164 lines
6.3 KiB
Python
164 lines
6.3 KiB
Python
from __future__ import annotations
|
|
|
|
from dataclasses import dataclass
|
|
from typing import TYPE_CHECKING, Optional
|
|
|
|
import torch
|
|
|
|
from sglang.srt.utils import get_compiler_backend
|
|
|
|
if TYPE_CHECKING:
|
|
from sglang.srt.managers.schedule_batch import ModelWorkerBatch
|
|
from sglang.srt.managers.scheduler import GenerationBatchResult
|
|
from sglang.srt.speculative.eagle_info import EagleDraftInput
|
|
from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
|
|
|
|
|
|
@torch.compile(dynamic=True, backend=get_compiler_backend())
|
|
def _resolve_future_token_ids(input_ids, future_token_ids_map):
|
|
input_ids[:] = torch.where(
|
|
input_ids < 0,
|
|
future_token_ids_map[torch.clamp(-input_ids, min=0)],
|
|
input_ids,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class FutureIndices:
|
|
indices: torch.Tensor
|
|
interval: Optional[slice] = None
|
|
|
|
|
|
class FutureMap:
|
|
def __init__(
|
|
self,
|
|
max_running_requests: int,
|
|
chunked_prefill_size: int,
|
|
context_len: int,
|
|
device: torch.device,
|
|
spec_algo: Optional[SpeculativeAlgorithm] = None,
|
|
):
|
|
# FIXME: the calculation of future_limit and future_buffer_len maybe too conservative
|
|
self.future_ct = 0
|
|
|
|
# Circular buffer layout (wraps in this order):
|
|
# Running decode batch -> Prefill chunk 1 -> ... -> Prefill chunk N
|
|
# A running decode batch's result will be resolved after all prefill chunks are done.
|
|
# reserve `max_num_chunks` extra future slots on top of `max_running_requests * 3`.
|
|
max_num_chunks = (
|
|
(context_len + chunked_prefill_size - 1) // chunked_prefill_size
|
|
if chunked_prefill_size
|
|
else 0
|
|
)
|
|
self.future_limit = max_running_requests * (3 + max_num_chunks)
|
|
# Adding 2 * max_running_requests to future_limit ensures the buffer is sufficiently large.
|
|
self.future_buffer_len = self.future_limit + 2 * max_running_requests
|
|
self.device = device
|
|
self.spec_algo = spec_algo
|
|
|
|
if self.spec_algo.is_none():
|
|
# For non-speculative decoding, we only need to store the token ids.
|
|
self.buf_initialized = True
|
|
self.token_ids_buf = torch.empty(
|
|
(self.future_buffer_len,), dtype=torch.int64, device=self.device
|
|
)
|
|
else:
|
|
# For speculative decoding, we lazily initialize the buffers
|
|
# This is to make the shape derivation easier.
|
|
self.buf_initialized = False
|
|
|
|
def _lazy_init_buf(self, draft_input: EagleDraftInput):
|
|
self.buf_initialized = True
|
|
|
|
# Get a reference for each tensor
|
|
topk_p0 = draft_input.topk_p[0]
|
|
topk_index0 = draft_input.topk_index[0]
|
|
hidden_states0 = draft_input.hidden_states[0]
|
|
verified_id0 = draft_input.verified_id[0]
|
|
new_seq_lens0 = draft_input.new_seq_lens[0]
|
|
|
|
self.topk_p_buf = torch.empty(
|
|
(self.future_buffer_len, *topk_p0.shape),
|
|
dtype=topk_p0.dtype,
|
|
device=self.device,
|
|
)
|
|
self.topk_index_buf = torch.empty(
|
|
(self.future_buffer_len, *topk_index0.shape),
|
|
dtype=topk_index0.dtype,
|
|
device=self.device,
|
|
)
|
|
self.hidden_states_buf = torch.empty(
|
|
(self.future_buffer_len, *hidden_states0.shape),
|
|
dtype=hidden_states0.dtype,
|
|
device=self.device,
|
|
)
|
|
self.verified_id_buf = torch.empty(
|
|
(self.future_buffer_len, *verified_id0.shape),
|
|
dtype=verified_id0.dtype,
|
|
device=self.device,
|
|
)
|
|
self.new_seq_lens_buf = torch.empty(
|
|
(self.future_buffer_len, *new_seq_lens0.shape),
|
|
dtype=new_seq_lens0.dtype,
|
|
device=self.device,
|
|
)
|
|
|
|
def alloc_future_indices(self, bs: int) -> FutureIndices:
|
|
"""Update the circular buffer pointer and allocate future indices."""
|
|
cur_future_ct = self.future_ct
|
|
self.future_ct = (cur_future_ct + bs) % self.future_limit
|
|
start = cur_future_ct + 1
|
|
end = cur_future_ct + 1 + bs
|
|
indices = torch.arange(start, end, dtype=torch.int64, device=self.device)
|
|
return FutureIndices(indices=indices, interval=slice(start, end))
|
|
|
|
def resolve_future(self, model_worker_batch: ModelWorkerBatch):
|
|
if self.spec_algo.is_eagle():
|
|
# TODO(lsyin): write future indices into spec_info.future_indices
|
|
draft_input: EagleDraftInput = model_worker_batch.spec_info
|
|
if draft_input is None:
|
|
# FIXME(lsyin): No future exists, only for prefill batch, not compatible with mixed mode
|
|
return
|
|
indices = draft_input.future_indices.indices
|
|
draft_input.topk_p = self.topk_p_buf[indices]
|
|
draft_input.topk_index = self.topk_index_buf[indices]
|
|
draft_input.hidden_states = self.hidden_states_buf[indices]
|
|
draft_input.verified_id = self.verified_id_buf[indices]
|
|
draft_input.new_seq_lens = self.new_seq_lens_buf[indices]
|
|
else:
|
|
_resolve_future_token_ids(model_worker_batch.input_ids, self.token_ids_buf)
|
|
|
|
def is_empty_slice(self, s: slice) -> bool:
|
|
start, stop, step = s.indices(self.future_buffer_len)
|
|
if step > 0:
|
|
return start >= stop
|
|
else:
|
|
return start <= stop
|
|
|
|
def store_to_map(
|
|
self, future_indices: FutureIndices, batch_result: GenerationBatchResult
|
|
):
|
|
if self.spec_algo.is_eagle():
|
|
draft_input: EagleDraftInput = batch_result.next_draft_input
|
|
self.store_to_map_for_new_batch(future_indices, draft_input)
|
|
else:
|
|
intv = future_indices.interval
|
|
self.token_ids_buf[intv] = batch_result.next_token_ids
|
|
|
|
def store_to_map_for_new_batch(
|
|
self, future_indices: FutureIndices, draft_input: EagleDraftInput
|
|
):
|
|
intv = future_indices.interval
|
|
if self.is_empty_slice(intv):
|
|
# idle indices in dp attention do not need store info
|
|
return
|
|
|
|
if not self.buf_initialized:
|
|
self._lazy_init_buf(draft_input)
|
|
|
|
self.topk_p_buf[intv] = draft_input.topk_p
|
|
self.topk_index_buf[intv] = draft_input.topk_index
|
|
self.hidden_states_buf[intv] = draft_input.hidden_states
|
|
self.verified_id_buf[intv] = draft_input.verified_id
|
|
self.new_seq_lens_buf[intv] = draft_input.new_seq_lens
|