# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import dataclasses
import itertools
from typing import Any, Dict, List, Optional, Tuple, Type, cast

import torch
import torch.distributed

from vllm.attention.backends.abstract import (AttentionBackend,
                                              AttentionMetadata)
from vllm.attention.backends.utils import PAD_SLOT_ID
from vllm.attention.selector import (get_env_variable_attn_backend,
                                     get_global_forced_attn_backend)
from vllm.config import VllmConfig
from vllm.forward_context import set_forward_context
from vllm.inputs import INPUT_REGISTRY, InputRegistry
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.model_executor import SamplingMetadata
from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalKwargs,
                             MultiModalRegistry)
from vllm.platforms import _Backend
from vllm.sampling_params import SamplingParams
from vllm.sequence import (IntermediateTensors, PoolerOutput,
                           SequenceGroupMetadata)
from vllm.utils import STR_NOT_IMPL_ENC_DEC_BACKEND, make_tensor_with_pad
from vllm.worker.model_runner import (GPUModelRunnerBase,
                                      ModelInputForGPUBuilder,
                                      ModelInputForGPUWithSamplingMetadata)
from vllm.worker.model_runner_base import (
    _add_attn_metadata_broadcastable_dict,
    _add_sampling_metadata_broadcastable_dict)
from vllm.worker.utils import assert_enc_dec_mr_supported_scenario

logger = init_logger(__name__)
LORA_WARMUP_RANK = 8


@dataclasses.dataclass(frozen=True)
class EncoderDecoderModelInput(ModelInputForGPUWithSamplingMetadata):
    """
    Used by the EncoderDecoderModelRunner.
    """
    encoder_input_tokens: Optional[torch.Tensor] = None
    encoder_input_positions: Optional[torch.Tensor] = None

    def as_broadcastable_tensor_dict(self) -> Dict[str, Any]:
        tensor_dict = {
            "input_tokens": self.input_tokens,
            "inputs_embeds": self.inputs_embeds,
            "input_positions": self.input_positions,
            "encoder_input_tokens": self.encoder_input_tokens,
            "encoder_input_positions": self.encoder_input_positions,
            "virtual_engine": self.virtual_engine,
            "request_ids_to_seq_ids": self.request_ids_to_seq_ids,
            "finished_requests_ids": self.finished_requests_ids,
            "multi_modal_kwargs": self.multi_modal_kwargs,
        }
        _add_attn_metadata_broadcastable_dict(tensor_dict, self.attn_metadata)
        _add_sampling_metadata_broadcastable_dict(tensor_dict,
                                                  self.sampling_metadata)
        return tensor_dict

    @classmethod
    def from_broadcasted_tensor_dict(
        cls,
        tensor_dict: Dict[str, Any],
        attn_backend: Optional["AttentionBackend"] = None,
    ) -> "EncoderDecoderModelInput":
        return cast(
            EncoderDecoderModelInput,
            super().from_broadcasted_tensor_dict(tensor_dict, attn_backend))


class EncoderDecoderModelRunner(GPUModelRunnerBase[EncoderDecoderModelInput]):
    _model_input_cls: Type[EncoderDecoderModelInput] = (
        EncoderDecoderModelInput)
    _builder_cls: Type[ModelInputForGPUBuilder] = (ModelInputForGPUBuilder)

    def __init__(
        self,
        vllm_config: VllmConfig,
        kv_cache_dtype: Optional[str] = "auto",
        is_driver_worker: bool = False,
        input_registry: InputRegistry = INPUT_REGISTRY,
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
    ):
        '''
        EncoderDecoderModelRunner constructor.

        `lora_config` and `prompt_adapter_config` are
        unused (since these features are not yet supported for encoder/decoder
        models) but these arguments are present here for compatibility with 
        the base-class constructor.
        '''
        self._maybe_force_supported_attention_backend()

        super().__init__(
            vllm_config=vllm_config,
            kv_cache_dtype=kv_cache_dtype,
            is_driver_worker=is_driver_worker,
            input_registry=input_registry,
            mm_registry=mm_registry,
        )

        # Crash for unsupported encoder/scenarios
        assert_enc_dec_mr_supported_scenario(self)

    def _maybe_force_supported_attention_backend(self):
        '''
        Force vLLM to use the XFormers attention backend,
        which is currently the only supported option.
        '''

        def raise_backend_err():
            # The user has specified an attention backend override
            # which is invalid for encoder/decoder models
            raise NotImplementedError(STR_NOT_IMPL_ENC_DEC_BACKEND)

        maybe_env_var_forced_backend = get_env_variable_attn_backend()
        maybe_global_forced_backend = get_global_forced_attn_backend()
        is_forced_by_global = maybe_global_forced_backend is not None
        is_forced_by_env_var = maybe_env_var_forced_backend is not None
        if is_forced_by_global:  # noqa: SIM102
            # Backend override enforced by global variable takes
            # precedence over vLLM backend environment variable.
            if maybe_global_forced_backend not in\
                 [_Backend.XFORMERS, _Backend.FLASH_ATTN]:
                raise_backend_err()
        elif is_forced_by_env_var:  # noqa: SIM102
            # Backend override enforced by vLLM backend
            # environment variable
            if maybe_env_var_forced_backend not in\
                 [_Backend.XFORMERS, _Backend.FLASH_ATTN]:
                raise_backend_err()

    def _list_to_int32_tensor(
        self,
        _list: List[int],
    ) -> torch.Tensor:
        return torch.tensor(_list, dtype=torch.int32, device=self.device)

    def _list_to_long_tensor(
        self,
        _list: List[int],
    ) -> torch.Tensor:
        return torch.tensor(_list, dtype=torch.long, device=self.device)

    def _empty_int32_tensor(self) -> torch.Tensor:
        return self._list_to_int32_tensor([])

    def _empty_long_tensor(self) -> torch.Tensor:
        return self._list_to_long_tensor([])

    @torch.inference_mode()
    def execute_model(
        self,
        model_input: EncoderDecoderModelInput,
        kv_caches: List[torch.Tensor],
        intermediate_tensors: Optional[IntermediateTensors] = None,
        num_steps: int = 1,
    ) -> Optional[List[PoolerOutput]]:
        if num_steps > 1:
            raise ValueError("num_steps > 1 is not supported in "
                             "EncoderDecoderModelRunner")
        if self.lora_config:
            assert model_input.lora_requests is not None
            assert model_input.lora_mapping is not None
            self.set_active_loras(model_input.lora_requests,
                                  model_input.lora_mapping)
        if (model_input.attn_metadata is not None
                and model_input.attn_metadata.prefill_metadata is None
                and model_input.attn_metadata.decode_metadata.use_cuda_graph):
            if model_input.inputs_embeds is None:
                assert model_input.input_tokens is not None
                graph_batch_size = model_input.input_tokens.shape[0]
                model_executable = (
                    self.graph_runners[model_input.virtual_engine][(
                        graph_batch_size, False)])
            else:
                graph_batch_size = model_input.inputs_embeds.shape[0]
                model_executable = (
                    self.graph_runners[model_input.virtual_engine][(
                        graph_batch_size, True)])
        else:
            model_executable = self.model

        seqlen_agnostic_kwargs = {
            "finished_requests_ids": model_input.finished_requests_ids,
            "request_ids_to_seq_ids": model_input.request_ids_to_seq_ids,
        } if self.has_inner_state else {}

        multi_modal_kwargs = model_input.multi_modal_kwargs or {}
        with set_forward_context(model_input.attn_metadata, self.vllm_config,
                                 model_input.virtual_engine):
            hidden_or_intermediate_states = model_executable(
                input_ids=model_input.input_tokens,
                inputs_embeds=model_input.inputs_embeds,
                positions=model_input.input_positions,
                encoder_input_ids=model_input.encoder_input_tokens,
                encoder_positions=model_input.encoder_input_positions,
                intermediate_tensors=intermediate_tensors,
                **MultiModalKwargs.as_kwargs(
                    multi_modal_kwargs,
                    device=self.device,
                ),
                **seqlen_agnostic_kwargs,
            )

        logits = self.model.compute_logits(hidden_or_intermediate_states,
                                           model_input.sampling_metadata)

        if not self.is_driver_worker:
            return []

        if model_input.async_callback is not None:
            model_input.async_callback()

        # Sample the next token.
        output: SamplerOutput = self.sampler(
            logits=logits,
            sampling_metadata=model_input.sampling_metadata,
        )

        return [output]

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

    def prepare_model_input(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        virtual_engine: int = 0,
        finished_requests_ids: Optional[List[str]] = None
    ) -> EncoderDecoderModelInput:
        """Prepare the model input based on a given sequence group, including
        metadata for the sampling step.

        Since chunked prefill is not supported for encoder/decoder models,
        `input_tokens` is assumed to be either entirely prefill tokens or
        entirely decode tokens.

        """
        model_input = self._prepare_model_input_tensors(
            seq_group_metadata_list, finished_requests_ids)
        (
            attn_metadata,
            encoder_input_tokens_tensor,
            encoder_input_positions_tensor,
        ) = (self._prepare_encoder_model_input_tensors(seq_group_metadata_list,
                                                       model_input))
        # Inject attn_metadata encoder/cross-attention fields &
        # encoder input tokens/positions into model_input.
        # Frozen dataclass fields cannot be modified, so use
        # dataclasses.replace to construct a new model input
        # instance.
        model_input = dataclasses.replace(
            model_input,
            attn_metadata=attn_metadata,
            encoder_input_tokens=encoder_input_tokens_tensor,
            encoder_input_positions=encoder_input_positions_tensor,
        )

        generators = self.get_generators(finished_requests_ids)
        sampling_metadata = SamplingMetadata.prepare(seq_group_metadata_list,
                                                     model_input.seq_lens,
                                                     model_input.query_lens,
                                                     self.device,
                                                     self.pin_memory,
                                                     generators=generators)
        is_prompt = (seq_group_metadata_list[0].is_prompt
                     if seq_group_metadata_list else None)
        return dataclasses.replace(model_input,
                                   sampling_metadata=sampling_metadata,
                                   is_prompt=is_prompt,
                                   virtual_engine=virtual_engine)

    @torch.inference_mode()
    def profile_run(self) -> None:
        # Enable top-k sampling to reflect the accurate memory usage.
        sampling_params = SamplingParams(top_p=0.99, top_k=self.vocab_size - 1)
        max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
        max_num_seqs = self.scheduler_config.max_num_seqs

        # This represents the maximum number of different requests
        # that will have unique loras, and therefore the max amount of
        # memory consumption. Create dummy lora request copies from the
        # lora request passed in, which contains a lora from the lora
        # warmup path.
        dummy_lora_requests: List[LoRARequest] = []
        dummy_lora_requests_per_seq: List[LoRARequest] = []
        if self.lora_config:
            dummy_lora_requests = self._add_dummy_loras(
                self.lora_config.max_loras)
            assert len(dummy_lora_requests) == self.lora_config.max_loras
            dummy_lora_requests_per_seq = [
                dummy_lora_requests[idx % len(dummy_lora_requests)]
                for idx in range(max_num_seqs)
            ]

        # Profile memory usage with max_num_sequences sequences and the total
        # number of tokens equal to max_num_batched_tokens.
        seqs: List[SequenceGroupMetadata] = []

        max_mm_tokens = self.mm_registry.get_max_multimodal_tokens(
            self.model_config)
        if max_mm_tokens > 0:
            logger.info("Starting profile run for multi-modal models.")

        batch_size = 0
        for group_id in range(max_num_seqs):
            seq_len = (max_num_batched_tokens // max_num_seqs +
                       (group_id < max_num_batched_tokens % max_num_seqs))
            batch_size += seq_len

            decoder_dummy_data = self.input_registry \
                .dummy_data_for_profiling(self.model_config,
                                          seq_len,
                                          self.mm_registry,
                                          is_encoder_data=False)
            encoder_dummy_data = self.input_registry \
                .dummy_data_for_profiling(self.model_config,
                                          seq_len,
                                          self.mm_registry,
                                          is_encoder_data=True)

            # Having more tokens is over-conservative but otherwise fine
            assert len(
                decoder_dummy_data.seq_data.prompt_token_ids
            ) >= seq_len, (
                f"Expected at least {seq_len} dummy tokens for profiling, "
                f"but got: {len(decoder_dummy_data.seq_data.prompt_token_ids)}"
            )

            assert decoder_dummy_data.multi_modal_data is None or \
            encoder_dummy_data.multi_modal_data is None, (
                "Multi-modal data can't be provided in both encoder and decoder"
            )

            seq = SequenceGroupMetadata(
                request_id=str(group_id),
                is_prompt=True,
                seq_data={group_id: decoder_dummy_data.seq_data},
                sampling_params=sampling_params,
                block_tables=None,
                encoder_seq_data=encoder_dummy_data.seq_data,
                cross_block_table=None,
                lora_request=dummy_lora_requests_per_seq[group_id]
                if dummy_lora_requests_per_seq else None,
                multi_modal_data=decoder_dummy_data.multi_modal_data
                or encoder_dummy_data.multi_modal_data,
                multi_modal_placeholders=decoder_dummy_data.
                multi_modal_placeholders
                or encoder_dummy_data.multi_modal_placeholders)
            seqs.append(seq)

        finished_requests_ids = [seq.request_id for seq in seqs]
        model_input = self.prepare_model_input(
            seqs, finished_requests_ids=finished_requests_ids)
        intermediate_tensors = None
        self.execute_model(model_input, None, intermediate_tensors)
        torch.cuda.synchronize()
        return

    def _prepare_encoder_model_input_tensors(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        model_input: EncoderDecoderModelInput,
    ) -> Tuple[AttentionMetadata, Optional[torch.Tensor],
               Optional[torch.Tensor]]:
        """Helper method to prepare the encoder- and cross-attn-related
        model inputs based on a given sequence group. These additional inputs
        are used to augment an already-computed `EncoderDecoderModelInput`
        data structure which already has decoder-related model inputs
        populated.

        Sets the following attn_metadata fields:
        * `num_encoder_tokens`
        * `encoder_seq_lens`
        * `encoder_seq_lens_tensor`
        * `max_encoder_seq_len`
        * `cross_slot_mapping`
        * `cross_block_tables`

        Constructs a new model inputs data structure, based on
        (1) the existing fields in the `model_inputs` argument,
        and (2) the following additional fields which are
        computed (or in the case of `attn_metadata`, updated) 
        by this function:
        * attn_metadata
        * encoder_input_tokens
        * encoder_input_positions

        Arguments:

        * seq_group_metadata_list: list of sequence groups for which to
                                   compute inputs
        * model_inputs: model inputs data structure with decoder-oriented
                        fields already computed.

        Return:

        * Updated model inputs data structure
        """

        if len(seq_group_metadata_list) == 0:
            return (model_input.attn_metadata, None, None)

        # Since we are not supporting chunked prefill either the entire
        # batch is prefill or it is decode
        is_prompt = seq_group_metadata_list[0].is_prompt

        # Build encoder inputs
        encoder_seq_lens: List[int] = []
        if is_prompt:
            # Prefill phase.
            cross_block_tables = self._empty_int32_tensor().view(
                len(seq_group_metadata_list), -1)

            # Extract input tokens/positions, cross-attention slot-mapping,
            # & seq len from each sequence group metadata
            (
                encoder_input_tokens,
                encoder_input_positions,
                cross_slot_mapping,
            ) = (
                [],
                [],
                [],
            )
            for seq_group_metadata in seq_group_metadata_list:
                # Build seq lens
                seq_len = seq_group_metadata.encoder_seq_data.get_len()
                token_ids = seq_group_metadata.encoder_seq_data.get_token_ids()
                encoder_seq_lens.append(seq_len)

                # Build slot mapping
                is_profile_run = (seq_group_metadata.block_tables is None)
                if is_profile_run:
                    # During memory profiling, the block tables are not
                    # initialized yet. In this case, we just use a dummy
                    # slot mapping.
                    # In embeddings, the block tables are {seq_id: None}.
                    cross_slot_mapping.extend([PAD_SLOT_ID] * seq_len)
                else:
                    for i in range(0, seq_len):
                        block_number = seq_group_metadata.cross_block_table[
                            i // self.block_size]
                        block_offset = i % self.block_size
                        slot = block_number * self.block_size + block_offset
                        cross_slot_mapping.append(slot)

                # Build encoder input tokens
                encoder_input_tokens.extend(token_ids)
                encoder_input_positions.extend(list(range(0, seq_len)))

            # Convert tokens/positions & cross-attention
            # slot-mapping to encoder input tensors
            encoder_input_tokens_tensor = self._list_to_long_tensor(
                encoder_input_tokens)
            encoder_input_positions_tensor = self._list_to_long_tensor(
                encoder_input_positions)
            cross_slot_mapping_tensor = self._list_to_long_tensor(
                cross_slot_mapping)

        else:
            # Decode phase.
            encoder_input_tokens_tensor = self._empty_long_tensor()
            encoder_input_positions_tensor = self._empty_long_tensor()
            cross_slot_mapping_tensor = self._empty_long_tensor()
            # Extract cross-attention block tables &
            # seq len from each sequence group metadata.
            # Cross-attention block tables are empty
            # during vLLM memory profiling.
            cross_block_tables = []
            for seq_group_metadata in seq_group_metadata_list:
                for _ in range(len(seq_group_metadata.seq_data)):
                    encoder_seq_lens.append(
                        seq_group_metadata.encoder_seq_data.get_len())
                    cross_block_table = seq_group_metadata.cross_block_table
                    cross_block_tables.append([] if (
                        cross_block_table is None) else cross_block_table)

            if (model_input.attn_metadata is not None
                    and model_input.attn_metadata.use_cuda_graph):
                # We will be using CUDA graph replay for this decode.
                max_len_of_block_table = self.get_max_block_per_batch()
                batch_size = len(encoder_seq_lens)
                graph_batch_size = self.vllm_config.pad_for_cudagraph(
                    batch_size)
                assert graph_batch_size >= batch_size
                cuda_graph_pad_size = graph_batch_size - batch_size
                # extend the cross_block_tables and encoder_seq_lens to match
                # the graph_batch_size.
                cross_block_tables.extend([[]
                                           for _ in range(cuda_graph_pad_size)
                                           ])
                encoder_seq_lens.extend(
                    itertools.repeat(1, cuda_graph_pad_size))

            else:
                max_len_of_block_table = max(
                    len(block_table) for block_table in cross_block_tables)

            cross_block_tables = make_tensor_with_pad(
                cross_block_tables,
                max_len=max_len_of_block_table,
                pad=0,
                dtype=torch.int32,
                device=self.device,
            )

        # Compute encoder sequence lengths & encoder
        # sequence starting offset tensors
        max_encoder_seq_len = max(encoder_seq_lens, default=0)
        encoder_seq_lens_tensor = self._list_to_int32_tensor(encoder_seq_lens)
        encoder_seq_start_loc = torch.zeros(encoder_seq_lens_tensor.shape[0] +
                                            1,
                                            dtype=torch.int32,
                                            device=self.device)
        torch.cumsum(encoder_seq_lens_tensor,
                     dim=0,
                     dtype=encoder_seq_start_loc.dtype,
                     out=encoder_seq_start_loc[1:])

        # Update attention metadata with encoder-oriented attributes
        attn_metadata = model_input.attn_metadata
        assert attn_metadata is not None
        (
            attn_metadata.num_encoder_tokens,
            attn_metadata.encoder_seq_lens,
            attn_metadata.encoder_seq_lens_tensor,
            attn_metadata.max_encoder_seq_len,
            attn_metadata.encoder_seq_start_loc,
            attn_metadata.cross_slot_mapping,
            attn_metadata.cross_block_tables,
        ) = (
            sum(encoder_seq_lens),
            encoder_seq_lens,
            encoder_seq_lens_tensor,
            max_encoder_seq_len,
            encoder_seq_start_loc,
            cross_slot_mapping_tensor,
            cross_block_tables,
        )

        return (attn_metadata, encoder_input_tokens_tensor,
                encoder_input_positions_tensor)
