Inv rope fp8 quant#42033
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Decode UE8M0 scale tensors before using them in ROCm fallback paths so E8M0 scales are not multiplied directly as float8 tensors. Use the current platform FP8 dtype for DeepSeek-V4 indexer and inverse-RoPE quantization, and select the Triton FNUZ FP8 type when required by the ROCm platform. Signed-off-by: bobofang11235 <bobo.fang@amd.com>
Route DeepSeek-V4 sparse FlashMLA prefill and decode calls through ROCm fallback implementations so ROCm can share the same FlashMLA API path as CUDA. Signed-off-by: bobofang11235 <bobo.fang@amd.com>
Wrap shuffled AITER MXFP4 weights as fresh Parameters so FP4 dtype metadata is preserved without changing non-ROCm MoE backend routing. Signed-off-by: bobofang11235 <bobo.fang@amd.com>
…s are -inf When both prefix and suffix have no tokens (e.g. chunked prefill with zero context length), both LSEs are -inf. IEEE 754: -inf - (-inf) = NaN, which propagates through exp and division into the output. Apply a branchless safe-softmax fix in the Triton kernel: - Clamp max_lse to a finite floor (-1e30) so the subtraction yields -inf instead of NaN, and exp() gives exactly 0. - Add epsilon (1e-10) to the denominator to prevent 0/0. The CUDA merge_attn_states kernel already handles this via an early-return branch on isinf(max_lse). This brings the Triton kernel to parity using an arithmetic-only approach. Add regression test covering the both-LSE-negative-inf edge case that the existing test explicitly excluded. Signed-off-by: MHYang <meng-hsuan.yang@amd.com>
Signed-off-by: MHYang <meng-hsuan.yang@amd.com>
Signed-off-by: MHYang <meng-hsuan.yang@amd.com>
Provide a ROCm-only torch fallback for fp8_einsum when DeepGEMM is unavailable while preserving the existing CUDA and non-ROCm dispatch behavior. Signed-off-by: bobofang11235 <bobo.fang@amd.com>
Signed-off-by: bobofang11235 <bobo.fang@amd.com>
Signed-off-by: bobofang11235 <bobo.fang@amd.com>
…ITER FP8 kernels Signed-off-by: MHYang <meng-hsuan.yang@amd.com>
bobofang11235
requested review from
LucasWilkinson,
MatthewBonanni,
WoosukKwon,
gshtras,
mgoin,
pavanimajety,
robertgshaw2-redhat,
tjtanaa,
tlrmchlsmth and
yewentao256
as code owners
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Code Review
This pull request implements ROCm support for DeepSeek-V4, primarily by introducing a fallback for FlashMLA sparse attention kernels and enhancing FP8 quantization handling. Key additions include a new ROCm-specific FlashMLA module using online softmax, support for FNUZ and UE8M0 scales in various layers, and fixes for numerical stability issues. A critical review comment identifies a potential NaN propagation bug in the _online_softmax_update function within the new ROCm fallback module, suggesting a more robust calculation for the scaling factor.
| finite_old = torch.isfinite(m) & torch.isfinite(new_m) # (T_q, H) | ||
| scale_old = torch.where( | ||
| finite_old, | ||
| torch.exp(m - torch.where(finite_old, new_m, m)), | ||
| torch.zeros_like(m), | ||
| ) # (T_q, H) |
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The calculation of scale_old can result in NaN when m is -inf. torch.where evaluates all its arguments, so m - torch.where(finite_old, new_m, m) can lead to -inf - (-inf) when finite_old is false. This will propagate NaNs into the output.
The _online_softmax_update_graph_safe function handles this more robustly. A similar approach should be used here to ensure numerical stability.
# Avoid -inf - -inf = nan when both old and new max are still -inf.
new_m_safe = torch.where(torch.isfinite(new_m), new_m, 0.0)
scale_old = torch.exp(m - new_m_safe) # (T_q, H)
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2 participants
Purpose
Use triton to implement inv_rope fp8 quant
Test Plan
Test Result
Use the same command in #41601
The e2e results is below
Compare with #41601
the performance is slightly drop
And the gsm8k accuracy task also become a little worse
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