# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
#
from typing import Union
from nvtripy import export
from nvtripy.common.exception import raise_error
from nvtripy.frontend import utils as frontend_utils
from nvtripy.types import IntLike
from nvtripy.utils import wrappers
[docs]
@export.public_api(document_under="operations/functions")
@wrappers.interface(
dtype_constraints={"input": "T1", wrappers.RETURN_VALUE: "T1"},
dtype_variables={
"T1": ["float32", "float16", "bfloat16", "int4", "float8", "int8", "int32", "int64", "bool"],
},
)
def repeat(input: "nvtripy.Tensor", repeats: IntLike, dim: int) -> "nvtripy.Tensor":
"""
Repeats each element of a tensor after itself along the specified dimension.
Args:
input: The input tensor.
repeats: The number of times to repeat each element.
dim: The dimension along which to repeat values.
Returns:
The new tensor.
.. code-block:: python
:linenos:
:caption: 1D tensor
inp = tp.arange(4, dtype=tp.int32)
out0 = tp.repeat(inp, 2, dim=0)
np_inp = np.from_dlpack(tp.copy(inp, device=tp.device("cpu"))) # doc: omit
ref_out0 = np.repeat(np_inp, 2, 0) # doc: omit
assert np.array_equal(ref_out0, np.from_dlpack(tp.copy(out0, device=tp.device("cpu"))))
.. code-block:: python
:linenos:
:caption: 2D tensor
inp = tp.reshape(tp.arange(4, dtype=tp.int32), (2, 2))
out0 = tp.repeat(inp, 2, dim=0)
out1 = tp.repeat(inp, 2, dim=1)
np_inp = np.from_dlpack(tp.copy(inp, device=tp.device("cpu"))) # doc: omit
ref_out0 = np.repeat(np_inp, 2, 0) # doc: omit
assert np.array_equal(ref_out0, np.from_dlpack(tp.copy(out0, device=tp.device("cpu"))))
ref_out1 = np.repeat(np_inp, 2, 1) # doc: omit
assert np.array_equal(ref_out1, np.from_dlpack(tp.copy(out1, device=tp.device("cpu"))))
"""
from nvtripy.frontend.dimension_size import DimensionSize
from nvtripy.frontend.ops.unsqueeze import unsqueeze
from nvtripy.frontend.trace.ops.expand import expand
from nvtripy.frontend.trace.ops.reshape import reshape
dim = frontend_utils.process_dim(dim, input.rank)
if isinstance(repeats, int):
if repeats < 0:
raise_error("`repeats` value must be non-negative.", [f"Got: repeats={repeats}."])
repeats = DimensionSize(repeats)
# By constraining repeats to be a single integer, we can use a very
# simple implementation for repeat.
# Imagine we have:
# a = [1, 2]
# out = tp.repeat(a, 2, dim=0)
#
# We achieve this by:
#
# [1, 2] -> [[1], -> [[1, 1], -> [1, 1, 2, 2]
# [2],] [2, 2],]
#
out = unsqueeze(input, dim + 1)
input_shape = input.shape
out = expand(out, input_shape[: dim + 1] + [repeats] + input_shape[dim + 1 :])
input_shape[dim] = input_shape[dim] * repeats
return reshape(out, input_shape)