240 lines
9.1 KiB
Python
240 lines
9.1 KiB
Python
from __future__ import annotations
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import bisect
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from typing import TYPE_CHECKING, Callable
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import torch
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from sglang.srt.model_executor.cuda_graph_runner import (
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CudaGraphRunner,
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get_batch_sizes_to_capture,
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get_global_graph_memory_pool,
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set_global_graph_memory_pool,
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set_torch_compile_config,
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)
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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.speculative.eagle_utils import EagleDraftInput
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if TYPE_CHECKING:
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from sglang.srt.speculative.eagle_worker import EAGLEWorker
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import logging
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logger = logging.getLogger(__name__)
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class EAGLEDraftCudaGraphRunner:
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def __init__(self, eagle_worker: EAGLEWorker):
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# Parse args
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self.eagle_worker = eagle_worker
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self.model_runner = model_runner = eagle_worker.model_runner
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self.graphs = {}
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self.output_buffers = {}
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self.enable_torch_compile = model_runner.server_args.enable_torch_compile
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self.disable_padding = model_runner.server_args.disable_cuda_graph_padding
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self.tp_size = self.model_runner.tp_size
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self.topk = model_runner.server_args.speculative_eagle_topk
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self.speculative_num_steps = model_runner.server_args.speculative_num_steps
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server_args = model_runner.server_args
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# Batch sizes to capture
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self.capture_bs, self.compile_bs = get_batch_sizes_to_capture(model_runner)
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self.num_tokens_per_bs = server_args.speculative_eagle_topk
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# Attention backend
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self.max_bs = max(self.capture_bs)
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self.max_num_token = self.max_bs * self.num_tokens_per_bs
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self.model_runner.draft_attn_backend.init_cuda_graph_state(self.max_num_token)
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self.seq_len_fill_value = self.model_runner.draft_attn_backend.attn_backends[
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0
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].get_cuda_graph_seq_len_fill_value()
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self.seq_lens_cpu = torch.full(
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(self.max_bs,), self.seq_len_fill_value, dtype=torch.int32
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)
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if self.enable_torch_compile:
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set_torch_compile_config()
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# Graph inputs
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with torch.device("cuda"):
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self.input_ids = torch.zeros((self.max_num_token,), dtype=torch.int64)
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self.req_pool_indices = torch.zeros((self.max_bs,), dtype=torch.int32)
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self.seq_lens = torch.full(
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(self.max_bs,), self.seq_len_fill_value, dtype=torch.int32
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)
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self.out_cache_loc = torch.zeros(
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(self.max_num_token * self.speculative_num_steps,), dtype=torch.int64
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)
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self.positions = torch.zeros((self.max_num_token,), dtype=torch.int64)
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self.topk_p = torch.zeros((self.max_bs, self.topk), dtype=torch.float32)
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self.topk_index = torch.zeros((self.max_bs, self.topk), dtype=torch.int64)
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self.hidden_states = torch.zeros(
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(self.max_bs, self.model_runner.model_config.hidden_size),
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dtype=self.model_runner.dtype,
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)
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# Capture
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try:
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self.capture()
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except RuntimeError as e:
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raise Exception(
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f"Capture cuda graph failed: {e}\n"
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"Possible solutions:\n"
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"1. disable cuda graph by --disable-cuda-graph\n"
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"2. set --mem-fraction-static to a smaller value (e.g., 0.8 or 0.7)\n"
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"3. disable torch compile by not using --enable-torch-compile\n"
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"4. specify --dtype to the same dtype (e.g. bfloat16)\n"
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"Open an issue on GitHub https://github.com/sgl-project/sglang/issues/new/choose \n"
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)
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def can_run(self, forward_batch: ForwardBatch):
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is_bs_supported = (
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forward_batch.batch_size in self.graphs
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if self.disable_padding
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else forward_batch.batch_size <= self.max_bs
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)
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return is_bs_supported
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def capture(self):
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CudaGraphRunner.capture(self)
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def capture_one_batch_size(self, num_seqs: int, forward: Callable):
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graph = torch.cuda.CUDAGraph()
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stream = self.stream
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num_tokens = num_seqs * self.num_tokens_per_bs
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# Graph inputs
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req_pool_indices = self.req_pool_indices[:num_seqs]
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seq_lens = self.seq_lens[:num_seqs]
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out_cache_loc = self.out_cache_loc[: num_tokens * self.speculative_num_steps]
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positions = self.positions[:num_tokens]
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topk_p = self.topk_p[:num_seqs]
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topk_index = self.topk_index[:num_seqs]
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hidden_states = self.hidden_states[:num_seqs]
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spec_info = EagleDraftInput(
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topk_p=topk_p,
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topk_index=topk_index,
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hidden_states=hidden_states,
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)
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# Forward batch
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forward_batch = ForwardBatch(
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forward_mode=ForwardMode.DECODE,
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batch_size=num_seqs,
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input_ids=None,
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req_pool_indices=req_pool_indices,
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seq_lens=seq_lens,
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req_to_token_pool=self.model_runner.req_to_token_pool,
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token_to_kv_pool=self.model_runner.token_to_kv_pool,
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out_cache_loc=out_cache_loc,
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seq_lens_sum=seq_lens.sum(),
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return_logprob=False,
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positions=positions,
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spec_algorithm=self.model_runner.spec_algorithm,
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spec_info=spec_info,
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capture_hidden_mode=(
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spec_info.capture_hidden_mode if spec_info else CaptureHiddenMode.NULL
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),
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)
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# Attention backend
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self.model_runner.draft_attn_backend.init_forward_metadata_capture_cuda_graph(
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forward_batch
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)
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# Run and capture
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def run_once():
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# Backup two fileds, which will be modified in-place in `draft_forward`.
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output_cache_loc_backup = forward_batch.out_cache_loc
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hidden_states_backup = forward_batch.spec_info.hidden_states
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ret = self.eagle_worker.draft_forward(forward_batch)
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forward_batch.out_cache_loc = output_cache_loc_backup
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forward_batch.spec_info.hidden_states = hidden_states_backup
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return ret
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for _ in range(2):
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torch.cuda.synchronize()
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self.model_runner.tp_group.barrier()
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run_once()
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with torch.cuda.graph(
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graph, pool=get_global_graph_memory_pool(), stream=stream
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):
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out = run_once()
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set_global_graph_memory_pool(graph.pool())
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return graph, out
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def _postprocess_output_to_raw_bs(self, out, raw_bs):
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score_list, token_list, parents_list = out
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score_list = [x[:raw_bs] for x in score_list]
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token_list = [x[:raw_bs] for x in token_list]
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parents_list = [x[:raw_bs] for x in parents_list]
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return (score_list, token_list, parents_list)
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def replay(self, forward_batch: ForwardBatch):
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assert forward_batch.out_cache_loc is not None
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raw_bs = forward_batch.batch_size
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raw_num_token = raw_bs * self.num_tokens_per_bs
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# Pad
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index = bisect.bisect_left(self.capture_bs, raw_bs)
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bs = self.capture_bs[index]
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if bs != raw_bs:
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self.seq_lens.fill_(1)
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self.out_cache_loc.zero_()
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self.positions.zero_()
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num_tokens = bs * self.num_tokens_per_bs
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# Common inputs
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self.req_pool_indices[:raw_bs].copy_(forward_batch.req_pool_indices)
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self.seq_lens[:raw_bs].copy_(forward_batch.seq_lens)
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self.out_cache_loc[: raw_num_token * self.speculative_num_steps].copy_(
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forward_batch.out_cache_loc
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)
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self.positions[:raw_num_token].copy_(forward_batch.positions)
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self.topk_p[:raw_bs].copy_(forward_batch.spec_info.topk_p)
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self.topk_index[:raw_bs].copy_(forward_batch.spec_info.topk_index)
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self.hidden_states[:raw_bs].copy_(forward_batch.spec_info.hidden_states)
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# Attention backend
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if bs != raw_bs:
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forward_batch.batch_size = bs
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forward_batch.seq_lens = self.seq_lens[:bs]
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forward_batch.req_pool_indices = self.req_pool_indices[:bs]
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forward_batch.positions = self.positions[:num_tokens]
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# Special handle for seq_len_cpu used when flashinfer mla is used
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if (forward_batch.decode_seq_lens_cpu is not None) and (bs != raw_bs):
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self.seq_lens_cpu.fill_(1)
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self.seq_lens_cpu[:raw_bs].copy_(forward_batch.decode_seq_lens_cpu)
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forward_batch.decode_seq_lens_cpu = self.seq_lens_cpu[:bs]
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self.model_runner.draft_attn_backend.init_forward_metadata_replay_cuda_graph(
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forward_batch, bs
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)
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# Replay
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self.graphs[bs].replay()
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out = self.output_buffers[bs]
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if bs != raw_bs:
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out = self._postprocess_output_to_raw_bs(out, raw_bs)
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forward_batch.batch_size = raw_bs
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forward_batch.positions = self.positions[:raw_num_token]
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forward_batch.seq_lens = self.seq_lens[:raw_bs]
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forward_batch.req_pool_indices = self.req_pool_indices[:raw_bs]
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if forward_batch.decode_seq_lens_cpu is not None:
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forward_batch.decode_seq_lens_cpu = self.seq_lens_cpu[:raw_bs]
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return out
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