chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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from .send_recv import send_u_recv, send_ue_recv, send_uv # noqa: F401
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__all__ = []
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@@ -0,0 +1,538 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import annotations
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from typing import TYPE_CHECKING, Literal, TypeAlias
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import numpy as np
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from paddle import _C_ops
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from paddle.base.data_feeder import (
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check_dtype,
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check_type,
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check_variable_and_dtype,
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)
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from paddle.base.framework import Variable
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from paddle.base.layer_helper import LayerHelper
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from paddle.framework import in_dynamic_or_pir_mode
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from .utils import (
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convert_out_size_to_list,
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get_out_size_tensor_inputs,
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reshape_lhs_rhs,
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)
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if TYPE_CHECKING:
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from paddle import Tensor
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_ReduceOp: TypeAlias = Literal[
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"sum",
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"mean",
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"max",
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"min",
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]
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_MessageOp: TypeAlias = Literal[
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"add",
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"sub",
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"mul",
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"div",
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]
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__all__ = []
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def send_u_recv(
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x: Tensor,
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src_index: Tensor,
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dst_index: Tensor,
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reduce_op: _ReduceOp = "sum",
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out_size: int | Tensor | None = None,
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name: str | None = None,
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) -> Tensor:
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"""
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Graph Learning message passing api.
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This api is mainly used in Graph Learning domain, and the main purpose is to reduce intermediate memory
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consumption in the process of message passing. Take `x` as the input tensor, we first use `src_index`
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to gather the corresponding data, and then use `dst_index` to update the corresponding position of output tensor
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in different reduce ops, like sum, mean, max, or min. Besides, we can use `out_size` to set necessary output shape.
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.. code-block:: text
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Given:
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x = [[0, 2, 3],
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[1, 4, 5],
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[2, 6, 7]]
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src_index = [0, 1, 2, 0]
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dst_index = [1, 2, 1, 0]
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reduce_op = "sum"
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out_size = None
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Then:
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out = [[0, 2, 3],
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[2, 8, 10],
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[1, 4, 5]]
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Args:
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x (Tensor): The input tensor, and the available data type is float32, float64, int32, int64.
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And we support float16 in gpu version.
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src_index (Tensor): An 1-D tensor, and the available data type is int32, int64.
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dst_index (Tensor): An 1-D tensor, and should have the same shape as `src_index`.
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The available data type is int32, int64.
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reduce_op (str): Different reduce ops, including `sum`, `mean`, `max`, `min`.
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Default value is `sum`.
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out_size (int|Tensor|None): We can set `out_size` to get necessary output shape. If not set or
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out_size is smaller or equal to 0, then this input will not be used.
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Otherwise, `out_size` should be equal with or larger than
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max(dst_index) + 1.
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name (str, optional): Name for the operation (optional, default is None).
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For more information, please refer to :ref:`api_guide_Name`.
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Returns:
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- out (Tensor), the output tensor, should have the same shape and same dtype as input tensor `x`.
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If `out_size` is set correctly, then it should have the same shape as `x` except the 0th dimension.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
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>>> indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
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>>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
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>>> out = paddle.geometric.send_u_recv(x, src_index, dst_index, reduce_op="sum")
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>>> print(out.numpy())
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[[ 0. 2. 3.]
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[ 2. 8. 10.]
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[ 1. 4. 5.]]
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>>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
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>>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
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>>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
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>>> out_size = paddle.max(dst_index) + 1
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>>> out = paddle.geometric.send_u_recv(x, src_index, dst_index, reduce_op="sum", out_size=out_size)
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>>> print(out.numpy())
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[[ 0. 2. 3.]
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[ 2. 8. 10.]]
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>>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
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>>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
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>>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
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>>> out = paddle.geometric.send_u_recv(x, src_index, dst_index, reduce_op="sum")
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>>> print(out.numpy())
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[[ 0. 2. 3.]
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[ 2. 8. 10.]
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[ 0. 0. 0.]]
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"""
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if reduce_op not in ["sum", "mean", "max", "min"]:
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raise ValueError(
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f"reduce_op should be `sum`, `mean`, `max` or `min`, but received {reduce_op}"
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)
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# TODO(daisiming): Should we add judgement for out_size: max(dst_index) + 1.
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if in_dynamic_or_pir_mode():
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out_size = convert_out_size_to_list(out_size, 'graph_send_recv')
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return _C_ops.send_u_recv(
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x, src_index, dst_index, reduce_op.upper(), out_size
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)
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else:
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check_variable_and_dtype(
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x,
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"X",
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("float32", "float64", "int32", "int64", "float16"),
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"graph_send_recv",
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)
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check_variable_and_dtype(
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src_index, "Src_index", ("int32", "int64"), "graph_send_recv"
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)
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check_variable_and_dtype(
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dst_index, "Dst_index", ("int32", "int64"), "graph_send_recv"
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)
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if out_size:
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check_type(
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out_size,
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'out_size',
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(int, np.int32, np.int64, Variable),
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'graph_send_recv',
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)
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if isinstance(out_size, Variable):
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check_dtype(
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out_size.dtype,
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'out_size',
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['int32', 'int64'],
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'graph_send_recv',
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)
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helper = LayerHelper("send_u_recv", **locals())
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out = helper.create_variable_for_type_inference(dtype=x.dtype)
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dst_count = helper.create_variable_for_type_inference(
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dtype="int32", stop_gradient=True
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)
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inputs = {"X": x, "Src_index": src_index, "Dst_index": dst_index}
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attrs = {"reduce_op": reduce_op.upper()}
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get_out_size_tensor_inputs(
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inputs=inputs,
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attrs=attrs,
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out_size=out_size,
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op_type='graph_send_recv',
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)
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helper.append_op(
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type="graph_send_recv",
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inputs=inputs,
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outputs={"Out": out, "Dst_count": dst_count},
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attrs=attrs,
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)
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return out
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def send_ue_recv(
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x: Tensor,
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y: Tensor,
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src_index: Tensor,
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dst_index: Tensor,
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message_op: _MessageOp = "add",
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reduce_op: _ReduceOp = "sum",
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out_size: int | Tensor | None = None,
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name: str | None = None,
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) -> Tensor:
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"""
|
||||
|
||||
Graph Learning message passing api.
|
||||
|
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This api is mainly used in Graph Learning domain, and the main purpose is to reduce intermediate memory
|
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consumption in the process of message passing. Take `x` as the input tensor, we first use `src_index`
|
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to gather the corresponding data, after computing with `y` in different message ops like add/sub/mul/div, then use `dst_index` to
|
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update the corresponding position of output tensor in different reduce ops, like sum, mean, max, or min.
|
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Besides, we can use `out_size` to set necessary output shape.
|
||||
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.. code-block:: text
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|
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Given:
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|
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x = [[0, 2, 3],
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[1, 4, 5],
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[2, 6, 7]]
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y = [1, 1, 1]
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src_index = [0, 1, 2, 0]
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dst_index = [1, 2, 1, 0]
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message_op = "add"
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reduce_op = "sum"
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out_size = None
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Then:
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out = [[1, 3, 4],
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[4, 10, 12],
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[2, 5, 6]]
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Args:
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x (Tensor): The input node feature tensor, and the available data type is float32, float64, int32, int64.
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And we support float16 in gpu version.
|
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y (Tensor): The input edge feature tensor, and the available data type is float32, float64, int32, int64.
|
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And we support float16 in gpu version.
|
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src_index (Tensor): An 1-D tensor, and the available data type is int32, int64.
|
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dst_index (Tensor): An 1-D tensor, and should have the same shape as `src_index`.
|
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The available data type is int32, int64.
|
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message_op (str, optional): Different message ops for x and e, including `add`, `sub`, `mul`, `div`.
|
||||
reduce_op (str, optional): Different reduce ops, including `sum`, `mean`, `max`, `min`.
|
||||
Default value is `sum`.
|
||||
out_size (int|Tensor, optional): We can set `out_size` to get necessary output shape. If not set or
|
||||
out_size is smaller or equal to 0, then this input will not be used.
|
||||
Otherwise, `out_size` should be equal with or larger than
|
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max(dst_index) + 1. Default value is `None`.
|
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name (str, optional): Name for the operation (optional, default is None).
|
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For more information, please refer to :ref:`api_guide_Name`.
|
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|
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Returns:
|
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- out (Tensor), the output tensor, should have the same shape and same dtype as input tensor `x`.
|
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If `out_size` is set correctly, then it should have the same shape as `x` except the 0th dimension.
|
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|
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
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>>> y = paddle.to_tensor([1, 1, 1, 1], dtype="float32")
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>>> indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
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>>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
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>>> out = paddle.geometric.send_ue_recv(x, y, src_index, dst_index, message_op="add", reduce_op="sum")
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>>> print(out.numpy())
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[[ 1. 3. 4.]
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[ 4. 10. 12.]
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[ 2. 5. 6.]]
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>>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
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>>> y = paddle.to_tensor([1, 1, 1], dtype="float32")
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>>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
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>>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
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>>> out_size = paddle.max(dst_index) + 1
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>>> out = paddle.geometric.send_ue_recv(x, y, src_index, dst_index, message_op="add", reduce_op="sum", out_size=out_size)
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>>> print(out.numpy())
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[[ 1. 3. 4.]
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[ 4. 10. 12.]]
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>>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
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>>> y = paddle.to_tensor([1, 1, 1], dtype="float32")
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>>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
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>>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
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>>> out = paddle.geometric.send_ue_recv(x, y, src_index, dst_index, message_op="add", reduce_op="sum")
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>>> print(out.numpy())
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[[ 1. 3. 4.]
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[ 4. 10. 12.]
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[ 0. 0. 0.]]
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"""
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if message_op not in ["add", "sub", "mul", "div"]:
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raise ValueError(
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f"message_op should be `add`, `sub`, `mul`, `div`, but received {message_op}"
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)
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|
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if reduce_op not in ["sum", "mean", "max", "min"]:
|
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raise ValueError(
|
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f"reduce_op should be `sum`, `mean`, `max` or `min`, but received {reduce_op}"
|
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)
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x, y = reshape_lhs_rhs(x, y)
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if message_op == 'sub':
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message_op = 'add'
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y = -y
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if message_op == "div":
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message_op = 'mul'
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y = 1.0 / (y + 1e-12)
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# TODO(daisiming): Should we add judgement for out_size: max(dst_index) + 1.
|
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|
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if in_dynamic_or_pir_mode():
|
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out_size = convert_out_size_to_list(out_size, 'graph_send_ue_recv')
|
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return _C_ops.send_ue_recv(
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x,
|
||||
y,
|
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src_index,
|
||||
dst_index,
|
||||
message_op.upper(),
|
||||
reduce_op.upper(),
|
||||
out_size,
|
||||
)
|
||||
else:
|
||||
check_variable_and_dtype(
|
||||
x,
|
||||
"X",
|
||||
("float32", "float64", "int32", "int64", "float16"),
|
||||
"graph_send_ue_recv",
|
||||
)
|
||||
check_variable_and_dtype(
|
||||
y,
|
||||
"Y",
|
||||
("float32", "float64", "int32", "int64", "float16"),
|
||||
"graph_send_ue_recv",
|
||||
)
|
||||
check_variable_and_dtype(
|
||||
src_index, "Src_index", ("int32", "int64"), "graph_send_ue_recv"
|
||||
)
|
||||
check_variable_and_dtype(
|
||||
dst_index, "Dst_index", ("int32", "int64"), "graph_send_ue_recv"
|
||||
)
|
||||
if out_size:
|
||||
check_type(
|
||||
out_size,
|
||||
'out_size',
|
||||
(int, np.int32, np.int64, Variable),
|
||||
'graph_send_ue_recv',
|
||||
)
|
||||
if isinstance(out_size, Variable):
|
||||
check_dtype(
|
||||
out_size.dtype,
|
||||
'out_size',
|
||||
['int32', 'int64'],
|
||||
'graph_send_ue_recv',
|
||||
)
|
||||
|
||||
helper = LayerHelper("send_ue_recv", **locals())
|
||||
out = helper.create_variable_for_type_inference(dtype=x.dtype)
|
||||
dst_count = helper.create_variable_for_type_inference(
|
||||
dtype="int32", stop_gradient=True
|
||||
)
|
||||
|
||||
inputs = {
|
||||
"X": x,
|
||||
"Y": y,
|
||||
"Src_index": src_index,
|
||||
"Dst_index": dst_index,
|
||||
}
|
||||
attrs = {
|
||||
"message_op": message_op.upper(),
|
||||
"reduce_op": reduce_op.upper(),
|
||||
}
|
||||
get_out_size_tensor_inputs(
|
||||
inputs=inputs,
|
||||
attrs=attrs,
|
||||
out_size=out_size,
|
||||
op_type='graph_send_ue_recv',
|
||||
)
|
||||
|
||||
helper.append_op(
|
||||
type="graph_send_ue_recv",
|
||||
inputs=inputs,
|
||||
outputs={"Out": out, "Dst_count": dst_count},
|
||||
attrs=attrs,
|
||||
)
|
||||
return out
|
||||
|
||||
|
||||
def send_uv(
|
||||
x: Tensor,
|
||||
y: Tensor,
|
||||
src_index: Tensor,
|
||||
dst_index: Tensor,
|
||||
message_op: _MessageOp = "add",
|
||||
name: str | None = None,
|
||||
) -> Tensor:
|
||||
"""
|
||||
|
||||
Graph Learning message passing api.
|
||||
|
||||
This api is mainly used in Graph Learning domain, and the main purpose is to reduce intermediate memory
|
||||
consumption in the process of message passing. Take `x` as the source node feature tensor, take `y` as
|
||||
the destination node feature tensor. Then we use `src_index` and `dst_index` to gather the corresponding data,
|
||||
and then compute the edge features in different message_ops like `add`, `sub`, `mul`, `div`.
|
||||
|
||||
.. code-block:: text
|
||||
|
||||
Given:
|
||||
|
||||
x = [[0, 2, 3],
|
||||
[1, 4, 5],
|
||||
[2, 6, 7]]
|
||||
|
||||
y = [[0, 1, 2],
|
||||
[2, 3, 4],
|
||||
[4, 5, 6]]
|
||||
|
||||
src_index = [0, 1, 2, 0]
|
||||
|
||||
dst_index = [1, 2, 1, 0]
|
||||
|
||||
message_op = "add"
|
||||
|
||||
Then:
|
||||
|
||||
out = [[2, 5, 7],
|
||||
[5, 9, 11],
|
||||
[4, 9, 11],
|
||||
[0, 3, 5]]
|
||||
|
||||
Args:
|
||||
x (Tensor): The source node feature tensor, and the available data type is float32, float64, int32, int64. And we support float16 in gpu version.
|
||||
y (Tensor): The destination node feature tensor, and the available data type is float32, float64, int32, int64. And we support float16 in gpu version.
|
||||
src_index (Tensor): An 1-D tensor, and the available data type is int32, int64.
|
||||
dst_index (Tensor): An 1-D tensor, and should have the same shape as `src_index`.
|
||||
The available data type is int32, int64.
|
||||
message_op (str): Different message ops for x and y, including `add`, `sub`, `mul` and `div`.
|
||||
name (str, optional): Name for the operation (optional, default is None).
|
||||
For more information, please refer to :ref:`api_guide_Name`.
|
||||
|
||||
Returns:
|
||||
- out (Tensor), the output tensor.
|
||||
|
||||
Examples:
|
||||
|
||||
.. code-block:: pycon
|
||||
|
||||
>>> import paddle
|
||||
|
||||
>>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
|
||||
>>> y = paddle.to_tensor([[0, 1, 2], [2, 3, 4], [4, 5, 6]], dtype="float32")
|
||||
>>> indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
|
||||
>>> src_index = indexes[:, 0]
|
||||
>>> dst_index = indexes[:, 1]
|
||||
>>> out = paddle.geometric.send_uv(x, y, src_index, dst_index, message_op="add")
|
||||
>>> print(out.numpy())
|
||||
[[ 2. 5. 7.]
|
||||
[ 5. 9. 11.]
|
||||
[ 4. 9. 11.]
|
||||
[ 0. 3. 5.]]
|
||||
|
||||
"""
|
||||
|
||||
if message_op not in ['add', 'sub', 'mul', 'div']:
|
||||
raise ValueError(
|
||||
f"message_op should be `add`, `sub`, `mul`, `div`, but received {message_op}"
|
||||
)
|
||||
|
||||
x, y = reshape_lhs_rhs(x, y)
|
||||
|
||||
if message_op == 'sub':
|
||||
message_op = 'add'
|
||||
y = -y
|
||||
if message_op == 'div':
|
||||
message_op = 'mul'
|
||||
y = 1.0 / (y + 1e-12)
|
||||
|
||||
if in_dynamic_or_pir_mode():
|
||||
return _C_ops.send_uv(x, y, src_index, dst_index, message_op.upper())
|
||||
else:
|
||||
helper = LayerHelper("graph_send_uv", **locals())
|
||||
check_variable_and_dtype(
|
||||
x,
|
||||
'x',
|
||||
['int32', 'int64', 'float32', 'float64', 'float16'],
|
||||
'graph_send_uv',
|
||||
)
|
||||
check_variable_and_dtype(
|
||||
y,
|
||||
'y',
|
||||
['int32', 'int64', 'float32', 'float64', 'float16'],
|
||||
'graph_send_uv',
|
||||
)
|
||||
check_variable_and_dtype(
|
||||
src_index, 'src_index', ['int32', 'int64'], 'graph_send_uv'
|
||||
)
|
||||
check_variable_and_dtype(
|
||||
dst_index, 'dst_index', ['int32', 'int64'], 'graph_send_uv'
|
||||
)
|
||||
out = helper.create_variable_for_type_inference(dtype=x.dtype)
|
||||
|
||||
inputs = {
|
||||
'x': x,
|
||||
'y': y,
|
||||
'src_index': src_index,
|
||||
'dst_index': dst_index,
|
||||
}
|
||||
attrs = {'message_op': message_op.upper()}
|
||||
helper.append_op(
|
||||
type="graph_send_uv",
|
||||
inputs=inputs,
|
||||
attrs=attrs,
|
||||
outputs={"out": out},
|
||||
)
|
||||
return out
|
||||
@@ -0,0 +1,111 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# 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.
|
||||
|
||||
import numpy as np
|
||||
|
||||
import paddle
|
||||
from paddle.base.data_feeder import check_dtype, convert_dtype
|
||||
from paddle.base.framework import Variable
|
||||
|
||||
|
||||
def convert_out_size_to_list(out_size, op_type):
|
||||
"""
|
||||
Convert out_size(int, np.int32, np.int64, Variable) to list
|
||||
in imperative mode.
|
||||
"""
|
||||
if out_size is None:
|
||||
out_size = [0]
|
||||
elif isinstance(out_size, (int, np.int32, np.int64)):
|
||||
out_size = [out_size]
|
||||
elif isinstance(out_size, (Variable, paddle.pir.Value)):
|
||||
out_size.stop_gradient = True
|
||||
check_dtype(
|
||||
out_size.dtype,
|
||||
'out_size',
|
||||
['int32', 'int64'],
|
||||
'op_type',
|
||||
'(When type of out_size in' + op_type + ' is Variable.)',
|
||||
)
|
||||
if convert_dtype(out_size.dtype) == 'int64':
|
||||
out_size = paddle.cast(out_size, 'int32')
|
||||
else:
|
||||
out_size = [int(out_size)]
|
||||
return out_size
|
||||
|
||||
|
||||
def get_out_size_tensor_inputs(inputs, attrs, out_size, op_type):
|
||||
"""
|
||||
Convert out_size(int, np.int32, np.int64, Variable) to inputs
|
||||
and attrs in static graph mode.
|
||||
"""
|
||||
if out_size is None:
|
||||
attrs['out_size'] = [0]
|
||||
elif isinstance(out_size, (int, np.int32, np.int64)):
|
||||
attrs['out_size'] = [out_size]
|
||||
elif isinstance(out_size, Variable):
|
||||
out_size.stop_gradient = True
|
||||
check_dtype(
|
||||
out_size.dtype,
|
||||
'out_size',
|
||||
['int32', 'int64'],
|
||||
'op_type',
|
||||
'(When type of out_size in' + op_type + ' is Variable.)',
|
||||
)
|
||||
if convert_dtype(out_size.dtype) == 'int64':
|
||||
out_size = paddle.cast(out_size, 'int32')
|
||||
inputs["Out_size"] = out_size
|
||||
else:
|
||||
raise TypeError("Out_size only supports Variable or int.")
|
||||
|
||||
|
||||
def reshape_lhs_rhs(x, y):
|
||||
"""
|
||||
Expand dims to ensure there will be no broadcasting issues with different
|
||||
number of dimensions.
|
||||
"""
|
||||
if len(x.shape) == 1:
|
||||
x = paddle.reshape(x, [-1, 1])
|
||||
if len(y.shape) == 1:
|
||||
y = paddle.reshape(y, [-1, 1])
|
||||
|
||||
x_shape = paddle.shape(x)
|
||||
y_shape = paddle.shape(y)
|
||||
if len(x.shape) != len(y.shape):
|
||||
max_ndims = max(len(x.shape), len(y.shape))
|
||||
x_pad_ndims = max_ndims - len(x.shape)
|
||||
y_pad_ndims = max_ndims - len(y.shape)
|
||||
new_x_shape = (
|
||||
[
|
||||
x_shape[0],
|
||||
]
|
||||
+ [
|
||||
1,
|
||||
]
|
||||
* x_pad_ndims
|
||||
+ list(x_shape[1:])
|
||||
)
|
||||
new_y_shape = (
|
||||
[
|
||||
y_shape[0],
|
||||
]
|
||||
+ [
|
||||
1,
|
||||
]
|
||||
* y_pad_ndims
|
||||
+ list(y_shape[1:])
|
||||
)
|
||||
x = paddle.reshape(x, new_x_shape)
|
||||
y = paddle.reshape(y, new_y_shape)
|
||||
|
||||
return x, y
|
||||
Reference in New Issue
Block a user