#
# SPDX-FileCopyrightText: Copyright (c) 2025 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 Optional
from nvtripy import export, utils
from nvtripy.common import datatype
from nvtripy.frontend.ops import utils as op_utils
from nvtripy.trace.ops.broadcast import Broadcast
from nvtripy.types import ShapeLike, TensorLike
from nvtripy.utils import wrappers
[docs]
@export.public_api(document_under="operations/initializers")
@wrappers.interface(
dtype_constraints={"dtype": "T1", wrappers.RETURN_VALUE: "T1"},
dtype_variables={
"T1": ["float32", "float16", "bfloat16", "int8", "int32", "int64", "bool"],
},
convert_to_tensors=True,
)
def full(shape: ShapeLike, value: TensorLike, dtype: "nvtripy.dtype" = datatype.float32) -> "nvtripy.Tensor":
"""
Returns a tensor of the desired shape with all values set to the specified value.
Args:
shape: The desired shape.
value: A scalar value to fill the resulting tensor.
dtype: The desired data type.
Returns:
A tensor of shape ``shape``.
.. code-block:: python
:linenos:
output = tp.full(shape=[2, 3], value=2)
assert np.array_equal(cp.from_dlpack(output).get(), np.full([2, 3], 2, dtype=np.float32))
"""
from nvtripy.frontend.ops.cast import cast
value_dtype = dtype
if dtype == datatype.int8:
# TODO (#580): Remove this workaround for broadcasting INT8 inputs:
value_dtype = datatype.int32
# We avoid using the `expand` API since it does extra things that we don't need.
out = op_utils.create_op(Broadcast, [cast(value, dtype=value_dtype), shape])
out = cast(out, dtype=dtype) # This will be a no-op if dtype == value_dtype
return out
[docs]
@export.public_api(document_under="operations/initializers")
@wrappers.interface(
dtype_constraints={"input": "T1", "dtype": "T2", wrappers.RETURN_VALUE: "T2"},
dtype_variables={
"T1": ["float32", "float16", "bfloat16", "float8", "int8", "int32", "int64", "bool"],
"T2": ["float32", "float16", "bfloat16", "int8", "int32", "int64", "bool"],
},
)
def full_like(input: "nvtripy.Tensor", value: TensorLike, dtype: Optional["nvtripy.dtype"] = None) -> "nvtripy.Tensor":
"""
Returns a tensor of the same shape and data type as the input tensor, with all values
set to the specified value.
Args:
input: Input tensor.
value: A scalar value to fill the resulting tensor.
dtype: The desired data type. This will override the data type inferred from the input tensor.
Returns:
A tensor of the same shape and data type (unless ``dtype`` is provided) as the input.
.. code-block:: python
:linenos:
input = tp.Tensor([[1, 2], [3, 4]])
output = tp.full_like(input, value=2)
assert np.array_equal(cp.from_dlpack(output).get(), np.array([[2, 2], [2, 2]], dtype=np.float32))
"""
return full(input.shape, value, utils.utils.default(dtype, input.dtype))