# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# 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.
#
"""
Pad last dimension.
"""
from typing import List
import jinja2
from aitemplate import backend
from aitemplate.backend import registry
from aitemplate.compiler.base import IntImm, Operator, Tensor
# pylint: disable=C0103,W0221
SHAPE_FUNC_TEMPLATE = jinja2.Template(
"""
{% for dim in shape %}
{{indent}}{{dtype}}X_DIM{{loop.index - 1}} = {{dim}};
{% endfor %}
{{indent}}{{dtype}}Y_OUT_DIM = {{out_dim}};
"""
)
SHAPE_ASSIGNMENT_TEMPLATE = jinja2.Template(
"""
{% for dim in shape %}
{{indent}}{{dtype}}{{dim}} = X_DIM{{loop.index - 1}};
{% endfor %}
{{indent}}{{dtype}}{{last_dim}} = Y_OUT_DIM;
"""
)
[docs]class pad_last_dim(Operator):
"""Pad the last dimension of the input data to the specified length."""
def __init__(self, ndim: int, out_dim: int):
super().__init__()
self._attrs["op"] = "pad_last_dim"
self._attrs["ndim"] = ndim
self._attrs["out_dim"] = out_dim
self.shape_eval_template = SHAPE_FUNC_TEMPLATE
self.shape_save_template = SHAPE_ASSIGNMENT_TEMPLATE
def _infer_shapes(self, x: Tensor):
"""Infers shapes for pad_last_dim."""
x_shape = x._attrs["shape"]
ndim = len(x_shape)
if self._attrs["out_dim"] <= max(x_shape[-1]._attrs["values"]):
raise RuntimeError("Output of padded dim must be larger than original dim")
if ndim != self._attrs["ndim"]:
raise RuntimeError("Data/Op dims mismatch")
if ndim > 4:
raise NotImplementedError
output_shape = list(x_shape)
output_shape[-1] = IntImm(self._attrs["out_dim"])
return output_shape
def __call__(self, x: Tensor) -> List[Tensor]:
self._attrs["inputs"] = [x]
self._set_depth()
output_shape = self._infer_shapes(x)
output = Tensor(output_shape, src_ops={self}, dtype=x._attrs["dtype"])
self._attrs["outputs"] = [output]
return output
def _get_op_attributes(self):
return {"ndim": self._attrs["ndim"], "out_dim": self._attrs["out_dim"]}
[docs] def gen_function(self) -> str:
target = backend.target.Target.current()
template_path = target.template_path()
func_key = "{target}.{op}.gen_function".format(
target=target.name(), op=self._attrs["op"]
)
func = registry.get(func_key)
return func(
self._attrs,
template_path,
self.shape_eval_template,
self.shape_save_template,
)