ckpt: support GLM-4.7-Flash test on ROCm (MI35x)#1620
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Two ROCm-only changes in tests/e2e/ckpt/test_glm47_flash_ckpt.py
(gated on IS_ROCM = torch.version.hip is not None):
1. Set extra_env_vars["SGLANG_USE_AITER"] = "0".
The rlsys/miles:MI350-355-latest Docker image bakes in
SGLANG_USE_AITER=1, which routes MoE topk, quantization, and the
GLM4MoE model forward pass through aiter's JIT-wrapped kernels.
Those wrappers (aiter/jit/utils/torch_guard.py) do Python-level
work -- torch.library registration, tensor allocations, first-call
JIT compilation -- on every dispatch, which corrupts the HIP stream
during CUDA graph capture and aborts downstream kernels (the
visible symptom is a Fatal Python error inside silu_and_mul during
warmup, but the cause is upstream aiter calls in the same captured
forward pass). Forcing the env var to "0" routes those paths
through SGLang's native Triton-based implementations, which
capture cleanly. EAGLE speculative decoding then works end-to-end.
2. Add --ci-disable-logprobs-checker on ROCm.
The Megatron-flash <-> SGLang-triton boundary produces a small but
non-zero training-vs-rollout log-probs delta on ROCm
(train_rollout_logprob_abs_diff ~0.28, train_rollout_kl ~0.11).
This test exists to verify the checkpoint save/load round-trip
(--ci-save-model-hash / --ci-check-model-hash), not cross-backend
numerical consistency, so the logprob check is gated off on ROCm.
The MTP loss and KL checkers stay on.
The save/load round-trip mechanism is unchanged. NVIDIA behavior is
unchanged (both blocks are gated on IS_ROCM).
External dependency: SGLang deepseek_v2.py patch
------------------------------------------------
This test also needs an upstream SGLang fix in
sglang/srt/models/deepseek_v2.py (NOT included in this PR -- it is
not a miles file). Without it, GLM-4.7-Flash fails to load on ROCm
with:
KeyError: 'original_max_position_embeddings'
at /sgl-workspace/aiter/aiter/rotary_embedding.py:1849
in get_rope_wrapper()
Root cause: at line ~1138 of deepseek_v2.py, the model init does an
unconditional truthy check on rope_scaling:
if rope_scaling:
rope_scaling["rope_type"] = "deepseek_yarn"
In transformers v5+, rope_parameters/rope_scaling is auto-populated
on every model config (including rope_type="default"), so this
truthy check misclassifies non-yarn models. GLM-4.7-Flash gets
stamped as deepseek_yarn, after which aiter's rope wrapper tries
to read rope_scaling["original_max_position_embeddings"] (a key
only yarn configs have) and crashes with KeyError.
The fix is to narrow the condition so it only fires for models that
already declare yarn scaling:
if rope_scaling and rope_scaling.get("rope_type") in ("yarn", "deepseek_yarn"):
rope_scaling["rope_type"] = "deepseek_yarn"
This needs to land upstream in sgl-project/sglang. Until then, the
fix has to be applied manually inside the running container at
/sgl-workspace/sglang/python/sglang/srt/models/deepseek_v2.py. A
permanent solution requires either an upstream SGLang PR or a
Docker image rebuild that includes the patch.
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Code Review
This pull request introduces ROCm support in the GLM-4.7-Flash checkpoint end-to-end test by conditionally disabling the logprobs checker and setting the SGLANG_USE_AITER environment variable. It also updates several configuration variables to be loaded from environment variables. However, the initial static assignments for ENABLE_EVAL and USE_DEEPEP were not removed, leading to redundant assignments and dead code.
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| ENABLE_EVAL = bool(int(os.environ.get("MILES_TEST_ENABLE_EVAL", "1"))) | ||
| TIGHT_HOST_MEMORY = bool(int(os.environ.get("MILES_TEST_TIGHT_HOST_MEMORY", "1"))) | ||
| USE_DEEPEP = bool(int(os.environ.get("MILES_TEST_USE_DEEPEP", "0"))) |
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The variables ENABLE_EVAL and USE_DEEPEP are redefined here using environment variables, but their initial static assignments (ENABLE_EVAL = 0 and USE_DEEPEP = 0 on lines 11 and 12) were not removed. This results in redundant assignments and dead code. Please remove the initial assignments on lines 11 and 12.
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Summary
Two ROCm-only changes in
tests/e2e/ckpt/test_glm47_flash_ckpt.py, gated onIS_ROCM = torch.version.hip is not None. NVIDIA behavior is unchanged.extra_env_vars["SGLANG_USE_AITER"] = "0"— therlsys/miles:MI350-355-latestimage bakes inSGLANG_USE_AITER=1, which routes MoE topk, quantization, and the GLM4MoE forward through aiter's JIT-wrapped kernels. Those wrappers do Python-level work (allocations,torch.libraryregistration, first-call JIT) inside the captured HIP graph and corrupt the stream — the visible symptom is aFatal Python errorinsilu_and_mul, but the cause is the aiter calls upstream of it. Forcing=0routes through SGLang's native Triton paths, which capture cleanly.--ci-disable-logprobs-checker— Megatron-flash ↔ SGLang-triton gives a small but non-zerotrain_rollout_logprob_abs_diff. This test verifies the checkpoint save/load round-trip (--ci-save-model-hash/--ci-check-model-hash), not cross-backend numerical consistency, so the logprob check is skipped on ROCm. MTP loss and KL checkers stay on.External dependency: SGLang
deepseek_v2.pypatch (not in this PR)This test also needs an upstream SGLang fix in
sglang/srt/models/deepseek_v2.py. Without it, GLM-4.7-Flash crashes at model load on ROCm. See the commit message for the root cause and the patch.Supersedes #1126