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# mypy: allow-untyped-defs
from itertools import chain
from operator import getitem
from typing import Callable, Dict, Optional, Set, Tuple, Type, Union
import torch
import torch.nn.functional as F
from torch import nn
from torch.ao.pruning.sparsifier.base_sparsifier import BaseSparsifier
from torch.fx import symbolic_trace
from torch.nn.utils import parametrize
from .match_utils import apply_match, MatchAllNode
from .parametrization import BiasHook, FakeStructuredSparsity, module_contains_param
from .prune_functions import (
prune_conv2d,
prune_conv2d_activation_conv2d,
prune_conv2d_activation_pool_conv2d,
prune_conv2d_conv2d,
prune_conv2d_pool_activation_conv2d,
prune_conv2d_pool_flatten_linear,
prune_linear,
prune_linear_activation_linear,
prune_linear_linear,
prune_lstm_output_layernorm_linear,
prune_lstm_output_linear,
)
def _get_supported_structured_pruning_modules():
SUPPORTED_STRUCTURED_PRUNING_MODULES = { # added to config if None given
nn.Linear,
nn.Conv2d,
nn.LSTM,
}
return SUPPORTED_STRUCTURED_PRUNING_MODULES
def _get_supported_activation_functions():
SUPPORTED_ACTIVATION_FUNCTIONS = {
F.relu,
F.rrelu,
F.hardtanh,
F.relu6,
F.sigmoid,
F.hardsigmoid,
F.tanh,
F.silu,
F.mish,
F.hardswish,
F.elu,
F.celu,
F.selu,
F.hardshrink,
F.leaky_relu,
F.logsigmoid,
F.softplus,
F.prelu,
F.softsign,
F.tanhshrink,
F.gelu,
}
return SUPPORTED_ACTIVATION_FUNCTIONS
def _get_supported_activation_modules():
SUPPORTED_ACTIVATION_MODULES = {
nn.ReLU,
nn.RReLU,
nn.Hardtanh,
nn.ReLU6,
nn.Sigmoid,
nn.Hardsigmoid,
nn.Tanh,
nn.SiLU,
nn.Mish,
nn.Hardswish,
nn.ELU,
nn.CELU,
nn.SELU,
nn.Hardshrink,
nn.LeakyReLU,
nn.LogSigmoid,
nn.Softplus,
nn.PReLU,
nn.Softsign,
nn.Tanhshrink,
nn.GELU,
}
return SUPPORTED_ACTIVATION_MODULES
def _get_default_structured_pruning_patterns() -> (
Dict[
Tuple[Union[Type[nn.Module], Callable, MatchAllNode, str], ...],
Callable[..., None],
]
):
"""
Returns the patterns for conv2d / linear conversion for each element in the activation functions/modules defined above.
"""
patterns: Dict[
Tuple[Union[Type[nn.Module], Callable, MatchAllNode, str], ...],
Callable[..., None],
] = {
# linear -> linear
(nn.Linear, "output"): prune_linear,
(nn.Linear, nn.Linear): prune_linear_linear,
# conv2d -> conv2d
(nn.Conv2d, "output"): prune_conv2d,
(nn.Conv2d, nn.Conv2d): prune_conv2d_conv2d,
# TODO LSTM Structured pruning does not support returned state currently.
# Should find a way to explicitly match getitem(0) instead of getitem.
# This will also require changing the pruning function.
# lstm -> getitem(0) -> linear
(nn.LSTM, getitem, nn.Linear): prune_lstm_output_linear,
# lstm -> getitem(0) -> layernorm -> linear
(nn.LSTM, getitem, nn.LayerNorm, nn.Linear): prune_lstm_output_layernorm_linear,
}
for activation in chain(
_get_supported_activation_functions(), _get_supported_activation_modules()
):
patterns.update(
{
# linear -> activation -> linear
(nn.Linear, activation, nn.Linear): prune_linear_activation_linear,
# conv2d -> activation -> conv2d
(nn.Conv2d, activation, nn.Conv2d): prune_conv2d_activation_conv2d,
# conv2d -> activation -> pool -> conv2d
(
nn.Conv2d,
activation,
nn.AvgPool2d,
nn.Conv2d,
): prune_conv2d_activation_pool_conv2d,
(
nn.Conv2d,
activation,
F.avg_pool2d,
nn.Conv2d,
): prune_conv2d_activation_pool_conv2d,
(
nn.Conv2d,
activation,
nn.MaxPool2d,
nn.Conv2d,
): prune_conv2d_activation_pool_conv2d,
(
nn.Conv2d,
activation,
F.max_pool2d,
nn.Conv2d,
): prune_conv2d_activation_pool_conv2d,
# conv2d -> pool -> activation -> conv2d
(
nn.Conv2d,
nn.AvgPool2d,
activation,
nn.Conv2d,
): prune_conv2d_pool_activation_conv2d,
(
nn.Conv2d,
F.avg_pool2d,
activation,
nn.Conv2d,
): prune_conv2d_pool_activation_conv2d,
(
nn.Conv2d,
nn.MaxPool2d,
activation,
nn.Conv2d,
): prune_conv2d_pool_activation_conv2d,
(
nn.Conv2d,
F.max_pool2d,
activation,
nn.Conv2d,
): prune_conv2d_pool_activation_conv2d,
# conv2d -> adaptive pool -> flatten -> linear
(
nn.Conv2d,
nn.AdaptiveAvgPool2d,
nn.Flatten,
nn.Linear,
): prune_conv2d_pool_flatten_linear,
(
nn.Conv2d,
nn.AdaptiveAvgPool2d,
torch.flatten,
nn.Linear,
): prune_conv2d_pool_flatten_linear,
(
nn.Conv2d,
nn.AdaptiveMaxPool2d,
nn.Flatten,
nn.Linear,
): prune_conv2d_pool_flatten_linear,
(
nn.Conv2d,
nn.AdaptiveMaxPool2d,
torch.flatten,
nn.Linear,
): prune_conv2d_pool_flatten_linear,
}
)
return patterns
class BaseStructuredSparsifier(BaseSparsifier):
r"""Base class for structured pruning.
Abstract methods that need to be implemented:
- update_mask: Function to compute a new mask for all keys in the
`groups` attribute.
Args:
- defaults [dict]: default configurations will be attached to the
configuration. Only the keys that don't exist in the `config` will
be updated.
"""
def __init__(self, defaults, patterns=None):
super().__init__(defaults)
if patterns is None:
patterns = _get_default_structured_pruning_patterns()
self.patterns = patterns
def make_config_from_model(
self,
model: nn.Module,
SUPPORTED_MODULES: Optional[Set[Type]] = None,
) -> None:
if SUPPORTED_MODULES is None:
SUPPORTED_MODULES = _get_supported_structured_pruning_modules()
super().make_config_from_model(model, SUPPORTED_MODULES=SUPPORTED_MODULES)
def _prepare(self, *args, **kwargs) -> None:
r"""This function will attach the FakeStructuredSparsity parameterizations
and BiasHooks at the appropriate points in the model.
"""
for config in self.groups:
module = config["module"]
tensor_name = config["tensor_name"]
parametrization = config.get("parametrization", FakeStructuredSparsity)
tensor = getattr(module, tensor_name)
mask = config.get(
"mask",
torch.ones(tensor.shape[0], dtype=torch.bool, device=tensor.device),
)
self.state[config["tensor_fqn"]]["mask"] = mask
parametrize.register_parametrization(
module, tensor_name, parametrization(mask)
)
# if linear / conv, we add in bias hooks
if isinstance(module, (nn.Linear, nn.Conv2d)):
prune_bias = config.get("prune_bias", True)
if module.bias is not None:
module.register_parameter(
"_bias", nn.Parameter(module.bias.detach())
)
module.bias = None
module.prune_bias = prune_bias
module.register_forward_hook(
BiasHook(module.parametrizations.weight[0], prune_bias)
)
def prune(self) -> None:
r"""
This function will FX symbolically trace the model and then find instances of the patterns
defined in self.patterns (by default SUPPORTED_STRUCTURED_PRUNING_PATTERNS ).
For each pattern, it will apply to corresponding conversion function, which will modify the output
and input size expected by the modules within the pattern
"""
self.traced = symbolic_trace(self.model)
modules = dict(self.traced.named_modules())
# Right now we check for matches simply by iterating across all the patterns
# if this is slow we can store patterns in a trie-structure and modify this code for faster lookup
for node in self.traced.graph.nodes:
for pattern, convert_fn in self.patterns.items():
matched = apply_match(modules, pattern, node, [])
if matched is None:
continue
first_module = modules.get(node.target)
# check if first module exists and has appropriate parameterization, otherwise skip
if (
first_module is not None
and parametrize.is_parametrized(first_module)
and module_contains_param(first_module, FakeStructuredSparsity)
):
convert_block = []
for node in matched:
if node.op == "call_module":
convert_block.append(modules.get(node.target))
elif node.op == "call_function":
convert_block.append(node.target)
convert_fn(*convert_block)
for module in self.traced.modules():
if module_contains_param(module, FakeStructuredSparsity):
raise Exception( # noqa: TRY002
f"Error: {module} still contains FakeStructuredSparsity parametrizations!"
)
self.traced.graph.lint()
self.traced.recompile()
return self.traced # type: ignore[return-value]
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