import dataclasses
import functools
from dataclasses import dataclass, field
from typing import (TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple,
                    Union)

import torch

from aphrodite.common.sequence import (CompletionSequenceGroupOutput,
                                       IntermediateTensors, Logprob,
                                       SequenceGroupMetadata, SequenceOutput)
from aphrodite.common.utils import PyObjectCache, async_tensor_h2d
from aphrodite.distributed import get_pp_group
from aphrodite.modeling.layers.sampler import (PromptLogprobs, SampleLogprobs,
                                               SamplerOutput, SamplingMetadata,
                                               get_logprobs,
                                               get_pythonized_sample_results)
from aphrodite.modeling.model_loader.tensorizer import TensorizerConfig
from aphrodite.worker.model_runner import (
    GPUModelRunnerBase, ModelInputForGPUWithSamplingMetadata)
from aphrodite.worker.model_runner_base import (
    BroadcastableModelInput, _init_attn_metadata_from_tensor_dict,
    _init_frozen_model_input_from_tensor_dict,
    _init_sampling_metadata_from_tensor_dict)

if TYPE_CHECKING:
    from aphrodite.attention.backends.abstract import AttentionBackend


MULTI_STEP_ATTENTION_BACKENDS = ["flash-attn", "flashinfer", "rocm-flash-attn"]
MULTI_STEP_CHUNKED_PREFILL_ATTENTION_BACKENDS = ["flash-attn"]
def _get_supported_attention_backends(chunked_prefill_enabled: bool) \
    -> List[str]:
    if chunked_prefill_enabled:
        return MULTI_STEP_CHUNKED_PREFILL_ATTENTION_BACKENDS
    else:
        return MULTI_STEP_ATTENTION_BACKENDS

def seq_output_builder():
    return SequenceOutput(
        0, 0,
        {0: Logprob(logprob=float('inf'), rank=None, decoded_token=None)})

def completion_seq_group_output_builder():
    return CompletionSequenceGroupOutput([], None)


# Used by pythonization to reduce python object allocations
class PythonizationCache:
    def __init__(self):
        self.cached_seq_output = PyObjectCache(seq_output_builder)
        self.cached_completion_seq_group_output = PyObjectCache(
            completion_seq_group_output_builder)

    def reset(self):
        self.cached_seq_output.reset()
        self.cached_completion_seq_group_output.reset()



@dataclass
class ModelOutput:
    """The output of a single model forward pass.
    The sampler_output_ready_event is set when the tensors in
    sampler_output are ready (the model+sampler forward pass has
    completed). We use the event to synchronize the GPU->CPU transfer,
    which we want to only run when the data has been written to the
    GPU tensors. Until the event is ready, the tensors in sampler_output
    will have garbage data.
    There are two scenarios:
    1. The output tensors are ready and we can pythonize them immediately.
    2. The output tensors are not ready and we need to wait for the event to be
    ready.
    """

    sampler_output: SamplerOutput
    sampler_output_ready_event: torch.cuda.Event
    sampled_token_ids: Optional[torch.Tensor] = None
    pythonized: bool = False
    # On-device tensor containing the logprobs of each token.
    logprobs: Optional["torch.Tensor"] = None
    pythonization_cache: Optional[PythonizationCache] = None

    def pythonize(
        self,
        input_metadata: "StatefulModelInput",
        copy_stream: torch.cuda.Stream,
        pinned_sampled_token_buffer: torch.Tensor,
    ) -> None:
        """Pythonize the output. Blocking."""
        if not self.pythonized:
            self._pythonize_sampler_output(
                input_metadata, copy_stream, pinned_sampled_token_buffer, True
            )
            self.pythonized = True

    def maybe_pythonize(
        self,
        input_metadata: "StatefulModelInput",
        copy_stream: torch.cuda.Stream,
        pinned_sampled_token_buffer: torch.Tensor,
    ) -> None:
        """Pythonize the output if ready, else return None. Non-blocking."""
        if not self.pythonized:
            self.pythonized = self._pythonize_sampler_output(
                input_metadata, copy_stream, pinned_sampled_token_buffer, False
            )

    def _pythonize_sampler_output(
        self,
        input_metadata: "StatefulModelInput",
        copy_stream: torch.cuda.Stream,
        pinned_sampled_token_buffer: torch.Tensor,
        blocking: bool,
    ) -> bool:
        """
        If blocking is set, will block until the forward pass for the output is
        ready and pythonize the output. Upon completing Pythonization, erases
        self.logprobs (note that a non-blocking call that is performed when
        the sampler output is not yet ready, will not erase self.logprobs.)
        """
        assert self.sampled_token_ids is not None
        if not blocking and not self.sampler_output_ready_event.query():
            return False
        if blocking:
            self.sampler_output_ready_event.synchronize()
        with torch.cuda.stream(copy_stream):
            _pythonize_sampler_output(
                input_metadata,
                self.sampler_output,
                pinned_sampled_token_buffer,
                self.sampled_token_ids, self.logprobs,
                self.pythonization_cache)

        # Erase the logprobs GPU-side tensor.
        # Note that although _pythonize_sampler_output() runs in its
        # own CUDA stream, nonetheless _pythonize_sampler_output()
        # cannot return until Pythonization is complete; therefore
        # we know that by the time the CPU reaches this point,
        # `self.logprobs` is no longer needed.
        self.logprobs = None
        return True


@dataclass(frozen=False)
class StatefulModelInput(BroadcastableModelInput):
    # actual frozen model input dataclass passed to _base_model_runner
    frozen_model_input: Optional[ModelInputForGPUWithSamplingMetadata] = None
    # list of model outputs for each step, may not be all pythonized
    cached_outputs: List[ModelOutput] = field(default_factory=list)
    # used to pass sampled token ids from the last step to the current step for
    # TP workers. Used to append to end of outputs and used by advance_step
    last_sampled_token_ids: Optional[torch.Tensor] = None
    current_step: int = 0
    is_multi_step: bool = True
    is_last_step: bool = False
    is_first_multi_step: bool = False
    base_output_proc_callback: Optional[Callable] = None
    # ping-pong data structures for multi-step to wait on the previous step
    step_cuda_events: List[torch.cuda.Event] = field(
        default_factory=lambda: [torch.cuda.Event(blocking=True)] * 2
    )
    num_seqs: int = -1
    num_queries: int = -1
    num_single_step_prefills: int = 0

    def as_broadcastable_tensor_dict(self) -> Dict[str, Any]:
        assert self.frozen_model_input is not None
        tensor_dict = self.frozen_model_input.as_broadcastable_tensor_dict()
        new_tensor_dict = {
            "last_sampled_token_ids": self.last_sampled_token_ids,
            "current_step": self.current_step,
            "is_multi_step": self.is_multi_step,
            "is_last_step": self.is_last_step,
            "is_first_multi_step": self.is_first_multi_step,
            "num_seqs": self.num_seqs,
            "num_queries": self.num_queries,
            'num_single_step_prefills': self.num_single_step_prefills,
        }
        tensor_dict.update(new_tensor_dict)
        return tensor_dict

    @classmethod
    def from_broadcasted_tensor_dict(
        cls,
        tensor_dict: Dict[str, Any],
        attn_backend: Optional["AttentionBackend"] = None,
    ) -> "StatefulModelInput":
        tensor_dict = _init_sampling_metadata_from_tensor_dict(tensor_dict)
        if attn_backend is not None:
            tensor_dict = _init_attn_metadata_from_tensor_dict(
                attn_backend, tensor_dict
            )
        tensor_dict = _init_frozen_model_input_from_tensor_dict(
            ModelInputForGPUWithSamplingMetadata, tensor_dict
        )
        return cls(**tensor_dict)

    def record_step_event(self, current_stream: torch.cuda.Stream):
        # record the event for the current step so that the next step can sync
        # on it. We modulo by 2 to keep the events in a circular buffer and
        # support any attn backends that may be supported in the future. ie
        # Flashinfer would want two DecodeWrappers to overlap the CPU and GPU.
        self.step_cuda_events[self.current_step & 1] = torch.cuda.Event(
            blocking=True
        )
        self.step_cuda_events[self.current_step & 1].record(current_stream)

    def wait_previous_step(self):
        # These cuda events are an explicit synchronization to ensure that
        # advance_step() (for other attn backends that may be supported in the
        # future) do not clobber any data structures that is also used by any
        # enqueued forwards steps. For distributed case, only a single event is
        # needed, but for single GPU case, since we can let the CPU run much
        # further ahead, two events allow us to overlap the advance_step with
        # the previous forward (ie using two DecodeWrappers for flashinfer
        # backend)
        self.step_cuda_events[(self.current_step + 1) & 1].wait()

    def add_sampler_output(
        self,
        sampler_output: SamplerOutput,
        sampled_token_ids: Optional[torch.Tensor] = None,
    ):
        self.cached_outputs.append(
            ModelOutput(
                sampler_output=sampler_output,
                sampler_output_ready_event=None,
                sampled_token_ids=sampled_token_ids,
                pythonized=False,
            )
        )

    def maybe_advance_sampling_metadata(self, device: str, pin_memory: bool):
        """
        sampling_metadata.selected_token_indices is constructed for the
        first-step in Multi-Step. However, when chunked-prefill is enabled with
        multi-step, the scheduled prompts are fully processed in the
        first-step and are processed as decodes in the rest of the steps.
        This function updates the sampling_metadata.selected_token_indices
        to account for this conversion.
        Example:
        Let 2 prompts and 2 decodes be scheduled together. Let the
        num-tokens to process for the 2 prompts be 5 and 8 respectively.
        In that case, sampling_metadata.sampled_token_indices will be,
        [4, 12, 13, 14] as it is constructed for the first-step in
        multi-step.
        However, the prompts turns to decodes after the first-step
        and the num-tokens for the previously-prompt sequences will
        be 1 and 1 as they are decodes now. The self.sampled_token_indices
        must be updated to [0,1,2,3].
        """
        assert self.current_step == 1 and self.num_single_step_prefills > 0
        if not get_pp_group().is_last_rank:
            return
        assert self.frozen_model_input is not None
        assert self.frozen_model_input.sampling_metadata is not None

        self.frozen_model_input.sampling_metadata.selected_token_indices =  \
            async_tensor_h2d(list(range(self.num_queries)),
                             dtype=torch.long,
                             target_device=device,
                             pin_memory=pin_memory)

    def maybe_advance_frozen_model_input(self, device: str, pin_memory: bool):
        """
        Advancing the datastructures of StatefulModelInput::frozen_model_input
        is only required when prefills are scheduled with decodes to run in
        multi-step. This advancement/correction is required to account for
        the conversion of Prefills to Decodes after the first multi-step.
        """
        if self.current_step != 1 or self.num_single_step_prefills == 0:
            return
        assert self.frozen_model_input is not None
        fmi = self.frozen_model_input
        # Truncate input_tokens
        assert fmi.input_tokens is not None
        assert fmi.input_tokens.shape[0] >= self.num_seqs
        fmi_new_input_tokens: torch.Tensor = fmi.input_tokens[:self.num_seqs]
        # Update frozen_model_input::input_positons.
        assert fmi.input_positions is not None
        assert fmi.input_positions.shape[0] >= self.num_seqs
        fmi_new_input_positions: torch.Tensor = fmi.input_positions[:self.
                                                                    num_seqs]
        # Assert unsupported
        assert fmi.lora_mapping is None
        assert fmi.lora_requests is not None
        assert len(fmi.lora_requests) == 0
        assert fmi.attn_metadata is not None
        assert fmi.prompt_adapter_mapping is None
        assert fmi.prompt_adapter_requests is not None
        assert len(fmi.prompt_adapter_requests) == 0
        assert fmi.multi_modal_kwargs is not None
        assert len(fmi.multi_modal_kwargs) == 0

        self.frozen_model_input = dataclasses.replace(
            self.frozen_model_input,
            input_tokens=fmi_new_input_tokens,
            input_positions=fmi_new_input_positions)
        self.maybe_advance_sampling_metadata(device, pin_memory)


# MutableModelInputForGPUWithMultiStepMetadata is not subclass of
# ModelInputForGPU but it wraps the actual input dataclass and adds multi-step
# metadata
# mypy: disable-error-code=type-var
class MultiStepModelRunner(GPUModelRunnerBase[StatefulModelInput]):
    # mypy: enable-error-code=type-var
    def __init__(self, base_model_runner: GPUModelRunnerBase, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # Check attention backend support.
        supported_attention_backends: List[str] = \
            _get_supported_attention_backends(
                self.scheduler_config.chunked_prefill_enabled)
        if self.attn_backend.get_name() not in supported_attention_backends:
            ms_config_str: str = "Multi-Step + Chunked-Prefill" \
                if self.scheduler_config.chunked_prefill_enabled \
                      else "Multi-Step"
            raise ValueError(
                f"{ms_config_str} not supported for attention backend: "
                f"{self.attn_backend.get_name()}. Set "
                "APHRODITE_ATTENTION_BACKEND "
                f"to a value from {supported_attention_backends}.")

        # uses the base model runner to execute the model and wraps it with
        # multi-step logic
        self._base_model_runner: GPUModelRunnerBase = base_model_runner
        self.is_multi_step = self.scheduler_config.is_multi_step
        self.pinned_sampled_token_ids: Optional[torch.Tensor] = None

        # Using the PythonizationCache in Pipeline-Parallel clobbers the
        # SequenceOutput and CompletionSequenceGroupOutput object.
        # When cache-reset happens at the last step of a multi-step
        # execution, there may be other on-going single-step/multi-step
        # executions. The current caching implementation does not check
        # for this.
        self.pythonization_cache = PythonizationCache() \
            if self.parallel_config.pipeline_parallel_size == 1 else None

    @functools.cached_property
    def _copy_stream(self):
        # used to copy tensors from GPU to CPU asynchronously
        return torch.cuda.Stream()

    def make_model_input_from_broadcasted_tensor_dict(
        self, tensor_dict: Dict[str, Any]
    ) -> StatefulModelInput:
        model_input = StatefulModelInput.from_broadcasted_tensor_dict(
            tensor_dict,
            attn_backend=self.attn_backend,
        )
        return model_input

    def prepare_model_input(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        virtual_engine: int = 0,
        finished_requests_ids: Optional[List[str]] = None,
    ) -> StatefulModelInput:
        frozen_model_input: ModelInputForGPUWithSamplingMetadata = \
              self._base_model_runner.prepare_model_input(
                    seq_group_metadata_list,
                    virtual_engine,
                    finished_requests_ids)
        assert frozen_model_input.query_lens is not None
        assert frozen_model_input.seq_lens is not None
        assert frozen_model_input.attn_metadata is not None
        num_queries = len(frozen_model_input.query_lens)
        num_seqs = len(frozen_model_input.seq_lens)
        num_single_step_prefills = frozen_model_input.attn_metadata.num_prefills

        model_input = StatefulModelInput(
            frozen_model_input=frozen_model_input,
            num_seqs=num_seqs,
            num_queries=num_queries,
            num_single_step_prefills=num_single_step_prefills)

        return model_input

    def _async_process_outputs(self, model_input: StatefulModelInput,
                               output_proc_callback: Callable):
        # Proceed with pythonization and output_proc in order.
        # Stop on the first one that fails to pythonize
        output_proc_callback()
        cont = True
        for step_num, model_output in enumerate(model_input.cached_outputs):
            if not model_output.pythonized:
                model_output.maybe_pythonize(model_input, self._copy_stream,
                                             self.pinned_sampled_token_ids)
                if model_output.pythonized:
                    ctx = output_proc_callback.keywords["ctx"]
                    ctx.append_output(
                        outputs=[model_output.sampler_output],
                        seq_group_metadata_list=ctx.seq_group_metadata_list,
                        scheduler_outputs=ctx.scheduler_outputs,
                        is_async=False,
                        is_last_step=False,
                        is_first_step_output=step_num == 0)
                    output_proc_callback()
                else:
                    cont = False
            if not cont:
                break
    def _final_process_outputs(self, model_input: StatefulModelInput,
                               output_proc_callback: Optional[Callable]):
        assert model_input.frozen_model_input is not None

        has_async_callback = output_proc_callback is not None
        outputs = []
        for step_num, output in enumerate(model_input.cached_outputs):
            is_last_step = step_num == len(model_input.cached_outputs) - 1
            # For non-async case:
            #   -- We simply add the outputs
            # For async case:
            #   -- Invoke callback, pythonize, add to callback queue and repeat
            #   -- For last output, just add to callback queue
            if has_async_callback:
                assert output_proc_callback is not None
                # Invoke callback before pythonize (to overlap with GPU)
                output_proc_callback()
                # Pythonize
                if not output.pythonized:
                    output.pythonize(model_input, self._copy_stream,
                                     self.pinned_sampled_token_ids)
                    # For non last step, add to callback queue to chain
                    # callbacks=>pythonize pairs (for GPU overlap)
                    if not is_last_step:
                        ctx = output_proc_callback.keywords[  # type: ignore
                            "ctx"]  # type: ignore
                        ctx.append_output(
                            outputs=[output.sampler_output],
                            seq_group_metadata_list=ctx.
                            seq_group_metadata_list,
                            scheduler_outputs=ctx.scheduler_outputs,
                            is_async=False,
                            is_last_step=False,
                            is_first_step_output=step_num == 0)
                    else:
                        outputs.append(output.sampler_output)
            else:
                output.pythonize(model_input, self._copy_stream,
                                 self.pinned_sampled_token_ids)
                outputs.append(output.sampler_output)
        return outputs


    @torch.inference_mode()
    def execute_model(
        self,
        model_input: StatefulModelInput,
        kv_caches: List[torch.Tensor],
        intermediate_tensors: Optional[IntermediateTensors] = None,
        num_steps: int = 1,
    ) -> Optional[Union[List[SamplerOutput], IntermediateTensors]]:
        """
        Execute the model for a single step and update multi-step
        metadata
        """
        assert num_steps == 1, "MultiStepModelRunner only supports num_steps=1"
        frozen_model_input = model_input.frozen_model_input
        assert frozen_model_input is not None
        # path for warm up runs
        if not model_input.is_multi_step:
            return self._base_model_runner.execute_model(
                frozen_model_input, kv_caches, intermediate_tensors, num_steps
            )
        # make sure we skip the sampler on the lask rank and only pythonize
        # if CPU is ahead.
        if self.is_driver_worker and get_pp_group().is_last_rank:
            if self.pinned_sampled_token_ids is None:
                self.pinned_sampled_token_ids = torch.zeros(
                    (self.scheduler_config.max_num_seqs, 1),
                    dtype=torch.long,
                    device="cpu",
                    pin_memory=True,
                )
            self._base_model_runner.model.sampler.include_gpu_probs_tensor = (
                True
            )
            if frozen_model_input.sampling_metadata:
                frozen_model_input.sampling_metadata.skip_sampler_cpu_output = (
                    True
                )
        # some pre-execute model logic for multi-step:
        #   - if it's the first step, we need to reset the sampling tensors
        #   - if it's not the first step, we need to advance the step using the
        #   appended sampler output from last iteration
        #   - also maybe pythonize if CPU is ahead of GPU
        current_stream = torch.cuda.current_stream()
        if not model_input.is_first_multi_step:
            # Explicitly block on the previous step's forward to make sure we
            # don't clobber any GPU tensors still in use.
            # This is not needed for flashattn backend, but for other attn
            # backends such as flashinfer that performs extra CPU operations on
            # input metadata we may need to synchronize any CPU operations that
            # might clobber enqueued forwards. (prevents CPU from running too
            # far ahead if needed)
            model_input.wait_previous_step()
            model_input = self._advance_step(
                model_input, model_input.cached_outputs[-1].sampler_output
            )

            # frozen_model_input may have been updated
            frozen_model_input = model_input.frozen_model_input
            assert frozen_model_input is not None

        if model_input.base_output_proc_callback is None:
            assert frozen_model_input is not None
            model_input.base_output_proc_callback = \
                        frozen_model_input.async_callback

        if frozen_model_input.async_callback is not None:
            assert model_input.base_output_proc_callback is not None
            async_callback = functools.partial(
                self._async_process_outputs,
                model_input=model_input,
                output_proc_callback=model_input.base_output_proc_callback)

            model_input.frozen_model_input = dataclasses.replace(  # type: ignore
                model_input.frozen_model_input,
                async_callback=async_callback)
            # Update the local instance
            frozen_model_input = model_input.frozen_model_input
            assert frozen_model_input is not None

        # Execute the model
        output = self._base_model_runner.execute_model(
            frozen_model_input, kv_caches, intermediate_tensors, num_steps=1
        )
        # record the event for the current step so that the next step can sync
        model_input.record_step_event(current_stream)
        if get_pp_group().is_last_rank and self.is_driver_worker:
            assert (
                len(output) == 1
            ), "MultiStepModelRunner requires single-step base_models"
            # event for the pythonization so that we only pythonize if the
            # tensors are ready. May be able to be combined with the step event
            output_ready_event = torch.cuda.Event()
            output_ready_event.record(current_stream)
            if self.parallel_config.pipeline_parallel_size > 1:
                output[0].sampled_token_ids_cpu = output[
                    0
                ].sampled_token_ids.cpu()
            model_input.cached_outputs.append(
                ModelOutput(
                    output[0],
                    output_ready_event,
                    output[0].sampled_token_ids, False,
                            output[0].logprobs, self.pythonization_cache))

            # These GPU tensors are not required by multi-step;
            # erase them to ensure they are not pythonized or
            # transferred to CPU
            output[0].sampled_token_ids = None
            output[0].sampled_token_probs = None
            output[0].logprobs = None
            # Pythonize the output if CPU is ahead and the previous step is
            # ready.
            if frozen_model_input.async_callback is None:
                for model_output in model_input.cached_outputs:
                    model_output.maybe_pythonize(model_input,
                                                 self._copy_stream,
                                                 self.pinned_sampled_token_ids)
        model_input.current_step += 1
        if not get_pp_group().is_last_rank:
            # Should be IntermediateTensors
            assert isinstance(output, IntermediateTensors)
            return output
        if not self.is_driver_worker:
            return []
        # Pythonize the output and block if needed since it is the last step
        if model_input.is_last_step:
            outputs = self._final_process_outputs(
                model_input, model_input.base_output_proc_callback)
            if self.pythonization_cache:
                self.pythonization_cache.reset()
            return outputs
        # should be [SamplerOutput]
        return output

    def _update_sampling_metadata(
        self, sampling_metadata, num_seqs, num_queries
    ):
        assert sampling_metadata.num_prompts == 0
        assert len(sampling_metadata.seq_groups) == num_queries
        assert sampling_metadata.selected_token_indices.shape == (num_queries,)
        # assert sampling_metadata.categorized_sample_indices == TODO: Add if needed # noqa: E501
        # Verify that all sequences are decodes
        for i in range(num_queries):
            seq_group = sampling_metadata.seq_groups[i]
            assert seq_group.is_prompt is False  # No prompt
            assert seq_group.prompt_logprob_indices == []  # No prompt
            assert seq_group.sample_indices == [i]  # Simple
            assert seq_group.seq_len is None  # Decode
            assert seq_group.query_len is None  # Decode

    def _advance_step(self, model_input: StatefulModelInput,
                      out: SamplerOutput) -> StatefulModelInput:

        model_input.maybe_advance_frozen_model_input(self.device,
                                                     self.pin_memory)
        frozen_model_input = model_input.frozen_model_input
        assert frozen_model_input is not None
        assert frozen_model_input.input_tokens is not None
        assert frozen_model_input.input_tokens.shape[0] == model_input.num_seqs
        assert frozen_model_input.attn_metadata is not None

        sampled_token_ids = model_input.cached_outputs[-1].sampled_token_ids
        num_seqs = model_input.num_seqs
        num_queries = model_input.num_queries
        frozen_model_input = model_input.frozen_model_input
        assert frozen_model_input is not None
        attn_metadata = frozen_model_input.attn_metadata
        assert attn_metadata is not None

        turn_prefills_into_decodes: bool = model_input.current_step == 1 and \
                                    model_input.num_single_step_prefills != 0
        attn_metadata.advance_step(
            frozen_model_input,
            sampled_token_ids,
            self.block_size,
            num_seqs,
            num_queries,
            turn_prefills_into_decodes=turn_prefills_into_decodes)

        return model_input

    def load_model(self) -> None:
        return self._base_model_runner.load_model()

    def save_sharded_state(
        self,
        path: str,
        pattern: Optional[str] = None,
        max_size: Optional[int] = None,
    ) -> None:
        return self._base_model_runner.save_sharded_state(
            path, pattern, max_size
        )

    def save_tensorized_model(
        self, tensorizer_config: TensorizerConfig
    ) -> None:
        return self._base_model_runner.save_tensorized_model(tensorizer_config)

    def profile_run(self) -> None:
        return self._base_model_runner.profile_run()

    def remove_all_loras(self):
        return self._base_model_runner.remove_all_loras()

    def capture_model(self, kv_caches: List[List]) -> None:
        return self._base_model_runner.capture_model(kv_caches)

    @property
    def vocab_size(self) -> int:
        return self._base_model_runner.vocab_size


DeferredLogprobsReturnType = Tuple[Optional[List[Optional[PromptLogprobs]]],
                                   Optional[List[SampleLogprobs]]]


def deferred_pythonize_logprobs(
    output: SamplerOutput,
    sampling_metadata: SamplingMetadata,
    logprobs_tensor: Optional[torch.Tensor],
) -> DeferredLogprobsReturnType:
    """Perform deferred logprob Pythonization.
    1. Pythonize GPU-side sampler result tensors into CPU-side sampler result.
    2. Pythonize GPU-side logprobs tensor into CPU-side logprobs lists,
       utilizing  the Pythonized sampler result computed in step 1.

    These deferred computations are not required for single-step scheduling
    or the `profile_run()` phase of multi-step scheduling.
    Args:
        output: sampler output (under deferred Pythonization)
        sampling_metadata

    Returns:
        prompt_logprobs (CPU), sample_logprobs (CPU)
    """

    # - Deferred pythonization of sample result
    sampler_result = get_pythonized_sample_results(
        output.deferred_sample_results_args)

    # - Erase the GPU-side deferred sample_result
    #   computation args to ensure it is never
    #   pythonized or transferred to CPU
    output.deferred_sample_results_args = None

    # - Deferred pythonization of logprobs
    (
        prompt_logprobs,
        sample_logprobs,
    ) = get_logprobs(logprobs_tensor, sampling_metadata, sampler_result)
    assert len(prompt_logprobs) == len(sampling_metadata.seq_groups)
    assert len(sample_logprobs) == len(sampling_metadata.seq_groups)

    return prompt_logprobs, sample_logprobs


def _pythonize_sampler_output(
    model_input: StatefulModelInput,
    output: SamplerOutput,
    pinned_sampled_token_buffer: torch.Tensor,
    sampled_token_ids: torch.Tensor,
    logprobs_tensor: Optional[torch.Tensor],
    cache: Optional[PythonizationCache],
) -> None:
    """ This function is only called when the output tensors are ready.
    See :class:`ModelOutput`.

    Modifies `output.outputs` and `pinned_sampled_token_buffer` in-place,
    adding a Pythonized output data structure
    (:class:`CompletionSequenceGroupOutput`) for each :class:`SequenceGroup`.
    Args:
      model_input
      output: sampler output
      pinned_sampled_token_token_buffer: CPU-side pinned memory
                                         (receives copy of
                                         GPU-side token buffer.)
      sampled_token_ids: GPU-side token buffer
      logprobs_tensor: GPU-side tensor containing
                       logprobs computed during sampling
    """
    assert model_input.frozen_model_input is not None
    frozen_model_input = model_input.frozen_model_input
    assert frozen_model_input.sampling_metadata is not None
    sampling_metadata = frozen_model_input.sampling_metadata
    # samples generation should have been skipped
    assert not output.outputs
    pinned_buffer = pinned_sampled_token_buffer[: model_input.num_queries]
    # We guarantee output tensors are ready, so it is safe to
    # pythonize the sampler output & obtain CPU-side logprobs.
    #
    # However we should check whether logprobs pythonization may
    # be skipped entirely, i.e. because no logprobs were requested
    # or pythonization was not deferred. To that end,
    #
    # * `prompt_logprobs_are_requested_for_prefill` signals that
    #   there are *any* prefill-phase requests which specify that
    #   prompt logprobs should be returned.
    #
    # * `any_logprobs_are_requested` signals that there are any
    #   requests which (1) specify that sample logprobs should be
    #   returned, or (2) are in the prefill phase AND specify that
    #   prompt logprobs should be returned.
    #
    # Later on, these flags cause adjustments to the pythonization
    # process to accommodate logprobs.
    seq_groups = sampling_metadata.seq_groups
    prompt_logprobs_are_requested_for_prefill = any([
        sg.sampling_params.prompt_logprobs is not None and sg.is_prompt
        for sg in seq_groups
    ])
    any_logprobs_are_requested = (
        prompt_logprobs_are_requested_for_prefill
        or any([sg.sampling_params.logprobs is not None for sg in seq_groups]))
    if prompt_logprobs_are_requested_for_prefill:
        # CPU GPU sync, after gathering *only* sampled tokens (since
        # requesting prompt logprobs leads `sampled_token_ids` to
        # include prompt token ids in addition to sampled token ids.)
        sample_idx_tensor = torch.tensor(
            [sdx for sg in seq_groups for sdx in sg.sample_indices])
        pinned_buffer = pinned_buffer.copy_(
            sampled_token_ids[sample_idx_tensor, :], non_blocking=False)
    else:
        # CPU GPU sync
        pinned_buffer = pinned_buffer.copy_(sampled_token_ids,
                                            non_blocking=False)
    # this will not block as the tensors are already on CPU
    samples_list = pinned_buffer.tolist()

    skip_sampler_cpu_output = (
        frozen_model_input.sampling_metadata.skip_sampler_cpu_output)

    # *Don't* skip logprobs pythonization *if*:
    # * Any requests require logprobs to be returned in this
    # iteration AND
    # * These requests are being scheduled in a fashion which
    # defers pythonization (i.e. multi-step scheduling.)
    do_pythonize_logprobs = (skip_sampler_cpu_output
                             and any_logprobs_are_requested)
    (
        prompt_logprobs,
        sample_logprobs,
    ) = (deferred_pythonize_logprobs(output, sampling_metadata,
                                     logprobs_tensor)
         if do_pythonize_logprobs else (None, None))

    for sgdx, (seq_group,
               sample_result) in enumerate(zip(seq_groups, samples_list)):
        if seq_group.sampling_params.logits_processors:
            assert len(seq_group.sampling_params.logits_processors) == 0, (
                "Logits Processors are not supported in multi-step decoding")

        if do_pythonize_logprobs:
            assert prompt_logprobs is not None
            assert sample_logprobs is not None

            (
                group_prompt_logprobs,
                group_sample_logprobs,
            ) = (  # Utilize deferred pythonization results
                prompt_logprobs[sgdx],
                sample_logprobs[sgdx],
            )
        elif any_logprobs_are_requested:
            (
                group_prompt_logprobs,
                group_sample_logprobs,
            ) = (
                # profile_run: use already-computed logprobs
                output.outputs[sgdx].prompt_logprobs,
                [sample.logprobs for sample in output.outputs[sgdx].samples])
        seq_ids = seq_group.seq_ids
        next_token_ids = sample_result
        parent_ids = [0]
        if cache is not None:
            completion_seq_group_output: CompletionSequenceGroupOutput = \
                cache.cached_completion_seq_group_output.get_object()
            completion_seq_group_output.samples.clear()
            seq_outputs: List[
                SequenceOutput] = completion_seq_group_output.samples
        else:
            seq_outputs = []
        for tdx, (parent_id,
                  next_token_id) in enumerate(zip(parent_ids, next_token_ids)):
            if cache is not None:
                seq_output: SequenceOutput = cache.cached_seq_output.get_object(
                )
                seq_output.parent_seq_id = seq_ids[parent_id]
                seq_output.output_token = next_token_id
                if any_logprobs_are_requested:
                    seq_output.logprobs = group_sample_logprobs[tdx]
                else:
                    logprobs = next(iter(seq_output.logprobs.values()))
                    seq_output.logprobs.clear()
                    logprobs.logprob = float('inf')
                    logprobs.rank = None
                    logprobs.decoded_token = None
                    seq_output.logprobs[next_token_id] = logprobs
                seq_outputs.append(seq_output)
            else:
                seq_outputs.append(
                    SequenceOutput(seq_ids[parent_id], next_token_id,
                                   (group_sample_logprobs[tdx]
                                    if any_logprobs_are_requested else {
                                        next_token_id:
                                        Logprob(logprob=float('inf'),
                                                rank=None,
                                                decoded_token=None)
                                    })))
        if cache is not None:
            completion_seq_group_output.prompt_logprobs = \
                group_prompt_logprobs if any_logprobs_are_requested else None
            output.outputs.append(completion_seq_group_output)
        else:
            output.outputs.append(
                CompletionSequenceGroupOutput(
                    seq_outputs, (group_prompt_logprobs
                                  if any_logprobs_are_requested else None)))
    assert len(output.outputs) > 0
