#
# 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 dataclasses import dataclass
from typing import Optional
import tripy.frontend.trace.ops.utils as op_utils
from tripy import constraints, export, utils
from tripy.common import datatype
from tripy.frontend import utils as frontend_utils
from tripy.frontend.trace.ops.base import BaseTraceOp
from tripy.types import ShapeLike
@dataclass(repr=False)
class Iota(BaseTraceOp):
dim: int
output_rank: int
dtype: datatype.dtype
infer_rank = op_utils.InferRankPolicies.same_as_shape_of_shape_input()
def infer_dtypes(self):
self.outputs[0].dtype = self.dtype
def infer_devices(self):
from tripy.common import device
self.outputs[0].device = device(("gpu", 0))
def to_flat_ir(self, inputs, outputs):
from tripy.flat_ir.ops import DynamicIotaOp
DynamicIotaOp.build(inputs, outputs, dim=self.dim)
def iota_impl(shape: "tripy.Tensor", dim: int, dtype: datatype.dtype, output_rank: int) -> "tripy.Tensor":
from tripy.frontend.trace.ops.cast import cast
# Allocate a float32 tensor and cast the output to dtype.
# `tensorrt.linspace` op result #0 must be 0D/1D/2D/3D/4D/5D/6D/7D/8D tensor of 32-bit float or 32-bit signless integer values.
if dtype not in (datatype.float32, datatype.int32, datatype.int64):
result = Iota.build([shape], dim, output_rank, datatype.float32)
return cast(result, dtype)
return Iota.build([shape], dim, output_rank, dtype)
[docs]
@export.public_api(document_under="operations/initializers")
@frontend_utils.convert_to_tensors(
preprocess_args=lambda shape, dim=None, dtype=None: (
{"dim": frontend_utils.process_dim(dim, len(shape))} if dim is not None else {}
)
)
@constraints.dtypes(
constraints={"dtype": "T1", constraints.RETURN_VALUE: "T1"},
variables={
"T1": ["float32", "float16", "bfloat16", "float8", "int4", "int8", "int32", "int64", "bool"],
},
)
def iota(shape: ShapeLike, dim: int = 0, dtype: datatype.dtype = datatype.float32) -> "tripy.Tensor":
"""
Fills an output tensor with consecutive values starting from zero along the given dimension.
Args:
shape: The desired shape.
dim: Dimension along which to perform the iota operation.
This cannot exceed the rank of the specified shape.
dtype: The desired data type.
Returns:
A tensor of shape ``shape`` and data type ``dtype``.
.. code-block:: python
:linenos:
:caption: Example
output = tp.iota((3,), dim=-1)
assert np.array_equal(cp.from_dlpack(output).get(), np.arange(0, 3, dtype=np.float32))
"""
return iota_impl(shape, dim, dtype, output_rank=None)
[docs]
@export.public_api(document_under="operations/initializers")
@constraints.dtypes(
constraints={"input": "T1", "dtype": "T2", constraints.RETURN_VALUE: "T2"},
variables={
"T1": ["float32", "float16", "bfloat16", "float8", "int4", "int8", "int32", "int64", "bool"],
"T2": ["float32", "float16", "bfloat16", "float8", "int4", "int8", "int32", "int64", "bool"],
},
)
def iota_like(input: "tripy.Tensor", dim: int = 0, dtype: Optional[datatype.dtype] = None) -> "tripy.Tensor":
"""
Returns a tensor of the same shape and data type as the input tensor, with consecutive values
starting from zero along the given dimension.
Args:
input: Input tensor.
dim: Dimension along which to perform the iota operation.
This cannot exceed the rank of the specified shape.
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:
:caption: Example
input = tp.Tensor([1, 2, 3])
output = tp.iota_like(input)
assert np.array_equal(cp.from_dlpack(output).get(), np.arange(0, 3, dtype=np.float32))
"""
dim = frontend_utils.process_dim(dim, input.rank)
return iota_impl(
frontend_utils.tensor_from_shape_like(input.shape),
dim,
utils.default(dtype, input.dtype),
output_rank=input.rank,
)