mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
554 lines
23 KiB
Python
554 lines
23 KiB
Python
from dataclasses import dataclass, field
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from typing import TYPE_CHECKING, List, Optional, Set
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import torch
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from torch import nn
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from tensorrt_llm.mapping import Mapping
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from ..attention_backend import AttentionMetadata
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from ..pyexecutor.guided_decoder import CapturableGuidedDecoder
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from ..pyexecutor.llm_request import LlmRequest
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from ..pyexecutor.resource_manager import BaseResourceManager, SlotManager
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from ..pyexecutor.sampler import TorchSampler
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from ..pyexecutor.scheduler import ScheduledRequests
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from .interface import SpecMetadata
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from .mtp import MTPSampler
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from .spec_tree_manager import SpecTreeManager
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if TYPE_CHECKING:
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from ...llmapi.llm_args import EagleDecodingConfig
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class Eagle3ResourceManager(BaseResourceManager):
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"""
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Eagle3 needs to save the hidden states for the draft model. When using
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Eagle3TwoModel, there will be two model engines, one for the target model
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and one for the draft model. Use this class to manage the hidden states.
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"""
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def __init__(self, config: "EagleDecodingConfig", dtype: torch.dtype,
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hidden_size: int, max_num_requests: int, max_seq_len: int,
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max_num_tokens: int):
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self.dtype = dtype
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self.max_draft_len = config.max_draft_len
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self.hidden_size = hidden_size
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self.max_num_requests = max_num_requests
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self.max_seq_len = max_seq_len
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self.slot_manager = SlotManager(max_num_requests)
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self.max_total_draft_tokens = config.max_total_draft_tokens
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# empty hidden states tensor
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max_num_tokens = min(max_num_tokens,
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max_num_requests * self.max_seq_len)
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self.hidden_states = torch.empty(
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(max_num_tokens, self.hidden_size * config.num_capture_layers),
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dtype=self.dtype,
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device='cuda')
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# sequence length, only used for metadata preparation
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self.seq_lens = {i: 0 for i in range(max_num_requests)}
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# start indices of each slot
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self.start_indices = {i: 0 for i in range(max_num_requests)}
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# whether the next draft forward is the first
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self.is_first_draft = True
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self.spec_tree_manager = None
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if config.eagle_choices is not None:
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self.spec_tree_manager = SpecTreeManager(
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max_num_requests=self.max_num_requests,
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use_dynamic_tree=config.use_dynamic_tree,
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max_draft_len=self.max_draft_len,
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max_total_draft_tokens=self.max_total_draft_tokens,
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eagle_choices=config.eagle_choices,
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dynamic_tree_max_topK=config.dynamic_tree_max_topK,
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)
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def prepare_resources(self, scheduled_batch: ScheduledRequests):
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context_batch = scheduled_batch.context_requests
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# allocate hidden state tensors and update slot ids
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self.slot_ids = []
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for req in context_batch:
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if req.is_first_context_chunk:
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slot_id = self.slot_manager.add_slot(req.request_id)
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self.slot_ids.append(slot_id)
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# reset the flag before model forward
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self.is_first_draft = True
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def update_resources(self, scheduled_batch: ScheduledRequests):
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pass
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def free_resources(self, request: LlmRequest):
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slot_id = self.slot_manager.get_slot(request.request_id)
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self.seq_lens[slot_id] = 0
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self.start_indices[slot_id] = 0
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self.slot_manager.remove_slot(request.request_id)
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def add_dummy_requests(self, request_ids: List[int]):
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for rid in request_ids:
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self.slot_manager.add_slot(rid)
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def shutdown(self):
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pass
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def get_max_resource_count(self) -> int:
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return self.max_num_requests
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def get_needed_resource_to_completion(self, request: LlmRequest):
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return 0
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@dataclass
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class Eagle3SpecMetadata(SpecMetadata):
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hidden_states: List[torch.Tensor] = field(default_factory=list)
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layers_to_capture: Optional[Set[int]] = None
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target_model_embed_tokens: Optional[torch.nn.Module] = None
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hidden_size: int = 0
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max_num_tokens: int = 0
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dtype: torch.dtype = torch.bfloat16
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is_draft_model: bool = False
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is_first_draft: bool = False
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eagle3_resource_manager: Optional[Eagle3ResourceManager] = None
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is_mtp_eagle: bool = False
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eagle_choices: Optional[List[List[int]]] = None
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max_total_draft_tokens: int = 0
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def __post_init__(self):
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if self.is_draft_model:
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self.layers_to_capture = (self.num_layers - 1, )
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elif self.layers_to_capture is None:
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if self.num_layers == 1 or self.is_mtp_eagle:
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self.layers_to_capture = (self.num_layers - 1, )
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else:
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if self.num_layers <= 5:
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raise ValueError(
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"Not enough hidden layers for default EAGLE3 capture")
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self.layers_to_capture = (1, self.num_layers // 2 - 1,
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self.num_layers - 4)
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else:
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self.layers_to_capture = sorted(list(self.layers_to_capture))
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if self.layers_to_capture[0] == -1:
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self.layers_to_capture = self.layers_to_capture[1:] + [
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self.layers_to_capture.pop(0)
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]
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self.num_capture_layers = len(self.layers_to_capture)
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# Initialize to 0 to avoid reading uninitialized memory during warmup
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self.hidden_states_read_indices = torch.zeros([self.max_num_tokens],
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dtype=torch.long,
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device='cuda')
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self.hidden_states_write_indices = torch.zeros([self.max_num_tokens],
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dtype=torch.long,
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device='cuda')
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self.hidden_states_read_indices_host = None
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self.hidden_states_write_indices_host = None
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if self.eagle_choices is not None:
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self.is_spec_dec_tree = True
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self.is_spec_dec_dynamic_tree = False
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def prepare(self):
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is_first_draft = self.eagle3_resource_manager.is_first_draft
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# Update start indices
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# Here, we assume the sequence lengths (seq_lens) during the draft model
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# forward will not exceed those of the target model. So pre-allocate
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# hidden state space before the target model forward.
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start_idx = 0
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if not self.is_draft_model:
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for req_id, seq_len in zip(self.request_ids, self.seq_lens):
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slot_id = self.eagle3_resource_manager.slot_manager.get_slot(
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req_id)
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self.eagle3_resource_manager.start_indices[slot_id] = start_idx
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start_idx += seq_len
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# Prepare hidden states gather ids
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hidden_states_read_indices = []
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hidden_states_write_indices = []
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for req_id, seq_len in zip(self.request_ids, self.seq_lens):
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slot_id = self.eagle3_resource_manager.slot_manager.get_slot(req_id)
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start_idx = self.eagle3_resource_manager.start_indices[slot_id]
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# If this is the first draft or the target model forward, we need to
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# read/write all of the hidden states, otherwise, only read the last token
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if is_first_draft or not self.is_draft_model:
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hidden_states_read_indices.extend(
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list(range(start_idx, start_idx + seq_len)))
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hidden_states_write_indices.extend(
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list(range(start_idx, start_idx + seq_len)))
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else:
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old_seq_len = self.eagle3_resource_manager.seq_lens[slot_id]
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hidden_states_read_indices.append(start_idx + old_seq_len - 1)
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hidden_states_write_indices.append(start_idx + seq_len - 1)
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self.eagle3_resource_manager.seq_lens[slot_id] = seq_len
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# Prepare hidden states gather ids
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self.hidden_states_read_indices_host = torch.tensor(
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hidden_states_read_indices, dtype=torch.long, pin_memory=True)
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self.hidden_states_write_indices_host = torch.tensor(
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hidden_states_write_indices, dtype=torch.long, pin_memory=True)
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self.is_first_draft = is_first_draft and self.is_draft_model
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if self.is_draft_model:
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self.eagle3_resource_manager.is_first_draft = False
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self.hidden_states_read_indices[:self.num_tokens].copy_(
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self.hidden_states_read_indices_host, non_blocking=True)
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self.hidden_states_write_indices[:self.num_tokens].copy_(
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self.hidden_states_write_indices_host, non_blocking=True)
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def is_layer_capture(self, layer_id: int):
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return layer_id in self.layers_to_capture
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def maybe_capture_hidden_states(
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self,
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layer_id: int,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor] = None) -> None:
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token_idx = self.hidden_states_write_indices[:self.num_tokens]
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eagle3_hidden_states = self.eagle3_resource_manager.hidden_states
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for i, captured_layer_id in enumerate(self.layers_to_capture):
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if captured_layer_id == layer_id:
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to_save = hidden_states + residual if residual is not None else hidden_states
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to_save = to_save.to(dtype=eagle3_hidden_states.dtype)
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eagle3_hidden_states[:, i * self.hidden_size:(i + 1) *
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self.hidden_size].index_copy_(
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0, token_idx, to_save)
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break
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def get_hidden_states(self):
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hidden_states = self.eagle3_resource_manager.hidden_states[
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self.hidden_states_read_indices[:self.num_tokens], :]
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if not self.is_first_draft:
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hidden_states = hidden_states[:, :self.hidden_size]
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return hidden_states
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@dataclass
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class Eagle3OneModelSpecMetadata(SpecMetadata):
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# The hidden states
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hidden_states: Optional[torch.Tensor] = None
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# The layers to be captured
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layers_to_capture: Optional[Set[int]] = None
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# The hidden size of the hidden states
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hidden_size: int = 0
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# The max number of tokens
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max_num_tokens: int = 0
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# The dtype of the hidden states
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dtype: torch.dtype = torch.bfloat16
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# The index of the batche inputs
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batch_indices_cuda: Optional[torch.Tensor] = None
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def __post_init__(self):
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if self.layers_to_capture is None:
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if self.num_layers == 1:
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self.layers_to_capture = (self.num_layers - 1, )
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else:
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if self.num_layers <= 5:
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raise ValueError(
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"Not enough hidden layers for default EAGLE3 capture")
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self.layers_to_capture = (1, self.num_layers // 2 - 1,
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self.num_layers - 4)
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else:
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self.layers_to_capture = sorted(list(self.layers_to_capture))
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self.num_capture_layers = len(self.layers_to_capture)
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self.hidden_states = torch.empty(
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(self.max_num_tokens,
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self.hidden_size * len(self.layers_to_capture)),
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dtype=self.dtype,
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device='cuda')
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self.batch_indices_cuda = torch.empty(
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[self.max_num_requests],
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dtype=torch.int,
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device='cuda',
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)
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# currently Eagle3 only supports linear tree
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self.is_spec_dec_tree = False
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# currently Eagle3 only supports static tree
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self.is_spec_dec_dynamic_tree = False
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def is_layer_capture(self, layer_id: int):
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return layer_id in self.layers_to_capture
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def prepare(self):
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assert self.request_ids is not None
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# update batch indeices
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num_seqs = len(self.request_ids)
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batch_indices = torch.arange(num_seqs,
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dtype=torch.int,
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device='cpu',
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pin_memory=True)
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self.batch_indices_cuda[:num_seqs].copy_(batch_indices,
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non_blocking=True)
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self.num_tokens -= (self.num_generations) * self.max_draft_len
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def maybe_capture_hidden_states(
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self,
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layer_id: int,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor] = None) -> None:
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for i, captured_layer_id in enumerate(self.layers_to_capture):
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if captured_layer_id == layer_id:
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num_tokens = hidden_states.shape[0]
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to_save = hidden_states + residual if residual is not None else hidden_states
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self.hidden_states[:num_tokens, i * self.hidden_size:(i + 1) *
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self.hidden_size].copy_(to_save,
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non_blocking=True)
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break
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class Eagle3OneModelSampler(MTPSampler):
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def __init__(self, args: TorchSampler.Args):
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super().__init__(args, nextn=args.max_draft_len)
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class Eagle3OneModelWorker(nn.Module):
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def __init__(self, spec_config: "EagleDecodingConfig", mapping: Mapping):
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super().__init__()
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self.spec_config = spec_config
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self.max_draft_len = self.spec_config.max_draft_len
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self.mapping = mapping
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self.guided_decoder: Optional[CapturableGuidedDecoder] = None
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# Skip torch.compile for now since current Torch is not compatible with Triton 3.4
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# @torch.compile(options={"max-autotune": True})
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def forward(self, input_ids, position_ids, hidden_states, logits,
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attn_metadata, spec_metadata, draft_model):
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batch_size = attn_metadata.num_seqs
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num_contexts = attn_metadata.num_contexts
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num_gens = batch_size - num_contexts
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raw_logits = logits
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if self.guided_decoder is not None:
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self.guided_decoder.execute(logits)
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# Sample and accept tokens
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accepted_tokens, num_accepted_tokens = self.sample_and_accept_draft_tokens(
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logits, attn_metadata, spec_metadata)
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# Save the old attn_metadata and spec_metadata
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attn_metadata.prepare_for_spec_dec("_seq_lens", "_seq_lens_cuda")
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# Prepare inputs for the 1st draft model forward
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position_ids = position_ids.squeeze(0)
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inputs = self.prepare_1st_drafter_inputs(
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input_ids=input_ids,
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position_ids=position_ids,
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hidden_states=hidden_states,
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accepted_tokens=accepted_tokens,
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attn_metadata=attn_metadata,
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spec_metadata=spec_metadata,
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draft_model=draft_model)
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# Predict draft tokens
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next_draft_tokens = []
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for i in range(self.max_draft_len):
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if i == 0:
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start_ids_gen = (spec_metadata.batch_indices_cuda[:num_gens] *
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(self.max_draft_len + 1)).long()
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gather_ids_gen = (start_ids_gen +
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num_accepted_tokens[num_contexts:] - 1 +
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attn_metadata.num_ctx_tokens)
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gather_ids = torch.concat(
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[spec_metadata.gather_ids[:num_contexts], gather_ids_gen],
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dim=0)
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else:
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# All of the seq_len are 1, use batch_indices_cuda as gather_ids
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gather_ids = spec_metadata.batch_indices_cuda[:batch_size]
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if self.guided_decoder is not None:
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new_tokens = inputs["input_ids"][gather_ids]
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self.guided_decoder.add_draft_batch(new_tokens,
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num_accepted_tokens,
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draft_step=i)
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hidden_states, hidden_states_to_save = draft_model.model(**inputs)
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# FIXME (jhaotingc): Currently we disable use_spec_decoding mode for Eagle engine nth steps except 1st step.
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# Eagle engine takes in draft_len tokens from the previous step, run spec-dec mode with those tokens,
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# then the following step can use regular decoding mode to generate 1 tokens per step.
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# Currently the spec-dec mask for chained tree is not implemented yet.
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# When token tree is supported, this can be removed and all steps may use spec-dec mode as well.
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attn_metadata.use_spec_decoding = False
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logits = draft_model.logits_processor(hidden_states[gather_ids],
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draft_model.lm_head,
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attn_metadata, True)
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if self.guided_decoder is not None:
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d2t = getattr(draft_model.model, "d2t", None)
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self.guided_decoder.execute_draft_batch(logits,
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d2t,
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draft_step=i)
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new_draft_token = self.draft_decoder(logits, draft_model)
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next_draft_tokens.append(new_draft_token)
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# update inputs
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hidden_states = hidden_states_to_save[gather_ids]
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position_ids = inputs["position_ids"][gather_ids] + 1
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# update attn_metadata
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if i == 0:
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attn_metadata._seq_lens[:batch_size].fill_(1)
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attn_metadata._seq_lens_cuda[:batch_size].fill_(1)
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attn_metadata.on_update()
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# cannot run generation if their is no kv cache
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if inputs["attn_metadata"].kv_cache_manager is not None:
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attn_metadata.host_request_types[:attn_metadata.
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num_contexts].fill_(1)
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attn_metadata.num_contexts = 0
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# update kv_lens_cuda
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if hasattr(attn_metadata, 'kv_lens_cuda'):
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attn_metadata.kv_lens_cuda[num_contexts:batch_size] -= (
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self.max_draft_len - num_accepted_tokens[num_contexts:])
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attn_metadata.kv_lens_cuda[:num_contexts] += 1
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elif hasattr(attn_metadata, 'kv_lens_cuda'):
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attn_metadata.kv_lens_cuda[:batch_size] += 1
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# support attention dp
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if spec_metadata.all_rank_num_tokens is not None:
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spec_metadata.all_rank_num_tokens = spec_metadata.all_rank_num_seqs
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inputs = {
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"input_ids": new_draft_token,
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"position_ids": position_ids,
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"hidden_states": hidden_states,
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"attn_metadata": attn_metadata,
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"spec_metadata": spec_metadata,
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}
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next_draft_tokens = torch.stack(next_draft_tokens, dim=1)
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# restore attn_metadata to support cuda graph
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attn_metadata.restore_from_spec_dec()
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attn_metadata.on_update()
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# prepare next new tokens to support overlap scheduler
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next_new_tokens = accepted_tokens[
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spec_metadata.batch_indices_cuda[:batch_size],
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num_accepted_tokens - 1].unsqueeze(1)
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next_new_tokens = torch.concat([next_new_tokens, next_draft_tokens],
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dim=1)
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attn_metadata.use_spec_decoding = True
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return {
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'logits': raw_logits,
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'new_tokens': accepted_tokens,
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'new_tokens_lens': num_accepted_tokens,
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'next_draft_tokens': next_draft_tokens,
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'next_new_tokens': next_new_tokens,
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}
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def sample_and_accept_draft_tokens(
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self,
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logits: torch.Tensor,
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attn_metadata: AttentionMetadata,
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spec_metadata: Eagle3OneModelSpecMetadata,
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):
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batch_size = attn_metadata.num_seqs
|
|
num_contexts = attn_metadata.num_contexts
|
|
num_gens = batch_size - num_contexts
|
|
|
|
if logits.dim() == 1:
|
|
logits = logits.unsqueeze(0)
|
|
|
|
# The return buffer
|
|
accepted_tokens = torch.empty((batch_size, (self.max_draft_len + 1)),
|
|
dtype=torch.int,
|
|
device=logits.device)
|
|
num_accepted_tokens = torch.ones(batch_size,
|
|
dtype=torch.int,
|
|
device=logits.device)
|
|
|
|
# Do greedy sampling for the input logits
|
|
target_tokens = torch.argmax(logits, dim=-1)
|
|
# context
|
|
accepted_tokens[:num_contexts, 0] = target_tokens[:num_contexts]
|
|
|
|
# generation
|
|
gen_target_tokens = target_tokens[num_contexts:].reshape(
|
|
num_gens, self.max_draft_len + 1)
|
|
accepted_tokens[num_contexts:, :] = gen_target_tokens
|
|
draft_tokens = spec_metadata.draft_tokens.reshape(
|
|
num_gens, self.max_draft_len)
|
|
num_accepted_tokens[num_contexts:] += torch.cumprod(
|
|
(draft_tokens == gen_target_tokens[:, :self.max_draft_len]).int(),
|
|
dim=-1).sum(1)
|
|
return accepted_tokens, num_accepted_tokens
|
|
|
|
def draft_decoder(
|
|
self,
|
|
logits: torch.Tensor,
|
|
draft_model: nn.Module,
|
|
):
|
|
'''
|
|
Sampling draft tokens.
|
|
|
|
Args:
|
|
logits: torch.Tensor
|
|
[num_tokens, vocab_size]
|
|
Logits produced by the draft model.
|
|
draft_model: nn.Module
|
|
The draft model.
|
|
|
|
Returns:
|
|
draft_tokens: torch.Tensor
|
|
[batch_size * max_draft_len]
|
|
Draft token ids. Flattened.
|
|
'''
|
|
|
|
draft_tokens = torch.argmax(logits, dim=-1)
|
|
|
|
# Apply d2t (offsets between draft model dictionary and main model dictionary).
|
|
if (d2t := getattr(draft_model.model, "d2t", None)) is not None:
|
|
draft_tokens = d2t[draft_tokens] + draft_tokens
|
|
|
|
draft_tokens = draft_tokens.type(torch.int32)
|
|
|
|
return draft_tokens
|
|
|
|
def prepare_1st_drafter_inputs(
|
|
self,
|
|
input_ids: torch.LongTensor,
|
|
position_ids: torch.LongTensor,
|
|
hidden_states: torch.Tensor,
|
|
accepted_tokens: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
|
|
spec_metadata: Eagle3OneModelSpecMetadata,
|
|
draft_model: nn.Module,
|
|
):
|
|
num_contexts = attn_metadata.num_contexts
|
|
num_tokens = input_ids.shape[0]
|
|
|
|
# prepare hidden states
|
|
hidden_size_up = spec_metadata.hidden_size * len(
|
|
spec_metadata.layers_to_capture)
|
|
hidden_states = spec_metadata.hidden_states[:num_tokens, :
|
|
hidden_size_up]
|
|
hidden_states = draft_model.apply_eagle3_fc(hidden_states)
|
|
|
|
# context
|
|
input_ctx_ids = input_ids[:attn_metadata.num_ctx_tokens]
|
|
input_ids_ctx = torch.empty_like(input_ctx_ids,
|
|
dtype=torch.int32,
|
|
device="cuda")
|
|
input_ids_ctx[:-1].copy_(input_ctx_ids[1:])
|
|
input_ids_ctx[
|
|
spec_metadata.
|
|
gather_ids[:num_contexts]] = accepted_tokens[:num_contexts, 0]
|
|
|
|
# generation
|
|
input_ids_gen = accepted_tokens[num_contexts:, :].flatten()
|
|
|
|
# get draft inputs
|
|
input_ids = torch.concat([input_ids_ctx, input_ids_gen], dim=0)
|
|
|
|
return {
|
|
"input_ids": input_ids,
|
|
"position_ids": position_ids,
|
|
"hidden_states": hidden_states,
|
|
"attn_metadata": attn_metadata,
|
|
"spec_metadata": spec_metadata,
|
|
}
|
|
|
|
def set_guided_decoder(self,
|
|
guided_decoder: CapturableGuidedDecoder) -> bool:
|
|
self.guided_decoder = guided_decoder
|
|
return True
|