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494 lines
17 KiB
494 lines
17 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""thor_ops"""
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from mindspore.ops import prim_attr_register, PrimitiveWithInfer
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from mindspore.ops.composite import multitype_ops as C
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import mindspore as ms
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__all__ = ["CusBatchMatMul",
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"CusCholeskyTrsm",
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"CusFusedAbsMax1",
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"CusImg2Col",
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"CusMatMulCubeDenseLeft",
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"CusMatMulCubeFraczRightMul",
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"CusMatMulCube",
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"CusMatrixCombine",
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"CusTranspose02314",
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"CusMatMulCubeDenseRight",
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"CusMatMulCubeFraczLeftCast",
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]
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class CusBatchMatMul(PrimitiveWithInfer):
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"""
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Multiplies matrix `a` by matrix `b` in batch.
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The rank of input tensors must be `3`.
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Inputs:
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- **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N, D, D)`.
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- **input_y** (Tensor) - The second tensor to be multiplied. The shape of the tensor is :math:`(N, D, D)`. If
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`transpose_b` is True.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(N, D, D)`.
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Examples:
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>>> input_x = Tensor(np.ones(shape=[2, 128, 128]), mindspore.float32)
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>>> input_y = Tensor(np.ones(shape=[2, 128, 128]), mindspore.float32)
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>>> cus_batch_matmul = P.CusBatchMatMul()
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>>> output = cus_batch_matmul(input_x, input_y)
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"""
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@prim_attr_register
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def __init__(self):
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"""init CusBatchMatMul"""
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self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
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from mindspore.ops._op_impl._custom_op.batch_matmul_impl import CusBatchMatMul
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def get_bprop(self):
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def bprop(x1, x2, out, dout):
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return (C.zeros_like(x1), C.zeros_like(x2))
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return bprop
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def infer_shape(self, data1_shape, data2_shape):
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return data1_shape
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def infer_dtype(self, data1_dtype, data2_dtype):
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return data1_dtype
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class CusCholeskyTrsm(PrimitiveWithInfer):
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"""
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L * LT = A.
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LT * (LT)^-1 = I.
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return (LT)^-1.
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Only compute the res of the diag part of input matrix with dim 128.
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The rank of input tensors must be `2`.
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Inputs:
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- **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N, N)`.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(N // Split_dim, Split_dim, Split_dim)`.
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Examples:
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>>> input_x = Tensor(np.ones(shape=[256, 256]), mindspore.float32)
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>>> cus_choleskytrsm = P.CusCholeskyTrsm()
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>>> output = matmul(input_x)
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"""
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@prim_attr_register
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def __init__(self):
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"""init CusCholeskyTrsm"""
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self.init_prim_io_names(inputs=['x1'], outputs=['y'])
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from mindspore.ops._op_impl._custom_op.cholesky_trsm_impl import CusCholeskyTrsm
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def infer_shape(self, data1_shape):
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ll = []
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m, _ = data1_shape
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if m >= 128:
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ll = [m // 128, 128, 128]
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else:
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ll = [1, 64, 64]
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return ll
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def infer_dtype(self, data1_dtype):
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return data1_dtype
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class CusFusedAbsMax1(PrimitiveWithInfer):
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"""
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Compute the abs max of Tensor input.
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The rank of input tensors must be `4` or `2`.
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Inputs:
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- **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N0, M0, N1, M1)`
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or math:`(32, 64)`.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(32, 64)` or math:`(1, )`.
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Examples:
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>>> input_x = Tensor(np.ones(shape=[1, 3]), mindspore.float32)
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>>> cus_fused_abs_max1 = P.CusFusedAbsMax1()
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>>> output = cus_fused_abs_max1(input_x)
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"""
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@prim_attr_register
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def __init__(self, origin_shape=[-1, -1]):
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"""init CusFusedAbsMax1"""
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self.init_prim_io_names(inputs=['x1'], outputs=['y'])
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self.origin_shape = origin_shape
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from mindspore.ops._op_impl._custom_op.fused_abs_max1_impl import CusFusedAbsMax1
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def get_bprop(self):
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def bprop(x, out, dout):
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return (C.zeros_like(x),)
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return bprop
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def infer_shape(self, data1_shape):
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ll = []
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if len(data1_shape) == 2:
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ll = [1,]
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else:
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ll = [32, 64]
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return ll
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def infer_dtype(self, data1_dtype):
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return data1_dtype
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class CusImg2Col(PrimitiveWithInfer):
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"""
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Img2col the feature map and the result in reorganized in NC1HWC0.
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Args:
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- **strides** (listInt) - the stride of the ops.
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- **ksizes** (listInt) - the kernel size of the ops.
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Inputs:
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- **input_x** (Tensor) - The shape of the tensor is :math:`(N, C, H, W)`.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(N * H_O * W_O, C1 * K_W * K_H * C0)`.
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Examples:
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>>> input_x = Tensor(np.ones(shape=[32, 3, 224, 224]), mindspore.float16)
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>>> cusimg2col = P.CusImg2Col()
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>>> output = cusimg2col(input_x)
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"""
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@prim_attr_register
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def __init__(self, ksizes, strides, dilates=(1, 1, 1, 1), mode="NC1HWC0"):
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"""init CusImg2Col"""
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self.init_prim_io_names(inputs=['x1'], outputs=['y'])
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self.ksizes = ksizes
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self.strides = strides
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self.dilates = dilates
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self.mode = mode
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from mindspore.ops._op_impl._custom_op.img2col_impl import CusImg2Col
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def get_bprop(self):
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def bprop(x, out, dout):
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return (C.zeros_like(x),)
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return bprop
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def infer_shape(self, data1_shape):
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bs, c, h, w = data1_shape
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_, stride_h, stride_w, _ = self.strides
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_, k_w, k_h, _ = self.ksizes
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# assert m == n
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c0 = 16
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c1 = c // 16
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if c1 == 0:
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c1 = 1
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shape = [bs * int(h // stride_h) * int(w // stride_w), k_w * k_h * c1 * c0]
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return shape
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def infer_dtype(self, data1_dtype):
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return data1_dtype
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class CusMatMulCubeDenseLeft(PrimitiveWithInfer):
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"""
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Multiplies matrix `a` by matrix `b`.
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The rank of input_x1 must be `4`, the fractal format of the normal matrix.
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The rank of input_x2 must be `2`.
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Inputs:
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- **input_x1** (Tensor) - The first tensor to be multiplied.
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The shape of the tensor is :math:`(N0, M0, N1, M1)`.
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- **input_x2** (Tensor) - The second tensor to be multiplied. The shape of the tensor is :math:`(M, C)`.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(N, C)`.
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Examples:
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>>> input_x = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16)
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>>> input_y = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
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>>> matmulcubedenseleft = P.CusMatMulCubeDenseLeft()
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>>> output = matmulcubedenseleft(input_x, input_y)
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"""
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@prim_attr_register
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def __init__(self):
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"""init CusMatMulCubeDenseLeft"""
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self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
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from mindspore.ops._op_impl._custom_op.matmul_cube_dense_left_impl import CusMatMulCubeDenseLeft
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def get_bprop(self):
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def bprop(x1, x2, out, dout):
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return (C.zeros_like(x1), C.zeros_like(x2))
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return bprop
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def infer_shape(self, data1_shape, data2_shape):
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return data2_shape
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def infer_dtype(self, data1_dtype, data2_dtype):
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return ms.common.dtype.tensor_type(getattr(ms, "float16"))
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class CusMatMulCubeFraczRightMul(PrimitiveWithInfer):
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"""
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Multiplies matrix `a` by matrix `b` and muls the result by scalar `c`.
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The rank of input_x1 tensors must be `2`.
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The rank of input_x2 tensors must be `4`.
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Inputs:
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- **input_x1** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N, C)`.
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- **input_x2** (Tensor) - The second tensor to be multiplied.
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The shape of the tensor is :math:`(C1, M1, C0, M0)`.
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- **input_x3** (Tensor) - The third tensor to be multiplied. The shape of the tensor if :math`(1, )`.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(N, M)`.
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Examples:
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>>> input_x1 = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
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>>> input_x2 = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16)
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>>> input_x3 = Tensor(np.ones(shape=[1, ]), mindspore.float16)
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>>> cusmatmulfraczrightmul = P.CusMatMulCubeFraczRightMul()
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>>> output = cusmatmulfraczrightmul(input_x1, input_x2, input_x3)
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"""
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@prim_attr_register
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def __init__(self):
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"""init CusMatMulCubeFraczRightMul"""
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self.init_prim_io_names(inputs=['x1', 'x2', 'x3'], outputs=['y'])
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from mindspore.ops._op_impl._custom_op.matmul_cube_fracz_right_mul_impl import CusMatMulCubeFraczRightMul
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def get_bprop(self):
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def bprop(x1, x2, x3, out, dout):
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return (C.zeros_like(x1), C.zeros_like(x2), C.zeros_like(x3))
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return bprop
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def infer_shape(self, data1_shape, data2_shape, data3_shape):
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return data1_shape
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def infer_dtype(self, data1_dtype, data2_dtype, data3_dtype):
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return ms.common.dtype.tensor_type(getattr(ms, "float32"))
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class CusMatMulCube(PrimitiveWithInfer):
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"""
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Multiplies matrix `a` by matrix `b`.
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The rank of input tensors must be `2`.
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Args:
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transpose_a (bool): If True, `a` is transposed before multiplication. Default: False.
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transpose_b (bool): If True, `b` is transposed before multiplication. Default: False.
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Inputs:
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- **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N, C)`. If
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`transpose_a` is True, its shape should be :math:`(N, C)` after transposing.
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- **input_y** (Tensor) - The second tensor to be multiplied. The shape of the tensor is :math:`(C, M)`. If
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`transpose_b` is True, its shape should be :math:`(C, M)` after transpose.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(N, M)`.
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Examples:
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>>> input_x = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
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>>> input_y = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
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>>> cusmatmulcube = P.CusMatMulCube()
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>>> output = matmul(input_x, input_y)
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"""
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@prim_attr_register
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def __init__(self, transpose_a=False, transpose_b=False):
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"""init CusMatMulCube"""
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self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
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self.transpose_a = transpose_a
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self.transpose_b = transpose_b
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from mindspore.ops._op_impl._custom_op.matmul_cube_impl import CusMatMulCube
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def get_bprop(self):
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def bprop(x1, x2, out, dout):
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return (C.zeros_like(x1), C.zeros_like(x2))
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return bprop
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def infer_shape(self, data1_shape, data2_shape):
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# shape = [1, data1_shape[1], data2_shape[2], 16, 16]
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# return shape
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if self.transpose_a:
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k1, m = data1_shape
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else:
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m, k1 = data1_shape
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if self.transpose_b:
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n, k2 = data2_shape
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else:
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k2, n = data2_shape
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assert k1 == k2
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shape = [m, n]
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return shape
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def infer_dtype(self, data1_dtype, data2_dtype):
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return ms.common.dtype.tensor_type(getattr(ms, "float32"))
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class CusMatrixCombine(PrimitiveWithInfer):
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"""
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move the batch matrix to result matrix diag part.
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The rank of input tensors must be `3`.
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Inputs:
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- **input_x** (Tensor) - The shape of the tensor is :math:`(N, D, D)`.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(N * D, N * D)`.
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Examples:
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>>> input_x = Tensor(np.ones(shape=[2, 128, 128]), mindspore.float32)
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>>> cusmatrixcombine = P.CusMatrixCombine()
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>>> output = cusmatrixcombine(input_x)
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"""
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@prim_attr_register
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def __init__(self):
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"""init CusMatrixCombine"""
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self.init_prim_io_names(inputs=['x'], outputs=['y'])
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from mindspore.ops._op_impl._custom_op.matrix_combine_impl import CusMatrixCombine
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def get_bprop(self):
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def bprop(x, out, dout):
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return (C.zeros_like(x),)
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return bprop
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def infer_shape(self, data_shape):
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a, b, c = data_shape
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shape = [a * b, a * c]
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return shape
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def infer_dtype(self, data_dtype):
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return data_dtype
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class CusTranspose02314(PrimitiveWithInfer):
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"""
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Permute input tensor with perm (0, 2, 3, 1, 4)
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The rank of input tensors must be `5` with format NC1HWC0.
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Inputs:
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- **input_x** (Tensor) - The shape of the tensor is :math:`(N, C1, H, W, C0)`.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(N, H, W, C1, C0)`.
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Examples:
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>>> input_x = Tensor(np.ones(shape=[32, 1, 224, 224, 16]), mindspore.float16)
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>>> custranspose02314 = P.CusTranspose02314()
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>>> output = custranspose02314(input_x)
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"""
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@prim_attr_register
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def __init__(self):
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"""init CusTranspose02314"""
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self.init_prim_io_names(inputs=['x1'], outputs=['y'])
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from mindspore.ops._op_impl._custom_op.transpose02314_impl import CusTranspose02314
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def get_bprop(self):
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def bprop(x, out, dout):
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return (C.zeros_like(x),)
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return bprop
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def infer_shape(self, data1_shape):
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assert len(data1_shape) == 4
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n, c, h, w = data1_shape
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c0 = 16
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c1 = c // 16
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shape = (n * h * w, c1 * c0)
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return shape
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def infer_dtype(self, data1_dtype):
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return data1_dtype
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class CusMatMulCubeDenseRight(PrimitiveWithInfer):
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"""
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Multiplies matrix `a` by matrix `b`.
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The rank of input_x1 tensor must be `2`.
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The rank of input_x2 tensor must be `4`.
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Inputs:
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- **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(N, C)`.
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- **input_y** (Tensor) - The second tensor to be multiplied.
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The shape of the tensor is :math:`(C1, M1, M0, C0)`.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(N, M)`.
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Examples:
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>>> input_x = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
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>>> input_y = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16)
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>>> cusmatmulcubedenseright = P.CusMatMulCubeDenseRight()
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>>> output = cusmatmulcubedenseright(input_x, input_y)
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"""
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@prim_attr_register
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def __init__(self):
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"""init CusMatMulCubeDenseRight"""
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self.init_prim_io_names(inputs=['x1', 'x2', 'x3'], outputs=['y'])
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from mindspore.ops._op_impl._custom_op.matmul_cube_dense_right_impl import CusMatMulCubeDenseRight
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def get_bprop(self):
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def bprop(x1, x2, x3, out, dout):
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return (C.zeros_like(x1), C.zeros_like(x2), C.zeros_like(x3))
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return bprop
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def infer_shape(self, data1_shape, data2_shape, data3_shape):
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return data1_shape
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def infer_dtype(self, data1_dtype, data2_dtype, data3_dtype):
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return ms.common.dtype.tensor_type(getattr(ms, "float32"))
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class CusMatMulCubeFraczLeftCast(PrimitiveWithInfer):
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"""
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Multiplies matrix `a` by matrix `b`.
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The rank of input_x1 tensor must be `4`.
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The rank of input_x2 tensors must be `2`.
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Inputs:
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- **input_x1** (Tensor) - The first tensor to be multiplied.
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The shape of the tensor is :math:`(C1, N1, N0, C0)`.
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- **input_x2** (Tensor) - The second tensor to be multiplied. The shape of the tensor is :math:`(C, M)`.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(N, M)`.
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Examples:
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>>> input_x = Tensor(np.ones(shape=[16, 16, 16, 16]), mindspore.float16)
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>>> input_y = Tensor(np.ones(shape=[256, 256]), mindspore.float16)
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>>> cusmatmulcubefraczleftcast = P.CusMatMulCubeFraczLeftCast()
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>>> output = cusmatmulcubefraczleftcast(input_x, input_y)
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"""
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@prim_attr_register
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def __init__(self):
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"""init CusMatMulCubeFraczLeftCast"""
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self.init_prim_io_names(inputs=['x1', 'x2'], outputs=['y'])
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from mindspore.ops._op_impl._custom_op.matmul_cube_fracz_left_cast_impl import CusMatMulCubeFraczLeftCast
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def get_bprop(self):
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def bprop(x1, x2, out, dout):
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return (C.zeros_like(x1), C.zeros_like(x2))
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return bprop
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def infer_shape(self, data1_shape, data2_shape):
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return data2_shape
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def infer_dtype(self, data1_dtype, data2_dtype):
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return ms.common.dtype.tensor_type(getattr(ms, "float16"))
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