forked from huawei/mindspore2022
676 lines
24 KiB
Python
676 lines
24 KiB
Python
# Copyright 2020-2021 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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import math
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from ..primitive import prim_attr_register, PrimitiveWithInfer
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from ...common import dtype as mstype
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from ..._checkparam import Validator as validator
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from ..operations.nn_ops import _check_positive_int_or_tuple
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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 = ops.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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"""Initialize 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 cus_batch_matmul
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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 = ops.CusCholeskyTrsm()
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>>> output = cus_choleskytrsm(input_x)
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"""
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@prim_attr_register
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def __init__(self):
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"""Initialize 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 cus_cholesky_trsm
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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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Computes 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 = ops.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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"""Initialize 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 cus_fused_abs_max1
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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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Img2cols 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 = ops.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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"""Initialize 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 cus_img2col
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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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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 = ops.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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"""Initialize 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 cus_matmul_cube_dense_left
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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 mstype.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 = ops.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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"""Initialize 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 cus_matmul_cube_fraczrightmul
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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 mstype.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 must 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 must 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 = ops.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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"""Initialize 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 cus_matmul_cube
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def infer_shape(self, data1_shape, data2_shape):
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if self.transpose_a:
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_, m = data1_shape
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else:
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m, _ = data1_shape
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if self.transpose_b:
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n, _ = data2_shape
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else:
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_, n = data2_shape
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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 mstype.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 = ops.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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"""Initialize 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 cus_matrix_combine
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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 = ops.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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"""Initialize 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 cus_transpose02314
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def get_bprop(self):
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"""Get backprop for CusTranspose02314."""
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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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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 = ops.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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"""Initialize 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 cus_matmul_cube_dense_right
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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 mstype.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 = ops.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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"""Initialize 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 cus_matmul_cube_fraczleftcast
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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 mstype.float16
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class Im2Col(PrimitiveWithInfer):
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"""
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extracts image paths from image.
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The rank of input_x1 must be `4`, data_format is "NCHW".
|
|
|
|
Inputs:
|
|
- **input_x1** (Tensor) - The feature map.
|
|
The shape of the tensor is :math:`(N, C, H, W)`.
|
|
Outputs:
|
|
Tensor.
|
|
Examples:
|
|
>>> input_x = Tensor(np.random.rand(32, 3, 224, 224).astype(np.float16))
|
|
>>> img2col = ops.CusMatMulCubeDenseLeft(kernel_size=7, pad=3, stride=2)
|
|
>>> output = img2col(input_x)
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|
"""
|
|
|
|
@prim_attr_register
|
|
def __init__(self,
|
|
kernel_size,
|
|
pad_mode="valid",
|
|
pad=0,
|
|
stride=1,
|
|
dilation=1):
|
|
"""Initialize Im2Col"""
|
|
self.init_prim_io_names(inputs=['x'], outputs=['output'])
|
|
self.kernel_size = _check_positive_int_or_tuple('kernel_size', kernel_size, self.name)
|
|
self.add_prim_attr('kernel_size', self.kernel_size)
|
|
self.stride = _check_positive_int_or_tuple('stride', stride, self.name, allow_four=True, ret_four=True)
|
|
self.add_prim_attr('stride', self.stride)
|
|
self.dilation = _check_positive_int_or_tuple('dilation', dilation, self.name, allow_four=True, ret_four=True)
|
|
self.add_prim_attr('dilation', self.dilation)
|
|
validator.check_value_type('pad', pad, (int,), self.name)
|
|
self.pad_mode = validator.check_string(pad_mode, ['valid', 'same', 'pad'], 'pad_mode', self.name)
|
|
self.pad = validator.check_pad_value_by_mode(pad_mode, pad, self.name)
|
|
if self.pad_mode == 'pad':
|
|
validator.check_non_negative_int(self.pad, 'pad', self.name)
|
|
self.add_prim_attr('data_format', "NCHW")
|
|
|
|
def infer_shape(self, x_shape):
|
|
validator.check_equal_int(len(x_shape), 4, "x rank", self.name)
|
|
kernel_size_h = self.kernel_size[0]
|
|
kernel_size_w = self.kernel_size[1]
|
|
stride_h = self.stride[2]
|
|
stride_w = self.stride[3]
|
|
dilation_h = self.dilation[2]
|
|
dilation_w = self.dilation[3]
|
|
if self.pad_mode == "valid":
|
|
h_out = math.ceil((x_shape[2] - dilation_h * (kernel_size_h - 1)) / stride_h)
|
|
w_out = math.ceil((x_shape[3] - dilation_w * (kernel_size_w - 1)) / stride_w)
|
|
pad_top, pad_bottom, pad_left, pad_right = 0, 0, 0, 0
|
|
elif self.pad_mode == "same":
|
|
h_out = math.ceil(x_shape[2] / stride_h)
|
|
w_out = math.ceil(x_shape[3] / stride_w)
|
|
pad_needed_h = max(0, (h_out - 1) * stride_h + dilation_h * (kernel_size_h - 1) + 1 - x_shape[2])
|
|
pad_top = math.floor(pad_needed_h / 2)
|
|
pad_bottom = pad_needed_h - pad_top
|
|
pad_needed_w = max(0, (w_out - 1) * stride_w + dilation_w * (kernel_size_w - 1) + 1 - x_shape[3])
|
|
pad_left = math.floor(pad_needed_w / 2)
|
|
pad_right = pad_needed_w - pad_left
|
|
elif self.pad_mode == 'pad':
|
|
pad_top, pad_bottom, pad_left, pad_right = self.pad, self.pad, self.pad, self.pad
|
|
h_out = 1 + (x_shape[2] + 2 * self.pad - kernel_size_h - (kernel_size_h - 1) * (dilation_h - 1)) / stride_h
|
|
w_out = 1 + (x_shape[3] + 2 * self.pad - kernel_size_w - (kernel_size_w - 1) * (dilation_w - 1)) / stride_w
|
|
h_out = math.floor(h_out)
|
|
w_out = math.floor(w_out)
|
|
self.pad_list = [pad_top, pad_bottom, pad_left, pad_right]
|
|
self.add_prim_attr('pad_list', (pad_top, pad_bottom, pad_left, pad_right))
|
|
batch_size = x_shape[0]
|
|
channel = x_shape[1]
|
|
k_h = kernel_size_h
|
|
k_w = kernel_size_w
|
|
out_shape = [channel, k_h, k_w, batch_size, h_out, w_out]
|
|
return out_shape
|
|
|
|
def infer_dtype(self, x_dtype):
|
|
valid_dtypes = [mstype.float16, mstype.float32]
|
|
validator.check_tensor_dtype_valid('x', x_dtype, valid_dtypes, self.name)
|
|
return x_dtype
|
|
|
|
|
|
class UpdateThorGradient(PrimitiveWithInfer):
|
|
"""
|
|
Updates Thor Gradient with Approximate Fisher info matrix(for GPU backend).
|
|
|
|
The rank of input_x1 must be `3`, which indicates the A matrix.
|
|
The rank of input_x2 must be `2`, which indicates the 1st-order gradient.
|
|
The rank of input_x3 must be `4`, which indicates the G matrix.
|
|
|
|
Inputs:
|
|
- **input_x1** (Tensor) - The first input is the diag part of the cov matrix of feature map.
|
|
Supported dtype [float32].
|
|
- **input_x2** (Tensor) - The second input is the corresponding 1st-order grad. Supported dtype [float32].
|
|
- **input_x3** (Tensor) - The third input is the diag part of the cov matrix of dout.
|
|
Supported dtype [float32].
|
|
|
|
Outputs:
|
|
Tensor, the shape is the same as the shape of input_x2, it will be used to update the weights.
|
|
|
|
Examples:
|
|
>>> input_x1 = Tensor(np.random.rand(16, 128, 128).astype(np.float32))
|
|
>>> input_x2 = Tensor(np.random.rand(2048, 1024).astype(np.float32))
|
|
>>> temp_x3 = np.random.rand(8, 128, 128).astype(np.float32)
|
|
>>> input_x3 = np.zeros(16,8,128,128).astype(np.float32)
|
|
>>> for i in range(16):
|
|
... input_x3[i,:,:,:] = temp_x3
|
|
>>> input_x3 = Tensor(input_x3)
|
|
>>> update_thor_gradient = ops.UpdateThorGradient(split_dim=128)
|
|
>>> output = update_thor_gradient(input_x1, input_x2, input_x3)
|
|
"""
|
|
|
|
@prim_attr_register
|
|
def __init__(self, split_dim=1):
|
|
"""Initialize UpdateThorGradient"""
|
|
self.init_prim_io_names(inputs=['x1', 'x2', 'x3'], outputs=['y'])
|
|
self.split_dim = split_dim
|
|
self.add_prim_attr('split_dim', self.split_dim)
|
|
|
|
def infer_shape(self, x1_shape, x2_shape, x3_shape):
|
|
return x2_shape
|
|
|
|
def infer_dtype(self, x1_dtype, x2_dtype, x3_dtype):
|
|
validator.check_tensors_dtypes_same_and_valid(
|
|
{'x1_dtype': x1_dtype, 'x2_dtype': x2_dtype, 'x3_dtype': x3_dtype},
|
|
[mstype.float32], self.name)
|
|
return x2_dtype
|
|
|
|
|
|
class _Cholesky(PrimitiveWithInfer):
|
|
"""
|
|
Inner API for _Cholesky base class.
|
|
"""
|
|
|
|
@prim_attr_register
|
|
def __init__(self, split_dim=0):
|
|
self.init_prim_io_names(inputs=['x1'], outputs=['y'])
|
|
self.split_dim = split_dim
|
|
self.add_prim_attr('split_dim', self.split_dim)
|
|
|
|
def infer_shape(self, x1_shape):
|
|
if self.split_dim != 0:
|
|
height = x1_shape[0]
|
|
width = x1_shape[1]
|
|
if height <= self.split_dim:
|
|
out_shape = [1, height, width]
|
|
else:
|
|
batch = height // self.split_dim
|
|
if height != batch * self.split_dim:
|
|
batch += 1
|
|
out_shape = [batch, self.split_dim, self.split_dim]
|
|
else:
|
|
out_shape = x1_shape
|
|
return out_shape
|
|
|
|
def infer_dtype(self, x1_dtype):
|
|
validator.check_tensor_dtype_valid('x1', x1_dtype, [mstype.float32], self.name)
|
|
return x1_dtype
|
|
|
|
|
|
class Cholesky(_Cholesky):
|
|
"""
|
|
Inner API for positive-definite matrix Cholesky decomposition GPU backend.
|
|
"""
|
|
|
|
|
|
class CholeskyTrsm(_Cholesky):
|
|
"""
|
|
Inner API for resnet50 THOR GPU backend.
|
|
"""
|
|
|
|
|
|
class DetTriangle(PrimitiveWithInfer):
|
|
"""
|
|
Calculate the determinant of triangle matrices.
|
|
|
|
Args:
|
|
fill_mode (tuple): The target shape to broadcast.
|
|
|
|
Inputs:
|
|
- **input_x** (Tensor) - The input tensor.
|
|
|
|
Outputs:
|
|
Tensor, with the given `shape` and the same data type as `input_x`.
|
|
|
|
Examples:
|
|
>>> shape = (2, 3)
|
|
>>> input_x = Tensor(np.array([1, 2, 3]).astype(np.float32))
|
|
>>> broadcast_to = P.BroadcastTo(shape)
|
|
>>> broadcast_to(input_x)
|
|
[[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]]
|
|
"""
|
|
|
|
@prim_attr_register
|
|
def __init__(self, fill_mode=0):
|
|
self.init_prim_io_names(inputs=['x1'], outputs=['y'])
|
|
self.fill_mode = fill_mode
|
|
self.add_prim_attr('fill_mode', self.fill_mode)
|
|
|
|
def infer_shape(self, x1_shape):
|
|
out_shape = x1_shape
|
|
del out_shape[-2:]
|
|
return out_shape
|
|
|
|
def infer_dtype(self, x1_dtype):
|
|
validator.check_tensor_dtype_valid('x1', x1_dtype, [mstype.float32], self.name)
|
|
return x1_dtype
|
|
|
|
|
|
class ProdForceSeA(PrimitiveWithInfer):
|
|
"""
|
|
ProdForceSeA.
|
|
"""
|
|
|
|
@prim_attr_register
|
|
def __init__(self, natoms=192):
|
|
self.init_prim_io_names(inputs=['net_deriv_tensor', "in_deriv_tensor", "nlist_tensor"], outputs=['y'])
|
|
self.natoms = natoms
|
|
self.add_prim_attr('natoms', self.natoms)
|
|
|
|
def infer_shape(self, x1_shape, x2_shape, x3_shape):
|
|
out_shape = [x3_shape[0], x3_shape[1], 3]
|
|
return out_shape
|
|
|
|
def infer_dtype(self, x1_dtype, x2_dtype, x3_dtype):
|
|
return x1_dtype
|