()
+
+        # Reset the seed to ensure that the random state is not affected by
+        # the model initialization and profiling.
+        set_random_seed(self.model_config.seed)
+
+    def get_model(self) -> nn.Module:
+        return self.model_runner.get_model()
+
+    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
+        return self.model_runner.get_supported_tasks()
+
+    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
+        return self.model_runner.get_kv_cache_spec()
+
+    def initialize_from_config(self, kv_cache_config: KVCacheConfig) -> None:
+        """Allocate GPU KV cache with the specified kv_cache_config."""
+        self.model_runner.initialize_kv_cache(kv_cache_config)
+
+    def check_health(self) -> None:
+        # worker will always be healthy as long as it's running.
+        return
+
+    def _init_tpu_worker_distributed_environment(
+        self,
+        vllm_config: VllmConfig,
+        rank: int,
+        distributed_init_method: Optional[str] = None,
+        local_rank: int = -1,
+    ) -> None:
+        """Initialize the distributed environment."""
+        if self.use_spmd:
+            xr.use_spmd()
+        # NOTE(woosuk): This is just to initialize the TP group and broadcast
+        # the input objects on CPU. The all-reduce and all-gather ops on TPU
+        # are invoked by `xm.all_reduce` and `xm.all_gather` which use their
+        # own context.
+        parallel_config = vllm_config.parallel_config
+        init_distributed_environment(
+            world_size=parallel_config.world_size,
+            rank=rank,
+            local_rank=local_rank,
+            distributed_init_method=distributed_init_method,
+            backend=current_platform.dist_backend,
+        )
+        ensure_model_parallel_initialized(
+            parallel_config.tensor_parallel_size,
+            parallel_config.pipeline_parallel_size)
+
+        ensure_kv_transfer_initialized(vllm_config)
+
+
+try:
+    from tpu_commons.worker import TPUWorker as TPUCommonsWorker
+    TPUWorker = TPUCommonsWorker  # type: ignore
+except ImportError:
+    logger.info("tpu_commons not found, using vLLM's TPUWorker.")
+    pass

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/utils.py

@@ -0,0 +1,300 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Optional
+
+import torch
+
+from vllm.attention.backends.abstract import AttentionBackend
+from vllm.config import ModelConfig, SchedulerConfig
+from vllm.model_executor.models.interfaces import MultiModalEmbeddings
+from vllm.model_executor.models.utils import extract_layer_index
+from vllm.multimodal.registry import MultiModalRegistry
+from vllm.v1.attention.backends.utils import AttentionMetadataBuilder
+from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
+from vllm.v1.kv_cache_interface import KVCacheGroupSpec
+
+if TYPE_CHECKING:
+    from vllm.attention.layer import Attention
+
+
+class MultiModalBudget:
+    """Helper class to calculate budget information for multi-modal models."""
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        scheduler_config: SchedulerConfig,
+        mm_registry: MultiModalRegistry,
+        *,
+        max_model_len: int,
+        max_num_reqs: int,
+    ) -> None:
+        super().__init__()
+
+        self.model_config = model_config
+        self.scheduler_config = scheduler_config
+        self.mm_registry = mm_registry
+
+        encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
+            model_config=model_config,
+            scheduler_config=scheduler_config,
+            mm_registry=mm_registry,
+        )
+
+        self.max_num_encoder_input_tokens = encoder_compute_budget
+        self.encoder_cache_size = encoder_cache_size
+        self.max_model_len = max_model_len
+        self.max_num_reqs = max_num_reqs
+
+        self.mm_limits = mm_registry.get_mm_limits_per_prompt(model_config)
+
+        max_items_per_prompt_by_modality = dict[str, int]()
+        max_items_per_batch_by_modality = dict[str, int]()
+
+        max_tokens_by_modality = mm_registry \
+            .get_max_tokens_per_item_by_nonzero_modality(model_config)
+
+        for modality, max_tokens in max_tokens_by_modality.items():
+            (
+                max_items_per_prompt,
+                max_items_per_batch,
+            ) = self.get_max_items(modality, max_tokens)
+
+            max_items_per_prompt_by_modality[modality] = max_items_per_prompt
+            max_items_per_batch_by_modality[modality] = max_items_per_batch
+
+        self.max_tokens_by_modality = max_tokens_by_modality
+        self.max_items_per_prompt_by_modality = max_items_per_prompt_by_modality
+        self.max_items_per_batch_by_modality = max_items_per_batch_by_modality
+
+    def get_modality_with_max_tokens(self) -> tuple[str, int]:
+        max_tokens_by_modality = self.max_tokens_by_modality
+        modality, max_tokens = max(max_tokens_by_modality.items(),
+                                   key=lambda item: item[1])
+
+        return modality, max_tokens
+
+    def get_encoder_budget(self) -> int:
+        return min(self.max_num_encoder_input_tokens, self.encoder_cache_size)
+
+    def get_max_items(
+        self,
+        modality: str,
+        max_tokens_per_item: int,
+    ) -> tuple[int, int]:
+        if max_tokens_per_item == 0:
+            return 0, 0
+
+        # Check how many items of this modality can be supported by
+        # the encoder budget.
+        encoder_budget = self.get_encoder_budget()
+
+        # TODO: handle encoder-decoder models once we support them.
+        if encoder_budget == 0:
+            return 0, 0
+
+        max_encoder_items_per_batch = encoder_budget // max_tokens_per_item
+
+        # Check how many items of this modality can be supported by
+        # the decoder budget.
+        mm_limit = self.mm_limits[modality]
+
+        max_items_per_prompt = max(
+            1,
+            min(mm_limit, self.max_model_len // max_tokens_per_item),
+        )
+
+        scheduler_config = self.scheduler_config
+        max_num_reqs = self.max_num_reqs
+
+        if not scheduler_config.enable_chunked_prefill:
+            max_num_reqs = min(
+                max_num_reqs,
+                scheduler_config.max_num_batched_tokens // max_tokens_per_item,
+            )
+
+        max_decoder_items_per_batch = max_num_reqs * max_items_per_prompt
+
+        max_items_per_batch = max(
+            1,
+            min(max_encoder_items_per_batch, max_decoder_items_per_batch),
+        )
+
+        return max_items_per_prompt, max_items_per_batch
+
+
+@dataclass
+class AttentionGroup:
+    backend: type[AttentionBackend]
+    metadata_builder: AttentionMetadataBuilder
+    layer_names: list[str]
+
+
+def sanity_check_mm_encoder_outputs(
+    mm_embeddings: MultiModalEmbeddings,
+    expected_num_items: int,
+) -> None:
+    """
+    Perform sanity checks for the result of
+    [`vllm.model_executor.models.SupportsMultiModal.get_multimodal_embeddings`][].
+    """
+    assert isinstance(mm_embeddings, (list, tuple, torch.Tensor)), (
+        "Expected multimodal embeddings to be a list/tuple of 2D tensors, "
+        f"or a single 3D tensor, but got {type(mm_embeddings)} "
+        "instead. This is most likely due to incorrect implementation "
+        "of the model's `get_multimodal_embeddings` method.")
+
+    assert len(mm_embeddings) == expected_num_items, (
+        "Expected number of multimodal embeddings to match number of "
+        f"input items: {expected_num_items}, but got {len(mm_embeddings)=} "
+        "instead. This is most likely due to incorrect implementation "
+        "of the model's `get_multimodal_embeddings` method.")
+
+    assert all(e.ndim == 2 for e in mm_embeddings), (
+        "Expected multimodal embeddings to be a sequence of 2D tensors, "
+        f"but got tensors with shapes {[e.shape for e in mm_embeddings]} "
+        "instead. This is most likely due to incorrect implementation "
+        "of the model's `get_multimodal_embeddings` method.")
+
+
+def scatter_mm_placeholders(
+    embeds: torch.Tensor,
+    is_embed: Optional[torch.Tensor],
+) -> torch.Tensor:
+    """
+    Scatter the multimodal embeddings into a contiguous tensor that represents
+    the placeholder tokens.
+
+    [`vllm.multimodal.processing.PromptUpdateDetails.is_embed`][].
+
+    Args:
+        embeds: The multimodal embeddings.
+          Shape: `(num_embeds, embed_dim)`
+        is_embed: A boolean mask indicating which positions in the placeholder
+          tokens need to be filled with multimodal embeddings.
+          Shape: `(num_placeholders, num_embeds)`
+    """
+    if is_embed is None:
+        return embeds
+
+    placeholders = embeds.new_full(
+        (is_embed.shape[0], embeds.shape[-1]),
+        fill_value=torch.nan,
+    )
+    placeholders[is_embed] = embeds
+    return placeholders
+
+
+def gather_mm_placeholders(
+    placeholders: torch.Tensor,
+    is_embed: Optional[torch.Tensor],
+) -> torch.Tensor:
+    """
+    Reconstructs the embeddings from the placeholder tokens.
+
+    This is the operation of [scatter_mm_placeholders][].
+    """
+    if is_embed is None:
+        return placeholders
+
+    return placeholders[is_embed]
+
+
+def initialize_kv_cache_for_kv_sharing(
+    shared_kv_cache_layers: dict[str, str],
+    kv_cache_groups: list[KVCacheGroupSpec],
+    kv_caches: dict[str, torch.Tensor],
+    # Optional for now to avoid breaking TPU
+    attn_groups: Optional[list[list[AttentionGroup]]] = None,
+) -> None:
+    """
+    Sets up KV cache sharing by reusing the allocated KV caches in `kv_caches`
+    for layers that do not allocate its own KV cache, based on the mapping in
+    `shared_kv_cache_layers`. Adds these layers to the corresponding KV cache
+    group, which is needed to ensure that attention metadata is assigned later.
+
+    Args:
+        shared_kv_cache_layers: 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]`.
+        kv_cache_groups: The KV cache groups of the model.
+        kv_caches: The allocated kv_caches with layer names as keys.
+            Note that layers in shared_kv_cache_layers.keys() are not
+            originally included as it only contains layers which have its own
+            KV cache allocation.
+        attn_groups: Optional list of attention groups. Layers in the same KV
+            cache group may be placed in different attention groups if they
+            have different attention backends.  Currently only provided by 
+            GPU model runner.
+    """
+    # mapping from layer name to tuple of (kv_cache_group_idx, attn_group_idx)
+    layer_to_attn_group_idx: dict[str, tuple[int, int]] = {}
+    if attn_groups:
+        for kv_cache_group_idx, kv_attn_groups in enumerate(attn_groups):
+            for attn_group_idx, attn_group in enumerate(kv_attn_groups):
+                for layer_name in attn_group.layer_names:
+                    layer_to_attn_group_idx[layer_name] = (kv_cache_group_idx,
+                                                           attn_group_idx)
+    else:
+        for kv_cache_group_idx, kv_cache_group in enumerate(kv_cache_groups):
+            for layer_name in kv_cache_group.layer_names:
+                # attn group idx default to 0 if not provided
+                layer_to_attn_group_idx[layer_name] = (kv_cache_group_idx, 0)
+
+    for layer_name, target_layer_name in shared_kv_cache_layers.items():
+        kv_caches[layer_name] = kv_caches[target_layer_name]
+        kv_cache_group_idx = layer_to_attn_group_idx[target_layer_name][0]
+        kv_cache_groups[kv_cache_group_idx].layer_names.append(layer_name)
+
+        if attn_groups:
+            attn_group_idx = layer_to_attn_group_idx[target_layer_name][1]
+            attn_groups[kv_cache_group_idx][attn_group_idx].layer_names.append(
+                layer_name)
+
+
+def bind_kv_cache(
+    kv_caches: dict[str, torch.Tensor],
+    forward_context: dict[str, "Attention"],
+    runner_kv_caches: list[torch.Tensor],
+) -> None:
+    """
+    Bind the allocated KV cache to both ModelRunner and forward context so
+    that the KV cache can be used in the forward pass.
+
+    This function:
+      1) Fills the ModelRunner's kv cache list (`runner_kv_caches`) with
+         kv_caches.
+      2) Associates each attention layer in the `forward_context` with its
+         corresponding KV cache in kv_caches.
+
+    Args:
+        kv_caches: The allocated kv_caches with layer names as keys.
+        forward_context: The global forward context containing all Attention
+        layers with layer names as keys.
+        runner_kv_caches: The kv_cache declared by ModelRunner.
+    """
+    # Bind kv_caches to ModelRunner
+    assert len(runner_kv_caches) == 0
+
+    # Convert kv_caches dict to a list of tensors in the order of layer_index.
+    index2name = defaultdict(list)
+    for layer_name in kv_caches:
+        index2name[extract_layer_index(layer_name)].append(layer_name)
+
+    for layer_index in sorted(index2name.keys()):
+        layer_names = index2name[layer_index]
+        if len(layer_names) > 1:
+            # One typical case is encoder-decoder model, e.g., bart.
+            # The cross attention and self attention in the same decoder layer
+            # has different layer_name but the same layer_index.
+            raise NotImplementedError
+        layer_name = layer_names[0]
+        runner_kv_caches.append(kv_caches[layer_name])
+
+    # Bind kv_caches to forward context
+    for layer_name, kv_cache in kv_caches.items():
+        # NOTE: Use list because of v0 PP virtual engine.
+        forward_context[layer_name].kv_cache = [kv_cache]

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/worker_base.py

@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+import torch
+import torch.nn as nn
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.v1.kv_cache_interface import KVCacheSpec
+from vllm.worker.worker_base import WorkerBase as WorkerBaseV0
+
+logger = init_logger(__name__)
+
+
+class WorkerBase(WorkerBaseV0):
+    """
+    Abstract class for v1 worker, mainly define some methods for v1.
+    For methods shared by v0 and v1, define them in v0 WorkerBase
+    """
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+        is_driver_worker: bool = False,
+    ):
+        """
+        Initialize common worker components.
+        
+        Args:
+            vllm_config: Complete vLLM configuration
+            local_rank: Local device index
+            rank: Global rank in distributed setup
+            distributed_init_method: Distributed initialization method
+            is_driver_worker: Whether this worker handles driver 
+            responsibilities
+        """
+        # Configuration storage
+        super().__init__(vllm_config=vllm_config)
+
+        self.parallel_config.rank = rank
+        self.local_rank = local_rank
+        self.rank = rank
+        self.distributed_init_method = distributed_init_method
+        self.is_driver_worker = is_driver_worker
+
+        # Device and model state
+        self.device: Optional[torch.device] = None
+        self.model_runner: Optional[nn.Module] = None
+
+    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
+        """Get specifications for KV cache implementation."""
+        raise NotImplementedError
+
+    def compile_or_warm_up_model(self) -> None:
+        """Prepare model for execution through compilation/warmup."""
+        raise NotImplementedError
+
+    def check_health(self) -> None:
+        """Basic health check (override for device-specific checks)."""
+        return

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/xpu_model_runner.py

@@ -0,0 +1,33 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import TYPE_CHECKING
+
+import torch
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.v1.worker.gpu_model_runner import GPUModelRunner
+
+if TYPE_CHECKING:
+    pass
+
+logger = init_logger(__name__)
+
+
+class XPUModelRunner(GPUModelRunner):
+    """A model runner for XPU devices."""
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        device: torch.device,
+    ):
+        super().__init__(vllm_config, device)
+        # FIXME: To be verified.
+        self.cascade_attn_enabled = False
+
+    def _init_device_properties(self) -> None:
+        self.num_sms = None
+
+    def _sync_device(self) -> None:
+        torch.xpu.synchronize()

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/worker/xpu_worker.py

@@ -0,0 +1,178 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import os
+
+import torch
+import torch.distributed
+
+import vllm.envs as envs
+from vllm.config import VllmConfig
+from vllm.distributed import get_world_group
+from vllm.logger import init_logger
+from vllm.model_executor import set_random_seed
+from vllm.platforms import current_platform
+from vllm.v1.worker.gpu_worker import (Worker,
+                                       init_worker_distributed_environment)
+from vllm.v1.worker.xpu_model_runner import XPUModelRunner
+
+logger = init_logger(__name__)
+
+
+class XPUWorker(Worker):
+    """A XPU worker class."""
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+        is_driver_worker: bool = False,
+    ):
+        super().__init__(vllm_config, local_rank, rank,
+                         distributed_init_method, is_driver_worker)
+        device_config = self.device_config
+        assert device_config.device_type == "xpu"
+        assert current_platform.is_xpu()
+
+        # Torch profiler. Enabled and configured through env vars:
+        # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
+        if envs.VLLM_TORCH_PROFILER_DIR:
+            torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
+            logger.info("Profiling enabled. Traces will be saved to: %s",
+                        torch_profiler_trace_dir)
+            logger.debug(
+                "Profiler config: record_shapes=%s,"
+                "profile_memory=%s,with_stack=%s,with_flops=%s",
+                envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
+                envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
+                envs.VLLM_TORCH_PROFILER_WITH_STACK,
+                envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
+            )
+            self.profiler = torch.profiler.profile(
+                activities=[
+                    torch.profiler.ProfilerActivity.CPU,
+                    torch.profiler.ProfilerActivity.XPU,
+                ],
+                record_shapes=envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
+                profile_memory=envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
+                with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
+                with_flops=envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
+                on_trace_ready=torch.profiler.tensorboard_trace_handler(
+                    torch_profiler_trace_dir, use_gzip=True))
+        else:
+            self.profiler = None
+
+    # we provide this function due to `torch.xpu.mem_get_info()` doesn't
+    # return correct free_gpu_memory on intel client GPU. We need to
+    # calculate/estiamte it.
+    def xpu_get_mem_info(self):
+        if current_platform.is_data_center_gpu():
+            return torch.xpu.mem_get_info()
+        else:
+            _, total_gpu_memory = torch.xpu.mem_get_info()
+            # FIXME: memory_allocated() doesn't count non-torch allocations,
+            # and we don't have any API to get it. so we mark it as 128MB.
+            used_memory = torch.xpu.memory_allocated()
+            non_torch_allocations = 128 * 1024 * 1024
+            free_gpu_memory = total_gpu_memory - (used_memory +
+                                                  non_torch_allocations)
+            return free_gpu_memory, total_gpu_memory
+
+    @torch.inference_mode()
+    def determine_available_memory(self) -> int:
+        """Profiles the peak memory usage of the model to determine how many
+        KV blocks may be allocated without OOMs.
+        The engine will first conduct a profiling of the existing memory usage.
+        Then, it calculate the maximum possible number of GPU and CPU blocks
+        that can be allocated with the remaining free memory.
+        .. tip::
+            You may limit the usage of GPU memory
+            by adjusting the `gpu_memory_utilization` parameter.
+        """
+        # Profile the memory usage of the model and get the maximum number of
+        # cache blocks that can be allocated with the remaining free memory.
+        torch.xpu.empty_cache()
+        torch.xpu.reset_peak_memory_stats()
+
+        free_gpu_memory, total_gpu_memory = torch.xpu.mem_get_info()
+        current_allocated_bytes = torch.xpu.memory_allocated()
+        msg = ("Before memory profiling run, "
+               f"total GPU memory: {total_gpu_memory / 1024**2:.2f} MB, "
+               f"model load takes {current_allocated_bytes / 1024**2:.2f} MB, "
+               f"free gpu memory is {free_gpu_memory / 1024**2:.2f} MB.")
+        logger.info(msg)
+        # Execute a forward pass with dummy inputs to profile the memory usage
+        # of the model.
+        self.model_runner.profile_run()
+
+        free_gpu_memory, _ = self.xpu_get_mem_info()
+        # NOTE(woosuk): Here we assume that the other processes using the same
+        # GPU did not change their memory usage during the profiling.
+        assert self.init_gpu_memory > free_gpu_memory, (
+            "Error in memory profiling. "
+            f"Initial free memory {self.init_gpu_memory}, current free memory"
+            f" {free_gpu_memory}. This happens when the GPU memory was "
+            "not properly cleaned up before initializing the vLLM instance.")
+
+        # Get the peak memory allocation recorded by torch
+        peak_memory = torch.xpu.memory_stats()["allocated_bytes.all.peak"]
+
+        torch.xpu.empty_cache()
+        torch_allocated_bytes = torch.xpu.memory_stats(
+        )["allocated_bytes.all.current"]
+        total_allocated_bytes = self.xpu_get_mem_info(
+        )[1] - self.xpu_get_mem_info()[0]
+
+        non_torch_allocations = total_allocated_bytes - torch_allocated_bytes
+        if non_torch_allocations > 0:
+            peak_memory += non_torch_allocations
+        available_kv_cache_memory = (
+            total_gpu_memory * self.cache_config.gpu_memory_utilization -
+            peak_memory)
+
+        msg = ("After memory profiling run, "
+               f"peak memory usage is {peak_memory / 1024**2:.2f} MB,"
+               f"torch mem is {torch_allocated_bytes / 1024**2:.2f} MB, "
+               f"non-torch mem is {non_torch_allocations / 1024**2:.2f} MB, "
+               f"free gpu memory is {free_gpu_memory / 1024**2:.2f} MB.")
+        logger.info(msg)
+
+        return int(available_kv_cache_memory)
+
+    def init_device(self):
+        if self.device_config.device.type == "xpu" and current_platform.is_xpu(
+        ):
+            self.device = torch.device(f"xpu:{self.local_rank}")
+            current_platform.set_device(self.device)
+            torch.xpu.empty_cache()
+            self.init_gpu_memory = torch.xpu.get_device_properties(
+                self.local_rank).total_memory
+        else:
+            raise RuntimeError(
+                f"Not support device type: {self.device_config.device}")
+
+        ENV_CCL_ZE_IPC_EXCHANGE = os.getenv("CCL_ZE_IPC_EXCHANGE", "pidfd")
+        ENV_CCL_ATL_TRANSPORT = os.getenv("CCL_ATL_TRANSPORT", "ofi")
+        ENV_LOCAL_WORLD_SIZE = os.getenv("LOCAL_WORLD_SIZE",
+                                         str(self.parallel_config.world_size))
+        os.environ["CCL_ZE_IPC_EXCHANGE"] = ENV_CCL_ZE_IPC_EXCHANGE
+        os.environ["CCL_ATL_TRANSPORT"] = ENV_CCL_ATL_TRANSPORT
+        os.environ["LOCAL_WORLD_SIZE"] = ENV_LOCAL_WORLD_SIZE
+        os.environ["LOCAL_RANK"] = str(self.local_rank)
+
+        init_worker_distributed_environment(self.vllm_config, self.rank,
+                                            self.distributed_init_method,
+                                            self.local_rank,
+                                            current_platform.dist_backend)
+
+        # global all_reduce needed for overall oneccl warm up
+        torch.distributed.all_reduce(torch.zeros(1).xpu(),
+                                     group=get_world_group().device_group)
+
+        # Set random seed.
+        set_random_seed(self.model_config.seed)
+
+        # Construct the model runner
+        self.model_runner = XPUModelRunner(  # type: ignore
+            self.vllm_config, self.device)