Sync the changes on cuda graph runners (#6932)
This commit is contained in:
@@ -20,7 +20,7 @@ import copy
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import uuid
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
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from sglang.srt.mm_utils import has_valid_data
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@@ -30,7 +30,7 @@ if TYPE_CHECKING:
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else:
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Image = Any
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from sglang.srt.managers.schedule_batch import BaseFinishReason, flatten_nested_list
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from sglang.srt.managers.schedule_batch import BaseFinishReason
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from sglang.srt.sampling.sampling_params import SamplingParams
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@@ -259,23 +259,8 @@ class CudaGraphRunner:
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}
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# Speculative_inference
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if (
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model_runner.spec_algorithm.is_eagle3()
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and not model_runner.is_draft_worker
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):
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self.hidden_states = torch.zeros(
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(
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self.max_num_token,
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3 * self.model_runner.model_config.hidden_size,
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),
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dtype=self.model_runner.dtype,
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)
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if model_runner.spec_algorithm.is_eagle3():
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self.model_runner.model.set_eagle3_layers_to_capture()
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elif model_runner.spec_algorithm.is_eagle():
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self.hidden_states = torch.zeros(
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(self.max_num_token, self.model_runner.model_config.hidden_size),
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dtype=self.model_runner.dtype,
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)
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if self.is_encoder_decoder:
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# NOTE: encoder_lens can influence the full_text_row_masked_out_mask tensor when doing mixed batch
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@@ -284,6 +269,7 @@ class CudaGraphRunner:
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)
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else:
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self.encoder_lens = None
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if self.enable_dp_attention or self.enable_sp_layernorm:
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# TODO(ch-wan): SP layernorm should use a different logic to manage gathered_buffer
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self.gathered_buffer = torch.zeros(
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@@ -303,13 +289,7 @@ class CudaGraphRunner:
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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. set --mem-fraction-static to a smaller value (e.g., 0.8 or 0.7)\n"
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"2. set --cuda-graph-max-bs to a smaller value (e.g., 16)\n"
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"3. disable torch compile by not using --enable-torch-compile\n"
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"4. disable CUDA graph by --disable-cuda-graph. (Not recommended. Huge performance loss)\n"
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"Open an issue on GitHub https://github.com/sgl-project/sglang/issues/new/choose \n"
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f"Capture cuda graph failed: {e}\n{CUDA_GRAPH_CAPTURE_FAILED_MSG}"
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)
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@contextmanager
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@@ -439,6 +419,7 @@ class CudaGraphRunner:
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self.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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if self.model_runner.server_args.lora_paths is not None:
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# Currently, if the lora_path in `lora_paths` is None, the lora backend will use a
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# different logic to handle lora, so we need to set `lora_paths` to a list of non-None
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@@ -467,9 +448,9 @@ class CudaGraphRunner:
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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=self.capture_hidden_mode,
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lora_paths=lora_paths,
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num_token_non_padded=self.num_token_non_padded,
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global_forward_mode=self.capture_forward_mode,
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lora_paths=lora_paths,
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)
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self.tbo_plugin.capture_one_batch_size(forward_batch, num_tokens=num_tokens)
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@@ -497,7 +478,9 @@ class CudaGraphRunner:
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self.pp_size > 1
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and "pp_proxy_tensors" in inspect.signature(forward).parameters
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):
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kwargs["pp_proxy_tensors"] = pp_proxy_tensors
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kwargs["pp_proxy_tensors"] = PPProxyTensors(
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{k: v.clone() for k, v in pp_proxy_tensors.tensors.items()}
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)
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logits_output_or_pp_proxy_tensors = forward(
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input_ids,
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@@ -590,9 +573,6 @@ class CudaGraphRunner:
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if self.enable_dp_attention or self.enable_sp_layernorm:
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self.global_num_tokens_gpu.copy_(forward_batch.global_num_tokens_gpu)
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if hasattr(forward_batch.spec_info, "hidden_states"):
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self.hidden_states[:raw_num_token] = forward_batch.spec_info.hidden_states
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# Attention backend
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self.model_runner.attn_backend.init_forward_metadata_replay_cuda_graph(
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bs,
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@@ -650,7 +630,7 @@ class CudaGraphRunner:
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else:
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spec_info = EagleVerifyInput(
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draft_token=None,
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custom_mask=torch.zeros(
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custom_mask=torch.ones(
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(num_tokens * self.model_runner.model_config.context_len),
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dtype=torch.bool,
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device="cuda",
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@@ -660,9 +640,20 @@ class CudaGraphRunner:
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retrive_next_token=None,
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retrive_next_sibling=None,
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retrive_cum_len=None,
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draft_token_num=self.model_runner.server_args.speculative_num_draft_tokens,
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spec_steps=self.model_runner.server_args.speculative_num_steps,
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topk=self.model_runner.server_args.speculative_eagle_topk,
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draft_token_num=self.model_runner.server_args.speculative_num_draft_tokens,
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capture_hidden_mode=CaptureHiddenMode.FULL,
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)
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return spec_info
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CUDA_GRAPH_CAPTURE_FAILED_MSG = (
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"Possible solutions:\n"
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"1. set --mem-fraction-static to a smaller value (e.g., 0.8 or 0.7)\n"
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"2. set --cuda-graph-max-bs to a smaller value (e.g., 16)\n"
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"3. disable torch compile by not using --enable-torch-compile\n"
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"4. disable CUDA graph by --disable-cuda-graph. (Not recommended. Huge performance loss)\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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@@ -447,7 +447,7 @@ class ServerArgs:
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self.speculative_num_steps,
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self.speculative_eagle_topk,
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self.speculative_num_draft_tokens,
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) = auto_choose_speculative_params(model_arch)
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) = auto_choose_speculative_params(self)
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if self.page_size > 1 and self.speculative_eagle_topk > 1:
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self.speculative_eagle_topk = 1
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@@ -1655,12 +1655,23 @@ def get_model_arch(args: ServerArgs):
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return hf_config.architectures[0]
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def auto_choose_speculative_params(arch: str):
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def auto_choose_speculative_params(self: ServerArgs):
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"""
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Automatically choose the parameters for speculative decoding.
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You can tune them on your own models and prompts with scripts/playground/bench_speculative.py
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"""
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kwargs = {}
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hf_config = get_config(
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self.model_path,
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trust_remote_code=self.trust_remote_code,
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revision=self.revision,
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model_override_args=json.loads(self.json_model_override_args),
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**kwargs,
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)
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arch = hf_config.architectures[0]
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if arch in ["LlamaForCausalLM"]:
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# The default value for llama
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return (5, 4, 8)
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@@ -4,7 +4,7 @@ from typing import List
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import torch
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from sglang.srt.utils import is_cuda, is_hip
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from sglang.srt.utils import is_cuda, is_hip, rank0_print
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if is_cuda() or is_hip():
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from sgl_kernel import (
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@@ -344,13 +344,13 @@ def test_build_tree_kernel_efficient():
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num_verify_tokens=num_draft_token,
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)
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first_rank_print("=========== build tree kernel efficient ==========")
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# first_rank_print(f"{tree_mask=}", flush=True)
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first_rank_print(f"{position=}", flush=True)
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first_rank_print(f"{retrive_index=}", flush=True)
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first_rank_print(f"{retrive_next_token=}", flush=True)
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first_rank_print(f"{retrive_next_sibling=}", flush=True)
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first_rank_print(f"{draft_tokens=}", flush=True)
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rank0_print("=========== build tree kernel efficient ==========")
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# rank0_print(f"{tree_mask=}", flush=True)
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rank0_print(f"{position=}", flush=True)
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rank0_print(f"{retrive_index=}", flush=True)
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rank0_print(f"{retrive_next_token=}", flush=True)
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rank0_print(f"{retrive_next_sibling=}", flush=True)
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rank0_print(f"{draft_tokens=}", flush=True)
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assert position.tolist() == [5, 6, 6, 7, 7, 8, 8, 9, 10, 11, 12, 12, 12, 12, 13, 14]
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assert retrive_index.tolist() == [
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[0, 1, 2, 3, 4, 5, 6, 7],
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@@ -6,6 +6,7 @@ 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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CUDA_GRAPH_CAPTURE_FAILED_MSG,
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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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@@ -73,7 +74,7 @@ class EAGLEDraftCudaGraphRunner:
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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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(self.max_num_token, self.model_runner.model_config.hidden_size),
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dtype=self.model_runner.dtype,
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)
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@@ -82,13 +83,7 @@ class EAGLEDraftCudaGraphRunner:
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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. set --mem-fraction-static to a smaller value (e.g., 0.8 or 0.7)\n"
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"2. set --cuda-graph-max-bs to a smaller value (e.g., 16)\n"
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"3. disable torch compile by not using --enable-torch-compile\n"
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"4. disable CUDA graph by --disable-cuda-graph. (Not recommended. Huge performance loss)\n"
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"Open an issue on GitHub https://github.com/sgl-project/sglang/issues/new/choose \n"
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f"Capture cuda graph failed: {e}\n{CUDA_GRAPH_CAPTURE_FAILED_MSG}"
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)
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def can_run(self, forward_batch: ForwardBatch):
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@@ -6,6 +6,7 @@ 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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CUDA_GRAPH_CAPTURE_FAILED_MSG,
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CudaGraphRunner,
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LogitsProcessorOutput,
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get_batch_sizes_to_capture,
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@@ -89,13 +90,7 @@ class EAGLEDraftExtendCudaGraphRunner:
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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. set --mem-fraction-static to a smaller value (e.g., 0.8 or 0.7)\n"
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"2. set --cuda-graph-max-bs to a smaller value (e.g., 16)\n"
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"3. disable torch compile by not using --enable-torch-compile\n"
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"4. disable CUDA graph by --disable-cuda-graph. (Not recommended. Huge performance loss)\n"
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"Open an issue on GitHub https://github.com/sgl-project/sglang/issues/new/choose \n"
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f"Capture cuda graph failed: {e}\n{CUDA_GRAPH_CAPTURE_FAILED_MSG}"
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)
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def can_run(self, forward_batch: ForwardBatch):
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@@ -200,7 +195,6 @@ class EAGLEDraftExtendCudaGraphRunner:
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# in the batch, which will not be counted as num_seqs
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raw_bs = forward_batch.batch_size
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num_tokens = forward_batch.input_ids.shape[0]
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assert raw_bs * self.num_tokens_per_bs == num_tokens
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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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@@ -224,9 +218,9 @@ class EAGLEDraftExtendCudaGraphRunner:
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self.seq_lens_cpu.fill_(1)
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self.seq_lens_cpu[:raw_bs].copy_(forward_batch.seq_lens_cpu)
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forward_batch.spec_info.positions = None
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if bs != raw_bs:
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forward_batch.spec_info.accept_length = self.accept_length[:bs]
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forward_batch.spec_info.positions = None
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self.eagle_worker.draft_extend_attn_backend.init_forward_metadata_replay_cuda_graph(
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bs=bs,
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@@ -232,8 +232,9 @@ class EagleVerifyInput:
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retrive_next_token: torch.Tensor
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retrive_next_sibling: torch.Tensor
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retrive_cum_len: torch.Tensor
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draft_token_num: int
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spec_steps: int
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topk: int
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draft_token_num: int
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capture_hidden_mode: CaptureHiddenMode
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grammar: BaseGrammarObject = None
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@@ -270,16 +271,17 @@ class EagleVerifyInput:
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)
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return cls(
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draft_tokens,
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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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None,
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num_verify_tokens,
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spec_steps,
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CaptureHiddenMode.FULL,
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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=spec_steps,
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topk=topk,
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draft_token_num=num_verify_tokens,
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capture_hidden_mode=CaptureHiddenMode.FULL,
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)
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def prepare_for_verify(self, batch: ScheduleBatch, page_size: int):
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