[Kernels] Use empty for modular MoE workspaces

[bnellnm]

The MoE workspaces only needed to be cleared for BatchedTritonExperts + fp8. Use empty for workspace instead.

TODO: defer workspace creation/clearing to individual Experts.

cc @tlrmchlsmth , @varun-sundar-rabindranath

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[gemini-code-assist]

Summary of Changes

Hello @bnellnm, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request optimizes memory allocation for Mixture of Experts (MoE) workspaces within the modular kernel implementation. By switching from zero-initialized tensors (torch.zeros) to uninitialized tensors (torch.empty) for the main workspaces, it avoids unnecessary overhead. The necessary zeroing for specific configurations, like FP8, is now handled conditionally within the fused batched MoE layer, ensuring correctness without impacting performance in other cases.

Highlights

• Memory Optimization: Changed the default allocation for MoE workspaces (workspace13 and workspace2) in the modular kernel from torch.zeros (zero-initialized) to torch.empty (uninitialized) to improve performance by avoiding unnecessary memory writes.
• Conditional Zeroing: Added a conditional zeroing step for the intermediate_cache1 workspace specifically when using FP8 weights and activations in the fused batched MoE layer, as zeroing is required for correctness in this configuration.

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[gemini-code-assist]

Code Review

This pull request optimizes MoE workspace initialization by using torch.empty instead of torch.zeros, which can improve performance by avoiding unnecessary zero-filling.

Concurrently, a necessary change is introduced in BatchedTritonExperts: intermediate_cache1 (derived from the now uninitialized workspace13) is explicitly zero-filled if use_fp8_w8a8 is true. My analysis suggests this is crucial for correctness because:

1. In BatchedTritonExperts, tokens beyond expert_num_tokens[expert] (i.e., padding tokens up to max_num_tokens) do not have their corresponding entries in intermediate_cache1 overwritten by the first GEMM. These entries would thus contain uninitialized data from torch.empty.
2. This intermediate_cache1 (including uninitialized padding) is then passed to the activation function.
3. The output of the activation, intermediate_cache2, would also contain garbage in these padded areas.
4. When intermediate_cache2 is subsequently quantized for FP8 (in moe_kernel_quantize_input), uninitialized large values could corrupt the FP8 scale calculation, leading to incorrect results in the second GEMM.

Zeroing intermediate_cache1 when use_fp8_w8a8 ensures that padded regions are zero, which behave predictably through activation and FP8 quantization.

The PR description correctly notes this clearing is specific to BatchedTritonExperts + fp8. Other expert types like TritonExperts (the non-batched version) appear to handle padding differently (e.g., via moe_align_block_size and tl.load(..., other=0.0)), which results in padded areas of intermediate_cache1 being effectively zeroed by the GEMM itself, thus not requiring this explicit fill_(0).

The changes appear well-reasoned and correctly implemented. The TODO item regarding deferring workspace creation/clearing to individual experts is also acknowledged, and this PR is a step in that direction.

No issues of medium or higher severity were identified in the diff.

[tlrmchlsmth]

Very nice

[mgoin]

Can you run an eval on a model that uses the non-fp8 pathway to make sure?

[bnellnm]

Update: I ran an lm_eval on deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct and didn't encounter any problems.