import logging
import time
from typing import List, Optional, Tuple

import torch

from sglang.srt.distributed import get_tp_group
from sglang.srt.layers.logits_processor import LogitsProcessorOutput
from sglang.srt.layers.sampler import get_token_ids_logprobs, get_top_logprobs
from sglang.srt.managers.schedule_batch import ScheduleBatch
from sglang.srt.managers.scheduler import GenerationBatchResult
from sglang.srt.managers.tp_worker import TpModelWorker
from sglang.srt.mem_cache.common import (
    alloc_paged_token_slots_extend,
    alloc_token_slots,
    get_last_loc,
)
from sglang.srt.model_executor.forward_batch_info import (
    CaptureHiddenMode,
    ForwardBatch,
    ForwardMode,
)
from sglang.srt.server_args import ServerArgs
from sglang.srt.speculative.draft_utils import DraftBackendFactory
from sglang.srt.speculative.eagle_draft_cuda_graph_runner import (
    EAGLEDraftCudaGraphRunner,
)
from sglang.srt.speculative.eagle_draft_extend_cuda_graph_runner import (
    EAGLEDraftExtendCudaGraphRunner,
)
from sglang.srt.speculative.eagle_info import (
    EagleDraftInput,
    EagleVerifyInput,
    EagleVerifyOutput,
)
from sglang.srt.speculative.eagle_utils import (
    build_tree_kernel_efficient,
    organize_draft_results,
)
from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
from sglang.srt.speculative.spec_utils import (
    assign_draft_cache_locs,
    detect_nan,
    draft_tp_context,
    fast_topk,
    generate_token_bitmask,
    load_token_map,
    select_top_k_tokens,
)
from sglang.srt.utils import (
    empty_context,
    get_available_gpu_memory,
    get_bool_env_var,
    is_cuda,
    next_power_of_2,
)

if is_cuda():
    from sgl_kernel import segment_packbits  # noqa: F401

logger = logging.getLogger(__name__)
SGLANG_RETURN_ORIGINAL_LOGPROB = get_bool_env_var("SGLANG_RETURN_ORIGINAL_LOGPROB")


class EAGLEWorker(TpModelWorker):

    def __init__(
        self,
        server_args: ServerArgs,
        gpu_id: int,
        tp_rank: int,
        dp_rank: Optional[int],
        moe_ep_rank: int,
        nccl_port: int,
        target_worker: TpModelWorker,
    ):
        # Parse arguments
        self.server_args = server_args
        self.topk = server_args.speculative_eagle_topk
        self.speculative_num_steps = server_args.speculative_num_steps
        self.speculative_num_draft_tokens = server_args.speculative_num_draft_tokens
        self.enable_nan_detection = server_args.enable_nan_detection
        self.gpu_id = gpu_id
        self.device = server_args.device
        self.target_worker = target_worker
        self.page_size = server_args.page_size
        self.speculative_algorithm = SpeculativeAlgorithm.from_string(
            server_args.speculative_algorithm
        )

        # Override the context length of the draft model to be the same as the target model.
        server_args.context_length = target_worker.model_runner.model_config.context_len

        # Do not capture cuda graph in `super().__init__()`
        # It will be captured later.
        backup_disable_cuda_graph = server_args.disable_cuda_graph
        server_args.disable_cuda_graph = True
        # Share the allocator with a target worker.
        # Draft and target worker own their own KV cache pools.
        self.req_to_token_pool, self.token_to_kv_pool_allocator = (
            target_worker.get_memory_pool()
        )

        # Load hot token ids
        if self.speculative_algorithm.is_eagle3():
            if server_args.speculative_token_map is not None:
                logger.warning(
                    "Speculative token map specified, but EAGLE3 models already have this. Ignoring the specified token map."
                )
            self.hot_token_id = None
        elif server_args.speculative_token_map is not None:
            self.hot_token_id = load_token_map(server_args.speculative_token_map)
            server_args.json_model_override_args = (
                f'{{"hot_vocab_size": {len(self.hot_token_id)}}}'
            )
        else:
            self.hot_token_id = None

        # Init draft worker
        with empty_context():
            super().__init__(
                server_args=server_args,
                gpu_id=gpu_id,
                tp_rank=tp_rank,
                pp_rank=0,  # FIXME
                dp_rank=dp_rank,
                moe_ep_rank=moe_ep_rank,
                nccl_port=nccl_port,
                is_draft_worker=True,
                req_to_token_pool=self.req_to_token_pool,
                token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
            )

        embed, head = self.target_worker.model_runner.model.get_embed_and_head()

        if self.speculative_algorithm.is_eagle3():
            # most cases EAGLE3 models don't share lm_head
            # but some models (e.g. nvidia/gpt-oss-120b-Eagle3) shares
            if (
                hasattr(self.draft_model_runner.model, "load_lm_head_from_target")
                and self.draft_model_runner.model.load_lm_head_from_target
            ):
                self.draft_model_runner.model.set_embed_and_head(embed, head)
            else:
                self.draft_model_runner.model.set_embed(embed)

            # grab hot token ids
            if self.draft_model_runner.model.hot_token_id is not None:
                self.hot_token_id = self.draft_model_runner.model.hot_token_id.to(
                    embed.device
                )

        else:
            if self.hot_token_id is not None:
                head = head.clone()
                self.hot_token_id = self.hot_token_id.to(head.device)
                head.data = head.data[self.hot_token_id]

            # Share the embedding and lm_head
            self.draft_model_runner.model.set_embed_and_head(embed, head)

        # Init attention backend and cuda graphs
        self.draft_model_runner.server_args.disable_cuda_graph = (
            backup_disable_cuda_graph
        )
        self.draft_tp_context = (
            draft_tp_context if server_args.enable_dp_attention else empty_context
        )
        with self.draft_tp_context(self.draft_model_runner.tp_group):
            self.init_attention_backend()
            self.init_cuda_graphs()

        # Some dummy tensors
        self.num_new_pages_per_topk = torch.empty(
            (), dtype=torch.int64, device=self.device
        )
        self.extend_lens = torch.empty((), dtype=torch.int64, device=self.device)

    def init_attention_backend(self):
        # Create multi-step attn backends and cuda graph runners
        draft_backend_factory = DraftBackendFactory(
            self.server_args,
            self.draft_model_runner,
            self.topk,
            self.speculative_num_steps,
        )

        # Initialize decode attention backend
        self.draft_attn_backend = draft_backend_factory.create_decode_backend()

        # Initialize draft extend attention backend (respects speculative_attention_mode setting)
        self.draft_extend_attn_backend = (
            draft_backend_factory.create_draft_extend_backend()
        )

        self.draft_model_runner.draft_attn_backend = self.draft_attn_backend

    def init_cuda_graphs(self):
        """Capture cuda graphs."""
        self.cuda_graph_runner = None
        self.cuda_graph_runner_for_draft_extend = None

        if self.server_args.disable_cuda_graph:
            return

        # Capture draft
        if self.speculative_num_steps > 1:
            tic = time.perf_counter()
            before_mem = get_available_gpu_memory(self.device, self.gpu_id)
            logger.info(
                f"Capture draft cuda graph begin. This can take up to several minutes. avail mem={before_mem:.2f} GB"
            )
            self.cuda_graph_runner = EAGLEDraftCudaGraphRunner(self)
            after_mem = get_available_gpu_memory(self.device, self.gpu_id)
            logger.info(
                f"Capture draft cuda graph end. Time elapsed: {time.perf_counter() - tic:.2f} s. mem usage={(before_mem - after_mem):.2f} GB. avail mem={after_mem:.2f} GB."
            )

        # Capture extend
        if self.draft_extend_attn_backend:
            tic = time.perf_counter()
            before_mem = get_available_gpu_memory(self.device, self.gpu_id)
            logger.info(
                f"Capture draft extend cuda graph begin. This can take up to several minutes. avail mem={before_mem:.2f} GB"
            )
            self.cuda_graph_runner_for_draft_extend = EAGLEDraftExtendCudaGraphRunner(
                self
            )
            after_mem = get_available_gpu_memory(self.device, self.gpu_id)
            logger.info(
                f"Capture draft extend cuda graph end. Time elapsed: {time.perf_counter() - tic:.2f} s. mem usage={(before_mem - after_mem):.2f} GB. avail mem={after_mem:.2f} GB."
            )

    @property
    def draft_model_runner(self):
        return self.model_runner

    def forward_batch_generation(self, batch: ScheduleBatch) -> GenerationBatchResult:
        """Run speculative decoding forward.

        NOTE: Many states of batch is modified as you go through. It is not guaranteed that
        the final output batch have the same state as the input.

        Args:
            batch: The batch to run forward. The state of the batch is modified as it runs.
        Returns:
            A tuple of the final logit output of the target model, next tokens accepted,
            the batch id (used for overlap schedule), and number of accepted tokens.
        """
        if batch.forward_mode.is_extend() or batch.is_extend_in_batch:
            logits_output, next_token_ids, seq_lens_cpu = self.forward_target_extend(
                batch
            )
            with self.draft_tp_context(self.draft_model_runner.tp_group):
                self.forward_draft_extend(
                    batch, logits_output.hidden_states, next_token_ids, seq_lens_cpu
                )
            return GenerationBatchResult(
                logits_output=logits_output,
                next_token_ids=next_token_ids,
                num_accepted_tokens=0,
                can_run_cuda_graph=False,
            )
        else:
            with self.draft_tp_context(self.draft_model_runner.tp_group):
                spec_info = self.draft(batch)
            logits_output, verify_output, model_worker_batch, can_run_cuda_graph = (
                self.verify(batch, spec_info)
            )

            with self.draft_tp_context(self.draft_model_runner.tp_group):
                # NOTE: We should use `check_forward_draft_extend_after_decode`
                # when DP attention is enabled, but it is slow. Skip it for now.
                if (
                    self.server_args.enable_dp_attention
                    or batch.spec_info.verified_id.shape[0] > 0
                ):
                    # decode is not finished
                    self.forward_draft_extend_after_decode(batch)

            return GenerationBatchResult(
                logits_output=logits_output,
                next_token_ids=verify_output.verified_id,
                num_accepted_tokens=sum(verify_output.accept_length_per_req_cpu),
                can_run_cuda_graph=can_run_cuda_graph,
            )

    def check_forward_draft_extend_after_decode(self, batch: ScheduleBatch):
        local_need_forward = batch.spec_info.verified_id.shape[0] > 0
        if not self.server_args.enable_dp_attention:
            return local_need_forward

        global_need_forward = torch.tensor(
            [
                (local_need_forward),
            ],
            dtype=torch.int64,
        )
        torch.distributed.all_reduce(
            global_need_forward, group=get_tp_group().cpu_group
        )
        global_need_forward_cnt = global_need_forward[0].item()
        need_forward = global_need_forward_cnt > 0
        return need_forward

    def forward_target_extend(
        self, batch: ScheduleBatch
    ) -> Tuple[LogitsProcessorOutput, torch.Tensor, int, Optional[torch.Tensor]]:
        """Run the target extend.

        Args:
            batch: The batch to run. States could be modified.

        Returns:
            logits_output: The output of logits. It will contain the full hidden states.
            next_token_ids: Next token ids generated.
        """
        # Forward with the target model and get hidden states.
        # We need the full hidden states to prefill the KV cache of the draft model.
        model_worker_batch = batch.get_model_worker_batch()
        model_worker_batch.capture_hidden_mode = CaptureHiddenMode.FULL
        batch_result = self.target_worker.forward_batch_generation(model_worker_batch)
        logits_output, next_token_ids = (
            batch_result.logits_output,
            batch_result.next_token_ids,
        )
        return (
            logits_output,
            next_token_ids,
            model_worker_batch.seq_lens_cpu,
        )

    def _draft_preprocess_decode(self, batch: ScheduleBatch):
        # Parse args
        num_seqs = batch.batch_size()
        spec_info = batch.spec_info

        # Accumulate penalty
        if batch.sampling_info.penalizer_orchestrator.is_required:
            # This is a relaxed version of penalties for speculative decoding.
            batch.sampling_info.penalizer_orchestrator.cumulate_output_tokens(
                spec_info.verified_id.to(torch.int64)
            )

        # Allocate cache locations
        # Layout of the out_cache_loc
        # [       topk 0         ] [       topk 1         ]
        # [iter=0, iter=1, iter=2] [iter=0, iter=1, iter=2]
        if self.page_size == 1:
            out_cache_loc, token_to_kv_pool_state_backup = alloc_token_slots(
                batch.tree_cache,
                num_seqs * self.speculative_num_steps * self.topk,
                backup_state=True,
            )
        else:
            if self.topk == 1:
                prefix_lens, seq_lens, last_loc = get_last_loc_large_page_size_top_k_1(
                    batch.req_to_token_pool.req_to_token,
                    batch.req_pool_indices,
                    batch.seq_lens,
                    self.speculative_num_steps,
                )
                prefix_lens_cpu = batch.seq_lens_cpu
                seq_lens_cpu = batch.seq_lens_cpu + self.speculative_num_steps
                extend_num_tokens = num_seqs * self.speculative_num_steps
            else:
                # In this case, the last partial page needs to be duplicated.
                # KV cache layout in batch.req_to_token_pool.req_to_token:
                #
                # | -------- | -- xxxx .. | -- xxxx .. | -- xxxx .. |
                #    prefix     top-k = 0    tok-k = 1    top-k = 2
                #
                #  "-" means prefix tokens
                #  "x" means speculative draft tokens
                #  "." means padded tokens

                # TODO(lmzheng): The current implementation is still a fake support
                # for page size > 1. In the `assign_draft_cache_locs` below,
                # we directly move the indices instead of the real kv cache.
                # This only works when the kernel backend runs with page size = 1.
                # If the kernel backend runs with page size > 1, we need to
                # duplicate the real KV cache. The overhead of duplicating KV
                # cache seems okay because the draft KV cache only has one layer.
                # see a related copy operation in MHATokenToKVPool::move_kv_cache.

                (
                    prefix_lens,
                    seq_lens,
                    last_loc,
                    self.num_new_pages_per_topk,
                    self.extend_lens,
                ) = get_last_loc_large_page_size_large_top_k(
                    batch.req_to_token_pool.req_to_token,
                    batch.req_pool_indices,
                    batch.seq_lens,
                    self.speculative_num_steps,
                    self.topk,
                    self.page_size,
                )
                prefix_lens_cpu = batch.seq_lens_cpu
                last_page_lens = prefix_lens_cpu % self.page_size
                num_new_pages_per_topk = (
                    last_page_lens + self.speculative_num_steps + self.page_size - 1
                ) // self.page_size
                seq_lens_cpu = (
                    prefix_lens_cpu // self.page_size * self.page_size
                    + num_new_pages_per_topk * (self.page_size * self.topk)
                )
                extend_num_tokens = torch.sum((seq_lens_cpu - prefix_lens_cpu)).item()

            out_cache_loc, token_to_kv_pool_state_backup = (
                alloc_paged_token_slots_extend(
                    batch.tree_cache,
                    prefix_lens,
                    prefix_lens_cpu,
                    seq_lens,
                    seq_lens_cpu,
                    last_loc,
                    extend_num_tokens,
                    backup_state=True,
                )
            )

        assign_draft_cache_locs[(num_seqs,)](
            batch.req_pool_indices,
            batch.req_to_token_pool.req_to_token,
            batch.seq_lens,
            self.extend_lens,
            self.num_new_pages_per_topk,
            out_cache_loc,
            batch.req_to_token_pool.req_to_token.shape[1],
            self.topk,
            self.speculative_num_steps,
            self.page_size,
            next_power_of_2(num_seqs),
            next_power_of_2(self.speculative_num_steps),
        )

        if self.page_size > 1 and self.topk > 1:
            # Remove padded slots
            out_cache_loc = out_cache_loc[
                : num_seqs * self.topk * self.speculative_num_steps
            ]

        batch.out_cache_loc = out_cache_loc
        batch.seq_lens_sum = torch.sum(batch.seq_lens).item()
        batch.return_hidden_states = False
        spec_info.positions = batch.seq_lens.repeat_interleave(self.topk, dim=0)
        self.token_to_kv_pool_allocator.restore_state(token_to_kv_pool_state_backup)

    def _draft_preprocess_idle(self, batch: ScheduleBatch):
        batch.spec_info = EagleDraftInput.create_idle_input(
            device=self.device,
            hidden_size=self.model_config.hidden_size,
            dtype=self.model_config.dtype,
            topk=self.topk,
            capture_hidden_mode=CaptureHiddenMode.LAST,
        )

    def draft(self, batch: ScheduleBatch):
        # Parse args
        if batch.forward_mode.is_idle():
            self._draft_preprocess_idle(batch)
        else:
            self._draft_preprocess_decode(batch)

        spec_info = batch.spec_info
        assert isinstance(spec_info, EagleDraftInput)

        spec_info.capture_hidden_mode = CaptureHiddenMode.LAST
        spec_info.num_tokens_per_batch = self.topk
        spec_info.num_tokens_for_logprob_per_batch = self.topk
        batch.return_hidden_states = False

        # Get forward batch
        model_worker_batch = batch.get_model_worker_batch()
        assert model_worker_batch.capture_hidden_mode == CaptureHiddenMode.LAST
        forward_batch = ForwardBatch.init_new(
            model_worker_batch, self.draft_model_runner
        )
        can_cuda_graph = self.cuda_graph_runner and self.cuda_graph_runner.can_run(
            forward_batch
        )
        if can_cuda_graph:
            parent_list, top_scores_index, draft_tokens = self.cuda_graph_runner.replay(
                forward_batch
            )
        else:
            forward_batch.can_run_dp_cuda_graph = False
            if (
                not forward_batch.forward_mode.is_idle()
                and self.speculative_num_steps > 1
            ):
                # Skip attention backend init for idle mode or 1-step draft
                self.draft_attn_backend.init_forward_metadata(forward_batch)
            # Run forward steps
            parent_list, top_scores_index, draft_tokens = self.draft_forward(
                forward_batch
            )

        if batch.forward_mode.is_idle():
            return EagleVerifyInput.create_idle_input(
                self.topk,
                self.speculative_num_steps,
                self.speculative_num_draft_tokens,
            )

        (
            tree_mask,
            position,
            retrive_index,
            retrive_next_token,
            retrive_next_sibling,
            draft_tokens,
        ) = build_tree_kernel_efficient(
            spec_info.verified_id,
            parent_list,
            top_scores_index,
            draft_tokens,
            batch.seq_lens,
            batch.seq_lens_sum,
            self.topk,
            self.speculative_num_steps,
            self.speculative_num_draft_tokens,
        )

        return EagleVerifyInput(
            draft_token=draft_tokens,
            custom_mask=tree_mask,
            positions=position,
            retrive_index=retrive_index,
            retrive_next_token=retrive_next_token,
            retrive_next_sibling=retrive_next_sibling,
            retrive_cum_len=None,
            spec_steps=self.speculative_num_steps,
            topk=self.topk,
            draft_token_num=self.server_args.speculative_num_draft_tokens,
            capture_hidden_mode=CaptureHiddenMode.FULL,
            seq_lens_sum=forward_batch.seq_lens_sum,
            seq_lens_cpu=forward_batch.seq_lens_cpu,
        )

    def draft_forward(self, forward_batch: ForwardBatch):
        # Parse args
        spec_info = forward_batch.spec_info
        assert isinstance(spec_info, EagleDraftInput)
        out_cache_loc = forward_batch.out_cache_loc
        topk_p, topk_index, hidden_states = (
            spec_info.topk_p,
            spec_info.topk_index,
            spec_info.hidden_states,
        )
        if self.hot_token_id is not None:
            topk_index = self.hot_token_id[topk_index]

        out_cache_loc = out_cache_loc.reshape(
            forward_batch.batch_size, self.topk, self.speculative_num_steps
        )
        out_cache_loc = out_cache_loc.permute((2, 0, 1)).reshape(
            self.speculative_num_steps, -1
        )

        # Return values
        score_list: List[torch.Tensor] = []
        token_list: List[torch.Tensor] = []
        parents_list: List[torch.Tensor] = []

        # Forward multiple steps
        scores = None
        for i in range(self.speculative_num_steps):
            input_ids, hidden_states, scores, tree_info = select_top_k_tokens(
                i, topk_p, topk_index, hidden_states, scores, self.topk
            )
            score_list.append(tree_info[0])
            token_list.append(tree_info[1])
            parents_list.append(tree_info[2])

            # We don't need to run the last forward. we get 1 token from draft prefill and (#spec steps - 1) tokens here
            if i == self.speculative_num_steps - 1:
                break

            # Set inputs
            forward_batch.input_ids = input_ids
            # This is a temporary fix for the case that the user is using standalone
            # speculative decoding and the draft model architecture is gpt-oss. gpt-oss
            # rope kernel needs cache_loc to be contiguous.
            if (
                self.server_args.speculative_algorithm == "STANDALONE"
                and self.model_config.hf_config.architectures[0] == "GptOssForCausalLM"
            ):
                out_cache_loc = out_cache_loc.contiguous()
            forward_batch.out_cache_loc = out_cache_loc[i]
            forward_batch.positions.add_(1)
            forward_batch.attn_backend = self.draft_attn_backend.attn_backends[i]
            spec_info.hidden_states = hidden_states

            # Run forward
            logits_output, _ = self.draft_model_runner.forward(
                forward_batch, skip_attn_backend_init=True
            )
            if self.server_args.enable_nan_detection:
                detect_nan(logits_output)
            probs = torch.softmax(logits_output.next_token_logits, dim=-1)
            topk_p, topk_index = fast_topk(probs, self.topk, dim=-1)
            if self.hot_token_id is not None:
                topk_index = self.hot_token_id[topk_index]
            hidden_states = logits_output.hidden_states

        parent_list, top_scores_index, draft_tokens = organize_draft_results(
            score_list, token_list, parents_list, self.speculative_num_draft_tokens
        )

        return parent_list, top_scores_index, draft_tokens

    def clear_cache_pool(self):
        # allocator and kv cache pool are shared with target worker
        pass

    def verify(self, batch: ScheduleBatch, spec_info: EagleVerifyInput):
        spec_info.prepare_for_verify(batch, self.page_size)
        batch.return_hidden_states = False
        batch.forward_mode = (
            ForwardMode.TARGET_VERIFY
            if not batch.forward_mode.is_idle()
            else ForwardMode.IDLE
        )
        batch.spec_info = spec_info

        model_worker_batch = batch.get_model_worker_batch(
            seq_lens_cpu_cache=spec_info.seq_lens_cpu
        )
        assert model_worker_batch.capture_hidden_mode == spec_info.capture_hidden_mode

        if batch.has_grammar:
            retrieve_next_token_cpu = spec_info.retrive_next_token.cpu()
            retrieve_next_sibling_cpu = spec_info.retrive_next_sibling.cpu()
            draft_tokens_cpu = spec_info.draft_token.view(
                spec_info.retrive_next_token.shape
            ).cpu()

        # Forward
        batch_result = self.target_worker.forward_batch_generation(
            model_worker_batch, is_verify=True
        )
        logits_output, can_run_cuda_graph = (
            batch_result.logits_output,
            batch_result.can_run_cuda_graph,
        )

        vocab_mask = None
        if batch.has_grammar:
            # Generate the logit mask for structured output.
            # Overlap the CPU operations for bitmask generation with the forward pass.
            vocab_mask = generate_token_bitmask(
                batch.reqs,
                spec_info,
                retrieve_next_token_cpu,
                retrieve_next_sibling_cpu,
                draft_tokens_cpu,
                batch.sampling_info.vocab_size,
            )

            if vocab_mask is not None:
                assert spec_info.grammar is not None
                vocab_mask = vocab_mask.to(spec_info.retrive_next_token.device)
                # NOTE (sk): otherwise, this vocab mask will be the one from the previous extend stage
                # and will be applied to produce wrong results
                batch.sampling_info.vocab_mask = None

        if self.enable_nan_detection:
            detect_nan(logits_output)

        spec_info.hidden_states = logits_output.hidden_states
        res: EagleVerifyOutput = spec_info.verify(
            batch,
            logits_output,
            self.token_to_kv_pool_allocator,
            self.page_size,
            vocab_mask,
        )

        # Post process based on verified outputs.
        # Pick indices that we care (accepted)
        logits_output.next_token_logits = logits_output.next_token_logits[
            res.accepted_indices
        ]
        logits_output.hidden_states = logits_output.hidden_states[res.accepted_indices]

        # QQ: can be optimized
        if self.target_worker.model_runner.hybrid_gdn_config is not None:
            # res.draft_input.accept_length is on GPU but may be empty for last verify?
            accepted_length = (
                torch.tensor(
                    res.accept_length_per_req_cpu,
                    device=logits_output.hidden_states.device,
                    dtype=torch.int32,
                )
                + 1
            )
            self.target_worker.model_runner.attn_backend.update_mamba_state_after_mtp_verify(
                accepted_length, self.target_worker.model_runner.model
            )

        if batch.return_logprob:
            self.add_logprob_values(batch, res, logits_output)

        # Prepare the batch for the next draft forwards.
        batch.forward_mode = (
            ForwardMode.DECODE if not batch.forward_mode.is_idle() else ForwardMode.IDLE
        )
        batch.spec_info = res.draft_input

        return logits_output, res, model_worker_batch, can_run_cuda_graph

    def add_logprob_values(
        self,
        batch: ScheduleBatch,
        res: EagleVerifyOutput,
        logits_output: LogitsProcessorOutput,
    ):
        # Extract args
        logits_output = res.logits_output
        top_logprobs_nums = batch.top_logprobs_nums
        token_ids_logprobs = batch.token_ids_logprobs
        accepted_indices = res.accepted_indices
        assert len(accepted_indices) == len(logits_output.next_token_logits)

        temperatures = batch.sampling_info.temperatures
        num_draft_tokens = batch.spec_info.draft_token_num
        # acceptance indices are the indices in a "flattened" batch.
        # dividing it to num_draft_tokens will yield the actual batch index.
        temperatures = temperatures[accepted_indices // num_draft_tokens]
        if SGLANG_RETURN_ORIGINAL_LOGPROB:
            logprobs = torch.nn.functional.log_softmax(
                logits_output.next_token_logits, dim=-1
            )
        else:
            logprobs = torch.nn.functional.log_softmax(
                logits_output.next_token_logits / temperatures, dim=-1
            )
        batch_next_token_ids = res.verified_id
        num_tokens_per_req = [accept + 1 for accept in res.accept_length_per_req_cpu]

        # We should repeat top_logprobs_nums to match num_tokens_per_req.
        top_logprobs_nums_repeat_interleaved = []
        token_ids_logprobs_repeat_interleaved = []
        for num, num_tokens in zip(top_logprobs_nums, num_tokens_per_req):
            top_logprobs_nums_repeat_interleaved.extend([num] * num_tokens)
        for token_ids, num_tokens in zip(token_ids_logprobs, num_tokens_per_req):
            token_ids_logprobs_repeat_interleaved.extend([token_ids] * num_tokens)

        # Extract logprobs
        if any(x > 0 for x in top_logprobs_nums):
            (
                logits_output.next_token_top_logprobs_val,
                logits_output.next_token_top_logprobs_idx,
            ) = get_top_logprobs(
                logprobs,
                top_logprobs_nums_repeat_interleaved,
            )

        if any(x is not None for x in token_ids_logprobs):
            (
                logits_output.next_token_token_ids_logprobs_val,
                logits_output.next_token_token_ids_logprobs_idx,
            ) = get_token_ids_logprobs(
                logprobs,
                token_ids_logprobs_repeat_interleaved,
            )

        logits_output.next_token_logprobs = logprobs[
            torch.arange(len(batch_next_token_ids), device=batch.sampling_info.device),
            batch_next_token_ids,
        ]

        # Add output logprobs to the request
        pt = 0
        next_token_logprobs = logits_output.next_token_logprobs.tolist()
        verified_ids = batch_next_token_ids.tolist()
        for req, num_tokens in zip(batch.reqs, num_tokens_per_req, strict=True):
            for _ in range(num_tokens):
                if req.return_logprob:
                    req.output_token_logprobs_val.append(next_token_logprobs[pt])
                    req.output_token_logprobs_idx.append(verified_ids[pt])
                    if req.top_logprobs_num > 0:
                        req.output_top_logprobs_val.append(
                            res.logits_output.next_token_top_logprobs_val[pt]
                        )
                        req.output_top_logprobs_idx.append(
                            res.logits_output.next_token_top_logprobs_idx[pt]
                        )
                pt += 1

    def forward_draft_extend(
        self,
        batch: ScheduleBatch,
        hidden_states: torch.Tensor,
        next_token_ids: torch.Tensor,
        seq_lens_cpu: Optional[torch.Tensor],
    ):
        """Run draft model extend. This API modifies the states of the batch.

        Args:
            batch: The batch to run.
            hidden_states: Hidden states from the target model forward
            next_token_ids: Next token ids generated from the target forward.
        """
        batch.spec_info = EagleDraftInput(
            hidden_states=hidden_states,
            verified_id=next_token_ids,
            num_tokens_per_batch=1,
            num_tokens_for_logprob_per_batch=1,
        )
        batch.return_hidden_states = False
        batch.spec_info.prepare_for_extend(batch)
        batch.spec_info.capture_hidden_mode = CaptureHiddenMode.LAST
        model_worker_batch = batch.get_model_worker_batch(
            seq_lens_cpu_cache=seq_lens_cpu
        )
        forward_batch = ForwardBatch.init_new(
            model_worker_batch, self.draft_model_runner
        )
        forward_batch.return_logprob = False
        logits_output, _ = self.draft_model_runner.forward(forward_batch)
        if self.enable_nan_detection:
            detect_nan(logits_output)
        assert isinstance(forward_batch.spec_info, EagleDraftInput)
        assert forward_batch.spec_info is batch.spec_info
        self.capture_for_decode(logits_output, forward_batch.spec_info)
        has_finished, unfinished_req_index = False, []
        for i, req in enumerate(batch.reqs):
            if req.finished():
                has_finished = True
            else:
                unfinished_req_index.append(i)
        if has_finished:
            unfinished_index_device = torch.tensor(
                unfinished_req_index,
                dtype=torch.int64,
                device=batch.spec_info.topk_p.device,
            )
            batch.spec_info.filter_batch(
                unfinished_index_device, has_been_filtered=False
            )

    def forward_draft_extend_after_decode(self, batch: ScheduleBatch):
        assert isinstance(batch.spec_info, EagleDraftInput)
        # Backup fields that will be modified in-place
        seq_lens_backup = batch.seq_lens.clone()
        seq_lens_cpu_backup = batch.seq_lens_cpu.clone()
        req_pool_indices_backup = batch.req_pool_indices
        accept_length_backup = batch.spec_info.accept_length
        return_logprob_backup = batch.return_logprob

        input_is_idle = batch.forward_mode.is_idle()

        if not input_is_idle and batch.spec_info.verified_id.numel() == 0:
            batch = batch.copy()
            batch.prepare_for_idle()
            hidden_size = (
                self.model_config.hidden_size * 3
                if self.speculative_algorithm.is_eagle3()
                else self.model_config.hidden_size
            )
            batch.spec_info = EagleDraftInput.create_idle_input(
                device=self.device,
                hidden_size=hidden_size,
                dtype=self.model_config.dtype,
                topk=self.topk,
                capture_hidden_mode=CaptureHiddenMode.LAST,
            )

        batch.spec_info.num_tokens_per_batch = self.speculative_num_steps + 1
        batch.spec_info.num_tokens_for_logprob_per_batch = 1
        batch.spec_info.prepare_extend_after_decode(
            batch,
            self.speculative_num_steps,
        )
        batch.forward_mode = (
            ForwardMode.DRAFT_EXTEND
            if not batch.forward_mode.is_idle()
            else ForwardMode.IDLE
        )

        batch.return_hidden_states = False
        model_worker_batch = batch.get_model_worker_batch()
        assert model_worker_batch.capture_hidden_mode == CaptureHiddenMode.LAST
        forward_batch = ForwardBatch.init_new(
            model_worker_batch, self.draft_model_runner
        )
        if forward_batch.seq_lens_cpu is not None:
            forward_batch.seq_lens_sum = forward_batch.seq_lens_cpu.sum().item()
        else:
            forward_batch.seq_lens_sum = batch.seq_lens.sum().item()

        # Run
        can_cuda_graph = (
            self.cuda_graph_runner_for_draft_extend
            and self.cuda_graph_runner_for_draft_extend.can_run(forward_batch)
        )
        if can_cuda_graph:
            logits_output = self.cuda_graph_runner_for_draft_extend.replay(
                forward_batch
            )
            forward_batch.spec_info.topk_p, forward_batch.spec_info.topk_index = (
                logits_output.topk_p,
                logits_output.topk_index,
            )
            forward_batch.spec_info.hidden_states = logits_output.hidden_states
        else:
            forward_batch.can_run_dp_cuda_graph = False
            if not forward_batch.forward_mode.is_idle():
                self.draft_model_runner.attn_backend.init_forward_metadata(
                    forward_batch
                )
            logits_output, _ = self.draft_model_runner.forward(
                forward_batch, skip_attn_backend_init=True
            )
            self.capture_for_decode(logits_output, forward_batch.spec_info)

        if self.enable_nan_detection:
            detect_nan(logits_output)

        # Restore backup.
        # This is because `seq_lens` can be modified in `prepare_extend_after_decode`
        batch.forward_mode = (
            ForwardMode.DECODE if not input_is_idle else ForwardMode.IDLE
        )
        batch.seq_lens = seq_lens_backup
        batch.seq_lens_cpu = seq_lens_cpu_backup
        batch.req_pool_indices = req_pool_indices_backup
        batch.spec_info.accept_length = accept_length_backup
        batch.return_logprob = return_logprob_backup

    def capture_for_decode(
        self, logits_output: LogitsProcessorOutput, draft_input: EagleDraftInput
    ):
        probs = torch.softmax(logits_output.next_token_logits, dim=-1)
        draft_input.topk_p, draft_input.topk_index = fast_topk(probs, self.topk, dim=-1)
        draft_input.hidden_states = logits_output.hidden_states


@torch.compile(dynamic=True)
def get_last_loc_large_page_size_top_k_1(
    req_to_token: torch.Tensor,
    req_pool_indices: torch.Tensor,
    seq_lens,
    speculative_num_steps: int,
):
    prefix_lens = seq_lens
    seq_lens = prefix_lens + speculative_num_steps
    last_loc = get_last_loc(
        req_to_token,
        req_pool_indices,
        prefix_lens,
    )
    return prefix_lens, seq_lens, last_loc


# Disable torch.compile for this function because it will be
# even slower.
# @torch.compile(dynamic=True)
def get_last_loc_large_page_size_large_top_k(
    req_to_token: torch.Tensor,
    req_pool_indices: torch.Tensor,
    seq_lens: torch.Tensor,
    speculative_num_steps: int,
    topk: int,
    page_size: int,
):
    prefix_lens = seq_lens
    last_page_lens = prefix_lens % page_size
    num_new_pages_per_topk = (
        last_page_lens + speculative_num_steps + page_size - 1
    ) // page_size
    seq_lens = prefix_lens // page_size * page_size + num_new_pages_per_topk * (
        page_size * topk
    )
    extend_lens = seq_lens - prefix_lens
    last_loc = get_last_loc(
        req_to_token,
        req_pool_indices,
        prefix_lens,
    )

    return prefix_lens, seq_lens, last_loc, num_new_pages_per_topk, extend_lens
