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https://github.com/tile-ai/tilelang.git
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307 lines
11 KiB
Python
307 lines
11 KiB
Python
import torch
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import tilelang.testing
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from tilelang import tvm as tvm
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import tilelang.language as T
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from tilelang.intrinsics import make_mfma_swizzle_layout as make_swizzle_layout
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from tilelang.intrinsics.mfma_macro_generator import MatrixCorePreshuffleIntrinEmitter
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from tilelang.transform import simplify_prim_func
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tilelang.testing.set_random_seed(0)
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@simplify_prim_func
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def tl_matmul(
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M,
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N,
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K,
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in_dtype,
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out_dtype,
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accum_dtype,
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a_transposed=False,
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b_transposed=True,
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k_pack=1,
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b_preshuffle=False,
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b_g2l_load=False,
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):
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micro_size_x = micro_size_y = micro_size_k = 16
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if in_dtype in {"float8_e4m3fnuz", "int8"}:
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micro_size_k = 32
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block_row_warps = 2
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block_col_warps = 2
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warp_row_tiles = 32
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warp_col_tiles = 32
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# for preshuffle_b, warp_layout = {1, 4}
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if b_preshuffle:
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block_row_warps = 1
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block_col_warps = 4
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warp_row_tiles = 64
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warp_col_tiles = 16
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chunk = 256 * k_pack
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pack_size_k = micro_size_k * k_pack
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shared_scope = "shared"
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block_M = block_row_warps * warp_row_tiles
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block_N = block_col_warps * warp_col_tiles
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block_K = chunk
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A_shape = (K, M) if a_transposed else (M, K)
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if b_preshuffle:
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B_shape = (N // micro_size_y, K // pack_size_k, micro_size_y,
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pack_size_k) if b_transposed else (K // pack_size_k, N // micro_size_y,
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pack_size_k, micro_size_y)
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else:
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B_shape = (N, K) if b_transposed else (K, N)
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A_shared_shape = (block_K, block_M) if a_transposed else (block_M, block_K)
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if b_preshuffle:
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B_shared_shape = (block_N // micro_size_y, block_K // pack_size_k, micro_size_y,
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pack_size_k) if b_transposed else (block_K // pack_size_k,
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block_N // micro_size_y, pack_size_k,
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micro_size_y)
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else:
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B_shared_shape = (block_N, block_K) if b_transposed else (block_K, block_N)
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warp_size = 64
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threads = warp_size * (block_row_warps * block_col_warps)
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local_size_a = (k_pack * micro_size_x * micro_size_k) // warp_size
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local_size_b = (k_pack * micro_size_y * micro_size_k) // warp_size
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local_size_c = (micro_size_x * micro_size_y) // warp_size
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warp_rows = warp_row_tiles // micro_size_x
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warp_cols = warp_col_tiles // micro_size_y
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# MMA Wrapper to Auto Generate Code for MMA
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mfma_emitter = MatrixCorePreshuffleIntrinEmitter(
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a_dtype=in_dtype,
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b_dtype=in_dtype,
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accum_dtype=accum_dtype,
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a_transposed=a_transposed,
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b_transposed=b_transposed,
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block_row_warps=block_row_warps,
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block_col_warps=block_col_warps,
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warp_row_tiles=warp_row_tiles,
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warp_col_tiles=warp_col_tiles,
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chunk=chunk,
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k_pack=k_pack,
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b_preshuffle=b_preshuffle,
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)
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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, scope=shared_scope)
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B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
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A_local = T.alloc_local((warp_rows * local_size_a), in_dtype)
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B_local = T.alloc_local((warp_cols * local_size_b), in_dtype)
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C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype)
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T.annotate_layout({
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A_shared: make_swizzle_layout(A_shared),
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})
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num_ko = K // block_K
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num_ki = block_K // (k_pack * micro_size_k)
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# Improve L2 Cache
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T.use_swizzle(panel_size=10)
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T.clear(C_local)
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for ko in T.Pipelined(num_ko, num_stages=0):
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# Load A into shared memory
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if a_transposed:
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T.copy(A[ko * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, ko * block_K], A_shared)
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# Load B into shared memory
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if b_g2l_load is False:
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if b_transposed:
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for j, k, jj, kk in T.Parallel(block_N // micro_size_y,
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block_K // pack_size_k, micro_size_y,
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pack_size_k):
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B_shared[j, k, jj, kk] = B[bx * block_N // micro_size_y + j,
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ko * block_K // pack_size_k + k, jj, kk]
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else:
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for k, j, kk, jj in T.Parallel(block_K // pack_size_k,
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block_N // micro_size_y, pack_size_k,
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micro_size_y):
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B_shared[k, j, kk, jj] = B[ko * block_K // pack_size_k + k,
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bx * block_N // micro_size_y + j, kk, jj]
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for ki in T.serial(0, num_ki):
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# Load A S2L
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mfma_emitter.ldmatrix_a(
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A_local,
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A_shared,
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ki,
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)
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if b_g2l_load:
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# Load B G2L
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mfma_emitter.ldmatrix_b(B_local, B, ki + ko * num_ki, pid_m=by, pid_n=bx)
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else:
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# Load B S2L
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mfma_emitter.ldmatrix_b(
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B_local,
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B_shared,
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ki,
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)
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# Perform Matrix Multiplication
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mfma_emitter.mfma(A_local, B_local, C_local)
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# Perform STMatrix
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mfma_emitter.stmatrix(
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C_local,
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C,
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pid_m=by,
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pid_n=bx,
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)
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return main
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def shuffle_weight(
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x: torch.Tensor,
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layout=(16, 32),
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k_pack=1,
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is_transpose=False,
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) -> torch.Tensor:
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IN, IK = layout
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BK = IK * k_pack
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BN = IN
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N, K = (x.shape[-2], x.shape[-1]) if is_transpose else (x.shape[-1], x.shape[-2])
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assert N % BN == 0
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assert K % BK == 0
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x = x.view(N // BN, BN, K // BK, BK) if is_transpose else x.view(K // BK, BK, N // BN, BN)
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x = x.permute(0, 2, 1, 3)
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return x.contiguous()
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def assert_tl_matmul_correctness(M,
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N,
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K,
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in_dtype,
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out_dtype,
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accum_dtype="float32",
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a_transposed=False,
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b_transposed=True,
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k_pack=1,
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b_preshuffle=False,
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b_g2l_load=False):
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matmul = tl_matmul(M, N, K, in_dtype, out_dtype, accum_dtype, a_transposed, b_transposed,
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k_pack, b_preshuffle, b_g2l_load)
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print(matmul)
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kernel = tilelang.compile(matmul)
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src_code = kernel.get_kernel_source()
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# src_code is the generated cuda source
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assert src_code is not None
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A_shape = (K, M) if a_transposed else (M, K)
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B_shape = (N, K) if b_transposed else (K, N)
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if in_dtype == "int8":
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A = torch.randint(-128, 127, A_shape, device="cuda", dtype=torch.int8)
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B = torch.randint(-128, 127, B_shape, device="cuda", dtype=torch.int8)
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elif in_dtype == "float8_e4m3fnuz":
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A = torch.rand(A_shape, device="cuda", dtype=torch.float16).to(getattr(torch, in_dtype))
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B = torch.rand(B_shape, device="cuda", dtype=torch.float16).to(getattr(torch, in_dtype))
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else:
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A = torch.rand(A_shape, device="cuda", dtype=getattr(torch, in_dtype))
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B = torch.rand(B_shape, device="cuda", dtype=getattr(torch, in_dtype))
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C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, out_dtype))
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B_preshuffle = B
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if b_preshuffle:
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B_preshuffle = shuffle_weight(B_preshuffle, k_pack=k_pack, is_transpose=b_transposed)
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kernel(A, B_preshuffle, C)
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else:
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kernel(A, B, C)
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print(kernel.get_kernel_source())
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profiler = kernel.get_profiler()
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latency = profiler.do_bench()
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# Ensure that the latency is not None
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assert latency is not None
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if a_transposed and b_transposed:
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# Get Reference Result
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ref_c = torch.matmul(A.T.to(torch.float32),
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B.T.to(torch.float32)).to(getattr(torch, out_dtype))
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elif a_transposed and not b_transposed:
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# Get Reference Result
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ref_c = torch.matmul(A.Tto(torch.float32),
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B.to(torch.float32)).to(getattr(torch, out_dtype))
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elif not a_transposed and b_transposed:
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# Get Reference Result
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ref_c = torch.matmul(A.to(torch.float32),
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B.T.to(torch.float32)).to(getattr(torch, out_dtype))
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else:
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# Get Reference Result
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ref_c = torch.matmul(A.to(torch.float32), B.to(torch.float32)).to(getattr(torch, out_dtype))
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print(C)
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print(ref_c)
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torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
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@tilelang.testing.requires_rocm
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def test_assert_tl_matmul():
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assert_tl_matmul_correctness(
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256, 256, 512, "int8", "int32", accum_dtype="int32", b_preshuffle=True)
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assert_tl_matmul_correctness(
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256, 256, 512, "int8", "int32", accum_dtype="int32", b_preshuffle=True)
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assert_tl_matmul_correctness(
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256, 256, 512, "int8", "int32", b_transposed=False, accum_dtype="int32", b_preshuffle=True)
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assert_tl_matmul_correctness(
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256, 256, 512, "int8", "int32", accum_dtype="int32", k_pack=2, b_preshuffle=True)
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assert_tl_matmul_correctness(
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256,
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256,
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512,
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"int8",
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"int32",
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b_transposed=False,
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accum_dtype="int32",
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k_pack=2,
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b_preshuffle=True)
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assert_tl_matmul_correctness(256, 256, 512, "float8_e4m3fnuz", "float32", b_preshuffle=True)
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assert_tl_matmul_correctness(
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256, 256, 512, "float8_e4m3fnuz", "float32", b_transposed=False, b_preshuffle=True)
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assert_tl_matmul_correctness(
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256, 256, 512, "float8_e4m3fnuz", "float32", k_pack=2, b_preshuffle=True)
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assert_tl_matmul_correctness(
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256,
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256,
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512,
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"float8_e4m3fnuz",
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"float32",
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k_pack=2,
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b_transposed=False,
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b_preshuffle=True)
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if __name__ == "__main__":
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tilelang.testing.main()
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