#
# Copyright (c) 2021, NVIDIA CORPORATION.
#
# 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 abc
import inspect
import logging
from collections import OrderedDict
from typing import List, Optional, Union
import torch
from torch.nn import Module
from merlin_standard_lib.utils.misc_utils import filter_kwargs
from ..utils import torch_utils
LOG = logging.getLogger("transformers4rec")
[docs]class BlockBase(torch_utils.OutputSizeMixin, torch.nn.Module, metaclass=abc.ABCMeta):
[docs] def to_model(self, prediction_task_or_head, inputs=None, **kwargs):
from ..model.base import Head, Model, PredictionTask
if isinstance(prediction_task_or_head, PredictionTask):
head = prediction_task_or_head.to_head(self, inputs=inputs, **kwargs)
elif isinstance(prediction_task_or_head, Head):
head = prediction_task_or_head
else:
raise ValueError(
"`prediction_task_or_head` needs to be a `Head` or `PredictionTask` "
f"found: {type(prediction_task_or_head)}"
)
return Model(head, **kwargs)
[docs] def as_tabular(self, name=None):
from ..tabular.base import AsTabular
if not name:
name = self.name
return SequentialBlock(self, AsTabular(name))
[docs]class Block(BlockBase):
def __init__(self, module: torch.nn.Module, output_size: Union[List[int], torch.Size]):
super().__init__()
self.module = module
self._output_size = output_size
[docs] def forward(self, inputs):
return self.module(inputs)
[docs] def forward_output_size(self, input_size):
if self._output_size[0] is None:
batch_size = torch_utils.calculate_batch_size_from_input_size(input_size)
return [batch_size] + self._output_size[1:]
return self._output_size
[docs]class SequentialBlock(BlockBase, torch.nn.Sequential):
def __init__(self, *args, output_size=None):
from transformers4rec.torch import TabularSequenceFeatures, TransformerBlock
if isinstance(args[0], TabularSequenceFeatures) and any(
isinstance(arg, TransformerBlock) for arg in args
):
masking = args[0].masking
for arg in args:
if isinstance(arg, TransformerBlock):
if arg.masking != masking:
LOG.warning(
"Masking is set in the input module but not in the "
"TransformerBlock, provide this through the masking argument"
)
super().__init__()
self._static_output_size = output_size
self.input_size = None
if len(args) == 1 and isinstance(args[0], OrderedDict):
last = None
for idx, key, module in enumerate(args[0].items()):
self.add_module_and_maybe_build(key, module, last, idx)
last = module
else:
if len(args) == 1 and isinstance(args[0], list):
args = args[0]
last = None
for idx, module in enumerate(args):
last = self.add_module_and_maybe_build(str(idx), module, last, idx)
@property
def inputs(self):
from transformers4rec.torch import TabularFeatures, TabularSequenceFeatures
first = list(self)[0]
if isinstance(first, (TabularSequenceFeatures, TabularFeatures)):
return first
[docs] def add_module(self, name: str, module: Optional[Module]) -> None:
from ..tabular.base import FilterFeatures
if isinstance(module, list):
module = FilterFeatures(module)
super().add_module(name, module)
[docs] def add_module_and_maybe_build(self, name: str, module, parent, idx) -> torch.nn.Module:
# Check if module needs to be built
if getattr(parent, "output_size", None) and getattr(module, "build", None):
module = module.build(parent.output_size())
if idx == 0:
self.input_size = getattr(module, "input_size", None)
self.add_module(name, module)
return module
def __rrshift__(self, other):
return right_shift_block(self, other)
def __rshift__(self, other):
# pylint: disable=arguments-out-of-order
return right_shift_block(other, self)
[docs] def forward(self, input, training=True, ignore_masking=True, **kwargs):
# from transformers4rec.torch import TabularSequenceFeatures
for i, module in enumerate(self):
if i == len(self) - 1:
filtered_kwargs = filter_kwargs(kwargs, module, filter_positional_or_keyword=False)
input = module(input, **filtered_kwargs)
elif "training" in inspect.signature(module.forward).parameters:
if "ignore_masking" in inspect.signature(module.forward).parameters:
input = module(input, training=training, ignore_masking=ignore_masking)
else:
input = module(input, training=training)
else:
input = module(input)
return input
[docs] def build(self, input_size, schema=None, **kwargs):
output_size = input_size
for module in self:
if not hasattr(module, "build"):
break
module.build(output_size, schema=schema)
output_size = module.output_size()
return super(SequentialBlock, self).build(input_size, schema=None, **kwargs)
[docs] def as_tabular(self, name=None):
from transformers4rec.torch import AsTabular
if not name:
name = self.name
return SequentialBlock(self, AsTabular(name))
def __add__(self, other):
from ..tabular.base import merge_tabular
return merge_tabular(self, other)
[docs] def forward_output_size(self, input_size):
if self._static_output_size:
return self._static_output_size
x = input_size
for module in list(self):
if getattr(module, "output_size", None):
x = module.output_size(x)
else:
# TODO log warning here
return None
return x
[docs] @staticmethod
def get_children_by_class_name(parent, *class_name):
children = []
def add_if_class_name_matches(to_check):
if to_check.__class__.__name__ in class_name:
children.append(to_check)
for child in parent:
if getattr(child, "merge_values", None):
for to_merge in child.merge_values:
add_if_class_name_matches(to_merge)
add_if_class_name_matches(child)
return children
[docs]def build_blocks(*modules):
return list(SequentialBlock(*modules))
[docs]class BuildableBlock(abc.ABC):
[docs] @abc.abstractmethod
def build(self, input_size) -> BlockBase:
raise NotImplementedError
def __rrshift__(self, other):
return right_shift_block(self, other)
[docs] def to_module(self, shape_or_module):
shape = shape_or_module
if isinstance(shape_or_module, torch.nn.Module):
shape = getattr(shape_or_module, "output_size", None)
if shape:
shape = shape()
return self.build(shape)
[docs]def right_shift_block(self, other):
from ..tabular.base import FilterFeatures
if isinstance(other, list):
left_side = [FilterFeatures(other)]
else:
left_side = list(other) if isinstance(other, SequentialBlock) else [other]
right_side = list(self) if isinstance(self, SequentialBlock) else [self]
# Maybe build right-side
if hasattr(left_side[-1], "output_size") and left_side[-1].output_size():
_right_side = []
x = left_side[-1].output_size()
for module in right_side:
if getattr(module, "build", None):
if "parents" in inspect.signature(module.build).parameters:
build = module.build(x, left_side)
else:
build = module.build(x)
if build:
module = build
x = module.output_size() if hasattr(module, "output_size") else None
_right_side.append(module)
right_side = _right_side
sequential = left_side + right_side
need_moving_to_gpu = False
if isinstance(self, torch.nn.Module):
need_moving_to_gpu = need_moving_to_gpu or torch_utils.check_gpu(self)
if isinstance(other, torch.nn.Module):
need_moving_to_gpu = need_moving_to_gpu or torch_utils.check_gpu(other)
out = SequentialBlock(*sequential)
if getattr(left_side[-1], "input_size", None) and left_side[-1].input_size:
out.input_size = left_side[-1].input_size
if need_moving_to_gpu:
out.to("cuda")
return out
BlockType = Union[BlockBase, BuildableBlock]
BlockOrModule = Union[BlockBase, BuildableBlock, torch.nn.Module]