mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-24 07:04:00 +08:00
Liu YunuoandLeiWang1999
b9a5907a20
* [Example] Migrate eligible examples to eager style * fix wrong shape annotation in gemm_split_k examples * fix incorrect dtype in gemm_streamk example * fix * revert some changes * Refactor attention functions to use new parameter structures and improve readability. Updated `example_amd_flash_attn_bwd.py` and `example_amd_flash_attn_fwd.py` to define input parameters more clearly. Enhanced shared memory usage in `flashattn_fwd` and `flashattn_sink` functions across various examples, ensuring compatibility with new configurations. Adjusted kernel definitions and added necessary checks for GPU capabilities. * lint fix * FIX --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
import tilelang
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import tilelang.language as T
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# @tilelang.jit(target="cuda")
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# target currently can be "cuda" or "hip" or "cpu".
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# if not specified, it will be inferred from the input tensors during compile time
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@tilelang.jit
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def matmul(A, B, block_M: int, block_N: int, block_K: int):
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M, N, K = T.const("M, N, K")
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dtype = T.float16
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accum_dtype = T.float32
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A: T.Tensor((M, K), dtype)
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B: T.Tensor((K, N), dtype)
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C = T.empty((M, N), dtype)
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# Initialize Kernel Context
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_K), dtype)
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B_shared = T.alloc_shared((block_K, block_N), dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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# Enable rasterization for better L2 cache locality (Optional)
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# T.use_swizzle(panel_size=10, enable=True)
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# Clear local accumulation
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T.clear(C_local)
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for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
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# Copy tile of A
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# This is a sugar syntax for parallelized copy
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T.copy(A[by * block_M, ko * block_K], A_shared)
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# Copy tile of B
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T.copy(B[ko * block_K, bx * block_N], B_shared)
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# Perform a tile-level GEMM on the shared buffers
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# Currently we dispatch to the cute/hip on Nvidia/AMD GPUs
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T.gemm(A_shared, B_shared, C_local)
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# relu
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for i, j in T.Parallel(block_M, block_N):
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C_local[i, j] = T.max(C_local[i, j], 0)
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# Copy result back to global memory
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T.copy(C_local, C[by * block_M, bx * block_N])
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return C
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M = 1024
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N = 1024
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K = 1024
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block_M = 128
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block_N = 128
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block_K = 32
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# Define the kernel (matmul) and compile/lower it into an executable module
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matmul_relu_kernel = matmul.compile(M=M, N=N, K=K, block_M=block_M, block_N=block_N, block_K=block_K)
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# Test the kernel in Python with PyTorch data
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import torch
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# Create random input tensors on the GPU
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a = torch.randn(M, K, device="cuda", dtype=torch.float16)
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b = torch.randn(K, N, device="cuda", dtype=torch.float16)
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# Run the kernel
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c = matmul_relu_kernel(a, b)
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print(c)
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# Reference multiplication using PyTorch
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ref_c = torch.relu(a @ b)
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# Validate correctness
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torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2)
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print("Kernel output matches PyTorch reference.")
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# Retrieve and inspect the generated CUDA source (optional)
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cuda_source = matmul_relu_kernel.get_kernel_source()
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print("Generated CUDA kernel:\n", cuda_source)
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# Profile latency with kernel
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profiler = matmul_relu_kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
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latency = profiler.do_bench()
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print(f"Latency: {latency} ms")
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