[ROCm] Fix biased wgrad with fp32 gradient accumulation#634
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XinyuJiangCMU wants to merge 5 commits into
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[ROCm] Fix biased wgrad with fp32 gradient accumulation#634XinyuJiangCMU wants to merge 5 commits into
XinyuJiangCMU wants to merge 5 commits into
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On ROCm, hipBLASLt has no algorithm for a bf16 -> fp32-accumulate wgrad GEMM that also fuses the bias-gradient (BGRADB) epilogue: the heuristic returns zero algorithms and the GEMM raises "Unable to find any suitable algorithms". This hits any LayerNormLinear with bias (e.g. Qwen2.5 QKV with add-qkv-bias) when training with fp32 gradient accumulation (--accumulate-allreduce-grads-in-fp32). When wgrad is accumulated into an fp32 main_grad on ROCm, skip the fused dbias and reduce grad_bias separately (grad_output.sum over tokens in fp32, cast to bias dtype) -- mathematically identical to the BGRADB epilogue. CUDA and all other paths are unchanged. Co-Authored-By: Jessica Jiang <jessicajiang324@gmail.com> Signed-off-by: Xinyu Jiang <xinyuj2@andrew.cmu.edu>
Move the BGRADB-unfuse workaround from the per-module LayerNormLinear backward up to general_gemm, the single chokepoint every wgrad path funnels through. This covers Linear, LayerNormLinear, LayerNormMLP and the delayed-wgrad store in one place, and fixes the delayed-wgrad path that the per-module version dropped the bias gradient on. CUDA, the forward bias-add path and fp8/fp4 are untouched. Co-Authored-By: Jessica Jiang <jessicajiang324@gmail.com> Signed-off-by: Xinyu Jiang <xinyuj2@andrew.cmu.edu>
Signed-off-by: Xinyu Jiang <xinyuj2@andrew.cmu.edu>
The hipBLASLt "no suitable algorithm" failure for the fused bias-grad (BGRADB) epilogue is driven by the fp32 output dtype, independent of accumulate, so the split must also cover the non-accumulating (e.g. first-microbatch) wgrad. Also exclude gelu, whose bias-grad is not a plain grad_output sum. Re-enable the ROCm numerics test that was skipped for this case. Co-authored-by: Zhiyao Jiang <jessicajiang324@gmail.com> Signed-off-by: Xinyu Jiang <xinyuj2@andrew.cmu.edu>
The hipBLASLt "no suitable algorithm" failure for the fused bias-grad (BGRADB) epilogue is driven by the fp32 output dtype, independent of accumulate, so the split must also cover non-accumulating (e.g. first-microbatch) wgrad. Also exclude gelu, whose bias-grad is not a plain grad_output sum. Re-enable the Linear / LayerNormLinear / LayerNormMLP wgrad numerics tests skipped for this case; GroupedLinear routes through general_grouped_gemm and stays skipped. Co-authored-by: Zhiyao Jiang <jessicajiang324@gmail.com> Signed-off-by: Xinyu Jiang <xinyuj2@andrew.cmu.edu>
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Problem
On ROCm, hipBLASLt cannot find a suitable algorithm for an fp32 weight gradient GEMM with fused bias gradient computation.
This causes training with --add-qkv-bias and --accumulate-allreduce-grads-in-fp32 to fail with:
Fix
Run the weight gradient GEMM without the fused bias gradient and compute the bias gradient separately by summing grad_output.
The fix is implemented in general_gemm, covering delayed weight gradient execution and other callers using the same path. CUDA behavior is unchanged.
Testing
Verified on MI350X:
The isolated reproduction passes.
Qwen2.5-0.5B GSM8K training passes the previously failing backward step.
Re-enabled the ROCm wgrad numerics tests previously skipped by grouped GEMM change 434:
All three use general_gemm.
Result: