[Bugfix][Kernel] Pass the correct expert count to WNA16 MoE block config#48573
[Bugfix][Kernel] Pass the correct expert count to WNA16 MoE block config#48573morluto wants to merge 9 commits into
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Bug 1 (moe_wna16.py): The expression was evaluated as
due to Python operator precedence, instead of the
intended . This meant the repeat_interleave was
applied to 'scales' weights even when group_size_div_factor was 1,
bypassing the guard. Currently latent (no-op with factor=1), but a
real correctness bug if the factor or logic changes.
Bug 2 (compressed_tensors.py): Two RuntimeError raises used
comma-separated string arguments instead of string concatenation,
producing unreadable tuple messages like ('msg1', 'msg2', ...).
Fixed to use implicit string concatenation.
Signed-off-by: williamlin1327
Co-authored-by: Codex Agent
Documents bugs found, fixes applied, and issues identified but not fixed due to scope constraints. Signed-off-by: williamlin1327 Co-authored-by: Codex Agent
The CUDA and Triton WNA16 (Weight N-bit Activation 16-bit) MoE dispatch paths in fused_moe.py were passing num_experts=B.size(1) instead of num_experts=B.size(0) when calling get_moe_wna16_block_config(). The MoE weight tensor B has shape (E, N, K) where E=num_experts, N=output_features (size_n), K=input_features (size_k). The code already correctly passed size_n=B.size(1) on the preceding line, then passed the same dimension for num_experts — which is the output feature count, not the expert count. Impact: - CUDA path: get_moe_wna16_block_config uses num_experts in the num_m_blocks heuristic. With num_experts set to size_n (hundreds-thousands), num_m_blocks is massively overestimated, causing incorrect tile configurations (BLOCK_SIZE_K/N) to be selected — a performance regression. In degenerate cases with very small size_n, it could select a tile violating the size_k % BLOCK_SIZE_K == 0 divisibility contract. - Triton path: no impact (returns early before num_experts is used). Fix: both call sites changed to num_experts=B.size(0).
…lection" This reverts commit 57b494d.
…tization" This reverts commit a630331.
This reverts commit 1d1bf89.
This reverts commit c14e762.
Assisted-by: OpenAI Codex Signed-off-by: morluto <76467478+morluto@users.noreply.github.com>
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Superseded by #48574; the replacement branch is based directly on current upstream main. |
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Purpose
The CUDA and Triton WNA16 MoE dispatchers passed
B.size(1)as bothsize_nandnum_expertswhen selecting a block configuration. For theexpert weight tensor,
B.size(0)is the expert count andB.size(1)isthe output dimension.
The incorrect expert count feeds the CUDA
num_m_blocksheuristic andcan select different
BLOCK_SIZE_NandBLOCK_SIZE_Kvalues. TheTriton selector currently returns before using
num_experts, but thisPR fixes both call sites to preserve the argument contract.
A regression test invokes the CUDA dispatcher with
E=8andN=1024while mocking only the native kernel launch. It verifies that the
dispatcher selects:
Passing
N=1024as the expert count instead would selectBLOCK_SIZE_N=1024andBLOCK_SIZE_K=256.This PR does not change the block-selection heuristic itself.
Duplicate search found no open issue or PR for this expert-dimension
error. Related PRs #44563 and #40547 address different WNA16 block
configuration problems.
Test Plan
Run the focused WNA16 dispatcher regression test:
Test Result
The warnings are unrelated TorchScript deprecation warnings.
No model evaluation was run because this change affects kernel
configuration selection, not model output semantics.
No performance benchmark was run, and this PR does not claim a measured
speedup.
AI assistance was used in preparing this change.