999858e82f
- Add test_xattn_kernels.py demonstrating flat_group_gemm_fuse_reshape and softmax_fuse_block_sum Triton kernels with structured data - Update testing.md with new test code style guidelines - Update xattn.py and xattn_bsa.py with improvements Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
"""
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Test: XAttention Triton kernels
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演示 XAttention 的两个核心 Triton kernel:
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1. flat_group_gemm_fuse_reshape: 计算 stride reshape 后的 attention scores (反对角线求和)
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2. softmax_fuse_block_sum: 对 attention scores 做 softmax 后按 block 求和
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数据流:
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Q, K [batch, heads, seq_len, head_dim]
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↓ flat_group_gemm_fuse_reshape
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attn_scores [batch, heads, seq_len/stride, seq_len/stride]
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↓ softmax_fuse_block_sum
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block_sums [batch, heads, q_blocks, k_blocks]
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"""
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import torch
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import sys
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sys.path.insert(0, "/home/zijie/Code/nano-vllm")
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from nanovllm.ops.xattn import flat_group_gemm_fuse_reshape, softmax_fuse_block_sum
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# ============================================================
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# 参数配置
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# ============================================================
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seq_len = 512 # Triton 要求 seq_len >= stride * BLOCK_M = 4 * 128 = 512
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head_dim = 128
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stride = 4
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block_size = 128 # softmax block size (in reshaped space)
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segment_size = 128 # Triton kernel 要求 segment_size >= block_size
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# ============================================================
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# 构造输入: 偶数位置=1, 奇数位置=2
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# ============================================================
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Q = torch.zeros(1, 1, seq_len, head_dim, dtype=torch.bfloat16).cuda()
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K = torch.zeros(1, 1, seq_len, head_dim, dtype=torch.bfloat16).cuda()
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for i in range(seq_len):
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if i % 2 == 0:
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Q[0, 0, i, :] = 1
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K[0, 0, i, :] = 1
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else:
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Q[0, 0, i, :] = 2
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K[0, 0, i, :] = 2
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# ============================================================
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# Step 1: flat_group_gemm_fuse_reshape
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# ============================================================
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attn_scores = flat_group_gemm_fuse_reshape(
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Q, K, stride,
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chunk_start=0,
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chunk_end=seq_len // stride,
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is_causal=False
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)
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# 验证: 反对角线求和
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# 每个 stride x stride 块的反对角线: Q[奇]*K[偶] + Q[偶]*K[奇] = 2*1 + 1*2 = 4
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# 反对角线有 stride/2 对，再乘以 head_dim
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expected_gemm = (2*1 + 1*2) * (stride // 2) * head_dim
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actual_gemm = attn_scores[0, 0, 0, 0].item()
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assert actual_gemm == expected_gemm, f"flat_group_gemm: {actual_gemm} != {expected_gemm}"
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# ============================================================
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# Step 2: softmax_fuse_block_sum
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# ============================================================
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reshaped_len = seq_len // stride
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scale = 1.4426950408889634 # log2(e) for exp2
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block_sums = softmax_fuse_block_sum(
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attn_scores,
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block_size,
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segment_size,
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chunk_start=0,
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chunk_end=reshaped_len,
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real_q_len=reshaped_len,
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scale=scale,
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is_causal=False
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)
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# 验证: 每个 block 的 softmax 结果求和
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# 所有 attn_scores 相同 → softmax 均匀分布 → block_sum = block_size^2 / reshaped_len
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expected_sum = block_size * block_size / reshaped_len
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actual_sum = block_sums[0, 0, 0, 0].item()
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assert actual_sum == expected_sum, f"softmax_fuse_block_sum: {actual_sum} != {expected_sum}"
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print("test_xattn_kernels: PASSED")
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