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https://github.com/tile-ai/tilelang.git
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5f202fe5c1
* tilelang frontend v2 * syntax sugar: defining a local var by annotation * [Refactor] fix type linting warning like `T.float32` * Add tl.local_var_init for new tl.float32 * allow passing default argument as function annotation * allow default arguments as annotation * fix lint error * minor fix * [Refactor] refactor tilelang.jit and tilelang.autotune * minor fix * minor fix * minor fix * fix metal get function name * add par_compile impl and tests * Type consistency on tvm datatype 1. isinstance(tl.float32, tvm.DataType) == True 2. Allow `tl.float32` as function annotations 3. Allow `tl.float32` as argument to be passed to `tl.alloc` or other functions * fix lint error * add more warning in frontend * update tvm version * Minor fix on tvm_ffi annotations * add document and examples * fix lint error * Simplify index calculations in example_chunk_o_bwd.py Refactor index calculations for dg_last_fragment assignment. * minor fix * lint fix --------- Co-authored-by: Lei Wang <leiwang1999@outlook.com> Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
import tilelang.testing
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import tilelang
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import torch
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@tilelang.jit(
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out_idx=-1, # create the output tensor during runtime
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verbose=True,
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)
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def matmul_kernel_jit(
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M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A=False,
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trans_B=True,
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in_dtype='float16',
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out_dtype='float32',
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accum_dtype='float32',
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num_stages=2,
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threads=128,
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):
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A_shape = (K, M) if trans_A else (M, K)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
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B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
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import tilelang.language as T
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@T.prim_func
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def main(
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A: T.Tensor(A_shape, in_dtype),
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B: T.Tensor(B_shape, in_dtype),
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C: T.Tensor((M, N), out_dtype),
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):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local, trans_A, trans_B)
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T.copy(C_local, C[by * block_M, bx * block_N])
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return main
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def test_par_compile():
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configs = [
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(1024, 1024, 1024, 128, 128, 32),
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(2048, 2048, 2048, 256, 256, 64),
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(4096, 4096, 4096, 64, 64, 128),
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]
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kernels = matmul_kernel_jit.par_compile(configs)
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for (M, N, K, _, _, _), kernel in zip(configs, kernels):
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A = torch.randn(M, K, dtype=torch.float16).cuda()
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B = torch.randn(N, K, dtype=torch.float16).cuda()
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ref = (A @ B.T).float()
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C = kernel(A, B)
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tilelang.testing.torch_assert_close(C, ref, rtol=1e-2, atol=1e-2)
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if __name__ == "__main__":
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tilelang.testing.main()
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