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Unverified Commit e10fc05d authored by chenbohua3's avatar chenbohua3 Committed by GitHub
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[model compression] reorganize quantizer files (#4341)

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docs/en_US/Compression/CompressionReference.rst
View file @ e10fc05d
...@@ -96,16 +96,22 @@ Pruners ...@@ -96,16 +96,22 @@ Pruners
Quantizers Quantizers
^^^^^^^^^^ ^^^^^^^^^^
.. autoclass:: nni.algorithms.compression.pytorch.quantization.quantizers.NaiveQuantizer .. autoclass:: nni.algorithms.compression.pytorch.quantization.NaiveQuantizer
:members: :members:
.. autoclass:: nni.algorithms.compression.pytorch.quantization.quantizers.QAT_Quantizer .. autoclass:: nni.algorithms.compression.pytorch.quantization.QAT_Quantizer
:members: :members:
.. autoclass:: nni.algorithms.compression.pytorch.quantization.quantizers.DoReFaQuantizer .. autoclass:: nni.algorithms.compression.pytorch.quantization.DoReFaQuantizer
:members: :members:
.. autoclass:: nni.algorithms.compression.pytorch.quantization.quantizers.BNNQuantizer .. autoclass:: nni.algorithms.compression.pytorch.quantization.BNNQuantizer
:members:
.. autoclass:: nni.algorithms.compression.pytorch.quantization.LsqQuantizer
:members:
.. autoclass:: nni.algorithms.compression.pytorch.quantization.ObserverQuantizer
:members: :members:
Model Speedup Model Speedup
......
nni/algorithms/compression/pytorch/auto_compress/constants.py
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...@@ -3,7 +3,7 @@ ...@@ -3,7 +3,7 @@
from ..pruning import LevelPruner, SlimPruner, L1FilterPruner, L2FilterPruner, FPGMPruner, TaylorFOWeightFilterPruner, \ from ..pruning import LevelPruner, SlimPruner, L1FilterPruner, L2FilterPruner, FPGMPruner, TaylorFOWeightFilterPruner, \
ActivationAPoZRankFilterPruner, ActivationMeanRankFilterPruner ActivationAPoZRankFilterPruner, ActivationMeanRankFilterPruner
from ..quantization.quantizers import NaiveQuantizer, QAT_Quantizer, DoReFaQuantizer, BNNQuantizer from ..quantization import NaiveQuantizer, QAT_Quantizer, DoReFaQuantizer, BNNQuantizer
PRUNER_DICT = { PRUNER_DICT = {
......
nni/algorithms/compression/pytorch/quantization/__init__.py
View file @ e10fc05d
# Copyright (c) Microsoft Corporation. # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license. # Licensed under the MIT license.
from .quantizers import * from .bnn_quantizer import BNNQuantizer
from .dorefa_quantizer import DoReFaQuantizer
from .lsq_quantizer import LsqQuantizer
from .native_quantizer import NaiveQuantizer
from .observer_quantizer import ObserverQuantizer
from .qat_quantizer import QAT_Quantizer
__all__ = ['NaiveQuantizer', 'QAT_Quantizer', 'DoReFaQuantizer', 'BNNQuantizer', 'LsqQuantizer', 'ObserverQuantizer']
nni/algorithms/compression/pytorch/quantization/bnn_quantizer.py 0 → 100644
View file @ e10fc05d
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import logging
import torch
from schema import Schema, And, Or, Optional
from nni.compression.pytorch.utils.config_validation import QuantizerSchema
from nni.compression.pytorch.compressor import Quantizer, QuantGrad
from nni.compression.pytorch.quantization.literal import QuantType
from nni.compression.pytorch.quantization.utils import get_bits_length
logger = logging.getLogger(__name__)
class ClipGrad(QuantGrad):
@staticmethod
def quant_backward(tensor, grad_output, quant_type, scale, zero_point, qmin, qmax):
if quant_type == QuantType.OUTPUT:
grad_output[torch.abs(tensor) > 1] = 0
return grad_output
class BNNQuantizer(Quantizer):
"""Binarized Neural Networks, as defined in:
Binarized Neural Networks: Training Deep Neural Networks with Weights and Outputs Constrained to +1 or -1
(https://arxiv.org/abs/1602.02830)
"""
def __init__(self, model, config_list, optimizer):
assert isinstance(optimizer, torch.optim.Optimizer), "unrecognized optimizer type"
super().__init__(model, config_list, optimizer)
device = next(model.parameters()).device
self.quant_grad = ClipGrad.apply
modules_to_compress = self.get_modules_to_compress()
for layer, config in modules_to_compress:
if "weight" in config.get("quant_types", []):
weight_bits = get_bits_length(config, 'weight')
layer.module.register_buffer('weight_bits', torch.Tensor([int(weight_bits)]))
self.bound_model.to(device)
def _del_simulated_attr(self, module):
"""
delete redundant parameters in quantize module
"""
del_attr_list = ['old_weight', 'weight_bits']
for attr in del_attr_list:
if hasattr(module, attr):
delattr(module, attr)
def validate_config(self, model, config_list):
"""
Parameters
----------
model : torch.nn.Module
Model to be pruned
config_list : list of dict
List of configurations
"""
schema = QuantizerSchema([{
Optional('quant_types'): Schema([lambda x: x in ['weight', 'output']]),
Optional('quant_bits'): Or(And(int, lambda n: 0 < n < 32), Schema({
Optional('weight'): And(int, lambda n: 0 < n < 32),
Optional('output'): And(int, lambda n: 0 < n < 32),
})),
Optional('op_types'): [str],
Optional('op_names'): [str],
Optional('exclude'): bool
}], model, logger)
schema.validate(config_list)
def quantize_weight(self, wrapper, **kwargs):
weight = wrapper.module.weight
weight = torch.sign(weight)
# remove zeros
weight[weight == 0] = 1
wrapper.module.weight = weight
wrapper.module.weight_bits = torch.Tensor([1.0])
return weight
def quantize_output(self, output, wrapper, **kwargs):
out = torch.sign(output)
# remove zeros
out[out == 0] = 1
return out
def export_model(self, model_path, calibration_path=None, onnx_path=None, input_shape=None, device=None):
"""
Export quantized model weights and calibration parameters(optional)
Parameters
----------
model_path : str
path to save quantized model weight
calibration_path : str
(optional) path to save quantize parameters after calibration
onnx_path : str
(optional) path to save onnx model
input_shape : list or tuple
input shape to onnx model
device : torch.device
device of the model, used to place the dummy input tensor for exporting onnx file.
the tensor is placed on cpu if ```device``` is None
Returns
-------
Dict
"""
assert model_path is not None, 'model_path must be specified'
self._unwrap_model()
calibration_config = {}
for name, module in self.bound_model.named_modules():
if hasattr(module, 'weight_bits'):
calibration_config[name] = {}
calibration_config[name]['weight_bits'] = int(module.weight_bits)
self._del_simulated_attr(module)
self.export_model_save(self.bound_model, model_path, calibration_config, calibration_path, onnx_path, input_shape, device)
return calibration_config
nni/algorithms/compression/pytorch/quantization/dorefa_quantizer.py 0 → 100644
View file @ e10fc05d
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import logging
import torch
from schema import Schema, And, Or, Optional
from nni.compression.pytorch.utils.config_validation import QuantizerSchema
from nni.compression.pytorch.compressor import Quantizer
from nni.compression.pytorch.quantization.utils import get_bits_length
logger = logging.getLogger(__name__)
class DoReFaQuantizer(Quantizer):
"""Quantizer using the DoReFa scheme, as defined in:
Zhou et al., DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
(https://arxiv.org/abs/1606.06160)
"""
def __init__(self, model, config_list, optimizer):
assert isinstance(optimizer, torch.optim.Optimizer), "unrecognized optimizer type"
super().__init__(model, config_list, optimizer)
device = next(model.parameters()).device
modules_to_compress = self.get_modules_to_compress()
for layer, config in modules_to_compress:
if "weight" in config.get("quant_types", []):
weight_bits = get_bits_length(config, 'weight')
layer.module.register_buffer('weight_bits', torch.Tensor([int(weight_bits)]))
self.bound_model.to(device)
def _del_simulated_attr(self, module):
"""
delete redundant parameters in quantize module
"""
del_attr_list = ['old_weight', 'weight_bits']
for attr in del_attr_list:
if hasattr(module, attr):
delattr(module, attr)
def validate_config(self, model, config_list):
"""
Parameters
----------
model : torch.nn.Module
Model to be pruned
config_list : list of dict
List of configurations
"""
schema = QuantizerSchema([{
Optional('quant_types'): Schema([lambda x: x in ['weight']]),
Optional('quant_bits'): Or(And(int, lambda n: 0 < n < 32), Schema({
Optional('weight'): And(int, lambda n: 0 < n < 32)
})),
Optional('op_types'): [str],
Optional('op_names'): [str],
Optional('exclude'): bool
}], model, logger)
schema.validate(config_list)
def quantize_weight(self, wrapper, **kwargs):
weight = wrapper.module.weight
weight_bits = int(wrapper.module.weight_bits)
weight = weight.tanh()
weight = weight / (2 * weight.abs().max()) + 0.5
weight = self.quantize(weight, weight_bits)
weight = 2 * weight - 1
wrapper.module.weight = weight
# wrapper.module.weight.data = weight
return weight
def quantize(self, input_ri, q_bits):
scale = pow(2, q_bits) - 1
output = torch.round(input_ri * scale) / scale
return output
def export_model(self, model_path, calibration_path=None, onnx_path=None, input_shape=None, device=None):
"""
Export quantized model weights and calibration parameters(optional)
Parameters
----------
model_path : str
path to save quantized model weight
calibration_path : str
(optional) path to save quantize parameters after calibration
onnx_path : str
(optional) path to save onnx model
input_shape : list or tuple
input shape to onnx model
device : torch.device
device of the model, used to place the dummy input tensor for exporting onnx file.
the tensor is placed on cpu if ```device``` is None
Returns
-------
Dict
"""
assert model_path is not None, 'model_path must be specified'
self._unwrap_model()
calibration_config = {}
for name, module in self.bound_model.named_modules():
if hasattr(module, 'weight_bits'):
calibration_config[name] = {}
calibration_config[name]['weight_bits'] = int(module.weight_bits)
self._del_simulated_attr(module)
self.export_model_save(self.bound_model, model_path, calibration_config, calibration_path, onnx_path, input_shape, device)
return calibration_config
nni/algorithms/compression/pytorch/quantization/lsq_quantizer.py 0 → 100644
View file @ e10fc05d
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import logging
import torch
from nni.compression.pytorch.compressor import BN_FOLD_TAG, Quantizer, QuantForward
from nni.compression.pytorch.quantization.utils import get_bits_length
logger = logging.getLogger(__name__)
class LsqQuantizer(Quantizer):
"""Quantizer defined in:
Learned Step Size Quantization (ICLR 2020)
https://arxiv.org/pdf/1902.08153.pdf
"""
def __init__(self, model, config_list, optimizer, dummy_input=None):
"""
Parameters
----------
model : torch.nn.Module
the model to be quantized
config_list : list of dict
list of configurations for quantization
supported keys for dict:
- quant_types : list of string
type of quantization you want to apply, currently support 'weight', 'input', 'output'
- quant_bits : int or dict of {str : int}
bits length of quantization, key is the quantization type, value is the length, eg. {'weight': 8},
when the type is int, all quantization types share same bits length
- quant_start_step : int
disable quantization until model are run by certain number of steps, this allows the network to enter a more stable
state where output quantization ranges do not exclude a significant fraction of values, default value is 0
- op_types : list of string
types of nn.module you want to apply quantization, eg. 'Conv2d'
- dummy_input : tuple of tensor
inputs to the model, which are used to get the graph of the module. The graph is used to find
Conv-Bn patterns. And then the batch normalization folding would be enabled. If dummy_input is not
given, the batch normalization folding would be disabled.
"""
assert isinstance(optimizer, torch.optim.Optimizer), "unrecognized optimizer type"
super().__init__(model, config_list, optimizer, dummy_input)
device = next(model.parameters()).device
self.quant_grad = QuantForward()
modules_to_compress = self.get_modules_to_compress()
self.bound_model.register_buffer("steps", torch.Tensor([1]))
for layer, config in modules_to_compress:
if "weight" in config.get("quant_types", []):
layer.module.register_parameter("weight_scale", torch.nn.Parameter(torch.Tensor([1.0])))
# todo: support per-channel quantization for weight since TensorRT use it for conv weight
weight_bits = get_bits_length(config, "weight")
layer.module.register_buffer('weight_bits', torch.Tensor([weight_bits]))
qmax = 2 ** (weight_bits - 1) - 1
qmin = -2 ** (weight_bits - 1)
init_weight_scale = layer.module.weight.data.detach().abs().mean() * 2 / (qmax ** 0.5)
layer.module.weight_scale = torch.nn.Parameter(init_weight_scale)
layer.module.weight_qmax = qmax
layer.module.weight_qmin = qmin
self.optimizer.add_param_group({"params": layer.module.weight_scale})
if "output" in config.get("quant_types", []):
# scale of output will be initialized using the first batch data
layer.module.register_parameter("output_scale", torch.nn.Parameter(torch.Tensor([1.0])))
output_bits = get_bits_length(config, "output")
layer.module.register_buffer('output_bits', torch.Tensor([output_bits]))
qmax = 2 ** (output_bits - 1) - 1
qmin = -2 ** (output_bits - 1)
layer.module.output_qmax = qmax
layer.module.output_qmin = qmin
self.optimizer.add_param_group({"params": layer.module.output_scale})
if "input" in config.get("quant_types", []):
# scale of input will be initialized using the first batch data
layer.module.register_parameter("input_scale", torch.nn.Parameter(torch.Tensor([1.0])))
input_bits = get_bits_length(config, "input")
layer.module.register_buffer('input_bits', torch.Tensor([input_bits]))
qmax = 2 ** (input_bits - 1) - 1
qmin = -2 ** (input_bits - 1)
layer.module.input_qmax = qmax
layer.module.input_qmin = qmin
self.optimizer.add_param_group({"params": layer.module.input_scale})
self.bound_model.to(device)
@staticmethod
def grad_scale(x, scale):
"""
Used to scale the gradient. Give tensor `x`, we have `y=grad_scale(x, scale)=x` in the forward pass,
which means that this function will not change the value of `x`. In the backward pass, we have:
.. math:
\frac{\alpha_L}{\alpha_x}=\frac{\alpha_L}{\alpha_y}*\frac{\alpha_y}{\alpha_x}=sclae*\frac{\alpha_L}{\alpha_x}
This means that the origin gradient of x is scaled by a factor of `scale`. Applying this function
to a nn.Parameter will scale the gradient of it without changing its value.
"""
y = x
y_grad = x * scale
return (y - y_grad).detach() + y_grad
@staticmethod
def round_pass(x):
"""
A simple way to achieve STE operation.
"""
y = x.round()
y_grad = x
return (y - y_grad).detach() + y_grad
def quantize(self, x, scale, qmin, qmax):
grad_scale_factor = 1.0 / ((qmax * x.numel()) ** 0.5)
scale = self.grad_scale(scale, grad_scale_factor)
x = x / scale
x = torch.clamp(x, qmin, qmax)
x = self.round_pass(x)
x = x * scale
return x
def quantize_weight(self, wrapper, **kwargs):
module = wrapper.module
weight = wrapper.module.weight
# todo: add support for quantize bias. If we use TensorRT as backend, there is no need to quantize
# bias
weight = self.quantize(weight, module.weight_scale, module.weight_qmin, module.weight_qmax)
module.weight = weight
return weight
def quantize_output(self, output, wrapper, **kwargs):
module = wrapper.module
# initialize the scale
if self.bound_model.steps == 1:
qmax = module.output_qmax
init_oup_scale = output.data.detach().abs().mean() * 2 / (qmax ** 0.5)
module.output_scale.data = init_oup_scale
output = self.quantize(output, module.output_scale, module.output_qmin, module.output_qmax)
return output
def quantize_input(self, inputs, wrapper, **kwargs):
module = wrapper.module
# initialize the scale
if self.bound_model.steps == 1:
qmax = module.input_qmax
init_oup_scale = inputs.data.detach().abs().mean() * 2 / (qmax ** 0.5)
module.input_scale.data = init_oup_scale
inputs = self.quantize(inputs, module.input_scale, module.input_qmin, module.input_qmax)
return inputs
def load_calibration_config(self, calibration_config):
modules_to_compress = self.get_modules_to_compress()
for layer, _ in modules_to_compress:
name, module = layer.name, layer.module
if name not in calibration_config:
if hasattr(module, 'weight_bits') or hasattr(module, 'output_bits') or hasattr(module, 'input_bits'):
logger.warning(f"Can not find module {name}'s parameter in input config.")
continue
if hasattr(module, 'weight_bits'):
assert calibration_config[name]['weight_bits'] == int(module.weight_bits), f"weight bits of module {name} fail to match"
if hasattr(module, 'input_bits'):
assert calibration_config[name]['input_bits'] == int(module.input_bits), f"input bits of module {name} fail to match"
module.input_scale.data = torch.Tensor([float(calibration_config[name]['tracked_max_input'] / module.input_qmax)])
if hasattr(module, 'output_bits'):
assert calibration_config[name]['output_bits'] == int(module.output_bits), f"output bits of module {name} fail to match"
module.output_scale.data = torch.Tensor([float(calibration_config[name]['tracked_max_output'] / module.output_qmax)])
def export_model(self, model_path, calibration_path=None, onnx_path=None, input_shape=None, device=None):
"""
Export quantized model weights and calibration parameters(optional)
Parameters
----------
model_path : str
path to save quantized model weight
calibration_path : str
(optional) path to save quantize parameters after calibration
onnx_path : str
(optional) path to save onnx model
input_shape : list or tuple
input shape to onnx model
device : torch.device
device of the model, used to place the dummy input tensor for exporting onnx file.
the tensor is placed on cpu if ```device``` is None
Returns
-------
Dict
"""
assert model_path is not None, 'model_path must be specified'
self._unwrap_model()
calibration_config = {}
for name, module in self.bound_model.named_modules():
if hasattr(module, 'input_bits') or hasattr(module, 'weight_bits') or hasattr(module, 'output_bits'):
calibration_config[name] = {}
if hasattr(module, 'weight_bits'):
calibration_config[name]['weight_bits'] = int(module.weight_bits)
abs_max_input = float(module.input_scale * module.input_qmax)
calibration_config[name]['tracked_min_input'] = -abs_max_input
calibration_config[name]['tracked_max_input'] = abs_max_input
actual_weight = getattr(module, 'old_weight', None)
if actual_weight is None:
logger.warning("Can not recover weight for layer %s. "
"This may lead to a wrong accuracy performance on the backend.", name)
delattr(module, 'weight')
module.register_parameter('weight', actual_weight)
if hasattr(module, BN_FOLD_TAG):
actual_bias = getattr(module, 'old_bias', None)
delattr(module, 'bias')
if actual_bias is not None:
module.register_parameter('bias', actual_bias)
else:
setattr(module, 'bias', None)
if hasattr(module, 'input_bits'):
calibration_config[name]['input_bits'] = int(module.input_bits)
abs_max_input = float(module.input_scale * module.input_qmax)
calibration_config[name]['tracked_min_input'] = -abs_max_input
calibration_config[name]['tracked_max_input'] = abs_max_input
if hasattr(module, 'output_bits'):
calibration_config[name]['output_bits'] = int(module.output_bits)
abs_max_output = float(module.output_scale * module.output_qmax)
calibration_config[name]['tracked_min_output'] = -abs_max_output
calibration_config[name]['tracked_max_output'] = abs_max_output
self._del_simulated_attr(module)
self.export_model_save(self.bound_model, model_path, calibration_config, calibration_path, onnx_path,
input_shape, device)
return calibration_config
def _del_simulated_attr(self, module):
"""
delete redundant parameters in quantize module
"""
del_attr_list = ['old_weight', 'tracked_min_input', 'tracked_max_input', 'tracked_min_output', \
'tracked_max_output', 'output_scale', 'input_scale', 'weight_scale','weight_bits', 'output_bits', 'input_bits', 'BN_FOLD_TAG']
for attr in del_attr_list:
if hasattr(module, attr):
delattr(module, attr)
def step_with_optimizer(self):
"""
override `compressor` `step` method, quantization only happens after certain number of steps
"""
self.bound_model.steps += 1
nni/algorithms/compression/pytorch/quantization/native_quantizer.py 0 → 100644
View file @ e10fc05d
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import logging
import torch
from schema import Or, Optional
from nni.compression.pytorch.utils.config_validation import QuantizerSchema
from nni.compression.pytorch.compressor import Quantizer
logger = logging.getLogger(__name__)
class NaiveQuantizer(Quantizer):
"""quantize weight to 8 bits
"""
def __init__(self, model, config_list, optimizer=None):
super().__init__(model, config_list, optimizer)
self.layer_scale = {}
def validate_config(self, model, config_list):
schema = QuantizerSchema([{
Optional('quant_types'): ['weight'],
Optional('quant_bits'): Or(8, {'weight': 8}),
Optional('op_types'): [str],
Optional('op_names'): [str],
Optional('exclude'): bool
}], model, logger)
schema.validate(config_list)
def quantize_weight(self, wrapper, **kwargs):
weight = wrapper.module.weight
new_scale = weight.abs().max() / 127
scale = max(self.layer_scale.get(wrapper.name, 0), new_scale)
self.layer_scale[wrapper.name] = scale
orig_type = weight.type() # TODO: user layer
weight = weight.div(scale).type(torch.int8).type(orig_type).mul(scale)
wrapper.module.weight = weight
return weight
nni/algorithms/compression/pytorch/quantization/observer_quantizer.py 0 → 100644
View file @ e10fc05d
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import logging
from collections import defaultdict
import torch
from schema import Schema, And, Or, Optional
from nni.compression.pytorch.utils.config_validation import QuantizerSchema
from nni.compression.pytorch.compressor import Quantizer, QuantForward
from nni.compression.pytorch.quantization.observers import default_weight_observer, default_histogram_observer
logger = logging.getLogger(__name__)
class ObserverQuantizer(Quantizer):
"""This quantizer uses observers to record weight/output statistics to get quantization information.
The whole process can be divided into three steps:
1. It will register observers to the place where quantization would happen (just like registering hooks).
2. The observers would record tensors' statistics during calibration.
3. Scale & zero point would be obtained after calibration.
Note that the observer type, tensor dtype and quantization qscheme are hard coded for now. Their customization
are under development and will be ready soon.
"""
def __init__(self, model, config_list, optimizer=None):
super().__init__(model, config_list, optimizer)
# NOTE: this quantizer is experimental for now. The dtype and qscheme of quantization
# is hard-coded.
# TODO:
# 1. support dtype and qscheme customization through config_list. Current settings:
# weight observer : per_tensor_symmetric, qint8
# output observer : per_tensor_affine, quint8, reduce_range=True
# 2. add more kinds of observers, such as Kullback-Leibler divergence.
# 3. add batch normalization folding
assert not model.training, "Currently the observer quantizer only works in evaluation mode."
self.quant_grad = QuantForward()
self.device = next(model.parameters()).device
modules_to_compress = self.get_modules_to_compress()
all_observers = defaultdict(dict)
weight_qmin, weight_qmax = -127, 127
output_qmin, output_qmax = 0, 127 # reduce_range is set to True
self.compressed = False
for layer, config in modules_to_compress:
layer_name = layer.name
module = layer.module
if "weight" in config.get("quant_types", []):
all_observers[layer_name]["weight"] = default_weight_observer()
setattr(module, "weight_qmax", weight_qmax)
setattr(module, "weight_qmin", weight_qmin)
if "input" in config.get("quant_types", []):
all_observers[layer_name]["input"] = default_histogram_observer()
setattr(module, "input_qmax", output_qmax)
setattr(module, "input_qmin", output_qmin)
if "output" in config.get("quant_types", []):
all_observers[layer_name]["output"] = default_histogram_observer()
setattr(module, "output_qmax", output_qmax)
setattr(module, "output_qmin", output_qmin)
self.all_observers = all_observers
self.bound_model.to(self.device)
def validate_config(self, model, config_list):
schema = QuantizerSchema([{
Optional('quant_types'): Schema([lambda x: x in ['weight', 'output', 'input']]),
Optional('quant_bits'): Or(And(int, lambda n: n == 8), Schema({
Optional('weight'): And(int, lambda n: n == 8),
Optional('output'): And(int, lambda n: n == 8),
Optional('input'): And(int, lambda n: n == 8),
})),
Optional('op_types'): [str],
Optional('op_names'): [str]
}], model, logger)
schema.validate(config_list)
def record(self, wrapper, quant_type, tensor):
name = wrapper.name
observer = self.all_observers[name][quant_type]
observer(tensor.cpu())
def calculate_qparams(self, name, quant_type):
observer = self.all_observers[name][quant_type]
scale, zero_point = observer.calculate_qparams()
return scale, zero_point
def _quantize(self, x, scale, zero_point, qmin, qmax):
x = x / scale + zero_point
x = torch.clamp(x, qmin, qmax)
x = torch.round(x)
x = (x - zero_point) * scale
return x
def quantize_input(self, inputs, wrapper, **kwargs):
if self.compressed:
module = wrapper.module
inputs = self._quantize(inputs,
module.input_scale,
module.input_zero_point,
module.input_qmin,
module.input_qmax)
else:
self.record(wrapper, 'input', inputs)
return inputs
def quantize_weight(self, wrapper, **kwargs):
# If ObserverQuantizer.compress is executed, the weight will be set to
# the Pseudo-quantized one. So there is no need to quantize it
if self.compressed:
return
weight = wrapper.module.weight
self.record(wrapper, 'weight', weight)
def quantize_output(self, output, wrapper, **kwargs):
if self.compressed:
module = wrapper.module
new_output = self._quantize(output,
module.output_scale,
module.output_zero_point,
module.output_qmin,
module.output_qmax)
else:
self.record(wrapper, 'output', output)
new_output = output
return new_output
def compress(self):
"""
Calculate quantization information of each tensor. Note that the inference of
the compressed model will no longer update the corresponding. Instead, the quantization
process will be simulated, which is used to test the accuracy of the quantization.
"""
modules_to_compress = self.get_modules_to_compress()
for layer, config in modules_to_compress:
module = layer.module
if "weight" in config.get("quant_types", []):
scale, zero_point = self.calculate_qparams(layer.name, 'weight')
module.register_buffer('weight_scale', scale.to(self.device))
module.register_buffer('weight_zero_point', zero_point.to(self.device))
weight = module.weight
quantized_weight = self._quantize(weight,
module.weight_scale,
module.weight_zero_point,
module.weight_qmin,
module.weight_qmax)
delattr(module, 'weight')
module.register_buffer('weight', quantized_weight)
if "input" in config.get("quant_types", []):
scale, zero_point = self.calculate_qparams(layer.name, 'input')
module.register_buffer('input_scale', scale.to(self.device))
module.register_buffer('input_zero_point', zero_point.to(self.device))
if "output" in config.get("quant_types", []):
scale, zero_point = self.calculate_qparams(layer.name, 'output')
module.register_buffer('output_scale', scale.to(self.device))
module.register_buffer('output_zero_point', zero_point.to(self.device))
self.compressed = True
super().compress()
def export_model(self, model_path, calibration_path=None, onnx_path=None, input_shape=None, device=None):
"""
Export quantized model weights and calibration parameters(optional)
Parameters
----------
model_path : str
path to save quantized model weight
calibration_path : str
(optional) path to save quantize parameters after calibration
onnx_path : str
(optional) path to save onnx model
input_shape : list or tuple
input shape to onnx model
device : torch.device
device of the model, used to place the dummy input tensor for exporting onnx file.
the tensor is placed on cpu if ```device``` is None
Returns
-------
Dict
"""
assert model_path is not None, 'model_path must be specified'
self._unwrap_model()
calibration_config = {}
for name, module in self.bound_model.named_modules():
if hasattr(module, 'weight_scale') or hasattr(module, 'input_scale') or hasattr(module, 'output_scale'):
calibration_config[name] = {}
if hasattr(module, 'weight_scale'):
calibration_config[name]['weight_bits'] = 8
val = float(module.weight_scale * module.weight_qmax)
calibration_config[name]['tracked_max_weight'] = val
calibration_config[name]['tracked_min_weight'] = -val
calibration_config[name]['tracked_qmin_weight'] = -127
calibration_config[name]['tracked_qmax_weight'] = 127
weight = module.weight
quantized_weight = self._quantize(weight,
module.weight_scale,
module.weight_zero_point,
module.weight_qmin,
module.weight_qmax)
delattr(module, 'weight')
module.register_parameter('weight', torch.nn.Parameter(quantized_weight))
# refactor these magic numbers when customizations of dtype and qscheme are ready.
if hasattr(module, 'input_scale'):
calibration_config[name]['input_bits'] = 8
max_input = float(module.input_scale * (module.input_qmax - module.input_zero_point))
min_input = float(module.input_scale * (module.input_qmin - module.input_zero_point))
calibration_config[name]['tracked_min_input'] = min_input
calibration_config[name]['tracked_max_input'] = max_input
calibration_config[name]['tracked_qmin_input'] = 0
calibration_config[name]['tracked_qmax_input'] = 127
if hasattr(module, 'output_scale'):
calibration_config[name]['output_bits'] = 8
max_input = float(module.output_scale * (module.output_qmax - module.output_zero_point))
min_input = float(module.output_scale * (module.output_qmin - module.output_zero_point))
calibration_config[name]['tracked_min_output'] = min_input
calibration_config[name]['tracked_max_output'] = max_input
calibration_config[name]['tracked_qmin_output'] = 0
calibration_config[name]['tracked_qmax_output'] = 127
self._del_simulated_attr(module)
self.export_model_save(self.bound_model, model_path, calibration_config, calibration_path, onnx_path,
input_shape, device)
return calibration_config
def _del_simulated_attr(self, module):
"""
delete redundant parameters in quantize module
"""
del_attr_list = ['old_weight', 'steps', 'weight_qmax', 'weight_qmin', 'input_qmax', 'input_qmin',
'output_qmax', 'output_qmin', 'weight_scale', 'weight_zero_point', 'input_scale',
'input_zero_point', 'output_scale', 'output_zero_point']
for attr in del_attr_list:
if hasattr(module, attr):
delattr(module, attr)
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