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[Fix] Try to fix nvcc compilation error (#21246)

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DarkSharpness
2026-03-26 10:59:36 +08:00
committed by GitHub
parent a34e9ed64a
commit bb29893689
7 changed files with 75 additions and 9 deletions
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.github/workflows/nightly-test-nvidia.yml
+33
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@@ -27,6 +27,7 @@ on:
- 'nightly-test-perf-8-gpu-b200'
- 'nightly-test-kernel-1-gpu-h100'
- 'nightly-test-diffusion-comparison'
- 'nightly-test-kernel-8-gpu-h200'
workflow_call:
inputs:
ref:
@@ -114,6 +115,38 @@ jobs:
- uses: ./.github/actions/upload-cuda-coredumps
if: always()
nightly-test-kernel-8-gpu-h200:
if: github.repository == 'sgl-project/sglang' && (inputs.job_filter == '' || inputs.job_filter == 'all' || inputs.job_filter == 'nightly-test-kernel-8-gpu-h200')
runs-on: 8-gpu-h200
timeout-minutes: 240
env:
SGLANG_JIT_KERNEL_RUN_FULL_TESTS: "1"
SGLANG_JIT_DEEPGEMM_FAST_WARMUP: true
SGLANG_PR_TEST_BYPASS_MAINTENANCE_ON_MAIN: ${{ github.ref == 'refs/heads/main' && 'true' || 'false' }}
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
ref: ${{ inputs.ref || github.ref }}
- uses: ./.github/actions/check-maintenance
with:
github-token: ${{ github.token }}
- name: Install dependencies
timeout-minutes: 20
run: |
bash scripts/ci/cuda/ci_install_dependency.sh
- name: Run multi-GPU jit kernel nightly suite
timeout-minutes: 90
run: |
cd test
python3 run_suite.py --hw cuda --suite nightly-kernel-8-gpu-h200 --nightly --continue-on-error
- uses: ./.github/actions/upload-cuda-coredumps
if: always()
# General tests - 4 GPU H100
nightly-test-general-4-gpu-h100:
if: github.repository == 'sgl-project/sglang' && (inputs.job_filter == '' || inputs.job_filter == 'all' || inputs.job_filter == 'nightly-test-general-4-gpu-h100')
.github/workflows/pr-test-jit-kernel.yml
+27
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@@ -62,6 +62,33 @@ jobs:
cd test/
python3 run_suite.py --hw cuda --suite stage-b-kernel-unit-1-gpu-large
jit-kernel-multigpu-unit-test:
if: |
github.event_name != 'schedule' &&
inputs.test_parallel_dispatch != 'true' &&
!inputs.target_stage
runs-on: 8-gpu-h200
timeout-minutes: 240
steps:
- uses: actions/checkout@v4
with:
ref: ${{ inputs.pr_head_sha || inputs.git_ref || github.sha }}
- uses: ./.github/actions/check-maintenance
with:
github-token: ${{ github.token }}
- name: Install dependencies
timeout-minutes: 20
run: |
bash scripts/ci/cuda/ci_install_dependency.sh diffusion
- name: Run multi-GPU test
timeout-minutes: 45
run: |
cd test/
python3 run_suite.py --hw cuda --suite stage-b-kernel-unit-8-gpu-h200
jit-kernel-benchmark-test:
if: |
github.event_name != 'schedule' &&
python/sglang/jit_kernel/all_reduce.py
+2
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@@ -131,6 +131,8 @@ def get_custom_all_reduce_cls() -> type[CustomAllReduceObj]:
@tvm_ffi.register_object("sgl.CustomAllReduce")
class CustomAllReduceObjReal(tvm_ffi.Object):
__slots__ = ("__dict__",)
def __init__(
self,
rank: int,
python/sglang/jit_kernel/include/sgl_kernel/distributed/custom_all_reduce.cuh
+2 -2
View File
@@ -194,8 +194,8 @@ struct CustomAllReduceBase : public tvm::ffi::Object {
for (const auto j : irange(new_registered_count)) {
/// NOTE: structural binding will cause intern compiler error...
const auto elem = array[j];
const auto offset = get<0>(elem);
const auto ipc_handle = get<1>(elem);
const auto offset = elem.get<0>();
const auto ipc_handle = elem.get<1>();
data[j].input[i] = pointer::offset(open_cached(ipc_handle), offset);
}
}
python/sglang/jit_kernel/tests/test_custom_all_reduce.py
+4 -6
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@@ -34,15 +34,13 @@ from sglang.srt.distributed.device_communicators.custom_all_reduce_v2 import (
from sglang.test.ci.ci_register import register_cuda_ci
register_cuda_ci(
est_time=120,
suite="stage-b-kernel-unit-1-gpu-large",
disabled="requires multi-GPU distributed setup",
est_time=500,
suite="stage-b-kernel-unit-8-gpu-h200",
)
register_cuda_ci(
est_time=120,
suite="nightly-kernel-1-gpu",
est_time=500,
suite="nightly-kernel-8-gpu-h200",
nightly=True,
disabled="requires multi-GPU distributed setup",
)
# ---------------------------------------------------------------------------
test/README.md
+5 -1
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@@ -189,9 +189,11 @@ Use dedicated kernel suites:
from sglang.test.ci.ci_register import register_cuda_ci
register_cuda_ci(est_time=30, suite="stage-b-kernel-unit-1-gpu-large")
register_cuda_ci(est_time=120, suite="stage-b-kernel-unit-8-gpu-h200")
register_cuda_ci(est_time=6, suite="stage-b-kernel-benchmark-1-gpu-large")
# Optional nightly registration
register_cuda_ci(est_time=120, suite="nightly-kernel-1-gpu", nightly=True)
register_cuda_ci(est_time=120, suite="nightly-kernel-8-gpu-h200", nightly=True)
```
Keep `est_time` and `suite` as literal values. `run_suite.py` collects them by statically parsing the file AST.
@@ -210,6 +212,7 @@ You can find the available suites for each hardware backend at [`test/run_suite.
| `stage-b-test-2-gpu-large` | `2-gpu-h100` | Two-GPU correctness and parallelism (TP/PP-style workloads) on H100 |
| `stage-b-test-4-gpu-b200` | `4-gpu-b200` | Early Blackwell coverage (e.g. SM100+ paths) on four GPUs |
| `stage-b-kernel-unit-1-gpu-large` | `1-gpu-h100` | JIT kernel correctness tests under `python/sglang/jit_kernel/tests/` |
| `stage-b-kernel-unit-8-gpu-h200` | `8-gpu-h200` | Multi-GPU JIT kernel correctness tests under `python/sglang/jit_kernel/tests/` |
| `stage-b-kernel-benchmark-1-gpu-large` | `1-gpu-h100` | JIT kernel benchmark files under `python/sglang/jit_kernel/benchmark/` |
| `stage-c-test-4-gpu-h100` | `4-gpu-h100` | Large 4-GPU H100 integration and scaling tests |
| `stage-c-test-8-gpu-h200` | `8-gpu-h200` | Large 8-GPU H200 runs for big models and parallelism |
@@ -242,6 +245,7 @@ Nightly registry suites are listed in `NIGHTLY_SUITES` in [`test/run_suite.py`](
- `nightly-1-gpu` (CUDA)
- `nightly-kernel-1-gpu` (CUDA, JIT kernel full grids)
- `nightly-kernel-8-gpu-h200` (CUDA, multi-GPU JIT kernel nightly coverage)
- `nightly-8-gpu-h200` (CUDA)
- `nightly-eval-vlm-2-gpu` (CUDA)
- `nightly-amd` (AMD)
@@ -254,7 +258,7 @@ Use the lightest suite that still meets your test's needs.
- Prefer the CPU suite (`stage-a-test-cpu`) when no GPU is required.
- For most small GPU workloads that fit a 5090-class card in CI, use `stage-b-test-1-gpu-small`. Most tests should go here.
- If you really need more GPU memory capacity or Hopper-specific features, use `stage-b-test-1-gpu-large`.
- For JIT kernel work under `python/sglang/jit_kernel/`, use `stage-b-kernel-unit-1-gpu-large` for correctness tests and `stage-b-kernel-benchmark-1-gpu-large` for benchmarks.
- For JIT kernel work under `python/sglang/jit_kernel/`, use `stage-b-kernel-unit-1-gpu-large` for single-GPU correctness tests, `stage-b-kernel-unit-8-gpu-h200` for multi-GPU correctness tests, and `stage-b-kernel-benchmark-1-gpu-large` for benchmarks.
- Use multi-GPU suites only when the test actually needs multiple GPUs or other advanced multi-GPU behavior.
In rare cases, if you need a new runner or custom setup, you might need to add a new suite.
test/run_suite.py
+2
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@@ -43,6 +43,7 @@ PER_COMMIT_SUITES = {
"stage-b-test-2-gpu-large",
"stage-b-test-4-gpu-b200",
"stage-b-kernel-unit-1-gpu-large",
"stage-b-kernel-unit-8-gpu-h200",
"stage-b-kernel-benchmark-1-gpu-large",
"stage-c-test-4-gpu-h100",
"stage-c-test-4-gpu-b200",
@@ -77,6 +78,7 @@ NIGHTLY_SUITES = {
"nightly-8-gpu-b200-basic", # Basic tests for large models on B200
"nightly-8-gpu-common", # Common tests that run on both H200 and B200
"nightly-kernel-1-gpu",
"nightly-kernel-8-gpu-h200",
# Eval and perf suites (2-gpu)
"nightly-eval-text-2-gpu",
"nightly-eval-vlm-2-gpu",