[CI] Add Analyzer and blocksparse_attention examples to CI (#472)

* yes

* [Bugfix] fix the unexpected keyword error of autotune

* format

* test

* [CI] Add Analyzer and blocksparse_attention examples to CI

* format

* try

* try

* try

* try

* t

* format

* d

---------

Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>

Author: yyttt6

examples/analyze/example_conv_analyze.py

@@ -49,7 +49,7 @@ def kernel(N,
     is_hopper = check_hopper()
 
     @T.prim_func
-    def main(
+    def conv(
             data: T.Tensor((N, H, W, C), dtype),
             kernel: T.Tensor((KH, KW, C, F), dtype),
             out: T.Tensor((N, OH, OW, F), dtype),
@@ -91,11 +91,16 @@ def main(
             T.copy(out_local, out_shared)
             T.copy(out_shared, out_flat[by * block_M, bx * block_N])
 
-    return main
+    return conv
 
 
-my_func = kernel(N, C, H, W, F, K, S, D, P, 64, 128, 32, 3, 256)
-cuda_device = CUDA("cuda")
-result = Analyzer.analysis(my_func, cuda_device)
-print(result)
-print(f"Analyzed FLOPs: {result.total_flops}")
+def main():
+    my_func = kernel(N, C, H, W, F, K, S, D, P, 64, 128, 32, 3, 256)
+    cuda_device = CUDA("cuda")
+    result = Analyzer.analysis(my_func, cuda_device)
+    print(result)
+    print(f"Analyzed FLOPs: {result.total_flops}")
+
+
+if __name__ == "__main__":
+    main()

examples/analyze/example_gemm_analyze.py

@@ -19,7 +19,7 @@ def kernel(
     accum_dtype = "float"
 
     @T.prim_func
-    def main(
+    def matmul(
             A: T.Tensor((M, K), dtype),
             B: T.Tensor((N, K), dtype),
             C: T.Tensor((M, N), dtype),
@@ -43,13 +43,18 @@ def main(
             T.copy(C_local, C_shared)
             T.copy(C_shared, C[by * block_M, bx * block_N])
 
-    return main
+    return matmul
 
 
-my_func = kernel(128, 128, 32, 3, 128, True)
+def main():
+    my_func = kernel(128, 128, 32, 3, 128, True)
 
-cuda_device = CUDA("cuda")
-result = Analyzer.analysis(my_func, cuda_device)
+    cuda_device = CUDA("cuda")
+    result = Analyzer.analysis(my_func, cuda_device)
 
-print(f"Analyzed FLOPs: {result.total_flops}")
-print(f"Expected FLOPs: {2 * M * N * K}")
+    print(f"Analyzed FLOPs: {result.total_flops}")
+    print(f"Expected FLOPs: {2 * M * N * K}")
+
+
+if __name__ == "__main__":
+    main()

examples/analyze/test_example_analyze.py

@@ -0,0 +1,17 @@
+# Copyright (c) Tile-AI Corporation.
+# Licensed under the MIT License.
+import tilelang.testing
+import example_gemm_analyze
+import example_conv_analyze
+
+
+def test_example_gemm_analyze():
+    example_gemm_analyze.main()
+
+
+def test_example_conv_analyze():
+    example_conv_analyze.main()
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()

examples/blocksparse_attention/block_sparse_attn_triton.py

@@ -379,6 +379,10 @@ def test_topk_sparse_attention_qlt_kl():
     print("Pass topk sparse attention test with qlen < klen")
 
 
-if __name__ == "__main__":
+def main():
     test_topk_sparse_attention()
     test_topk_sparse_attention_qlt_kl()
+
+
+if __name__ == "__main__":
+    main()

examples/blocksparse_attention/example_tilelang_block_sparse_attn.py

@@ -118,7 +118,7 @@ def Rescale(
                 acc_o[i, j] *= scores_scale[i]
 
         @T.prim_func
-        def main(
+        def blocksparse_flashattn(
                 Q: T.Tensor(shape, dtype),
                 K: T.Tensor(shape, dtype),
                 V: T.Tensor(shape, dtype),
@@ -165,7 +165,7 @@ def main(
                 T.copy(acc_o, O_shared)
                 T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :])
 
-        return main
+        return blocksparse_flashattn
 
     return kernel_func(block_M, block_N, num_stages, threads)
 
@@ -219,5 +219,9 @@ def test_topk_sparse_attention():
     print("Pass topk sparse attention test with qlen == klen")
 
 
-if __name__ == "__main__":
+def main():
     test_topk_sparse_attention()
+
+
+if __name__ == "__main__":
+    main()

examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_indice.py

@@ -223,6 +223,7 @@ def forward(self, query, key, value, block_indices, cache_seqlens):
         heads = self.heads
         heads_kv = self.heads_kv
         dim_v = self.dim_v
+        dim = self.dim
         block_size = self.block_size
         max_selected_blocks = block_indices.shape[-1]
 
@@ -397,30 +398,20 @@ def debug(name, expect, actual, atol=1e-3, rtol=1e-3):
         print(f"Index: {first_index}, expect: {expect[first_index]}, actual: {actual[first_index]}")
 
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--batch', type=int, default=8, help='batch size')
-    parser.add_argument('--heads', type=int, default=32, help='heads')
-    parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv')
-    parser.add_argument(
-        '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length')
-    parser.add_argument('--dim', type=int, default=128, help='dim')
-    parser.add_argument('--dim_v', type=int, default=128, help='dim_v')
-    parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio')
-    parser.add_argument('--block_size', type=int, default=32, help='block_size')
-    args = parser.parse_args()
-
-    batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v
-    sparse_ratio = args.sparse_ratio
-    block_size = args.block_size
-    qk_flops = 2 * batch * heads * max_cache_seqlen * dim
-    pv_flops = 2 * batch * heads * max_cache_seqlen * dim_v
-    total_flops = qk_flops + pv_flops
-
+def main(batch=8,
+         heads=32,
+         heads_kv=8,
+         max_cache_seqlen=8192,
+         dim=128,
+         dim_v=128,
+         sparse_ratio=0.8,
+         block_size=32):
+    batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v
+    sparse_ratio = sparse_ratio
+    block_size = block_size
     max_selected_blocks = int(math.ceil(max_cache_seqlen * (1 - sparse_ratio) / block_size))
     print("max_selected_blocks: ", max_selected_blocks)
     dtype = torch.float16
-    block_H = 64
 
     Q = torch.randn((batch, heads, dim), dtype=dtype, device='cuda')
     K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device='cuda')
@@ -494,3 +485,19 @@ def debug(name, expect, actual, atol=1e-3, rtol=1e-3):
         out = sparse_kernel(Q, K, V, block_indices, cache_seqlens)
     torch.cuda.synchronize()
     print("sparse time: ", (time.time() - start) / 100 * 1000)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch', type=int, default=8, help='batch size')
+    parser.add_argument('--heads', type=int, default=32, help='heads')
+    parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv')
+    parser.add_argument(
+        '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length')
+    parser.add_argument('--dim', type=int, default=128, help='dim')
+    parser.add_argument('--dim_v', type=int, default=128, help='dim_v')
+    parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio')
+    parser.add_argument('--block_size', type=int, default=32, help='block_size')
+    args = parser.parse_args()
+    main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v,
+         args.sparse_ratio, args.block_size)

examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_mask.py

@@ -209,6 +209,7 @@ def forward(self, query, key, value, block_mask, cache_seqlens):
         heads = self.heads
         heads_kv = self.heads_kv
         dim_v = self.dim_v
+        dim = self.dim
         block_size = self.block_size
         block_H = self.block_H
         max_cache_seqlen = key.shape[1]
@@ -370,30 +371,20 @@ def debug(name, expect, actual, atol=1e-3, rtol=1e-3):
         print(f"Index: {first_index}, expect: {expect[first_index]}, actual: {actual[first_index]}")
 
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--batch', type=int, default=8, help='batch size')
-    parser.add_argument('--heads', type=int, default=32, help='heads')
-    parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv')
-    parser.add_argument(
-        '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length')
-    parser.add_argument('--dim', type=int, default=128, help='dim')
-    parser.add_argument('--dim_v', type=int, default=128, help='dim_v')
-    parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio')
-    parser.add_argument('--block_size', type=int, default=32, help='block_size')
-    args = parser.parse_args()
-
-    batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v
-    sparse_ratio = args.sparse_ratio
-    block_size = args.block_size
-    qk_flops = 2 * batch * heads * max_cache_seqlen * dim
-    pv_flops = 2 * batch * heads * max_cache_seqlen * dim_v
-    total_flops = qk_flops + pv_flops
-
+def main(batch=8,
+         heads=32,
+         heads_kv=8,
+         max_cache_seqlen=8192,
+         dim=128,
+         dim_v=128,
+         sparse_ratio=0.8,
+         block_size=32):
+    batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v
+    sparse_ratio = sparse_ratio
+    block_size = block_size
     max_selected_blocks = int(math.ceil(max_cache_seqlen * (1 - sparse_ratio) / block_size))
     print("max_selected_blocks: ", max_selected_blocks)
     dtype = torch.float16
-    block_H = 64
 
     Q = torch.randn((batch, heads, dim), dtype=dtype, device='cuda')
     K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device='cuda')
@@ -457,3 +448,19 @@ def debug(name, expect, actual, atol=1e-3, rtol=1e-3):
         out = model(Q, K, V, block_mask, cache_seqlens)
     torch.cuda.synchronize()
     print("sparse time: ", (time.time() - start) / 100 * 1000)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch', type=int, default=8, help='batch size')
+    parser.add_argument('--heads', type=int, default=32, help='heads')
+    parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv')
+    parser.add_argument(
+        '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length')
+    parser.add_argument('--dim', type=int, default=128, help='dim')
+    parser.add_argument('--dim_v', type=int, default=128, help='dim_v')
+    parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio')
+    parser.add_argument('--block_size', type=int, default=32, help='block_size')
+    args = parser.parse_args()
+    main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v,
+         args.sparse_ratio, args.block_size)

examples/blocksparse_attention/example_triton_sparse_gqa_decode_varlen_indice.py

@@ -350,22 +350,18 @@ def ref_program_fa(query, key, value, cache_seqlens):
     return output
 
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--batch', type=int, default=64, help='batch size')
-    parser.add_argument('--heads', type=int, default=32, help='heads')
-    parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv')
-    parser.add_argument(
-        '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length')
-    parser.add_argument('--dim', type=int, default=128, help='dim')
-    parser.add_argument('--dim_v', type=int, default=128, help='dim_v')
-    parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio')
-    parser.add_argument('--block_size', type=int, default=32, help='block_size')
-    args = parser.parse_args()
-
-    batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v
-    sparse_ratio = args.sparse_ratio
-    block_size = args.block_size
+def main(batch=64,
+         heads=32,
+         heads_kv=8,
+         max_cache_seqlen=8192,
+         dim=128,
+         dim_v=128,
+         sparse_ratio=0.8,
+         block_size=32):
+
+    batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v
+    sparse_ratio = sparse_ratio
+    block_size = block_size
     qk_flops = 2 * batch * heads * max_cache_seqlen * dim
     pv_flops = 2 * batch * heads * max_cache_seqlen * dim_v
     total_flops = qk_flops + pv_flops
@@ -466,3 +462,19 @@ def ref_program_fa(query, key, value, cache_seqlens):
     print(f"Average time of ref: {avg_time_ref:.6f} seconds")
 
     print(f"Speedup: {avg_time_ref / avg_time:.2f}x")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch', type=int, default=64, help='batch size')
+    parser.add_argument('--heads', type=int, default=32, help='heads')
+    parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv')
+    parser.add_argument(
+        '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length')
+    parser.add_argument('--dim', type=int, default=128, help='dim')
+    parser.add_argument('--dim_v', type=int, default=128, help='dim_v')
+    parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio')
+    parser.add_argument('--block_size', type=int, default=32, help='block_size')
+    args = parser.parse_args()
+    main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v,
+         args.sparse_ratio, args.block_size)

examples/blocksparse_attention/example_triton_sparse_gqa_decode_varlen_mask.py

@@ -348,28 +348,23 @@ def ref_program_fa(query, key, value, cache_seqlens):
     return output
 
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--batch', type=int, default=64, help='batch size')
-    parser.add_argument('--heads', type=int, default=32, help='heads')
-    parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv')
-    parser.add_argument(
-        '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length')
-    parser.add_argument('--dim', type=int, default=128, help='dim')
-    parser.add_argument('--dim_v', type=int, default=128, help='dim_v')
-    parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio')
-    parser.add_argument('--block_size', type=int, default=32, help='block_size')
-    args = parser.parse_args()
-
-    batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v
-    block_size = args.block_size
-    sparse_ratio = args.sparse_ratio
+def main(batch=64,
+         heads=32,
+         heads_kv=8,
+         max_cache_seqlen=8192,
+         dim=128,
+         dim_v=128,
+         sparse_ratio=0.8,
+         block_size=32):
+
+    batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v
+    block_size = block_size
+    sparse_ratio = sparse_ratio
     qk_flops = 2 * batch * heads * max_cache_seqlen * dim
     pv_flops = 2 * batch * heads * max_cache_seqlen * dim_v
     total_flops = qk_flops + pv_flops
 
     dtype = torch.float16
-    block_H = 64
 
     Q = torch.randn((batch, heads, dim), dtype=dtype, device='cuda')
     K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device='cuda')
@@ -435,6 +430,7 @@ def ref_program_fa(query, key, value, cache_seqlens):
     avg_time = elapsed_time / 1000
     avg_flops = total_flops / avg_time
     print(f"Average time: {avg_time:.6f} seconds")
+    print(f"Average flops: {avg_flops:.2f} GFLOPS")
 
     # Measure performance of reference implementation
     start = time.time()
@@ -446,5 +442,22 @@ def ref_program_fa(query, key, value, cache_seqlens):
     avg_time_ref = elapsed_time_ref / 1000
     avg_flops_ref = total_flops / avg_time_ref
     print(f"Average time of ref: {avg_time_ref:.6f} seconds")
+    print(f"Average flops of ref: {avg_flops_ref:.2f} GFLOPS")
 
     print(f"Speedup: {avg_time_ref / avg_time:.2f}x")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch', type=int, default=64, help='batch size')
+    parser.add_argument('--heads', type=int, default=32, help='heads')
+    parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv')
+    parser.add_argument(
+        '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length')
+    parser.add_argument('--dim', type=int, default=128, help='dim')
+    parser.add_argument('--dim_v', type=int, default=128, help='dim_v')
+    parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio')
+    parser.add_argument('--block_size', type=int, default=32, help='block_size')
+    args = parser.parse_args()
+    main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v,
+         args.sparse_ratio, args.block_size)