754 lines
30 KiB
Python
754 lines
30 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import logging
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import time
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from typing import TYPE_CHECKING, List, Optional, Tuple
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import torch
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from sglang.srt.distributed import get_tp_group
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from sglang.srt.layers.dp_attention import get_attention_tp_group
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from sglang.srt.layers.logits_processor import LogitsProcessorOutput
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from sglang.srt.layers.moe.utils import speculative_moe_backend_context
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from sglang.srt.layers.utils.logprob import add_output_logprobs_for_spec_v1
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from sglang.srt.managers.schedule_batch import ScheduleBatch
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from sglang.srt.managers.scheduler import GenerationBatchResult
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from sglang.srt.managers.tp_worker import TpModelWorker
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from sglang.srt.model_executor.forward_batch_info import (
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CaptureHiddenMode,
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ForwardBatch,
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ForwardMode,
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)
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from sglang.srt.server_args import ServerArgs
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from sglang.srt.speculative.draft_utils import DraftBackendFactory
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from sglang.srt.speculative.eagle_info import (
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EagleDraftInput,
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EagleVerifyInput,
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EagleVerifyOutput,
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)
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from sglang.srt.speculative.eagle_utils import (
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build_tree_kernel_efficient,
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organize_draft_results,
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)
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from sglang.srt.speculative.multi_layer_eagle_draft_extend_cuda_graph_runner import (
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MultiLayerEagleDraftExtendCudaGraphRunner,
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)
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from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
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from sglang.srt.speculative.spec_utils import (
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draft_tp_context,
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fast_topk,
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generate_token_bitmask,
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load_token_map,
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maybe_detect_nan,
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select_top_k_tokens,
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)
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from sglang.srt.utils import empty_context, get_available_gpu_memory, is_cuda, is_npu
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if TYPE_CHECKING:
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from sglang.srt.model_executor.model_runner import ModelRunner
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_is_npu = is_npu()
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if is_cuda():
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from sgl_kernel import segment_packbits # noqa: F401
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logger = logging.getLogger(__name__)
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class MultiLayerEagleWorker(TpModelWorker):
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def __init__(
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self,
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server_args: ServerArgs,
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gpu_id: int,
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tp_rank: int,
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dp_rank: Optional[int],
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moe_ep_rank: int,
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attn_cp_rank: int,
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moe_dp_rank: int,
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nccl_port: int,
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target_worker: TpModelWorker,
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):
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# Parse arguments
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self.server_args = server_args
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self.topk = server_args.speculative_eagle_topk
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self.speculative_num_steps = server_args.speculative_num_steps
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self.speculative_num_draft_tokens = server_args.speculative_num_draft_tokens
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self.gpu_id = gpu_id
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self.device = server_args.device
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self.target_worker = target_worker
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self.page_size = server_args.page_size
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self.speculative_algorithm = SpeculativeAlgorithm.from_string(
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server_args.speculative_algorithm
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)
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self.draft_extend_attn_backend_list = []
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# Override the context length of the draft model to be the same as the target model.
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server_args.context_length = target_worker.model_runner.model_config.context_len
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# Do not capture cuda graph in `super().__init__()`
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# It will be captured later.
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backup_disable_cuda_graph = server_args.disable_cuda_graph
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server_args.disable_cuda_graph = True
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# Share the allocator with a target worker.
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# Draft and target worker own their own KV cache pools.
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self.req_to_token_pool, self.token_to_kv_pool_allocator = (
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target_worker.get_memory_pool()
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)
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# Load hot token ids
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if self.speculative_algorithm.is_eagle3():
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if server_args.speculative_token_map is not None:
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logger.warning(
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"Speculative token map specified, but EAGLE3 models already have this. Ignoring the specified token map."
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)
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self.hot_token_id = None
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elif server_args.speculative_token_map is not None:
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self.hot_token_id = load_token_map(server_args.speculative_token_map)
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server_args.json_model_override_args = (
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f'{{"hot_vocab_size": {len(self.hot_token_id)}}}'
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)
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else:
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self.hot_token_id = None
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# Init draft worker
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if server_args.enable_dp_attention and self.speculative_algorithm.is_eagle3():
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ctx = draft_tp_context(get_attention_tp_group())
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else:
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ctx = empty_context()
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with ctx, speculative_moe_backend_context():
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super().__init__(
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server_args=server_args,
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gpu_id=gpu_id,
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tp_rank=tp_rank,
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pp_rank=0, # FIXME
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dp_rank=dp_rank,
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moe_ep_rank=moe_ep_rank,
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attn_cp_rank=attn_cp_rank,
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moe_dp_rank=moe_dp_rank,
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nccl_port=nccl_port,
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is_draft_worker=True,
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req_to_token_pool=self.req_to_token_pool,
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token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
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memory_pool_config=target_worker.model_runner.memory_pool_config,
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is_multi_layer_eagle=True,
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)
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embed, head = self.target_worker.model_runner.model.get_embed_and_head()
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if self.speculative_algorithm.is_eagle3():
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# most cases EAGLE3 models don't share lm_head
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# but some models (e.g. nvidia/gpt-oss-120b-Eagle3) shares
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if (
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hasattr(self.draft_model_runner.model, "load_lm_head_from_target")
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and self.draft_model_runner.model.load_lm_head_from_target
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):
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self.draft_model_runner.model.set_embed_and_head(embed, head)
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else:
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self.draft_model_runner.model.set_embed(embed)
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# grab hot token ids
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if self.draft_model_runner.model.hot_token_id is not None:
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self.hot_token_id = self.draft_model_runner.model.hot_token_id.to(
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embed.device
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)
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else:
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if self.hot_token_id is not None:
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head = head.clone()
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self.hot_token_id = self.hot_token_id.to(head.device)
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head.data = head.data[self.hot_token_id]
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# Share the embedding and lm_head
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for i in range(self.speculative_num_steps):
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self.mtp_model_runner(i).model.set_embed_and_head(embed, head)
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# Init attention backend and cuda graphs
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for i in range(self.speculative_num_steps):
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self.mtp_model_runner(i).server_args.disable_cuda_graph = (
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backup_disable_cuda_graph
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)
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self.draft_tp_context = (
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draft_tp_context if server_args.enable_dp_attention else empty_context
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)
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with self.draft_tp_context(
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self.mtp_model_runner(0).tp_group
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), speculative_moe_backend_context():
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self.init_attention_backend()
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self.init_cuda_graphs()
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# Some dummy tensors
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self.num_new_pages_per_topk = torch.empty(
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(), dtype=torch.int64, device=self.device
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)
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self.extend_lens = torch.empty((), dtype=torch.int64, device=self.device)
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def init_attention_backend(self):
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# Create multi-step attn backends and cuda graph runners
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for step in range(self.speculative_num_steps):
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draft_backend_factory = DraftBackendFactory(
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self.server_args,
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self.mtp_model_runner(step),
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self.topk,
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self.speculative_num_steps,
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)
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# Initialize draft extend attention backend (respects speculative_attention_mode setting)
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self.draft_extend_attn_backend_list.append(
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draft_backend_factory.create_draft_extend_backend()
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)
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def init_cuda_graphs(self):
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"""Capture cuda graphs."""
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self.cuda_graph_runner_for_draft_extend_list = []
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if self.server_args.disable_cuda_graph:
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return
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# Capture extend
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for step in range(self.speculative_num_steps):
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if self.draft_extend_attn_backend_list[step] and not _is_npu:
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tic = time.perf_counter()
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before_mem = get_available_gpu_memory(self.device, self.gpu_id)
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logger.info(
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f"Capture draft extend cuda graph begin. This can take up to several minutes. avail mem={before_mem:.2f} GB"
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)
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self.cuda_graph_runner_for_draft_extend_list.append(
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MultiLayerEagleDraftExtendCudaGraphRunner(self, step)
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)
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after_mem = get_available_gpu_memory(self.device, self.gpu_id)
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logger.info(
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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."
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)
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def mtp_model_runner(self, layer_id: int) -> ModelRunner:
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return self.model_runner_list[layer_id]
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def forward_batch_generation(self, batch: ScheduleBatch) -> GenerationBatchResult:
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"""Run speculative decoding forward.
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NOTE: Many states of batch is modified as you go through. It is not guaranteed that
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the final output batch have the same state as the input.
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Args:
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batch: The batch to run forward. The state of the batch is modified as it runs.
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Returns:
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A tuple of the final logit output of the target model, next tokens accepted,
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the batch id (used for overlap schedule), and number of accepted tokens.
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"""
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if batch.forward_mode.is_extend() or batch.is_extend_in_batch:
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logits_output, next_token_ids, seq_lens_cpu = self.forward_target_extend(
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batch
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)
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with self.draft_tp_context(
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self.mtp_model_runner(0).tp_group
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), speculative_moe_backend_context():
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self.forward_draft_extend(
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batch, logits_output.hidden_states, next_token_ids, seq_lens_cpu
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)
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return GenerationBatchResult(
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logits_output=logits_output,
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next_token_ids=next_token_ids,
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num_accepted_tokens=0,
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can_run_cuda_graph=False,
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)
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else:
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with self.draft_tp_context(
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self.mtp_model_runner(0).tp_group
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), speculative_moe_backend_context():
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spec_info = self.draft(batch)
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logits_output, verify_output, model_worker_batch, can_run_cuda_graph = (
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self.verify(batch, spec_info)
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)
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with self.draft_tp_context(
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self.mtp_model_runner(0).tp_group
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), speculative_moe_backend_context():
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# NOTE: We should use `check_forward_draft_extend_after_decode`
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# when DP attention is enabled, but it is slow. Skip it for now.
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if (
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self.server_args.enable_dp_attention
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or batch.spec_info.verified_id.shape[0] > 0
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):
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# decode is not finished
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self.forward_draft_extend_after_decode(batch)
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return GenerationBatchResult(
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logits_output=logits_output,
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next_token_ids=verify_output.verified_id,
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num_accepted_tokens=sum(verify_output.accept_length_per_req_cpu),
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can_run_cuda_graph=can_run_cuda_graph,
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)
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def check_forward_draft_extend_after_decode(self, batch: ScheduleBatch):
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local_need_forward = batch.spec_info.verified_id.shape[0] > 0
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if not self.server_args.enable_dp_attention:
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return local_need_forward
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global_need_forward = torch.tensor(
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[
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(local_need_forward),
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],
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dtype=torch.int64,
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)
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torch.distributed.all_reduce(
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global_need_forward, group=get_tp_group().cpu_group
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)
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global_need_forward_cnt = global_need_forward[0].item()
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need_forward = global_need_forward_cnt > 0
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return need_forward
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def forward_target_extend(
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self, batch: ScheduleBatch
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) -> Tuple[LogitsProcessorOutput, torch.Tensor, int, Optional[torch.Tensor]]:
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"""Run the target extend.
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Args:
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batch: The batch to run. States could be modified.
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Returns:
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logits_output: The output of logits. It will contain the full hidden states.
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next_token_ids: Next token ids generated.
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"""
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# Forward with the target model and get hidden states.
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# We need the full hidden states to prefill the KV cache of the draft model.
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model_worker_batch = batch.get_model_worker_batch()
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model_worker_batch.capture_hidden_mode = CaptureHiddenMode.FULL
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model_worker_batch.return_hidden_states_before_norm = True
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batch_result = self.target_worker.forward_batch_generation(model_worker_batch)
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logits_output, next_token_ids = (
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batch_result.logits_output,
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batch_result.next_token_ids,
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)
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return (
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logits_output,
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next_token_ids,
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model_worker_batch.seq_lens_cpu,
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)
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def _draft_preprocess_decode(self, batch: ScheduleBatch):
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from sglang.srt.speculative.eagle_worker import EAGLEWorker
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# FIXME: migrate multi-layer eagle worker to eagle worker
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return EAGLEWorker._draft_preprocess_decode(self, batch)
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def _draft_preprocess_idle(self, batch: ScheduleBatch):
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from sglang.srt.speculative.eagle_worker import EAGLEWorker
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# FIXME: migrate multi-layer eagle worker to eagle worker
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return EAGLEWorker._draft_preprocess_idle(self, batch)
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def draft(self, batch: ScheduleBatch):
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# Parse args
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if batch.forward_mode.is_idle():
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self._draft_preprocess_idle(batch)
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else:
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self._draft_preprocess_decode(batch)
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spec_info = batch.spec_info
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assert isinstance(spec_info, EagleDraftInput)
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spec_info.capture_hidden_mode = CaptureHiddenMode.LAST
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spec_info.num_tokens_per_req = self.topk
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spec_info.num_tokens_for_logprob_per_req = self.topk
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batch.return_hidden_states = False
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# Get forward batch
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model_worker_batch = batch.get_model_worker_batch()
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assert model_worker_batch.capture_hidden_mode == CaptureHiddenMode.LAST
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forward_batch = ForwardBatch.init_new(
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model_worker_batch, self.mtp_model_runner(0)
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)
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forward_batch.can_run_dp_cuda_graph = False
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forward_batch.return_hidden_states_before_norm = True
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# Parse args
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assert isinstance(spec_info, EagleDraftInput)
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topk_p, topk_index, hidden_states = (
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spec_info.topk_p,
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spec_info.topk_index,
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spec_info.hidden_states,
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)
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maybe_detect_nan(topk_p, "draft: NaN in initial topk_p from spec_info")
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# Return values
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score_list: List[torch.Tensor] = []
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token_list: List[torch.Tensor] = []
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parents_list: List[torch.Tensor] = []
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# Forward multiple steps
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scores = None
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input_ids, hidden_states, scores, tree_info = select_top_k_tokens(
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0, topk_p, topk_index, hidden_states, scores, self.topk
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)
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if self.speculative_num_steps == 1:
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score_list.append(tree_info[0])
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token_list.append(tree_info[1])
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parents_list.append(tree_info[2])
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else:
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for i in range(self.speculative_num_steps):
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score_list.append(tree_info[0][:, :, i].unsqueeze(-1))
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token_index = tree_info[1][:, i].unsqueeze(-1)
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token_list.append(token_index)
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if i == 0:
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parents_list.append(tree_info[2])
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else:
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parents_list.append(
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torch.full(
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(tree_info[2].size(0), 1),
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i,
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dtype=torch.long,
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device=self.device,
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)
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)
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parent_list, top_scores_index, draft_tokens = organize_draft_results(
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score_list, token_list, parents_list, self.speculative_num_draft_tokens
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)
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if batch.forward_mode.is_idle():
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return EagleVerifyInput.create_idle_input(
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self.topk,
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self.speculative_num_steps,
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self.speculative_num_draft_tokens,
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)
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(
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tree_mask,
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position,
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retrive_index,
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retrive_next_token,
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retrive_next_sibling,
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draft_tokens,
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) = build_tree_kernel_efficient(
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spec_info.verified_id,
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parent_list,
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top_scores_index,
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draft_tokens,
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batch.seq_lens,
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batch.seq_lens_sum,
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self.topk,
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self.speculative_num_steps,
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self.speculative_num_draft_tokens,
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)
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return EagleVerifyInput(
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draft_token=draft_tokens,
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custom_mask=tree_mask,
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positions=position,
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retrive_index=retrive_index,
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retrive_next_token=retrive_next_token,
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retrive_next_sibling=retrive_next_sibling,
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retrive_cum_len=None,
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spec_steps=self.speculative_num_steps,
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topk=self.topk,
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draft_token_num=self.server_args.speculative_num_draft_tokens,
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capture_hidden_mode=CaptureHiddenMode.FULL,
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seq_lens_sum=forward_batch.seq_lens_sum,
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seq_lens_cpu=forward_batch.seq_lens_cpu,
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)
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def clear_cache_pool(self):
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# allocator and kv cache pool are shared with target worker
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pass
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def verify(self, batch: ScheduleBatch, spec_info: EagleVerifyInput):
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|
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
|
|
model_worker_batch.return_hidden_states_before_norm = True
|
|
|
|
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
|
|
|
|
maybe_detect_nan(logits_output.next_token_logits, "verify: target model logits")
|
|
|
|
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]
|
|
|
|
if self.target_worker.model_runner.hybrid_gdn_config is not None:
|
|
accepted_length = (
|
|
torch.tensor(
|
|
res.accept_length_per_req_cpu,
|
|
device=logits_output.hidden_states.device,
|
|
dtype=torch.int64,
|
|
)
|
|
+ 1
|
|
)
|
|
|
|
# If topk > 1, we need to use retrieve_next_token and retrieve_next_sibling to handle the eagle tree custom attention mask
|
|
# res.accepted_indices.shape[0] > 0 skips DP attn idle batch
|
|
if spec_info.topk > 1 and res.accepted_indices.shape[0] > 0:
|
|
# accepted_indices=[0,2,3,4,5,7,9,10,11], accepted_length=[4, 3, 2], cumulative_accepted_lengths=[4, 7, 9]
|
|
# first_token_indices_per_req=prepend(0, accepted_indices[cumulative_accepted_lengths[:-1]]) = [0, 5, 10]
|
|
# last_token_indices_per_req=accepted_indices[cumulative_accepted_lengths - 1] = [4, 9, 11] (last token ID of each req)
|
|
# max_relative_indices_per_req = [4,4,1]; those are the per-req spec-decoding step offsets that contain the correct mamba caches
|
|
cumulative_accepted_lengths = torch.cumsum(accepted_length, dim=0)
|
|
req_start_positions = torch.cat(
|
|
[
|
|
torch.zeros(
|
|
1,
|
|
dtype=cumulative_accepted_lengths.dtype,
|
|
device=cumulative_accepted_lengths.device,
|
|
),
|
|
cumulative_accepted_lengths[:-1],
|
|
]
|
|
)
|
|
first_token_indices_per_req = res.accepted_indices[req_start_positions]
|
|
last_token_indices_per_req = res.accepted_indices[
|
|
cumulative_accepted_lengths - 1
|
|
]
|
|
max_relative_indices_per_req = (
|
|
last_token_indices_per_req - first_token_indices_per_req
|
|
)
|
|
else:
|
|
max_relative_indices_per_req = accepted_length - 1
|
|
self.target_worker.model_runner.attn_backend.update_mamba_state_after_mtp_verify(
|
|
max_relative_indices_per_req, self.target_worker.model_runner.model
|
|
)
|
|
|
|
if batch.return_logprob:
|
|
add_output_logprobs_for_spec_v1(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 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_req=1,
|
|
num_tokens_for_logprob_per_req=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.mtp_model_runner(0)
|
|
)
|
|
forward_batch.return_logprob = False
|
|
forward_batch.return_hidden_states_before_norm = True
|
|
topk_p_list = []
|
|
topk_index_list = []
|
|
for step in range(self.speculative_num_steps):
|
|
logits_output = (
|
|
self.mtp_model_runner(step).forward(forward_batch).logits_output
|
|
)
|
|
maybe_detect_nan(
|
|
logits_output.next_token_logits,
|
|
f"draft_extend_for_prefill step {step}",
|
|
)
|
|
probs = torch.softmax(logits_output.next_token_logits, dim=-1)
|
|
topk_p, topk_index = fast_topk(probs, self.topk, dim=-1)
|
|
topk_p_list.append(topk_p)
|
|
topk_index_list.append(topk_index)
|
|
pt = 0
|
|
if forward_batch.extend_seq_lens is not None:
|
|
for i, extend_len in enumerate(forward_batch.extend_seq_lens):
|
|
input_ids = forward_batch.input_ids[pt : pt + extend_len]
|
|
forward_batch.input_ids[pt : pt + extend_len] = torch.cat(
|
|
(input_ids[1:], topk_index[i].reshape(1))
|
|
)
|
|
pt += extend_len
|
|
|
|
assert isinstance(forward_batch.spec_info, EagleDraftInput)
|
|
assert forward_batch.spec_info is batch.spec_info
|
|
forward_batch.spec_info.topk_p = torch.cat(topk_p_list, dim=1)
|
|
forward_batch.spec_info.topk_index = torch.cat(topk_index_list, dim=1)
|
|
|
|
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_req = self.speculative_num_steps + 1
|
|
batch.spec_info.num_tokens_for_logprob_per_req = 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.mtp_model_runner(0)
|
|
)
|
|
forward_batch.return_hidden_states_before_norm = True
|
|
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()
|
|
topk_p_list = []
|
|
topk_index_list = []
|
|
# Run
|
|
for step in range(self.speculative_num_steps):
|
|
can_cuda_graph = len(
|
|
self.cuda_graph_runner_for_draft_extend_list
|
|
) and self.cuda_graph_runner_for_draft_extend_list[step].can_run(
|
|
forward_batch
|
|
)
|
|
if can_cuda_graph:
|
|
logits_output = self.cuda_graph_runner_for_draft_extend_list[
|
|
step
|
|
].replay(forward_batch)
|
|
else:
|
|
forward_batch.can_run_dp_cuda_graph = False
|
|
if not forward_batch.forward_mode.is_idle():
|
|
self.mtp_model_runner(step).attn_backend.init_forward_metadata(
|
|
forward_batch
|
|
)
|
|
logits_output = (
|
|
self.mtp_model_runner(step)
|
|
.forward(forward_batch, skip_attn_backend_init=True)
|
|
.logits_output
|
|
)
|
|
|
|
maybe_detect_nan(
|
|
logits_output.next_token_logits,
|
|
f"draft_extend_after_decode step {step} (cuda_graph={can_cuda_graph})",
|
|
)
|
|
probs = torch.softmax(logits_output.next_token_logits, dim=-1)
|
|
topk_p, topk_index = fast_topk(probs, self.topk, dim=-1)
|
|
topk_p_list.append(topk_p)
|
|
topk_index_list.append(topk_index)
|
|
pt = 0
|
|
if forward_batch.extend_seq_lens is not None:
|
|
for i, extend_len in enumerate(forward_batch.extend_seq_lens):
|
|
input_ids = forward_batch.input_ids[pt : pt + extend_len]
|
|
forward_batch.input_ids[pt : pt + extend_len] = torch.cat(
|
|
(input_ids[1:], topk_index[i].reshape(1))
|
|
)
|
|
pt += extend_len
|
|
|
|
forward_batch.spec_info.topk_p = torch.cat(topk_p_list, dim=1)
|
|
forward_batch.spec_info.topk_index = torch.cat(topk_index_list, dim=1)
|
|
|
|
# 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
|