def __init__(self, shape, dtype, kernel_name):
"""
init the parameters
Parameters
----------
shape: tuple or list
the shape of input tensor
dtype: string
the dtype of input tensor
kernel_name: str
kernel name, default value is "reverse_ext2"
Returns
-------
None
"""
self.tik_instance = tik.Tik(tik.Dprofile())
self.aicore_num = cce.cce_conf.get_soc_spec(cce.cce_conf.CORE_NUM)
self.shape = list(shape)
self.dtype = dtype
self.kernel_name = kernel_name
block_byte_size = 32
dtype_byte_size = cce.cce_intrin.get_bit_len(dtype) // 8
self.data_each_block = block_byte_size // dtype_byte_size
ub_byte_size = (cce.cce_conf.get_soc_spec(cce.cce_conf.UB_SIZE) -
block_byte_size)
self.ub_element_number = (ub_byte_size // dtype_byte_size //
self.data_each_block * self.data_each_block)
self.input_total_num = functools_reduce(lambda x, y: x * y, shape)
self.data_num_each_core = self.input_total_num // self.aicore_num
self.last_data_num = self.input_total_num % self.aicore_num
self.input_gm = self.tik_instance.Tensor(self.dtype,
self.shape,
name="input_gm",
scope=tik.scope_gm)
self.output_gm = self.tik_instance.Tensor(self.dtype,
self.shape,
name="output_gm",
scope=tik.scope_gm)
self.input_ub = None
def init_tik_instance(self):
"""
init the tik_instance
Parameters
----------
Returns
-------
None
"""
profile = tik.Dprofile()
self.tik_instance = tik.Tik(profile)
self.real_core_num = profile.get_aicore_num()
self.l1_buffer_size = profile.get_l1_buffer_size()
def __init__(self, input_dict):
"""
init the Crop parameters
Parameters
----------
input_dict: input_dict is a dict, the keys as follow:
x1: dict,shape and datatype,datatype supports int8,uint8,
int16,uint16,int32,uint32,int64,uint64,float16,float32
x2: dict,shape and datatype,datatype supports int8,uint8,
int16,uint16,int32,uint32,int64,uint64,float16,float32
y: dict,shape and datatype,datatype supports int8,uint8,
int16,uint16,int32,uint32,int64,uint64,float16,float32
axis: crop start with axis
offsets: crop start offset of each axis
kernel_name: cce kernel name, default value is "crop"
Returns
-------
None
"""
self.instance = tik.Tik(tik.Dprofile())
self.dtype = input_dict.get("x1").get("dtype").lower()
self.dsize = common_util.get_data_size(self.dtype)
total_size = tbe_platform.cce_conf.get_soc_spec(
tbe_platform.cce_conf.UB_SIZE)
ub_size = (total_size - RESERVE_SIZE) // (2 * self.dsize)
burnest_len = constant.BLOCK_SIZE // self.dsize
ub_size = ((ub_size + burnest_len - 1) // burnest_len) * burnest_len
self.one_max_size = ub_size
x1_len = get_shape_total_number(input_dict.get("x1").get("shape"))
x1_len = ((x1_len + burnest_len - 1) // burnest_len) * burnest_len
mod = input_dict.get("y").get("shape")[-1] % burnest_len
if mod != 0:
x1_len = x1_len + burnest_len
self.x1_gm = self.instance.Tensor(self.dtype, (x1_len, ),
name="x1_gm",
scope=tik.scope_gm)
self.x2_gm = self.instance.Tensor(self.dtype, (32, ),
name="x2_gm",
scope=tik.scope_gm)
y_len = get_shape_total_number(input_dict.get("y").get("shape"))
y_len = ((y_len + burnest_len - 1) // burnest_len) * burnest_len
if mod != 0:
y_len = y_len + burnest_len
self.y_gm = self.instance.Tensor(self.dtype, (y_len, ),
name="y_gm",
scope=tik.scope_gm)
self.input_dict = input_dict
def __init__(self, input_dict, stride_h, stride_w):
self.dprofile = tik.Dprofile()
self.tik_instance = tik.Tik(self.dprofile)
self.ub_size = self.dprofile.get_unified_buffer_size()
self.dtype = input_dict.get("x").get("dtype").lower()
self.x_shape = input_dict.get("x").get("shape")
self.dsize = get_data_size(self.dtype)
self.y_shape = cal_out_shape(self.x_shape, stride_h, stride_w)
self.x_gm = self.tik_instance.Tensor(self.dtype,
self.x_shape,
name="x_gm",
scope=tik.scope_gm)
self.y_gm = self.tik_instance.Tensor(self.dtype,
self.y_shape,
name="y_gm",
scope=tik.scope_gm)
def map_index(x_dic, data_seq_dic, level_index_dic, y_dic,
kernel_name="map_index"):
"""
:param x_dic:
:param data_seq_dic:
:param level_index_dic:
:param y_dic:
:param kernel_name:
:return:
"""
check_list = ["int32"]
x_shape = x_dic.get("shape")
x_dtype = x_dic.get("dtype")
check_dtype(x_dtype.lower(), check_list, param_name="x")
data_seq_shape = data_seq_dic.get("shape")
data_seq_dtype = data_seq_dic.get("dtype")
check_dtype(data_seq_dtype.lower(), check_list,
param_name="data_seq")
y_dtype = y_dic.get("dtype")
check_dtype(y_dtype.lower(), check_list, param_name="y")
if x_shape[0] > 8:
raise RuntimeError("the length of x should "
"be less than or equal to 8")
if data_seq_shape[0] % x_shape[0] != 0:
raise RuntimeError("the length of data_seq must "
"be multiple of the length of x")
tik_instance = tik.Tik(tik.Dprofile())
if level_index_dic:
level_index_dtype = level_index_dic.get("dtype")
check_dtype(level_index_dtype.lower(), check_list,
param_name="level_index")
map_index_result = MapIndexProcess((tik_instance, x_dic, data_seq_dic,
y_dic, level_index_dic))
else:
map_index_result = MapIndexProcess((tik_instance, x_dic, data_seq_dic,
y_dic))
return map_index_result.cce_map_index(kernel_name)
def __init__(self, input_dict):
"""
init the permute parameters
"""
self.instance = tik.Tik(tik.Dprofile())
self.dtype = input_dict.get("x").get("dtype").lower()
self.dsize = 2
size = get_shape_size(input_dict.get("x").get("shape"))
self.x_gm = self.instance.Tensor(self.dtype, (size, ),
name="x_gm",
scope=tik.scope_gm)
self.y_gm = self.instance.Tensor(self.dtype, (size, ),
name="y_gm",
scope=tik.scope_gm)
ub_size = (UB_SIZE_B - 1024) // 4 // self.dsize // 256 * 256
self.ub_size = ub_size
self.input_dict = input_dict
def init_param(self, pooled_hw, dicts, spatial_scale_list, kernel_name):
"""
init parameters
Parameters
----------
pooled_hw: (pooled_h, pooled_w)
dicts: (x_dict, rois_dict, actual_dict, y_dict)
spatial_scale_list: (spatial_scale_h, spatial_scale_w)
kernel_name: kernel name
Returns
-------
None
"""
self.tik_instance = tik.Tik(tik.Dprofile())
self.pooled_h = pooled_hw[0]
self.pooled_w = pooled_hw[1]
self.dtype = dicts[0].get("dtype").lower()
self.shape = dicts[0].get("shape")
self.rois_dtype = dicts[1].get("dtype").lower()
self.rois_shape = dicts[1].get("shape")
self.output_shape = dicts[3].get("shape")
self.spatial_scale_h = spatial_scale_list[0]
self.spatial_scale_w = spatial_scale_list[1]
self.roi_actual_num_effect = (dicts[2] is not None)
self.kernel_name = kernel_name
self.feature_batch = self.shape[0]
self.fm_c1 = self.shape[1]
self.fm_h = self.shape[2]
self.fm_w = self.shape[3]
self.fm_c0 = self.shape[4]
self.device_core_num = \
tbe_platform.cce_conf.get_soc_spec(tbe_platform.cce_conf.CORE_NUM)
self.proposal_num_per_tiling = 128
self.roi_max_num = self.rois_shape[2]
def clip_boxes_d_compute(boxes_input, img_w, img_h, kernel_name="clip_boxes"):
"""
the compute process of clip_boxes
input:
boxes_input:a dict, include shape, and dtype
img_w: width of the image
img_h: height of the image
kernel_name: the kernel name
return:
the tik container
"""
const_num = ConstList()
tiling_para = TilingFunc(boxes_input.get("shape"))
# start the TIK container
tik_instance = tik.Tik(tik.Dprofile(), True)
anchors = tik_instance.Tensor("float16",
(tiling_para.tot_of_blk*const_num.num_d,
const_num.num_d),
name="anchors",
scope=tik.scope_gm)
res_anchors = tik_instance.Tensor("float16",
(tiling_para.tot_of_blk*const_num.num_d,
const_num.num_d),
name="res_anchors",
scope=tik.scope_gm)
with tik_instance.for_range(0, tiling_para.loop_time - CONFIG_ONE,
thread_num=tiling_para.thread_num) as loop_i:
processing_one_loop(tik_instance,
(anchors, res_anchors),
tiling_para,
(img_h, img_w),
loop_i)
# the tail processing
processing_tail(tik_instance,
(anchors, res_anchors),
tiling_para,
(img_h, img_w))
tik_instance.BuildCCE(kernel_name=kernel_name, inputs=[anchors], outputs=[res_anchors])
return tik_instance
def __init__(self, input_data, block_size):
"""
init space_to_depth base parameters
Parameters
----------
input_data: shape and data type,data type supports float16,float32,
int32,uint32,int16,uint16,int8,uint8,int64,uint64
block_size: must be greater than one. It indicates the block size
"""
self.input_shape = input_data.get("shape")
self.dtype = input_data.get("dtype").lower()
self.dtype_size = common_util.get_data_size(self.dtype)
self.block_size = block_size
self.tik_instance = tik.Tik(tik.Dprofile())
self.output_shape = (self.input_shape[0],
self.input_shape[1] // block_size,
self.input_shape[2] // block_size,
self.input_shape[3] * block_size * block_size)
def check_shape_dtype_format(input_shape, input_dtype, input_format):
"""
check shape, dtype and format
Parameters
----------
input_shape: input dic shape
input_dtype: input dtype
input_format: input format, NC1HWC0
The common check rule for tensor shape, just for 5hd
Returns
-------
None
"""
tik_name = tik.Dprofile().get_product_name()
if tik_name == "hisi-es":
check_list = ["float16"]
else:
check_list = ["float16", "float32"]
if input_dtype not in check_list:
raise RuntimeError("upsample only support %s while dtype is %s"
% (",".join(check_list), input_dtype))
util.check_shape_rule(input_shape)
if len(input_shape) != DIM_5HD:
raise RuntimeError(
"The dim of tensor must be %d"
", actual dim is %d" % (DIM_5HD, len(input_shape)))
n, c1, h, w, c0 = input_shape
shape_c0 = C0
if input_shape[DIM_5HD - 1] != shape_c0:
raise RuntimeError(
"The value of C0 must be 16")
if input_format != "NC1HWC0":
raise RuntimeError(
"The format must be NC1HWC0, while actual format is %s" %(input_format))
def __init__(self, kernel_name):
self.hidden_size = 32
self.feature_size = 32
self.block_size = 16
self.feature_block_num = self.feature_size // self.block_size
self.hidden_block_size = self.hidden_size // self.block_size
self.batch_size = 32
self.batch_blocks = self.batch_size // self.block_size
self.num_step = 16
self.forget_bias = 1.0
self.use_fixpipe = True
self.matmul_init_l1out = self.use_fixpipe
self.feature_hidden_size = self.feature_size + self.hidden_size
self.feature_hidden_block = self.feature_hidden_size // self.block_size
self.tik_instance = tik.Tik(tik.Dprofile())
self.fixpipe_workspace = self.tik_instance.Tensor(
"float16", (1, 4 * self.hidden_block_size, self.batch_blocks,
self.block_size, self.block_size),
name="fixpipe_workspace",
scope=tik.scope_gm,
is_workspace=True)
self.declare_gm_tensor()
self.init_core()
self.tik_instance.BuildCCE(
kernel_name,
inputs=[
self.gm_x, self.gm_init_h, self.gm_init_c, self.gm_weight,
self.gm_b
],
outputs=[self.gm_output_h, self.gm_output_c])
def __init__(self, shape_info, param_info):
classes = param_info['classes']
coords = param_info['coords']
boxes = param_info['boxes']
dtype = param_info['dtype']
batch = shape_info['batch']
height = shape_info['height']
width = shape_info['width']
dtype_size = 2 if (param_info['dtype'] == "float16") else 4
self.product_name = ""
self.total_ub_size = 0
self.tik_inst = tik.Tik(tik.Dprofile())
# in order to solve 32B not enough when do the last data_mov
batch_padding = 32 // (
(boxes * (coords + 1 + classes)) * height * width * dtype_size) + 1
self.yolo_din = \
self.tik_inst.Tensor(dtype, (batch+batch_padding,
boxes*(coords+1+classes),
height, width),
scope=tik.scope_gm, name="yolo_din")
# shape defined by fp16 for infershape dtype can not be determined
self.crd_dout = \
self.tik_inst.Tensor(dtype, (batch, boxes*coords,
ceil_x(height*width*2+32, 32)//2),
scope=tik.scope_gm, name="crd_dout")
self.obj_dout = \
self.tik_inst.Tensor(dtype, (batch, ceil_x(boxes*height*width *
2+32, 32)//2),
scope=tik.scope_gm, name="obj_dout")
self.cls_dout = \
self.tik_inst.Tensor(dtype, (batch, classes,
ceil_x(boxes*height*width *
2+32, 32)//2),
scope=tik.scope_gm, name="cls_dout")
def __init__(self, input_dict):
"""
init the ShuffleChannel parameters
Parameters
----------
input_dict: input_dict is a dict, the keys as follow:
x: dict,shape and datatype,datatype supports int8,uint8,int16,
uint16,int32,uint32,int64,uint64,float16,float32
y: dict,shape and datatype,datatype supports int8,uint8,int16,
uint16,int32,uint32,int64,uint64,float16,float32
group: 1 channel group
kernel_name: cce kernel name, default value is "shuffle_channel"
Returns
-------
None
"""
self.instance = tik.Tik(tik.Dprofile())
self.dtype = input_dict.get("x").get("dtype").lower()
self.dsize = common_util.get_data_size(self.dtype)
total_size = tbe_platform.cce_conf.get_soc_spec(tbe_platform.cce_conf.UB_SIZE)
ub_size = (total_size - RESERVE_SIZE) // (2 * self.dsize)
burnest_len = constant.BLOCK_SIZE // self.dsize
ub_size = ((ub_size + burnest_len - 1) // burnest_len) * burnest_len
self.one_max_size = ub_size
x_len = get_shape_total_number(input_dict.get("x").get("shape"))
x_len = ((x_len + burnest_len - 1) // burnest_len) * burnest_len
hw = input_dict.get("y").get("shape")[2] * \
input_dict.get("y").get("shape")[3]
mod = hw % burnest_len
if mod != 0:
x_len = x_len + burnest_len
self.x_gm = self.instance.Tensor(self.dtype, (x_len,), name="x_gm",
scope=tik.scope_gm)
self.y_gm = self.instance.Tensor(self.dtype, (x_len,), name="y_gm",
scope=tik.scope_gm)
self.input_dict = input_dict
def __init__(self, x, y, split_dim, num_split, kernel_name):
"""
Init split_d parameters
Parameters
----------
x: dict
the dict of input tensor.
y: list or tuple
the list of output tensor.
split_dim: int
the dimension along which to split_d.
num_split: int
an integer indicating the number of split_d along `split_dim`.
kernel_name: str
cce kernel name, default value is "split_d".
Returns
-------
None
"""
self.tik_instance = tik.Tik(tik.Dprofile())
self.split_dim = split_dim
self.num_split = num_split
self.kernel_name = kernel_name
self.input_dtype = x.get("dtype").lower()
self.output_dtype = y[0].get("dtype").lower()
self.input_dtype_bytes_size = tbe_platform.cce_intrin.get_bit_len(
self.input_dtype) // EIGHT_BIT
self.input_data_each_block = BLOCK_BYTES // self.input_dtype_bytes_size
self.core_num = tbe_platform.cce_conf.get_soc_spec(
tbe_platform.cce_conf.CORE_NUM)
self.ub_size = tbe_platform.cce_conf.get_soc_spec(
tbe_platform.cce_conf.UB_SIZE) - RESERVED_UB_SIZE
self.ub_number = self.ub_size // self.input_dtype_bytes_size
self.ub_number = (self.ub_number // self.input_data_each_block
) * self.input_data_each_block
self.tiling_gm, self.input_gm, self.outs_gm = self.init_gm_tensor()
self.check_input_params()
self.ub_number_new = None
self.input_ub = None
self.temp_ub = None
self.tiling_ub = None
self.select_mode = None
self.input_size_split = None
self.output_size_split = None
self.act_core_num = None
self.loop_each_core = None
self.loop_last_core = None
self.data_each_core = None
self.data_last_core = None
self.loop_num = None
self.last_num = None
self.loop_num_last_core = None
self.last_num_last_core = None
self.input_num = None
self.loop_each = None
self.loop_last = None
self.loop_each_last_core = None
self.loop_last_last_core = None
self.loop_burst_len = None
def __init__(self, var, indices, updates, var_out, axis, kernel_name, compute_type):
"""
Init scatter axis parameters
Parameters
----------
var: dict
data of input
datatype suports float32,float16,int32,int8,uint8
indices: dict
data of indices
datatype supports int32
updates: dict
data of updates
datatype supports float32,float16,int32,int8,uint8
var_out: dict
data of input
axis: bool
axis
kernel_name: str
the name of the operator
compute_type: str
the compute type of scatter
Returns
-------
example: var(2, 6, 8, 8) axis=1
process uint is var[axis:] (6,8,8) slice shape
small slice shape is var[axis+1:] (8,8)
slice num is 2 and divide in each core to proc
each proc of slice data(6,8,8)
updates_date proc by indices info to copy
"""
self.tik_instance = tik.Tik(tik.Dprofile())
self.var_shape = var.get("shape")
self.var_dtype = var.get("dtype").lower()
self.indices_shape = indices.get("shape")
self.indices_dtype = indices.get("dtype").lower()
self.updates_shape = updates.get("shape")
self.updates_dtype = updates.get("dtype").lower()
self.var_ele_num = functools_reduce(lambda x, y: x * y, self.var_shape)
self.indices_num = functools_reduce(lambda x, y: x * y, self.indices_shape)
self.updates_num = functools_reduce(lambda x, y: x * y, self.updates_shape)
self.axis = axis
self.kernel_name = kernel_name
self.compute_type = compute_type
self.ub_size_bytes = (tik.Dprofile().get_unified_buffer_size() - UB_RESERVE_SIZE)
self.ai_core_num = tik.Dprofile().get_aicore_num()
self.var_dtype_bytes_size = tbe_platform.cce_intrin.get_bit_len(self.var_dtype) // 8
self.indices_dtype_bytes_size = tbe_platform.cce_intrin.get_bit_len(self.indices_dtype) // 8
self.var_data_each_block = 32 // self.var_dtype_bytes_size
self.indices_data_each_block = 32 // self.indices_dtype_bytes_size
self.check_param(var_out)
# indices buf size in ub
self.indices_ub_number = 0
# var and updates buf size in ub
self.updates_ub_number = 0
# slice is var[axis:], one uint of process
if axis == 0:
self.slice_num = 1
else:
self.slice_num = functools_reduce(lambda x, y: x * y, self.var_shape[0:axis])
self.slice_shape = self.var_shape[axis:]
self.slice_data_num = functools_reduce(lambda x, y: x * y, self.var_shape[axis:])
self.small_elem_num = self.slice_data_num // self.var_shape[axis]
self.slice_size = self.slice_data_num * self.var_dtype_bytes_size
self.max_num_one_repeat = 128
if self.var_dtype in ("float32", "int32"):
self.max_num_one_repeat = 64
# decide block num
if self.slice_num == 1:
self.block_num = 1
self.slice_step = 0
else:
self.slice_step = math.ceil(self.slice_num / self.ai_core_num)
self.block_num = math.ceil(self.slice_num / self.slice_step)
# each loop data buf now is one slice data var[axis:] date
self.update_data_num = self.slice_data_num
self.vconv_dst_dtype = "float16"
self.init_gm_tensor()
self.init_ub_tensor_para()
self.init_scalar_val()
def CusMatMulCubeFraczLeftCast(input_x1,
input_x2,
bias=None,
output_y={},
trans_a=False,
trans_b=False,
kernel_name="CusMatMulCubeFraczLeftCast"):
"""
calculating matrix multiplication with bias, C = A*B + bias, support input
data with fractal format.
Parameters:
shape_a: list or tuple
Shape of the first tensor a with rank > 1
shape_b: list or tuple
Shape of the second tensor b with the same type with a,
and shape_a, shape_b must be 2 dims
src_dtype: str
The data type of input, support "float32", "float16"
dst_dtype: str
The data type of output, support "float32", "float16"
trans_a: bool
If True, shape_a == transposed before multiplication
trans_b: bool
If True, shape_b == transposed before multiplication
is_fractal: bool
If True, the input data format of a and b must be fractal format
shape_bias: list or tuple
Shape of bias, only support the input data format with ND
Returns
-------
None
"""
shape_a = input_x1.get("ori_shape")
shape_b = input_x2.get("ori_shape")
print("============")
print(input_x1.get("format"), input_x2.get("format"))
print(shape_a, shape_b)
print("============")
if input_x2.get("format") == "FRACTAL_Z":
n, c, h, w = shape_b
c0 = 16
c1 = c // c0
if c1 == 0:
c1 = 1
shape_b = [n, c1 * h * w * c0]
shape_a = [n, n]
if input_x1.get("format") == "FRACTAL_Z":
n, c, h, w = shape_a
c0 = 16
c1 = c // c0
if c1 == 0:
c1 = 1
shape_a = [n, c1 * h * w * c0]
shape_b = [c1 * h * w * c0, c1 * h * w * c0]
if input_x2.get("format") == "FRACTAL_NZ":
shape_a = [shape_b[0], shape_b[0]]
shape_b = shape_b
if input_x1.get("format") == "FRACTAL_NZ":
shape_a = shape_a
shape_b = [shape_a[1], shape_a[1]]
shape_a = list(shape_a)
shape_b = list(shape_b)
shape_a = _get_input_shape(shape_a)
shape_b = _get_input_shape(shape_b)
util.check_kernel_name(kernel_name)
util.check_shape_rule(shape_a)
util.check_shape_rule(shape_b)
util.check_shape_size(shape_a, SHAPE_SIZE_LIMIT)
util.check_shape_size(shape_b, SHAPE_SIZE_LIMIT)
shape_a = [shape_a[1], shape_a[0]]
trans_a = bool(1 - trans_a)
shape_b = [shape_b[1], shape_b[0]]
trans_b = bool(1 - trans_b)
shape_bias = ()
if bias is not None and bool(bias):
shape_bias = bias.get("shape")
shape_bias = list(shape_bias)
shape_bias = _get_bias(shape_bias)
src_dtype = input_x1.get("dtype").lower()
_shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b)
m_shape = shape_a[len(shape_a) - 2]
km_shape = shape_a[len(shape_a) - 1]
kn_shape = shape_b[len(shape_a) - 2]
n_shape = shape_b[len(shape_a) - 1]
if src_dtype == "float16":
block_reduce = cce.BLOCK_REDUCE
block_in = cce.BLOCK_IN
block_out = cce.BLOCK_OUT
if trans_a and km_shape == 1:
block_in = cce.BLOCK_VECTOR
if not trans_a and m_shape == 1:
block_in = cce.BLOCK_VECTOR
if trans_b and kn_shape == 1:
block_out = cce.BLOCK_VECTOR
if not trans_b and n_shape == 1:
block_out = cce.BLOCK_VECTOR
if trans_a:
shape_a_temp = (m_shape // block_reduce, km_shape // block_in,
block_reduce, block_in)
else:
shape_a_temp = (m_shape // block_in, km_shape // block_reduce,
block_in, block_reduce)
if trans_b:
shape_b_temp = (kn_shape // block_out, n_shape // block_reduce,
block_reduce, block_out)
else:
shape_b_temp = (kn_shape // block_reduce, n_shape // block_out,
block_out, block_reduce)
shape_a_temp = (shape_a_temp[0], shape_a_temp[1], shape_a_temp[2],
shape_a_temp[3])
shape_b_temp = (shape_b_temp[0], shape_b_temp[1], shape_b_temp[2],
shape_b_temp[3])
if util.get_product_version() == util.VERSION_MINI:
tik_instance = tik.Tik(tik.Dprofile("v100", "mini"))
else:
tik_instance = tik.Tik(tik.Dprofile("v100", "cloud"))
input_x1 = tik_instance.Tensor(input_x1.get("dtype"),
shape_a_temp,
name="left_matrix",
scope=tik.scope_gm)
input_x2 = tik_instance.Tensor(input_x2.get("dtype"),
shape_b_temp,
name="right_matrix",
scope=tik.scope_gm)
res_matmul = tik_instance.Tensor(output_y.get("dtype"),
output_y.get("shape"),
name="output",
scope=tik.scope_gm)
DIAG_SIZE = 128
mo_tile, ko_tile, no_tile, diag_opt = get_cus_tile_info(
input_x1, input_x2, DIAG_SIZE)
cus_cube_matmul_cast(tik_instance,
input_x1,
trans_a,
input_x2,
trans_b,
res_matmul,
mo_tile=mo_tile,
ko_tile=ko_tile,
no_tile=no_tile,
diag_opt=diag_opt,
diag_size=DIAG_SIZE)
tik_instance.BuildCCE(kernel_name=kernel_name,
inputs=[input_x1, input_x2],
outputs=[res_matmul])
return tik_instance
def CusCholeskyTrsm(input_x, output, kernel_name):
"""CusCholeskyTrsm"""
input_x_shape = input_x.get("shape")
output_shape = output.get("shape")
split_dim = 128
matrix_dim = input_x_shape[0]
split_dim = min(matrix_dim, split_dim)
vector_repeat_times = int(split_dim // 64)
blocks = int(matrix_dim // split_dim)
if blocks == 0:
blocks = 1
if util.get_product_version() == util.VERSION_MINI:
tik_instance = tik.Tik(tik.Dprofile("v100", "mini"))
else:
tik_instance = tik.Tik(tik.Dprofile("v100", "cloud"))
input_x = tik_instance.Tensor("float32",
input_x_shape,
name="input_x",
scope=tik.scope_gm)
res = tik_instance.Tensor("float32",
output_shape,
name="res",
scope=tik.scope_gm)
with tik_instance.for_range(0, blocks, block_num=blocks) as block_index:
input_x_ub = tik_instance.Tensor("float32", (split_dim, split_dim),
name="input_x_ub",
scope=tik.scope_ubuf)
temp_ub = tik_instance.Tensor("float32", (split_dim, split_dim),
name="temp_ub",
scope=tik.scope_ubuf)
assist_1_ub = tik_instance.Tensor("float32", (split_dim, ),
name="assist_1_ub",
scope=tik.scope_ubuf)
assist_2_ub = tik_instance.Tensor("float32", (split_dim, ),
name="assist_2_ub",
scope=tik.scope_ubuf)
with tik_instance.for_range(0, split_dim) as i:
tik_instance.data_move(
input_x_ub[i, 0], input_x[block_index * split_dim + i,
block_index * split_dim], 0, 1,
vector_repeat_times * 8, 0, 0)
scalar1 = tik_instance.Scalar("float32", init_value=-0.5)
with tik_instance.for_range(0, split_dim) as i:
scalar2 = tik_instance.Scalar("float32")
tik_instance.vln(64, assist_1_ub[0], input_x_ub[i, 0],
vector_repeat_times, 1, 1, 8, 8)
tik_instance.vmuls(64, assist_2_ub[0], assist_1_ub[0], scalar1,
vector_repeat_times, 1, 1, 8, 8)
tik_instance.vexp(64, assist_1_ub[0], assist_2_ub[0],
vector_repeat_times, 1, 1, 8, 8)
scalar2.set_as(assist_1_ub[i])
tik_instance.vmuls(64, input_x_ub[i, 0], input_x_ub[i, 0], scalar2,
vector_repeat_times, 1, 1, 8, 8)
with tik_instance.for_range(i + 1, split_dim) as j:
scalar3 = tik_instance.Scalar("float32")
scalar3.set_as(input_x_ub[i, j])
tik_instance.vmuls(64, temp_ub[j, 0], input_x_ub[i, 0],
scalar3, vector_repeat_times, 1, 1, 8, 8)
tik_instance.vsub(64, input_x_ub[i + 1, 0], input_x_ub[i + 1, 0],
temp_ub[i + 1, 0],
(split_dim - 1 - i) * vector_repeat_times, 1, 1,
1, 8, 8, 8)
zero = tik_instance.Scalar("float32")
zero.set_as(0.0)
one = tik_instance.Scalar("float32")
one.set_as(1.0)
with tik_instance.for_range(0, split_dim) as i:
tik_instance.vector_dup(64, temp_ub[i, 0], zero,
vector_repeat_times, 1, 8)
temp_ub.__setitem__(i * split_dim + i, one)
chol_diag_element_final = tik_instance.Scalar("float32")
chol_diag_element_final.set_as(input_x_ub[split_dim * split_dim - 1])
trsm_diag_element = tik_instance.Scalar("float32")
trsm_diag_element.set_as(1.0 / chol_diag_element_final)
temp_ub.__setitem__(split_dim * split_dim - 1, trsm_diag_element)
with tik_instance.for_range(1, split_dim) as i:
index = split_dim - i - 1
tik_instance.vector_dup(64, assist_1_ub, zero, vector_repeat_times,
1, 8)
with tik_instance.for_range(0, i) as j:
chol_diag_element_loop = tik_instance.Scalar("float32")
chol_diag_element_loop.set_as(input_x_ub[index, index + 1 + j])
tik_instance.vmuls(64, assist_2_ub, temp_ub[j + index + 1, 0],
chol_diag_element_loop, vector_repeat_times,
1, 1, 8, 8)
tik_instance.vadd(64, assist_1_ub, assist_2_ub, assist_1_ub,
vector_repeat_times, 1, 1, 1, 8, 8, 8)
temp_scalar = tik_instance.Scalar("float32")
temp_scalar.set_as(input_x_ub[index, index])
chol_diag_element = tik_instance.Scalar("float32")
chol_diag_element.set_as(1.0 / temp_scalar)
tik_instance.vsub(64, temp_ub[index, 0], temp_ub[index,
0], assist_1_ub,
vector_repeat_times, 1, 1, 1, 8, 8, 8)
tik_instance.vmuls(64, temp_ub[index, 0], temp_ub[index, 0],
chol_diag_element, vector_repeat_times, 1, 1, 8,
8)
tik_instance.data_move(res[block_index, 0, 0], temp_ub, 0, 1,
8 * vector_repeat_times * split_dim, 0, 0)
tik_instance.BuildCCE(kernel_name=kernel_name,
inputs=[input_x],
outputs=[res])
return tik_instance
def __init__(self, params_dict, indices_dict, axis_dict, y_dict, kernel_name):
"""
constructor of GatherV2
Parameters
----------
params_dict: dict
shape and dtype of input params
indices_dict: dict
shape and dtype of input indices
axis_dict: dict
shape and dtype of input axis
y_dict: dict
shape and dtype of output, should be same dtype as input
kernel_name: str
kernel name, default value is "GatherV2"
Returns
-------
None
"""
self.params_dtype = params_dict.get("dtype").lower()
self.indices_dtype = indices_dict.get("dtype").lower()
self.axis_dtype = axis_dict.get("dtype").lower()
self.y_dtype = y_dict.get("dtype").lower()
self.tiling_dtype = INT32
dtype_list = ("int8", "int16", "int32", "int64", "uint8", "uint16",
"uint32", "uint64", "float16", "float32")
indices_support_dtype_list = ("int32", "int64")
check_dtype(self.params_dtype, dtype_list, param_name="x")
check_dtype(self.indices_dtype, indices_support_dtype_list, param_name="indices")
check_dtype(self.axis_dtype, (INT32,), param_name="axis")
if self.y_dtype != self.params_dtype:
error_manager_vector.raise_err_inputs_dtype_not_equal(kernel_name, "y", "x",
self.y_dtype, self.params_dtype)
profile = tik.Dprofile()
self.ub_size = profile.get_unified_buffer_size()
self.l1_size = profile.get_l1_buffer_size()
self.core_num = profile.get_aicore_num()
self.tik_instance = tik.Tik(profile, disable_debug=True)
self.kernel_name = kernel_name
self.axis_shape = (1,)
self.x_shape = (PARAMS_SIZE,)
self.indices_shape = (INDICES_NUM,)
self.y_shape = (PARAMS_SIZE,)
self.params_dsize = TYPE_LEN_DICT.get(self.params_dtype)
self.indices_dsize = TYPE_LEN_DICT.get(self.indices_dtype)
self.block_elem = BLOCK_SIZE // self.params_dsize
self.x = None
self.indices = None
self.axis = None
self.tiling_gm = None
self.y = None
self.params_pre = None
self.params_axis = None
self.params_row = None
self.indices_num = None
self.cache_params = None
self.need_core_num = None
self.tail_process_core = None
self.indices_num_each_core = None
self.indices_num_remaining = None
self.indices_loop_num = None
self.indices_row_num_once = None
self.indices_row_num_last = None
self.row_num_once_ub = None
self.row_num_once_tail_ub = None
self.inner_loop_num = None
self.row_num_last_ub = None
self.row_num_last_tail_ub = None
self.inner_loop_num_last = None
def __init__(self, padding, dtype, kernel_name, tik_obj, fuse_mark):
"""
Function: store pad_d's parameters of compilation
"""
self.dtype = dtype.lower()
self.ori_padding = padding.copy()
self.padding = padding.copy()
self.kernel_name = kernel_name
self.num_bit = tbe_platform.cce_intrin.get_bit_len(self.dtype) // 8
self.fuse_mark = fuse_mark
self.mask = 128
if self.num_bit == 4:
self.mask = 64
self.max_ub_size = tik.Dprofile().get_unified_buffer_size() - 1024
self.max_core = tik.Dprofile().get_aicore_num()
self.tiling_gm = None
self.input_gm = None
self.output_gm = None
self.tiling_buf = None
self.tiling_buf_size = None
self.buf = None
self.buf_size = None
self.help_buf = None
self.tik_instance = tik_obj
# circulation
self.axis_amount = None
self.branch = None
self.depth = None
self.top_vol = None
self.top_address = None
self.top_div_core = None
self.top_total_core = None
self.top_core_vol_0 = None
self.top_core_vol_1 = None
self.top_core_gap_0 = None
self.top_core_gap_1 = None
self.bottom_vol = None
self.bottom_address = None
self.bottom_div_core = None
self.bottom_total_core = None
self.bottom_core_vol_0 = None
self.bottom_core_vol_1 = None
self.bottom_core_gap_0 = None
self.bottom_core_gap_1 = None
# recursion
self.recur_total_core = None
self.recur_div_core = None
self.recur_in_vol = None
self.recur_loop_0 = None
self.recur_loop_1 = None
self.recur_gap_0 = None
self.recur_gap_1 = None
self.recur_cond = None
self.recur_start_address = None
self.new_in_shape = None
self.new_out_shape = None
self.new_padding_top = None
self.new_padding_bottom = None
self.recur_model = None
self.recur_dup_mk = None
self.recur_gm2buf_mk = None
self.prod_new_in = None
self.prod_new_out = None
self.tiling_arg_kind = None
self.tiling_arg_num = None
self.tiling_arg_idx = None
def CusMatMulCubeDenseLeft(input_x1,
input_x2,
bias=None,
output_y={},
trans_a=False,
trans_b=False,
kernel_name="matmulcube"):
"""
calculating matrix multiplication with bias, C = A*B + bias, support input
data with fractal format.
Parameters:
shape_a: list or tuple
Shape of the first tensor a with rank > 1
shape_b: list or tuple
Shape of the second tensor b with the same type with a,
and shape_a, shape_b must be 2 dims
src_dtype: str
The data type of input, support "float32", "float16"
dst_dtype: str
The data type of output, support "float32", "float16"
trans_a: bool
If True, shape_a == transposed before multiplication
trans_b: bool
If True, shape_b == transposed before multiplication
is_fractal: bool
If True, the input data format of a and b must be fractal format
shape_bias: list or tuple
Shape of bias, only support the input data format with ND
Returns
-------
None
"""
print("!!!!come into zzt~~~~~~~!!!!")
shape_a = input_x1.get("ori_shape")
shape_b = input_x2.get("ori_shape")
shape_output = output_y.get("ori_shape")
print("============")
print(input_x1.get("format"), input_x2.get("format"))
print(shape_a, shape_b)
print("============")
if input_x2.get("format") == "FRACTAL_Z":
n, c, h, w = shape_b
c0 = 16
c1 = c // c0
if c1 == 0:
c1 = 1
shape_b = [n, c1 * h * w * c0]
shape_a = [n, n]
if input_x1.get("format") == "FRACTAL_Z":
n, c, h, w = shape_a
c0 = 16
c1 = c // c0
if c1 == 0:
c1 = 1
shape_a = [n, c1 * h * w * c0]
shape_b = [c1 * h * w * c0, c1 * h * w * c0]
if input_x2.get("format") == "FRACTAL_NZ":
shape_a = [shape_b[0], shape_b[0]]
shape_b = shape_b
if input_x1.get("format") == "FRACTAL_NZ":
shape_a = shape_a
shape_b = [shape_a[1], shape_a[1]]
shape_a = list(shape_a)
shape_b = list(shape_b)
shape_a = _get_input_shape(shape_a)
shape_b = _get_input_shape(shape_b)
util.check_kernel_name(kernel_name)
util.check_shape_rule(shape_a)
util.check_shape_rule(shape_b)
util.check_shape_size(shape_a, SHAPE_SIZE_LIMIT)
util.check_shape_size(shape_b, SHAPE_SIZE_LIMIT)
shape_a = [shape_a[1], shape_a[0]]
trans_a = bool(1 - trans_a)
shape_b = [shape_b[1], shape_b[0]]
trans_b = bool(1 - trans_b)
shape_bias = ()
if bias is not None and bool(bias):
shape_bias = bias.get("shape")
shape_bias = list(shape_bias)
shape_bias = _get_bias(shape_bias)
src_dtype = input_x1.get("dtype").lower()
dst_dtype = output_y.get("dtype").lower()
_shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b)
m_shape = shape_a[len(shape_a) - 2]
km_shape = shape_a[len(shape_a) - 1]
kn_shape = shape_b[len(shape_a) - 2]
n_shape = shape_b[len(shape_a) - 1]
if src_dtype == "float16":
block_reduce = cce.BLOCK_REDUCE
block_in = cce.BLOCK_IN
block_out = cce.BLOCK_OUT
if trans_a and km_shape == 1:
block_in = cce.BLOCK_VECTOR
if not trans_a and m_shape == 1:
block_in = cce.BLOCK_VECTOR
if trans_b and kn_shape == 1:
block_out = cce.BLOCK_VECTOR
if not trans_b and n_shape == 1:
block_out = cce.BLOCK_VECTOR
if trans_a:
shape_a_temp = (m_shape // block_reduce, km_shape // block_in,
block_reduce, block_in)
else:
shape_a_temp = (m_shape // block_in, km_shape // block_reduce,
block_in, block_reduce)
if trans_b:
shape_b_temp = (kn_shape // block_out, n_shape // block_reduce,
block_reduce, block_out)
else:
shape_b_temp = (kn_shape // block_reduce, n_shape // block_out,
block_out, block_reduce)
shape_a_temp = (shape_a_temp[0], shape_a_temp[1], shape_a_temp[2],
shape_a_temp[3])
format_a = "FRACTAL_NZ"
shape_b_temp = (shape_b_temp[0], shape_b_temp[1], shape_b_temp[2],
shape_b_temp[3])
format_b = "FRACTAL_NZ"
print("=======================================")
print(shape_a_temp, shape_b_temp)
print(format_a, format_b)
print("=======================================")
tensor_bias = None
tensor_a = tvm.placeholder(shape_a_temp, name='tensor_a', dtype=src_dtype)
tensor_b = tvm.placeholder(shape_b_temp, name='tensor_b', dtype=src_dtype)
if shape_bias:
tensor_bias = tvm.placeholder(shape_bias,
name='tensor_bias',
dtype=dst_dtype)
if shape_a_temp[0] == 63 and shape_a_temp[1] == 63 and shape_b_temp[
0] == 128 and shape_b_temp[1] == 63:
if util.get_product_version() == util.VERSION_MINI:
tik_instance = tik.Tik(tik.Dprofile("v100", "mini"))
else:
tik_instance = tik.Tik(tik.Dprofile("v100", "cloud"))
input_x1 = tik_instance.Tensor("float16",
shape_a_temp,
name="left_matrix",
scope=tik.scope_gm)
input_x2 = tik_instance.Tensor("float16",
shape_b_temp,
name="right_matrix",
scope=tik.scope_gm)
resMatmul = tik_instance.Tensor("float16",
shape_output,
name="output",
scope=tik.scope_gm)
with tik_instance.for_range(0, 32, block_num=32) as block_index:
resMatmul_local_UB = tik_instance.Tensor("float16", (128 * 256, ),
scope=tik.scope_ubuf,
name="resMatmul_local_UB")
resMatmul_local_UB_local_L0C = tik_instance.Tensor(
"float32", (128 * 256, ),
scope=tik.scope_cc,
name="resMatmul_local_UB")
input_1_local_L1_local_L0A = tik_instance.Tensor(
"float16", (128 * 128, ),
scope=tik.scope_ca,
name="input_1_local_L1_local_L0A")
input_2_local_L1 = tik_instance.Tensor("float16", (128 * 256, ),
scope=tik.scope_cbuf,
name="input_2_local_L1")
input_1_local_L1 = tik_instance.Tensor("float16", (128 * 128, ),
scope=tik.scope_cbuf,
name="input_1_local_L1")
input_2_local_L1_local_L0B = tik_instance.Tensor(
"float16", (128 * 256, ),
scope=tik.scope_cb,
name="input_2_local_L1_local_L0B")
core_m_idx = block_index % 8
core_n_idx = block_index // 8
with tik_instance.if_scope(core_m_idx != 7):
tik_instance.data_move(
input_1_local_L1,
input_x1[core_m_idx * (8 * 256 + 128 * 1008)], 0, 8, 128,
55 * 16, 0)
tik_instance.data_move(
input_2_local_L1,
input_x2[core_m_idx * 8 * 256 + core_n_idx * 512 * 1008],
0, 32, 128, 55 * 16, 0)
with tik_instance.for_range(0, 8) as cc12:
tik_instance.load2dv1(
input_1_local_L1_local_L0A[cc12 * 2048],
input_1_local_L1[cc12 * 256], 0, 8, 8, 0, False)
with tik_instance.for_range(0, 2) as cc6:
with tik_instance.for_range(0, 8) as cc121:
tik_instance.load2dv1(
input_2_local_L1_local_L0B[cc121 * 4096],
input_2_local_L1[cc6 * 32768 + cc121 * 256], 0, 16,
8, 0, True)
tik_instance.mmad(resMatmul_local_UB_local_L0C,
input_1_local_L1_local_L0A,
input_2_local_L1_local_L0B, 128, 128,
256, 0)
tik_instance.data_move(resMatmul_local_UB,
resMatmul_local_UB_local_L0C, 0, 1,
128, 0, 0, 1)
tik_instance.data_move(
resMatmul[cc6 * 256 * 1008 + core_m_idx * 8 * 256 +
core_n_idx * 512 * 1008], resMatmul_local_UB,
0, 16, 256 // 2, 0, 55 * 16 * 2 // 2)
with tik_instance.else_scope():
tik_instance.data_move(
input_1_local_L1,
input_x1[core_m_idx * (8 * 256 + 128 * 1008)], 0, 7, 112,
56 * 16, 0)
tik_instance.data_move(
input_2_local_L1,
input_x2[core_m_idx * 8 * 256 + core_n_idx * 512 * 1008],
0, 32, 112, 56 * 16, 0)
with tik_instance.for_range(0, 7) as cc10:
tik_instance.load2dv1(
input_1_local_L1_local_L0A[cc10 * 1792],
input_1_local_L1[cc10 * 256], 0, 7, 7, 0, False)
with tik_instance.for_range(0, 2) as cc5:
with tik_instance.for_range(0, 7) as cc101:
tik_instance.load2dv1(
input_2_local_L1_local_L0B[cc101 * 4096],
input_2_local_L1[cc5 * 28672 + cc101 * 256], 0, 16,
7, 0, True)
tik_instance.mmad(resMatmul_local_UB_local_L0C,
input_1_local_L1_local_L0A,
input_2_local_L1_local_L0B, 112, 112,
256, 0)
tik_instance.data_move(resMatmul_local_UB,
resMatmul_local_UB_local_L0C, 0, 1,
112, 0, 0, 1)
tik_instance.data_move(
resMatmul[cc5 * 256 * 1008 + core_m_idx * 8 * 256 +
core_n_idx * 512 * 1008], resMatmul_local_UB,
0, 16, 224 // 2, 0, 56 * 16 * 2 // 2)
tik_instance.BuildCCE(kernel_name=kernel_name,
inputs=[input_x1, input_x2],
outputs=[resMatmul])
return tik_instance
print("come into tbe, shape is error!")
result = te.lang.cce.matmul(tensor_a,
tensor_b,
trans_a,
trans_b,
format_a=format_a,
format_b=format_b,
dst_dtype=dst_dtype,
tensor_bias=tensor_bias)
with tvm.target.cce():
schedule = generic.auto_schedule(result)
tensor_list = [tensor_a, tensor_b, result]
if shape_bias:
tensor_list = [tensor_a, tensor_b, tensor_bias, result]
config = {
"print_ir": False,
"name": kernel_name,
"tensor_list": tensor_list
}
te.lang.cce.cce_build_code(schedule, config)
def decode_bbox(box_predictions,
anchors,
decoded_boxes,
decode_clip,
kernel_name="decode_bbox"):
"""
calculating data
Parameters
----------
box_predictions : shape and dtype of input
anchors : shape and dtype of input
decoded_boxes : shape and dtype of output, s
hould be same shape and type as input
decode_clip : decode_clip
kernel_name : kernel name, default value is "decode_bbox"
Returns
-------
None
"""
# check param & data
shape_box_predictions = box_predictions.get("shape")
shape_anchors = anchors.get("shape")
shape_decoded_boxes = decoded_boxes.get("shape")
util.check_kernel_name(kernel_name)
format_box_predictions = box_predictions.get("format")
format_anchors = anchors.get("format")
format_decoded_boxes = decoded_boxes.get("format")
check_format_shape(format_box_predictions, format_anchors,
format_decoded_boxes)
util.check_shape_rule(shape_box_predictions, CONFIG_THREE, CONFIG_FOUR,
None)
util.check_shape_rule(shape_anchors, CONFIG_THREE, CONFIG_FOUR, None)
util.check_shape_rule(shape_decoded_boxes, CONFIG_TWO, CONFIG_TWO, None)
util.check_shape_size(shape_box_predictions, SHAPE_SIZE_LIMIT)
util.check_shape_size(shape_anchors, SHAPE_SIZE_LIMIT)
util.check_shape_size(shape_decoded_boxes, SHAPE_SIZE_LIMIT)
util.check_dtype_rule(box_predictions.get("dtype").lower(), ("float16", ))
util.check_dtype_rule(anchors.get("dtype").lower(), ("float16", ))
util.check_dtype_rule(decoded_boxes.get("dtype").lower(), ("float16", ))
if shape_box_predictions != shape_anchors:
raise RuntimeError("the input shape_box_predictions and anchors)"
"must be same")
if (reduce(lambda x, y: x * y, shape_box_predictions[:])) \
!= (reduce(lambda x, y: x * y, shape_decoded_boxes[:])):
raise RuntimeError("the input shape (box_predictions and anchors"
"is not equal to out shape(decoded_boxes)")
if (shape_box_predictions[-1] == CONFIG_FOUR
and len(shape_box_predictions) == CONFIG_THREE):
if shape_decoded_boxes[1] != CONFIG_FOUR:
raise RuntimeError("the output shape_decoded_boxes must be 4")
else:
if (shape_box_predictions[0] == CONFIG_FOUR
and len(shape_box_predictions) == CONFIG_FOUR):
if shape_decoded_boxes[0] != CONFIG_FOUR:
raise RuntimeError("the output shape_decoded_boxes must be 4")
else:
raise RuntimeError("the input shape not in {(4,C,H,W), (H,W,4)}")
if not isinstance(decode_clip, (float, int)):
raise RuntimeError("input param type of decode_clip should be Float")
if decode_clip < 0 or decode_clip > 10:
raise RuntimeError(
"input param decode_clip can't be negtive and shoud be [0,10]! ")
# init the tiling shape
print("shape_box_predictions", shape_box_predictions)
shape = TilingFunc(shape_box_predictions)
# calculate the deocede_bbox
tik_instance = tik.Tik(tik.Dprofile())
data_tensor = InitTensor(tik_instance, shape)
if shape.input_shape[-1] == CONFIG_FOUR \
and len(shape.input_shape) == CONFIG_THREE:
decode_bbox_compute(tik_instance, shape, data_tensor, decode_clip,
kernel_name)
if shape.input_shape[0] == CONFIG_FOUR \
and len(shape.input_shape) == CONFIG_FOUR:
decode_bbox_compute_transpose(tik_instance, shape, data_tensor,
decode_clip, kernel_name)
return tik_instance
def __init__(self,
input0,
gamma0,
beta0,
output0,
kernel_name="BatchNorm"):
self.tik_instance = tik.Tik(tik.Dprofile("v100", "mini"))
self.sclar_gamma = self.tik_instance.Scalar("float16")
self.sclar_beta = self.tik_instance.Scalar("float16")
#
self.input_n = self.tik_instance.InputScalar(dtype="int32",
name="inputscalar_n")
self.input_c = self.tik_instance.InputScalar(dtype="int32",
name="inputscalar_c")
self.input_h = self.tik_instance.InputScalar(dtype="int32",
name="inputscalar_h")
self.input_w = self.tik_instance.InputScalar(dtype="int32",
name="inputscalar_w")
self.inputtype = \
self.tik_instance.InputScalar(dtype="int32",
name="inputscalar_dtype")
self.output_n = self.tik_instance.InputScalar(dtype="int32",
name="outputscalar_n")
self.output_c = self.tik_instance.InputScalar(dtype="int32",
name="outputscalar_c")
self.output_h = self.tik_instance.InputScalar(dtype="int32",
name="outputscalar_h")
self.output_w = self.tik_instance.InputScalar(dtype="int32",
name="outputscalar_w")
self.outputtype = \
self.tik_instance.InputScalar(dtype="int32",
name="outputscalar_dtype")
self.gamma_c = self.tik_instance.InputScalar(dtype="int32",
name="gammascalar")
self.gammatype = \
self.tik_instance.InputScalar(dtype="int32",
name="gammascalar_dtype")
self.beta_c = self.tik_instance.InputScalar(dtype="int32",
name="betascalar")
self.betatype = self.tik_instance.InputScalar(dtype="int32",
name="betascalar_dtype")
self.param1 = self.tik_instance.InputScalar(dtype="int32",
name="param1")
self.param2 = self.tik_instance.InputScalar(dtype="int32",
name="param2")
self.param3 = self.tik_instance.InputScalar(dtype="int32",
name="param3")
self.param4 = self.tik_instance.InputScalar(dtype="int32",
name="param4")
self.param5 = self.tik_instance.InputScalar(dtype="int32",
name="param5")
self.param6 = self.tik_instance.InputScalar(dtype="int32",
name="param6")
self.param7 = self.tik_instance.InputScalar(dtype="int32",
name="param7")
self.param8 = self.tik_instance.InputScalar(dtype="int32",
name="param8")
self.param9 = self.tik_instance.InputScalar(dtype="int32",
name="param9")
self.param10 = self.tik_instance.InputScalar(dtype="int32",
name="param10")
self.byte_fp16 = 2
self.input_dtype = "float16"
self.kernel_name = kernel_name
# gm buffer
self.gamma_gm = self.tik_instance.Tensor("float16", (MAX_CHANNEL, ),
name="gamma_gm",
scope=tik.scope_gm)
self.beta_gm = self.tik_instance.Tensor("float16", (MAX_CHANNEL, ),
name="beta_gm",
scope=tik.scope_gm)
self.input_gm = self.tik_instance.\
Tensor("float16",
(MAX_BATCH*MAX_CHANNEL*MAX_HEIGHT*MAX_WIDTH,),
name="input_gm", scope=tik.scope_gm)
self.output_gm = self.tik_instance.\
Tensor("float16",
(MAX_BATCH*MAX_CHANNEL*MAX_HEIGHT*MAX_WIDTH,),
name="output_gm", scope=tik.scope_gm)
self.gamma_ub = self.tik_instance.\
Tensor("float16", (MAX_CHANNEL, ),
name="gamma_ub", scope=tik.scope_ubuf)
self.beta_ub = self.tik_instance.\
Tensor("float16", (MAX_CHANNEL, ),
name="beta_ub", scope=tik.scope_ubuf)
align_c = ceil_div_mul(self.input_c, 16)
#clear to zero
self.tik_instance.vec_muls(128, self.gamma_ub, self.gamma_ub, 0,
MAX_CHANNEL // 128, 8, 8)
self.tik_instance.vec_muls(128, self.beta_ub, self.beta_ub, 0,
MAX_CHANNEL // 128, 8, 8)
self.tik_instance.data_move(self.gamma_ub, self.gamma_gm, 0, 1,
align_c // 16, 0, 0)
self.tik_instance.data_move(self.beta_ub, self.beta_gm, 0, 1,
align_c // 16, 0, 0)
# 1/var
self.tik_instance.vrec(16, self.beta_ub, self.beta_ub, align_c // 16,
1, 1, 1, 1)
# -mean
self.tik_instance.vec_muls(16, self.gamma_ub, self.gamma_ub, -1.0,
align_c // 16, 1, 1)
def CusBatchMatMul(input_x1, input_x2, output, transpose_a=False, transpose_b=True, kernel_name="batchmatmul"):
"""CusBatchMatMul"""
if util.get_product_version() == util.VERSION_MINI:
tik_instance = tik.Tik(tik.Dprofile("v100", "mini"))
else:
tik_instance = tik.Tik(tik.Dprofile("v100", "cloud"))
x1_shape = input_x1.get("shape")
dtype = input_x1.get("dtype").lower()
x2_shape = input_x2.get("shape")
if dtype != input_x2.get("dtype").lower():
raise RuntimeError("dtype of input_x1 and input_x2 must be same, but got %s vs %s" % (
dtype, input_x2.get("dtype").lower()))
input_shape = (tuple(x1_shape), tuple(x2_shape), dtype, transpose_a, transpose_b)
support_shape = [((8, 128, 128), (8, 128, 128), "float32", False, True),
((36, 128, 128), (36, 128, 128), "float32", False, True),
((5, 128, 128), (5, 128, 128), "float32", False, True),
((18, 128, 128), (18, 128, 128), "float32", False, True),
((16, 128, 128), (16, 128, 128), "float32", False, True),
((9, 128, 128), (9, 128, 128), "float32", False, True),
((1, 64, 64), (1, 64, 64), "float32", False, True),
((1, 128, 128), (1, 128, 128), "float32", False, True),
((4, 128, 128), (4, 128, 128), "float32", False, True),
((2, 128, 128), (2, 128, 128), "float32", False, True)]
if input_shape not in support_shape:
raise RuntimeError("input_shape %s is not supported" % str(input_shape))
# if not transpose_a and transpose_b:
batch, m, k = x1_shape
input1_shape = _get_flattern_shape(x1_shape)
input1 = tik_instance.Tensor(dtype, input1_shape, name="input1", scope=tik.scope_gm)
input2_shape = _get_flattern_shape(x2_shape)
input2 = tik_instance.Tensor(dtype, input2_shape, name="input2", scope=tik.scope_gm)
output_shape = x1_shape
res_shape = _get_flattern_shape(output_shape)
res = tik_instance.Tensor(dtype, res_shape, name="res", scope=tik.scope_gm)
if input_shape == ((36, 128, 128), (36, 128, 128), "float32", False, True):
with tik_instance.for_range(0, 18, block_num=18) as block_idx:
with tik_instance.for_range(0, 2) as cc0:
with tik_instance.for_range(0, 128, thread_num=2) as cc1:
input1_index = block_idx * 32768 + cc0 * 16384 + cc1 * 128
input2_index = block_idx * 32768 + cc0 * 16384
res_index = block_idx * 32768 + cc0 * 16384 + cc1 * 128
_inner_matmul_new(tik_instance, dtype,
input1, input1_index,
input2, input2_index,
res, res_index)
if input_shape == ((5, 128, 128), (5, 128, 128), "float32", False, True):
with tik_instance.for_range(0, 30, block_num=30) as block_idx:
with tik_instance.for_range(0, 11) as cc1_db:
with tik_instance.for_range(0, 2, thread_num=2) as thread_idx:
with tik_instance.if_scope(((((block_idx % 6) * 22) + (cc1_db * 2) + thread_idx) < 128)):
input_1_local_UB = tik_instance.Tensor(dtype, [128], name="input_1_local_UB",
scope=tik.scope_ubuf)
t_1_0_local_UB = tik_instance.Tensor(dtype, [64 * 128], name="t_1_0_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_1_local_UB, input1[
(block_idx // 6) * 16384 + (block_idx % 6) * 2816 + cc1_db * 256 + thread_idx * 128], 0, 1,
16, 0, 0)
with tik_instance.for_range(0, 2) as vec_i:
tik_instance.vadds(64, t_1_0_local_UB[vec_i * 64], input_1_local_UB[vec_i * 64], 0,
64, 1, 1, 16, 0)
with tik_instance.for_range(0, 2, thread_num=2) as thread_idx2:
input_2_local_UB = tik_instance.Tensor(dtype, [64 * 128], name="input_2_local_UB",
scope=tik.scope_ubuf)
t_1_local_UB = input_2_local_UB
bisec_last_axis_local_UB = input_2_local_UB
matmul_hybrid_f_t_local_UB = tik_instance.Tensor(dtype, [64],
name="matmul_hybrid_f_t_local_UB",
scope=tik.scope_ubuf)
matmul_hybrid_f_t_local_UB_dst_tmp = tik_instance.Tensor(dtype, [64],
name="matmul_hybrid_f_t_local_UB_dst_tmp",
scope=tik.scope_ubuf)
tik_instance.vector_dup(64, matmul_hybrid_f_t_local_UB, 0, 1, 1, 8)
tik_instance.data_move(input_2_local_UB,
input2[(block_idx // 6) * 16384 + thread_idx2 * 8192], 0, 1,
1024, 0, 0)
tik_instance.vmul(64, t_1_local_UB, t_1_0_local_UB, input_2_local_UB, 128, 1, 1, 1, 8, 8, 8)
tik_instance.vadd(64, bisec_last_axis_local_UB, t_1_local_UB, t_1_local_UB[64], 64, 1, 1, 1,
16, 16, 16)
tik_instance.vector_dup(64, matmul_hybrid_f_t_local_UB_dst_tmp, 0, 1, 1, 8)
with tik_instance.for_range(0, 64) as cc6:
tik_instance.vcadd(64, matmul_hybrid_f_t_local_UB_dst_tmp[cc6],
bisec_last_axis_local_UB[cc6 * 128],
1, 1, 1, 8)
tik_instance.vadd(64, matmul_hybrid_f_t_local_UB, matmul_hybrid_f_t_local_UB_dst_tmp,
matmul_hybrid_f_t_local_UB, 1, 1, 1, 1, 8, 8, 8)
tik_instance.data_move(
res[(block_idx // 6) * 16384 + (block_idx % 6) * 2816 + cc1_db * 256 +
thread_idx * 128 + thread_idx2 * 64],
matmul_hybrid_f_t_local_UB, 0, 1, 8, 0, 0)
if input_shape == ((18, 128, 128), (18, 128, 128), "float32", False, True):
with tik_instance.for_range(0, 18, block_num=18) as block_idx:
with tik_instance.for_range(0, 128, thread_num=2) as cc0:
input1_index = block_idx * 16384 + cc0 * 128
input2_index = block_idx * 16384
res_index = block_idx * 16384 + cc0 * 128
_inner_matmul_new(tik_instance, dtype,
input1, input1_index,
input2, input2_index,
res, res_index)
if input_shape == ((9, 128, 128), (9, 128, 128), "float32", False, True):
with tik_instance.for_range(0, 27, block_num=27) as block_idx:
with tik_instance.for_range(0, 42, thread_num=2) as cc0:
input1_index = (block_idx // 3) * 16384 + (block_idx % 3) * 5504 + cc0 * 128
input2_index = (block_idx // 3) * 16384
res_index = (block_idx // 3) * 16384 + (block_idx % 3) * 5504 + cc0 * 128
_inner_matmul_new(tik_instance, dtype,
input1, input1_index,
input2, input2_index,
res, res_index)
with tik_instance.if_scope((block_idx % 3) < 2):
input1_index = (block_idx // 3) * 16384 + (block_idx % 3) * 5504 + 42 * 128
input2_index = (block_idx // 3) * 16384
res_index = (block_idx // 3) * 16384 + (block_idx % 3) * 5504 + 42 * 128
_inner_matmul_new(tik_instance, dtype,
input1, input1_index,
input2, input2_index,
res, res_index)
if input_shape == ((1, 64, 64), (1, 64, 64), "float32", False, True):
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
with tik_instance.for_range(0, 2, thread_num=2) as cc0:
input1_index = block_idx * 128 + cc0 * 64
input2_index = 0
res_index = block_idx * 128 + cc0 * 64
_inner_matmul_new_1_64_32_64(tik_instance, dtype,
input1, input1_index,
input2, input2_index,
res, res_index)
input_shape_list = [((1, 128, 128), (1, 128, 128), "float32", False, True),
((2, 128, 128), (2, 128, 128), "float32", False, True),
((4, 128, 128), (4, 128, 128), "float32", False, True),
((8, 128, 128), (8, 128, 128), "float32", False, True),
((16, 128, 128), (16, 128, 128), "float32", False, True)
]
if input_shape in input_shape_list:
block_num = 32
input1_unit_size = 128
input2_unint_size = 128 * 128
with tik_instance.for_range(0, block_num, block_num=block_num) as block_idx:
block_process_ele_num = (batch * m * k) // block_num
loop_time = (batch * m * k) // block_num // input1_unit_size
thread_num = 2
with tik_instance.for_range(0, loop_time, thread_num=thread_num) as cc0:
input1_index = block_idx * block_process_ele_num + cc0 * input1_unit_size
if batch > 1:
input2_index = block_idx // (block_num // batch) * input2_unint_size
else:
input2_index = 0
res_index = block_idx * block_process_ele_num + cc0 * input1_unit_size
_inner_matmul_new(tik_instance, dtype,
input1, input1_index,
input2, input2_index,
res, res_index)
tik_instance.BuildCCE(kernel_name, inputs=[input1, input2], outputs=[res])
return tik_instance
def sort(x, y1, y2, axis=-1, descending=False, kernel_name="sort"):
"""
Function: Sorts the elements of the input tensor along a given dimension in ascending order by value.
Modify : 2020-08-03
Init base parameters
Parameters
----------
input(x): dict
data of input
output(y1): dict
data of output
indices(y2): dict
data of indices
dim(axis): int
descending: bool
kernel_name: str
the name of the operator
----------
"""
shape, dtype, allnum, num = cheak(x, y1, y2, axis, kernel_name)
tik_instance = tik.Tik(tik.Dprofile())
add16 = (16 - (num % 16)) % 16
total = num + add16
big_shape = list(shape)
big_shape[-1] = total
input_gm = tik_instance.Tensor(dtype, shape, name="x", scope=tik.scope_gm)
data_out = tik_instance.Tensor(dtype,
big_shape,
name="data_out",
scope=tik.scope_gm,
is_workspace=True)
data_indices = tik_instance.Tensor("int32",
big_shape,
name="data_indices",
scope=tik.scope_gm,
is_workspace=True)
data_out_ = tik_instance.Tensor(dtype,
shape,
name="data_out_",
scope=tik.scope_gm)
data_indices_ = tik_instance.Tensor("int32",
shape,
name="data_indices_",
scope=tik.scope_gm)
# to figure the index of input_gm
L = len(shape)
distance = []
big_distance = []
tmp = allnum
big_tmp = allnum // num * total
for i in range(L - 1):
tmp = tmp // shape[i]
distance.append(tmp)
big_tmp = big_tmp // shape[i]
big_distance.append(big_tmp)
rounds = allnum // num
available_aicore_num = tik.Dprofile().get_aicore_num()
used_aicore_num = available_aicore_num if rounds > available_aicore_num else rounds
batch_num_per_aicore_process = rounds // used_aicore_num
batch_tail = rounds % used_aicore_num
with tik_instance.for_range(0, used_aicore_num,
block_num=used_aicore_num) as i:
with tik_instance.for_range(0, batch_num_per_aicore_process) as k:
data_out, data_indices = sort_compute(tik_instance, dtype, total,
i + k * used_aicore_num,
descending, num, distance,
shape, big_distance,
data_out, data_indices,
input_gm, L)
with tik_instance.if_scope(i < batch_tail):
data_out, data_indices = sort_compute(
tik_instance, dtype, total,
batch_num_per_aicore_process * used_aicore_num + i, descending,
num, distance, shape, big_distance, data_out, data_indices,
input_gm, L)
float_ub = tik_instance.Tensor("float16", [total],
name="float_ub",
scope=tik.scope_ubuf)
int_ub = tik_instance.Tensor("int32", [total],
name="int_ub",
scope=tik.scope_ubuf)
with tik_instance.for_range(0, rounds) as i:
tik_instance.data_move(float_ub[0], data_out[i * total], 0, 1,
total // 16, 0, 0)
tik_instance.data_move(data_out_[i * num], float_ub[0], 0, 1,
total // 16, 0, 0)
tik_instance.data_move(int_ub[0], data_indices[i * total], 0, 1,
total // 8, 0, 0)
tik_instance.data_move(data_indices_[i * num], int_ub[0], 0, 1,
total // 8, 0, 0)
tik_instance.BuildCCE(kernel_name=kernel_name,
inputs=[input_gm],
outputs=[data_out_, data_indices_])
return tik_instance
def safe_check(dicts, kernel_name):
"""
check if the inputs are legal
Parameters
----------
dicts: (x_dict, rois_dict, actual_dict, y_dict)
kernel_name: kernel name
Returns
-------
None
"""
x_shape = dicts[0].get("shape")
x_dtype = dicts[0].get("dtype").lower()
rois_shape = dicts[1].get("shape")
rois_dtype = dicts[1].get("dtype").lower()
y_dtype = dicts[3].get("dtype").lower()
y_shape = dicts[3].get("shape")
profile = tik.Dprofile()
tik_name_check = tbe_platform.cce_conf.get_soc_spec("SOC_VERSION")
if tik_name_check in ("Ascend310", "Ascend910", "Hi3796CV300ES",
"Hi3796CV300CS"):
op_utils.check_dtype(x_dtype, ["float16"], param_name="input_x")
op_utils.check_dtype(rois_dtype, ["float16"], param_name="input_rois")
else:
op_utils.check_dtype(x_dtype, ["float16", "float32"],
param_name="input_x")
op_utils.check_dtype(rois_dtype, ["float16", "float32"],
param_name="input_rois")
if x_dtype != rois_dtype or x_dtype != y_dtype:
error_info = {}
error_info['errCode'] = 'E81012'
error_info['op_name'] = 'roi_pooling'
error_info['real_dtypes'] = str((x_dtype, rois_dtype, y_dtype))
raise RuntimeError(
error_info,
"In op[roi_pooling], the dtype of tensor x, rois and y should be the same, but actually they are [%s]."
% error_info['real_dtypes'])
op_utils.check_shape(x_shape, min_rank=5, max_rank=5, param_name="input_x")
op_utils.check_shape(rois_shape,
min_rank=3,
max_rank=3,
param_name="input_rois")
op_utils.check_shape(y_shape,
min_rank=5,
max_rank=5,
param_name="output_y")
roi_max_num = rois_shape[2]
if roi_max_num > 6000 or roi_max_num % 16 != 0:
error_info = {}
error_info['errCode'] = 'E81013'
error_info['real_rois_shape[2]'] = str(rois_shape[2])
raise RuntimeError(
error_info,
"In op[roi_pooling], the rois_shape[2] should be less than 6000 and can be divided by 16, but actually is [%s]."
% error_info['real_rois_shape[2]'])
def conv2d_tik_compute(params):
te_set_l2_mode(1)
tik_instance = tik.Tik(tik.Dprofile(params["arch"], params["version"]),
err_msg_level=1)
n, c1, h, w, c0 = params["fm_shape"]
c1, kh, kw, cout, c0 = params["weight_shape"]
stride_h, stride_w = params["stride_list"]
dilation_h, dilation_w = params["dilation_list"]
pad_top, pad_bot, pad_left, pad_right = params["pad_list"]
kh_dilation = (kh - 1) * dilation_h + 1
kw_dilation = (kw - 1) * dilation_w + 1
ho = int(np.ceil((h + pad_top + pad_bot - kh_dilation + 1) / stride_h))
wo = int(np.ceil((w + pad_right + pad_left - kw_dilation + 1) / stride_w))
round_howo = ceil_div(ho * wo, 16) * 16
fm_gm = tik_instance.Tensor(params['fm_dtype'], (n, c1, h, w, c0),
name='fm_gm',
scope=tik.scope_gm)
weight_gm = tik_instance.Tensor(params['weight_type'],
(c1, kh, kw, cout, c0),
name='weight_gm',
scope=tik.scope_gm)
if params['dst_gm_type'] in ("int8", "uint8"):
dst_gm = tik_instance.Tensor(params['dst_gm_type'],
[n, cout // 32, ho, wo, 32],
name='dst_gm',
scope=tik.scope_gm)
else:
dst_gm = tik_instance.Tensor(params['dst_gm_type'],
[n, cout // 16, ho, wo, 16],
name='dst_gm',
scope=tik.scope_gm)
core_num = 2
pre_core_cout = cout // core_num
cout_iter_num = pre_core_cout // params["cout_split_factor"]
Cin_blocks = c1
with tik_instance.for_range(0, core_num, block_num=core_num) as cout_o:
with tik_instance.for_range(0, cout_iter_num, thread_num=1) as cout_i:
weight_L1 = tik_instance.Tensor(
params['weight_type'],
(Cin_blocks, kh, kw, params["cout_split_factor"], c0),
name='weight_l1',
scope=tik.scope_cbuf)
tik_instance.data_move(
weight_L1,
weight_gm.flatten()[cout_o * pre_core_cout * c0 +
params["cout_split_factor"] * cout_i * c0],
0, Cin_blocks * kh * kw, params["cout_split_factor"],
(cout - params["cout_split_factor"]), 0)
with tik_instance.for_range(0, n, thread_num=2) as n_index:
feature_map_l1 = tik_instance.Tensor(params['fm_dtype'],
(c1, h, w, c0),
name='feature_map_l1',
scope=tik.scope_cbuf)
tik_instance.data_move(feature_map_l1,
fm_gm[n_index, :, :, :, :], 0, 1,
c1 * h * w, 0, 0)
dst_l0c = tik_instance.Tensor(
params['dst_l0c_type'],
[params["cout_split_factor"] // 16, round_howo, 16],
name='dst_l0c',
scope=tik.scope_cbuf_out)
tik_instance.conv2d(
dst_l0c, feature_map_l1, weight_L1, (c1, h, w, c0),
(Cin_blocks, kh, kw, params["cout_split_factor"], c0),
params['stride_list'], params['pad_list'],
params['dilation_list'], params['pad_value'])
tik_instance.fixpipe(
dst_gm[n_index, (cout_o * pre_core_cout +
params["cout_split_factor"] * cout_i) //
(32 // DTYPE_SIZE[params['dst_gm_type']]), 0, 0, 0],
dst_l0c,
params["cout_split_factor"] // 16,
ho * wo * 16 * DTYPE_SIZE[params['dst_l0c_type']] // 32,
0,
0,
extend_params={
"bias": None,
"quantize_params": params["quantize_params"]
})
tik_instance.BuildCCE(kernel_name=params["kernel_name"],
inputs=[fm_gm, weight_gm],
outputs=[dst_gm])
return tik_instance
def decode_boundaries_target(boundary_predictions, anchors, boundary_encoded,
kernel_name="cce_decode_boundaries_target_fpLINE"):
"""
calculating data
Parameters
----------
boundary_predictions : dict
shape and dtype of input
anchors : dict
shape and dtype of input
boundary_encoded : dict
shape and dtype of output, should be same shape and type as input
kernel_name : str
kernel name, default value is "decode_boundaries_target"
Returns
-------
None
"""
util.check_kernel_name(kernel_name)
input_info = InputInfo(
shape_boundary_predictions=boundary_predictions.get("shape"),
shape_anchors=anchors.get("shape"),
dtype_boundary_predictions=boundary_predictions.get("dtype").lower(),
dtype_anchors=anchors.get("dtype").lower()
)
input_info.set_nmax(n_max=NMAX)
output_info = Output()
total_handling_times, last_handling_n = check_input(
boundary_predictions=boundary_predictions,
anchors=anchors,
boundary_encoded=boundary_encoded,
n_max=input_info.n_max)
tik_instance = tik.Tik(tik.Dprofile(), True)
# tensor init
data_boundary_predictions, data_anchors, \
data_z = get_gm(tik_instance=tik_instance,
dtype=input_info.dtype_anchors,
shape1=input_info.shape_boundary_predictions,
shape2=input_info.shape_anchors,
name1="data_boundary_predictions",
name2="data_anchors",
name3="data_z",
scope=tik.scope_gm)
if total_handling_times > 0:
with tik_instance.for_range(0, total_handling_times) as current_handling_times:
# current_handling_times:
output_info.set_burst_num(burst_num=input_info.n_max)
# number of LINE*LINE
output_info.update(
n_vector=int_ceil_div(output_info.burst_num, MATRIX_NUM),
n_matrix=int_ceil_div(output_info.burst_num * FOUR, MATRIX_NUM)
)
output_info.update(
shape_vector=(output_info.n_vector, LINE, LINE),
shape_matrix=(output_info.n_matrix * FOUR, LINE, LINE)
)
# move x_gm to ub times
# move y_gm to ub times
input_info.update(
burst_x=int_ceil_div(output_info.burst_num, LINE),
burst_y=int_ceil_div(output_info.burst_num * FOUR, LINE)
)
output_info.update(
rep=output_info.burst_num // VECTOR,
overflow=0
)
process_calculate(tik_instance=tik_instance,
input_info=input_info,
output_info=output_info,
current_handling_times=current_handling_times,
data_boundary_predictions=data_boundary_predictions,
data_anchors=data_anchors,
data_z=data_z)
current_handling_times = total_handling_times
if last_handling_n > 0:
output_info.set_burst_num(burst_num=last_handling_n)
# number of LINE*LINE
output_info.update(
n_vector=int_ceil_div(output_info.burst_num, MATRIX_NUM),
n_matrix=int_ceil_div(output_info.burst_num * FOUR, MATRIX_NUM)
)
output_info.update(
shape_vector=(output_info.n_vector, LINE, LINE),
shape_matrix=(output_info.n_matrix * FOUR, LINE, LINE)
)
# move x_gm to ub times
# move y_gm to ub times
input_info.update(
burst_x=int_ceil_div(output_info.burst_num, LINE),
burst_y=int_ceil_div(output_info.burst_num * FOUR, LINE)
)
output_info.update(
rep=0,
overflow=output_info.burst_num - VECTOR *
(output_info.burst_num // VECTOR)
)
process_end(tik_instance=tik_instance,
input_info=input_info,
output_info=output_info,
current_handling_times=current_handling_times,
data_boundary_predictions=data_boundary_predictions,
data_anchors=data_anchors,
data_z=data_z)
# build_cce
tik_instance.BuildCCE(
kernel_name=kernel_name,
inputs=[data_boundary_predictions, data_anchors],
outputs=[data_z])
return tik_instance
def __init__(self, var, indices, updates, var_out, nd_flag, kernel_name,
compute_type):
"""
Init scatter base parameters
Parameters
----------
var: dict
data of input
datatype suports float32,float16,int32,int8,uint8
indices: dict
data of indices
datatype supports int32
updates: dict
data of updates
datatype supports float32,float16,int32,int8,uint8
var_out: dict
data of input
nd_flag: bool
if this op is nd operator
kernel_name: str
the name of the operator
compute_type: str
the compute type of scatter
Returns
-------
None
"""
self.tik_instance = tik.Tik(tik.Dprofile())
self.nd_flag = nd_flag
self.var_shape = var.get("shape")
self.var_dtype = var.get("dtype").lower()
self.indices_shape = indices.get("shape")
self.indices_dtype = indices.get("dtype").lower()
self.updates_shape = updates.get("shape")
self.updates_dtype = updates.get("dtype").lower()
self.var_ele_num = functools_reduce(lambda x, y: x * y, self.var_shape)
self.indices_num = functools_reduce(lambda x, y: x * y,
self.indices_shape)
self.updates_num = functools_reduce(lambda x, y: x * y,
self.updates_shape)
self.kernel_name = kernel_name
if self.indices_shape == (1,) and \
len(self.var_shape)-len(self.updates_shape) == 1:
if not nd_flag:
self.updates_shape = (1, ) + self.updates_shape
self.check_param(var_out)
if nd_flag:
if self.indices_shape[-1] == len(self.var_shape):
self.update_data_num = 1
else:
self.update_data_num = functools_reduce(
lambda x, y: x * y,
self.var_shape[self.indices_shape[-1]:])
self.max_indice = functools_reduce(
lambda x, y: x * y, self.var_shape[0:self.indices_shape[-1]])
self.index_dims = self.indices_shape[-1]
else:
if len(self.var_shape) > 1:
self.update_data_num = functools_reduce(
lambda x, y: x * y, self.var_shape[1:])
else:
self.update_data_num = 1
self.max_indice = self.var_shape[0]
self.index_dims = 1
self.compute_type = compute_type
self.ub_size_bytes = (
tbe_platform.cce_conf.get_soc_spec(tbe_platform.cce_conf.UB_SIZE) -
8192)
self.var_dtype_bytes_size = tbe_platform.cce_intrin.get_bit_len(
self.var_dtype) // 8
self.indices_dtype_bytes_size = tbe_platform.cce_intrin.get_bit_len(
self.indices_dtype) // 8
self.var_data_each_block = 32 // self.var_dtype_bytes_size
self.indices_data_each_block = 32 // self.indices_dtype_bytes_size
self.indices_ub_number = 0
self.updates_ub_number = 0
self.index_loop_num = 0
self.max_num_one_repeat = 128
if self.var_dtype in ("float32", "int32"):
self.max_num_one_repeat = 64
if self.update_data_num < self.var_data_each_block:
self.block_num = 1
else:
ai_core_num = tbe_platform.cce_conf.get_soc_spec(
tbe_platform.cce_conf.CORE_NUM)
self.indice_step = math.ceil(self.max_indice / ai_core_num)
self.block_num = math.ceil(self.max_indice / self.indice_step)
self.var_gm = self.tik_instance.Tensor(self.var_dtype,
self.var_shape,
name="var_gm",
scope=tik.scope_gm)
self.indices_gm = self.tik_instance.Tensor(self.indices_dtype,
self.indices_shape,
name="indices_gm",
scope=tik.scope_gm)
self.updates_gm = self.tik_instance.Tensor(self.updates_dtype,
self.updates_shape,
name="updates_gm",
scope=tik.scope_gm)
self.out_gm = self.tik_instance.Tensor(self.var_dtype,
self.var_shape,
name="out_gm",
scope=tik.scope_gm)
self.vconv_dst_dtype = "float16"
self.init_ub_tensor_para()
self.var_vconv_ub = None
self.updates_vconv_ub = None
self.var_tile_vconv_ub = None
self.updates_tile_vconv_ub = None
self.var_ub = None
self.updates_ub = None
self.indices_ub = None
self.var_tile_ub = None
self.updates_tile_ub = None
self.var_read_index = None
self.updates_read_index = None
self.indices_loop_index = None
self.indices_tmp = None
def CusMatMulCubeDenseRight(input_x1,
input_x2,
input_x3,
bias=None,
output_y={},
trans_a=False,
trans_b=False,
kernel_name="matmulcube"):
"""CusMatMulCubeDenseRight"""
shape_a_temp = (128, 63, 16, 16)
shape_b_temp = (128, 128, 16, 16)
shape_output = output_y.get("shape")
matrix_max_shape = (1, )
support_shape = [
(shape_a_temp, shape_b_temp, matrix_max_shape),
]
shape_a_input = input_x1.get("shape")
shape_b_input = input_x2.get("shape")
matrix_max_input = input_x3.get("shape")
input_shape = (tuple(shape_a_input), tuple(shape_b_input),
tuple(matrix_max_input))
if input_shape not in support_shape:
raise RuntimeError("input_shape %s is not supported" %
str(input_shape))
if shape_a_temp[0] == 128 and shape_a_temp[1] == 63 and shape_b_temp[
0] == 128 and shape_b_temp[1] == 128:
if util.get_product_version() == util.VERSION_MINI:
tik_instance = tik.Tik(tik.Dprofile("v100", "mini"))
else:
tik_instance = tik.Tik(tik.Dprofile("v100", "cloud"))
input_x1 = tik_instance.Tensor("float16",
shape_a_temp,
name="left_matrix",
scope=tik.scope_gm)
input_x2 = tik_instance.Tensor("float16",
shape_b_temp,
name="right_matrix",
scope=tik.scope_gm)
input_x3 = tik_instance.Tensor("float32", [
1,
],
name="matrix_max",
scope=tik.scope_gm)
resMatmul = tik_instance.Tensor("float32",
shape_output,
name="output",
scope=tik.scope_gm)
with tik_instance.for_range(0, 32, block_num=32) as block_index:
core_m_idx = block_index // 16
core_n_idx = block_index % 16
matrix_max_scalar = tik_instance.Scalar("float32")
matrix_max_local_UB = tik_instance.Tensor(
"float32", (8, ),
scope=tik.scope_ubuf,
name="matrix_max_local_UB")
tik_instance.data_move(matrix_max_local_UB, input_x3, 0, 1, 1, 0,
0)
matrix_max_scalar.set_as(matrix_max_local_UB[0])
resMatmul_local_UB = tik_instance.Tensor("float32", (256 * 128, ),
scope=tik.scope_ubuf,
name="resMatmul_local_UB")
resMatmul_local_UB1 = tik_instance.Tensor(
"float32", (240 * 128, ),
scope=tik.scope_ubuf,
name="resMatmul_local_UB1")
resMatmul_local_UB_local_L0C = tik_instance.Tensor(
"float32", (256 * 128, ),
scope=tik.scope_cc,
name="resMatmul_local_UB_local_L0C")
resMatmul_local_UB_local_L0C1 = tik_instance.Tensor(
"float32", (240 * 128, ),
scope=tik.scope_cc,
name="resMatmul_local_UB_local_L0C1")
input_1_local_L1_local_L0A = tik_instance.Tensor(
"float16", (256 * 128, ),
scope=tik.scope_ca,
name="input_1_local_L1_local_L0A")
input_2_local_L1 = tik_instance.Tensor("float16", (8 * 128 * 16, ),
scope=tik.scope_cbuf,
name="input_2_local_L1")
input_2_local_L11 = tik_instance.Tensor("float16",
(8 * 128 * 16, ),
scope=tik.scope_cbuf,
name="input_2_local_L11")
input_1_local_L1 = tik_instance.Tensor("float16", (8 * 256 * 16, ),
scope=tik.scope_cbuf,
name="input_1_local_L1")
input_1_local_L11 = tik_instance.Tensor("float16",
(8 * 240 * 16, ),
scope=tik.scope_cbuf,
name="input_1_local_L11")
input_2_local_L1_local_L0B = tik_instance.Tensor(
"float16", (128 * 128, ),
scope=tik.scope_cb,
name="input_2_local_L1_local_L0B")
input_2_local_L1_local_L0B1 = tik_instance.Tensor(
"float16", (128 * 128, ),
scope=tik.scope_cb,
name="input_2_local_L1_local_L0B1")
with tik_instance.if_scope(core_m_idx == 0):
with tik_instance.for_range(0, 2) as cc1:
tik_instance.data_move(
input_2_local_L1,
input_x2[core_n_idx * 262144 + core_n_idx * 2048], 0,
8, 128, 1920, 0)
tik_instance.data_move(
input_1_local_L1,
input_x1[core_n_idx * 129024 + cc1 * 4096], 0, 8, 256,
752, 0)
with tik_instance.for_range(0, 8) as cc10:
tik_instance.load2dv1(
input_2_local_L1_local_L0B[cc10 * 2048],
input_2_local_L1[cc10 * 256], 0, 8, 8, 0, True)
with tik_instance.for_range(0, 16) as cc101:
tik_instance.load2dv1(
input_1_local_L1_local_L0A[cc101 * 2048],
input_1_local_L1[cc101 * 256], 0, 8, 16, 0, False)
tik_instance.mmad(resMatmul_local_UB_local_L0C,
input_1_local_L1_local_L0A,
input_2_local_L1_local_L0B, 256, 128,
128, 0)
tik_instance.data_move(resMatmul_local_UB,
resMatmul_local_UB_local_L0C, 0, 1,
128, 0, 0)
tik_instance.vmuls(64, resMatmul_local_UB,
resMatmul_local_UB, matrix_max_scalar,
255, 1, 1, 8, 8)
tik_instance.vmuls(64, resMatmul_local_UB[255 * 64],
resMatmul_local_UB[255 * 64],
matrix_max_scalar, 255, 1, 1, 8, 8)
tik_instance.vmuls(64, resMatmul_local_UB[510 * 64],
resMatmul_local_UB[510 * 64],
matrix_max_scalar, 2, 1, 1, 8, 8)
tik_instance.data_move(
resMatmul[core_n_idx * 129024 + cc1 * 4096],
resMatmul_local_UB, 0, 8, 512, 0, 1504)
with tik_instance.else_scope():
tik_instance.data_move(
input_2_local_L1,
input_x2[core_n_idx * 262144 + core_n_idx * 2048], 0, 8,
128, 1920, 0)
tik_instance.data_move(
input_1_local_L1, input_x1[core_n_idx * 129024 + 2 * 4096],
0, 8, 256, 752, 0)
with tik_instance.for_range(0, 8) as cc10:
tik_instance.load2dv1(
input_2_local_L1_local_L0B[cc10 * 2048],
input_2_local_L1[cc10 * 256], 0, 8, 8, 0, True)
with tik_instance.for_range(0, 16) as cc101:
tik_instance.load2dv1(
input_1_local_L1_local_L0A[cc101 * 2048],
input_1_local_L1[cc101 * 256], 0, 8, 16, 0, False)
tik_instance.mmad(resMatmul_local_UB_local_L0C,
input_1_local_L1_local_L0A,
input_2_local_L1_local_L0B, 256, 128, 128, 0)
tik_instance.data_move(resMatmul_local_UB,
resMatmul_local_UB_local_L0C, 0, 1, 128,
0, 0)
tik_instance.vmuls(64, resMatmul_local_UB, resMatmul_local_UB,
matrix_max_scalar, 255, 1, 1, 8, 8)
tik_instance.vmuls(64, resMatmul_local_UB[255 * 64],
resMatmul_local_UB[255 * 64],
matrix_max_scalar, 255, 1, 1, 8, 8)
tik_instance.vmuls(64, resMatmul_local_UB[510 * 64],
resMatmul_local_UB[510 * 64],
matrix_max_scalar, 2, 1, 1, 8, 8)
tik_instance.data_move(
resMatmul[core_n_idx * 129024 + 2 * 4096],
resMatmul_local_UB, 0, 8, 512, 0, 1504)
tik_instance.data_move(
input_2_local_L11,
input_x2[core_n_idx * 262144 + core_n_idx * 2048], 0, 8,
128, 1920, 0)
tik_instance.data_move(input_1_local_L11,
input_x1[core_n_idx * 129024 + 12288],
0, 8, 240, 768, 0)
with tik_instance.for_range(0, 8) as cc102:
tik_instance.load2dv1(
input_2_local_L1_local_L0B1[cc102 * 2048],
input_2_local_L11[cc102 * 256], 0, 8, 8, 0, True)
with tik_instance.for_range(0, 16) as cc103:
tik_instance.load2dv1(
input_1_local_L1_local_L0A[cc103 * 2048],
input_1_local_L11[cc103 * 256], 0, 8, 15, 0, False)
tik_instance.mmad(resMatmul_local_UB_local_L0C1,
input_1_local_L1_local_L0A,
input_2_local_L1_local_L0B1, 240, 128, 128,
0)
tik_instance.data_move(resMatmul_local_UB1,
resMatmul_local_UB_local_L0C1, 0, 1,
120, 0, 0)
tik_instance.vmuls(64, resMatmul_local_UB1,
resMatmul_local_UB1, matrix_max_scalar, 255,
1, 1, 8, 8)
tik_instance.vmuls(64, resMatmul_local_UB1[255 * 64],
resMatmul_local_UB1[255 * 64],
matrix_max_scalar, 225, 1, 1, 8, 8)
tik_instance.data_move(resMatmul[core_n_idx * 129024 + 12288],
resMatmul_local_UB1, 0, 8, 480, 0, 1536)
tik_instance.BuildCCE(kernel_name=kernel_name,
inputs=[input_x1, input_x2, input_x3],
outputs=[resMatmul])
return tik_instance
def CusTranspose02314(input_x, output, kernel_name="transpose021354"):
"""CusTranspose02314"""
input_x_shape = input_x.get("shape")
output_shape = output.get("shape")
perm = (0, 2, 3, 1, 4)
input_x_shape = tuple(input_x_shape)
support_shape = [(32, 128, 7, 7, 16),
(32, 32, 7, 7, 16),
(32, 32, 14, 14, 16),
(32, 64, 14, 14, 16),
(32, 16, 14, 14, 16),
(32, 16, 28, 28, 16),
(32, 32, 28, 28, 16),
(32, 8, 28, 28, 16),
(32, 8, 56, 56, 16),
(32, 16, 56, 56, 16),
(32, 4, 56, 56, 16),
(32, 4, 112, 112, 16)]
if input_x_shape not in support_shape:
raise RuntimeError("input_shape %s is not supported" % str(input_x_shape))
if util.get_product_version() == util.VERSION_MINI:
tik_instance = tik.Tik(tik.Dprofile("v100", "mini"))
else:
tik_instance = tik.Tik(tik.Dprofile("v100", "cloud"))
input_x = tik_instance.Tensor("float16", input_x_shape, name="input_x", scope=tik.scope_gm)
res = tik_instance.Tensor("float16", output_shape, name="res", scope=tik.scope_gm)
dtype = "float16"
if tuple(input_x_shape) == (32, 4, 112, 112, 16):
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
with tik_instance.for_range(0, 14) as cc1_db:
with tik_instance.for_range(0, 2, thread_num=2) as db_idx:
input_1_local_UB = tik_instance.Tensor(dtype, [28672], name="input_1_local_UB",
scope=tik.scope_ubuf)
T_transpose_local_UB = tik_instance.Tensor(dtype, [28672], name="T_transpose_local_UB",
scope=tik.scope_ubuf)
zero = tik_instance.Scalar(dtype="float16", init_value=0)
tik_instance.data_move(input_1_local_UB,
input_x[block_idx * 802816 + cc1_db * 14336 + 7168 * db_idx], 0, 4, 448,
12096, 0)
with tik_instance.for_range(0, 448) as cc7:
with tik_instance.for_range(0, 4) as cc8:
tik_instance.vadds(16, T_transpose_local_UB[cc7 * 64 + cc8 * 16],
input_1_local_UB[7168 * cc8 + cc7 * 16], zero, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 802816 + cc1_db * 57344 + 28672 * db_idx],
T_transpose_local_UB, 0, 1, 1792, 0, 0)
elif tuple(input_x_shape) == (32, 4, 56, 56, 16):
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
zero = tik_instance.Scalar(dtype="float16", init_value=0)
with tik_instance.for_range(0, 3) as cc1_db:
with tik_instance.for_range(0, 2, thread_num=2) as db_idx:
input_1_local_UB = tik_instance.Tensor(dtype, [28672], name="input_1_local_UB",
scope=tik.scope_ubuf)
T_transpose_local_UB = tik_instance.Tensor(dtype, [28672], name="T_transpose_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_1_local_UB,
input_x[block_idx * 200704 + cc1_db * 14336 + 7168 * db_idx], 0, 4, 448,
2688, 0)
with tik_instance.for_range(0, 448) as cc7:
with tik_instance.for_range(0, 4) as cc8:
tik_instance.vadds(16, T_transpose_local_UB[cc7 * 64 + cc8 * 16],
input_1_local_UB[7168 * cc8 + cc7 * 16], zero, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 200704 + cc1_db * 57344 + 28672 * db_idx],
T_transpose_local_UB, 0, 1, 1792, 0, 0)
input_1_local_UB2 = tik_instance.Tensor(dtype, [28672], name="input_1_local_UB2", scope=tik.scope_ubuf)
T_transpose_local_UB2 = tik_instance.Tensor(dtype, [28672], name="T_transpose_local_UB2",
scope=tik.scope_ubuf)
tik_instance.data_move(input_1_local_UB2, input_x[block_idx * 200704 + 43008], 0, 4, 448, 2688, 0)
with tik_instance.for_range(0, 448) as cc72:
with tik_instance.for_range(0, 4) as cc82:
tik_instance.vadds(16, T_transpose_local_UB2[cc72 * 64 + cc82 * 16],
input_1_local_UB2[7168 * cc82 + cc72 * 16], zero, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 200704 + 172032], T_transpose_local_UB2, 0, 1, 1792, 0, 0)
elif tuple(input_x_shape) == (32, 16, 56, 56, 16):
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
zero = tik_instance.Scalar(dtype="float16", init_value=0)
with tik_instance.for_range(0, 14) as cc1_db:
with tik_instance.for_range(0, 2, thread_num=2) as db_idx:
input_1_local_UB = tik_instance.Tensor(dtype, [28672], name="input_1_local_UB",
scope=tik.scope_ubuf)
T_transpose_local_UB = tik_instance.Tensor(dtype, [28672], name="T_transpose_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_1_local_UB,
input_x[block_idx * 802816 + cc1_db * 3584 + 1792 * db_idx], 0, 16, 112,
3024, 0)
with tik_instance.for_range(0, 112) as cc7:
with tik_instance.for_range(0, 16) as cc8:
tik_instance.vadds(16, T_transpose_local_UB[cc7 * 256 + cc8 * 16],
input_1_local_UB[1792 * cc8 + cc7 * 16], zero, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 802816 + cc1_db * 57344 + 28672 * db_idx],
T_transpose_local_UB, 0, 1, 1792, 0, 0)
elif tuple(input_x_shape) == (32, 8, 56, 56, 16):
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
zero = tik_instance.Scalar(dtype="float16", init_value=0)
with tik_instance.for_range(0, 7) as cc1_db:
with tik_instance.for_range(0, 2, thread_num=2) as db_idx:
input_1_local_UB = tik_instance.Tensor(dtype, [28672], name="input_1_local_UB",
scope=tik.scope_ubuf)
T_transpose_local_UB = tik_instance.Tensor(dtype, [28672], name="T_transpose_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_1_local_UB,
input_x[block_idx * 401408 + cc1_db * 7168 + 3584 * db_idx], 0, 8, 224, 2912,
0)
with tik_instance.for_range(0, 224) as cc7:
with tik_instance.for_range(0, 16) as cc8:
tik_instance.vadds(16, T_transpose_local_UB[cc7 * 128 + cc8 * 16],
input_1_local_UB[3584 * cc8 + cc7 * 16], zero, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 401408 + cc1_db * 57344 + 28672 * db_idx],
T_transpose_local_UB, 0, 1, 1792, 0, 0)
elif tuple(input_x_shape) == (32, 8, 28, 28, 16):
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
zero = tik_instance.Scalar(dtype="float16", init_value=0)
with tik_instance.for_range(0, 2) as cc1_db:
with tik_instance.for_range(0, 2, thread_num=2) as db_idx:
input_1_local_UB = tik_instance.Tensor(dtype, [25088], name="input_1_local_UB",
scope=tik.scope_ubuf)
T_transpose_local_UB = tik_instance.Tensor(dtype, [25088], name="T_transpose_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_1_local_UB,
input_x[block_idx * 100352 + cc1_db * 6272 + 3136 * db_idx], 0, 8, 196, 588,
0)
with tik_instance.for_range(0, 196) as cc7:
with tik_instance.for_range(0, 8) as cc8:
tik_instance.vadds(16, T_transpose_local_UB[cc7 * 128 + cc8 * 16],
input_1_local_UB[3136 * cc8 + cc7 * 16], zero, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 100352 + cc1_db * 50176 + 25088 * db_idx],
T_transpose_local_UB, 0, 1, 1568, 0, 0)
elif tuple(input_x_shape) == (32, 32, 28, 28, 16):
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
zero = tik_instance.Scalar(dtype="float16", init_value=0)
with tik_instance.for_range(0, 7) as cc1_db:
with tik_instance.for_range(0, 2, thread_num=2) as db_idx:
input_1_local_UB = tik_instance.Tensor(dtype, [28672], name="input_1_local_UB",
scope=tik.scope_ubuf)
T_transpose_local_UB = tik_instance.Tensor(dtype, [28672], name="T_transpose_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_1_local_UB, input_x[block_idx * 401408 + cc1_db * 1792 + 896 * db_idx],
0, 32, 56, 728, 0)
with tik_instance.for_range(0, 56) as cc7:
with tik_instance.for_range(0, 32) as cc8:
tik_instance.vadds(16, T_transpose_local_UB[cc7 * 512 + cc8 * 16],
input_1_local_UB[896 * cc8 + cc7 * 16], zero, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 401408 + cc1_db * 57344 + 28672 * db_idx],
T_transpose_local_UB, 0, 1, 1792, 0, 0)
elif tuple(input_x_shape) == (32, 16, 28, 28, 16):
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
zero = tik_instance.Scalar(dtype="float16", init_value=0)
with tik_instance.for_range(0, 3) as cc1_db:
with tik_instance.for_range(0, 2, thread_num=2) as db_idx:
input_1_local_UB = tik_instance.Tensor(dtype, [28672], name="input_1_local_UB",
scope=tik.scope_ubuf)
T_transpose_local_UB = tik_instance.Tensor(dtype, [28672], name="T_transpose_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_1_local_UB,
input_x[block_idx * 200704 + cc1_db * 3584 + 1792 * db_idx], 0, 16, 112, 672,
0)
with tik_instance.for_range(0, 112) as cc7:
with tik_instance.for_range(0, 16) as cc8:
tik_instance.vadds(16, T_transpose_local_UB[cc7 * 256 + cc8 * 16],
input_1_local_UB[1792 * cc8 + cc7 * 16], zero, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 200704 + cc1_db * 57344 + 28672 * db_idx],
T_transpose_local_UB, 0, 1, 1792, 0, 0)
input_1_local_UB2 = tik_instance.Tensor(dtype, [28672], name="input_1_local_UB2", scope=tik.scope_ubuf)
T_transpose_local_UB2 = tik_instance.Tensor(dtype, [28672], name="T_transpose_local_UB2",
scope=tik.scope_ubuf)
tik_instance.data_move(input_1_local_UB2, input_x[block_idx * 200704 + 10752], 0, 16, 112, 672, 0)
with tik_instance.for_range(0, 112) as cc7:
with tik_instance.for_range(0, 16) as cc8:
tik_instance.vadds(16, T_transpose_local_UB2[cc7 * 256 + cc8 * 16],
input_1_local_UB2[1792 * cc8 + cc7 * 16], zero, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 200704 + 172032], T_transpose_local_UB2, 0, 1, 1792, 0, 0)
elif tuple(input_x_shape) == (32, 16, 14, 14, 16):
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
zero = tik_instance.Scalar(dtype="float16", init_value=0)
with tik_instance.for_range(0, 2, thread_num=2) as db_idx:
input_1_local_UB = tik_instance.Tensor(dtype, [25088], name="input_1_local_UB", scope=tik.scope_ubuf)
T_transpose_local_UB = tik_instance.Tensor(dtype, [25088], name="T_transpose_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_1_local_UB, input_x[block_idx * 50176 + 1568 * db_idx], 0, 16, 98, 98, 0)
with tik_instance.for_range(0, 98) as cc7:
with tik_instance.for_range(0, 16) as cc8:
tik_instance.vadds(16, T_transpose_local_UB[cc7 * 256 + cc8 * 16],
input_1_local_UB[1568 * cc8 + cc7 * 16], zero, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 50176 + 25088 * db_idx], T_transpose_local_UB, 0, 1, 1568, 0, 0)
elif tuple(input_x_shape) == (32, 128, 7, 7, 16) and tuple(perm) == (0, 2, 3, 1, 4) and dtype == "float16":
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
with tik_instance.for_range(0, 7, thread_num=2) as cc1:
input_x_ub = tik_instance.Tensor(dtype, [1, 128, 1, 7, 16], name="input_1_local_UB",
scope=tik.scope_ubuf)
transpose_ub = tik_instance.Tensor(dtype, [1, 1, 7, 128, 16], name="transpose_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_x_ub, input_x[block_idx, 0, cc1, 0, 0], 0, 128, 7, 42, 0)
with tik_instance.for_range(0, 7) as cc7:
with tik_instance.for_range(0, 128) as cc8:
tik_instance.vadds(16, transpose_ub[0, 0, cc7, cc8, 0], input_x_ub[0, cc8, 0, cc7, 0], 0,
1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 100352 + 14336 * cc1], transpose_ub, 0, 1, 896, 0, 0)
elif tuple(input_x_shape) == (32, 32, 7, 7, 16) and tuple(perm) == (0, 2, 3, 1, 4) and dtype == "float16":
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
input_x_ub = tik_instance.Tensor(dtype, [1, 32, 7, 7, 16], name="input_1_local_UB",
scope=tik.scope_ubuf)
transpose_ub = tik_instance.Tensor(dtype, [1, 7, 7, 32, 16], name="transpose_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_x_ub, input_x[block_idx, 0, 0, 0, 0], 0, 1, 1568, 0, 0)
with tik_instance.for_range(0, 7) as cc1:
with tik_instance.for_range(0, 7) as cc2:
with tik_instance.for_range(0, 32) as cc3:
tik_instance.vadds(16, transpose_ub[0, cc1, cc2, cc3, 0], input_x_ub[0, cc3, cc1, cc2, 0], 0,
1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 25088], transpose_ub, 0, 1, 1568, 0, 0)
elif tuple(input_x_shape) == (32, 32, 14, 14, 16) and tuple(perm) == (0, 2, 3, 1, 4) and dtype == "float16":
def _inner_compute(split_index):
input_x_ub = tik_instance.Tensor(dtype, [1, 32, 2, 14, 16], name="input_1_local_UB",
scope=tik.scope_ubuf)
transpose_ub = tik_instance.Tensor(dtype, [1, 2, 14, 32, 16], name="transpose_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_x_ub, input_x[block_idx, 0, split_index * 2, 0, 0], 0, 32, 28, 168, 0)
with tik_instance.for_range(0, 2) as cc2:
with tik_instance.for_range(0, 14) as cc3:
with tik_instance.for_range(0, 32) as cc4:
tik_instance.vadds(16, transpose_ub[0, cc2, cc3, cc4, 0], input_x_ub[0, cc4, cc2, cc3, 0],
0, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 100352 + split_index * 2 * 7168], transpose_ub, 0, 1, 896, 0, 0)
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
with tik_instance.for_range(0, 6, thread_num=2) as cc1:
_inner_compute(cc1)
_inner_compute(6)
elif tuple(input_x_shape) == (32, 64, 14, 14, 16) and tuple(perm) == (0, 2, 3, 1, 4) and dtype == "float16":
def _inner_compute(split_index, block_idx):
input_x_ub = tik_instance.Tensor(dtype, [1, 64, 2, 14, 16], name="input_1_local_UB",
scope=tik.scope_ubuf)
transpose_ub = tik_instance.Tensor(dtype, [1, 2, 14, 64, 16], name="transpose_local_UB",
scope=tik.scope_ubuf)
tik_instance.data_move(input_x_ub, input_x[block_idx, 0, split_index * 2, 0, 0], 0, 64, 28, 168, 0)
with tik_instance.for_range(0, 2) as cc2:
with tik_instance.for_range(0, 14) as cc3:
with tik_instance.for_range(0, 64) as cc4:
tik_instance.vadds(16, transpose_ub[0, cc2, cc3, cc4, 0], input_x_ub[0, cc4, cc2, cc3, 0],
0, 1, 1, 1, 0, 0)
tik_instance.data_move(res[block_idx * 200704 + split_index * 2 * 14336], transpose_ub, 0, 1, 1792, 0, 0)
with tik_instance.for_range(0, 32, block_num=32) as block_idx:
with tik_instance.for_range(0, 6, thread_num=2) as cc1:
_inner_compute(cc1, block_idx)
_inner_compute(6, block_idx)
tik_instance.BuildCCE(kernel_name, inputs=[input_x], outputs=[res])
return tik_instance
