# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Model runner for instances that only run the multi-modal encoder.

An encoder-only instance -- `--mm-encoder-only`, or the producer side of
encoder-cache disaggregation -- encodes the multi-modal items and publishes the
embeddings. It runs no language model: no KV cache, sampler, or decoder graphs.
"""

import time
from collections.abc import Iterator
from contextlib import contextmanager
from typing import TYPE_CHECKING, Any

import torch

from vllm.config import VllmConfig
from vllm.config.compilation import CUDAGraphMode
from vllm.logger import init_logger
from vllm.sequence import IntermediateTensors
from vllm.utils.gc_utils import freeze_gc_for_cudagraph_capture
from vllm.v1.kv_cache_interface import KVCacheSpec
from vllm.v1.outputs import (
    ModelRunnerOutput,
    make_empty_encoder_model_runner_output,
)
from vllm.v1.worker.gpu.cudagraph_utils import BatchExecutionDescriptor
from vllm.v1.worker.gpu.mm.lora import set_active_mm_loras
from vllm.v1.worker.gpu.model_runner import GPUModelRunner
from vllm.v1.worker.workspace import lock_workspace

if TYPE_CHECKING:
    from vllm.v1.core.sched.output import SchedulerOutput

logger = init_logger(__name__)


class MMEncoderModelRunner(GPUModelRunner):
    """Encoder-only variant of the V2 GPU model runner."""

    def __init__(self, vllm_config: VllmConfig, device: torch.device):
        super().__init__(vllm_config, device)
        assert self.supports_mm_inputs, (
            "An encoder-only instance must serve a multi-modal model."
        )
        assert self.dp_size == 1, "An encoder-only instance does not support DP."

        depth = vllm_config.max_concurrent_batches
        # Blocking (sleep) events: busy-polling the driver lock can make this
        # rank a straggler under contention.
        self._input_events = [torch.Event(blocking=True) for _ in range(depth)]
        self._input_event_idx = 0

    @contextmanager
    def input_tensor_semaphore(self) -> Iterator[None]:
        """Guard the host writes into the pooled input buffers.

        `UvaBufferPool` recycles a slot every `max_concurrent_batches` steps and
        the device reads the pooled host buffers in place. A sampling step is
        ordered by the device wait in `AsyncOutput.get_output()`; this one never
        waits, so entering blocks until the step that last wrote these slots is
        done with them and leaving records this step's last write.
        """
        idx = self._input_event_idx
        input_event = self._input_events[idx]
        input_event.synchronize()
        try:
            yield
        finally:
            input_event.record()
            self._input_event_idx = (idx + 1) % len(self._input_events)

    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
        return {}

    @torch.inference_mode()
    def capture_model(self, *, profile_only: bool = False) -> int:
        encoder_runner = self.model_state.encoder_runner
        if not encoder_runner.has_cudagraph():
            return 0

        start_time = time.perf_counter()
        with freeze_gc_for_cudagraph_capture():
            torch.accelerator.empty_cache()
            start_free_memory = torch.accelerator.get_memory_info()[0]
            encoder_runner.capture()
            graph_memory = start_free_memory - torch.accelerator.get_memory_info()[0]

        if not profile_only:
            lock_workspace()

        manager = encoder_runner.cudagraph_manager
        assert manager is not None
        logger.info(
            "Encoder graph capturing finished in %.0f secs, took %.2f GiB (%d graphs)",
            time.perf_counter() - start_time,
            graph_memory / (1 << 30),
            manager.get_num_graphs_to_capture(),
        )
        return graph_memory

    def needs_cudagraph_capture(self) -> bool:
        return False

    def _dummy_run(
        self, *args: Any, **kwargs: Any
    ) -> tuple[torch.Tensor, torch.Tensor]:
        empty = torch.empty(0, device=self.device)
        return empty, empty

    def _dummy_sampler_run(self, hidden_states: torch.Tensor) -> None:
        return

    def _dummy_pooler_run(self, hidden_states: torch.Tensor) -> None:
        return

    def _no_forward(self, scheduler_output: "SchedulerOutput") -> ModelRunnerOutput:
        return self._merge_ec_connector_no_forward(
            scheduler_output, self.kv_connector.no_forward(scheduler_output)
        )

    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
        intermediate_tensors: IntermediateTensors | None = None,
        dummy_run: bool = False,
        skip_attn_for_dummy_run: bool = False,
        is_profile: bool = False,
        context_len: int = 0,
    ) -> ModelRunnerOutput:
        assert not dummy_run, "An encoder-only instance runs no dummy batch."

        with self.input_tensor_semaphore():
            self.update_pp_decode_requests()
            self.finish_requests(scheduler_output)
            self.free_states(scheduler_output)
            self.add_requests(scheduler_output)
            self.update_requests(scheduler_output)
            self.block_tables.apply_staged_writes()
            if scheduler_output.total_num_scheduled_tokens == 0:
                return self._no_forward(scheduler_output)

            batch_req_state, _ = self.gather_batch_req_state(scheduler_output, False)
            assert batch_req_state is not None
            # No decoder graph or DP padding; encoder graphs run independently.
            batch_desc = BatchExecutionDescriptor(
                cg_mode=CUDAGraphMode.NONE,
                num_tokens=batch_req_state.num_tokens,
                num_reqs=None,
            )
            self.prepare_inputs(
                scheduler_output, batch_req_state, batch_desc, num_active_loras=0
            )

        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if self.lora_config is not None:
            set_active_mm_loras(
                model=self.model,
                lora_manager=self.lora_manager,
                encoder_cache=self.encoder_cache,
                req_id_to_index=self.req_states.req_id_to_index,
                lora_state=self.lora_state,
                scheduled_encoder_inputs=scheduled_encoder_inputs,
            )

        with self.ec_connector.maybe_get_output(
            scheduler_output
        ) as ec_connector_output:
            self.model_state.execute_mm_encoder(scheduled_encoder_inputs)

        return ModelRunnerOutput.with_ec_conn_output(
            make_empty_encoder_model_runner_output(scheduler_output),
            ec_connector_output,
        )