matmul样例
本样例实现了输入是固定shape的matmul接口的端到端调用示例。
其中输入a的shape为 [16, 64]，输入b的shape为[64, 1024]。
from tbe import tik
DTYPE_SIZE = {
'int8': 1,
'float16': 2,
'float32': 4,
}
def MK_TO_K1MK0(tik_instance, mk_input_tensor, k1mk0_tensor, dtype, k1, m, k0):
"""change data format mk to k1mk0"""
src_ub = tik_instance.Tensor(dtype, (k1, m, k0), name="src_ub", scope=tik.scope_ubuf)
# data_move(m,k) --> (k1,m,k0)
with tik_instance.for_range(0, k1) as i:
tik_instance.data_move(src_ub[i * m * k0:], mk_input_tensor[i * k0:], 0, m, k0 * DTYPE_SIZE[dtype] // 32,
(k1 - 1) * k0 * DTYPE_SIZE[dtype] // 32, 0)
# data_move out
tik_instance.data_move(k1mk0_tensor, src_ub, 0, 1, k1 * m * k0 * DTYPE_SIZE[dtype] // 32, 0, 0)
def KN_TO_K1NK0(tik_instance, kn_input_tensor, k1nk0_tensor, dtype, k1, n, k0):
"""change data format kn to k1nk0"""
with tik_instance.for_range(0, k1) as index:
k1nk0_ub = tik_instance.Tensor(dtype, (n, k0), tik.scope_ubuf, "k1nk0_ub")
src_ub = tik_instance.Tensor(dtype, (k0, n), tik.scope_ubuf, "src_ub")
burst_len = k0 * n * DTYPE_SIZE[dtype] // 32
tik_instance.data_move(src_ub, kn_input_tensor[index * k0 * n], 0, 1, burst_len, 0, 0)
dst_list = [k1nk0_ub[16 * i] for i in range(16)]
src_list = [src_ub[n * i] for i in range(16)]
rep_times = n // k0
dst_rep_stride = k0
src_rep_stride = 1
tik_instance.vec_trans_scatter(False, False, dst_list, src_list, rep_times, dst_rep_stride, src_rep_stride)
tik_instance.data_move(k1nk0_tensor[index * k0 * n], k1nk0_ub, 0, 1, burst_len, 0, 0)
def N1MN0_TO_MN(tik_instance, mn_output_tensor, n1mn0_tensor, dtype, n1, m, n0):
"""change data format n1mn0 to mn"""
src_ub = tik_instance.Tensor(dtype, (m, n1 * n0), name="src_ub", scope=tik.scope_ubuf)
# data_move (n1,m,n0) --> (m,n)
with tik_instance.for_range(0, n1) as i:
tik_instance.data_move(src_ub[i * n0:], n1mn0_tensor[i * m * n0:], 0, m,
n0 * DTYPE_SIZE[dtype] // 32, 0, (n1 - 1) * n0 * DTYPE_SIZE[dtype] // 32)
# data_move out
tik_instance.data_move(mn_output_tensor, src_ub, 0, 1, m * n1 * n0 * DTYPE_SIZE[dtype] // 32, 0, 0)
def matmul_tik_compute(params, kernel_name):
"""
matmul tik compute
@param params: matmul data
@param kernel_name: kernel name
@return: tik instance
"""
tik_instance = tik.Tik()
if not isinstance(params, dict):
params = params.__dict__
m_size, k_size, n_size = params['M'], params['K'], params['N']
data_type = params["data_type"]
m_tiling_size = int(params["m_tiling_size"])
n_tiling_size = int(params["n_tiling_size"])
k_tiling_size = int(params['k_tiling_size'])
m_cycle_times = params["m_cycle_times"]
n_cycle_times = params["n_cycle_times"]
k_cycle_times = params["k_cycle_times"]
# Determine the output type
if data_type == "float16":
C_loc_out_type = "float32"
K0 = 16
else:
C_loc_out_type = "int32"
K0 = 32
block_size = 16
n_thread_num = params['n_thread_num']
m_thread_num = params['m_thread_num']
k_thread_num = params['k_thread_num']
mk_gm_input = tik_instance.Tensor(data_type, (m_size, k_size), name="mk_input_gm", scope=tik.scope_gm)
kn_gm_input = tik_instance.Tensor(data_type, (k_size, n_size), name="kn_input_gm", scope=tik.scope_gm)
k1mk0_workspace = tik_instance.Tensor(data_type, (k_size // K0, m_size, K0), name="k1mk0_workspace",
scope=tik.scope_gm, is_workspace=True)
k1nk0_workspace = tik_instance.Tensor(data_type, (k_size // K0, n_size, K0), name="k1nk0_workspace",
scope=tik.scope_gm, is_workspace=True)
mn_gm_output = tik_instance.Tensor(C_loc_out_type, (m_size, n_size), tik.scope_gm, "mn_output_gm")
nmk0_workspace = tik_instance.Tensor(C_loc_out_type, (n_size // block_size, m_size, block_size),
name="nmk0_workspace", scope=tik.scope_gm, is_workspace=True)
MK_TO_K1MK0(tik_instance, mk_gm_input, k1mk0_workspace, data_type, k_size // K0, m_size, K0)
KN_TO_K1NK0(tik_instance, kn_gm_input, k1nk0_workspace, data_type, k_size // K0, n_size, K0)
# Tiling is realized through the for_range() loop.
with tik_instance.for_range(0, 2, block_num=1) as core_id:
with tik_instance.for_range(0, n_cycle_times // 2, thread_num=n_thread_num) as n_idx:
with tik_instance.for_range(0, m_cycle_times, thread_num=m_thread_num) as m_idx:
dst_l0c = tik_instance.Tensor(C_loc_out_type, [n_tiling_size // 16, m_tiling_size, 16], name='dst_l0c',
scope=tik.scope_cbuf_out)
with tik_instance.for_range(0, k_cycle_times,
thread_num=k_thread_num) as k_idx:
# Calculation result data transfer.
inputa_l1 = tik_instance.Tensor(params['data_type'], [k_tiling_size // K0, m_tiling_size, K0],
name="A_tiling_l1", scope=tik.scope_cbuf)
tik_instance.data_move(inputa_l1,
k1mk0_workspace[k_idx * k_tiling_size // K0, m_idx * m_tiling_size, :],
0, k_tiling_size // K0, m_tiling_size, m_size - m_tiling_size, 0)
inputb_l1 = tik_instance.Tensor(params["data_type"], [k_tiling_size // K0, n_tiling_size, K0],
name="B_tiling_l1", scope=tik.scope_cbuf)
if n_size - n_tiling_size > 65535:
with tik_instance.for_range(0, k_tiling_size // K0) \
as dma_k_idx:
tik_instance.data_move(inputb_l1[dma_k_idx, :, :],
k1nk0_workspace[k_idx * k_tiling_size // K0 + dma_k_idx,
(core_id * n_cycle_times // 2 + n_idx) * n_tiling_size, :],
0, 1, n_tiling_size, 0, 0)
else:
tik_instance.data_move(inputb_l1, k1nk0_workspace[k_idx * k_tiling_size // K0,
(core_id * n_cycle_times // 2 + n_idx) * n_tiling_size, :], 0,
k_tiling_size // K0, n_tiling_size, n_size - n_tiling_size, 0)
# Call matmul API to matrix multiplication calculation.
with tik_instance.if_scope(k_idx == 0):
tik_instance.matmul(dst_l0c, inputa_l1, inputb_l1, m_tiling_size, k_tiling_size, n_tiling_size,
init_l1out=True)
with tik_instance.else_scope():
tik_instance.matmul(dst_l0c, inputa_l1, inputb_l1, m_tiling_size, k_tiling_size, n_tiling_size,
init_l1out=False)
tik_instance.fixpipe(nmk0_workspace[n_tiling_size // 16 * (core_id * n_cycle_times // 2 + n_idx),
m_idx * m_tiling_size, :], dst_l0c, n_tiling_size // 16, m_tiling_size * 16 *
DTYPE_SIZE[C_loc_out_type] // 32,
(m_size - m_tiling_size) * 16 * DTYPE_SIZE[C_loc_out_type] // 32, 0)
N1MN0_TO_MN(tik_instance, mn_gm_output, nmk0_workspace, C_loc_out_type, n_size // K0, m_size, K0)
tik_instance.BuildCCE(kernel_name=kernel_name,
inputs=[mk_gm_input, kn_gm_input], outputs=[mn_gm_output],
config={'l2_mode': 1})
return tik_instance
def test_matmul_tik():
shape_a = [16, 64]
shape_b = [64, 1024]
# 输入参数和tiling信息
params = {
'M': shape_a[0],
'K': shape_a[1],
'N': shape_b[1],
'data_type': "float16",
'm_tiling_size': 16,
'm_cycle_times': 1,
'm_thread_num': 1,
'n_tiling_size': 64,
'n_cycle_times': 16,
'n_thread_num': 1,
'k_tiling_size': 32,
'k_cycle_times': 2,
'k_thread_num': 2,
}
tik_instance = matmul_tik_compute(params, "simple_matmul")
示例结果：
输入数据： 左矩阵： [[1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] ... [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.] [1. 1. 1. ... 1. 1. 1.]] 右矩阵： [[2. 2. 2. ... 2. 2. 2.] [2. 2. 2. ... 2. 2. 2.] [2. 2. 2. ... 2. 2. 2.] ... [2. 2. 2. ... 2. 2. 2.] [2. 2. 2. ... 2. 2. 2.] [2. 2. 2. ... 2. 2. 2.]] 计算结果： [[128. 128. 128. ... 128. 128. 128.] [128. 128. 128. ... 128. 128. 128.] [128. 128. 128. ... 128. 128. 128.] ... [128. 128. 128. ... 128. 128. 128.] [128. 128. 128. ... 128. 128. 128.] [128. 128. 128. ... 128. 128. 128.]]
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