# SPDX-License-Identifier: Apache-2.0
from typing import Optional

import numpy as np
import torch
import torch.nn as nn
from vllm.attention.layer import Attention
from vllm.config import (CompilationLevel, VllmConfig,
                         get_layers_from_vllm_config)
from vllm.distributed.parallel_state import get_pp_group
from vllm.logger import logger
from vllm.model_executor.model_loader import get_model
from vllm.model_executor.models import supports_multimodal
from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata

from vllm_ascend.ascend_forward_context import set_ascend_forward_context
from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType

PADDING_SLOT_ID = -1


class EagleProposer(Proposer):

    def __init__(self,
                 vllm_config: VllmConfig,
                 device: torch.device,
                 runner=None):
        self.name = SpecDcodeType.EAGLE if vllm_config.speculative_config.method == "eagle" else SpecDcodeType.EAGLE3
        self.vllm_config = vllm_config
        self.device = device
        self.runner = runner

        self.block_size = vllm_config.cache_config.block_size
        # We need to get the hidden size from the draft model config because
        # the draft model's hidden size can be different from the target model's
        # hidden size (e.g., Llama 3.3 70B).
        self.hidden_size = vllm_config.speculative_config.draft_model_config.get_hidden_size(
        )

        self.use_cuda_graph = (self.vllm_config.compilation_config.level
                               == CompilationLevel.PIECEWISE and
                               not self.vllm_config.model_config.enforce_eager)
        self.cudagraph_batch_sizes = list(
            reversed(
                self.vllm_config.compilation_config.cudagraph_capture_sizes))

        # persistent buffers for cuda graph
        self.input_ids = torch.zeros(
            self.vllm_config.scheduler_config.max_num_batched_tokens,
            dtype=torch.int32,
            device=device)
        self.positions = torch.zeros(
            self.vllm_config.scheduler_config.max_num_batched_tokens,
            dtype=torch.int64,
            device=device)
        self.hidden_states = torch.zeros(
            (self.vllm_config.scheduler_config.max_num_batched_tokens,
             self.hidden_size),
            dtype=self.vllm_config.model_config.dtype,
            device=device)
        # We need +1 here because the arange is used to set query_start_loc,
        # which has one more element than batch_size.
        self.arange = torch.arange(vllm_config.scheduler_config.max_num_seqs +
                                   1,
                                   device=device,
                                   dtype=torch.int32)
        attn_mask_len = self.vllm_config.model_config.max_model_len
        self.attn_mask_builder = AttentionMaskBuilder(
            attn_mask_len, self.vllm_config.model_config.dtype)

    def load_model(self, model: nn.Module) -> None:
        target_attn_layer_names = set(
            get_layers_from_vllm_config(self.vllm_config, Attention).keys())
        self.model = get_model(vllm_config=self.vllm_config,
                               model_config=self.vllm_config.
                               speculative_config.draft_model_config)
        draft_attn_layer_names = (
            get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
            target_attn_layer_names)
        self.attn_layer_name = next(iter(draft_attn_layer_names))

        # share embed_tokens with the target model if needed
        if get_pp_group().world_size == 1:
            logger.info(
                "The EAGLE head shares the same vocab embedding" \
                " with the target model."
            )
            self.model.model.embed_tokens = model.model.embed_tokens
        else:
            logger.info(
                "Since PP > 1, the EAGLE head loaded its own vocab embedding" \
                " weights instead of sharing them with the target model."
            )

        # share lm_head with the target model if needed
        # some model definition do not define lm_head explicitly
        # and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
        if self.name == SpecDcodeType.EAGLE and hasattr(model, "lm_head"):
            logger.info("Loading EAGLE LM head weights from the target model.")
            if supports_multimodal(model):
                self.model.lm_head = model.get_language_model().lm_head
            else:
                self.model.lm_head = model.lm_head

    @torch.inference_mode()
    def dummy_run(self,
                  num_tokens: int,
                  with_prefill: bool = False,
                  skip_attn: bool = False,
                  num_reqs: int = 0,
                  num_tokens_across_dp: Optional[torch.Tensor] = None):
        moe_comm_type = self.runner._select_moe_comm_method(
            num_tokens, with_prefill)
        with set_ascend_forward_context(None,
                                        self.vllm_config,
                                        moe_comm_type=moe_comm_type,
                                        num_tokens=num_tokens):
            self.model(
                input_ids=self.input_ids[:num_tokens],
                positions=self.positions[:num_tokens],
                hidden_states=self.hidden_states[:num_tokens],
            )

    def generate_token_ids(self,
                           valid_sampled_token_ids: list[list[int]],
                           sampling_metadata: SamplingMetadata = None,
                           scheduler_output: SchedulerOutput = None,
                           spec_decode_metadata: SpecDecodeMetadata = None,
                           positions: torch.Tensor = None,
                           num_scheduled_tokens: int = 0,
                           hidden_states: torch.Tensor = None,
                           attn_metadata=None,
                           aux_hidden_states: torch.Tensor = None):

        attn_metadata = self._get_eagle_atten_dict(scheduler_output)
        next_token_ids: list[int] = []
        for i, token_ids in enumerate(valid_sampled_token_ids):
            if token_ids:
                # Common case.
                next_token_id = token_ids[-1]
            else:
                # Partial prefill (rare case).
                # Get the next token id from the request state.
                req_id = self.runner.input_batch.req_ids[i]
                req_state = self.runner.requests[req_id]
                seq_len = (req_state.num_computed_tokens +
                           scheduler_output.num_scheduled_tokens[req_id])

                next_token_id = req_state.get_token_id(seq_len)
            next_token_ids.append(next_token_id)
        next_token_ids = torch.tensor(next_token_ids,
                                      dtype=torch.int32,
                                      device=self.device)
        eagle_attn_metadata = attn_metadata[self.attn_layer_name]
        if spec_decode_metadata is None:
            # input_ids can be None for multimodal models.
            target_token_ids = self.runner.input_ids[:num_scheduled_tokens]
            target_positions = positions[:num_scheduled_tokens]
            if self.name == SpecDcodeType.EAGLE3:
                target_hidden_states = torch.cat(
                    [h[:num_scheduled_tokens] for h in aux_hidden_states],
                    dim=-1)
            else:
                target_hidden_states = hidden_states[:num_scheduled_tokens]
            target_slot_mapping = eagle_attn_metadata.slot_mapping
            cu_num_tokens = eagle_attn_metadata.query_start_loc
        else:
            num_draft_tokens = spec_decode_metadata.num_draft_tokens
            num_rejected_tokens = [
                n + 1 - len(valid_sampled_token_ids[i]) if n > 0 else 0
                for i, n in enumerate(num_draft_tokens)
            ]
            num_rejected_tokens = torch.tensor(
                num_rejected_tokens,
                dtype=torch.int32,
                device=self.device,
            )
            num_tokens = num_scheduled_tokens - sum(num_rejected_tokens)
            cu_num_tokens, token_indices = self._prepare_inputs(
                eagle_attn_metadata.query_start_loc, num_rejected_tokens,
                num_tokens)
            target_token_ids = self.runner.input_ids[token_indices]
            target_positions = positions[token_indices]
            if self.name == SpecDcodeType.EAGLE3:
                target_hidden_states = torch.cat(
                    [h[token_indices] for h in aux_hidden_states], dim=-1)
            else:
                target_hidden_states = hidden_states[token_indices]
            target_slot_mapping = eagle_attn_metadata.slot_mapping[
                token_indices]

        draft_token_ids = self._propose(
            target_token_ids=target_token_ids,
            target_positions=target_positions,
            target_hidden_states=target_hidden_states,
            target_slot_mapping=target_slot_mapping,
            next_token_ids=next_token_ids,
            cu_num_tokens=cu_num_tokens,
            block_table=eagle_attn_metadata.block_tables,
            sampling_metadata=sampling_metadata,
        )
        spec_token_ids = draft_token_ids.tolist()
        return spec_token_ids

    def _get_eagle_atten_dict(
        self,
        scheduler_output: "SchedulerOutput",
    ):
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        assert total_num_scheduled_tokens > 0
        num_reqs = self.runner.input_batch.num_reqs
        assert num_reqs > 0

        # OPTIMIZATION: Start copying the block table first.
        # This way, we can overlap the copy with the following CPU operations.
        self.runner.input_batch.block_table.commit_block_table(num_reqs)

        # Get the number of scheduled tokens for each request.
        req_ids = self.runner.input_batch.req_ids
        tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
        num_scheduled_tokens = np.array(tokens, dtype=np.int32)
        max_num_scheduled_tokens = max(tokens)
        self.runner.query_lens = torch.from_numpy(num_scheduled_tokens)
        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
        req_indices = np.repeat(self.runner.arange_np[:num_reqs],
                                num_scheduled_tokens)

        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        cu_num_tokens, arange = self._get_cumsum_and_arange(
            num_scheduled_tokens)

        # Get positions.
        positions_np = self.runner.positions_np[:total_num_scheduled_tokens]
        np.add(self.runner.input_batch.num_computed_tokens_cpu[req_indices],
               arange,
               out=positions_np)

        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
        if self.runner.uses_mrope:
            self.runner._calc_mrope_positions(scheduler_output)

        # 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.runner.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.runner.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            torch.from_numpy(token_indices),
            out=self.runner.input_ids_cpu[:total_num_scheduled_tokens])

        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
        # NOTE(Chen): there is exactly one KV cache group that contains all
        # attetnion layers in the model for now, so the current logic for
        # getting attn_metadata is not related to kv_cache_group information.
        # Will extend this part to support multiple KV cache groups later.
        for kv_cache_group_id, kv_cache_group_spec in enumerate(
                self.runner.kv_cache_config.kv_cache_groups):
            block_size = kv_cache_group_spec.kv_cache_spec.block_size
            block_table = self.runner.input_batch.block_table[
                kv_cache_group_id]
            # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
            # -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
            # where K is the max_num_blocks_per_req and the block size is 2.
            # NOTE(woosuk): We can't simply use `token_indices // block_size`
            # here because M (max_model_len) is not necessarily divisible by
            # block_size.
            block_table_indices = (
                req_indices * block_table.max_num_blocks_per_req +
                positions_np // block_size)
            block_table_cpu = block_table.get_cpu_tensor()
            block_numbers = block_table_cpu.flatten(
            )[block_table_indices].numpy()
            block_offsets = positions_np % block_size
            np.add(
                block_numbers * block_size,
                block_offsets,
                out=block_table.slot_mapping_np[:total_num_scheduled_tokens])

        # Prepare the attention metadata.
        self.runner.query_start_loc_np[0] = 0
        self.runner.query_start_loc_np[1:num_reqs + 1] = cu_num_tokens

        self.runner.seq_lens_np[:num_reqs] = (
            self.runner.input_batch.num_computed_tokens_cpu[:num_reqs] +
            num_scheduled_tokens)

        # Copy the tensors to the NPU.
        self.runner.input_ids[:total_num_scheduled_tokens].copy_(
            self.runner.input_ids_cpu[:total_num_scheduled_tokens],
            non_blocking=True)
        if self.runner.uses_mrope:
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
            self.runner.mrope_positions[:, :total_num_scheduled_tokens].copy_(
                self.runner.
                mrope_positions_cpu[:, :total_num_scheduled_tokens],
                non_blocking=True)
        else:
            # Common case (1D positions)
            self.runner.positions[:total_num_scheduled_tokens].copy_(
                self.runner.positions_cpu[:total_num_scheduled_tokens],
                non_blocking=True)

        self.runner.query_start_loc[:num_reqs + 1].copy_(
            self.runner.query_start_loc_cpu[:num_reqs + 1], non_blocking=True)
        self.runner.seq_lens[:num_reqs].copy_(
            self.runner.seq_lens_cpu[:num_reqs], non_blocking=True)

        # Fill unused with -1. Needed for reshape_and_cache
        self.runner.seq_lens[num_reqs:].fill_(0)
        self.runner.query_start_loc[num_reqs + 1:].fill_(-1)

        attn_metadata = {}
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
        for kv_cache_group_id, kv_cache_group_spec in enumerate(
                self.runner.kv_cache_config.kv_cache_groups):
            common_attn_metadata = AscendCommonAttentionMetadata(
                query_start_loc=self.runner.query_start_loc[:num_reqs + 1],
                query_start_loc_cpu=self.runner.query_start_loc_cpu[:num_reqs +
                                                                    1],
                seq_lens_cpu=self.runner.seq_lens_cpu,
                num_reqs=num_reqs,
                max_query_len=max_num_scheduled_tokens,
                num_actual_tokens=total_num_scheduled_tokens,
                actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
                block_table_tensor=self.runner.input_batch.block_table[0].
                get_device_tensor(),
                slot_mapping=self.runner.slot_mapping,
                positions=self.runner.positions,
                attn_mask=self.runner.attn_mask,
                spec_attn_mask=self.runner.spec_attn_mask,
                attn_state=self.runner.attn_state,
                decode_token_per_req=self.runner.decode_token_per_req,
                num_computed_tokens_cpu=None,
                seq_lens=None)
            builder = self.runner.attn_groups[0][0].get_metadata_builder()
            attn_metadata_i = builder.build(0, common_attn_metadata,
                                            self.runner.get_model())
            for layer_name in kv_cache_group_spec.layer_names:
                attn_metadata[layer_name] = attn_metadata_i

        return attn_metadata

    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
        cumsum_dtype: Optional[np.dtype] = None,
    ) -> tuple[np.ndarray, np.ndarray]:
        """Get the cumulative sum and batched arange of the given array.
        # E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2])
        # Equivalent to but faster than:
        # np.concatenate([np.arange(n) for n in num_tokens])
        """
        # Step 1. [2, 5, 3] -> [2, 7, 10]
        cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype)
        total_num_tokens = cu_num_tokens[-1]
        # Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7]
        cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens)
        # Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        arange = self.runner.arange_np[:total_num_tokens] - cumsums_offsets

        return cu_num_tokens, arange

    def _propose(
        self,
        # [num_tokens]
        target_token_ids: torch.Tensor,
        # [num_tokens]
        target_positions: torch.Tensor,
        # [num_tokens, hidden_size]
        target_hidden_states: torch.Tensor,
        # [num_tokens]
        target_slot_mapping: torch.Tensor,
        # [batch_size]
        next_token_ids: torch.Tensor,
        # [batch_size + 1] starting with 0
        cu_num_tokens: torch.Tensor,
        # [batch_size, max_num_blocks_per_req]
        block_table: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> torch.Tensor:
        device = cu_num_tokens.device
        cu_num_tokens = cu_num_tokens.cpu()
        block_table = block_table.cpu()
        num_tokens = target_token_ids.shape[0]
        batch_size = next_token_ids.shape[0]
        last_token_indices = cu_num_tokens[1:] - 1
        target_positions = target_positions.cpu()
        if self.name == SpecDcodeType.EAGLE3:
            assert isinstance(self.model, Eagle3LlamaForCausalLM)
            target_hidden_states = self.model.combine_hidden_states(
                target_hidden_states)
            assert target_hidden_states.shape[-1] == self.hidden_size

        # Shift the input ids by one token.
        # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
        self.input_ids[:num_tokens - 1] = target_token_ids[1:]
        # Replace the last token with the next token.
        # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
        self.input_ids[last_token_indices] = next_token_ids
        seq_lens = (target_positions[last_token_indices] + 1).int()

        query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1]
        max_query_len = query_lens.max().item()
        attn_mask = self.attn_mask_builder.get_splitfuse_attn_mask(
            seq_lens, target_positions, self.vllm_config.model_config.dtype,
            self.device)

        common_attn_metadata = AscendCommonAttentionMetadata(
            query_start_loc=cu_num_tokens.to(device),
            query_start_loc_cpu=cu_num_tokens,
            seq_lens_cpu=seq_lens.cpu(),
            max_query_len=max_query_len,
            num_reqs=batch_size,
            num_actual_tokens=num_tokens,
            actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
            block_table_tensor=self.runner.input_batch.block_table[0].
            get_device_tensor(),
            slot_mapping=target_slot_mapping,
            positions=target_positions,
            attn_mask=attn_mask,
            spec_attn_mask=self.runner.spec_attn_mask,
            attn_state=self.runner.attn_state,
            decode_token_per_req=self.runner.decode_token_per_req,
            num_computed_tokens_cpu=None,
            seq_lens=None)
        # FIXME(woosuk): The below two ops cause synchronization. Optimize.
        builder = self.runner.attn_groups[0][0].get_metadata_builder()
        attn_metadata = builder.build(0, common_attn_metadata,
                                      self.runner.get_model())
        if self.use_cuda_graph and \
            num_tokens <= self.cudagraph_batch_sizes[-1]:
            num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
        else:
            num_input_tokens = num_tokens

        with_prefill = attn_metadata.attn_state not in [
            AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding
        ]
        moe_comm_type = self.runner._select_moe_comm_method(
            num_input_tokens, with_prefill)

        # copy inputs to buffer for cudagraph
        self.positions[:num_tokens] = target_positions.to(device)
        self.hidden_states[:num_tokens] = target_hidden_states
        attn_metadata.block_tables = block_table.to(device)
        with set_ascend_forward_context(attn_metadata,
                                        self.vllm_config,
                                        moe_comm_type=moe_comm_type,
                                        num_tokens=num_input_tokens):
            last_hidden_states, hidden_states = self.model(
                input_ids=self.input_ids[:num_input_tokens],
                positions=self.positions[:num_input_tokens],
                hidden_states=self.hidden_states[:num_input_tokens],
            )
        sample_hidden_states = last_hidden_states[last_token_indices]
        logits = self.model.compute_logits(sample_hidden_states)
        draft_token_ids = logits.argmax(dim=-1)

        # Early exit if there is only one draft token to be generated.
        if self.vllm_config.speculative_config.num_speculative_tokens == 1:
            # [batch_size, 1]
            return draft_token_ids.view(-1, 1)

        # Generate the remaining draft tokens.
        draft_token_ids_tensor = torch.zeros(
            (self.vllm_config.speculative_config.num_speculative_tokens,
             *draft_token_ids.shape),
            dtype=draft_token_ids.dtype)
        draft_token_ids_tensor[0] = draft_token_ids

        positions_cpu = target_positions[last_token_indices].cpu().to(
            torch.int64)
        hidden_states = hidden_states[last_token_indices]
        if self.use_cuda_graph and \
            batch_size <= self.cudagraph_batch_sizes[-1]:
            input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
        else:
            input_batch_size = batch_size

        moe_comm_type = self.runner._select_moe_comm_method(
            input_batch_size, False)

        attn_metadata.num_actual_tokens = batch_size
        attn_metadata.max_query_len = 1
        attn_metadata.query_start_loc = self.arange[:batch_size + 1]
        query_lens.fill_(1)
        attn_metadata.query_lens = query_lens

        attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
        for now_speculative in range(
                self.vllm_config.speculative_config.num_speculative_tokens -
                1):
            # Update the inputs.
            # cast to int32 is crucial when eagle model is compiled.
            # tensor.argmax() returns int64 by default.
            input_ids = draft_token_ids_tensor[now_speculative].to(device)
            positions_cpu += 1

            # NOTE(woosuk): We should handle the case where the draft model
            # generates tokens beyond the max model length. Since it is complex
            # to remove such requests from the batch, we keep them in the batch
            # but adjust the position ids and slot mappings to avoid the
            # out-of-range access during the model execution. The draft tokens
            # generated with this adjustment should be ignored.
            exceeds_max_model_len = positions_cpu >= self.vllm_config.model_config.max_model_len
            # Mask out the position ids that exceed the max model length.
            # Otherwise, we may get out-of-range error in RoPE.
            clamped_positions_cpu = torch.where(exceeds_max_model_len, 0,
                                                positions_cpu)
            clamped_positions = clamped_positions_cpu.to(device)

            # TODO: Increment the sequence lengths.

            attn_metadata.seq_lens += 1
            # TODO: Consider max model length.
            # attn_metadata.max_seq_len = min(attn_metadata.max_seq_len,
            #                                 self.max_model_len)
            # For the requests that exceed the max model length, we set the
            # TODO: sequence length to 1 to minimize their overheads in attention.

            # Compute the slot mapping.
            block_numbers = (clamped_positions_cpu // self.block_size)
            block_ids = block_table.gather(dim=1,
                                           index=block_numbers.view(-1, 1))
            block_ids = block_ids.view(-1)
            slot_mapping_cpu = (
                block_ids * self.vllm_config.cache_config.block_size +
                clamped_positions_cpu % self.block_size)

            # Mask out the slot mappings that exceed the max model length.
            # Otherwise, the KV cache will be inadvertently updated with the
            # padding tokens.
            slot_mapping_cpu.masked_fill_(exceeds_max_model_len,
                                          PADDING_SLOT_ID)
            # NOTE: ASCEND slot_mapping must on cpu
            attn_metadata.slot_mapping = slot_mapping_cpu.to(
                torch.int32).to(device)
            # copy inputs to buffer for cudagraph
            self.input_ids[:batch_size] = input_ids
            self.positions[:batch_size] = clamped_positions
            self.hidden_states[:batch_size] = hidden_states
            attn_mask = self.attn_mask_builder.get_splitfuse_attn_mask(
                attn_metadata.seq_lens, positions_cpu,
                self.vllm_config.model_config.dtype, self.device)

            attn_metadata.attn_mask = attn_mask
            attn_metadata.block_tables = block_table.to(device)
            # Run the model.
            with set_ascend_forward_context(attn_metadata,
                                            self.vllm_config,
                                            moe_comm_type=moe_comm_type,
                                            num_tokens=input_batch_size):

                last_hidden_states, hidden_states = self.model(
                    input_ids=self.input_ids[:input_batch_size],
                    positions=self.positions[:input_batch_size],
                    hidden_states=self.hidden_states[:input_batch_size],
                )
            hidden_states = hidden_states[:batch_size]
            logits = self.model.compute_logits(last_hidden_states[:batch_size])

            # TODO(wenlong): get more than one token for tree attention
            draft_token_ids = logits.argmax(dim=-1)
            draft_token_ids_tensor[now_speculative + 1] = draft_token_ids.cpu()

        # [batch_size, num_speculative_tokens]
        draft_token_ids = draft_token_ids_tensor.swapaxes(0, 1)
        return draft_token_ids

    def _prepare_inputs(
        self,
        # [batch_size + 1]
        cu_target_query_lens: torch.Tensor,
        # [batch_size]
        num_rejected_tokens: torch.Tensor,
        num_tokens: int,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # cu_target_query_lens: [0, a, a + b, a + b + c]
        # num_rejected_tokens: [n1, n2, n3]
        # num_tokens_per_req: [a - n1, b - n2, c - n3]
        # cu_num_tokens: [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
        # token_indices: [0, 1, ..., a - n1 - 1,
        #                 a, a + 1, ..., a + b - n2 - 1,
        #                 a + b, a + b + 1, ..., a + b + c - n3 - 1]

        # [0, a, a + b, a + b + c] -> [a, b, c]
        query_len_per_req = (cu_target_query_lens[1:] -
                             cu_target_query_lens[:-1])
        # [a, b, c] -> [a - n1, b - n2, c - n3]
        num_tokens_per_req = query_len_per_req - num_rejected_tokens

        # [a - n1, b - n2, c - n3] ->
        # [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
        cu_num_tokens = torch.zeros_like(cu_target_query_lens)
        torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
        token_indices = torch.empty(
            num_tokens,
            dtype=torch.int32,
            device=cu_target_query_lens.device,
        )
        BLOCK_SIZE = 1024
        self._prepare_eagle_input_sequential(
            token_indices,
            cu_target_query_lens,
            cu_num_tokens,
            block_size=BLOCK_SIZE,
        )
        return cu_num_tokens, token_indices

    def _prepare_eagle_input_sequential(self, out_tensor: torch.Tensor,
                                        cu_query_lens: torch.Tensor,
                                        cu_num_tokens: torch.Tensor,
                                        block_size: int):
        num_programs = len(cu_num_tokens) - 1
        for pid in range(num_programs):
            start_pos = cu_num_tokens[pid].item()
            end_pos = cu_num_tokens[pid + 1].item()
            num_tokens = end_pos - start_pos
            index_start = cu_query_lens[pid].item()
            num_blocks = int(
                torch.ceil(torch.tensor(num_tokens / block_size)).item())

            for i in range(num_blocks):
                offset_tensor = torch.arange(0,
                                             block_size,
                                             dtype=torch.int32,
                                             device=out_tensor.device)
                global_start_offset = i * block_size
                target_indices = torch.tensor(
                    start_pos + global_start_offset,
                    dtype=torch.int32,
                    device=out_tensor.device) + offset_tensor
                values_to_store = torch.tensor(
                    index_start + global_start_offset,
                    dtype=torch.int32,
                    device=out_tensor.device) + offset_tensor
                mask = (target_indices >= start_pos) & \
                    (target_indices < end_pos) & \
                    (offset_tensor < num_tokens)
                out_tensor[target_indices[mask]] = values_to_store[mask]
