mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-25 07:14:16 +08:00
Chaofan Lin
76571366e7
* [Target] Add TileLang target registry * extend * fix gitignore * [Backend] Register target and execution backend resolution * [Backend] Remove JIT execution backend shim * [Backend] Move target helpers under backend * [Backend] Use registration order for target detection * [Backend] Use registration order for execution backend * [Backend] Split target helpers by backend * [Backend] Remove supported target registry * remove lazy detector * fix debug print
431 lines
16 KiB
Python
431 lines
16 KiB
Python
import pytest
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import torch
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import tilelang
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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.rocm.intrinsics import make_mfma_swizzle_layout as make_swizzle_layout
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from tilelang.rocm.intrinsics.mfma_macro_generator import MatrixCorePreshuffleIntrinEmitter
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from tilelang.transform import simplify_prim_func
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from tilelang.language.fp8 import determine_fp8_type
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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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a_preshuffle=False,
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b_preshuffle=False,
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b_g2l_load=False,
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block_row_warps=None,
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block_col_warps=None,
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warp_row_tiles=None,
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warp_col_tiles=None,
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chunk=None,
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num_stages=0,
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panel_size=10,
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mfma_shape=None,
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):
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"""Build a TileLang MFMA kernel for ``A @ B^T`` (with optional preshuffle).
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The (block_*_warps, warp_*_tiles, chunk, num_stages, panel_size) parameters
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expose the underlying CK-style template knobs so that an external autotuner
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can pick per-shape tile / wave / pipeline configurations.
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``mfma_shape`` selects which MFMA instruction to use, as an ``(M, N, K)``
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tuple. Supported int8 shapes on CDNA4 (gfx950):
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(16, 16, 32) — default, ``v_mfma_i32_16x16x32_i8``
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(16, 16, 64) — doubled-K, ``v_mfma_i32_16x16x64_i8``
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(32, 32, 32) — doubled-MN, ``v_mfma_i32_32x32x32_i8``
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"""
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if mfma_shape is not None:
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micro_size_x, micro_size_y, micro_size_k = mfma_shape
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else:
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micro_size_x = micro_size_y = 16
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micro_size_k = 32 if in_dtype.bits == 8 else 16
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if block_row_warps is None:
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block_row_warps = 2
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if block_col_warps is None:
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block_col_warps = 2
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if warp_row_tiles is None:
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warp_row_tiles = max(32, micro_size_x)
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if warp_col_tiles is None:
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warp_col_tiles = max(32, micro_size_y)
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# Legacy heuristic: if the caller did not override any tile knob and we are
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# in B-only preshuffle mode, keep the historical 1x4 warp grid.
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_all_tile_defaults = (
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block_row_warps == 2
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and block_col_warps == 2
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and warp_row_tiles == max(32, micro_size_x)
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and warp_col_tiles == max(32, micro_size_y)
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)
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if _all_tile_defaults and b_preshuffle and not a_preshuffle:
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block_row_warps, block_col_warps = 1, 4
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warp_row_tiles = max(64, micro_size_x)
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warp_col_tiles = max(16, micro_size_y)
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if chunk is None:
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chunk = 256 * k_pack
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# ---- structural validation (catch invalid configs early) ----
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assert warp_row_tiles % micro_size_x == 0, f"warp_row_tiles={warp_row_tiles} must be a multiple of micro_size_x={micro_size_x}"
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assert warp_col_tiles % micro_size_y == 0, f"warp_col_tiles={warp_col_tiles} must be a multiple of micro_size_y={micro_size_y}"
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assert chunk % (k_pack * micro_size_k) == 0, f"chunk={chunk} must be a multiple of k_pack*micro_size_k={k_pack * micro_size_k}"
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block_M_check = block_row_warps * warp_row_tiles
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block_N_check = block_col_warps * warp_col_tiles
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assert M % block_M_check == 0, f"M={M} must be a multiple of block_M={block_M_check}"
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assert N % block_N_check == 0, f"N={N} must be a multiple of block_N={block_N_check}"
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assert K % chunk == 0, f"K={K} must be a multiple of chunk={chunk}"
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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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if a_preshuffle:
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A_shape = (
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(K // pack_size_k, M // micro_size_x, pack_size_k, micro_size_x)
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if a_transposed
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else (M // micro_size_x, K // pack_size_k, micro_size_x, pack_size_k)
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)
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else:
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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 = (
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(N // micro_size_y, K // pack_size_k, micro_size_y, pack_size_k)
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if b_transposed
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else (K // pack_size_k, N // micro_size_y, pack_size_k, micro_size_y)
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)
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else:
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B_shape = (N, K) if b_transposed else (K, N)
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if a_preshuffle:
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A_shared_shape = (
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(block_K // pack_size_k, block_M // micro_size_x, pack_size_k, micro_size_x)
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if a_transposed
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else (block_M // micro_size_x, block_K // pack_size_k, micro_size_x, pack_size_k)
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)
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else:
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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 = (
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(block_N // micro_size_y, block_K // pack_size_k, micro_size_y, pack_size_k)
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if b_transposed
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else (block_K // pack_size_k, block_N // micro_size_y, pack_size_k, micro_size_y)
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)
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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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a_preshuffle=a_preshuffle,
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b_preshuffle=b_preshuffle,
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mfma_shape=mfma_shape,
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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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layout_map = {}
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if not a_preshuffle:
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layout_map[A_shared] = make_swizzle_layout(A_shared)
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if not b_preshuffle:
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layout_map[B_shared] = make_swizzle_layout(B_shared)
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if layout_map:
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T.annotate_layout(layout_map)
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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=panel_size)
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T.clear(C_local)
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for ko in T.Pipelined(num_ko, num_stages=num_stages):
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# Load A into shared memory
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if a_preshuffle:
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if a_transposed:
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for k, i, kk, ii in T.Parallel(block_K // pack_size_k, block_M // micro_size_x, pack_size_k, micro_size_x):
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A_shared[k, i, kk, ii] = A[ko * block_K // pack_size_k + k, by * block_M // micro_size_x + i, kk, ii]
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else:
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for i, k, ii, kk in T.Parallel(block_M // micro_size_x, block_K // pack_size_k, micro_size_x, pack_size_k):
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A_shared[i, k, ii, kk] = A[by * block_M // micro_size_x + i, ko * block_K // pack_size_k + k, ii, kk]
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else:
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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_preshuffle:
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if b_transposed:
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for j, k, jj, kk in T.Parallel(block_N // micro_size_y, block_K // pack_size_k, micro_size_y, pack_size_k):
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B_shared[j, k, jj, kk] = B[bx * block_N // micro_size_y + j, 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, block_N // micro_size_y, pack_size_k, micro_size_y):
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B_shared[k, j, kk, jj] = B[ko * block_K // pack_size_k + k, bx * block_N // micro_size_y + j, kk, jj]
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else:
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if b_transposed:
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T.copy(B[bx * block_N, ko * block_K], B_shared)
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else:
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T.copy(B[ko * block_K, bx * block_N], B_shared)
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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(
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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=T.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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a_preshuffle=False,
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b_preshuffle=False,
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b_g2l_load=False,
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mfma_shape=None,
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):
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matmul = 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,
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b_transposed,
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k_pack,
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a_preshuffle,
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b_preshuffle,
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b_g2l_load,
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mfma_shape=mfma_shape,
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)
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kernel = tilelang.compile(matmul)
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assert kernel.get_kernel_source() 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 == T.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 "float8" in str(in_dtype):
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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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shuf_layout = (mfma_shape[0], mfma_shape[2]) if mfma_shape else (16, 32)
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A_in = shuffle_weight(A, layout=shuf_layout, k_pack=k_pack, is_transpose=not a_transposed) if a_preshuffle else A
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B_in = shuffle_weight(B, layout=shuf_layout, k_pack=k_pack, is_transpose=b_transposed) if b_preshuffle else B
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kernel(A_in, B_in, C)
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if a_transposed and b_transposed:
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ref_c = torch.matmul(A.T.to(torch.float32), 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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ref_c = torch.matmul(A.T.to(torch.float32), B.to(torch.float32)).to(getattr(torch, out_dtype))
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elif not a_transposed and b_transposed:
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ref_c = torch.matmul(A.to(torch.float32), B.T.to(torch.float32)).to(getattr(torch, out_dtype))
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else:
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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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torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
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@pytest.mark.parametrize(
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"M, N, K, in_dtype, out_dtype, accum_dtype, a_transposed, b_transposed, k_pack, a_preshuffle, b_preshuffle, b_g2l_load",
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[
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# B-only preshuffle
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(256, 256, 512, T.int8, T.int32, T.int32, False, True, 1, False, True, False),
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(256, 256, 512, T.int8, T.int32, T.int32, False, False, 1, False, True, False),
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(256, 256, 512, T.int8, T.int32, T.int32, False, True, 2, False, True, False),
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(256, 256, 512, T.int8, T.int32, T.int32, False, False, 2, False, True, False),
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(256, 256, 512, determine_fp8_type(), T.float32, T.float32, False, True, 1, False, True, False),
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(256, 256, 512, determine_fp8_type(), T.float32, T.float32, False, False, 1, False, True, False),
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(256, 256, 512, determine_fp8_type(), T.float32, T.float32, False, True, 2, False, True, False),
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(256, 256, 512, determine_fp8_type(), T.float32, T.float32, False, False, 2, False, True, False),
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# No preshuffle
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(256, 256, 512, T.int8, T.int32, T.int32, False, True, 1, False, False, False),
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(256, 256, 512, determine_fp8_type(), T.float32, T.float32, False, True, 1, False, False, False),
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# A-only preshuffle
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(256, 256, 512, T.int8, T.int32, T.int32, False, True, 1, True, False, False),
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(256, 256, 512, T.int8, T.int32, T.int32, True, True, 1, True, False, False),
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(256, 256, 512, determine_fp8_type(), T.float32, T.float32, False, True, 1, True, False, False),
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(256, 256, 512, determine_fp8_type(), T.float32, T.float32, True, True, 1, True, False, False),
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# A+B preshuffle together (default 2x2 warp grid)
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(256, 256, 512, T.int8, T.int32, T.int32, False, True, 1, True, True, False),
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(256, 256, 512, determine_fp8_type(), T.float32, T.float32, False, True, 1, True, True, False),
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],
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)
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@tilelang.testing.requires_cdna
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def test_assert_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,
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b_transposed,
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k_pack,
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a_preshuffle,
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b_preshuffle,
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b_g2l_load,
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):
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assert_tl_matmul_correctness(
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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=accum_dtype,
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a_transposed=a_transposed,
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b_transposed=b_transposed,
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k_pack=k_pack,
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a_preshuffle=a_preshuffle,
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b_preshuffle=b_preshuffle,
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b_g2l_load=b_g2l_load,
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)
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# ---- CDNA4 extended MFMA shapes: 16x16x64 and 32x32x32 for int8 ----
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@pytest.mark.parametrize(
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"M, N, K, in_dtype, out_dtype, accum_dtype, b_transposed, k_pack, b_preshuffle, mfma_shape",
|
|
[
|
|
# v_mfma_i32_16x16x64_i8 — doubled K throughput (kp=1 only, micro_k=64)
|
|
(256, 256, 512, T.int8, T.int32, T.int32, True, 1, False, (16, 16, 64)),
|
|
(256, 256, 512, T.int8, T.int32, T.int32, True, 1, True, (16, 16, 64)),
|
|
# v_mfma_i32_32x32x32_i8 — doubled MN throughput
|
|
(256, 256, 512, T.int8, T.int32, T.int32, True, 1, False, (32, 32, 32)),
|
|
(256, 256, 512, T.int8, T.int32, T.int32, True, 1, True, (32, 32, 32)),
|
|
],
|
|
)
|
|
@tilelang.testing.requires_gfx950
|
|
def test_assert_tl_matmul_extended_mfma(
|
|
M,
|
|
N,
|
|
K,
|
|
in_dtype,
|
|
out_dtype,
|
|
accum_dtype,
|
|
b_transposed,
|
|
k_pack,
|
|
b_preshuffle,
|
|
mfma_shape,
|
|
):
|
|
assert_tl_matmul_correctness(
|
|
M,
|
|
N,
|
|
K,
|
|
in_dtype,
|
|
out_dtype,
|
|
accum_dtype=accum_dtype,
|
|
b_transposed=b_transposed,
|
|
k_pack=k_pack,
|
|
b_preshuffle=b_preshuffle,
|
|
mfma_shape=mfma_shape,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tilelang.testing.main()
|