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# pylint: skip-file
"""Top level API for Adaptive Rounding - Post-Training Quantization (PTQ)"""
import copy
from contextlib import contextmanager
import os
import tempfile
import json
from typing import Tuple, Dict, List, Callable, Collection
from onnx import onnx_pb
from onnxruntime.quantization.onnx_quantizer import ONNXModel
from tqdm import tqdm
import numpy as np
# Import AIMET specific modules
from aimet_common import quantsim
from aimet_common.utils import AimetLogger, deprecated
from aimet_common.defs import QuantScheme, QuantizationDataType, qtype
from aimet_onnx.adaround.adaround_loss import AdaroundHyperParameters
from aimet_onnx.adaround.adaround_tensor_quantizer import AdaroundTensorQuantizer
from aimet_onnx.quantsim import QuantizationSimModel
from aimet_onnx.qc_quantize_op import OpMode
from aimet_onnx.meta.utils import get_module_act_func_pair, get_ordered_ops
from aimet_onnx.meta.connectedgraph import ConnectedGraph
from aimet_onnx import utils
from aimet_onnx.adaround.adaround_optimizer import AdaroundOptimizer
from aimet_onnx.adaround.utils import ModelData, ModuleInfo
logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Quant)
# The following modules with weights are supported by Adaround
AdaroundSupportedModules = ["Conv", "ConvTranspose", "MatMul", "Gemm"]
[docs]
def apply_adaround(
sim: QuantizationSimModel,
inputs: Collection[Dict[str, np.ndarray]],
num_iterations: int = 10000,
):
"""
Optimizes the rounding direction of weights in the QuantizationSimModel to reduce quantization error.
After applying AdaRound to a QuantizationSimModel object, the quantization encodings will be frozen
for optimized weights and the sim model will contain updated weight tensors.
Args:
sim (QuantizationSimModel): Calibrated QuantizationSimModel instance to optimize
inputs (Collection[Dict[str, np.ndarray]]): The set of input samples to use during optimization.
num_iterations (int): Number of optimization steps to take for each layer. Recommended value is
10K for weight bitwidths >= 8-bits, 15K for weight bitwidths < 8 bits.
"""
parameters = AdaroundParameters(
inputs,
len(inputs),
num_iterations,
AdaroundParameters.DEFAULT_REG_PARAM,
AdaroundParameters.DEFAULT_BETA_RANGE,
AdaroundParameters.DEFAULT_WARM_START,
)
module_act_func_pair = get_module_act_func_pair(sim.connected_graph)
# TODO: Refactor AdaRound to remove the need for separate unquantized model
model = copy.deepcopy(sim.model.model)
model = QuantizationSimModel.remove_quantizers(model)
with utils.disable_quantizers(sim, set(sim.activation_names)):
Adaround._adaround_model(
ONNXModel(model),
sim,
module_act_func_pair,
parameters,
use_cuda="CUDAExecutionProvider" in sim.session.get_providers(),
user_onnx_libs=sim._user_onnx_libs,
device=int(
sim.session.get_provider_options()
.get("CUDAExecutionProvider", {})
.get("device_id", "0")
),
)
# Re-build session since weights have been updated
sim._rebuild_session()
class AdaroundParameters:
"""
Configuration parameters for Adaround
"""
DEFAULT_REG_PARAM: float = 0.01
DEFAULT_BETA_RANGE: Tuple = (20, 2)
DEFAULT_WARM_START: float = 0.2
def __init__(
self,
data_loader,
num_batches: int,
default_num_iterations: int = None,
default_reg_param: float = DEFAULT_REG_PARAM,
default_beta_range: Tuple = DEFAULT_BETA_RANGE,
default_warm_start: float = DEFAULT_WARM_START,
forward_fn: Callable = None,
forward_pass_callback_args=None,
):
"""
:param data_loader: Data loader
:param num_batches: Number of batches to be used for Adaround.
A commonly recommended value for this parameter is the smaller value among (1) len(data_loader) and (2) ceil(2000/batch_size)
:param default_num_iterations: Number of iterations to adaround each layer.
The default value is 10K for models with 8- or higher bit weights, and 15K for models with lower than 8 bit weights.
:param default_reg_param: Regularization parameter, trading off between rounding loss vs reconstruction loss.
Default 0.01
:param default_beta_range: Start and stop beta parameter for annealing of rounding loss (start_beta, end_beta).
Default (20, 2)
:param default_warm_start: warm up period, during which rounding loss has zero effect. Default 20% (0.2)
:param forward_fn: Function to compute encodings for sim
:param forward_pass_callback_args: These argument(s) are passed to the forward_pass_callback as-is. Up to
the user to determine the type of this parameter. E.g. could be simply an integer representing the number
of data samples to use. Or could be a tuple of parameters or an object representing something more complex.
If set to None, forward_pass_callback will be invoked with no parameters.
"""
if len(data_loader) < num_batches:
raise ValueError(
f"Can not fetch {num_batches} batches from "
f"a data loader of length {len(data_loader)}."
)
self.data_loader = data_loader
self.num_batches = num_batches
self.num_iterations = default_num_iterations
self.reg_param = default_reg_param
self.beta_range = default_beta_range
self.warm_start = default_warm_start
self.forward_fn = forward_fn
self.forward_pass_callback_args = forward_pass_callback_args
class Adaround:
"""
Weight-rounding mechanism for Post Training Quantization (PTQ)
"""
@classmethod
@deprecated(f"Use `aimet_onnx.apply_adaround` instead")
def apply_adaround(
cls,
model: onnx_pb.ModelProto,
params: AdaroundParameters,
path: str,
filename_prefix: str,
default_param_bw: int = 4,
param_bw_override_list: List[Tuple[str, int]] = None,
ignore_quant_ops_list: List[str] = None,
default_quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
default_config_file: str = None,
use_cuda: bool = False,
device: int = 0,
user_onnx_libs: List[str] = None,
) -> onnx_pb.ModelProto:
"""
Returns model with optimized weight rounding of every module (Conv and Linear) and also saves the
corresponding quantization encodings to a separate JSON-formatted file that can then be imported by
QuantSim for inference or QAT
:param model: Model to Adaround
:param params: Parameters for Adaround
:param path: path where to store parameter encodings
:param filename_prefix: Prefix to use for filename of the encodings file
:param default_param_bw: Default bitwidth (4-31) to use for quantizing layer parameters
:param param_bw_override_list: List of Tuples. Each Tuple is a param name and the corresponding parameter bitwidth
to be used for that param.
:param ignore_quant_ops_list: Ops listed here are skipped during quantization needed for AdaRounding. Do not
specify Conv and Linear modules in this list. Doing so, will affect accuracy.
:param default_quant_scheme: Quantization scheme. Supported options are using Quant Scheme Enum
QuantScheme.post_training_tf or QuantScheme.post_training_tf_enhanced
:param default_config_file: Default configuration file for model quantizers
:param use_cuda: If we should use cuda
:param device: CUDA device ID
:param user_onnx_libs: List of paths to all compiled ONNX custom ops libraries
:return: Model with Adarounded weights and saves corresponding parameter encodings JSON file at provided path
"""
# pylint: disable=too-many-arguments
# Create Quant sim with given parameters
if not isinstance(model, ONNXModel):
model = ONNXModel(model)
quant_sim = QuantizationSimModel(
copy.deepcopy(model),
quant_scheme=default_quant_scheme,
param_type=qtype.int(default_param_bw),
config_file=default_config_file,
user_onnx_libs=user_onnx_libs,
use_cuda=use_cuda,
)
# For the params in the param_bw_override_list, override the default parameter bitwidths in the QuantSim
if param_bw_override_list:
cls._override_param_bitwidth(quant_sim, param_bw_override_list)
if ignore_quant_ops_list:
cls._exclude_modules(quant_sim, ignore_quant_ops_list)
# Compute only param encodings
cls._compute_param_encodings(quant_sim, params)
return cls._apply_adaround(
quant_sim,
model,
params,
path,
filename_prefix,
use_cuda,
device,
user_onnx_libs,
)
@classmethod
def _apply_adaround(
cls,
quant_sim: QuantizationSimModel,
model: onnx_pb.ModelProto,
params: AdaroundParameters,
path: str,
filename_prefix: str,
use_cuda: bool = False,
device: int = 0,
user_onnx_libs: List[str] = None,
) -> onnx_pb.ModelProto:
"""
Returns model with optimized weight rounding of every module (Conv and Linear) and also saves the
corresponding quantization encodings to a separate JSON-formatted file that can then be imported by
QuantSim for inference or QAT
:param quant_sim: QuantizationSimModel object to optimize weight rounding.
The activation quantizers are expected to have been disabled.
:param model: Original fp32 model from which quant_sim was created.
:param params: Parameters for Adaround
:param path: path where to store parameter encodings
:param filename_prefix: Prefix to use for filename of the encodings file
:param use_cuda: If we should use cuda
:param device: CUDA device ID
:param user_onnx_libs: List of paths to all compiled ONNX custom ops libraries
:return: Model with Adarounded weights and saves corresponding parameter encodings JSON file at provided path
"""
# Sanity check: All the input/output quantizers should be disabled
for quantizer_name in quant_sim.activation_names:
assert not quant_sim.qc_quantize_op_dict[quantizer_name].enabled
# Get the module - activation function pair using ConnectedGraph
module_act_func_pair = get_module_act_func_pair(ConnectedGraph(model))
cls._adaround_model(
model,
quant_sim,
module_act_func_pair,
params,
use_cuda,
device,
user_onnx_libs,
)
# Export quantization encodings to JSON-formatted file
cls._export_encodings_to_json(path, filename_prefix, quant_sim)
adaround_model = quant_sim.remove_quantizers(quant_sim.model)
logger.info("Completed Adarounding Model")
return adaround_model
@classmethod
def _adaround_model(
cls,
model: onnx_pb.ModelProto,
quant_sim: QuantizationSimModel,
module_act_func_pair: Dict,
params: AdaroundParameters,
use_cuda: bool = False,
device: int = 0,
user_onnx_libs: List[str] = None,
):
"""
Optimize weight rounding of every module (AdaroundSupportedModules) of model in sequential manner
based on occurrence
:param model: Original fp32 model from which quant_sim was created.
:param quant_sim: QuantizationSimModel object to optimize weight rounding.
The activation quantizers are expected to have been disabled.
:param module_act_func_pair: Dictionary of module to immediate following activation function
:param params: Adaround parameters
:param use_cuda: If we should use cuda
:param device: CUDA device ID
:param user_onnx_libs: List of paths to all compiled ONNX custom ops libraries
"""
# pylint: disable=too-many-locals, protected-access
num_iterations = params.num_iterations
if num_iterations is None:
lowest_weight_bw = 32
for param_name in quant_sim.param_names:
quantizer = quant_sim.qc_quantize_op_dict[param_name]
if (
quantizer.enabled
and quantizer.data_type == QuantizationDataType.int
):
lowest_weight_bw = min(lowest_weight_bw, quantizer.bitwidth)
# If the lowest wegith bitwidth is < 8, then set num_iterations to 15K by default
if lowest_weight_bw < 8:
num_iterations = 15000
else:
num_iterations = 10000
with tempfile.TemporaryDirectory() as tmp_dir:
# Cache model input data to temporary directory
cached_dataset = utils.CachedDataset(
params.data_loader, params.num_batches, tmp_dir
)
# Optimization Hyper parameters
opt_params = AdaroundHyperParameters(
num_iterations, params.reg_param, params.beta_range, params.warm_start
)
param_to_tensor_quantizer_dict = (
Adaround._create_param_to_tensor_quantizer_dict(quant_sim)
)
model_data = ModelData(model.model)
quantized_layer_to_input_tensor_name = (
Adaround._get_quantized_layer_input_tensor_name(quant_sim)
)
# AdaRound must be applied to modules in the order of occurrence
modules = get_ordered_ops(model)
for module in tqdm(modules):
name = module.name
module_info = model_data.module_to_info[name]
if cls._is_supported_layer_type(module_info):
# Get module's next following activation function
act_func = module_act_func_pair[name]
quantized_input_name = quantized_layer_to_input_tensor_name[name]
logger.info(
"Started Optimizing weight rounding of module: %s", name
)
AdaroundOptimizer.adaround_module(
module_info,
quantized_input_name,
model,
quant_sim.model,
act_func,
cached_dataset,
opt_params,
param_to_tensor_quantizer_dict,
use_cuda,
device,
user_onnx_libs,
)
# Freeze quantizer's encodings
weight_name = module_info.params["weight"].name
quant_sim.qc_quantize_op_dict[weight_name].freeze_encodings()
@classmethod
def _is_supported_layer_type(cls, module_info: ModuleInfo):
if not module_info.type in AdaroundSupportedModules:
return False
if not "weight" in module_info.params:
return False
if module_info.type in ("Conv", "ConvTranspose"):
# Only 2d conv/convtranspose is supported
return len(module_info.params["weight"].shape) == 4
return True
@staticmethod
def _compute_param_encodings(
quant_sim: QuantizationSimModel, params: AdaroundParameters
):
"""
Compute encodings for parameters, needed for initializing Adaround quantizers
:param quant_sim: Quant sim
:param params: Adaround params
"""
for op_name, qc_op in quant_sim.qc_quantize_op_dict.items():
if op_name in quant_sim.activation_names:
qc_op.enabled = False
else:
qc_op.op_mode = OpMode.oneShotQuantizeDequantize
params.forward_fn(quant_sim.session, params.forward_pass_callback_args)
for op_name, qc_op in quant_sim.qc_quantize_op_dict.items():
if op_name in quant_sim.param_names:
qc_op.compute_encodings()
qc_op.op_mode = OpMode.quantizeDequantize
@staticmethod
def _create_param_to_tensor_quantizer_dict(
quant_sim: QuantizationSimModel,
) -> Dict[str, AdaroundTensorQuantizer]:
"""
Create Adaround tensor quantizers for weight tensor
:param quant_sim: Quant sim
:return: Dict of param name to AdaroundTensorQuantizer
"""
param_to_tq_dict = {}
for param_name in quant_sim.param_names:
quantizer = quant_sim.qc_quantize_op_dict[param_name]
ch_axis = -1
if quantizer.quant_info.usePerChannelMode:
ch_axis = quantizer.quant_info.channelAxis
adaround_quantizer = AdaroundTensorQuantizer(
quantizer.bitwidth,
"Adaptive",
quantizer.quant_scheme,
quantizer.use_symmetric_encodings,
quantizer.enabled,
ch_axis,
)
adaround_quantizer.use_strict_symmetric = quantizer.use_strict_symmetric
adaround_quantizer.use_unsigned_symmetric = quantizer.use_unsigned_symmetric
# Set the encodings and replace by Adaround tensor quantizer
adaround_quantizer.encoding = quantizer.encodings
param_to_tq_dict[param_name] = adaround_quantizer
return param_to_tq_dict
@classmethod
def _export_encodings_to_json(
cls, path: str, filename_prefix: str, quant_sim: QuantizationSimModel
):
"""
Save Adadrounded module's parameter encodings to JSON file
:param path: path where to store param encodings
:param filename_prefix: filename to store exported weight encodings in JSON format
:param quant_sim: QunatSim that contains the model and Adaround tensor quantizers
"""
# pylint: disable=protected-access
param_encodings = quant_sim._get_encodings(
quant_sim.param_names, quantsim.encoding_version
)
# export encodings to JSON file
os.makedirs(os.path.abspath(path), exist_ok=True)
encoding_file_path = os.path.join(path, filename_prefix + ".encodings")
with open(encoding_file_path, "w") as encoding_fp:
json.dump(param_encodings, encoding_fp, sort_keys=True, indent=4)
@staticmethod
def _override_param_bitwidth(
quant_sim: QuantizationSimModel, param_bw_override_list: List[Tuple[str, int]]
):
"""
For the QuantSim, for the list of modules in the param_bw_override_list,
overrides the default parameter bitwidths with the provided bitwidth.
:param quant_sim: The QuantSim that was created using a deepcopy of the original model.
:param param_bw_override_list: List of Tuples. Each Tuple is a param name and the corresponding parameter bitwidth
to be used for that param.
"""
# For the params specified in the param_bw_override_list, set the weight quantizer bitwidth
for param_name, bw in param_bw_override_list:
quant_sim.qc_quantize_op_dict[param_name] = bw
@classmethod
def _exclude_modules(
cls, quant_sim: QuantizationSimModel, ignore_quant_ops_list: List[str]
):
"""
For the modules mentioned in the ignore_quant_ops_list, remove the corresponding quant wrappers from the
quantSim and excludes modules from adaround optimization.
:param model: The original model
:param quant_sim: The QuantSim that was created using a deepcopy of the original model.
:param ignore_quant_ops_list: The list of quantizers for which the Quantization wrappers are removed from the
QuantSim object.
"""
@staticmethod
def _get_quantized_layer_input_tensor_name(sim):
quantized_layer_to_input_tensor_name = {}
for node in sim.model.model.graph.node:
if node.op_type in AdaroundSupportedModules:
quantized_layer_to_input_tensor_name[node.name] = node.input[0]
return quantized_layer_to_input_tensor_name