mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-25 07:14:16 +08:00
Lei Wang
09385e7deb
* Remove unnecessary swizzled layout annotations from various attention sink examples and kernel analysis script for improved clarity and performance. * Remove unnecessary swizzled layout annotations from various examples to enhance code clarity and performance. * lint fix * Uncomment main test execution for vectorized cast * Add swizzled layout annotation for B_shared in dequantize GEMM example This change introduces a layout annotation for the B_shared tensor in the example_dequant_gemm_bf16_mxfp4_hopper.py file, enhancing the memory layout optimization for better performance during matrix multiplication. * Remove unnecessary cache disabling in GQA sink example for improved clarity and performance. * lint fix * Refactor layout functions to support row-major linear layout for any dimension - Renamed `makeGemmLayoutLinear` to `makeLinearLayout` and updated its implementation to handle arbitrary dimensions. - Updated related function calls in `gemm_layouts.cc` and `layout.cc` to use the new layout function. - Enhanced layout inference in `CumSumOpNode` to enforce linear layout for shared buffers in strict mode. * Refactor `make_linear_layout` to accept a single argument and support arbitrary dimensions - Updated the function signature to take a `Buffer`, `BufferLoad`, or `BufferRegion` directly. - Simplified the implementation by removing argument checks and directly obtaining the shape from the buffer info. - Enhanced the documentation to clarify the function's purpose and usage. * skip callback test * Remove example_dequant_gemm_bf16_mxfp4_hopper_tma.py file, eliminating unused code related to dequantization GEMM example. * typo fix
129 lines
5.1 KiB
Python
129 lines
5.1 KiB
Python
import torch
|
|
import tilelang
|
|
from tilelang.autotuner import *
|
|
import tilelang.language as T
|
|
import argparse
|
|
|
|
|
|
def check_hopper():
|
|
if not torch.cuda.is_available():
|
|
return None
|
|
props = torch.cuda.get_device_properties(0)
|
|
compute_capability = props.major, props.minor
|
|
return compute_capability == (9, 0)
|
|
|
|
|
|
def ref_program(stride, padding, dilation):
|
|
def main(A, B):
|
|
A = A.permute(0, 3, 1, 2) # N, H, W, C -> N, C, H, W
|
|
B = B.permute(3, 2, 0, 1) # H, W, C, F -> F, C, H, W
|
|
C = torch.conv2d(A, B, stride=stride, padding=padding, dilation=dilation)
|
|
C = C.permute(0, 2, 3, 1) # N, C, H, W -> N, H, W, C
|
|
return C
|
|
|
|
return main
|
|
|
|
|
|
@tilelang.jit(out_idx=[2])
|
|
def convolution(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages, threads, dtype=T.float16, accum_dtype=T.float32):
|
|
KH, KW = K, K
|
|
OH = (H + 2 * P - D * (K - 1) - 1) // S + 1
|
|
OW = (W + 2 * P - D * (K - 1) - 1) // S + 1
|
|
dtype = T.float16
|
|
accum_dtype = T.float32
|
|
is_hopper = check_hopper()
|
|
|
|
@T.prim_func
|
|
def main(
|
|
data: T.Tensor((N, H, W, C), dtype),
|
|
kernel: T.Tensor((KH, KW, C, F), dtype),
|
|
out: T.Tensor((N, OH, OW, F), dtype),
|
|
):
|
|
with T.Kernel(T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M), threads=threads) as (bx, by):
|
|
data_shared = T.alloc_shared((block_M, block_K), dtype)
|
|
kernel_shared = T.alloc_shared((block_K, block_N), dtype)
|
|
out_local = T.alloc_fragment((block_M, block_N), accum_dtype)
|
|
out_shared = T.alloc_shared((block_M, block_N), dtype)
|
|
|
|
kernel_flat = T.Tensor((KH * KW * C, F), dtype, kernel.data)
|
|
out_flat = T.Tensor((N * OH * OW, F), dtype, out.data)
|
|
|
|
T.clear(out_local)
|
|
for k_iter in T.Pipelined(T.ceildiv(KH * KW * C, block_K), num_stages=num_stages):
|
|
if is_hopper:
|
|
T.c2d_im2col(data, data_shared, by, k_iter, KH, S, D, P)
|
|
else:
|
|
for i, j in T.Parallel(block_M, block_K):
|
|
k = k_iter * block_K + j
|
|
m = by * block_M + i
|
|
access_h = m % (OH * OW) // OW * S + k // (KW * C) * D - P
|
|
access_w = m % OW * S + k // C % KW * D - P
|
|
in_bound = (access_h >= 0) and (access_w >= 0) and (access_h < H) and (access_w < W)
|
|
data_shared[i, j] = T.if_then_else(in_bound, data[m // (OH * OW), access_h, access_w, k % C], 0)
|
|
T.copy(kernel_flat[k_iter * block_K, bx * block_N], kernel_shared)
|
|
T.gemm(data_shared, kernel_shared, out_local)
|
|
|
|
T.copy(out_local, out_shared)
|
|
T.copy(out_shared, out_flat[by * block_M, bx * block_N])
|
|
|
|
return main
|
|
|
|
|
|
def main(argv=None):
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--n", type=int, default=128, help="n")
|
|
parser.add_argument("--c", type=int, default=128, help="c")
|
|
parser.add_argument("--h", type=int, default=64, help="h")
|
|
parser.add_argument("--w", type=int, default=64, help="w")
|
|
parser.add_argument("--f", type=int, default=128, help="f")
|
|
parser.add_argument("--k", type=int, default=3, help="k")
|
|
parser.add_argument("--s", type=int, default=1, help="s")
|
|
parser.add_argument("--d", type=int, default=1, help="d")
|
|
parser.add_argument("--p", type=int, default=1, help="p")
|
|
|
|
args = parser.parse_args(argv)
|
|
N, C, H, W, F, K, S, D, P = args.n, args.c, args.h, args.w, args.f, args.k, args.s, args.d, args.p
|
|
a = torch.randn(N, H, W, C).cuda().half()
|
|
b = torch.randn(K, K, C, F).cuda().half()
|
|
|
|
block_m = 64
|
|
block_n = 128
|
|
block_k = 32
|
|
num_stages = 3
|
|
threads = 256
|
|
kernel = convolution(N, C, H, W, F, K, S, D, P, block_m, block_n, block_k, num_stages, threads)
|
|
|
|
out_c = kernel(a, b)
|
|
ref_c = ref_program(S, P, D)(a, b)
|
|
torch.testing.assert_close(out_c, ref_c, rtol=1e-2, atol=1e-2)
|
|
print("All checks passed.✅")
|
|
|
|
|
|
def run_regression_perf(argv=None):
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--n", type=int, default=128, help="n")
|
|
parser.add_argument("--c", type=int, default=128, help="c")
|
|
parser.add_argument("--h", type=int, default=64, help="h")
|
|
parser.add_argument("--w", type=int, default=64, help="w")
|
|
parser.add_argument("--f", type=int, default=128, help="f")
|
|
parser.add_argument("--k", type=int, default=3, help="k")
|
|
parser.add_argument("--s", type=int, default=1, help="s")
|
|
parser.add_argument("--d", type=int, default=1, help="d")
|
|
parser.add_argument("--p", type=int, default=1, help="p")
|
|
|
|
args = parser.parse_args(argv)
|
|
N, C, H, W, F, K, S, D, P = args.n, args.c, args.h, args.w, args.f, args.k, args.s, args.d, args.p
|
|
|
|
block_m = 64
|
|
block_n = 128
|
|
block_k = 32
|
|
num_stages = 3
|
|
threads = 256
|
|
kernel = convolution(N, C, H, W, F, K, S, D, P, block_m, block_n, block_k, num_stages, threads)
|
|
profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Auto)
|
|
return profiler.do_bench(backend="cupti")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|