[core] Fix crashes on the gpu_only spec_v2 path (#26738)

This commit is contained in:
Liangsheng Yin
2026-05-29 19:12:03 -07:00
committed by GitHub
parent c2ac37dbcc
commit 1f850e67f2
5 changed files with 44 additions and 13 deletions
@@ -537,9 +537,13 @@ class TritonAttnBackend(AttentionBackend):
dtype=torch.int32,
device=self.device,
)
# Different with flashinfer kv_indptr and kv_indices construction
# Different with flashinfer kv_indptr and kv_indices construction.
# gpu_only: seq_lens_sum may be None; ub-allocate is safe (ragged write).
seq_lens_sum = forward_batch.seq_lens_sum
if seq_lens_sum is None:
seq_lens_sum = bs * self.max_context_len
kv_indices = torch.empty(
forward_batch.seq_lens_sum, dtype=torch.int64, device=self.device
seq_lens_sum, dtype=torch.int64, device=self.device
)
kv_indptr = self._fill_kv_indptr_and_indices(
bs,
@@ -605,8 +609,14 @@ class TritonAttnBackend(AttentionBackend):
attn_logits = None
attn_lse = None
else:
# gpu_only leaves _cpu unset; ub-allocate is safe (ragged write
# from GPU tensor, extra tail unused).
if forward_batch.extend_prefix_lens_cpu is not None:
kv_indices_len = sum(forward_batch.extend_prefix_lens_cpu)
else:
kv_indices_len = bs * self.max_context_len
kv_indices = torch.empty(
sum(forward_batch.extend_prefix_lens_cpu),
kv_indices_len,
dtype=torch.int64,
device=self.device,
)
@@ -641,7 +651,12 @@ class TritonAttnBackend(AttentionBackend):
mask_indptr = None
attn_logits = None
attn_lse = None
max_extend_len = max(forward_batch.extend_seq_lens_cpu)
# Caller usually supplies extend_seq_lens_cpu (eagle_info gpu_only
# sets host-constant mirror); defensive GPU-max fallback if not.
if forward_batch.extend_seq_lens_cpu is not None:
max_extend_len = max(forward_batch.extend_seq_lens_cpu)
else:
max_extend_len = int(forward_batch.extend_seq_lens.max())
num_kv_splits = None
self.forward_metadata = ForwardMetadata(
+5 -1
View File
@@ -32,7 +32,11 @@ def decide_needs_cpu_seq_lens(
if not server_args.disable_piecewise_cuda_graph:
# FIXME: support PCG without seq lens cpu value
return True
return any(b.needs_cpu_seq_lens for b in attn_backends)
# Skip unset slots (e.g. draft_extend_attn_backend on some spec configs);
# missing flag -> True so undeclared backends stay on the legacy path.
return any(
getattr(b, "needs_cpu_seq_lens", True) for b in attn_backends if b is not None
)
_is_cuda = is_cuda()
@@ -266,6 +266,10 @@ class EagleDraftInputV2Mixin:
if not gpu_only:
forward_batch.seq_lens_cpu = forward_batch.seq_lens_cpu + num_draft_tokens
forward_batch.seq_lens_sum = int(forward_batch.seq_lens_cpu.sum())
else:
# Supply CPU mirror (extend_seq_lens are all num_draft_tokens) so
# backend max() reads from list without a per-iter D2H sync.
forward_batch.extend_seq_lens_cpu = [num_draft_tokens] * bs
can_cuda_graph = cuda_graph_runner and cuda_graph_runner.can_run(forward_batch)
if not batch.forward_mode.is_idle() and not can_cuda_graph:
draft_model_runner.attn_backend.init_forward_metadata(forward_batch)
+6 -4
View File
@@ -41,7 +41,7 @@ class FwdOccupancyMixin:
# in /server_info); silently skipped otherwise. EAGLE3 3/1/4 on
# 5090 + Llama-3.1-8B measured ~2.0 in CI; 1.8 leaves a small
# buffer while still catching silent fallback to vanilla (~1.0).
spec_accept_length_threshold: float = 1.8
fwd_occupancy_acc_length_threshold: float = 1.8
# Warmup: one short request to fill cuda graphs + get the
# device-timer past its first NaN window.
@@ -51,7 +51,9 @@ class FwdOccupancyMixin:
# Measurement: one long single-batch request -- max_new_tokens must
# span several decode_log_interval windows for enough samples.
fwd_occupancy_max_new_tokens: int = 2048
fwd_occupancy_prompt: str = "Write a long, detailed, multi-paragraph story about "
fwd_occupancy_prompt: str = (
"Human: Give me a fully functional FastAPI server. Show the python code.\n\nAssistant:"
)
def _scrape_fwd_occupancy(self):
"""Max non-NaN gauge value across exposed labels (e.g. dp ranks);
@@ -202,8 +204,8 @@ class FwdOccupancyMixin:
print(f"avg_spec_accept_length = {avg_accept:.3f}")
self.assertGreater(
avg_accept,
self.spec_accept_length_threshold,
self.fwd_occupancy_acc_length_threshold,
f"avg_spec_accept_length={avg_accept:.3f} did not exceed "
f"threshold {self.spec_accept_length_threshold} -- spec "
f"threshold {self.fwd_occupancy_acc_length_threshold} -- spec "
"barely accepted, possibly degraded to vanilla decode",
)
@@ -8,8 +8,8 @@ from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
from sglang.test.kits.basic_api_contract_kit import BasicAPIContractMixin
from sglang.test.kits.basic_decode_correctness_kit import BasicDecodeCorrectnessMixin
from sglang.test.kits.basic_scheduler_stress_kit import BasicSchedulerStressMixin
from sglang.test.kits.eval_accuracy_kit import GSM8KMixin
from sglang.test.kits.fwd_occupancy_kit import FwdOccupancyMixin
from sglang.test.kits.hellaswag_kit import HellaswagMixin
from sglang.test.test_utils import (
DEFAULT_DRAFT_MODEL_EAGLE3,
DEFAULT_TARGET_MODEL_EAGLE3,
@@ -29,7 +29,7 @@ class TestBasicSanityEagle3(
BasicDecodeCorrectnessMixin,
BasicSchedulerStressMixin,
FwdOccupancyMixin,
HellaswagMixin,
GSM8KMixin,
CustomTestCase,
):
served_model_name = DEFAULT_TARGET_MODEL_EAGLE3
@@ -38,6 +38,11 @@ class TestBasicSanityEagle3(
# measurement window to avoid too few non-NaN samples.
fwd_occupancy_threshold = 80.0 if is_in_amd_ci() else 97.0
fwd_occupancy_max_new_tokens = 4096 if is_in_amd_ci() else 2048
fwd_occupancy_acc_length_threshold: float = 1.6
model = DEFAULT_TARGET_MODEL_EAGLE3
gsm8k_num_questions = 1400
gsm8k_accuracy_thres = 0.74
@classmethod
def setUpClass(cls):
@@ -59,16 +64,17 @@ class TestBasicSanityEagle3(
"--speculative-draft-model-path",
DEFAULT_DRAFT_MODEL_EAGLE3,
"--speculative-num-steps",
"3",
"1",
"--speculative-eagle-topk",
"1",
"--speculative-num-draft-tokens",
"4",
"2",
"--cuda-graph-max-bs",
"4",
"--mem-fraction-static",
"0.7",
"--enable-metrics",
"--disable-piecewise-cuda-graph",
],
env={"SGLANG_ENABLE_METRICS_DEVICE_TIMER": "1"},
)