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Splitting attention _fwd_grouped_kernel_stage1 to improve occupancy #19774
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Summary of Changes
Hello @ekuznetsov139, 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 introduces a significant optimization for attention computation, particularly beneficial for models like DeepSeek-V3 handling very long context lengths. By splitting a critical Triton kernel into two stages and leveraging a temporary buffer, the changes aim to improve GPU occupancy and overall generation throughput, addressing a key performance bottleneck.
Highlights
- Performance Optimization: The core
_fwd_grouped_kernel_stage1
Triton kernel, identified as a bottleneck for large context lengths, has been split into two new kernels:_fwd_grouped_kernel_stage1_part1
and_fwd_grouped_kernel_stage1_part2
. This refactoring aims to improve GPU occupancy and generation throughput, especially for DeepSeek-V3 with context lengths exceeding 100k characters. - Temporary Buffer for Intermediate Results: A temporary buffer (
attn_buf
) is introduced to store intermediate attention calculation results between the two new kernel stages. This buffer is managed by a global cache (attn_buf_cache
) invllm/attention/backends/triton_mla.py
to optimize memory allocation. - Kernel Execution Re-orchestration: The
_fwd_grouped_kernel_stage1_part1
kernel is designed to be launched with a larger grid size, writing partial attention sums and logsumexp values to the temporary buffer. The_fwd_grouped_kernel_stage1_part2
kernel then aggregates these partial results from the buffer to compute the final attention output, allowing for more efficient GPU utilization.
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Code Review
This pull request introduces a performance optimization by splitting the _fwd_grouped_kernel_stage1
Triton kernel into two parts to improve GPU occupancy, which is beneficial for large context lengths. The core logic for the kernel split and the use of a temporary buffer (attn_buf
) appears to be implemented.
Key areas for review include:
- The management of the global
attn_buf_cache
, which could lead to memory issues without proper eviction or lifecycle management. - The use of magic numbers (e.g.,
32
fortemp_page_count
) that should be defined as constants for clarity. - Minor points on code clarity within the Triton kernels, such as an unused variable and a potentially misleading comment.
- A check for potential division by zero in the second stage of the new kernel.
- Removal of development artifacts like an
if True:
block.
Addressing these points will enhance the robustness and maintainability of the changes.
tl.store(Att_Out + offs_mid_o + offs_dv, | ||
acc / e_sum, | ||
mask=mask_dv) |
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There's a potential division by zero if e_sum
is 0.0
. This can occur if all attention scores (qk values) for a particular head and split were masked to -inf
, leading to p
(from tl.exp(qk - n_e_max[:, None])
) being all zeros, and thus tl.sum(p,1)
being zero.
If e_sum
is zero, acc / e_sum
will result in NaN or Inf. Is this scenario handled by upstream logic, or should a check be added here? For example, tl.where(e_sum == 0, 0.0, acc / e_sum)
if the output should be zero in such cases.
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Signed-off-by: Eugene Kuznetsov <eugene.kuznetsov@amd.com>
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Bump |
@gshtras can we get this PR reviewed? thanks |
This splits _fwd_grouped_kernel_stage1 operation in two parts, using a temporary buffer.
The rationale is as follows. _fwd_grouped_kernel_stage1 becomes a significant bottleneck in DeepSeek-V3 with large context lengths. It is slow, because it severely under-utilizes the GPU (it is launched with a very small grid size). This patch breaks it into _fwd_grouped_kernel_stage1_part1 (launched using a larger grid, writes into a temporary buffer) and _fwd_grouped_kernel_stage1_part2 (a small grid but much lighter).
Performance (context length vs. generation throughput in token/s, 4x AMD Instinct MI325, deepseek-ai/DeepSeek-V3-0324, --max_seq_len_to_capture 131072):