用Auto-TensorCore代码生成优化matmul
用Auto-TensorCore代码生成优化matmul
将演示如何使用TVM Auto TensorCore CodeGen在Volta/Turing GPU上编写高性能matmul调度。这是一个透明的解决方案，可以生成大多数在ir过程中完成的转换的tensorcore内核。用户还可以编写带有tensorize的调度来生成TensorCore代码。两个解决方案使用相同的tensorcore内部函数。有关详细信息，请参阅如何使用TensorCores优化卷积资料。 准备和算法
支持2种输入数据类型：float16和int8。对于float16，累加器为float32。对于int8，累加器为int32。对于数据布局，“N”表示无转置，“T”表示转置。
import logging
import sys
import numpy as np
import tvm
from tvm import te
from tvm import autotvm
from tvm.contrib import nvcc
def matmul_nn(A, B, L, dtype="float16", layout="NN"):
k = te.reduce_axis((0, L), name="k")
if dtype == "float16":
out_type = "float"
elif dtype == "int8":
out_type = "int"
elif dtype == "int4" or dtype == "int1":
out_type = "int"
if layout == "NN":
return te.compute(
(N, M), lambda i, j: te.sum(A[i, k].astype(out_type) * B[k, j].astype(out_type), axis=k)
)
if layout == "NT":
return te.compute(
(N, M), lambda i, j: te.sum(A[k, i].astype(out_type) * B[k, j].astype(out_type), axis=k)
)
if layout == "TN":
return te.compute(
(N, M), lambda i, j: te.sum(A[i, k].astype(out_type) * B[j, k].astype(out_type), axis=k)
)
if layout == "TT":
return te.compute(
(N, M), lambda i, j: te.sum(A[k, i].astype(out_type) * B[j, k].astype(out_type), axis=k)
)
Scheduling the Computation
这个调度和GPU上的非tensorcore matmul调度没有什么不同。有关优化matmul调度的基础知识，请参考How to optimize GEMM on CPU资料。当设置“tensor_core”pragma时，“rewrite for tensorcore”ir pass将自动转换tensorcore codegen的调度，否则将生成性能较低但功能相同的普通CUDA代码。
注意
TensorCore要求
请注意，在以下两种情况下，即使设置了“tensor_core”标注，TVM仍将返回到正常的CUDA codegen：（1）输入矩阵的m、n或k不是16的倍数；（2）CUDA9上的扭曲分片大小不是{16x16x16，32x8x16，8x32x16}中的一种。
在这个项目中，storage_align用于减少共享内存的库冲突。有关storage_align原语的用法，请参阅本文档。简言之，需要为一些共享内存缓冲区添加偏移量，以减少内存冲突。根据wmma doc，load_matrix_sync的步长必须是16字节的倍数，因此选择8作为float16的偏移量，16作为int8的偏移量。
使用AutoTVM搜索此计划中的最佳配置。
@autotvm.template("tutorial/auto_tensorcore/test_gemm")
def test_gemm(N, L, M, dtype, layout):
if layout == "NN":
shape_a = (N, L)
shape_b = (L, M)
elif layout == "NT":
shape_a = (L, N)
shape_b = (L, M)
elif layout == "TN":
shape_a = (N, L)
shape_b = (M, L)
elif layout == "TT":
shape_a = (L, N)
shape_b = (M, L)
else:
print("Unsupported layout:", layout)
sys.exit(1)
A = te.placeholder(shape_a, name="A", dtype=dtype)
B = te.placeholder(shape_b, name="B", dtype=dtype)
C = matmul_nn(A, B, L, dtype, layout)
s = te.create_schedule(C.op)
y, x = s[C].op.axis
k = s[C].op.reduce_axis[0]
# storage_align params
factor = 16
offset = 8
if dtype == "int8":
factor = 32
offset = 16
elif dtype == "int4":
factor = 64
offset = 32
elif dtype == "int1":
factor = 256
offset = 128
# create cache stages
AA = s.cache_read(A, "shared", [C])
if layout == "NN" or layout == "TN":
s[AA].storage_align(AA.op.axis[0], factor, offset)
AL = s.cache_read(AA, "local", [C])
BB = s.cache_read(B, "shared", [C])
if layout == "TT" or layout == "NT":
s[BB].storage_align(BB.op.axis[0], factor, offset)
BL = s.cache_read(BB, "local", [C])
CL = s.cache_write(C, "local")
# autotvm search space definition
cfg = autotvm.get_config()
cfg.define_knob("bx", [2, 4, 8])
cfg.define_knob("by", [8, 16, 32, 64])
cfg.define_knob("step_k", [1, 2, 4, 8, 16, 32])
cfg.define_knob("v", [4, 8, 16, 32])
by = cfg["by"].val
bx = cfg["bx"].val
step_k = cfg["step_k"].val
v = cfg["v"].val
# thread tile
TX = 8
TY = 1
if dtype == "int4" or dtype == "int1":
TX = 2
# warp tile
warp_tile_m = 16 # it could also be 8 or 32 on CUDA version >= 10.0
warp_tile_k = 16 # it must be 16 for fp16/int8 data type
if dtype == "int4":
warp_tile_m = 8
warp_tile_k = 32
elif dtype == "int1":
warp_tile_m = 8
warp_tile_k = 128
# block tile
tile_x = bx * TX
tile_y = by * TY
yo, ty = s[C].split(y, tile_y)
ty, yi = s[C].split(ty, TY)
# schedule for C stage
xo, xi = s[C].split(x, tile_x)
WX = min(warp_tile_m, tile_x)
tz, xi = s[C].split(xi, WX)
tx, xi = s[C].split(xi, TX)
s[C].reorder(yo, xo, tz, ty, tx, yi, xi)
s[C].bind(yo, te.thread_axis("blockIdx.y"))
s[C].bind(xo, te.thread_axis("blockIdx.x"))
s[C].bind(ty, te.thread_axis("threadIdx.y"))
s[C].bind(tz, te.thread_axis("threadIdx.z"))
s[C].bind(tx, te.thread_axis("threadIdx.x"))
# schedule for CL stage
ko, ki = s[CL].split(k, step_k * warp_tile_k)
kl, ki = s[CL].split(ki, warp_tile_k)
s[CL].compute_at(s[C], tx)
yo, xo = CL.op.axis
s[CL].reorder(ko, kl, ki, yo, xo)
# schedule for AA stage
s[AA].compute_at(s[CL], ko)
xo, xi = s[AA].split(s[AA].op.axis[1], factor=bx * v)
tz, tx = s[AA].split(xi, factor=(WX // TX) * v)
tx, vec = s[AA].split(tx, factor=v)
fused = s[AA].fuse(s[AA].op.axis[0], xo)
_, ty = s[AA].split(fused, factor=by)
s[AA].bind(ty, te.thread_axis("threadIdx.y"))
s[AA].bind(tz, te.thread_axis("threadIdx.z"))
s[AA].bind(tx, te.thread_axis("threadIdx.x"))
# vectorization is very important for float16/int8 inputs
s[AA].vectorize(vec)
# schedule for BB stage
s[BB].compute_at(s[CL], ko)
xo, xi = s[BB].split(s[BB].op.axis[1], factor=bx * v)
tz, tx = s[BB].split(xi, factor=(WX // TX) * v)
tx, vec = s[BB].split(tx, factor=v)
fused = s[BB].fuse(s[BB].op.axis[0], xo)
_, ty = s[BB].split(fused, factor=by)
s[BB].bind(ty, te.thread_axis("threadIdx.y"))
s[BB].bind(tz, te.thread_axis("threadIdx.z"))
s[BB].bind(tx, te.thread_axis("threadIdx.x"))
s[BB].vectorize(vec)
s[AL].compute_at(s[CL], kl)
s[BL].compute_at(s[CL], kl)
# set the 'tensor_core' pragma for tensorcore codegen
s[CL].pragma(ko, "tensor_core")
return s, [A, B, C]
AutoTune and Test
最后，使用一个调谐器来调整调度，生成具有最佳配置的代码，并运行内核与numpy进行比较，以检查结果是否正确。
# check whether the gpu has tensorcore
if not tvm.gpu(0).exist or not tvm.runtime.enabled("cuda"):
raise Exception("skip building this tutorial because cuda is not enabled..")
ctx = tvm.gpu()
if not nvcc.have_tensorcore(ctx.compute_version):
raise Exception("the gpu has no tensorcore, skipping...")
M, N, L = 512, 32, 512
dtype = "float16"
layout = "NN"
if len(sys.argv) >= 4:
M, N, L = int(sys.argv[1]), int(sys.argv[2]), int(sys.argv[3])
if len(sys.argv) >= 5:
dtype = sys.argv[4]
if len(sys.argv) >= 6:
layout = sys.argv[5]
# check whether current gpu arch support support current dtype's wmma codegen
cuda_compute_capability = tvm.runtime._ffi_api.GetDeviceAttr(2, 0, 4)
major, minor = nvcc.parse_compute_version(cuda_compute_capability)
if dtype == "int8":
assert major == 7 and minor >= 2
elif dtype == "int4" or dtype == "int1":
# int4/int1 only support layout TN
assert major == 7 and minor == 5 and layout == "TN"
def tune_and_evaluate(M, N, L, dtype, layout):
task = autotvm.task.create(
"tutorial/auto_tensorcore/test_gemm", args=(N, L, M, dtype, layout), target="cuda"
)
print(task.config_space)
logging.getLogger("autotvm").setLevel(logging.DEBUG)
logging.getLogger("autotvm").addHandler(logging.StreamHandler(sys.stdout))
measure_option = autotvm.measure_option(builder="local", runner=autotvm.LocalRunner(number=5))
tuner = autotvm.tuner.XGBTuner(task)
tuner.tune(
n_trial=1000,
measure_option=measure_option,
callbacks=[autotvm.callback.log_to_file("matmul.log")],
)
dispatch_context = autotvm.apply_history_best("matmul.log")
best_config = dispatch_context.query(task.target, task.workload)
print("\nBest config:")
print(best_config)
with autotvm.apply_history_best("matmul.log"):
with tvm.target.Target("cuda"):
s, arg_bufs = test_gemm(N, L, M, dtype, layout)
print(tvm.lower(s, arg_bufs, simple_mode=True))
func = tvm.build(s, arg_bufs)
dev_module = func.imported_modules[0]
print(dev_module.get_source())
# check correctness
if layout == "NN":
shape_a = (N, L)
shape_b = (L, M)
elif layout == "NT":
shape_a = (L, N)
shape_b = (L, M)
elif layout == "TN":
shape_a = (N, L)
shape_b = (M, L)
elif layout == "TT":
shape_a = (L, N)
shape_b = (M, L)
a_np = None
b_np = None
c_np = None
c_np_type = None
if dtype == "float16":
c_np_type = np.float32
a_np = np.random.uniform(size=shape_a).astype(np.float16)
b_np = np.random.uniform(size=shape_b).astype(np.float16)
if layout == "NN":
c_np = np.dot(a_np, b_np)
elif layout == "NT":
c_np = np.dot(a_np.T, b_np)
elif layout == "TN":
c_np = np.dot(a_np, b_np.T)
elif layout == "TT":
c_np = np.dot(a_np.T, b_np.T)
elif dtype == "int8":
c_np_type = np.int32
a_np = np.random.randint(low=-128, high=127, size=shape_a).astype(np.int8)
b_np = np.random.randint(low=-128, high=127, size=shape_b).astype(np.int8)
if layout == "NN":
c_np = np.dot(a_np.astype(np.int32), b_np.astype(np.int32))
elif layout == "NT":
c_np = np.dot(a_np.astype(np.int32).T, b_np.astype(np.int32))
elif layout == "TN":
c_np = np.dot(a_np.astype(np.int32), b_np.astype(np.int32).T)
elif layout == "TT":
c_np = np.dot(a_np.astype(np.int32).T, b_np.astype(np.int32).T)
elif dtype == "int4":
c_np_type = np.int32
a_np_int = np.random.randint(low=-8, high=7, size=shape_a).astype(np.int32)
b_np_int = np.random.randint(low=-8, high=7, size=shape_b).astype(np.int32)
# "TN"
c_np = np.dot(a_np_int.astype(np.int32), b_np_int.astype(np.int32).T)
a_np = np.zeros(shape=(N, int(L / 8)), dtype=np.int32)
b_np = np.zeros(shape=(M, int(L / 8)), dtype=np.int32)
# a_np --> col_major
for i in range(N):
for j in range(int(L / 8)):
for k in range(8):
a_np[i, j] = a_np[i, j] | ((a_np_int[i, j * 8 + k] & 0xF) << ((7 - k) * 4))
# b_np --> row_major
for i in range(M):
for j in range(int(L / 8)):
for k in range(8):
b_np[i, j] = b_np[i, j] | ((b_np_int[i, j * 8 + k] & 0xF) << ((7 - k) * 4))
elif dtype == "int1":
c_np_type = np.int32
a_np_int = np.random.randint(low=0, high=1, size=shape_a).astype(np.int32)
b_np_int = np.random.randint(low=0, high=1, size=shape_b).astype(np.int32)
# "TN"
c_np = np.dot(a_np_int.astype(np.int32), b_np_int.astype(np.int32).T)
a_np = np.zeros(shape=(N, int(L / 32)), dtype=np.int32)
b_np = np.zeros(shape=(M, int(L / 32)), dtype=np.int32)
for i in range(N):
for j in range(int(L / 32)):
for k in range(32):
a_np[i, j] = a_np[i, j] | ((a_np_int[i, j * 32 + k] & 0xF) << (31 - k))
for i in range(M):
for j in range(int(L / 32)):
for k in range(32):
b_np[i, j] = b_np[i, j] | ((b_np_int[i, j * 32 + k] & 0xF) << (31 - k))
c_tvm = tvm.nd.array(np.zeros(c_np.shape, dtype=c_np_type), ctx=ctx)
a_tvm = tvm.nd.array(a_np, ctx=ctx)
b_tvm = tvm.nd.array(b_np, ctx=ctx)
func(a_tvm, b_tvm, c_tvm)
tvm.testing.assert_allclose(c_np, c_tvm.asnumpy(), rtol=1e-3)
evaluator = func.time_evaluator(func.entry_name, ctx, number=100)
print("Time cost of this operator: %f" % evaluator(a_tvm, b_tvm, c_tvm).mean)
# We do not run the tuning in our webpage server since it takes some time.
# Uncomment the following line to run it by yourself.
# tune_and_evaluate(M, N, L, dtype, layout)
Sample Output
Best config:
[('bx', 4), ('by', 32), ('step_k', 16), ('v', 8)],,None,40
Finish loading 162 records
produce compute {
// attr [iter_var(blockIdx.y, , blockIdx.y)] thread_extent = 1
// attr [compute.local] storage_scope = "wmma.accumulator"
allocate compute.local[float32 * 256]
// attr [A.shared] storage_scope = "shared"
allocate A.shared[float16 * 8448]
// attr [B.shared] storage_scope = "shared"
allocate B.shared[float16 * 8192]
// attr [A.shared.local] storage_scope = "wmma.matrix_b"
allocate A.shared.local[float16 * 256]
// attr [B.shared.local] storage_scope = "wmma.matrix_a"
allocate B.shared.local[float16 * 256]
// attr [iter_var(blockIdx.x, , blockIdx.x)] thread_extent = 16
// attr [iter_var(threadIdx.z, , threadIdx.z)] thread_extent = 2
// attr [iter_var(threadIdx.y, , threadIdx.y)] thread_extent = 32
// attr [iter_var(threadIdx.x, , threadIdx.x)] thread_extent = 2
produce compute.local {
for (j.c.init, 0, 1) {
tvm_fill_fragment(compute.local, 16, 16, 16, 0, 0f)
}
// attr [iter_var(k.outer, )] pragma_tensor_core = 1
for (k.outer, 0, 2) {
produce A.shared {
for (ax0.ax1.outer.fused.outer, 0, 8) {
// attr [iter_var(threadIdx.y, , threadIdx.y)] thread_extent = 32
// attr [iter_var(threadIdx.z, , threadIdx.z)] thread_extent = 2
// attr [iter_var(threadIdx.x, , threadIdx.x)] thread_extent = 2
A.shared[ramp((((((ax0.ax1.outer.fused.outer*1056) + (floordiv(threadIdx.y, 8)*264)) + (floormod(threadIdx.y, 8)*32)) + (threadIdx.z*16)) + (threadIdx.x*8)), 1, 8)] = A[ramp(((((((ax0.ax1.outer.fused.outer*2048) + (floordiv(threadIdx.y, 8)*512)) + (k.outer*256)) + (floormod(threadIdx.y, 8)*32)) + (threadIdx.z*16)) + (threadIdx.x*8)), 1, 8)]
}
}
produce B.shared {
for (ax0.ax1.outer.fused.outer, 0, 8) {
// attr [iter_var(threadIdx.y, , threadIdx.y)] thread_extent = 32
// attr [iter_var(threadIdx.z, , threadIdx.z)] thread_extent = 2
// attr [iter_var(threadIdx.x, , threadIdx.x)] thread_extent = 2
B.shared[ramp(((((ax0.ax1.outer.fused.outer*1024) + (threadIdx.y*32)) + (threadIdx.z*16)) + (threadIdx.x*8)), 1, 8)] = B[ramp(((((((k.outer*131072) + (ax0.ax1.outer.fused.outer*16384)) + (threadIdx.y*512)) + (blockIdx.x*32)) + (threadIdx.z*16)) + (threadIdx.x*8)), 1, 8)]
}
}
for (k.inner.outer, 0, 16) {
produce A.shared.local {
for (ax1, 0, 1) {
tvm_load_matrix_sync(A.shared.local, 16, 16, 16, 0, &(A.shared[(((threadIdx.y/16)*4224) + (k.inner.outer*16))]), 264, "col_major")
}
}
produce B.shared.local {
for (ax0, 0, 1) {
for (ax1, 0, 1) {
tvm_load_matrix_sync(B.shared.local, 16, 16, 16, 0, &(B.shared[((k.inner.outer*512) + (threadIdx.z*16))]), 32, "col_major")
}
}
}
for (k.inner.inner, 0, 1) {
for (j.c, 0, 1) {
tvm_mma_sync(compute.local, 0, B.shared.local, 0, A.shared.local, 0, compute.local, 0)
}
}
}
}
}
for (j.inner.inner.inner, 0, 1) {
tvm_store_matrix_sync(compute.local, 16, 16, 16, 0, &(compute[((((threadIdx.y/16)*8192) + (blockIdx.x*32)) + (threadIdx.z*16))]), 512, "col_major")
}
}
#include <cuda_fp16.h>
__device__ half max(const half a, const half b)
{
return __hgt(__half(a), __half(b)) ? a : b;
}
__device__ half min(const half a, const half b)
{
return __hlt(__half(a), __half(b)) ? a : b;
}
__device__ half operator+(const volatile __half &a, const volatile __half &b)
{
return __hadd(a, b);
}
__device__ half operator<=(const volatile __half &a, const volatile __half &b)
{
return __hlt(a, b);
}
__device__ half operator*(const volatile __half &a, const volatile __half &b)
{
return __hmul(a, b);
}
#include <mma.h>
extern "C" __global__ void default_function_kernel0( half* __restrict__ A, half* __restrict__ B, float* __restrict__ compute) {
nvcuda::wmma::fragment<nvcuda::wmma::accumulator, 16, 16, 16, float> compute_local[1];
__shared__ half A_shared[8448];
__shared__ half B_shared[8192];
nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, 16, 16, 16, half, nvcuda::wmma::col_major> A_shared_local[1];
nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, 16, 16, 16, half, nvcuda::wmma::col_major> B_shared_local[1];
for (int j_c_init = 0; j_c_init < 1; ++j_c_init) {
(void)nvcuda::wmma::fill_fragment(compute_local[0], 0.000000e+00f);
}
for (int k_outer = 0; k_outer < 2; ++k_outer) {
__syncthreads();
for (int ax0_ax1_outer_fused_outer = 0; ax0_ax1_outer_fused_outer < 8; ++ax0_ax1_outer_fused_outer) {
((__shared__ float4*)(A_shared + (((((ax0_ax1_outer_fused_outer * 1056) + ((((int)threadIdx.y) >> 3) * 264)) + ((((int)threadIdx.y) & 7) * 32)) + (((int)threadIdx.z) * 16)) + (((int)threadIdx.x) * 8))))[0] = (( float4*)(A + ((((((ax0_ax1_outer_fused_outer * 2048) + ((((int)threadIdx.y) >> 3) * 512)) + (k_outer * 256)) + ((((int)threadIdx.y) & 7) * 32)) + (((int)threadIdx.z) * 16)) + (((int)threadIdx.x) * 8))))[0];
}
for (int ax0_ax1_outer_fused_outer1 = 0; ax0_ax1_outer_fused_outer1 < 8; ++ax0_ax1_outer_fused_outer1) {
((__shared__ float4*)(B_shared + ((((ax0_ax1_outer_fused_outer1 * 1024) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.z) * 16)) + (((int)threadIdx.x) * 8))))[0] = (( float4*)(B + ((((((k_outer * 131072) + (ax0_ax1_outer_fused_outer1 * 16384)) + (((int)threadIdx.y) * 512)) + (((int)blockIdx.x) * 32)) + (((int)threadIdx.z) * 16)) + (((int)threadIdx.x) * 8))))[0];
}
__syncthreads();
for (int k_inner_outer = 0; k_inner_outer < 16; ++k_inner_outer) {
for (int ax1 = 0; ax1 < 1; ++ax1) {
(void)nvcuda::wmma::load_matrix_sync(A_shared_local[0], &(A_shared[(((((int)threadIdx.y) / 16) * 4224) + (k_inner_outer * 16))]), 264);
}
for (int ax0 = 0; ax0 < 1; ++ax0) {
for (int ax11 = 0; ax11 < 1; ++ax11) {
(void)nvcuda::wmma::load_matrix_sync(B_shared_local[0], &(B_shared[((k_inner_outer * 512) + (((int)threadIdx.z) * 16))]), 32);
}
}
for (int k_inner_inner = 0; k_inner_inner < 1; ++k_inner_inner) {
for (int j_c = 0; j_c < 1; ++j_c) {
(void)nvcuda::wmma::mma_sync(compute_local[0], B_shared_local[0], A_shared_local[0], compute_local[0]);
}
}
}
}
for (int j_inner_inner_inner = 0; j_inner_inner_inner < 1; ++j_inner_inner_inner) {
(void)nvcuda::wmma::store_matrix_sync(&(compute[((((((int)threadIdx.y) / 16) * 8192) + (((int)blockIdx.x) * 32)) + (((int)threadIdx.z) * 16))]), compute_local[0], 512, nvcuda::wmma::mem_col_major);
}
}
Time cost of this operator: 0.000008
Summary
演示如何使用TVM的AutoSensorCoreCodegen生成tensorcore内核。
下载Python源代码：opt_matmul_auto_tensorcore.py
下载Jupyter笔记：opt\u matmul_auto_tensorcore.ipynb网站
https://tvm.apache.org/docs/tutorials/optimize/opt_matmul_auto_tensorcore.html#sample-output
Download Python source code: opt_matmul_auto_tensorcore.py
Download Jupyter notebook: opt_matmul_auto_tensorcore.ipynb
