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75
example/resnet50_imagenet2012_THOR/cus_ops/batch_matmul_impl.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""batch_matmul_impl"""
from mindspore.ops.op_info_register import op_info_register
@op_info_register("""{
"op_name": "CusBatchMatMul",
"imply_type": "TBE",
"fusion_type": "OPAQUE",
"async_flag": false,
"binfile_name": "batchmatmul.so",
"compute_cost": 10,
"kernel_name": "CusBatchMatMul",
"partial_flag": true,
"attr": [
],
"inputs": [
{
"index": 0,
"dtype": [
"float32"
],
"format": [
"DefaultFormat"
],
"name": "x1",
"need_compile": false,
"param_type": "required",
"shape": "all"
},
{
"index": 1,
"dtype": [
"float32"
],
"format": [
"DefaultFormat"
],
"name": "x2",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
],
"outputs": [
{
"index": 0,
"dtype": [
"float32"
],
"format": [
"DefaultFormat"
],
"name": "y",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
]
}""")
def CusBatchMatMul(input_x1, input_x2, output, transpose_a=False, transpose_b=True, kernel_name="batchmatmul"):
"""CusBatchMatMul"""
return

63
example/resnet50_imagenet2012_THOR/cus_ops/cholesky_trsm.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusCholeskyTrsm"""
from mindspore.ops.op_info_register import op_info_register
@op_info_register("""{
"op_name": "CusCholeskyTrsm",
"imply_type": "TBE",
"fusion_type": "OPAQUE",
"async_flag": false,
"binfile_name": "choleskytrsm.so",
"compute_cost": 10,
"kernel_name": "CusCholeskyTrsm",
"partial_flag": true,
"attr": [
],
"inputs": [
{
"index": 0,
"dtype": [
"float32"
],
"format": [
"DefaultFormat"
],
"name": "x1",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
],
"outputs": [
{
"index": 0,
"dtype": [
"float32"
],
"format": [
"DefaultFormat"
],
"name": "y",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
]
}""")
def CusCholeskyTrsm(input_x, output, kernel_name):
"""CusCholeskyTrsm"""
return

37
example/resnet50_imagenet2012_THOR/cus_ops/cus_batch_matmul.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusBatchMatMul"""
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
from mindspore.ops.composite import multitype_ops as C
class CusBatchMatMul(PrimitiveWithInfer):
"""CusMatMulCube definition"""
@prim_attr_register
def __init__(self):
"""init CusMatMulCube"""
self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
def get_bprop(self):
def bprop(x1, x2, out, dout):
return (C.zeros_like(x1), C.zeros_like(x2))
return bprop
def infer_shape(self, data1_shape, data2_shape):
return data1_shape
def infer_dtype(self, data1_dtype, data2_dtype):
return data1_dtype

37
example/resnet50_imagenet2012_THOR/cus_ops/cus_cholesky_trsm.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusCholeskyTrsm"""
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
class CusCholeskyTrsm(PrimitiveWithInfer):
"""CusCholeskyTrsm definition"""
@prim_attr_register
def __init__(self):
"""init CusCholeskyTrsm"""
self.init_prim_io_names(inputs=['x1'], outputs=['y'])
def infer_shape(self, data1_shape):
ll = []
m, _ = data1_shape
if m >= 128:
ll = [m // 128, 128, 128]
else:
ll = [1, 64, 64]
return ll
def infer_dtype(self, data1_dtype):
return data1_dtype

44
example/resnet50_imagenet2012_THOR/cus_ops/cus_fused_abs_max1.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusFusedAbsMax1"""
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
from mindspore.ops.composite import multitype_ops as C
class CusFusedAbsMax1(PrimitiveWithInfer):
"""CusCholeskyTrsm definition"""
@prim_attr_register
def __init__(self, origin_shape=[-1, -1]):
"""init CusCholeskyTrsm"""
self.init_prim_io_names(inputs=['x1'], outputs=['y'])
self.origin_shape = origin_shape
def get_bprop(self):
def bprop(x, out, dout):
return (C.zeros_like(x),)
return bprop
def infer_shape(self, data1_shape):
ll = []
if len(data1_shape) == 2:
ll = [1,]
else:
ll = [32, 64]
return ll
def infer_dtype(self, data1_dtype):
return data1_dtype

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example/resnet50_imagenet2012_THOR/cus_ops/cus_img2col.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusImg2Col"""
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
from mindspore.ops.composite import multitype_ops as C
class CusImg2Col(PrimitiveWithInfer):
"""CusImg2Col definition"""
@prim_attr_register
def __init__(self, ksizes, strides, dilates=(1, 1, 1, 1), mode="NC1HWC0"):
"""init CusImg2Col"""
self.init_prim_io_names(inputs=['x1'], outputs=['y'])
self.ksizes = ksizes
self.strides = strides
self.dilates = dilates
self.mode = mode
def get_bprop(self):
def bprop(x, out, dout):
return (C.zeros_like(x),)
return bprop
def infer_shape(self, data1_shape):
bs, c, h, w = data1_shape
_, stride_h, stride_w, _ = self.strides
_, k_w, k_h, _ = self.ksizes
# assert m == n
c0 = 16
c1 = c // 16
if c1 == 0:
c1 = 1
shape = [bs * int(h // stride_h) * int(w // stride_w), k_w * k_h * c1 * c0]
return shape
def infer_dtype(self, data1_dtype):
return data1_dtype

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example/resnet50_imagenet2012_THOR/cus_ops/cus_matmul_cube.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusMatMulCube"""
import mindspore as ms
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
from mindspore.ops.composite import multitype_ops as C
# y = x^2
class CusMatMulCube(PrimitiveWithInfer):
"""CusMatMulCube definition"""
@prim_attr_register
def __init__(self, transpose_a=False, transpose_b=False):
"""init CusMatMulCube"""
self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
self.transpose_a = transpose_a
self.transpose_b = transpose_b
def get_bprop(self):
def bprop(x1, x2, out, dout):
return (C.zeros_like(x1), C.zeros_like(x2))
return bprop
def infer_shape(self, data1_shape, data2_shape):
# shape = [1, data1_shape[1], data2_shape[2], 16, 16]
# return shape
if self.transpose_a:
k1, m = data1_shape
else:
m, k1 = data1_shape
if self.transpose_b:
n, k2 = data2_shape
else:
k2, n = data2_shape
assert k1 == k2
shape = [m, n]
return shape
def infer_dtype(self, data1_dtype, data2_dtype):
return ms.common.dtype.tensor_type(getattr(ms, "float32"))

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example/resnet50_imagenet2012_THOR/cus_ops/cus_matmul_cube_dense_left.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusMatMulCubeDenseLeft"""
import mindspore as ms
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
from mindspore.ops.composite import multitype_ops as C
# y = x^2
class CusMatMulCubeDenseLeft(PrimitiveWithInfer):
"""CusMatMulCube definition"""
@prim_attr_register
def __init__(self):
"""init CusMatMulCube"""
self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
def get_bprop(self):
def bprop(x1, x2, out, dout):
return (C.zeros_like(x1), C.zeros_like(x2))
return bprop
def infer_shape(self, data1_shape, data2_shape):
return data2_shape
def infer_dtype(self, data1_dtype, data2_dtype):
return ms.common.dtype.tensor_type(getattr(ms, "float16"))

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example/resnet50_imagenet2012_THOR/cus_ops/cus_matmul_cube_fracz_right_mul.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusMatMulCubeFraczRightMul"""
import mindspore as ms
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
from mindspore.ops.composite import multitype_ops as C
class CusMatMulCubeFraczRightMul(PrimitiveWithInfer):
"""CusMatMulCubeFraczRightMul definition"""
@prim_attr_register
def __init__(self):
"""init CusMatMulCubeFraczRightMul"""
self.init_prim_io_names(inputs=['x1', 'x2', 'x3'], outputs=['y'])
def get_bprop(self):
def bprop(x1, x2, x3, out, dout):
return (C.zeros_like(x1), C.zeros_like(x2), C.zeros_like(x3))
return bprop
def infer_shape(self, data1_shape, data2_shape, data3_shape):
return data1_shape
def infer_dtype(self, data1_dtype, data2_dtype, data3_dtype):
return ms.common.dtype.tensor_type(getattr(ms, "float32"))

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example/resnet50_imagenet2012_THOR/cus_ops/cus_matrix_combine.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusMatrixCombine"""
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
from mindspore.ops.composite import multitype_ops as C
class CusMatrixCombine(PrimitiveWithInfer):
"""CusMatMulCube definition"""
@prim_attr_register
def __init__(self):
"""init CusMatMulCube"""
self.init_prim_io_names(inputs=['x'], outputs=['y'])
def get_bprop(self):
def bprop(x, out, dout):
return (C.zeros_like(x),)
return bprop
def infer_shape(self, data_shape):
a, b, c = data_shape
shape = [a * b, a * c]
return shape
def infer_dtype(self, data_dtype):
return data_dtype

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example/resnet50_imagenet2012_THOR/cus_ops/cus_transpose02314.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusTranspose02314"""
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
from mindspore.ops.composite import multitype_ops as C
class CusTranspose02314(PrimitiveWithInfer):
"""CusTranspose02314 definition"""
@prim_attr_register
def __init__(self):
"""init CusTranspose02314"""
self.init_prim_io_names(inputs=['x1'], outputs=['y'])
def get_bprop(self):
def bprop(x, out, dout):
return (C.zeros_like(x),)
return bprop
def infer_shape(self, data1_shape):
assert len(data1_shape) == 4
n, c, h, w = data1_shape
c0 = 16
c1 = c // 16
shape = (n * h * w, c1 * c0)
return shape
def infer_dtype(self, data1_dtype):
return data1_dtype

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example/resnet50_imagenet2012_THOR/cus_ops/fused_abs_max1.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusFusedAbsMax1"""
from mindspore.ops.op_info_register import op_info_register
@op_info_register("""{
"op_name": "CusFusedAbsMax1",
"imply_type": "TBE",
"fusion_type": "OPAQUE",
"async_flag": false,
"binfile_name": "fusedabsmax1.so",
"compute_cost": 10,
"kernel_name": "CusFusedAbsMax1",
"partial_flag": true,
"attr": [
{
"name": "origin_shape",
"param_type": "required",
"type": "listInt",
"value": "all"
}
],
"inputs": [
{
"index": 0,
"dtype": [
"float32"
],
"format": [
"DefaultFormat"
],
"name": "x1",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
],
"outputs": [
{
"index": 0,
"dtype": [
"float32"
],
"format": [
"DefaultFormat"
],
"name": "y",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
]
}""")
def CusFusedAbsMax1(input_x, output, origin_shape=None, kernel_name="fused_abs_max1"):
"""CusFusedAbsMax1"""
return

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example/resnet50_imagenet2012_THOR/cus_ops/img2col_impl.py
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusImg2ColNC1HWC0"""
from mindspore.ops.op_info_register import op_info_register
@op_info_register("""{
"op_name": "CusImg2ColNC1HWC0",
"imply_type": "TBE",
"fusion_type": "OPAQUE",
"async_flag": false,
"binfile_name": "img2colnc1hwc0.so",
"compute_cost": 10,
"kernel_name": "CusImg2ColNC1HWC0",
"partial_flag": true,
"attr": [
{
"name": "ksizes",
"param_type": "required",
"type": "listInt",
"value": "all"
},
{
"name": "strides",
"param_type": "required",
"type": "listInt",
"value": "all"
},
{
"name": "dilates",
"param_type": "required",
"type": "listInt",
"value": "all"
},
{
"name": "padding",
"param_type": "required",
"type": "str",
"value": "all"
}
],
"inputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"NC1HWC0"
],
"name": "x1",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
],
"outputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"FRACTAL_NZ"
],
"name": "y",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
]
}""")
def CusImg2ColNC1HWC0(input_x, output, ksizes, strides, dilates, padding, kernel_name="img2col"):
"""CusImg2ColNC1HWC0"""
return

102
example/resnet50_imagenet2012_THOR/cus_ops/matmul_cube_dense_left.py
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
copyright 2020 Huawei Technologies Co., Ltd
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License == distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
matmul
"""
from __future__ import absolute_import
from mindspore.ops.op_info_register import op_info_register
from topi.cce import util
# General limitation of the size for input shape: 2**31
SHAPE_SIZE_LIMIT = 2147483648
NoneType = type(None)
@op_info_register("""{
"op_name": "CusMatMulCubeDenseLeft",
"imply_type": "TBE",
"fusion_type": "OPAQUE",
"async_flag": false,
"binfile_name": "matmulcubedenseleft.so",
"compute_cost": 10,
"kernel_name": "CusMatMulCubeDenseLeft",
"partial_flag": true,
"attr": [
],
"inputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"DefaultFormat"
],
"name": "x1",
"need_compile": false,
"param_type": "required",
"shape": "all"
},
{
"index": 1,
"dtype": [
"float16"
],
"format": [
"FRACTAL_NZ"
],
"name": "x2",
"need_compile": false,
"param_type": "required",
"shape": "all"
},
{
"index": 2,
"dtype": [
"float16"
],
"format": [
"DefaultFormat"
],
"name": "x3",
"need_compile": false,
"param_type": "optional",
"shape": "all"
}
],
"outputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"FRACTAL_NZ"
],
"name": "y",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
]
}""")
@util.check_input_type(dict, dict, (dict, NoneType), dict, bool, bool, str)
def CusMatMulCubeDenseLeft(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False,
kernel_name="matmulcube"):
"""CusMatMulCubeDenseLeft"""
return

103
example/resnet50_imagenet2012_THOR/cus_ops/matmul_cube_fracz_left_cast_impl.py
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@ -1,103 +0,0 @@
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
copyright 2020 Huawei Technologies Co., Ltd
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License == distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
matmul
"""
from __future__ import absolute_import
from mindspore.ops.op_info_register import op_info_register
from topi.cce import util
# General limitation of the size for input shape: 2**31
SHAPE_SIZE_LIMIT = 2147483648
NoneType = type(None)
@op_info_register("""{
"op_name": "CusMatMulCubeFraczLeftCast",
"imply_type": "TBE",
"fusion_type": "OPAQUE",
"async_flag": false,
"binfile_name": "matmulcubefraczleftcast.so",
"compute_cost": 10,
"kernel_name": "CusMatMulCubeFraczLeftCast",
"partial_flag": true,
"attr": [
],
"inputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"DefaultFormat"
],
"name": "x1",
"need_compile": false,
"param_type": "required",
"shape": "all"
},
{
"index": 1,
"dtype": [
"float32"
],
"format": [
"FracZ"
],
"name": "x2",
"need_compile": false,
"param_type": "required",
"shape": "all"
},
{
"index": 2,
"dtype": [
"float16"
],
"format": [
"DefaultFormat"
],
"name": "x3",
"need_compile": false,
"param_type": "optional",
"shape": "all"
}
],
"outputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"FracZ"
],
"name": "y",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
]
}""")
# pylint: disable=locally-disabled,too-many-arguments, too-many-locals, too-many-statements
@util.check_input_type(dict, dict, (dict, NoneType), dict, bool, bool, str)
def CusMatMulCubeFraczLeftCast(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False,
kernel_name="CusMatMulCubeFraczLeftCast"):
"""CusMatMulCubeFraczLeftCast"""
return

113
example/resnet50_imagenet2012_THOR/cus_ops/matmul_cube_fracz_right_mul_impl.py
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@ -1,113 +0,0 @@
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
copyright 2020 Huawei Technologies Co., Ltd
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License == distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
matmul
"""
from __future__ import absolute_import
from mindspore.ops.op_info_register import op_info_register
# General limitation of the size for input shape: 2**31
SHAPE_SIZE_LIMIT = 2147483648
NoneType = type(None)
@op_info_register("""{
"op_name": "CusMatMulCubeFraczRightMul",
"imply_type": "TBE",
"fusion_type": "OPAQUE",
"async_flag": false,
"binfile_name": "matmulcubefraczrightmul.so",
"compute_cost": 10,
"kernel_name": "CusMatMulCubeFraczRightMul",
"partial_flag": true,
"attr": [
],
"inputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"FracZ"
],
"name": "x1",
"need_compile": false,
"param_type": "required",
"shape": "all"
},
{
"index": 1,
"dtype": [
"float16"
],
"format": [
"DefaultFormat"
],
"name": "x2",
"need_compile": false,
"param_type": "required",
"shape": "all"
},
{
"index": 2,
"dtype": [
"float32"
],
"format": [
"DefaultFormat"
],
"name": "x3",
"need_compile": false,
"param_type": "required",
"shape": "all"
},
{
"index": 3,
"dtype": [
"float16"
],
"format": [
"DefaultFormat"
],
"name": "x4",
"need_compile": false,
"param_type": "optional",
"shape": "all"
}
],
"outputs": [
{
"index": 0,
"dtype": [
"float32"
],
"format": [
"FracZ"
],
"name": "y",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
]
}""")
def CusMatMulCubeFraczRightMul(input_x1, input_x2, input_x3, bias=None, output_y={}, trans_a=False, trans_b=False,
kernel_name="matmulcube"):
"""CusMatMulCubeFraczRightMul"""
return

114
example/resnet50_imagenet2012_THOR/cus_ops/matmul_cube_impl.py
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@ -1,114 +0,0 @@
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
copyright 2020 Huawei Technologies Co., Ltd
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License == distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
matmul
"""
from __future__ import absolute_import
from mindspore.ops.op_info_register import op_info_register
from topi.cce import util
# General limitation of the size for input shape: 2**31
SHAPE_SIZE_LIMIT = 2147483648
NoneType = type(None)
@op_info_register("""{
"op_name": "CusMatMulCube",
"imply_type": "TBE",
"fusion_type": "OPAQUE",
"async_flag": false,
"binfile_name": "matmulcube.so",
"compute_cost": 10,
"kernel_name": "CusMatMulCube",
"partial_flag": true,
"attr": [
{
"name": "transpose_a",
"param_type": "required",
"type": "bool",
"value": "all"
},
{
"name": "transpose_b",
"param_type": "required",
"type": "bool",
"value": "all"
}
],
"inputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"FRACTAL_NZ"
],
"name": "x1",
"need_compile": false,
"param_type": "required",
"shape": "all"
},
{
"index": 1,
"dtype": [
"float16"
],
"format": [
"FRACTAL_NZ"
],
"name": "x2",
"need_compile": false,
"param_type": "required",
"shape": "all"
},
{
"index": 2,
"dtype": [
"float16"
],
"format": [
"DefaultFormat"
],
"name": "x3",
"need_compile": false,
"param_type": "optional",
"shape": "all"
}
],
"outputs": [
{
"index": 0,
"dtype": [
"float32"
],
"format": [
"FRACTAL_NZ"
],
"name": "y",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
]
}""")
# pylint: disable=locally-disabled,too-many-arguments, too-many-locals, too-many-statements
@util.check_input_type(dict, dict, (dict, NoneType), dict, bool, bool, str)
def CusMatMulCube(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False, kernel_name="matmulcube"):
"""CusMatMulCube"""
return

63
example/resnet50_imagenet2012_THOR/cus_ops/matrix_combine_impl.py
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@ -1,63 +0,0 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusMatrixCombine"""
from mindspore.ops.op_info_register import op_info_register
@op_info_register("""{
"op_name": "CusMatrixCombine",
"imply_type": "TBE",
"fusion_type": "OPAQUE",
"async_flag": false,
"binfile_name": "matrixcombine.so",
"compute_cost": 10,
"kernel_name": "CusMatrixCombine",
"partial_flag": true,
"attr": [
],
"inputs": [
{
"index": 0,
"dtype": [
"float32"
],
"format": [
"DefaultFormat"
],
"name": "x1",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
],
"outputs": [
{
"index": 0,
"dtype": [
"float32"
],
"format": [
"DefaultFormat"
],
"name": "y",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
]
}""")
def CusMatrixCombine(input_x, output, kernel_name="matrix_combine"):
"""CusMatrixCombine"""
return

63
example/resnet50_imagenet2012_THOR/cus_ops/transpose02314_impl.py
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@ -1,63 +0,0 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""CusTranspose02314"""
from mindspore.ops.op_info_register import op_info_register
@op_info_register("""{
"op_name": "CusTranspose02314",
"imply_type": "TBE",
"fusion_type": "OPAQUE",
"async_flag": false,
"binfile_name": "transpose02314.so",
"compute_cost": 10,
"kernel_name": "CusTranspose02314",
"partial_flag": true,
"attr": [
],
"inputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"NC1HWC0"
],
"name": "x1",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
],
"outputs": [
{
"index": 0,
"dtype": [
"float16"
],
"format": [
"DefaultFormat"
],
"name": "y",
"need_compile": false,
"param_type": "required",
"shape": "all"
}
]
}""")
def CusTranspose02314(input_x, output, kernel_name="transpose021354"):
"""CusTranspose02314"""
return