The GitHub issue check had issues:
1. Network latency on import
2. Issue being closed does not mean the fix is in the installed vLLM version
Now skip the PDL workaround if vLLM version > 0.13.2, which is when
the upstream fix is expected to be included.
- Patch vllm.lora.ops.triton_ops.utils directly where supports_pdl is defined
- Clear lru_cache before patching to prevent stale cached results
- Add fused_moe_lora_op to consumer modules list
- Use *args, **kwargs in fake function for compatibility
- Add _spec_exists helper function to reduce duplication
- Scan all GPUs for SM100 instead of just device 0
- Use loop for module patching to improve maintainability
When using base models with custom chat templates applied after loading,
vLLM's internal tokenizer may not have the chat_template set. This causes
issues during RL training with vLLM inference.
This fix syncs the chat_template from the processing_class (the tokenizer
you loaded and configured) to vLLM's internal tokenizer during trainer
initialization, but only if vLLM's tokenizer does not already have one set.
vLLM's LoRA Triton kernels use tl.extra.cuda.gdc_wait() for PDL
optimization on SM90+ GPUs. This fails on SM100 (Blackwell) during
CUDA graph capture because Triton's pipeliner cannot handle gdc_wait
in complex kernels.
This fix:
- Detects SM100 GPUs and applies the workaround automatically
- Sets TRITON_DISABLE_PDL=1 environment variable
- Monkey-patches supports_pdl to return False in lora_expand_op and
lora_shrink_op
- Checks GitHub issue #30872 status (with 3s timeout) to auto-disable
the workaround once the upstream fix is merged
- Includes quick internet connectivity check (0.5s) to avoid delays
when offline
Fixes the error:
'tt.elementwise_inline_asm' op pipeliner doesn't know how to predicate this op
LLVM ERROR: Fatal pipeliner error
See: https://github.com/vllm-project/vllm/issues/30872
When users load a model with fast_inference=False but then try to use
vLLM-style arguments with fast_generate, they previously got confusing
errors. This adds a wrapper that detects common mistakes and provides
helpful guidance:
- Using sampling_params: explains to use HF generate args instead
- Using lora_request: explains LoRA weights are already merged
- Passing text strings: shows how to tokenize input first
Changes:
- Add make_fast_generate_wrapper to _utils.py
- Apply wrapper in llama.py when fast_inference=False
- Apply wrapper in vision.py when fast_inference=False
Gemma3 models have a large vocabulary (262144 tokens) which causes
training loss to explode when using int8 embedding quantization.
This fix auto-detects Gemma3 models and switches from int8-int4
(phone-deployment) to int4 weight-only QAT for stable training.
