From 7827a43952ec8acd6776241cf3800705c06efbf1 Mon Sep 17 00:00:00 2001 From: Alessandro Pappalardo Date: Thu, 11 Jul 2019 15:05:55 +0100 Subject: [PATCH] Brevitas, first commit Signed-off-by: Alessandro Pappalardo --- LICENSE | 54 +- README.md | 47 +- .../function => brevitas}/__init__.py | 0 brevitas/core/__init__.py | 2 + brevitas/core/bit_width.py | 244 ++++++ brevitas/core/function_wrapper.py | 124 ++++ brevitas/core/quant.py | 245 ++++++ brevitas/core/restrict_val.py | 118 +++ brevitas/core/scaling.py | 210 ++++++ brevitas/core/stats.py | 265 +++++++ brevitas/function/__init__.py | 1 + brevitas/function/ops.py | 130 ++++ brevitas/loss/weighted_bit_width.py | 153 ++++ brevitas/nn/__init__.py | 5 + brevitas/nn/quant_accumulator.py | 120 +++ brevitas/nn/quant_activation.py | 239 ++++++ brevitas/nn/quant_avg_pool.py | 95 +++ brevitas/nn/quant_conv.py | 233 ++++++ brevitas/nn/quant_layer.py | 64 ++ brevitas/nn/quant_linear.py | 165 +++++ brevitas/nn/quant_recurrent.py | 109 +++ 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quantization/src/quantized_fused_rnn_cuda_wrapper.h delete mode 100644 quantization/src/quantized_fused_rnn_cuda_wrapper_preproc.h delete mode 100644 quantization/src/wrapper_helper.h delete mode 100644 quantization/utils/__init__.py delete mode 100644 quantization/utils/__init__.pyc delete mode 100644 quantization/utils/hls_utils.pyc delete mode 100644 quantization/utils/quantization_utils.pyc delete mode 100644 quantization/utils/rnn.py delete mode 100644 quantization/utils/rnn.pyc create mode 100644 setup.py delete mode 100644 xilinx/__init__.py delete mode 100644 xilinx/__init__.pyc delete mode 100644 xilinx/torch/__init__.py delete mode 100644 xilinx/torch/__init__.pyc delete mode 100644 xilinx/torch/_torch.so diff --git a/LICENSE b/LICENSE index b0a818b6c..a2a3263a5 100644 --- a/LICENSE +++ b/LICENSE @@ -1,7 +1,3 @@ -Components: pytorch - - -/* Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) Copyright (c) 2016- Facebook, Inc (Adam Paszke) Copyright (c) 2014- Facebook, Inc (Soumith Chintala) @@ -25,9 +21,9 @@ modification, are permitted provided that the following conditions are met: notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. -3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived +3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, + NEC Laboratories America and IDIAP Research Institute nor the names + of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" @@ -40,46 +36,4 @@ SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -## Mostly based on the Pytorch-Encoding source code, due MIT copyright below -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -## Created by: Hang Zhang -## ECE Department, Rutgers University -## Email: zhang.hang@rutgers.edu -## Copyright (c) 2017 -## -## This source code is licensed under the MIT-style license found below -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -# MIT License - -# Copyright (c) 2017 Hang Zhang - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - - -Copyright 2012-2014 Deepmind Technologies -Copyright 2018 Xilinx Inc -Copyright 2017 Hang Zhang -Copyright 2011-2013 NYU -Copyright 2014, 2016 Facebook Inc -Copyright 2001-2014 Idiap Research Institute -Copyright 2006-2012 NEC Laboratories America \ No newline at end of file +POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/README.md b/README.md index 7f492498b..2aa769ff3 100644 --- a/README.md +++ b/README.md @@ -1,35 +1,40 @@ -# Pytorch Quantization +# Brevitas -## Introduction - -This repository implements a set of quantization strategies to be applied to supported type of layers. +Brevitas is a Pytorch library for training-aware quantization. -The code originally started from the Pytorch and ATen implementation of a fused GRU/LSTM, extracted as a CFFI extension and expanded from there. +*Brevitas is currently in alpha stage and under active development. APIs might and probably will change. Documentation, examples, and pretrained models will be progressively released.* ## Requirements -Building currently requires an appropriate CUDA environment, but execution is supported on CPU as well. +* [Pytorch](https://pytorch.org) >= 1.1.0 -* Nvidia CUDA Toolkit (tested with CUDA 9.0) -* [Pytorch](https://pytorch.org) (tested with version 0.3.1) +## Introduction -## Installation +Brevitas implements a set of building blocks to model a reduced precision hardware data-path at training time. +While partially biased towards modelling dataflow-style, very low-precision implementations, the building blocks can be parametrized and assembled together to target all sorts of reduced precision hardware. -1. Run `python build.py` -2. Add current path to the python path: `EXPORT PYTHONPATH=/path/to/pytorch-quantization:PYTHONPATH` +The implementations tries to adhere to the following design principles: +- Idiomatic Pytorch, when possible. +- Modularity first, at the cost of some verbosity. +- Easily extendible. -## Usage +## Target audience +Brevitas is mainly targeted at researchers and practicioners in the fields of training for reduced precision inference. -The following quantization modes are implemented for weights: +The implementation is quite rich in options and allows for very fine grained control over the trained model. However, compared to other software solutions in this space, the burden of correctly modelling the target data-path is currently placed on the user. -* FP: full-precision, no quantization performed. -* SIGNED_FIXED_UNIT: fixed point quantization between [-1,1). +## Features -The following quantization modes are implemented for activations: +Soon. + +## Installation + +Soon. + +## Usage +Soon. -* FP: full-precision, no quantization performed. -* SIGNED_FIXED_UNIT: fixed point quantization between [-1,1). +## Author -The following quantized layers are implemented: +Alessandro Pappalardo @ Xilinx Research Labs. -* QuantizedLinear -* QuantizedLSTM \ No newline at end of file +## diff --git a/quantization/function/__init__.py b/brevitas/__init__.py similarity index 100% rename from quantization/function/__init__.py rename to brevitas/__init__.py diff --git a/brevitas/core/__init__.py b/brevitas/core/__init__.py new file mode 100644 index 000000000..22ee5b382 --- /dev/null +++ b/brevitas/core/__init__.py @@ -0,0 +1,2 @@ +ZERO_HW_SENTINEL_NAME = 'zero_hw_sentinel' +ZERO_HW_SENTINEL_VALUE = 0.0 \ No newline at end of file diff --git a/brevitas/core/bit_width.py b/brevitas/core/bit_width.py new file mode 100644 index 000000000..9ee513e24 --- /dev/null +++ b/brevitas/core/bit_width.py @@ -0,0 +1,244 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from typing import Optional +from enum import auto + +import torch +from torch import Tensor +from torch.nn import Parameter + +from brevitas.utils.python_utils import AutoName +from brevitas.function.ops import tensor_clamp_ste, tensor_clamp +from .restrict_val import RestrictValueOpImplType, RestrictValueType, RestrictValue, FloatToIntImplType + + +MIN_INT_BIT_WIDTH = 2 +NON_ZERO_EPSILON = 1e-6 +REMOVE_ZERO_BIT_WIDTH = 0.1 + + +class BitWidthImplType(AutoName): + CONST = auto() + PARAMETER = auto() + + +class ZeroLsbTruncBitWidth(torch.jit.ScriptModule): + + def forward(self, input_bit_width: Tensor, zero_hw_sentinel: Tensor): + return zero_hw_sentinel + + +class BitWidthConst(torch.jit.ScriptModule): + __constants__ = ['bit_width'] + + def __init__(self, bit_width_init: int, restrict_bit_width_type: RestrictValueType) -> None: + super(BitWidthConst, self).__init__() + + if restrict_bit_width_type != RestrictValueType.INT: + raise Exception("When bit width is predefined, it has to be an INT value.") + + self.bit_width = int(bit_width_init) + + @torch.jit.script_method + def forward(self, zero_hw_sentinel: Tensor) -> Tensor: + return self.bit_width + zero_hw_sentinel + + +class BitWidthParameter(torch.jit.ScriptModule): + __constants__ = ['bit_width_base', 'max_bit_width', 'override_pretrained'] + + def __init__(self, + bit_width_init: int, + min_overall_bit_width: Optional[int], + max_overall_bit_width: Optional[int], + restrict_bit_width_type: RestrictValueType, + override_pretrained: bool) -> None: + super(BitWidthParameter, self).__init__() + + if min_overall_bit_width is None: + min_overall_bit_width = MIN_INT_BIT_WIDTH + if not (restrict_bit_width_type == RestrictValueType.FP + or restrict_bit_width_type == RestrictValueType.INT + or restrict_bit_width_type == RestrictValueType.POWER_OF_TWO): + raise Exception("Restriction on bit width {} not supported".format(restrict_bit_width_type)) + if bit_width_init < MIN_INT_BIT_WIDTH or min_overall_bit_width < MIN_INT_BIT_WIDTH: + raise Exception("Int bit width has to be at least {}, instead is {}." + .format(MIN_INT_BIT_WIDTH, bit_width_init)) + + self.override_pretrained = override_pretrained + bit_width_init_op = RestrictValue.restrict_value_op(restrict_bit_width_type, + restrict_value_op_impl_type=RestrictValueOpImplType.MATH) + self.restrict_bit_width = RestrictValue(restrict_bit_width_type, + float_to_int_impl_type=FloatToIntImplType.ROUND) + self.bit_width_base = bit_width_init_op(min_overall_bit_width) + self.max_bit_width = bit_width_init_op(min_overall_bit_width) if max_overall_bit_width is not None else None + bit_width_offset_init = bit_width_init_op(bit_width_init) - self.bit_width_base + self.bit_width_offset = Parameter(torch.tensor(float(bit_width_offset_init))) + + @torch.jit.script_method + def forward(self, zero_hw_sentinel: Tensor) -> Tensor: + if self.max_bit_width is not None: + raise Exception("Not implemented yet.") + bit_width = torch.abs(self.bit_width_offset) + self.bit_width_base + bit_width = self.restrict_bit_width(bit_width) + return bit_width + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + super(BitWidthParameter, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + bit_width_offset_key = prefix + 'bit_width_offset' + if bit_width_offset_key in missing_keys: + missing_keys.remove(bit_width_offset_key) + if self.override_pretrained and bit_width_offset_key in state_dict: + del state_dict[bit_width_offset_key] + + +class RemoveBitwidthParameter(torch.jit.ScriptModule): + __constants__ = ['min_overall_bit_width', 'non_zero_epsilon', 'override_pretrained', 'remove_at_least_init_val'] + + def __init__(self, bit_width_to_remove, remove_at_least_init_val, restrict_bit_width_impl, override_pretrained): + super(RemoveBitwidthParameter, self).__init__() + + if bit_width_to_remove < 0: + raise Exception("Bit width to clamp has to be at least 0, instead is {}." + .format(bit_width_to_remove)) + elif bit_width_to_remove == 0: + bit_width_coeff_init = 1 / REMOVE_ZERO_BIT_WIDTH + else: + bit_width_coeff_init = 1 / bit_width_to_remove + self.bit_width_coeff = Parameter(torch.tensor(bit_width_coeff_init)) + self.restrict_bit_width_impl = restrict_bit_width_impl + self.non_zero_epsilon = NON_ZERO_EPSILON + self.override_pretrained = override_pretrained + self.remove_at_least_init_val = remove_at_least_init_val + + @torch.jit.script_method + def forward(self, zero_hw_sentinel) -> Tensor: + bit_width_to_remove = 1.0 / (self.non_zero_epsilon + torch.abs(self.bit_width_coeff)) + bit_width_to_remove = self.restrict_bit_width_impl(bit_width_to_remove) + return bit_width_to_remove + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + super(RemoveBitwidthParameter, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + bit_width_coeff_key = prefix + 'bit_width_coeff' + if bit_width_coeff_key in missing_keys: + missing_keys.remove(bit_width_coeff_key) + if self.override_pretrained and bit_width_coeff_key in state_dict: + del state_dict[bit_width_coeff_key] + + +class MsbClampParameterBitWidth(torch.jit.ScriptModule): + __constants__ = ['min_overall_bit_width', 'max_overall_bit_width'] + + def __init__(self, + ms_bit_width_to_clamp: int, + clamp_at_least_init_val: bool, + min_overall_bit_width: int, + max_overall_bit_width: int, + bit_width_impl_type: BitWidthImplType, + override_pretrained: bool) -> None: + super(MsbClampParameterBitWidth, self).__init__() + + self.min_overall_bit_width = min_overall_bit_width + self.max_overall_bit_width = max_overall_bit_width + + if bit_width_impl_type == BitWidthImplType.CONST: + self.bit_width_to_remove_impl = BitWidthConst(ms_bit_width_to_clamp, RestrictValueType.INT) + elif bit_width_impl_type == BitWidthImplType.PARAMETER: + restrict_bit_width_impl = RestrictValue(RestrictValueType.INT, + float_to_int_impl_type=FloatToIntImplType.ROUND) + self.bit_width_to_remove_impl = RemoveBitwidthParameter(bit_width_to_remove=ms_bit_width_to_clamp, + remove_at_least_init_val=clamp_at_least_init_val, + restrict_bit_width_impl=restrict_bit_width_impl, + override_pretrained=override_pretrained) + else: + raise Exception("Bit width implementation type {} not recognized for clamping accumulator." + .format(bit_width_impl_type)) + + @torch.jit.script_method + def forward(self, input_bit_width: Tensor, zero_hw_sentinel: Tensor) -> Tensor: + bit_width_to_remove = self.bit_width_to_remove_impl(zero_hw_sentinel) + output_bit_width = torch.abs(input_bit_width - bit_width_to_remove) + output_bit_width = tensor_clamp_ste(output_bit_width, + self.min_overall_bit_width + zero_hw_sentinel, + self.max_overall_bit_width + zero_hw_sentinel) + return output_bit_width + + +class LsbTruncParameterBitWidth(torch.jit.ScriptModule): + __constants__ = ['is_const', 'min_overall_bit_width', 'max_overall_bit_width'] + + def __init__(self, + ls_bit_width_to_trunc: int, + trunc_at_least_init_val: bool, + min_overall_bit_width: int, + max_overall_bit_width: int, + bit_width_impl_type: BitWidthImplType, + override_pretrained: bool): + super(LsbTruncParameterBitWidth, self).__init__() + + self.min_overall_bit_width = min_overall_bit_width + self.max_overall_bit_width = max_overall_bit_width + + if bit_width_impl_type == BitWidthImplType.CONST: + self.bit_width_to_remove_impl = BitWidthConst(ls_bit_width_to_trunc, RestrictValueType.INT) + elif bit_width_impl_type == BitWidthImplType.PARAMETER: + restrict_bit_width_impl = RestrictValue(RestrictValueType.INT, + float_to_int_impl_type=FloatToIntImplType.ROUND) + self.bit_width_to_remove_impl = RemoveBitwidthParameter(bit_width_to_remove=ls_bit_width_to_trunc, + remove_at_least_init_val=trunc_at_least_init_val, + restrict_bit_width_impl=restrict_bit_width_impl, + override_pretrained=override_pretrained) + else: + raise Exception("Bit width implementation type {} not recognized for truncating accumulator." + .format(bit_width_impl_type)) + + @torch.jit.script_method + def forward(self, input_bit_width: Tensor, zero_hw_sentinel: Tensor) -> Tensor: + bit_width_to_remove = self.bit_width_to_remove_impl(zero_hw_sentinel) + max_bit_width_to_remove = input_bit_width - self.min_overall_bit_width + bit_width_to_remove = torch.where(bit_width_to_remove > max_bit_width_to_remove, + max_bit_width_to_remove, + bit_width_to_remove) + return bit_width_to_remove \ No newline at end of file diff --git a/brevitas/core/function_wrapper.py b/brevitas/core/function_wrapper.py new file mode 100644 index 000000000..840de9e1f --- /dev/null +++ b/brevitas/core/function_wrapper.py @@ -0,0 +1,124 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from enum import auto + +import torch + +from brevitas.utils.python_utils import AutoName +from brevitas.function.ops import ceil_ste, round_ste, floor_ste +from brevitas.function import tensor_clamp, tensor_clamp_ste + + +class TensorClampImplType(AutoName): + STE = auto() + DIFFERENTIABLE = auto() + + +class Identity(torch.jit.ScriptModule): + def __init__(self) -> None: + super(Identity, self).__init__() + + @torch.jit.script_method + def forward(self, x: torch.Tensor): + return x + + +class RoundSte(torch.jit.ScriptModule): + def __init__(self) -> None: + super(RoundSte, self).__init__() + + @torch.jit.script_method + def forward(self, x: torch.Tensor): + return round_ste(x) + + +class FloorSte(torch.jit.ScriptModule): + def __init__(self) -> None: + super(FloorSte, self).__init__() + + @torch.jit.script_method + def forward(self, x: torch.Tensor): + return floor_ste(x) + + +class CeilSte(torch.jit.ScriptModule): + def __init__(self) -> None: + super(CeilSte, self).__init__() + + @torch.jit.script_method + def forward(self, x: torch.Tensor): + return ceil_ste(x) + + +class PowerOfTwo(torch.jit.ScriptModule): + def __init__(self) -> None: + super(PowerOfTwo, self).__init__() + + @torch.jit.script_method + def forward(self, x: torch.Tensor): + return 2.0 ** x + + +class LogTwo(torch.jit.ScriptModule): + def __init__(self) -> None: + super(LogTwo, self).__init__() + + @torch.jit.script_method + def forward(self, x: torch.Tensor): + return torch.log2(x) + + +class TensorClampSte(torch.jit.ScriptModule): + def __init__(self) -> None: + super(TensorClampSte, self).__init__() + + @torch.jit.script_method + def forward(self, x: torch.Tensor, min_val: torch.Tensor, max_val: torch.Tensor): + return tensor_clamp_ste(x, min_val=min_val, max_val=max_val) + + +class TensorClamp(torch.jit.ScriptModule): + def __init__(self) -> None: + super(TensorClamp, self).__init__() + + @torch.jit.script_method + def forward(self, x: torch.Tensor, min_val: torch.Tensor, max_val: torch.Tensor): + return tensor_clamp(x, min_val=min_val, max_val=max_val) diff --git a/brevitas/core/quant.py b/brevitas/core/quant.py new file mode 100644 index 000000000..76df49a8e --- /dev/null +++ b/brevitas/core/quant.py @@ -0,0 +1,245 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from enum import auto +from typing import Optional, Tuple, Union + +import torch +from torch import Tensor +from torch.nn import Module + +from brevitas.utils.python_utils import AutoName +from brevitas.function.ops import min_int, max_int, max_uint +from brevitas.function import binary_sign_ste, ternary_sign_ste + + +__all__ = ['QuantType', 'BinaryQuant', 'TernaryQuant', 'RescalingIntQuant', 'PrescaledIntQuant', + 'PrescaledRestrictIntQuant'] + + +class QuantType(AutoName): + BINARY = auto() + TERNARY = auto() + INT = auto() + FP = auto() + + +class IdentityQuant(torch.jit.ScriptModule): + + @torch.jit.script_method + def forward(self, x: Tensor, zero_hw_sentinel: Tensor) -> Tuple[Tensor, Tensor, Tensor]: + return x, zero_hw_sentinel, zero_hw_sentinel + + +class BinaryQuant(torch.jit.ScriptModule): + __constants__ = ['threshold', 'bit_width'] + + def __init__(self, scaling_impl: Module): + super(BinaryQuant, self).__init__() + self.scaling_impl = scaling_impl + self.bit_width = 1 + + @torch.jit.script_method + def forward(self, x: Tensor, zero_hw_sentinel: Tensor) -> Tuple[Tensor, Tensor, Tensor]: + scale = self.scaling_impl(zero_hw_sentinel) + y = binary_sign_ste(x) * scale + return y, scale, zero_hw_sentinel + self.bit_width + + +class TernaryQuant(torch.jit.ScriptModule): + __constants__ = ['threshold', 'bit_width'] + + def __init__(self, scaling_impl: Module, threshold: float): + super(TernaryQuant, self).__init__() + self.scaling_impl = scaling_impl + self.threshold = threshold + self.bit_width = 2 + + @torch.jit.script_method + def forward(self, x: Tensor, zero_hw_sentinel: Tensor) -> Tuple[Tensor, Tensor, Tensor]: + scale = self.scaling_impl(zero_hw_sentinel) + mask = x.abs().ge(self.threshold * scale) + y = mask.float() * ternary_sign_ste(x) + y = y * scale + return y, scale, zero_hw_sentinel + self.bit_width + + +class PrescaledRestrictIntQuant(torch.jit.ScriptModule): + + def __init__(self, + narrow_range: bool, + signed: bool, + tensor_clamp_impl: Module, + msb_clamp_bit_width_impl: Module, + float_to_int_impl: Module): + super(PrescaledRestrictIntQuant, self).__init__() + self.int_quant = IntQuant(signed=signed, + narrow_range=narrow_range, + tensor_clamp_impl=tensor_clamp_impl, + float_to_int_impl=float_to_int_impl) + self.msb_clamp_bit_width_impl = msb_clamp_bit_width_impl + + @torch.jit.script_method + def forward(self, + x: Tensor, + scale: Tensor, + input_bit_width: Tensor, + zero_hw_sentinel: Tensor) -> Tuple[Tensor, Tensor, Tensor]: + msb_clamp_bit_width = self.msb_clamp_bit_width_impl(input_bit_width, zero_hw_sentinel) + y = self.int_quant(scale, zero_hw_sentinel + 1, msb_clamp_bit_width, x) + return y, scale, msb_clamp_bit_width + + +class IdentityPrescaledIntQuant(torch.jit.ScriptModule): + + @torch.jit.script_method + def forward(self, x, input_scale, input_bit_width, zero_hw_sentinel) -> Tuple[Tensor, Tensor, Tensor]: + return x, input_scale, input_bit_width + + +class PrescaledIntQuant(torch.jit.ScriptModule): + + def __init__(self, + narrow_range: bool, + signed: bool, + tensor_clamp_impl: Module, + float_to_int_impl: Module): + super(PrescaledIntQuant, self).__init__() + self.int_quant = IntQuant(signed=signed, + narrow_range=narrow_range, + tensor_clamp_impl=tensor_clamp_impl, + float_to_int_impl=float_to_int_impl) + + @torch.jit.script_method + def forward(self, + x: Tensor, + scale: Tensor, + bit_width: Tensor, + zero_hw_sentinel: Tensor) -> Tuple[Tensor, Tensor, Tensor]: + y = self.int_quant(scale, zero_hw_sentinel + 1, bit_width, x) + return y, scale, bit_width + + +class RescalingIntQuant(torch.jit.ScriptModule): + + def __init__(self, + narrow_range: bool, + signed: bool, + scaling_impl: Module, + int_scaling_impl: Module, + tensor_clamp_impl: Module, + msb_clamp_bit_width_impl: Module, + float_to_int_impl: Module): + super(RescalingIntQuant, self).__init__() + self.int_quant = IntQuant(signed=signed, + narrow_range=narrow_range, + tensor_clamp_impl=tensor_clamp_impl, + float_to_int_impl=float_to_int_impl) + self.scaling_impl = scaling_impl + self.int_scaling_impl = int_scaling_impl + self.msb_clamp_bit_width_impl = msb_clamp_bit_width_impl + + @staticmethod + def scaling_init_from_min_max(min_val_init: Union[int, float], max_val_init: Union[int, float]) -> float: + scaling_init = max(abs(float(min_val_init)), abs(float(max_val_init))) + return scaling_init + + @torch.jit.script_method + def forward(self, + x: Tensor, + zero_hw_sentinel: Tensor) -> Tuple[Tensor, Tensor, Tensor]: + msb_clamp_bit_width = self.msb_clamp_bit_width_impl(zero_hw_sentinel) + scale = self.scaling_impl(zero_hw_sentinel) + int_scale = self.int_scaling_impl(msb_clamp_bit_width) + y = self.int_quant(scale, int_scale, msb_clamp_bit_width, x) + output_bit_width = msb_clamp_bit_width + output_scale = scale / int_scale + return y, output_scale, output_bit_width + + +class IntQuant(torch.jit.ScriptModule): + __constants__ = ['signed', 'narrow_range'] + + def __init__(self, + narrow_range: bool, + signed: bool, + float_to_int_impl: Module, + tensor_clamp_impl: Module): + super(IntQuant, self).__init__() + self.float_to_int_impl = float_to_int_impl + self.tensor_clamp_impl = tensor_clamp_impl + self.signed = signed + self.narrow_range = narrow_range + + def to_int(self, + scale: Tensor, + int_scale: Tensor, + msb_clamp_bit_width: Tensor, + x: Tensor) -> Tensor: + y = x / scale + y = y * int_scale + min_int_val = self.min_int(msb_clamp_bit_width) + max_int_val = self.max_int(msb_clamp_bit_width) + y = self.tensor_clamp_impl(y, min_val=min_int_val, max_val=max_int_val) + y = self.float_to_int_impl(y) + return y + + @torch.jit.script_method + def min_int(self, bit_width): + return min_int(self.signed, self.narrow_range, bit_width) + + @torch.jit.script_method + def max_int(self, bit_width): + return max_int(self.signed, bit_width) + + @torch.jit.script_method + def max_uint(self, bit_width): + return max_uint(self.narrow_range, bit_width) + + @torch.jit.script_method + def forward(self, + scale: Tensor, + int_scale: Tensor, + msb_clamp_bit_width: Tensor, + x: Tensor) -> Tensor: + y_int = self.to_int(scale, int_scale, msb_clamp_bit_width, x) + y = y_int / int_scale + y = y * scale + return y diff --git a/brevitas/core/restrict_val.py b/brevitas/core/restrict_val.py new file mode 100644 index 000000000..65fddd834 --- /dev/null +++ b/brevitas/core/restrict_val.py @@ -0,0 +1,118 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from enum import auto +from typing import Callable, Union +import math + +import torch +from torch.nn import Sequential + +from brevitas.utils.python_utils import AutoName +from .function_wrapper import RoundSte, CeilSte, Identity, PowerOfTwo, LogTwo, FloorSte + + +class RestrictValueType(AutoName): + FP = auto() + LOG_FP = auto() + INT = auto() + POWER_OF_TWO = auto() + + +class FloatToIntImplType(AutoName): + ROUND = auto() + CEIL = auto() + FLOOR = auto() + + +class RestrictValueOpImplType(AutoName): + MATH = auto() + TORCH_FN = auto() + TORCH_MODULE = auto() + + +class RestrictValue(torch.jit.ScriptModule): + + def __init__(self, + restrict_value_type: RestrictValueType, + float_to_int_impl_type: FloatToIntImplType) -> None: + super(RestrictValue, self).__init__() + + if float_to_int_impl_type == FloatToIntImplType.ROUND: + float_to_int_impl = RoundSte() + elif float_to_int_impl_type == FloatToIntImplType.CEIL: + float_to_int_impl = CeilSte() + elif float_to_int_impl_type == FloatToIntImplType.FLOOR: + float_to_int_impl = FloorSte() + else: + raise Exception("Float to int impl type {} not supported for restrict value" + .format(str(float_to_int_impl_type))) + + if restrict_value_type == RestrictValueType.FP: + self.forward_impl = Identity() + elif restrict_value_type == RestrictValueType.LOG_FP: + self.forward_impl = PowerOfTwo() + elif restrict_value_type == RestrictValueType.INT: + self.forward_impl = float_to_int_impl + elif restrict_value_type == RestrictValueType.POWER_OF_TWO: + self.forward_impl = Sequential(float_to_int_impl, PowerOfTwo()) + else: + raise Exception("Restrict value type {} not recognized".format(str(restrict_value_type))) + + @staticmethod + def restrict_value_op(restrict_value_type: RestrictValueType, restrict_value_op_impl_type: RestrictValueOpImplType): + if restrict_value_type == RestrictValueType.FP or restrict_value_type == RestrictValueType.INT: + return lambda x: x + elif restrict_value_type == RestrictValueType.LOG_FP or restrict_value_type == RestrictValueType.POWER_OF_TWO: + if restrict_value_op_impl_type == RestrictValueOpImplType.TORCH_FN: + return torch.log2 + elif restrict_value_op_impl_type == RestrictValueOpImplType.MATH: + return math.log2 + elif restrict_value_op_impl_type == RestrictValueOpImplType.TORCH_MODULE: + return LogTwo() + else: + raise Exception("Type of implementation {} not recognized".format(str(restrict_value_op_impl_type))) + else: + raise Exception("Restriction of type {} not recognized".format(str(restrict_value_type))) + + @torch.jit.script_method + def forward(self, value: torch.Tensor) -> torch.Tensor: + value = self.forward_impl(value) + return value diff --git a/brevitas/core/scaling.py b/brevitas/core/scaling.py new file mode 100644 index 000000000..46f22a374 --- /dev/null +++ b/brevitas/core/scaling.py @@ -0,0 +1,210 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from enum import auto +from typing import Callable, Tuple, Optional, List + +import torch +from torch.nn import Module, Parameter + +from brevitas.utils.python_utils import AutoName +from brevitas.function.ops import min_int, max_int +from .stats import StatsOp, StatsInputViewShapeImpl, ParameterListStats +from .restrict_val import RestrictValue, RestrictValueType, FloatToIntImplType, RestrictValueOpImplType + +SCALING_SCALAR_SHAPE = () + + +class ScalingImplType(AutoName): + CONST = auto() + STATS = auto() + PARAMETER = auto() + PARAMETER_FROM_STATS = auto() + + +class StandaloneScaling(torch.jit.ScriptModule): + + def __init__(self, + scaling_init: float, + is_parameter: bool, + parameter_shape: Optional[Tuple[int, ...]], + restrict_scaling_type: RestrictValueType) -> None: + super(StandaloneScaling, self).__init__() + + if len(parameter_shape) > 1 and not is_parameter: + raise Exception("Standalone scaling shape has to be a scalar when scaling is not learned.") + if not (restrict_scaling_type == RestrictValueType.FP + or restrict_scaling_type == RestrictValueType.LOG_FP + or restrict_scaling_type == RestrictValueType.POWER_OF_TWO): + raise Exception("Restriction of type {} is not supported for standalone scaling." + .format(str(restrict_scaling_type))) + + self.restrict_value = RestrictValue(restrict_scaling_type, FloatToIntImplType.CEIL) + scaling_init_op = RestrictValue.restrict_value_op(restrict_scaling_type, + restrict_value_op_impl_type=RestrictValueOpImplType.MATH) + scaling_init = scaling_init_op(scaling_init) + if is_parameter: + self.value = Parameter(torch.full(parameter_shape, scaling_init)) + else: + self.value = torch.tensor(scaling_init) + + @torch.jit.script_method + def forward(self, zero_hw_sentinel: torch.Tensor) -> torch.Tensor: + value = self.value + zero_hw_sentinel + value = self.restrict_value(value) + return value + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + super(StandaloneScaling, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + value_key = prefix + 'value' + if value_key in missing_keys: + missing_keys.remove(value_key) + + +class StatsScaling(torch.jit.ScriptModule): + + def __init__(self, + stats_op: StatsOp, + restrict_scaling_type: RestrictValueType, + tracked_parameter_list: List[torch.nn.Parameter], + stats_input_view_shape_impl: StatsInputViewShapeImpl, + stats_output_shape: Tuple[int, ...], + stats_input_concat_dim: Optional[int], + sigma: Optional[float], + stats_reduce_dim: Optional[int]) -> None: + super(StatsScaling, self).__init__() + + if not (restrict_scaling_type == RestrictValueType.FP + or restrict_scaling_type == RestrictValueType.LOG_FP + or restrict_scaling_type == RestrictValueType.POWER_OF_TWO): + raise Exception("Restriction of type {} is not supported for stats scaling." + .format(str(restrict_scaling_type))) + if stats_op == StatsOp.MAX_AVE and stats_reduce_dim is not None: + raise Exception("Scaling with MAX_AVE stats can't be over output channels.") + + self.restrict_scaling = RestrictValue(restrict_scaling_type, FloatToIntImplType.CEIL) + self.restrict_scaling_preprocess = RestrictValue.restrict_value_op(restrict_scaling_type, + restrict_value_op_impl_type= + RestrictValueOpImplType.TORCH_MODULE) + self.parameter_list_stats = ParameterListStats(stats_op=stats_op, + stats_output_shape=stats_output_shape, + stats_reduce_dim=stats_reduce_dim, + stats_input_view_shape_impl=stats_input_view_shape_impl, + tracked_parameter_list=tracked_parameter_list, + stats_input_concat_dim=stats_input_concat_dim, + sigma=sigma) + + @torch.jit.script_method + def forward(self, zero_hw_sentinel: torch.Tensor) -> torch.Tensor: + stats = self.parameter_list_stats() + stats = self.restrict_scaling_preprocess(stats) + stats = self.restrict_scaling(stats) + return stats + + +class FpIntScale(torch.jit.ScriptModule): + __constants__ = ['signed', 'narrow_range'] + + def __init__(self, signed, narrow_range): + super(FpIntScale, self).__init__() + self.signed = signed + self.narrow_range = narrow_range + + @torch.jit.script_method + def forward(self, bit_width): + return - min_int(self.signed, self.narrow_range, bit_width) + + +class LogFpIntScale(torch.jit.ScriptModule): + __constants__ = ['signed'] + + def __init__(self, signed): + super(LogFpIntScale, self).__init__() + self.signed = signed + + @torch.jit.script_method + def forward(self, bit_width): + return max_int(self.signed, bit_width) + + +class PowerOfTwoIntScale(torch.jit.ScriptModule): + __constants__ = ['signed'] + + def __init__(self, signed): + super(PowerOfTwoIntScale, self).__init__() + self.signed = signed + + @torch.jit.script_method + def forward(self, bit_width): + return max_int(self.signed, bit_width) + 1 + + +class IntScaling(torch.jit.ScriptModule): + + def __init__(self, + narrow_range: bool, + signed: bool, + restrict_scaling_type: RestrictValueType) -> None: + super(IntScaling, self).__init__() + + if not (restrict_scaling_type == RestrictValueType.FP + or restrict_scaling_type == RestrictValueType.LOG_FP + or restrict_scaling_type == RestrictValueType.POWER_OF_TWO): + raise Exception("Restriction of type {} is not supported for int scaling." + .format(str(restrict_scaling_type))) + + if signed and not restrict_scaling_type == RestrictValueType.POWER_OF_TWO: # FP or LOG_FP + self.forward_impl = FpIntScale(signed, narrow_range) + elif not signed and not restrict_scaling_type == RestrictValueType.POWER_OF_TWO: # FP or LOG_FP + self.forward_impl = LogFpIntScale(signed) + elif restrict_scaling_type == RestrictValueType.POWER_OF_TWO: + self.forward_impl = PowerOfTwoIntScale(signed) + else: + raise Exception("Restrict value type {} not recognized".format(restrict_scaling_type)) + + @torch.jit.script_method + def forward(self, bit_width): + int_scale = self.forward_impl(bit_width) + return int_scale + + + diff --git a/brevitas/core/stats.py b/brevitas/core/stats.py new file mode 100644 index 000000000..2c843dfab --- /dev/null +++ b/brevitas/core/stats.py @@ -0,0 +1,265 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from typing import Tuple, Optional, List, Union +from enum import Enum + +import torch +from torch import nn +from torch.nn import Parameter + + +__all__ = ['StatsInputViewShapeImpl', 'StatsOp', 'ParameterListStats'] + + +STD_DEV_EPSILON = 1e-8 + + +def _over_tensor(x): + return (-1) + + +def _over_output_channels(x): + return (x.shape[0], -1) + + +def _over_batch_over_tensor(x): + return (x.shape[0], -1) + + +def _over_batch_over_output_channels(x): + return (x.shape[0], x.shape[1], -1) + + +class StatsInputViewShapeImpl(object): + OVER_TENSOR = _over_tensor + OVER_OUTPUT_CHANNELS = _over_output_channels + OVER_BATCH_OVER_TENSOR = _over_batch_over_tensor + OVER_BATCH_OVER_OUTPUT_CHANNELS = _over_batch_over_output_channels + + +class StatsOp(Enum): + MAX = 'MAX' + AVE = 'AVE' + MAX_AVE = 'MAX_AVE' + MEAN_SIGMA_STD = 'MEAN_SIGMA_STD' + MEAN_LEARN_SIGMA_STD = 'MEAN_LEARN_SIGMA_STD' + + +class _ViewParameterWrapper(torch.jit.ScriptModule): + __constants__ = ['shape'] + + def __init__(self, parameter, view_shape_impl): + super(_ViewParameterWrapper, self).__init__() + self.parameter = parameter + self.shape = view_shape_impl(parameter) + + @torch.jit.script_method + def forward(self): + return self.parameter.view(self.shape) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + super(_ViewParameterWrapper, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + parameter_key = prefix + 'parameter' + if parameter_key in missing_keys: + missing_keys.remove(parameter_key) + + +class _ViewCatParameterWrapper(torch.jit.ScriptModule): + __constants__ = ['shape', 'cat_dim'] + + def __init__(self, parameter, view_shape_impl, cat_dim): + super(_ViewCatParameterWrapper, self).__init__() + self.parameter = parameter + self.shape = view_shape_impl(parameter) + self.cat_dim = cat_dim + + @torch.jit.script_method + def forward(self, x: torch.Tensor): + return torch.cat([self.parameter.view(self.shape), x], dim=self.cat_dim) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + super(_ViewCatParameterWrapper, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + parameter_key = prefix + 'parameter' + if parameter_key in missing_keys: + missing_keys.remove(parameter_key) + + +class AbsMax(torch.jit.ScriptModule): + __constants__ = ['reduce_dim'] + + def __init__(self, reduce_dim) -> None: + super(AbsMax, self).__init__() + self.reduce_dim = reduce_dim + + @torch.jit.script_method + def forward(self, x: torch.Tensor): + if self.reduce_dim is None: + return torch.max(torch.abs(x)) + else: + return torch.max(torch.abs(x), dim=self.reduce_dim)[0] + + +class AbsMaxAve(torch.jit.ScriptModule): + __constants__ = ['reduce_dim'] + + def __init__(self, reduce_dim) -> None: + super(AbsMaxAve, self).__init__() + self.reduce_dim = reduce_dim + + @torch.jit.script_method + def forward(self, x: torch.Tensor): + return torch.mean(torch.max(torch.abs(x), dim=self.reduce_dim)[0]) + + +class AbsAve(torch.jit.ScriptModule): + __constants__ = ['reduce_dim'] + + def __init__(self, reduce_dim) -> None: + super(AbsAve, self).__init__() + self.reduce_dim = reduce_dim + + @torch.jit.script_method + def forward(self, x: torch.Tensor): + if self.reduce_dim is None: + return torch.mean(torch.abs(x)) + else: + return torch.mean(torch.abs(x), dim=self.reduce_dim) + + +class MeanSigmaStd(torch.jit.ScriptModule): + __constants__ = ['reduce_dim', 'sigma', 'output_shape'] + + def __init__(self, reduce_dim, const_sigma, learned_sigma, output_shape) -> None: + super(MeanSigmaStd, self).__init__() + self.reduce_dim = reduce_dim + self.const_sigma = const_sigma + self.learned_sigma = learned_sigma + self.output_shape = output_shape + + @torch.jit.script_method + def forward(self, x: torch.Tensor): + abs_val = torch.abs(x) + if self.reduce_dim is None: + mean_val = torch.mean(abs_val) + std_val = torch.sqrt(torch.var(abs_val) + STD_DEV_EPSILON) + else: + mean_val = torch.mean(torch.abs(x), dim=self.reduce_dim) + mean_val = mean_val.view(self.output_shape) + std_val = torch.sqrt(torch.var(abs_val, dim=self.reduce_dim) + STD_DEV_EPSILON) + std_val = std_val.view(self.output_shape) + if self.const_sigma is not None: + return mean_val + self.const_sigma * std_val + else: + return mean_val + self.learned_sigma * std_val + + +class ParameterListStats(torch.jit.ScriptModule): + __constants__ = ['stats_input_view_shape_impl', + 'stats_input_concat_dim', + 'stats_output_shape', + 'stats_reduce_dim', + 'stats_op', + 'extra_tracked_params_list', + 'sigma'] + + def __init__(self, + stats_op: StatsOp, + stats_input_view_shape_impl: StatsInputViewShapeImpl, + stats_reduce_dim: Optional[int], + stats_input_concat_dim: int, + stats_output_shape: Tuple[int, ...], + tracked_parameter_list: List[torch.nn.Parameter], + sigma: Optional[float]) -> None: + super(ParameterListStats, self).__init__() + + if stats_reduce_dim is not None and len(stats_output_shape) < 2: + raise Exception("Defining a reduce dimension requires the output view shape to have at least 2 dims.") + if len(stats_output_shape) > 1 and stats_reduce_dim is None: + raise Exception("Defining an output view shape with more than 1 dims assumes a not None reduce dim.") + if (stats_op == StatsOp.MEAN_SIGMA_STD or stats_op == StatsOp.MEAN_LEARN_SIGMA_STD) and sigma is None: + raise Exception("Stats of type {} requires to define a value for sigma.".format(str(stats_op))) + + self.stats_input_view_shape_impl = stats_input_view_shape_impl + self.stats_input_concat_dim = stats_input_concat_dim + self.stats_output_shape = stats_output_shape + self.stats_reduce_dim = stats_reduce_dim + self.first_tracked_param = _ViewParameterWrapper(tracked_parameter_list[0], stats_input_view_shape_impl) + if len(tracked_parameter_list) > 1: + extra_list = [_ViewCatParameterWrapper(param, stats_input_view_shape_impl, stats_input_concat_dim) + for param in tracked_parameter_list[1:]] + self.extra_tracked_params_list = torch.nn.ModuleList(extra_list) + else: + self.extra_tracked_params_list = None + + if stats_op == StatsOp.MAX: + self.stats_impl = AbsMax(reduce_dim=stats_reduce_dim) + elif stats_op == StatsOp.AVE: + self.stats_impl = AbsAve(reduce_dim=stats_reduce_dim) + elif stats_op == StatsOp.MAX_AVE: + self.stats_impl = AbsMaxAve(reduce_dim=stats_reduce_dim) + elif stats_op == StatsOp.MEAN_SIGMA_STD or stats_op == StatsOp.MEAN_LEARN_SIGMA_STD: + const_sigma = None + learned_sigma = None + if stats_op == StatsOp.MEAN_LEARN_SIGMA_STD: + learned_sigma = Parameter(torch.full(stats_output_shape, sigma)) + else: + const_sigma = sigma + self.stats_impl = MeanSigmaStd(stats_reduce_dim, const_sigma, learned_sigma, stats_output_shape) + else: + raise Exception("Stats op {} not recognized".format(str(stats_op))) + + @torch.jit.script_method + def forward(self) -> torch.Tensor: + stats_input = self.first_tracked_param() + if self.extra_tracked_params_list is not None: + for extra_tracked_param in self.extra_tracked_params_list: + stats_input = extra_tracked_param(stats_input) + stats = self.stats_impl(stats_input) + stats = stats.view(self.stats_output_shape) + return stats + + + + diff --git a/brevitas/function/__init__.py b/brevitas/function/__init__.py new file mode 100644 index 000000000..6965f8963 --- /dev/null +++ b/brevitas/function/__init__.py @@ -0,0 +1 @@ +from .ops import * diff --git a/brevitas/function/ops.py b/brevitas/function/ops.py new file mode 100644 index 000000000..5e4ec02ad --- /dev/null +++ b/brevitas/function/ops.py @@ -0,0 +1,130 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +import torch + + +@torch.jit.script +def _ste(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: + return x + (y - x).detach() + + +@torch.jit.script +def round_ste(x: torch.Tensor) -> torch.Tensor: + y = torch.round(x) + return _ste(x, y) + + +@torch.jit.script +def tensor_clamp(x: torch.Tensor, min_val: torch.Tensor, max_val: torch.Tensor): + out = torch.where(x > max_val, max_val, x) + out = torch.where(out < min_val, min_val, out) + return out + + +@torch.jit.script +def tensor_clamp_(x: torch.Tensor, min_val: torch.Tensor, max_val: torch.Tensor): + torch.min(x, max_val, out=x) + torch.max(x, min_val, out=x) + return x + + +@torch.jit.script +def max_uint(narrow_range: bool, bit_width: torch.Tensor): + if narrow_range: + value = (2 ** bit_width) - 2 + else: + value = (2 ** bit_width) - 1 + value = round_ste(value) + return value + + +@torch.jit.script +def max_int(signed: bool, bit_width: torch.Tensor): + if signed: + value = (2 ** (bit_width - 1)) - 1 + else: + value = (2 ** bit_width) - 1 + value = round_ste(value) + return value + + +@torch.jit.script +def min_int(signed: bool, narrow_range: bool, bit_width: torch.Tensor): + if signed and narrow_range: + value = - (2 ** (bit_width - 1)) + 1 + elif signed and not narrow_range: + value = - (2 ** (bit_width - 1)) + else: + value = 0 * bit_width + value = round_ste(value) + return value + + +@torch.jit.script +def tensor_clamp_ste(x: torch.Tensor, min_val: torch.Tensor, max_val: torch.Tensor) -> torch.Tensor: + y = tensor_clamp(x, min_val, max_val) + return _ste(x, y) + + +@torch.jit.script +def ceil_ste(x: torch.Tensor) -> torch.Tensor: + y = torch.ceil(x) + return _ste(x, y) + + +@torch.jit.script +def floor_ste(x: torch.Tensor) -> torch.Tensor: + y = torch.floor(x) + return _ste(x, y) + + +@torch.jit.script +def binary_sign_ste(x: torch.Tensor) -> torch.Tensor: + positive_mask = torch.ge(x, 0.0) + negative_mask = torch.lt(x, 0.0) + y = positive_mask.float() - negative_mask.float() + return _ste(x, y) + + +@torch.jit.script +def ternary_sign_ste(x: torch.Tensor) -> torch.Tensor: + y = torch.sign(x) + return _ste(x, y) \ No newline at end of file diff --git a/brevitas/loss/weighted_bit_width.py b/brevitas/loss/weighted_bit_width.py new file mode 100644 index 000000000..dcb390e52 --- /dev/null +++ b/brevitas/loss/weighted_bit_width.py @@ -0,0 +1,153 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from typing import List +from abc import ABCMeta, abstractmethod + +from functools import reduce +from operator import mul + +import torch +from torch import nn + +from brevitas.utils.quant_utils import * +from brevitas.nn.quant_linear import QuantLinear +from brevitas.nn.quant_conv import QuantConv2d +from brevitas.nn.quant_avg_pool import QuantAvgPool2d + +MEGA = 10e6 + +class BitWidthWeighted(object): + __metaclass__ = ABCMeta + + def __init__(self, model): + self.model: nn.Module = model + self.weighted_bit_width_list: List[torch.Tensor] = [] + self.tot_num_elements: int = 0 + self.register_hooks() + + @abstractmethod + def register_hooks(self): + pass + + def zero_accumulated_values(self): + del self.weighted_bit_width_list + del self.tot_num_elements + self.weighted_bit_width_list = [] + self.tot_num_elements = 0 + + def retrieve(self, as_average=True): + if self.tot_num_elements != 0 and self.weighted_bit_width_list: + if as_average: + value = sum(self.weighted_bit_width_list) / self.tot_num_elements + else: + value = [bit_width / self.tot_num_elements for bit_width in self.weighted_bit_width_list] + else: + raise Exception("Number of elements to penalize can't be zero") + return value + + def log(self): + return self.retrieve(as_average=True).detach().clone() + + +class WeightBitWidthWeightedBySize(BitWidthWeighted): + + def __init__(self, model): + super(WeightBitWidthWeightedBySize, self).__init__(model=model) + pass + + def register_hooks(self): + + def hook_fn(module, input, output): + (quant_weight, output_scale, output_bit_width) = output + num_elements = reduce(mul, quant_weight.size(), 1) + self.weighted_bit_width_list.append(num_elements * output_bit_width) + self.tot_num_elements += num_elements + + for name, module in self.model.named_modules(): + if has_learned_weight_bit_width(module): + module.register_forward_hook(hook_fn) + + +class ActivationBitWidthWeightedBySize(BitWidthWeighted): + + def __init__(self, model): + super(ActivationBitWidthWeightedBySize, self).__init__(model=model) + pass + + def register_hooks(self): + + def hook_fn(module, input, output): + (quant_act, output_scale, output_bit_width) = output + num_elements = reduce(mul, quant_act.size()[1:], 1) # exclude batch size + self.weighted_bit_width_list.append(num_elements * output_bit_width) + self.tot_num_elements += num_elements + + for name, module in self.model.named_modules(): + if has_learned_activation_bit_width(module): + module.register_forward_hook(hook_fn) + + +class QuantLayerOutputBitWidthWeightedByOps(BitWidthWeighted): + + def __init__(self, model, layer_types=(QuantConv2d, QuantLinear)): + self.layer_types = layer_types + super(QuantLayerOutputBitWidthWeightedByOps, self).__init__(model=model) + pass + + def register_hooks(self): + + def hook_fn(module, input, output): + if module.return_quant_tensor: + output, output_scale, output_bit_width = output + output_size = reduce(mul, output.size()[1:], 1) # exclude batch size + num_mops = output_size * module.per_elem_ops / MEGA + self.weighted_bit_width_list.append(output_bit_width * num_mops) + self.tot_num_elements += num_mops + + for name, module in self.model.named_modules(): + if isinstance(module, self.layer_types) \ + and module.return_quant_tensor \ + and module.compute_output_bit_width\ + and module.per_elem_ops is not None: + module.register_forward_hook(hook_fn) + + + diff --git a/brevitas/nn/__init__.py b/brevitas/nn/__init__.py new file mode 100644 index 000000000..f1d373a2b --- /dev/null +++ b/brevitas/nn/__init__.py @@ -0,0 +1,5 @@ +from .quant_accumulator import ClampQuantAccumulator, TruncQuantAccumulator +from .quant_activation import QuantReLU, QuantSigmoid, QuantTanh, QuantHardTanh +from .quant_avg_pool import QuantAvgPool2d +from .quant_linear import QuantLinear +from .quant_conv import QuantConv2d, PaddingType \ No newline at end of file diff --git a/brevitas/nn/quant_accumulator.py b/brevitas/nn/quant_accumulator.py new file mode 100644 index 000000000..00ce05f65 --- /dev/null +++ b/brevitas/nn/quant_accumulator.py @@ -0,0 +1,120 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from abc import ABCMeta + +from torch.nn import Module + +from brevitas.core.bit_width import BitWidthImplType +from brevitas.core.quant import QuantType +from brevitas.proxy.runtime_quant import ClampQuantProxy, TruncQuantProxy +from .quant_layer import QuantLayer + + +class QuantAccumulator(QuantLayer, Module): + __metaclass__ = ABCMeta + + def __init__(self): + QuantLayer.__init__(self, + compute_output_scale=True, + compute_output_bit_width=True, + return_quant_tensor=True) + Module.__init__(self) + + @property + def acc_quant_proxy(self): + return self._act_quant_proxy + + @acc_quant_proxy.setter + def acc_quant_proxy(self, act_quant_proxy): + self._acc_quant_proxy = act_quant_proxy + + def forward(self, input): + tensor, input_scale, input_bit_width = self.unpack_input(input) + output, output_scale, output_bit_width = self.acc_quant_proxy(tensor, input_scale, input_bit_width) + return self.pack_output(output, output_scale, output_bit_width) + + +class ClampQuantAccumulator(QuantAccumulator): + + def __init__(self, + ms_bit_width_to_clamp: int = 0, + signed: bool = True, + narrow_range: bool = True, + min_overall_bit_width: int = 2, + max_overall_bit_width: int = 32, + quant_type: QuantType = QuantType.INT, + msb_clamp_bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST, + per_elem_ops: int = None, + clamp_at_least_init_val=False, + override_pretrained_bit_width: bool = False): + super(ClampQuantAccumulator, self).__init__() + self.per_elem_ops = per_elem_ops + self.acc_quant_proxy = ClampQuantProxy(signed=signed, + narrow_range=narrow_range, + quant_type=quant_type, + ms_bit_width_to_clamp=ms_bit_width_to_clamp, + min_overall_bit_width=min_overall_bit_width, + max_overall_bit_width=max_overall_bit_width, + msb_clamp_bit_width_impl_type=msb_clamp_bit_width_impl_type, + clamp_at_least_init_val=clamp_at_least_init_val, + override_pretrained_bit_width=override_pretrained_bit_width) + + +class TruncQuantAccumulator(QuantAccumulator): + + def __init__(self, + ls_bit_width_to_trunc: int = 0, + signed: bool = True, + min_overall_bit_width: int = 2, + max_overall_bit_width: int = 32, + quant_type: QuantType = QuantType.INT, + lsb_trunc_bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST, + trunc_at_least_init_val=False, + override_pretrained_bit_width: bool = False): + super(TruncQuantAccumulator, self).__init__() + self.acc_quant_proxy = TruncQuantProxy(signed=signed, + quant_type=quant_type, + ls_bit_width_to_trunc=ls_bit_width_to_trunc, + min_overall_bit_width=min_overall_bit_width, + max_overall_bit_width=max_overall_bit_width, + trunc_at_least_init_val=trunc_at_least_init_val, + lsb_trunc_bit_width_impl_type=lsb_trunc_bit_width_impl_type, + override_pretrained_bit_width=override_pretrained_bit_width) \ No newline at end of file diff --git a/brevitas/nn/quant_activation.py b/brevitas/nn/quant_activation.py new file mode 100644 index 000000000..f5e66b6c2 --- /dev/null +++ b/brevitas/nn/quant_activation.py @@ -0,0 +1,239 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from abc import ABCMeta +from typing import Optional, Union, Tuple + +from torch import nn +from torch.nn import Module + +from brevitas.core.bit_width import BitWidthParameter, BitWidthImplType +from brevitas.core.function_wrapper import Identity +from brevitas.core.quant import QuantType +from brevitas.core.restrict_val import RestrictValueType, FloatToIntImplType +from brevitas.core.scaling import ScalingImplType +from brevitas.proxy.runtime_quant import ActivationQuantProxy +from .quant_layer import QuantLayer + + +class QuantActivation(QuantLayer, Module): + __metaclass__ = ABCMeta + + def __init__(self, return_quant_tensor): + QuantLayer.__init__(self, + compute_output_scale=True, + compute_output_bit_width=True, + return_quant_tensor=return_quant_tensor) + Module.__init__(self) + + @property + def act_quant_proxy(self): + return self._act_quant_proxy + + @act_quant_proxy.setter + def act_quant_proxy(self, act_quant_proxy): + self._act_quant_proxy = act_quant_proxy + + def forward(self, input): + tensor, _, _ = self.unpack_input(input) + output, output_scale, output_bit_width = self.act_quant_proxy(tensor) + return self.pack_output(output, output_scale, output_bit_width) + + +class QuantReLU(QuantActivation): + + def __init__(self, + bit_width: int, + max_val: float, + quant_type: QuantType = QuantType.FP, + float_to_int_impl_type: FloatToIntImplType = FloatToIntImplType.ROUND, + scaling_impl_type: ScalingImplType = ScalingImplType.PARAMETER, + scaling_per_channel: bool = False, + per_channel_broadcastable_shape: Optional[Tuple[int, ...]] = None, + min_overall_bit_width: Optional[int] = 2, + max_overall_bit_width: Optional[int] = None, + bit_width_impl_override: Union[BitWidthParameter] = None, + bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST, + restrict_bit_width_type: RestrictValueType = RestrictValueType.INT, + restrict_scaling_type: RestrictValueType = RestrictValueType.LOG_FP, + override_pretrained_bit_width: bool = False, + return_quant_tensor: bool = False): + super(QuantReLU, self).__init__(return_quant_tensor=return_quant_tensor) + activation_impl = nn.ReLU() + self.act_quant_proxy = ActivationQuantProxy(activation_impl=activation_impl, + bit_width=bit_width, + signed=False, + narrow_range=False, + min_val=0.0, + max_val=max_val, + quant_type=quant_type, + float_to_int_impl_type=float_to_int_impl_type, + scaling_impl_type=scaling_impl_type, + scaling_per_channel=scaling_per_channel, + per_channel_broadcastable_shape=per_channel_broadcastable_shape, + min_overall_bit_width=min_overall_bit_width, + max_overall_bit_width=max_overall_bit_width, + bit_width_impl_override=bit_width_impl_override, + bit_width_impl_type=bit_width_impl_type, + restrict_bit_width_type=restrict_bit_width_type, + restrict_scaling_type=restrict_scaling_type, + override_pretrained_bit_width=override_pretrained_bit_width) + + +class QuantSigmoid(QuantActivation): + + def __init__(self, + bit_width: int, + narrow_range: bool = False, + quant_type: QuantType = QuantType.FP, + float_to_int_impl_type: FloatToIntImplType = FloatToIntImplType.ROUND, + scaling_impl_type: ScalingImplType = ScalingImplType.PARAMETER, + scaling_per_channel: bool = False, + per_channel_broadcastable_shape: Optional[Tuple[int, ...]] = None, + min_overall_bit_width: Optional[int] = 2, + max_overall_bit_width: Optional[int] = None, + bit_width_impl_override: Union[BitWidthParameter] = None, + bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST, + restrict_bit_width_type: RestrictValueType = RestrictValueType.INT, + restrict_scaling_type: RestrictValueType = RestrictValueType.LOG_FP, + override_pretrained_bit_width: bool = False, + return_quant_tensor = False): + super(QuantSigmoid, self).__init__(return_quant_tensor=return_quant_tensor) + activation_impl = nn.Sigmoid() + self.act_quant_proxy = ActivationQuantProxy(activation_impl=activation_impl, + bit_width=bit_width, + signed=False, + narrow_range=narrow_range, + min_val=0.0, + max_val=1.0, + quant_type=quant_type, + float_to_int_impl_type=float_to_int_impl_type, + scaling_impl_type=scaling_impl_type, + scaling_per_channel=scaling_per_channel, + per_channel_broadcastable_shape=per_channel_broadcastable_shape, + min_overall_bit_width=min_overall_bit_width, + max_overall_bit_width=max_overall_bit_width, + bit_width_impl_override=bit_width_impl_override, + bit_width_impl_type=bit_width_impl_type, + restrict_bit_width_type=restrict_bit_width_type, + restrict_scaling_type=restrict_scaling_type, + override_pretrained_bit_width=override_pretrained_bit_width) + + +class QuantTanh(QuantActivation): + + def __init__(self, + bit_width: int, + narrow_range: bool = False, + quant_type: QuantType = QuantType.FP, + float_to_int_impl_type: FloatToIntImplType = FloatToIntImplType.ROUND, + scaling_impl_type: ScalingImplType = ScalingImplType.PARAMETER, + scaling_per_channel: bool = False, + per_channel_broadcastable_shape: Optional[Tuple[int, ...]] = None, + min_overall_bit_width: Optional[int] = 2, + max_overall_bit_width: Optional[int] = None, + bit_width_impl_override: Union[BitWidthParameter] = None, + bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST, + restrict_bit_width_type: RestrictValueType = RestrictValueType.INT, + restrict_scaling_type: RestrictValueType = RestrictValueType.LOG_FP, + override_pretrained_bit_width: bool = False, + return_quant_tensor: bool = False): + super(QuantTanh, self).__init__(return_quant_tensor=return_quant_tensor) + activation_impl = nn.Tanh() + self.act_quant_proxy = ActivationQuantProxy(activation_impl=activation_impl, + bit_width=bit_width, + signed=True, + narrow_range=narrow_range, + min_val=-1.0, + max_val=1.0, + quant_type=quant_type, + float_to_int_impl_type=float_to_int_impl_type, + scaling_impl_type=scaling_impl_type, + scaling_per_channel=scaling_per_channel, + per_channel_broadcastable_shape=per_channel_broadcastable_shape, + min_overall_bit_width=min_overall_bit_width, + max_overall_bit_width=max_overall_bit_width, + bit_width_impl_override=bit_width_impl_override, + bit_width_impl_type=bit_width_impl_type, + restrict_bit_width_type=restrict_bit_width_type, + restrict_scaling_type=restrict_scaling_type, + override_pretrained_bit_width=override_pretrained_bit_width) + + +class QuantHardTanh(QuantActivation): + + def __init__(self, + bit_width: int, + min_val: float = -1.0, + max_val: float = 1.0, + narrow_range: bool = False, + quant_type: QuantType = QuantType.FP, + float_to_int_impl_type: FloatToIntImplType = FloatToIntImplType.ROUND, + scaling_impl_type: ScalingImplType = ScalingImplType.PARAMETER, + scaling_per_channel: bool = False, + per_channel_broadcastable_shape: Optional[Tuple[int, ...]] = None, + min_overall_bit_width: Optional[int] = 2, + max_overall_bit_width: Optional[int] = None, + bit_width_impl_override: Union[BitWidthParameter] = None, + bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST, + restrict_bit_width_type: RestrictValueType = RestrictValueType.INT, + restrict_scaling_type: RestrictValueType = RestrictValueType.LOG_FP, + override_pretrained_bit_width: bool = False, + return_quant_tensor: bool = False): + super(QuantHardTanh, self).__init__(return_quant_tensor=return_quant_tensor) + activation_impl = Identity() + self.act_quant_proxy = ActivationQuantProxy(activation_impl=activation_impl, + bit_width=bit_width, + signed=True, + narrow_range=narrow_range, + min_val=min_val, + max_val=max_val, + quant_type=quant_type, + float_to_int_impl_type=float_to_int_impl_type, + scaling_impl_type=scaling_impl_type, + scaling_per_channel=scaling_per_channel, + per_channel_broadcastable_shape=per_channel_broadcastable_shape, + min_overall_bit_width=min_overall_bit_width, + max_overall_bit_width=max_overall_bit_width, + bit_width_impl_override=bit_width_impl_override, + bit_width_impl_type=bit_width_impl_type, + restrict_bit_width_type=restrict_bit_width_type, + restrict_scaling_type=restrict_scaling_type, + override_pretrained_bit_width=override_pretrained_bit_width) diff --git a/brevitas/nn/quant_avg_pool.py b/brevitas/nn/quant_avg_pool.py new file mode 100644 index 000000000..29deb29b7 --- /dev/null +++ b/brevitas/nn/quant_avg_pool.py @@ -0,0 +1,95 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from typing import Optional + +import math +import torch +from torch.nn import AvgPool2d + +from brevitas.core.bit_width import BitWidthImplType +from brevitas.core.quant import QuantType +from brevitas.function.ops import ceil_ste, max_uint +from brevitas.nn.quant_layer import QuantLayer +from brevitas.proxy.runtime_quant import TruncQuantProxy +from brevitas.quant_tensor import pack_quant_tensor + + +class QuantAvgPool2d(QuantLayer, AvgPool2d): + + def __init__(self, + kernel_size: int, + stride: int = None, + signed: bool = True, + min_overall_bit_width: Optional[int] = 2, + max_overall_bit_width: Optional[int] = 32, + quant_type: QuantType = QuantType.FP, + lsb_trunc_bit_width_impl_type = BitWidthImplType.CONST): + QuantLayer.__init__(self, + compute_output_scale=True, + compute_output_bit_width=True, + return_quant_tensor=True) + AvgPool2d.__init__(self, + kernel_size=kernel_size, + stride=stride) + ls_bit_width_to_trunc = math.ceil(math.log2(kernel_size * kernel_size)) + self.signed = signed + self.accumulator_quant = TruncQuantProxy(signed=signed, + quant_type=quant_type, + trunc_at_least_init_val=True, + ls_bit_width_to_trunc=ls_bit_width_to_trunc, + min_overall_bit_width=min_overall_bit_width, + max_overall_bit_width=max_overall_bit_width, + lsb_trunc_bit_width_impl_type=lsb_trunc_bit_width_impl_type, + override_pretrained_bit_width=False) + + def forward(self, input): + input_tensor, input_scale, input_bit_width = self.unpack_input(input) + x = super(QuantAvgPool2d, self).forward(input_tensor) + output_bit_width = self.max_output_bit_width(input_bit_width) + output_scale = input_scale / (self.kernel_size * self.kernel_size) + x, output_scale, output_bit_width = self.accumulator_quant(x, output_scale, output_bit_width) + return pack_quant_tensor(x, output_scale, output_bit_width) + + def max_output_bit_width(self, input_bit_width): + max_uint_input = max_uint(bit_width=input_bit_width, narrow_range=False) + max_uint_output = max_uint_input * self.kernel_size * self.kernel_size + max_output_bit_width = ceil_ste(torch.log2(max_uint_output)) + return max_output_bit_width diff --git a/brevitas/nn/quant_conv.py b/brevitas/nn/quant_conv.py new file mode 100644 index 000000000..737b89ad9 --- /dev/null +++ b/brevitas/nn/quant_conv.py @@ -0,0 +1,233 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from enum import auto +from typing import Union, Optional, Tuple + +import math +import torch +from torch.nn import Conv2d +from torch.nn import functional as F +from torch.nn.functional import conv2d +from torch.nn.parameter import Parameter + +from brevitas.core.bit_width import BitWidthParameter, BitWidthConst, BitWidthImplType +from brevitas.core.quant import QuantType +from brevitas.core.restrict_val import RestrictValueType +from brevitas.core.scaling import ScalingImplType, SCALING_SCALAR_SHAPE +from brevitas.core.stats import StatsInputViewShapeImpl, StatsOp +from brevitas.function.ops import ceil_ste, max_uint +from brevitas.proxy.parameter_quant import WeightQuantProxy, BiasQuantProxy +from brevitas.utils.python_utils import AutoName +from .quant_layer import QuantLayer + +__all__ = ['QuantConv2d'] + + +class PaddingType(AutoName): + STANDARD = auto() + SAME = auto() + + +class QuantConv2d(QuantLayer, Conv2d): + + def __init__(self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, Tuple[int, int]], + stride: Union[int, Tuple[int, int]] = 1, + padding: Union[int, Tuple[int, int]] = 0, + padding_type: PaddingType = PaddingType.STANDARD, + dilation: Union[int, Tuple[int, int]] = 1, + groups: int = 1, + bias: bool = True, + bias_quant_type: QuantType = QuantType.FP, + bias_narrow_range: bool = False, + weight_quant_override: WeightQuantProxy = None, + weight_quant_type: QuantType = QuantType.FP, + weight_narrow_range: bool = False, + weight_bit_width_impl_override: Union[BitWidthParameter, BitWidthConst] = None, + weight_bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST, + weight_restrict_bit_width_type: RestrictValueType = RestrictValueType.INT, + weight_bit_width: int = 32, + weight_min_overall_bit_width: Optional[int] = 2, + weight_max_overall_bit_width: Optional[int] = None, + weight_scaling_impl_type: ScalingImplType = ScalingImplType.STATS, + weight_scaling_stats_op: StatsOp = StatsOp.MAX, + weight_scaling_per_output_channel: bool = False, + weight_ternary_threshold: float = 0.5, + weight_restrict_scaling_type: RestrictValueType = RestrictValueType.LOG_FP, + weight_scaling_stats_sigma: float = 3.0, + weight_override_pretrained_bit_width: bool = False, + compute_output_scale: bool = False, + compute_output_bit_width: bool = False, + return_quant_tensor: bool = False) -> None: + QuantLayer.__init__(self, + compute_output_scale=compute_output_scale, + compute_output_bit_width=compute_output_bit_width, + return_quant_tensor=return_quant_tensor) + Conv2d.__init__(self, + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias) + if weight_quant_type == QuantType.FP and compute_output_bit_width: + raise Exception("Computing output bit width requires enabling quantization") + if bias_quant_type != QuantType.FP and not (compute_output_scale and compute_output_bit_width): + raise Exception("Quantizing bias requires to compute output scale and output bit width") + + self.per_elem_ops = 2 * self.kernel_size[0] * self.kernel_size[1] * (in_channels // groups) + self.padding_type = padding_type + + if weight_quant_override is not None: + self.weight_quant = weight_quant_override + self.weight_quant.add_tracked_parameter(self.weight) + else: + weight_scaling_stats_input_concat_dim = 0 + if weight_scaling_per_output_channel: + weight_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS + weight_scaling_shape = self.per_output_channel_broadcastable_shape + weight_scaling_stats_reduce_dim = 1 + else: + weight_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_TENSOR + weight_scaling_shape = SCALING_SCALAR_SHAPE + weight_scaling_stats_reduce_dim = None + + if weight_scaling_stats_op == StatsOp.MAX_AVE: + weight_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS + weight_scaling_stats_reduce_dim = 1 + + self.weight_quant = WeightQuantProxy(bit_width=weight_bit_width, + quant_type=weight_quant_type, + narrow_range=weight_narrow_range, + restrict_scaling_type=weight_restrict_scaling_type, + scaling_stats_op=weight_scaling_stats_op, + scaling_impl_type=weight_scaling_impl_type, + scaling_stats_reduce_dim=weight_scaling_stats_reduce_dim, + scaling_shape=weight_scaling_shape, + bit_width_impl_type=weight_bit_width_impl_type, + bit_width_impl_override=weight_bit_width_impl_override, + restrict_bit_width_type=weight_restrict_bit_width_type, + min_overall_bit_width=weight_min_overall_bit_width, + max_overall_bit_width=weight_max_overall_bit_width, + tracked_parameter_list_init=self.weight, + ternary_threshold=weight_ternary_threshold, + scaling_stats_input_view_shape_impl=weight_stats_input_view_shape_impl, + scaling_stats_input_concat_dim=weight_scaling_stats_input_concat_dim, + scaling_stats_sigma=weight_scaling_stats_sigma, + override_pretrained_bit_width=weight_override_pretrained_bit_width) + self.bias_quant = BiasQuantProxy(quant_type=bias_quant_type, + narrow_range=bias_narrow_range) + + @property + def per_output_channel_broadcastable_shape(self): + if self.transposed: + raise Exception("Transposed filters are not supported.") + else: + output_dim = 0 + per_channel_size = [1] * len(self.weight.size()) + per_channel_size[output_dim] = self.out_channels + per_channel_size = tuple(per_channel_size) + return per_channel_size + + def forward(self, input): + output_scale = None + output_bit_width = None + input, input_scale, input_bit_width = self.unpack_input(input) + quant_weight, quant_weight_scale, quant_weight_bit_width = self.weight_quant(self.weight) + + if self.compute_output_bit_width: + output_bit_width = self.max_output_bit_width(input_bit_width, quant_weight_bit_width) + if self.compute_output_scale: + output_scale = input_scale * quant_weight_scale + + if self.bias is not None: + quant_bias = self.bias_quant(self.bias, output_scale, output_bit_width) + output = self.conv2d(input, quant_weight, quant_bias) + else: + output = self.conv2d(input, quant_weight, None) + return self.pack_output(output, output_scale, output_bit_width) + + def conv2d(self, x, weight, bias): + if self.padding_type == PaddingType.SAME: + out = self.conv2d_same_padding(x, weight, bias) + else: + out = conv2d(x, weight, bias, self.stride, self.padding, self.dilation, self.groups) + return out + + def conv2d_same_padding(self, x, weight, bias): + ih, iw = x.size()[-2:] + kh, kw = weight.size()[-2:] + sh, sw = self.stride + oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) + pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) + pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) + if pad_h > 0 or pad_w > 0: + x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]) + out = F.conv2d(x, weight, bias, self.stride, 0, self.dilation, self.groups) + return out + + def merge_bn_in(self, bn, affine_only): + if affine_only and not bn.affine: + raise Exception("Affine-only merging requires BN to have affine scaling enabled.") + per_output_channel_mul_factor = bn.weight.data + per_output_channel_add_factor = bn.bias.data * torch.sqrt(bn.running_var + bn.eps) + per_output_channel_add_factor -= bn.running_mean * (bn.weight.data - 1.0) + if not affine_only: + per_output_channel_mul_factor *= 1.0 / torch.sqrt(bn.running_var + bn.eps) + per_output_channel_add_factor -= bn.running_mean + per_output_channel_add_factor *= 1.0 / torch.sqrt(bn.running_var + bn.eps) + self.weight.data *= per_output_channel_mul_factor.view(self.per_output_channel_broadcastable_shape) + if self.bias is not None: + self.bias.data += per_output_channel_add_factor + else: + self.bias = Parameter(per_output_channel_add_factor) + + def max_output_bit_width(self, input_bit_width, weight_bit_width): + max_uint_input = max_uint(bit_width=input_bit_width, narrow_range=False) + max_kernel_val = self.weight_quant.tensor_quant.int_quant.max_uint(weight_bit_width) + group_size = self.out_channels // self.groups + max_uint_output = max_uint_input * max_kernel_val * self.kernel_size[0] * self.kernel_size[1] * group_size + max_output_bit_width = ceil_ste(torch.log2(max_uint_output)) + return max_output_bit_width diff --git a/brevitas/nn/quant_layer.py b/brevitas/nn/quant_layer.py new file mode 100644 index 000000000..a653788f7 --- /dev/null +++ b/brevitas/nn/quant_layer.py @@ -0,0 +1,64 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from brevitas.quant_tensor import QuantTensor + + +class QuantLayer(object): + + def __init__(self, compute_output_scale, compute_output_bit_width, return_quant_tensor): + self.compute_output_scale = compute_output_scale + self.compute_output_bit_width = compute_output_bit_width + self.return_quant_tensor = return_quant_tensor + + def unpack_input(self, input): + if isinstance(input, QuantTensor): + return input + else: + return input, None, None + + def pack_output(self, + output, + output_scale, + output_bit_width): + if self.return_quant_tensor: + return QuantTensor(tensor=output, scale=output_scale, bit_width=output_bit_width) + else: + return output \ No newline at end of file diff --git a/brevitas/nn/quant_linear.py b/brevitas/nn/quant_linear.py new file mode 100644 index 000000000..7fa1a3352 --- /dev/null +++ b/brevitas/nn/quant_linear.py @@ -0,0 +1,165 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from typing import Optional, Union + +import torch +from torch.nn import Linear +from torch.nn.functional import linear + +from brevitas.core.bit_width import BitWidthParameter, BitWidthConst, BitWidthImplType +from brevitas.core.quant import QuantType +from brevitas.core.restrict_val import RestrictValueType +from brevitas.core.scaling import ScalingImplType, SCALING_SCALAR_SHAPE +from brevitas.core.stats import StatsInputViewShapeImpl +from brevitas.core.stats import StatsOp +from brevitas.function.ops import ceil_ste, max_uint +from brevitas.proxy.parameter_quant import WeightQuantProxy, BiasQuantProxy +from .quant_layer import QuantLayer + +__all__ = ['QuantLinear'] + + +class QuantLinear(QuantLayer, Linear): + + def __init__(self, + in_features: int, + out_features: int, + bias: bool, + bias_quant_type: QuantType = QuantType.FP, + bias_narrow_range: bool = False, + weight_quant_override: WeightQuantProxy = None, + weight_quant_type: QuantType = QuantType.FP, + weight_narrow_range: bool = False, + weight_bit_width_impl_override: Union[BitWidthParameter, BitWidthConst] = None, + weight_bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST, + weight_restrict_bit_width_type: RestrictValueType = RestrictValueType.INT, + weight_bit_width: int = 32, + weight_min_overall_bit_width: Optional[int] = 2, + weight_max_overall_bit_width: Optional[int] = None, + weight_scaling_impl_type: ScalingImplType = ScalingImplType.STATS, + weight_scaling_stats_op: StatsOp = StatsOp.MAX, + weight_scaling_per_output_channel: bool = False, + weight_ternary_threshold: float = 0.5, + weight_restrict_scaling_type: RestrictValueType = RestrictValueType.LOG_FP, + weight_scaling_stats_sigma: float = 3.0, + weight_override_pretrained_bit_width: bool = False, + compute_output_scale: bool = False, + compute_output_bit_width: bool = False, + return_quant_tensor: bool = False) -> None: + QuantLayer.__init__(self, + compute_output_scale=compute_output_scale, + compute_output_bit_width=compute_output_bit_width, + return_quant_tensor=return_quant_tensor) + Linear.__init__(self, + in_features=in_features, + out_features=out_features, + bias=bias) + if weight_quant_type == QuantType.FP and compute_output_bit_width: + raise Exception("Computing output bit width requires enabling quantization") + if bias_quant_type != QuantType.FP and not (compute_output_scale and compute_output_bit_width): + raise Exception("Quantizing bias requires to compute output scale and output bit width") + + self.per_elem_ops = 2 * in_features + + if weight_quant_override is not None: + self.weight_quant = weight_quant_override + self.weight_quant.add_tracked_tensor(self.weight) + else: + weight_scaling_stats_input_concat_dim = 0 + if weight_scaling_per_output_channel: + weight_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS + weight_scaling_shape = (self.out_features, 1) + weight_scaling_stats_reduce_dim = 1 + else: + weight_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_TENSOR + weight_scaling_shape = SCALING_SCALAR_SHAPE + weight_scaling_stats_reduce_dim = None + self.weight_quant = WeightQuantProxy(bit_width=weight_bit_width, + quant_type=weight_quant_type, + narrow_range=weight_narrow_range, + restrict_scaling_type=weight_restrict_scaling_type, + scaling_stats_op=weight_scaling_stats_op, + scaling_impl_type=weight_scaling_impl_type, + scaling_stats_reduce_dim=weight_scaling_stats_reduce_dim, + scaling_shape=weight_scaling_shape, + bit_width_impl_type=weight_bit_width_impl_type, + bit_width_impl_override=weight_bit_width_impl_override, + restrict_bit_width_type=weight_restrict_bit_width_type, + min_overall_bit_width=weight_min_overall_bit_width, + max_overall_bit_width=weight_max_overall_bit_width, + tracked_parameter_list_init=self.weight, + ternary_threshold=weight_ternary_threshold, + scaling_stats_input_view_shape_impl=weight_stats_input_view_shape_impl, + scaling_stats_input_concat_dim=weight_scaling_stats_input_concat_dim, + scaling_stats_sigma=weight_scaling_stats_sigma, + override_pretrained_bit_width=weight_override_pretrained_bit_width) + self.bias_quant = BiasQuantProxy(quant_type=bias_quant_type, + narrow_range=bias_narrow_range) + + def forward(self, input): + output_scale = None + output_bit_width = None + + input, input_scale, input_bit_width = self.unpack_input(input) + + quant_weight, quant_weight_scale, quant_weight_bit_width = self.weight_quant(self.weight) + + if self.compute_output_bit_width: + output_bit_width = self.max_output_bit_width(input_bit_width, quant_weight_bit_width) + if self.compute_output_scale: + output_scale = input_scale * quant_weight_scale + + if self.bias is not None: + quant_bias = self.bias_quant(self.bias, output_scale, output_bit_width) + output = linear(input, quant_weight, quant_bias) + else: + output = linear(input, quant_weight, None) + return self.pack_output(output, output_scale, output_bit_width) + + def max_output_bit_width(self, input_bit_width, weight_bit_width): + max_input_val = max_uint(bit_width=input_bit_width, narrow_range=False) + max_fc_val = self.weight_quant.tensor_quant.int_quant.max_uint(weight_bit_width) + max_output_val = max_input_val * max_fc_val * self.in_features + output_bit_width = ceil_ste(torch.log2(max_output_val)) + return output_bit_width + + + diff --git a/brevitas/nn/quant_recurrent.py b/brevitas/nn/quant_recurrent.py new file mode 100644 index 000000000..d9c2acde5 --- /dev/null +++ b/brevitas/nn/quant_recurrent.py @@ -0,0 +1,109 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +import torch +from torch import Tensor +from torch.nn import Parameter + +from brevitas.proxy.max_bit_width_mod import MaxLinearOutputBitWidth +from brevitas.core.quant import QuantType, IntQuant +from brevitas.proxy.parameter_quant import WeightQuant, BiasQuant +from brevitas.core.bit_width import BitWidthParameter, BitWidthConst, BitWidthImplType +from brevitas.core.restrict_val import RestrictValueType +from brevitas.core.scaling import ScalingImplType, SCALING_SCALAR_SHAPE +from brevitas.core.stats import StatsInputViewShapeImpl +from brevitas.core.stats import StatsOp +from brevitas.nn.quant_activation import QuantAccumulator, QuantSigmoid, QuantTanh +from brevitas.proxy.parameter_quant import WeightQuant, BiasQuant + + +class LSTMCell(torch.jit.ScriptModule): + + def __init__(self, + input_size, + hidden_size, + weight_quant_type: QuantType = QuantType.FP, + weight_narrow_range: bool = False, + weight_bit_width_impl_override: Union[BitWidthParameter, BitWidthConst] = None, + weight_bit_width_impl_type: BitWidthImplType = BitWidthImplType.CONST, + weight_restrict_bit_width_type: RestrictValueType = RestrictValueType.INT, + weight_bit_width: int = 32, + weight_min_overall_bit_width: Optional[int] = 2, + weight_max_overall_bit_width: Optional[int] = None, + weight_scaling_impl_type: ScalingImplType = ScalingImplType.STATS, + weight_scaling_stats_op: StatsOp = StatsOp.MAX, + weight_scaling_per_output_channel: bool = False, + weight_ternary_threshold: float = 0.5, + weight_restrict_scaling_type: RestrictValueType = RestrictValueType.LOG_FP, + weight_scaling_stats_sigma: float = 3.0): + super(LSTMCell, self).__init__() + self.input_size = input_size + self.hidden_size = hidden_size + self.weight_ih = Parameter(torch.randn(4 * hidden_size, input_size)) + self.weight_hh = Parameter(torch.randn(4 * hidden_size, hidden_size)) + self.bias_ih = Parameter(torch.randn(4 * hidden_size)) + self.bias_hh = Parameter(torch.randn(4 * hidden_size)) + + + self.weight_ih_quant = WeightQuant() + self.weight_hh_quant = WeightQuant() + self.bias_ih_quant = BiasQuant() + self.bias_hh_quant = BiasQuant() + self.ingate_quant_sigmoid = torch.jit.trace(QuantSigmoid(), torch.randn(size=hidden_size)) + self.forgetgate_quant_sigmoid = torch.jit.trace(QuantSigmoid(), torch.randn(size=hidden_size)) + self.cellgate_quant_tanh = torch.jit.trace(QuantTanh(), torch.randn(size=hidden_size)) + self.outgate_quant_sigmoid = torch.jit.trace(QuantSigmoid(), torch.randn(size=hidden_size)) + + @torch.jit.script_method + def forward(self, input, state): + hx, cx = state + gates = (torch.mm(input, self.weight_ih.t()) + self.bias_ih + + torch.mm(hx, self.weight_hh.t()) + self.bias_hh) + ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1) + + ingate = torch.sigmoid(ingate) + forgetgate = torch.sigmoid(forgetgate) + cellgate = torch.tanh(cellgate) + outgate = torch.sigmoid(outgate) + + cy = (forgetgate * cx) + (ingate * cellgate) + hy = outgate * torch.tanh(cy) + + return hy, (hy, cy) diff --git a/brevitas/proxy/__init__.py b/brevitas/proxy/__init__.py new file mode 100644 index 000000000..d35180e21 --- /dev/null +++ b/brevitas/proxy/__init__.py @@ -0,0 +1,2 @@ +from .parameter_quant import * +from .runtime_quant import * \ No newline at end of file diff --git a/brevitas/proxy/parameter_quant.py b/brevitas/proxy/parameter_quant.py new file mode 100644 index 000000000..5da385da7 --- /dev/null +++ b/brevitas/proxy/parameter_quant.py @@ -0,0 +1,291 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from abc import ABCMeta +from typing import Tuple, Optional, Union, List +from functools import partial + +import torch +from torch import nn, Tensor + +from brevitas.core.scaling import ScalingImplType, StatsScaling, StatsInputViewShapeImpl, IntScaling +from brevitas.core.scaling import StandaloneScaling, SCALING_SCALAR_SHAPE +from brevitas.core.stats import StatsOp +from brevitas.core.quant import QuantType, BinaryQuant, TernaryQuant, RescalingIntQuant, PrescaledIntQuant +from brevitas.core.quant import IdentityQuant +from brevitas.core import ZERO_HW_SENTINEL_NAME, ZERO_HW_SENTINEL_VALUE +from brevitas.core.bit_width import BitWidthConst, BitWidthParameter, BitWidthImplType +from brevitas.core.restrict_val import RestrictValueType, FloatToIntImplType, RestrictValue +from brevitas.core.function_wrapper import TensorClampSte, TensorClamp, RoundSte + + +__all__ = ['WeightQuantProxy', 'BiasQuantProxy'] + + +OVER_BATCH_OVER_CHANNELS_SHAPE = (1, -1, 1, 1) + + +class ParameterQuantProxy(nn.Module): + __metaclass__ = ABCMeta + + def __init__(self): + super(ParameterQuantProxy, self).__init__() + self.register_buffer(ZERO_HW_SENTINEL_NAME, torch.tensor(ZERO_HW_SENTINEL_VALUE)) + + @property + def tensor_quant(self): + return self._tensor_quant + + @tensor_quant.setter + def tensor_quant(self, tensor_quant): + self._tensor_quant = tensor_quant + + @tensor_quant.deleter + def tensor_quant(self): + del self._tensor_quant + + +def _weight_quant_init_impl(bit_width: Optional[int], + quant_type: QuantType, + narrow_range: bool, + restrict_scaling_type: RestrictValueType, + scaling_stats_op: StatsOp, + scaling_impl_type: ScalingImplType, + scaling_stats_reduce_dim: Optional[int], + scaling_shape: Tuple[int, ...], + bit_width_impl_type: Optional[BitWidthImplType], + restrict_bit_width_type: Optional[RestrictValueType], + min_overall_bit_width: Optional[int], + max_overall_bit_width: Optional[int], + bit_width_impl_override: Optional[Union[BitWidthConst, BitWidthParameter]], + scaling_stats_input_view_shape_impl: StatsInputViewShapeImpl, + scaling_stats_input_concat_dim: int, + ternary_threshold: Optional[float], + scaling_stats_sigma: Optional[float], + tracked_parameter_list: List[torch.nn.Parameter], + zero_hw_sentinel: torch.Tensor, + override_pretrained_bit_width: bool): + if quant_type == QuantType.FP: + tensor_quant = IdentityQuant() + else: + if scaling_impl_type == ScalingImplType.STATS or scaling_impl_type == ScalingImplType.PARAMETER_FROM_STATS: + stats_scaling_init = partial(StatsScaling, + stats_op=scaling_stats_op, + restrict_scaling_type=restrict_scaling_type, + tracked_parameter_list=tracked_parameter_list, + stats_input_view_shape_impl=scaling_stats_input_view_shape_impl, + stats_input_concat_dim=scaling_stats_input_concat_dim, + sigma=scaling_stats_sigma, + stats_reduce_dim=scaling_stats_reduce_dim) + if scaling_impl_type == ScalingImplType.PARAMETER_FROM_STATS: + stats_scaling = stats_scaling_init(stats_reduce_dim=None, + stats_output_shape=SCALING_SCALAR_SHAPE) + scaling_init = stats_scaling(zero_hw_sentinel).detach().item() + scaling_impl = StandaloneScaling(scaling_init=scaling_init, + parameter_shape=scaling_shape, + restrict_scaling_type=restrict_scaling_type, + is_parameter=True) + else: + scaling_impl = stats_scaling_init(stats_reduce_dim=scaling_stats_reduce_dim, + stats_output_shape=scaling_shape) + else: + raise Exception("Scaling type {} not supported for weight quantization" + .format(str(scaling_impl_type))) + + if bit_width == 1 and quant_type == QuantType.BINARY: + tensor_quant = BinaryQuant(scaling_impl=scaling_impl) + + elif bit_width == 2 and quant_type == QuantType.TERNARY: + tensor_quant = TernaryQuant(scaling_impl=scaling_impl, threshold=ternary_threshold) + + elif bit_width >= 2 and quant_type == QuantType.INT: + if bit_width_impl_override is None: + if (bit_width_impl_type is None + or bit_width is None + or restrict_bit_width_type is None): + raise Exception("Bit width is not defined properly") + + if bit_width_impl_type == BitWidthImplType.CONST: + tensor_clamp_impl = TensorClampSte() + bit_width_impl = BitWidthConst(bit_width, restrict_bit_width_type) + elif bit_width_impl_type == BitWidthImplType.PARAMETER: + tensor_clamp_impl = TensorClamp() + bit_width_impl = BitWidthParameter(bit_width_init=bit_width, + restrict_bit_width_type=restrict_bit_width_type, + min_overall_bit_width=min_overall_bit_width, + max_overall_bit_width=max_overall_bit_width, + override_pretrained=override_pretrained_bit_width) + else: + raise Exception("Bit width type {} not supported for weight quantization." + .format(str(bit_width_impl_type))) + else: + tensor_clamp_impl = TensorClamp() + bit_width_impl = bit_width_impl_override + + float_to_int_impl = RestrictValue(restrict_value_type=RestrictValueType.INT, + float_to_int_impl_type=FloatToIntImplType.ROUND) + int_scaling_impl = IntScaling(narrow_range, + signed=True, + restrict_scaling_type=restrict_scaling_type) + tensor_quant = RescalingIntQuant(narrow_range=narrow_range, + signed=True, + scaling_impl=scaling_impl, + int_scaling_impl=int_scaling_impl, + tensor_clamp_impl=tensor_clamp_impl, + msb_clamp_bit_width_impl=bit_width_impl, + float_to_int_impl=float_to_int_impl) + else: + raise Exception('Unsupported weight quantization: {} bit width, {} quantization.' + .format(bit_width, str(quant_type))) + return tensor_quant + + +class WeightQuantProxy(ParameterQuantProxy): + + def __init__(self, + bit_width: Optional[int], + quant_type: QuantType, + narrow_range: bool, + restrict_scaling_type: RestrictValueType, + scaling_stats_op: StatsOp, + scaling_impl_type: ScalingImplType, + scaling_stats_reduce_dim: Optional[int], + scaling_shape: Tuple[int, ...], + bit_width_impl_type: Optional[BitWidthImplType], + restrict_bit_width_type: Optional[RestrictValueType], + min_overall_bit_width: Optional[int], + max_overall_bit_width: Optional[int], + tracked_parameter_list_init: torch.nn.Parameter, + bit_width_impl_override: Optional[Union[BitWidthConst, BitWidthParameter]], + scaling_stats_input_view_shape_impl: StatsInputViewShapeImpl, + scaling_stats_input_concat_dim: int, + ternary_threshold: Optional[float], + scaling_stats_sigma: Optional[float], + override_pretrained_bit_width: bool) -> None: + super(WeightQuantProxy, self).__init__() + zero_hw_sentinel = getattr(self, ZERO_HW_SENTINEL_NAME) + self.lazy_tensor_quant_init = partial(_weight_quant_init_impl, + bit_width=bit_width, + quant_type=quant_type, + narrow_range=narrow_range, + restrict_scaling_type=restrict_scaling_type, + scaling_stats_op=scaling_stats_op, + scaling_impl_type=scaling_impl_type, + scaling_stats_reduce_dim=scaling_stats_reduce_dim, + scaling_shape=scaling_shape, + bit_width_impl_type=bit_width_impl_type, + restrict_bit_width_type=restrict_bit_width_type, + min_overall_bit_width=min_overall_bit_width, + max_overall_bit_width=max_overall_bit_width, + bit_width_impl_override=bit_width_impl_override, + scaling_stats_input_view_shape_impl=scaling_stats_input_view_shape_impl, + scaling_stats_input_concat_dim=scaling_stats_input_concat_dim, + ternary_threshold=ternary_threshold, + scaling_stats_sigma=scaling_stats_sigma, + zero_hw_sentinel=zero_hw_sentinel, + override_pretrained_bit_width=override_pretrained_bit_width) + self._tracked_parameter_list = [tracked_parameter_list_init] + self.scale_output_shape = OVER_BATCH_OVER_CHANNELS_SHAPE + self.re_init_tensor_quant() + + def re_init_tensor_quant(self): + self.tensor_quant = self.lazy_tensor_quant_init(tracked_parameter_list=self._tracked_parameter_list) + + def add_tracked_parameter(self, x: torch.nn.Parameter) -> None: + self._tracked_parameter_list.append(x) + if not isinstance(self.tensor_quant, IdentityQuant): + del self.tensor_quant + self.re_init_tensor_quant() + + def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + zero_hw_sentinel = getattr(self, ZERO_HW_SENTINEL_NAME) + out, scale, bit_width = self.tensor_quant(x, zero_hw_sentinel) + reshaped_scale = scale.view(self.scale_output_shape) + return out, reshaped_scale, bit_width + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + super(WeightQuantProxy, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + zero_hw_sentinel_key = prefix + ZERO_HW_SENTINEL_NAME + if zero_hw_sentinel_key in missing_keys: + missing_keys.remove(zero_hw_sentinel_key) + + +class BiasQuantProxy(ParameterQuantProxy): + + def __init__(self, + quant_type: QuantType, + narrow_range: bool) -> None: + super(BiasQuantProxy, self).__init__() + + if quant_type == QuantType.FP: + self.tensor_quant = None + elif quant_type == QuantType.INT: + tensor_clamp_impl = TensorClamp() + float_to_int_impl = RestrictValue(restrict_value_type=RestrictValueType.INT, + float_to_int_impl_type=FloatToIntImplType.ROUND) + self.tensor_quant = PrescaledIntQuant(narrow_range=narrow_range, + signed=True, + tensor_clamp_impl=tensor_clamp_impl, + float_to_int_impl=float_to_int_impl) + else: + raise Exception('Quantization type {} not supported for bias quant.' + .format(str(quant_type))) + + def forward(self, + x: Tensor, + scale: Tensor, + bit_width: Tensor) -> Tensor: + if self.tensor_quant is not None: + zero_hw_sentinel = getattr(self, ZERO_HW_SENTINEL_NAME) + reshaped_scale = scale.view(-1) + out = self.tensor_quant(x, reshaped_scale, bit_width, zero_hw_sentinel) + return out + else: + return x + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + super(BiasQuantProxy, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + zero_hw_sentinel_key = prefix + ZERO_HW_SENTINEL_NAME + if zero_hw_sentinel_key in missing_keys: + missing_keys.remove(zero_hw_sentinel_key) diff --git a/brevitas/proxy/runtime_quant.py b/brevitas/proxy/runtime_quant.py new file mode 100644 index 000000000..3046c1c13 --- /dev/null +++ b/brevitas/proxy/runtime_quant.py @@ -0,0 +1,271 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from typing import Optional, Union, Tuple, List + +import torch +from torch.nn import Module + +from brevitas.core.bit_width import BitWidthImplType, MsbClampParameterBitWidth, BitWidthConst +from brevitas.core.bit_width import BitWidthParameter, LsbTruncParameterBitWidth, ZeroLsbTruncBitWidth +from brevitas.core import ZERO_HW_SENTINEL_NAME, ZERO_HW_SENTINEL_VALUE +from brevitas.core.function_wrapper import Identity, TensorClamp, RoundSte, FloorSte +from brevitas.core.quant import RescalingIntQuant, PrescaledRestrictIntQuant, IdentityQuant +from brevitas.core.quant import QuantType, IdentityPrescaledIntQuant, PrescaledIntQuant +from brevitas.core.restrict_val import RestrictValueType, RestrictValue, FloatToIntImplType +from brevitas.core.scaling import ScalingImplType, StandaloneScaling, IntScaling, SCALING_SCALAR_SHAPE + + +class FusedActivationQuantProxy(torch.jit.ScriptModule): + + def __init__(self, + activation_impl, + tensor_quant): + super(FusedActivationQuantProxy, self).__init__() + self.activation_impl = activation_impl + self.tensor_quant = tensor_quant + + @torch.jit.script_method + def forward(self, x, zero_hw_sentinel): + x = self.activation_impl(x) + x, output_scale, output_bit_width = self.tensor_quant(x, zero_hw_sentinel) + return x, output_scale, output_bit_width + + +class ActivationQuantProxy(Module): + + def __init__(self, + activation_impl: Module, + bit_width: int, + signed: bool, + narrow_range: bool, + min_val: float, + max_val: float, + quant_type: QuantType, + float_to_int_impl_type: FloatToIntImplType, + scaling_impl_type: ScalingImplType, + scaling_per_channel: bool, + per_channel_broadcastable_shape: Optional[Tuple[int, ...]], + min_overall_bit_width: Optional[int], + max_overall_bit_width: Optional[int], + bit_width_impl_override: Module, + bit_width_impl_type: BitWidthImplType, + restrict_bit_width_type: RestrictValueType, + restrict_scaling_type: RestrictValueType, + override_pretrained_bit_width: bool): + super(ActivationQuantProxy, self).__init__() + + if not signed and min_val != 0.0: + raise Exception("Min val has to be 0.0 when quantization is unsigned.") + if scaling_per_channel and per_channel_broadcastable_shape is None: + raise Exception("Per channel scaling requires to specify number of channels.") + + self.register_buffer(ZERO_HW_SENTINEL_NAME, torch.tensor(ZERO_HW_SENTINEL_VALUE)) + + if scaling_per_channel: + scaling_shape = per_channel_broadcastable_shape + else: + scaling_shape = SCALING_SCALAR_SHAPE + + if quant_type == QuantType.FP: + tensor_quant = IdentityQuant() + + elif quant_type == QuantType.INT: + + if scaling_impl_type == ScalingImplType.CONST or scaling_impl_type == ScalingImplType.PARAMETER: + scaling_init = RescalingIntQuant.scaling_init_from_min_max(min_val, max_val) + scaling_impl = StandaloneScaling(is_parameter=scaling_impl_type == ScalingImplType.PARAMETER, + parameter_shape=scaling_shape, + restrict_scaling_type=restrict_scaling_type, + scaling_init=scaling_init) + else: + raise Exception("Scaling type {} not supported for int runtime quantization" + .format(str(scaling_impl_type))) + + if bit_width_impl_override is None: + if bit_width_impl_type is None or bit_width is None or restrict_bit_width_type is None: + raise Exception("Bit width is not defined properly") + + if bit_width_impl_type == BitWidthImplType.CONST: + tensor_clamp_impl = TensorClamp() # If it's const, don't pass gradients to clipped values + msb_clamp_bit_width_impl = BitWidthConst(bit_width, restrict_bit_width_type) + elif bit_width_impl_type == BitWidthImplType.PARAMETER: + tensor_clamp_impl = TensorClamp() # if it's learned, I pass gradients to the bit width + msb_clamp_bit_width_impl = BitWidthParameter(bit_width, + min_overall_bit_width, + max_overall_bit_width, + restrict_bit_width_type, + override_pretrained_bit_width) + else: + raise Exception("Bit width type {} not supported for weight quantization" + .format(str(bit_width_impl_type))) + else: + msb_clamp_bit_width_impl = bit_width_impl_override + tensor_clamp_impl = TensorClamp() # if there is an override, it's learned + + float_to_int_impl = RestrictValue(restrict_value_type=RestrictValueType.INT, + float_to_int_impl_type=float_to_int_impl_type) + int_scaling_impl = IntScaling(narrow_range, + signed=signed, + restrict_scaling_type=restrict_scaling_type) + tensor_quant = RescalingIntQuant(signed=signed, + narrow_range=narrow_range, + scaling_impl=scaling_impl, + int_scaling_impl=int_scaling_impl, + tensor_clamp_impl=tensor_clamp_impl, + msb_clamp_bit_width_impl=msb_clamp_bit_width_impl, + float_to_int_impl=float_to_int_impl) + else: + raise Exception("Quantization type {} not supported for activations.".format(quant_type)) + + self.fused_activation_quant_proxy = FusedActivationQuantProxy(activation_impl, tensor_quant) + + def forward(self, x): + output, output_scale, output_bit_width = self.fused_activation_quant_proxy(x, self.zero_hw_sentinel) + return output, output_scale, output_bit_width + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + super(ActivationQuantProxy, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + zero_hw_sentinel_key = prefix + ZERO_HW_SENTINEL_NAME + if zero_hw_sentinel_key in missing_keys: + missing_keys.remove(zero_hw_sentinel_key) + + +class ClampQuantProxy(Module): + + def __init__(self, + signed: bool, + narrow_range: bool, + quant_type: QuantType, + ms_bit_width_to_clamp: int, + clamp_at_least_init_val: bool, + min_overall_bit_width: Optional[int], + max_overall_bit_width: Optional[int], + msb_clamp_bit_width_impl_type: BitWidthImplType, + override_pretrained_bit_width: bool): + super(ClampQuantProxy, self).__init__() + self.register_buffer(ZERO_HW_SENTINEL_NAME, torch.tensor(ZERO_HW_SENTINEL_VALUE)) + + if quant_type == QuantType.FP: + self.tensor_quant = IdentityPrescaledIntQuant() + + elif quant_type == QuantType.INT: + msb_clamp_bit_width_impl = MsbClampParameterBitWidth(ms_bit_width_to_clamp=ms_bit_width_to_clamp, + clamp_at_least_init_val=clamp_at_least_init_val, + min_overall_bit_width=min_overall_bit_width, + max_overall_bit_width=max_overall_bit_width, + bit_width_impl_type=msb_clamp_bit_width_impl_type, + override_pretrained=override_pretrained_bit_width) + tensor_clamp_impl = TensorClamp() + float_to_int_impl = RestrictValue(restrict_value_type=RestrictValueType.INT, + float_to_int_impl_type=FloatToIntImplType.ROUND) + self.tensor_quant = PrescaledRestrictIntQuant(signed=signed, + narrow_range=narrow_range, + tensor_clamp_impl=tensor_clamp_impl, + float_to_int_impl=float_to_int_impl, + msb_clamp_bit_width_impl=msb_clamp_bit_width_impl) + else: + raise Exception("Quantization type {} not supported for accumulators.".format(quant_type)) + + def forward(self, x, input_scale, input_bit_width): + x, output_scale, output_bit_width = self.tensor_quant(x, input_scale, input_bit_width, self.zero_hw_sentinel) + return x, output_scale, output_bit_width + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + super(ClampQuantProxy, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + zero_hw_sentinel_key = prefix + ZERO_HW_SENTINEL_NAME + if zero_hw_sentinel_key in missing_keys: + missing_keys.remove(zero_hw_sentinel_key) + + +class TruncQuantProxy(Module): + + def __init__(self, + signed: bool, + quant_type: QuantType, + ls_bit_width_to_trunc: int, + trunc_at_least_init_val: bool, + min_overall_bit_width: Optional[int], + max_overall_bit_width: Optional[int], + lsb_trunc_bit_width_impl_type: BitWidthImplType, + override_pretrained_bit_width: bool): + super(TruncQuantProxy, self).__init__() + self.register_buffer(ZERO_HW_SENTINEL_NAME, torch.tensor(ZERO_HW_SENTINEL_VALUE)) + + if quant_type == QuantType.FP: + self.lsb_trunc_bit_width_impl = ZeroLsbTruncBitWidth() + self.tensor_quant = IdentityPrescaledIntQuant() + + elif quant_type == QuantType.INT: + self.lsb_trunc_bit_width_impl = LsbTruncParameterBitWidth(ls_bit_width_to_trunc=ls_bit_width_to_trunc, + trunc_at_least_init_val=trunc_at_least_init_val, + min_overall_bit_width=min_overall_bit_width, + max_overall_bit_width=max_overall_bit_width, + bit_width_impl_type=lsb_trunc_bit_width_impl_type, + override_pretrained=override_pretrained_bit_width) + tensor_clamp_impl = TensorClamp() + float_to_int_impl = RestrictValue(restrict_value_type=RestrictValueType.INT, + float_to_int_impl_type=FloatToIntImplType.FLOOR) + self.tensor_quant = PrescaledIntQuant(signed=signed, + narrow_range=False, + tensor_clamp_impl=tensor_clamp_impl, + float_to_int_impl=float_to_int_impl) + else: + raise Exception("Quantization type {} not supported for accumulators.".format(quant_type)) + + def forward(self, x, input_scale, input_bit_width): + trunc_bit_width = self.lsb_trunc_bit_width_impl(input_bit_width, self.zero_hw_sentinel) + trunc_scale = 2.0 ** trunc_bit_width + output_scale = trunc_scale * input_scale + x, output_scale, input_bit_width = self.tensor_quant(x, output_scale, input_bit_width, self.zero_hw_sentinel) + output_bit_width = input_bit_width - trunc_bit_width + return x, output_scale, output_bit_width + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + super(TruncQuantProxy, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + zero_hw_sentinel_key = prefix + ZERO_HW_SENTINEL_NAME + if zero_hw_sentinel_key in missing_keys: + missing_keys.remove(zero_hw_sentinel_key) diff --git a/brevitas/quant_tensor.py b/brevitas/quant_tensor.py new file mode 100644 index 000000000..115b1a6f7 --- /dev/null +++ b/brevitas/quant_tensor.py @@ -0,0 +1,159 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from collections import namedtuple + +import torch + +from brevitas.function.ops import max_uint, ceil_ste, round_ste + + +def pack_quant_tensor(tensor, scale, bit_width): + return QuantTensor._make([tensor, scale, bit_width]) + + +class QuantTensor(namedtuple("QuantTensor", ["tensor", "scale", "bit_width"])): + + @staticmethod + def check_input_type(other): + if not isinstance(other, QuantTensor): + raise Exception("Other tensor is not a QuantTensor") + + def check_scaling_factors_same(self, other): + if not torch.allclose(self.scale, other.scale): + raise Exception("Other tensor is not a QuantTensor") + + # Reference: https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types + + def __neg__(self): + return QuantTensor._make([- self.tensor, self.scale, self.bit_width]) + + def __add__(self, other): + QuantTensor.check_input_type(other) + self.check_scaling_factors_same(other) + output_tensor = self.tensor + other.tensor + output_scale = (self.scale + other.scale) / 2 + max_uint_val = max_uint(narrow_range=False, bit_width=self.bit_width) + max_uint_val += max_uint(narrow_range=False, bit_width=other.bit_width) + output_bit_width = ceil_ste(torch.log2(max_uint_val)) + output = pack_quant_tensor(output_tensor, output_scale, output_bit_width) + return output + + def __mul__(self, other): + QuantTensor.check_input_type(other) + output_tensor = self.tensor * other.tensor + output_scale = self.scale * other.scale + output_bit_width = self.bit_width + other.bit_width + output = pack_quant_tensor(output_tensor, output_scale, output_bit_width) + return output + + def __sub__(self, other): + return self.__add__(- other) + + def __truediv__(self, other): + QuantTensor.check_input_type(other) + output_tensor = self.tensor / other.tensor + output_scale = self.scale / other.scale + output_bit_width = self.bit_width - other.bit_width + output = pack_quant_tensor(output_tensor, output_scale, output_bit_width) + return output + + def __abs__(self): + return QuantTensor._make([abs(self.tensor), self.scale, self.bit_width]) + + def __pos__(self): + return self + + def __int__(self): + return round_ste(self.tensor / self.scale) + + def __float__(self): + return self.tensor + + def __index__(self): + raise NotImplementedError + + def __round__(self): + raise NotImplementedError + + def __trunc__(self): + raise NotImplementedError + + def __floor__(self): + raise NotImplementedError + + def __ceil__(self): + raise NotImplementedError + + def __complex__(self): + raise NotImplementedError + + def __invert__(self): + raise NotImplementedError + + def __matmul__(self, other): + raise NotImplementedError + + def __floordiv__(self, other): + raise NotImplementedError + + def __mod__(self, other): + raise NotImplementedError + + def __divmod__(self, other): + raise NotImplementedError + + def __pow__(self, other): + raise NotImplementedError + + def __lshift__(self, other): + raise NotImplementedError + + def __rshift__(self, other): + raise NotImplementedError + + def __and__(self, other): + raise NotImplementedError + + def __xor__(self, other): + raise NotImplementedError + + def __or__(self, other): + raise NotImplementedError diff --git a/quantization/modules/__init__.py b/brevitas/utils/__init__.py similarity index 100% rename from quantization/modules/__init__.py rename to brevitas/utils/__init__.py diff --git a/brevitas/utils/logging.py b/brevitas/utils/logging.py new file mode 100644 index 000000000..5462e8d86 --- /dev/null +++ b/brevitas/utils/logging.py @@ -0,0 +1,91 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from typing import Dict +from abc import ABCMeta, abstractmethod +from functools import partial + +import torch +from torch import nn + +from brevitas.utils.quant_utils import * + + +class LogBitWidth(object): + __metaclass__ = ABCMeta + + def __init__(self, model): + self.model: nn.Module = model + self.bit_width_dict: Dict[str, int] = {} + self.register_hooks() + + @abstractmethod + def register_hooks(self): + pass + + +class LogWeightBitWidth(LogBitWidth): + + def __init__(self, model): + super(LogWeightBitWidth, self).__init__(model=model) + pass + + def register_hooks(self): + for name, module in self.model.named_modules(): + def hook_fn(module, input, output, name): + (quant_weight, scale, bit_width) = output + self.bit_width_dict[name] = bit_width.detach().clone() + if has_learned_weight_bit_width(module): + module.register_forward_hook(partial(hook_fn, name=name)) + + +class LogActivationBitWidth(LogBitWidth): + + def __init__(self, model): + super(LogActivationBitWidth, self).__init__(model=model) + pass + + def register_hooks(self): + for name, module in self.model.named_modules(): + def hook_fn(module, input, output, name): + (quant_act, scale, bit_width) = output + self.bit_width_dict[name] = bit_width.detach().clone() + if has_learned_activation_bit_width(module): + module.register_forward_hook(partial(hook_fn, name=name)) \ No newline at end of file diff --git a/brevitas/utils/python_utils.py b/brevitas/utils/python_utils.py new file mode 100644 index 000000000..dedb835ad --- /dev/null +++ b/brevitas/utils/python_utils.py @@ -0,0 +1,49 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from enum import Enum + + +class AutoName(Enum): + def _generate_next_value_(name, start, count, last_values): + return name + + def __str__(self): + return self.value \ No newline at end of file diff --git a/brevitas/utils/quant_utils.py b/brevitas/utils/quant_utils.py new file mode 100644 index 000000000..d2a483cb2 --- /dev/null +++ b/brevitas/utils/quant_utils.py @@ -0,0 +1,66 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from brevitas.proxy.parameter_quant import WeightQuantProxy +from brevitas.core.quant import RescalingIntQuant +from brevitas.proxy.runtime_quant import ActivationQuantProxy, FusedActivationQuantProxy +from brevitas.core.bit_width import BitWidthParameter +from brevitas.nn.quant_layer import QuantTensor + + +def has_learned_weight_bit_width(module): + if isinstance(module, WeightQuantProxy) \ + and isinstance(module.tensor_quant, RescalingIntQuant) \ + and isinstance(module.tensor_quant.msb_clamp_bit_width_impl, + BitWidthParameter): + return True + else: + return False + + +def has_learned_activation_bit_width(module): + if isinstance(module, ActivationQuantProxy) \ + and isinstance(module.fused_activation_quant_proxy, FusedActivationQuantProxy) \ + and isinstance(module.fused_activation_quant_proxy.tensor_quant, RescalingIntQuant) \ + and isinstance(module.fused_activation_quant_proxy.tensor_quant.msb_clamp_bit_width_impl, + BitWidthParameter): + return True + else: + return False \ No newline at end of file diff --git a/brevitas/utils/torch_utils.py b/brevitas/utils/torch_utils.py new file mode 100644 index 000000000..0261e4f59 --- /dev/null +++ b/brevitas/utils/torch_utils.py @@ -0,0 +1,57 @@ +# Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) +# Copyright (c) 2016- Facebook, Inc (Adam Paszke) +# Copyright (c) 2014- Facebook, Inc (Soumith Chintala) +# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) +# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) +# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) +# Copyright (c) 2011-2013 NYU (Clement Farabet) +# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) +# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) +# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) + +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, +# NEC Laboratories America and IDIAP Research Institute nor the names +# of its contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +from torch.nn import Sequential + + +class TupleSequential(Sequential): + + def output(self, mod, input): + if isinstance(input, tuple): + return mod(*input) + else: + return mod(input) + + def forward(self, *input): + modules = list(self._modules.values()) + out = self.output(modules[0], input) + for mod in modules[1:]: + out = self.output(mod, out) + return out \ No newline at end of file diff --git a/build.py b/build.py deleted file mode 100644 index ae46a33ea..000000000 --- a/build.py +++ /dev/null @@ -1,99 +0,0 @@ -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -## Mostly based on the Pytorch-Encoding source code, due MIT copyright below -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -## Created by: Hang Zhang -## ECE Department, Rutgers University -## Email: zhang.hang@rutgers.edu -## Copyright (c) 2017 -## -## This source code is licensed under the MIT-style license found below -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -# MIT License - -# Copyright (c) 2017 Hang Zhang - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import os -import torch -import platform -import subprocess -from torch.utils.ffi import create_extension - -lib_path = os.path.join(os.path.dirname(torch.__file__), 'lib') -cwd = os.path.dirname(os.path.realpath(__file__)) -quantization_lib_path = os.path.join(cwd, "quantization", "lib") - -# clean the build files -clean_cmd = ['bash', 'clean.sh'] -subprocess.check_call(clean_cmd) - -# build CUDA library -os.environ['TORCH_LIB_DIR'] = lib_path -if platform.system() == 'Darwin': - os.environ['TH_LIBRARIES'] = os.path.join(lib_path,'libATen.1.dylib') - QUANTIZATION_LIB = os.path.join(cwd, 'quantization/lib/libQUANTIZATION.dylib') - -else: - os.environ['TH_LIBRARIES'] = os.path.join(lib_path,'libATen.so.1') - QUANTIZATION_LIB = os.path.join(cwd, 'quantization/lib/libQUANTIZATION.so') - -build_all_cmd = ['bash', 'make.sh'] -subprocess.check_call(build_all_cmd, env=dict(os.environ)) - -# build FFI -sources = [ - 'quantization/src/quantized_fused_rnn_cuda_wrapper.cpp' - ] -headers = [ - 'quantization/src/quantized_fused_rnn_cuda_wrapper.h' -] -defines = [('WITH_CUDA', None)] -with_cuda = True - -include_path = [os.path.join(lib_path, 'include'), - os.path.join(cwd,'quantization/kernel'), - os.path.join(cwd,'quantization/kernel/include'), - os.path.join(cwd,'quantization/kernel/thcunn_include'), - os.path.join(cwd,'quantization/src')] - -def make_relative_rpath(path): - if platform.system() == 'Darwin': - return '-Wl,-rpath,' + path - else: - return '-Wl,-rpath,' + path - -ffi = create_extension( - 'xilinx.torch', - package=False, - headers=headers, - sources=sources, - define_macros=defines, - relative_to=__file__, - with_cuda=with_cuda, - include_dirs = include_path, - extra_link_args = [ - make_relative_rpath(lib_path), - make_relative_rpath(quantization_lib_path), - QUANTIZATION_LIB, - ], -) - -if __name__ == '__main__': - ffi.build() \ No newline at end of file diff --git a/clean.sh b/clean.sh deleted file mode 100644 index 8012dbbc4..000000000 --- a/clean.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/usr/bin/env bash - -rm -rf build/ dist/ quantization.egg-info/ quantization/lib/ quantization/_ext/ __pycache__ quantization/__pycache__ diff --git a/make.sh b/make.sh deleted file mode 100644 index 8f3716e35..000000000 --- a/make.sh +++ /dev/null @@ -1,5 +0,0 @@ -#!/bin/bash - -mkdir -p quantization/lib && cd quantization/lib -cmake .. -make diff --git a/quantization/CMakeLists.txt b/quantization/CMakeLists.txt deleted file mode 100644 index 98fd614ee..000000000 --- a/quantization/CMakeLists.txt +++ /dev/null @@ -1,100 +0,0 @@ -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -## Mostly based on the Pytorch-Encoding source code, due MIT copyright below -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -## Created by: Hang Zhang -## ECE Department, Rutgers University -## Email: zhang.hang@rutgers.edu -## Copyright (c) 2017 -## -## This source code is licensed under the MIT-style license found below -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -# MIT License - -# Copyright (c) 2017 Hang Zhang - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -CMAKE_MINIMUM_REQUIRED(VERSION 2.8 FATAL_ERROR) -CMAKE_POLICY(VERSION 2.8) - -INCLUDE(${CMAKE_CURRENT_SOURCE_DIR}/cmake/FindTorch.cmake) - -IF(NOT CUDA_FOUND) -FIND_PACKAGE(CUDA 6.5 REQUIRED) -ENDIF() - -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -## Boilerplate compiler flags -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - -# Detect CUDA architecture and get best NVCC flags -IF(NOT COMMAND CUDA_SELECT_NVCC_ARCH_FLAGS OR MSVC) - INCLUDE(${CMAKE_CURRENT_SOURCE_DIR}/cmake/select_compute_arch.cmake) -ENDIF() -LIST(APPEND CUDA_NVCC_FLAGS $ENV{TORCH_NVCC_FLAGS}) -CUDA_SELECT_NVCC_ARCH_FLAGS(NVCC_FLAGS_EXTRA $ENV{TORCH_CUDA_ARCH_LIST}) -LIST(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA}) - -if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU") - if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "4.9.3") - if(CUDA_VERSION VERSION_LESS "8.0") - MESSAGE(STATUS "Found gcc >=5 and CUDA <= 7.5, adding workaround C++ flags") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_FORCE_INLINES -D_MWAITXINTRIN_H_INCLUDED -D__STRICT_ANSI__") - endif(CUDA_VERSION VERSION_LESS "8.0") - endif(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "4.9.3") -endif(CMAKE_CXX_COMPILER_ID STREQUAL "GNU") - -IF(MSVC) - LIST(APPEND CUDA_NVCC_FLAGS "-Xcompiler /wd4819") - ADD_DEFINITIONS(-DTH_EXPORTS) -ENDIF() - -SET(CMAKE_MACOSX_RPATH 1) -SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") - -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -## Actual compilation stuff -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - -FILE(GLOB kernel_src kernel/*.cu) - -MESSAGE(STATUS "TORCH_INCLUDE_DIR:" ${TORCH_INCLUDE_DIR}) -MESSAGE(STATUS "TORCH_TH_INCLUDE_DIR:" ${TORCH_TH_INCLUDE_DIR}) -MESSAGE(STATUS "TORCH_THC_INCLUDE_DIR:" ${TORCH_THC_INCLUDE_DIR}) - - -CUDA_INCLUDE_DIRECTORIES( - ${CMAKE_CURRENT_SOURCE_DIR}/kernel - ${CMAKE_CURRENT_SOURCE_DIR}/kernel/include - ${CMAKE_CURRENT_SOURCE_DIR}/kernel/thcunn_include - ${TORCH_INCLUDE_DIR} - ${TORCH_TH_INCLUDE_DIR} - ${TORCH_THC_INCLUDE_DIR} -) - -CUDA_ADD_LIBRARY(QUANTIZATION SHARED ${kernel_src}) - -IF(MSVC) - SET_TARGET_PROPERTIES(QUANTIZATION PROPERTIES PREFIX "lib" IMPORT_PREFIX "lib") -ENDIF() - -TARGET_LINK_LIBRARIES(QUANTIZATION - ${THC_LIBRARIES} - ${TH_LIBRARIES} -) \ No newline at end of file diff --git a/quantization/__init__.py b/quantization/__init__.py deleted file mode 100644 index 9315556fb..000000000 --- a/quantization/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from quantization.modules.rnn import QuantizedGRU, QuantizedLSTM \ No newline at end of file diff --git a/quantization/__init__.pyc b/quantization/__init__.pyc deleted file mode 100644 index 0a687692ae2b38ce00f717c1ad99c9dd5b099bf9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 239 zcmZSn%*)m6ofDnR00m4y+5w1*rGP{V5HT|3FfimYGDI;lE-69l;)%s z>lNkY1p(z4f*65>28e|qiuHh8{esGp{G#NHcx3JRD4h8C%)HE!_;@{_QVyUN8;Id{ KAR~)`m;nHDpgFPt diff --git a/quantization/cmake/FindTorch.cmake b/quantization/cmake/FindTorch.cmake deleted file mode 100644 index b3f3aa3fc..000000000 --- a/quantization/cmake/FindTorch.cmake +++ /dev/null @@ -1,43 +0,0 @@ -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -## Created by: Hang Zhang -## ECE Department, Rutgers University -## Email: zhang.hang@rutgers.edu -## Copyright (c) 2017 -## -## This source code is licensed under the MIT-style license found below -##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -# MIT License - -# Copyright (c) 2017 Hang Zhang - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# Set the envrionment variable via python -SET(TORCH_LIB_DIR "$ENV{TORCH_LIB_DIR}") -MESSAGE(STATUS "TORCH_LIB_DIR: " ${TORCH_LIB_DIR}) - -# Find the include files -SET(TORCH_TH_INCLUDE_DIR "${TORCH_LIB_DIR}/include/TH") -SET(TORCH_THC_INCLUDE_DIR "${TORCH_LIB_DIR}/include/THC") -SET(TORCH_ATEN_INCLUDE_DIR "${TORCH_LIB_DIR}/include/THC") -SET(TORCH_INCLUDE_DIR "${TORCH_LIB_DIR}/include") - -# Find the libs. We need to find libraries one by one. -SET(TH_LIBRARIES "$ENV{TH_LIBRARIES}") -SET(THC_LIBRARIES "$ENV{THC_LIBRARIES}") diff --git a/quantization/cmake/select_compute_arch.cmake b/quantization/cmake/select_compute_arch.cmake deleted file mode 100644 index 17458c3ea..000000000 --- a/quantization/cmake/select_compute_arch.cmake +++ /dev/null @@ -1,236 +0,0 @@ -# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -# Copyright (c) 2011-2013 NYU (Clement Farabet) -# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -# Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. - -# 3. Neither the names of NEC Laboratories American and IDIAP Research -# Institute nor the names of its contributors may be used to endorse or -# promote products derived from this software without specific prior -# written permission. - -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -# POSSIBILITY OF SUCH DAMAGE. - -# Synopsis: -# CUDA_SELECT_NVCC_ARCH_FLAGS(out_variable [target_CUDA_architectures]) -# -- Selects GPU arch flags for nvcc based on target_CUDA_architectures -# target_CUDA_architectures : Auto | Common | All | LIST(ARCH_AND_PTX ...) -# - "Auto" detects local machine GPU compute arch at runtime. -# - "Common" and "All" cover common and entire subsets of architectures -# ARCH_AND_PTX : NAME | NUM.NUM | NUM.NUM(NUM.NUM) | NUM.NUM+PTX -# NAME: Fermi Kepler Maxwell Kepler+Tegra Kepler+Tesla Maxwell+Tegra Pascal -# NUM: Any number. Only those pairs are currently accepted by NVCC though: -# 2.0 2.1 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.2 -# Returns LIST of flags to be added to CUDA_NVCC_FLAGS in ${out_variable} -# Additionally, sets ${out_variable}_readable to the resulting numeric list -# Example: -# CUDA_SELECT_NVCC_ARCH_FLAGS(ARCH_FLAGS 3.0 3.5+PTX 5.2(5.0) Maxwell) -# LIST(APPEND CUDA_NVCC_FLAGS ${ARCH_FLAGS}) -# -# More info on CUDA architectures: https://en.wikipedia.org/wiki/CUDA -# - -# This list will be used for CUDA_ARCH_NAME = All option -set(CUDA_KNOWN_GPU_ARCHITECTURES "Fermi" "Kepler" "Maxwell") - -# This list will be used for CUDA_ARCH_NAME = Common option (enabled by default) -set(CUDA_COMMON_GPU_ARCHITECTURES "3.0" "3.5" "5.0") - -if (CUDA_VERSION VERSION_GREATER "6.5") - list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Kepler+Tegra" "Kepler+Tesla" "Maxwell+Tegra") - list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2") -endif () - -if (CUDA_VERSION VERSION_GREATER "7.5") - list(APPEND CUDA_KNOWN_GPU_ARCHITECTURES "Pascal") - list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "6.0" "6.1" "6.1+PTX") -else() - list(APPEND CUDA_COMMON_GPU_ARCHITECTURES "5.2+PTX") -endif () - - - -################################################################################################ -# A function for automatic detection of GPUs installed (if autodetection is enabled) -# Usage: -# CUDA_DETECT_INSTALLED_GPUS(OUT_VARIABLE) -# -function(CUDA_DETECT_INSTALLED_GPUS OUT_VARIABLE) - if(NOT CUDA_GPU_DETECT_OUTPUT) - set(cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu) - - file(WRITE ${cufile} "" - "#include \n" - "int main()\n" - "{\n" - " int count = 0;\n" - " if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n" - " if (count == 0) return -1;\n" - " for (int device = 0; device < count; ++device)\n" - " {\n" - " cudaDeviceProp prop;\n" - " if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n" - " std::printf(\"%d.%d \", prop.major, prop.minor);\n" - " }\n" - " return 0;\n" - "}\n") - - execute_process(COMMAND "${CUDA_NVCC_EXECUTABLE}" "--run" "${cufile}" - "-ccbin" ${CMAKE_CXX_COMPILER} - WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/" - RESULT_VARIABLE nvcc_res OUTPUT_VARIABLE nvcc_out - ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) - - if(nvcc_res EQUAL 0) - # only keep the last line of nvcc_out - string(REGEX REPLACE ";" "\\\\;" nvcc_out "${nvcc_out}") - string(REGEX REPLACE "\n" ";" nvcc_out "${nvcc_out}") - list(GET nvcc_out -1 nvcc_out) - string(REPLACE "2.1" "2.1(2.0)" nvcc_out "${nvcc_out}") - set(CUDA_GPU_DETECT_OUTPUT ${nvcc_out} CACHE INTERNAL "Returned GPU architetures from detect_gpus tool" FORCE) - endif() - endif() - - if(NOT CUDA_GPU_DETECT_OUTPUT) - message(STATUS "Automatic GPU detection failed. Building for common architectures.") - set(${OUT_VARIABLE} ${CUDA_COMMON_GPU_ARCHITECTURES} PARENT_SCOPE) - else() - set(${OUT_VARIABLE} ${CUDA_GPU_DETECT_OUTPUT} PARENT_SCOPE) - endif() -endfunction() - - -################################################################################################ -# Function for selecting GPU arch flags for nvcc based on CUDA architectures from parameter list -# Usage: -# SELECT_NVCC_ARCH_FLAGS(out_variable [list of CUDA compute archs]) -function(CUDA_SELECT_NVCC_ARCH_FLAGS out_variable) - set(CUDA_ARCH_LIST "${ARGN}") - - if("X${CUDA_ARCH_LIST}" STREQUAL "X" ) - set(CUDA_ARCH_LIST "Auto") - endif() - - set(cuda_arch_bin) - set(cuda_arch_ptx) - - if("${CUDA_ARCH_LIST}" STREQUAL "All") - set(CUDA_ARCH_LIST ${CUDA_KNOWN_GPU_ARCHITECTURES}) - elseif("${CUDA_ARCH_LIST}" STREQUAL "Common") - set(CUDA_ARCH_LIST ${CUDA_COMMON_GPU_ARCHITECTURES}) - elseif("${CUDA_ARCH_LIST}" STREQUAL "Auto") - CUDA_DETECT_INSTALLED_GPUS(CUDA_ARCH_LIST) - message(STATUS "Autodetected CUDA architecture(s): ${CUDA_ARCH_LIST}") - endif() - - # Now process the list and look for names - string(REGEX REPLACE "[ \t]+" ";" CUDA_ARCH_LIST "${CUDA_ARCH_LIST}") - list(REMOVE_DUPLICATES CUDA_ARCH_LIST) - foreach(arch_name ${CUDA_ARCH_LIST}) - set(arch_bin) - set(add_ptx FALSE) - # Check to see if we are compiling PTX - if(arch_name MATCHES "(.*)\\+PTX$") - set(add_ptx TRUE) - set(arch_name ${CMAKE_MATCH_1}) - endif() - if(arch_name MATCHES "(^[0-9]\\.[0-9](\\([0-9]\\.[0-9]\\))?)$") - set(arch_bin ${CMAKE_MATCH_1}) - set(arch_ptx ${arch_bin}) - else() - # Look for it in our list of known architectures - if(${arch_name} STREQUAL "Fermi") - set(arch_bin "2.0 2.1(2.0)") - elseif(${arch_name} STREQUAL "Kepler+Tegra") - set(arch_bin 3.2) - elseif(${arch_name} STREQUAL "Kepler+Tesla") - set(arch_bin 3.7) - elseif(${arch_name} STREQUAL "Kepler") - set(arch_bin 3.0 3.5) - set(arch_ptx 3.5) - elseif(${arch_name} STREQUAL "Maxwell+Tegra") - set(arch_bin 5.3) - elseif(${arch_name} STREQUAL "Maxwell") - set(arch_bin 5.0 5.2) - set(arch_ptx 5.2) - elseif(${arch_name} STREQUAL "Pascal") - set(arch_bin 6.0 6.1) - set(arch_ptx 6.1) - else() - message(SEND_ERROR "Unknown CUDA Architecture Name ${arch_name} in CUDA_SELECT_NVCC_ARCH_FLAGS") - endif() - endif() - if(NOT arch_bin) - message(SEND_ERROR "arch_bin wasn't set for some reason") - endif() - list(APPEND cuda_arch_bin ${arch_bin}) - if(add_ptx) - if (NOT arch_ptx) - set(arch_ptx ${arch_bin}) - endif() - list(APPEND cuda_arch_ptx ${arch_ptx}) - endif() - endforeach() - - # remove dots and convert to lists - string(REGEX REPLACE "\\." "" cuda_arch_bin "${cuda_arch_bin}") - string(REGEX REPLACE "\\." "" cuda_arch_ptx "${cuda_arch_ptx}") - string(REGEX MATCHALL "[0-9()]+" cuda_arch_bin "${cuda_arch_bin}") - string(REGEX MATCHALL "[0-9]+" cuda_arch_ptx "${cuda_arch_ptx}") - - if(cuda_arch_bin) - list(REMOVE_DUPLICATES cuda_arch_bin) - endif() - if(cuda_arch_ptx) - list(REMOVE_DUPLICATES cuda_arch_ptx) - endif() - - set(nvcc_flags "") - set(nvcc_archs_readable "") - - # Tell NVCC to add binaries for the specified GPUs - foreach(arch ${cuda_arch_bin}) - if(arch MATCHES "([0-9]+)\\(([0-9]+)\\)") - # User explicitly specified ARCH for the concrete CODE - list(APPEND nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1}) - list(APPEND nvcc_archs_readable sm_${CMAKE_MATCH_1}) - else() - # User didn't explicitly specify ARCH for the concrete CODE, we assume ARCH=CODE - list(APPEND nvcc_flags -gencode arch=compute_${arch},code=sm_${arch}) - list(APPEND nvcc_archs_readable sm_${arch}) - endif() - endforeach() - - # Tell NVCC to add PTX intermediate code for the specified architectures - foreach(arch ${cuda_arch_ptx}) - list(APPEND nvcc_flags -gencode arch=compute_${arch},code=compute_${arch}) - list(APPEND nvcc_archs_readable compute_${arch}) - endforeach() - - string(REPLACE ";" " " nvcc_archs_readable "${nvcc_archs_readable}") - set(${out_variable} ${nvcc_flags} PARENT_SCOPE) - set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE) -endfunction() diff --git a/quantization/function/__init__.pyc b/quantization/function/__init__.pyc deleted file mode 100644 index 65c0a2392c38a58c2a998eb4c01984e267fa1273..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 142 zcmZSn%*)m6ofDnR00oRd+5w1*S%5?e14FO|NW@PANHCxg#pXaU{esGp{G#NH_`=e} yypqhS#FEVXJbe^ST4`P~STsI9GcU6wK3)%~hXbhBCO1E&G$+*#WJ56!GXMZ~X&^-a diff --git a/quantization/function/activation.pyc b/quantization/function/activation.pyc deleted file mode 100644 index b124bf8aae7a7a9485384440e68fbb0e43b78786..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 395 zcmb_XO%DMv5S=bUgqs9^K=x$M5<%kbz>R~{`daDkqTLPR#t-!$`~cpR1b36pn>W+G zzBY3&7ll$2pWfl1_K>;`m}3c2L}L(#t{{RZBR7NH;DH$Utl-Qc`$*jY;E*BO2s#V8 zeDO0;4Sfx@S{b>l8>|3`Ti3RR?=kV-UmG@&GyNLp(S-6aO{Fpd{NMt}f^LC%+oM%Y zcZrNlaFQm7Bk!kKCXsKa5+$_^e3HM8F8OEd*votKHpWk2AMS z*Q{-@C4~2ZC&U}?hzEqU;)z%IODpXUSibL6caQC`f+aJUv8!Emx~977)OWsf>a+_V zEzbXC_2lQFq@M!*uA?MxIU*5$9qEen9NBTC=gN*N4yCSi3$jzt9tG(ZrB{-jlD$`y zZdrPBvNLDfCFxeASCySA+GTlPWM^KYIf+Zsb|k7$awhV#oof9Rz(Bwki&BbVNk=yoPu$)$CP z?qt$ea_KE9^f?Z#HJz>aZ08I1g}8}x=DcPuNLs3)tqoQNE#R!_Bl;z%7k3F*p- zEA`u3I;mENPqo9AP-}MwfoXCqQ^X}4^drO8k^e^e_su4nqL*YHwbzHoW-to({paJL zZ#usWIP=#pG}`HUE=c`OZ`fVKm-*Pk{dg2#LnYx;#aVDv5l^ESLz?4|LVl>n4V2{T zxWqt_Be6>a5{Y z5l$ZWU@}jwO~qn7PJvHIIJ>f2E7pn%uC2g~M}di7G?&-V@jo#a*av|S(k8iKM2bS@ z0V*d2qNaXY(i%AAv`m{hDar{%WjQP4kRsGPe!0m}?AFn|d!0Vf>Ax3rjkbfMyv=7O zVbF~ge#(~Y&aND!hpwnk6gnMUQjhofWa?7fd1v0q4JKbbJ4M+qbkHtdSgE42HR#8x zJYB7K17Df;TnVVpqolUeq{h6PywrKQ#mj7wN6Y;ziqbTYSJqwJ9)aM2yB{XIFAqH` zoqItpQs{jL6?iP5uYL-s6&a7z;a{;2H*W!qfZzfAT8R#T*eq!We1kX9vXtN2!!Y=K z_)c1pb=@s>KkC4=2LCr0snuK1Qm4Be;~#b+v+sjjO}bWV_=GE^z_djzw}b#cQg1mJ zn`vW!`@4pckOYX=WvAv`pJ4XilKgDp`3+9Z`kgC)=c{-+-E{`fitg!?&QTn_2+WrZ zA-L1TX-*RBXry$g?iF-s0Z4SZ6LcOZmUFNk6d3ttQ04ZunjBNvUyQ#oOyelkyk!)|f$Cmu_X?Vf z4Hy*~8lf$##{a`fx z^AZ_^h0LySA+kVrFyR-@OH@Yd(n0AsHhS3b81&iN49*y8s5^KL#xSIAh%ppE*meyD(kEGeLL&B ztm&agWf$#q7WRmFr3nL2+f`ZOS22m7aw4fE77-LvpWE?hCZkKde*#g;{Xx0P?T_`$f$WhOBkfT|rhJ+L?d@t4$jX56LO48Zu+n9vFG;Z)kWp450Don6O@*GbYrA0e&uY937nNWpM_OI=IotgfxN+FW`QOVnT>gKs;X|&7z+rfDRW{7MMdXv)a0xcdkSPm6o+9TLqOBj| zWCJDn7A|p?gJkLv=|*^qEEx$~+bL(lEG0?d9Mh1rGfgybI5O!EP*f2ZAhS$}#5xfr zQ?)+Q4AWG5$+Q7Rn4h6o?^(#xAe`MM<=m#rv*S8ud!+m=AI--|p-G&d%Xmo+Q3+6Cs?nb-Odm!#%_!evP6rVc99W1!uWfaFif7yPe@Mjxq#==%yboWCJC6 z4VN4}(CYi2kiou;6%L?SnV32wb4MHoFg*Q$1Jwl}n$ZTxpUGYV?L5D+ZJ4G?g7;z{ zCm%mM4^1fT68JnG_+|mS-*Xz=TyYm2{wVke^&*Ob?^mc;W_uT&4Qm38TW{vdo+^Ou zE*BOuA)-A9HE~4mhdSH@9H7l)B50~LJXO6>DIlfa8_)6-%rC=My z&&M6OKRt3Z8PutmYK(mQUM5k0+K&$>s#MR46XiG2-=pq5@`p`O1l=T_1cvr-qsGqT z8Iz!PRLwo}tYe!V4*=8N4I0ZIP|_pgf^!Q$cisEWzf9+-XPlXRFS%46NotN&0R?cPnS$nHj;KxKzUI)4a2pE%*L;GNmin|L^1Jf8-3({V#f7 zK`TVj4^_2+@?TRa_f@I%bEsHu0S73Df(P)Lo2`^YK?4&KWyVb{+a~>HG?L_5j&BvaO(r3Q~n_zC7pdw!BBU+C&?7+S}ws zGkM=-?IM?Yk14x{i#>_NL0{Q>k9S@g7o0cj1vUlqM!yr?q4qH(vUG6)G6yp?&xJHg%FvCk=POU2>M(r9!pT Us5KT*UTrKl8jb6XdgIzZ0S*~4+5i9m diff --git a/quantization/function/quantization_scheme.py b/quantization/function/quantization_scheme.py deleted file mode 100644 index 4d94e2975..000000000 --- a/quantization/function/quantization_scheme.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright (c) 2018- Xilinx, Inc - -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. - -# 3. Neither the names of Xilinx nor the names of its contributors -# may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -# POSSIBILITY OF SUCH DAMAGE. - -from abc import ABCMeta, abstractmethod, abstractproperty - -from functools import partial -import math - -from quantization_impl import Identity, FixedUnitWeight, FixedUnitActivation - - -class QuantizationParams(object): - - def __init__(self, bit_width, q_type): - self.bit_width = bit_width - self.q_type = q_type - - def gen_hls_typedef(self, name): - return "typedef ap_fixed<{} , {}, AP_RND_ZERO, AP_WRAP> t_fixed_{};".format(self.bit_width, self.int_bit_width, name) - - -class SignedFixedUnitQuantizationParams(QuantizationParams): - - def __init__(self, bit_width, int_bit_width, q_type): - super(SignedFixedUnitQuantizationParams, self).__init__(bit_width, q_type) - self.int_bit_width = int_bit_width #including implicit sign bit - self.frac_bit_width = bit_width - int_bit_width - self.prescale = 2 ** self.frac_bit_width - self.postscale = 2 ** (- self.frac_bit_width) - self.min_val = - (2 ** (bit_width - self.frac_bit_width - 1)) - self.max_val = - self.min_val - self.postscale - - -class UnsignedFixedUnitQuantizationParams(QuantizationParams): - - def __init__(self, bit_width, q_type): - super(UnsignedFixedUnitQuantizationParams, self).__init__(bit_width, q_type) - self.prescale = 2 ** self.bit_width - self.postscale = 1.0 / self.prescale - self.min_val = 0.0 - self.max_val = 1.0 - self.postscale - - -class QuantizationScheme(object): - __metaclass__ = ABCMeta - - @abstractmethod - def __init__(self, bit_width, q_type, scale_factor=None): - if bit_width == 32 and q_type == 'FP': - self.q_params = QuantizationParams(bit_width, q_type=q_type) - elif q_type == 'UNSIGNED_FIXED_UNIT': - self.q_params = UnsignedFixedUnitQuantizationParams(bit_width, q_type=q_type) - elif q_type == 'SIGNED_FIXED_UNIT': - self.q_params = SignedFixedUnitQuantizationParams(bit_width, int_bit_width=1, q_type=q_type) - else: - raise Exception('Unknown activation quantization scheme: {} bit width, {} quantization.'.format(bit_width, q_type)) - - @property - def q_forward(self): - return partial(self.q_impl.apply, self.q_params) - - @property - def to_int(self): - return partial(self.q_impl.to_int, self.q_params) - - @abstractproperty - def q_impl(self): - pass - - -class ActivationQuantizationScheme(QuantizationScheme): - - def __init__(self, bit_width, q_type, scale_factor=None): - super(ActivationQuantizationScheme, self).__init__(bit_width, q_type, scale_factor) - - @property - def q_impl(self): - if self.q_params.bit_width == 32 and self.q_params.q_type == 'FP': - return Identity - elif self.q_params.q_type == 'SIGNED_FIXED_UNIT' or self.q_params.q_type == 'UNSIGNED_FIXED_UNIT': - return FixedUnitActivation - else: - raise Exception('Unknown activation quantization scheme: {} bit width, {} quantization.'.format(self.q_params.bit_width, self.q_params.q_type)) - - -class WeightQuantizationScheme(QuantizationScheme): - - def __init__(self, bit_width, q_type, scale_factor=None): - super(WeightQuantizationScheme, self).__init__(bit_width, q_type, scale_factor) - - @property - def q_impl(self): - if self.q_params.bit_width == 32 and self.q_params.q_type == 'FP': - return Identity - elif self.q_params.q_type == 'SIGNED_FIXED_UNIT' or self.q_params.q_type == 'UNSIGNED_FIXED_UNIT': - return FixedUnitWeight - else: - raise Exception('Unknown activation quantization scheme: {} bit width, {} quantization.'.format(self.q_params.bit_width, self.q_params.q_type)) - - ''' - (Non Autograd) function to apply to full precision - values during forward - Performed in place on a Variable's data - ''' - @property - def fp_transform_(self): - return lambda x: None - -class BiasQuantizationScheme(WeightQuantizationScheme): - - def __init__(self, bit_width, q_type, scale_factor=None): - super(BiasQuantizationScheme, self).__init__(bit_width, q_type, scale_factor) - - - diff --git a/quantization/function/quantized_fused_rnn.py b/quantization/function/quantized_fused_rnn.py deleted file mode 100644 index 809bb7d5b..000000000 --- a/quantization/function/quantized_fused_rnn.py +++ /dev/null @@ -1,466 +0,0 @@ -# Copyright (c) 2018- Xilinx, Inc - -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. - -# 3. Neither the names of Xilinx nor the names of its contributors -# may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -# POSSIBILITY OF SUCH DAMAGE. - -from functools import partial - -import torch -from quantization.function.quantized_linear import quantized_linear -from torch.autograd import Variable -from torch.autograd.function import Function, InplaceFunction, once_differentiable -import torch.nn.functional as F -import xilinx.torch - -class QuantizedFusedGRU(Function): - @staticmethod - def forward(ctx, input_gate, hidden_gate, hx, q_ibias=False, q_hbias=None): - - hy = input_gate.new() - workspace = input_gate.new(hx.numel() * 5) - - ctx.has_bias = False - if q_ibias is not None: - ctx.has_bias = True - if q_ibias.dim() == 1: - q_ibias = q_ibias.unsqueeze(0) - if q_hbias.dim() == 1: - q_hbias = q_hbias.unsqueeze(0) - - if isinstance(input_gate, torch.cuda.FloatTensor): - with torch.cuda.device_of(input_gate): - xilinx.torch.quantized_fused_gru_Cudaforward( - input_gate, hidden_gate, q_ibias, q_hbias, hx, hy, workspace) - - elif isinstance(input_gate, torch.cuda.DoubleTensor): - with torch.cuda.device_of(input_gate): - xilinx.torch.quantized_fused_gru_CudaDoubleforward( - input_gate, hidden_gate, q_ibias, q_hbias, hx, hy, workspace) - else: - raise RuntimeError('Unimplemented Tensor type.') - - else: - if isinstance(input_gate, torch.cuda.FloatTensor): - with torch.cuda.device_of(input_gate): - xilinx.torch.quantized_fused_gru_nobias_Cudaforward( - input_gate, hidden_gate, hx, hy, workspace) - - elif isinstance(input_gate, torch.cuda.DoubleTensor): - with torch.cuda.device_of(input_gate): - xilinx.torch.quantized_fused_gru_nobias_CudaDoubleforward( - input_gate, hidden_gate, hx, hy, workspace) - else: - raise RuntimeError('Unimplemented Tensor type.') - - ctx.workspace = workspace - ctx.igate_size = input_gate.size() - ctx.hgate_size = hidden_gate.size() - - return hy - - @staticmethod - @once_differentiable - def backward(ctx, gradOutput): - - gradInputHx = gradOutput.new() - gradInInput = gradOutput.new(*ctx.igate_size) - gradInHidden = gradOutput.new(*ctx.hgate_size) - - if isinstance(gradInInput, torch.cuda.FloatTensor): - with torch.cuda.device_of(gradInInput): - xilinx.torch.quantized_fused_gru_Cudabackward( - gradInInput, gradInHidden, gradOutput, gradInputHx, ctx.workspace) - - elif isinstance(gradInInput, torch.cuda.DoubleTensor): - with torch.cuda.device_of(gradInInput): - xilinx.torch.quantized_fused_gru_CudaDoublebackward( - gradInInput, gradInHidden, gradOutput, gradInputHx, ctx.workspace) - - else: - raise RuntimeError('Unimplemented Tensor type.') - - gb1 = gb2 = None - if ctx.has_bias: - gb1 = gradInInput.sum(0, keepdim=False) - gb2 = gradInHidden.sum(0, keepdim=False) - return gradInInput, gradInHidden, gradInputHx, gb1, gb2 - - -class QuantizedFusedLSTM(Function): - - @staticmethod - def forward(ctx, input_gate, hidden_gate, cx, q_ibias=None, q_hbias=None, internal_activation_bit_width=None): - hy = input_gate.new() - cy = input_gate.new() - - ctx.has_bias = False - if q_ibias is not None: - ctx.has_bias = True - if q_ibias.dim() == 1: - q_ibias = q_ibias.unsqueeze(0) - if q_hbias.dim() == 1: - q_hbias = q_hbias.unsqueeze(0) - - # input_gate gets overwritten with some intermediate values to use in backwards - if isinstance(input_gate, torch.cuda.FloatTensor): - bit_width_tensor = torch.cuda.FloatTensor([internal_activation_bit_width]) - with torch.cuda.device_of(input_gate): - xilinx.torch.quantized_fused_lstm_Cudaforward( - input_gate, hidden_gate, q_ibias, q_hbias, cx, hy, cy, bit_width_tensor) - - elif isinstance(input_gate, torch.cuda.DoubleTensor): - bit_width_tensor = torch.cuda.DoubleTensor([internal_activation_bit_width]) - with torch.cuda.device_of(input_gate): - xilinx.torch.quantized_fused_lstm_CudaDoubleforward( - input_gate, hidden_gate, q_ibias, q_hbias, cx, hy, cy, bit_width_tensor) - else: - raise RuntimeError('Unimplemented Tensor type.') - else: - # input_gate gets overwritten with some intermediate values to use in backwards - if isinstance(input_gate, torch.cuda.FloatTensor): - bit_width_tensor = torch.cuda.FloatTensor([internal_activation_bit_width]) - with torch.cuda.device_of(input_gate): - xilinx.torch.quantized_fused_lstm_nobias_Cudaforward( - input_gate, hidden_gate, cx, hy, cy, bit_width_tensor) - - elif isinstance(input_gate, torch.cuda.DoubleTensor): - bit_width_tensor = torch.cuda.DoubleTensor([internal_activation_bit_width]) - with torch.cuda.device_of(input_gate): - xilinx.torch.quantized_fused_lstm_nobias_CudaDoubleforward( - input_gate, hidden_gate, cx, hy, cy, bit_width_tensor) - else: - raise RuntimeError('Unimplemented Tensor type.') - - ctx.hgate_size = hidden_gate.size() - ctx.save_for_backward(input_gate, cx, cy) - - return hy, cy - - @staticmethod - @once_differentiable - def backward(ctx, *gradOutput): - gradInputCx = gradOutput[0].new() - gradInGates = gradOutput[0].new(*ctx.hgate_size) - - saved_tens, cx, cy = ctx.saved_tensors - - if isinstance(gradInputCx, torch.cuda.FloatTensor): - with torch.cuda.device_of(gradInputCx): - xilinx.torch.quantized_fused_lstm_Cudabackward( - saved_tens, gradInGates, cx, cy, - gradOutput[0], gradOutput[1], gradInputCx) - - elif isinstance(gradInputCx, torch.cuda.DoubleTensor): - with torch.cuda.device_of(gradInputCx): - xilinx.torch.quantized_fused_lstm_CudaDoublebackward( - saved_tens, gradInGates, cx, cy, - gradOutput[0], gradOutput[1], gradInputCx) - - else: - raise RuntimeError('Unimplemented Tensor type.') - - gb1 = gb2 = None - if ctx.has_bias: - gb1 = gradInGates.sum(0, keepdim=False) - gb2 = gradInGates.sum(0, keepdim=False) - - return gradInGates, gradInGates, gradInputCx, gb1, gb2, None - - -def QuantizedLSTMCell(input, hidden, w_ih, w_hh, - b_ih=None, b_hh=None, - weight_quantization_scheme=None, - activation_quantization_scheme=None, - bias_quantization_scheme=None, - tanh_quantization_scheme=None, - sigmoid_quantization_scheme=None): - if input.is_cuda: - igates = quantized_linear(input, w_ih, weight_quantization_scheme=weight_quantization_scheme) - hgates = quantized_linear(hidden[0], w_hh, weight_quantization_scheme=weight_quantization_scheme) - state = QuantizedFusedLSTM.apply - if b_ih is None: - output = state(igates, hgates, hidden[1], tanh_quantization_scheme.q_params.bit_width) - else: - bias_quantization_scheme.fp_transform_(b_ih) - bias_quantization_scheme.fp_transform_(b_hh) - q_b_ih = bias_quantization_scheme.q_forward(b_ih) - q_b_hh = bias_quantization_scheme.q_forward(b_hh) - output = state(igates, hgates, hidden[1], q_b_ih, q_b_hh, tanh_quantization_scheme.q_params.bit_width) - quantized_h = activation_quantization_scheme.q_forward(output[0]) - return (quantized_h, output[1]) - else: - - igates = quantized_linear(input, w_ih, weight_quantization_scheme=weight_quantization_scheme) - hgates = quantized_linear(hidden[0], w_hh, weight_quantization_scheme=weight_quantization_scheme) - - def state(igates, hgates, cell_state, i_bias=None, h_bias=None): - hidden_size = igates.size()[1] / 4 #first dimension is batch size - if i_bias is not None and h_bias is not None: - gates = igates + hgates + i_bias + h_bias - else: - gates = igates + hgates - ig = sigmoid_quantization_scheme.q_forward(torch.nn.functional.sigmoid(gates[:, 0:hidden_size])) - fg = sigmoid_quantization_scheme.q_forward(torch.nn.functional.sigmoid(gates[:, hidden_size:2*hidden_size])) - cg = tanh_quantization_scheme.q_forward(torch.nn.functional.tanh(gates[:, 2*hidden_size:3*hidden_size])) - og = sigmoid_quantization_scheme.q_forward(torch.nn.functional.sigmoid(gates[:, 3*hidden_size:4*hidden_size])) - cg = ig * cg - fg = cell_state * fg - cg = fg + cg - og = tanh_quantization_scheme.q_forward(torch.nn.functional.tanh(cg)) * og - return (og, cg) - - if b_ih is None: - output = state(igates, hgates, hidden[1]) - else: - bias_quantization_scheme.fp_transform_(b_ih) - bias_quantization_scheme.fp_transform_(b_hh) - q_b_ih = bias_quantization_scheme.q_forward(b_ih) - q_b_hh = bias_quantization_scheme.q_forward(b_hh) - output = state(igates, hgates, hidden[1], q_b_ih, q_b_hh) - quantized_h = activation_quantization_scheme.q_forward(output[0]) - return (quantized_h, output[1]) - - - - -def QuantizedGRUCell(input, hidden, w_ih, w_hh, - b_ih=None, b_hh=None, - weight_quantization_scheme=None, - activation_quantization_scheme=None, - bias_quantization_scheme=None): - - if input.is_cuda: - gi = quantized_linear(input, w_ih, weight_quantization_scheme=weight_quantization_scheme) - gh = quantized_linear(hidden, w_hh, weight_quantization_scheme=weight_quantization_scheme) - state = QuantizedFusedGRU.apply - if b_ih is None: - output = state(gi, gh, hidden) - else: - bias_quantization_scheme.fp_transform_(b_ih) - bias_quantization_scheme.fp_transform_(b_hh) - q_b_ih = bias_quantization_scheme.q_forward(b_ih) - q_b_hh = bias_quantization_scheme.q_forward(b_hh) - output = state(gi, gh, hidden, q_b_ih, q_b_hh) - return activation_quantization_scheme.q_forward(output) - - else: - raise Exception('No QuantizedGRUCell CPU implementation') - - -def QuantizedAutogradRNN(mode, input_size, hidden_size, weight_quantization_scheme, - activation_quantization_scheme, sigmoid_quantization_scheme, - tanh_quantization_scheme, num_layers=1, batch_first=False, - dropout=0, train=True, bidirectional=False, batch_sizes=None, - dropout_state=None, flat_weight=None, bias_quantization_scheme=None): - - if mode == 'QLSTM': - cell = partial(QuantizedLSTMCell, - weight_quantization_scheme=weight_quantization_scheme, - bias_quantization_scheme=bias_quantization_scheme, - activation_quantization_scheme=activation_quantization_scheme, - sigmoid_quantization_scheme=sigmoid_quantization_scheme, - tanh_quantization_scheme=tanh_quantization_scheme) - elif mode == 'QGRU': - cell = partial(QuantizedGRUCell, - weight_quantization_scheme=weight_quantization_scheme, - bias_quantization_scheme=bias_quantization_scheme, - activation_quantization_scheme=activation_quantization_scheme) - else: - raise Exception('Unknown mode: {}'.format(mode)) - - if batch_sizes is None: - rec_factory = Recurrent - else: - rec_factory = variable_recurrent_factory(batch_sizes) - - if bidirectional: - layer = (rec_factory(cell), rec_factory(cell, reverse=True)) - else: - layer = (rec_factory(cell),) - - func = StackedRNN(layer, - num_layers, - (mode == 'QLSTM'), - dropout=dropout, - train=train) - - def forward(input, weight, hidden): - if batch_first and batch_sizes is None: - input = input.transpose(0, 1) - - nexth, output = func(input, hidden, weight) - - if batch_first and batch_sizes is None: - output = output.transpose(0, 1) - - return output, nexth - - return forward - - - -def StackedRNN(inners, num_layers, lstm=False, dropout=0, train=True): - - num_directions = len(inners) - total_layers = num_layers * num_directions - - def forward(input, hidden, weight): - assert(len(weight) == total_layers) - next_hidden = [] - - if lstm: - hidden = list(zip(*hidden)) - - for i in range(num_layers): - all_output = [] - for j, inner in enumerate(inners): - l = i * num_directions + j - - hy, output = inner(input, hidden[l], weight[l]) - next_hidden.append(hy) - all_output.append(output) - - input = torch.cat(all_output, input.dim() - 1) - - if dropout != 0 and i < num_layers - 1: - input = F.dropout(input, p=dropout, training=train, inplace=False) - - if lstm: - next_h, next_c = zip(*next_hidden) - next_hidden = ( - torch.cat(next_h, 0).view(total_layers, *next_h[0].size()), - torch.cat(next_c, 0).view(total_layers, *next_c[0].size()) - ) - else: - next_hidden = torch.cat(next_hidden, 0).view( - total_layers, *next_hidden[0].size()) - - return next_hidden, input - - return forward - - -def Recurrent(inner, reverse=False): - def forward(input, hidden, weight): - output = [] - steps = range(input.size(0) - 1, -1, -1) if reverse else range(input.size(0)) - for i in steps: - hidden = inner(input[i], hidden, *weight) - # hack to handle LSTM - output.append(hidden[0] if isinstance(hidden, tuple) else hidden) - - if reverse: - output.reverse() - output = torch.cat(output, 0).view(input.size(0), *output[0].size()) - - return hidden, output - - return forward - - -def variable_recurrent_factory(batch_sizes): - def fac(inner, reverse=False): - if reverse: - return VariableRecurrentReverse(batch_sizes, inner) - else: - return VariableRecurrent(batch_sizes, inner) - return fac - - -def VariableRecurrent(batch_sizes, inner): - def forward(input, hidden, weight): - output = [] - input_offset = 0 - last_batch_size = batch_sizes[0] - hiddens = [] - flat_hidden = not isinstance(hidden, tuple) - if flat_hidden: - hidden = (hidden,) - for batch_size in batch_sizes: - step_input = input[input_offset:input_offset + batch_size] - input_offset += batch_size - - dec = last_batch_size - batch_size - if dec > 0: - hiddens.append(tuple(h[-dec:] for h in hidden)) - hidden = tuple(h[:-dec] for h in hidden) - last_batch_size = batch_size - - if flat_hidden: - hidden = (inner(step_input, hidden[0], *weight),) - else: - hidden = inner(step_input, hidden, *weight) - - output.append(hidden[0]) - hiddens.append(hidden) - hiddens.reverse() - - hidden = tuple(torch.cat(h, 0) for h in zip(*hiddens)) - assert hidden[0].size(0) == batch_sizes[0] - if flat_hidden: - hidden = hidden[0] - output = torch.cat(output, 0) - - return hidden, output - - return forward - - -def VariableRecurrentReverse(batch_sizes, inner): - def forward(input, hidden, weight): - output = [] - input_offset = input.size(0) - last_batch_size = batch_sizes[-1] - initial_hidden = hidden - flat_hidden = not isinstance(hidden, tuple) - if flat_hidden: - hidden = (hidden,) - initial_hidden = (initial_hidden,) - hidden = tuple(h[:batch_sizes[-1]] for h in hidden) - for batch_size in reversed(batch_sizes): - inc = batch_size - last_batch_size - if inc > 0: - hidden = tuple(torch.cat((h, ih[last_batch_size:batch_size]), 0) - for h, ih in zip(hidden, initial_hidden)) - last_batch_size = batch_size - step_input = input[input_offset - batch_size:input_offset] - input_offset -= batch_size - - if flat_hidden: - hidden = (inner(step_input, hidden[0], *weight),) - else: - hidden = inner(step_input, hidden, *weight) - output.append(hidden[0]) - - output.reverse() - output = torch.cat(output, 0) - if flat_hidden: - hidden = hidden[0] - return hidden, output - - return forward \ No newline at end of file diff --git a/quantization/function/quantized_fused_rnn.pyc b/quantization/function/quantized_fused_rnn.pyc deleted file mode 100644 index 551bb1c65d6f7dfcf14a75578a739c092c21061c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 13071 zcmcgy-ESP%bw77@x#W_|5AjQ6t5#%E$7>sr;@C}F*DxeYmSZxGfNJ+ zoF!*wCDJAPVXBtYq;Z<0E$Y1Vp$|oY0zuJ-0tJc!{XkH(KwtWlrvgF$fdYLh+Wvm$ z&dd&JwPu2KrQOSW@7#}b?>*=DJNL|%{(XFO|8noEElK~4;_oGV<6$G>;bWvDvTbD5 zh+)~0jw7p%J}XJ5B->?KEz9|7896{D0t*r1^xS2*PhsC{7FQHqjf`qd0?-hLGzk|RBMmBZh4=ZW@pd_2< zs;4`PK@Nu{apXXxQIdl)k9Mmn(X2^^`jwj%?cm6kBhfmW;fTEw1vo5Ah-rr8SevhD zP2OgRZLm?0CaKEisBDhO5r+DHR;=+6M*HB5m;|R?X5l>}Z$O;uZ|;6*j&+{U&O;_0 z>*G5YMhxG7lpxnnH&*z{@y=F|Z=AAoO^Qo97}our)MJ_A{_+gY$blo9d2w0}D$v0p z9Op{FG@ud&_)7c}NN$C}_D;v&hMxJ}(oH{%yU|jzzvEx1Qxg*^M(E$gS>kVR+wW-Z zTC)@Ts<#`>xUm*ACm@cJ+d7?6F8MjCXs!|E`}9B7!&w~K*U^%0 z)Y{N~t)ACR#?azgr`t@dG}_vrWUDUeaI#6=);yJU_H$8tulO8uD%<(ACqZ zcN-g;*T?%Vx37+`O=4Q-#hra!Yh%|Ma+4N+@mQgq;y_mGdyO`Yy%B}s1$5^`?QV3p z83DMkvM(T!Qq3$nRZ}xnGj0Dnb7rPAm!^}YVyT9flV;LsQBDq*n|t+`xx=K@^r2KR z_pgf_!o2euM{CO5GvEPsdD9v&Y;rp*LO<2Q)h0)XIz^kLDHJ6WCCfUEMh*^GsA_$C=Zolgv#qA*Q>e2bb#S@-S2fnAYJD|u*=U5#ZNJgbbv7E?U9Z<+ky{6(NP^b3pKNqJ-4?rB9N(o5 zyN@I3FGzC(GB=m-NBoK@WW|)s^yf+@;gKEZ;xFB}`Q;XB^&hd4<{tkn5!dKfO=V$H@ITWMZ4suku10=X%__$zWX8)dJzaR8 zm;cqkde9;r0(OcKKSi?Uh~qTIAAQ8aG~xH?aP=n`)d`NW2}XbP3Bq4KKB5)*U$PN@ z43R@%SwUpdH{1ft<{)DG?q;(zqLV$%e}ow%>k2%*>xO67^O3c0VYSC$4-I-}reX;?xWYvp}sN(~M5- zU()OGnwm3dW}Fs1Ep9CB_+@KU5M3T4A-W6YSkE9^l?t9dme2HB&!NYYNY~tsZPYF(O&|(HGn1Nly21uRA-k_ zS0Gi8?3E(Pz6`f8R<7#L>wcEwTx9YXlg}U-ympf7KET(~YiY{NJ4&x*b@s}0eR_3P ztouM2+$Wek$wb%v|H7S5VY;{QwcP2H%-l02(;~hQJ7^RoKK*+d1(Zo7`XS*MHej{+ zaEz9}33WjOz3v_2qGlT?jU0|=4WMVRsRPC0!--V04*{N*3r8e6k^>ace}Y&FN?UvY zUv^lIU|&-bULv9*@*_>_@Ior zh}DBx8Y~8%`}i0#0Br7kM~dt-_-rUdXT~1Eb&%Il*3|~ezz*hniX+Z1Iz>TY1 zoEvtXTb{6A!0T(QS-t97$ZGkWPJ>RhZyVYBJKJyAEc>VTUA-#v80|Faj?GK1&#CC)33$Fe}kYT17d8AA?SMkfrB!tA!Zi$i#gmh9y3pv z&*J~6{+}^*V!Ynig1EtOgG(%g^Ng5-Zbv5w{buAo#ab@BxwF&RSJc!%MDBlRX_r0^ zZ#;D#fwm8LBePp>Z(cmLyN$pGIPNw!Y-TO1Sz`^2xHz_rkY*p1*FKo>-1DFv4SBR8P4;5g*q*8%Qs=pn4cpTUw=x=Yuy z$S{J!+_t4FuiaY8BYWyp>t);JKB&sOSDAZ>34z4D#)KH6iv7}F%im#is;*atSNQN% zCcnhwOH97ZWQECBn7qd1IujR(Ld1ID-pKPCs&MX2wz$RQbtDP{1I2HK~#Jm1R=#fBipjMjk^ahTo7l;u4O9-{f8sKdTYH-Z(V8~2aPyhi%kPhC2 zK!1-)&O;}4Gwy>;Ay`e`N6bF(lFlX#=eHxT^fs`twnUT8(mT8@8L#E3sBltY+Y+6t&K)Ih+-Smvt`n* z4XHC8JV*mk+h4{aV(Pa5IOm)NhpD}>v(kN&$=gimzw27SbKA|N4-l^3>P37zm5^`m zG$VaW)rhj{MjO1qrL0fdrl$zApEYE`zw(vj5~9&mxfuLlKqqKY|l!spiZf zR8e(mPQ@%clSoT=Rzc0&nEK%r^v&;F4A26`EQCBP$%C>yD9P-@p}xuAFUkF~+(!-K z;mSQUe}%Y|d(<|$?L1<&%|C};VOV>8doV=Uix&u%fHCY6tPdth1Vtd zMXpgR@xB3_zyxduk#K<+VnmOy4(I6_KW}j1?-{PT#5X6zqA|gJHl0`BUSl?dJh5*; zblw_m81UrQ8)(7C@H$3EU@cSHM1j;7rSfM{&Ewr>GuMp3X1$~(+aJT=iffqz$TJ4!%`wAL+Yu>~!g7~8Oy z7j^0bg^ea|JgJ)t6_rgDCjEc+(c6N|{wq+wJ@1UtxYEO&LojBRnEg&-G z=o7n8XzBoxZi3imidjS|#gGBvg@V$vSVH_HMi$(8DzN-HX9kwP2*-HIjGHsgIp>_I zqkh_X%v>l~GBY-~BuoV$?+e-&3dAW<@cIOQa8>81(~WQYcS4A_cZc^I8~_Fr^xd%kO5-+p|;nTF9yc|B|~TmLjY%J!2qU$h_-$aA|HtW9caor zp_(#nui&cKAD$4QU4W6;P{FS;r^?A~NqYDJfZJdljnMsdCclBi>IU7JzL8lYVYfK( zcVZVJr0&rBY{yXsuHbJ`5BSbapw`bprRSX|q0ZyxX-DU>gq+V`P+IDMn?D$oZUz|j zP0GZ*h6^>(LLt{l>Zj@@R0pcb-!4HfmE~|unO;(|Op8@7e+CtJ9?d8*t4`tQZvdaH zg0bN7-p9Gp9}e-&%Im4?l{;i^>QA3})I;mJ<~w3>ppBM;JT!%I_pTo%XMMh-dp7 zeef13-W`)Rv~s6TPzH*}QIF?iQLjb&$PV}!Fgoaiv zHF9y`bC4fFP*qA*XU`w#H7q6~Kfmt7)7Xh#z=9>F%oRDE_)Gt*`0lqcjQbrVD!co; z%;7tz8_IFPad;mQyv)h?T}HxvKg)dw7{V5Lpa^X^V&%`^kbs(ETAzX}Nc5M(HlN%| zKIpGRL&WwK;x{m-t8A(Ch}EPF`Yv-hTiUDhE3A8!NsGx%B)gT5 zAT2r)Sg2f8G5?N&_N0mRC|v_-oDtMh=pJm>iwe<639mrZT2nK*AXsvv$sy=O>7qVx^cma%JTSHj0yANK_ z>$XyXy}cHJ8A z7$>2GBWif)lf#ogqV^Raon9d|Nf+>Jx_~D&4gg&Mqi6VtUBwaOY?k59pB^kZejodl z#DWdG?hpCs_mO;ZtNr7Awe|&@y=L%+;SxeMl90xEJy%h*52}3>rL~>7(<_R)zsE5q zkRTMn-_55NeD@Dn&y^!k?0y%2?SgYSIkEmhp$k#*yIe8Pk-qI@j*5IU#dl9c#c5o@ zD~gd717y_Kk<*HbRw?z!a#=rdO#CKf{g6_pn0UUey6mSo!U&UoteNJa;bOPP z93ud3zyw$;E+O6J8tmpFGf;h26@A|R?(_w&FTQ|e3W0}tGhLp4ZT?M0RPF!TMVqh9 RlLn_RPCrqbu2!p${}1b@Gf@Bl diff --git a/quantization/function/quantized_linear.py b/quantization/function/quantized_linear.py deleted file mode 100644 index 7743ec54f..000000000 --- a/quantization/function/quantized_linear.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright (c) 2018- Xilinx, Inc - -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. - -# 3. Neither the names of Xilinx nor the names of its contributors -# may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -# POSSIBILITY OF SUCH DAMAGE. - -import torch -import math -import numpy as np - -from torch.nn.functional import linear - -def quantized_linear_hls_bias_string(bias, quantization_scheme, hls_var_name, pe=None): - int_bias = quantization_scheme.to_int(bias).numpy().astype(int) - if pe is not None: - assert(int_bias.shape[0] == 1) - int_bias = np.vstack([int_bias[0][i::pe] for i in xrange(0, pe)]) #create pe subarrays selecting every pe element - return hls_matrix_string(int_bias, quantization_scheme, hls_var_name) - -def quantized_linear_hls_weight_string(weight, quantization_scheme, hls_var_name, simd=None, pe=None): - int_weight = quantization_scheme.to_int(weight).numpy().astype(int) - if pe is not None and simd is not None: - int_weight_simd_pe = np.zeros(shape=(simd, int_weight.shape[0]/simd, pe, int_weight.shape[1]/pe)) - for b in xrange(0, int_weight.shape[0]/simd): - for a in xrange(0, simd): - int_weight_simd_pe[a,b,:,:] = np.vstack([int_weight[b*simd+a][j::pe] for j in xrange(0, pe)]) - return hls_matrix_string(int_weight_simd_pe.astype(int), quantization_scheme, hls_var_name) - else: - return hls_matrix_string(int_weight, quantization_scheme, hls_var_name) - -def int_to_hex(x, bit_width): - return hex(int(np.binary_repr(x, bit_width), 2)) - -def hls_matrix_string(int_x, quantization_scheme, hls_var_name): - x_bit_width = quantization_scheme.q_params.bit_width - decl = "const ap_uint<{}> {} ".format(x_bit_width, hls_var_name) - decl += ''.join(["[{}]".format(int_x.shape[i]) for i in range(int_x.ndim)]) - string_list = [" = "] - matrix_to_string_list(int_x, int_x.ndim - 1, x_bit_width, string_list) - decl += ''.join(string_list) - decl +=";\n" - return quantization_scheme.q_params.gen_hls_typedef(hls_var_name), decl - -def matrix_to_string_list(int_x, dims, bit_width, string_list): - string_list.append("{ ") - for i in xrange(0, int_x.shape[0]): - if dims > 0: - matrix_to_string_list(int_x[i], dims - 1, bit_width, string_list) - else: - string_list.append(str(int_to_hex(int_x[i], bit_width))) - if i < int_x.shape[0] - 1: - string_list.append(", \n") - else: - string_list.append("\n") - string_list.append("}\n") - return - -def quantized_linear(input, weight, weight_quantization_scheme, bias=None, bias_quantization_scheme=None): - weight_quantization_scheme.fp_transform_(weight) - q_weight = weight_quantization_scheme.q_forward(weight) - if bias is not None: - bias_quantization_scheme.fp_transform_(bias) - q_bias = bias_quantization_scheme.q_forward(bias) - output = linear(input, q_weight, q_bias) - else: - output = linear(input, q_weight) - return output \ No newline at end of file diff --git a/quantization/kernel/generic/quantized_generic_fused_rnn_kernel.cu b/quantization/kernel/generic/quantized_generic_fused_rnn_kernel.cu deleted file mode 100644 index 04bb6e9a0..000000000 --- a/quantization/kernel/generic/quantized_generic_fused_rnn_kernel.cu +++ /dev/null @@ -1,222 +0,0 @@ -/* -Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef THC_GENERIC_FILE -#define THC_GENERIC_FILE "generic/quantized_generic_fused_rnn_kernel.cu" -#else - -#include -#include -#include -#include - -void THNN_(QuantizedLSTMFused_updateOutput)( - THCState *state, - THCTensor *input, - THCTensor *hidden, - THCTensor *bias1, - THCTensor *bias2, - THCTensor *cx, - THCTensor *hy, - THCTensor *cy, - THCTensor *quantizationBitWidth) -{ - THCTensor_(resizeAs)(state, hy, cx); - THCTensor_(resizeAs)(state, cy, cx); - THNN_(FusedRNNAssertSizes)(state, 4, 5, input, hidden, hy, cy, cx); - - bool canUse32bi = THNN_(canUse32BitIndexMath) - (state, 7, input, hidden, bias1, bias2, hy, cy, cx); - - - if (canUse32bi) { - THNN_(QuantizedLSTM_forw_ind_wrap) - (state, input, hidden, bias1, bias2, cx, hy, cy, quantizationBitWidth); - } else { - THNN_(QuantizedLSTM_forw_ind_wrap) - (state, input, hidden, bias1, bias2, cx, hy, cy, quantizationBitWidth); - } - THCudaCheck(cudaGetLastError()); -} - -void THNN_(QuantizedLSTMFusedNoBias_updateOutput)( - THCState *state, - THCTensor *input, - THCTensor *hidden, - THCTensor *cx, - THCTensor *hy, - THCTensor *cy, - THCTensor *quantizationBitWidth) -{ - THCTensor_(resizeAs)(state, hy, cx); - THCTensor_(resizeAs)(state, cy, cx); - THNN_(FusedRNNAssertSizes)(state, 4, 5, input, hidden, hy, cy, cx); - - bool canUse32bi = THNN_(canUse32BitIndexMath) - (state, 5, input, hidden, hy, cy, cx); - - if(canUse32bi){ - THNN_(QuantizedLSTM_forw_ind_wrap) - (state, input, hidden, NULL, NULL, cx, hy, cy, quantizationBitWidth); - } else { - THNN_(QuantizedLSTM_forw_ind_wrap) - (state, input, hidden, NULL, NULL, cx, hy, cy, quantizationBitWidth); - } - THCudaCheck(cudaGetLastError()); -} - -void THNN_(QuantizedLSTMFused_updateGradInput)( - THCState *state, - THCTensor *storage, - THCTensor *gradInGates, - THCTensor *cx, - THCTensor *cy, - THCTensor *gradOutput, - THCTensor *gradOutputCell, - THCTensor *gradInputCx) -{ - THCTensor_(resizeAs)(state, gradInputCx, gradOutput); - THCUNN_assertSameGPU(state, 7, storage, gradInGates, cx, cy, - gradOutput, gradOutputCell, gradInputCx); - THNN_(FusedRNNAssertSizes) - (state, 4, 7, storage, gradInGates, cx, cy, - gradOutput, gradOutputCell, gradInputCx); - - bool canUse32bi = THNN_(canUse32BitIndexMath) - (state, 7, storage, gradInGates, cx, cy, - gradOutput, gradOutputCell, gradInputCx); - - if(canUse32bi){ - THNN_(QuantizedLSTM_back_ind_wrap) - (state, storage, gradInGates, cx, cy, - gradOutput, gradOutputCell, gradInputCx); - }else{ - THNN_(QuantizedLSTM_back_ind_wrap) - (state, storage, gradInGates, cx, cy, - gradOutput, gradOutputCell, gradInputCx); - } - THCudaCheck(cudaGetLastError()); -} - -void THNN_(QuantizedGRUFused_updateOutput)( - THCState *state, - THCTensor *input, - THCTensor *hidden, - THCTensor *bias1, - THCTensor *bias2, - THCTensor *hx, - THCTensor *hy, - THCTensor *storage) -{ - THCTensor_(resizeAs)(state, hy, hx); - THNN_(FusedRNNAssertSizes)(state, 3, 4, input, hidden, hx, hy); - THArgCheck(THCTensor_(nElement)(state, storage) == - THCTensor_(nElement)(state, hx)*5, - 3, "Storage tensor for fused kernel was not sized correctly."); - - bool canUse32bi = THNN_(canUse32BitIndexMath) - (state, 7, input, hidden, hx, hy, bias1, bias2, storage); - - if (canUse32bi) { - THNN_(QuantizedGRU_forw_ind_wrap) - (state, input, hidden, bias1, bias2, hx, hy, storage); - } else { - THNN_(QuantizedGRU_forw_ind_wrap) - (state, input, hidden, bias1, bias2, hx, hy, storage); - } - THCudaCheck(cudaGetLastError()); -} - -void THNN_(QuantizedGRUFusedNoBias_updateOutput)( - THCState *state, - THCTensor *input, - THCTensor *hidden, - THCTensor *hx, - THCTensor *hy, - THCTensor *storage) -{ - THCTensor_(resizeAs)(state, hy, hx); - THNN_(FusedRNNAssertSizes)(state, 3, 4, input, hidden, hx, hy); - THArgCheck(THCTensor_(nElement)(state, storage) == - THCTensor_(nElement)(state, hx)*5, - 3, "Storage tensor for fused kernel was not sized correctly."); - - bool canUse32bi = THNN_(canUse32BitIndexMath) - (state, 5, input, hidden, hx, hy, storage); - - if (canUse32bi) { - THNN_(QuantizedGRU_forw_ind_wrap) - (state, input, hidden, NULL, NULL, hx, hy, storage); - } else { - THNN_(QuantizedGRU_forw_ind_wrap) - (state, input, hidden, NULL, NULL, hx, hy, storage); - } - THCudaCheck(cudaGetLastError()); -} - -void THNN_(QuantizedGRUFused_updateGradInput)( - THCState *state, - THCTensor *gradInInput, - THCTensor *gradInHidden, - THCTensor *gradOutput, - THCTensor *gradInputHx, - THCTensor *storage) -{ - THCTensor_(resizeAs)(state, gradInputHx, gradOutput); - THCUNN_assertSameGPU(state, 5, gradInInput, gradInHidden, gradOutput, gradInputHx, storage); - THNN_(FusedRNNAssertSizes)(state, 3, 4, gradInInput, gradInHidden, gradOutput, gradInputHx); - bool canUse32bi = THNN_(canUse32BitIndexMath)(state, 5, gradInInput, gradInHidden, - gradOutput, gradInputHx, storage); - if(canUse32bi){ - THNN_(QuantizedGRU_back_ind_wrap) - (state, gradInInput, gradInHidden, gradOutput, gradInputHx, storage); - }else{ - THNN_(QuantizedGRU_back_ind_wrap) - (state, gradInInput, gradInHidden, gradOutput, gradInputHx, storage); - } - - THCudaCheck(cudaGetLastError()); -} - -#include - -#endif diff --git a/quantization/kernel/generic/quantized_generic_fused_rnn_kernel.h b/quantization/kernel/generic/quantized_generic_fused_rnn_kernel.h deleted file mode 100644 index 13ef42f6b..000000000 --- a/quantization/kernel/generic/quantized_generic_fused_rnn_kernel.h +++ /dev/null @@ -1,103 +0,0 @@ -/* -Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef THC_GENERIC_FILE -#define THC_GENERIC_FILE "generic/quantized_generic_fused_rnn_kernel.h" -#else - -void THNN_(QuantizedLSTMFused_updateOutput)( - THCState *state, - THCTensor *input, - THCTensor *hidden, - THCTensor *bias1, - THCTensor *bias2, - THCTensor *cx, - THCTensor *hy, - THCTensor *cy, - THCTensor *quantizationBitWidth); - -void THNN_(QuantizedLSTMFusedNoBias_updateOutput)( - THCState *state, - THCTensor *input, - THCTensor *hidden, - THCTensor *cx, - THCTensor *hy, - THCTensor *cy, - THCTensor *quantizationBitWidth); - -void THNN_(QuantizedLSTMFused_updateGradInput)( - THCState *state, - THCTensor *storage, - THCTensor *gradInGates, - THCTensor *cx, - THCTensor *cy, - THCTensor *gradOutput, - THCTensor *gradOutputCell, - THCTensor *gradInputCx); - -void THNN_(QuantizedGRUFused_updateOutput)( - THCState *state, - THCTensor *input, - THCTensor *hidden, - THCTensor *bias1, - THCTensor *bias2, - THCTensor *hx, - THCTensor *hy, - THCTensor *storage); - -void THNN_(QuantizedGRUFusedNoBias_updateOutput)( - THCState *state, - THCTensor *input, - THCTensor *hidden, - THCTensor *hx, - THCTensor *hy, - THCTensor *storage); - -void THNN_(QuantizedGRUFused_updateGradInput)( - THCState *state, - THCTensor *gradInInput, - THCTensor *gradInHidden, - THCTensor *gradOutput, - THCTensor *gradInputHx, - THCTensor *storage); - -#endif diff --git a/quantization/kernel/include/internal_common.h b/quantization/kernel/include/internal_common.h deleted file mode 100644 index 183c683d8..000000000 --- a/quantization/kernel/include/internal_common.h +++ /dev/null @@ -1,48 +0,0 @@ -/* -Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef INTERNAL_COMMON_H -#define INTERNAL_COMMON_H - -#define THNN_(NAME) TH_CONCAT_3(THNN_, CReal, NAME) - -#endif diff --git a/quantization/kernel/include/math_generics.cuh b/quantization/kernel/include/math_generics.cuh deleted file mode 100644 index 628a7d932..000000000 --- a/quantization/kernel/include/math_generics.cuh +++ /dev/null @@ -1,101 +0,0 @@ -#ifndef MATH_GENERICS_INC -#define MATH_GENERICS_INC - -#include - -namespace math_generics { - - template - __device__ __forceinline__ T max(T a, T b) { - static_assert(sizeof(T) != sizeof(T), "Function max hasn't implemented the specialized type."); - } - - template - __device__ __forceinline__ T min(T a, T b) { - static_assert(sizeof(T) != sizeof(T), "Function min hasn't implemented the specialized type."); - } - - template - __device__ __forceinline__ T exp(T x) { - static_assert(sizeof(T) != sizeof(T), "Function exp hasn't implemented the specialized type."); - } - - template - __device__ __forceinline__ T tanh(T x) { - static_assert(sizeof(T) != sizeof(T), "Function tanh hasn't implemented the specialized type."); - } - - template - __device__ __forceinline__ T pow(T b, T x) { - static_assert(sizeof(T) != sizeof(T), "Function pow hasn't implemented the specialized type."); - } - - template - __device__ __forceinline__ T round(T x) { - static_assert(sizeof(T) != sizeof(T), "Function round hasn't implemented the specialized type."); - } - - - template <> - __device__ __forceinline__ float max(float a, float b) { - return fmaxf(a, b); - } - - template <> - __device__ __forceinline__ double max(double a, double b) { - return fmax(a, b); - } - - template <> - __device__ __forceinline__ float min(float a, float b) { - return fminf(a, b); - } - - template <> - __device__ __forceinline__ double min(double a, double b) { - return fmin(a, b); - } - - template <> - __device__ __forceinline__ float exp(float x) { - return expf(x); - } - - template <> - __device__ __forceinline__ double exp(double x) { - return exp(x); - } - - template <> - __device__ __forceinline__ float tanh(float x) { - return tanhf(x); - } - - template <> - __device__ __forceinline__ double tanh(double x) { - return tanh(x); - } - - template <> - __device__ __forceinline__ float pow(float b, float x) { - return powf(b, x); - } - - template <> - __device__ __forceinline__ double pow(double b, double x) { - return pow(b, x); - } - - template <> - __device__ __forceinline__ float round(float x) { - return rintf(x); - } - - template <> - __device__ __forceinline__ double round(double x) { - return rint(x); - } -} - - -#endif // MATH_GENERICS_INC diff --git a/quantization/kernel/include/quantized_fused_rnn_kernel_cleanup.h b/quantization/kernel/include/quantized_fused_rnn_kernel_cleanup.h deleted file mode 100644 index e8cdd9b02..000000000 --- a/quantization/kernel/include/quantized_fused_rnn_kernel_cleanup.h +++ /dev/null @@ -1,57 +0,0 @@ -/* -Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef RNN_CLEANUP_H -#define RNN_CLEANUP_H - -#undef DEVICE_LINEAR_GET -#undef H2F -#undef F2H -#undef EXPAND_FUNCTION -#undef EXPAND_DIM -#undef EXPAND_TYPE -#undef FILL_TYPES_FORWARD -#undef FILL_FORWARD -#undef FILL_TYPES_BACKWARD -#undef FILL_BACKWARD - -#endif diff --git a/quantization/kernel/include/quantized_generic_fused_rnn_kernel_helper.cuh b/quantization/kernel/include/quantized_generic_fused_rnn_kernel_helper.cuh deleted file mode 100644 index e04786627..000000000 --- a/quantization/kernel/include/quantized_generic_fused_rnn_kernel_helper.cuh +++ /dev/null @@ -1,117 +0,0 @@ -/* -Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -//No pragma once since the header is included multiple times during the compilation process -//once per generated type - -#define DATATYPE TensorUtils::DataType - -#define DEVICE_LINEAR_GET(D_TENSOR, INDEX) \ - D_TENSOR.data[IndexToOffset::get(INDEX, D_TENSOR)] - -//factor will be 3 for GRU and 4 for LSTM -void THNN_(FusedRNNAssertSizes)(THCState *state, int factor, int count, ...) -{ - va_list list; - va_start(list, count); - THCTensor *input = va_arg(list, THCTensor*); - THCTensor *hidden = va_arg(list, THCTensor*); - THArgCheck(THCTensor_(nElement)(state, input) == - THCTensor_(nElement)(state, hidden), - 3, "Input and Hidden tensor sizes should be the same."); - - THAssertMsg(TensorUtils::getDims(state, input) <= MAX_CUTORCH_DIMS, - "Tensor dimension is too large."); - - THAssertMsg(TensorUtils::getDims(state, hidden) <= MAX_CUTORCH_DIMS, - "Tensor dimension is too large."); - - for (int arg=2; arg < count; ++arg){ - THCTensor *tens = va_arg(list, THCTensor*); - THArgCheck(THCTensor_(nElement)(state, input) == - THCTensor_(nElement)(state, tens)*factor, - 3, "A pointwise tensor was not the right size, should have 1/%u the elements of input/hidden tensor.", arg, factor); - THAssertMsg(TensorUtils::getDims(state, tens) <= MAX_CUTORCH_DIMS, - "Tensor dimension is too large."); - } - - va_end(list); -} - -int THNN_(minIndexType)(THCState *state, int count, ...) -{ - va_list list; - va_start(list, count); - - THCTensor* tens = va_arg(list, THCTensor*); - int startDim = TensorUtils::getDims(state, tens); - bool canCollapse = THCTensor_(isContiguous)(state,tens); - - for (int arg=1; arg < count; ++arg){ - tens = va_arg(list, THCTensor*); - canCollapse = canCollapse && THCTensor_(isContiguous)(state, tens); - if(TensorUtils::getDims(state, tens) != startDim){ - va_end(list); - return -1; - } - } - va_end(list); - if(canCollapse) return -2; - return startDim; -} - -bool THNN_(canUse32BitIndexMath)(THCState *state, int count, ...) -{ - va_list list; - va_start(list, count); - - for (int arg=0; arg < count; ++arg){ - THCTensor *tens = va_arg(list, THCTensor*); - if (!TensorUtils::canUse32BitIndexMath(state, tens)){ - va_end(list); - return false; - } - } - va_end(list); - return true; -} - diff --git a/quantization/kernel/include/quantized_generic_fused_rnn_kernel_impl.cuh b/quantization/kernel/include/quantized_generic_fused_rnn_kernel_impl.cuh deleted file mode 100644 index 6eba8c5f1..000000000 --- a/quantization/kernel/include/quantized_generic_fused_rnn_kernel_impl.cuh +++ /dev/null @@ -1,311 +0,0 @@ -/* -Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -//No pragma once since the header is included multiple times during the compilation process -//once per generated type - -template -#if __CUDA_ARCH__ >= 350 -__launch_bounds__(32 * 16, 4) -#endif -__global__ void -THNN_(QuantizedGRUForward)(TensorInfo Input, - TensorInfo Hidden, - TensorInfo Bias1, - TensorInfo Bias2, - TensorInfo _hx, - TensorInfo _hy, - TensorInfo storage, - IndexType hsz, - IndexType totalElements) -{ - for (IndexType linearIndex = blockIdx.x * blockDim.x + threadIdx.x; - linearIndex < totalElements; - linearIndex += gridDim.x * blockDim.x) - { - - IndexType offset = (linearIndex/hsz)*3*hsz+linearIndex%hsz; - - T ir = DEVICE_LINEAR_GET(Input, offset+0*hsz); - T ii = DEVICE_LINEAR_GET(Input, offset+1*hsz); - T in = DEVICE_LINEAR_GET(Input, offset+2*hsz); - T hr = DEVICE_LINEAR_GET(Hidden,offset+0*hsz); - T hi = DEVICE_LINEAR_GET(Hidden,offset+1*hsz); - T hn = DEVICE_LINEAR_GET(Hidden, offset+2*hsz); - - T hx = DEVICE_LINEAR_GET(_hx, linearIndex); - T* hy = &DEVICE_LINEAR_GET(_hy, linearIndex); - - bool has_bias = (Bias1.data != NULL); - - T b1r, b1i, b1n, b2r, b2i, b2n; - - if(has_bias) { - b1r = DEVICE_LINEAR_GET(Bias1, linearIndex%hsz+0*hsz); - b1i = DEVICE_LINEAR_GET(Bias1, linearIndex%hsz+1*hsz); - b1n = DEVICE_LINEAR_GET(Bias1, linearIndex%hsz+2*hsz); - - b2r = DEVICE_LINEAR_GET(Bias2, linearIndex%hsz+0*hsz); - b2i = DEVICE_LINEAR_GET(Bias2, linearIndex%hsz+1*hsz); - b2n = DEVICE_LINEAR_GET(Bias2, linearIndex%hsz+2*hsz); - } else { - b1r = 0.0; b1i = 0.0; b1n = 0.0; - b2r = 0.0; b2i = 0.0; b2n = 0.0; - } - - offset = (linearIndex/hsz)*5*hsz+linearIndex%hsz; - - T rg, ig, ng; - - rg = ir + hr + b1r + b2r; - ig = ii + hi + b1i + b2i; - - TensorSigmoidOp()(&rg, &rg); - TensorSigmoidOp()(&ig, &ig); - ng = in + b1n + rg * (hn + b2n); - ng = math_generics::tanh(ng); - *hy = ng + ig * (hx - ng); - - //SAVE FOR BACKWARDS - DEVICE_LINEAR_GET(storage, offset+0*hsz) = rg; - DEVICE_LINEAR_GET(storage, offset+1*hsz) = ig; - DEVICE_LINEAR_GET(storage, offset+2*hsz) = ng; - DEVICE_LINEAR_GET(storage, offset+3*hsz) = hx; - DEVICE_LINEAR_GET(storage, offset+4*hsz) = hn + b2n; - } -} - -template -#if __CUDA_ARCH__ >= 350 -__launch_bounds__(32 * 16, 4) -#endif -__global__ void -THNN_(QuantizedGRUBackward)(TensorInfo gradInInput, - TensorInfo gradInHidden, - TensorInfo gradOutput, - TensorInfo gradInputHx, - TensorInfo storage, - IndexType hsz, - IndexType totalElements) -{ - for (IndexType linearIndex = blockIdx.x * blockDim.x + threadIdx.x; - linearIndex < totalElements; - linearIndex += gridDim.x * blockDim.x) { - IndexType offset = (linearIndex/hsz)*5*hsz+linearIndex%hsz; - - T rg = DEVICE_LINEAR_GET(storage, offset+0*hsz); - T ig = DEVICE_LINEAR_GET(storage, offset+1*hsz); - T ng = DEVICE_LINEAR_GET(storage, offset+2*hsz); - T hx = DEVICE_LINEAR_GET(storage, offset+3*hsz); - T hn = DEVICE_LINEAR_GET(storage, offset+4*hsz); - - T go = DEVICE_LINEAR_GET(gradOutput, linearIndex); - - offset = (linearIndex/hsz)*3*hsz+linearIndex%hsz; - - T gig = go*(hx-ng)*(1-ig)*(ig); - T ghx = go*(ig); - T gin = go*(1-ig)*(1-ng*ng); - T ghn = gin *rg; - T grg = gin*hn*(1-rg)*rg; - - DEVICE_LINEAR_GET(gradInputHx, linearIndex) = ghx; - DEVICE_LINEAR_GET(gradInInput, offset+0*hsz) = grg; - DEVICE_LINEAR_GET(gradInInput, offset+1*hsz) = gig; - DEVICE_LINEAR_GET(gradInInput, offset+2*hsz) = gin; - DEVICE_LINEAR_GET(gradInHidden, offset+0*hsz) = grg; - DEVICE_LINEAR_GET(gradInHidden, offset+1*hsz) = gig; - DEVICE_LINEAR_GET(gradInHidden, offset+2*hsz) = ghn; - } -} - -template -#if __CUDA_ARCH__ >= 350 -__launch_bounds__(32 * 16, 4) -#endif -__global__ void - THNN_(QuantizedLSTMForward)(TensorInfo input, - TensorInfo hidden, - TensorInfo bias1, - TensorInfo bias2, - TensorInfo _cx, - TensorInfo _hy, - TensorInfo _cy, - TensorInfo quantizationBitWidth, - IndexType hsz, - IndexType totalElements) -{ - T quantization_bit_width = (T) DEVICE_LINEAR_GET(quantizationBitWidth, 0); - - for (IndexType linearIndex = blockIdx.x * blockDim.x + threadIdx.x; - linearIndex < totalElements; - linearIndex += gridDim.x * blockDim.x) - { - - IndexType offset = (linearIndex/hsz)*4*hsz+linearIndex%hsz; - - - T* iig = &DEVICE_LINEAR_GET(input, offset+0*hsz); - T* ifg = &DEVICE_LINEAR_GET(input, offset+1*hsz); - T* icg = &DEVICE_LINEAR_GET(input, offset+2*hsz); - T* iog = &DEVICE_LINEAR_GET(input, offset+3*hsz); - - T hig = DEVICE_LINEAR_GET(hidden, offset+0*hsz); - T hfg = DEVICE_LINEAR_GET(hidden, offset+1*hsz); - T hcg = DEVICE_LINEAR_GET(hidden, offset+2*hsz); - T hog = DEVICE_LINEAR_GET(hidden, offset+3*hsz); - - T cx = DEVICE_LINEAR_GET(_cx, linearIndex); - - T* hy = &DEVICE_LINEAR_GET(_hy, linearIndex); - T* cy = &DEVICE_LINEAR_GET(_cy, linearIndex); - - bool has_bias = (bias1.data != NULL); - - T b1i, b1f, b1c, b1o; - T b2i, b2f, b2c, b2o; - - if(has_bias) { - b1i = DEVICE_LINEAR_GET(bias1, linearIndex%hsz+0*hsz); - b1f = DEVICE_LINEAR_GET(bias1, linearIndex%hsz+1*hsz); - b1c = DEVICE_LINEAR_GET(bias1, linearIndex%hsz+2*hsz); - b1o = DEVICE_LINEAR_GET(bias1, linearIndex%hsz+3*hsz); - - b2i = DEVICE_LINEAR_GET(bias2, linearIndex%hsz+0*hsz); - b2f = DEVICE_LINEAR_GET(bias2, linearIndex%hsz+1*hsz); - b2c = DEVICE_LINEAR_GET(bias2, linearIndex%hsz+2*hsz); - b2o = DEVICE_LINEAR_GET(bias2, linearIndex%hsz+3*hsz); - - } else { - b1i = 0.0; b1f = 0.0; b1c = 0.0; b1o = 0.0; - b2i = 0.0; b2f = 0.0; b2c = 0.0; b2o = 0.0; - } - - T ig, fg, cg, og; - - ig = *iig + hig + b1i + b2i; - fg = *ifg + hfg + b1f + b2f; - cg = *icg + hcg + b1c + b2c; - og = *iog + hog + b1o + b2o; - - QuantizedTensorSigmoidOp()(&ig, &ig, &quantization_bit_width); - QuantizedTensorSigmoidOp()(&fg, &fg, &quantization_bit_width); - QuantizedTensorTanhOp()(&cg, &cg, &quantization_bit_width); - QuantizedTensorSigmoidOp()(&og, &og, &quantization_bit_width); - - *cy = (fg * cx) + (ig * cg); - T cy_tanhed; - QuantizedTensorTanhOp()(&cy_tanhed, cy, &quantization_bit_width); - *hy = og * cy_tanhed; - - //SAVE FOR BACKWARDS - //Also need cy and cx but can be saved easily in python - *iig = ig; - *ifg = fg; - *icg = cg; - *iog = og; - } -} - -template -#if __CUDA_ARCH__ >= 350 -__launch_bounds__(32 * 16, 4) -#endif -__global__ void - THNN_(QuantizedLSTMBackward)(TensorInfo storage, - TensorInfo gradInGates, - TensorInfo _cx, - TensorInfo _cy, - TensorInfo gradoutput, - TensorInfo gradoutputcell, - TensorInfo gradInputCx, - IndexType hsz, - IndexType totalElements) -{ - for (IndexType linearIndex = blockIdx.x * blockDim.x + threadIdx.x; - linearIndex < totalElements; - linearIndex += gridDim.x * blockDim.x) { - IndexType offset = (linearIndex/hsz)*4*hsz+linearIndex%hsz; - - T ig = DEVICE_LINEAR_GET(storage, offset+0*hsz); - T fg = DEVICE_LINEAR_GET(storage, offset+1*hsz); - T cg = DEVICE_LINEAR_GET(storage, offset+2*hsz); - T og = DEVICE_LINEAR_GET(storage, offset+3*hsz); - - T* ih = &DEVICE_LINEAR_GET(gradInGates, offset+0*hsz); - T* fh = &DEVICE_LINEAR_GET(gradInGates, offset+1*hsz); - T* ch = &DEVICE_LINEAR_GET(gradInGates, offset+2*hsz); - T* oh = &DEVICE_LINEAR_GET(gradInGates, offset+3*hsz); - - //will return hidden grads here - T cx = DEVICE_LINEAR_GET(_cx, linearIndex); - T cy = DEVICE_LINEAR_GET(_cy, linearIndex); - - T* gi = &DEVICE_LINEAR_GET(gradInputCx, linearIndex); - - T go = DEVICE_LINEAR_GET(gradoutput, linearIndex); - T goc= DEVICE_LINEAR_GET(gradoutputcell, linearIndex); - T gcx = math_generics::tanh(cy); - - T gog = go * gcx; - gcx = go * og * ( 1 - gcx*gcx) + goc; - - T gig = gcx * cg; - T gfg = gcx * cx; - T gcg = gcx * ig; - - gcx = gcx * fg; - - gig = gig * (1-ig) * ig; - gfg = gfg * (1-fg) * fg; - gcg = gcg * (1-cg*cg); - gog = gog * (1-og) * og; - - *ih = gig; - *fh = gfg; - *ch = gcg; - *oh = gog; - - *gi = gcx; - } -} - diff --git a/quantization/kernel/include/quantized_generic_fused_rnn_kernel_internal_wrap.cuh b/quantization/kernel/include/quantized_generic_fused_rnn_kernel_internal_wrap.cuh deleted file mode 100644 index 6dce3d822..000000000 --- a/quantization/kernel/include/quantized_generic_fused_rnn_kernel_internal_wrap.cuh +++ /dev/null @@ -1,344 +0,0 @@ -/* -Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -//No pragma once since the header is included multiple times during the compilation process -//once per generated type - -// ************ START Create function calls ********** // -#define FILL_FUNCTION(ITYPE, DIM, FUNCTION) FUNCTION(ITYPE, DIM) - -#define FILL_DIM(ITYPE, DIM, FUNCTION) \ - switch (DIM) { \ - case -2: \ - FILL_FUNCTION(ITYPE, -2, FUNCTION); \ - break; \ - case 1: \ - FILL_FUNCTION(ITYPE, 1, FUNCTION); \ - break; \ - case 2: \ - FILL_FUNCTION(ITYPE, 2, FUNCTION); \ - break; \ - default: \ - FILL_FUNCTION(ITYPE, -1, FUNCTION); \ - break; \ - } - -#define QUANTIZED_LSTM_FORWARD(ITYPE, DIM) THNN_(QuantizedLSTMForward) \ - \ - <<>> \ - (inputI, hiddenI, \ - bias1I, bias2I, cxI, hyI, cyI, \ - quantizationBitWidthI, \ - hid_size, totalElements); - -#define QUANTIZED_LSTM_BACKWARD(ITYPE, DIM) THNN_(QuantizedLSTMBackward) \ - \ - <<>> \ - (storageI, gradingatesI, cxI, cyI, \ - gradoutI, gradoutcI, gradincxI, \ - hid_size, totalElements); - -#define QUANTIZED_GRU_FORWARD(ITYPE, DIM) THNN_(QuantizedGRUForward) \ - \ - <<>> \ - (inputI, hiddenI, bias1I, bias2I, hxI, hyI, storageI, \ - hid_size, totalElements); - -#define QUANTIZED_GRU_BACKWARD(ITYPE, DIM) THNN_(QuantizedGRUBackward) \ - \ - <<>> \ - (gradininputI, gradinhiddenI, gradoutI, gradinhxI, storageI, \ - hid_size, totalElements); - -// ************ END Create actual function calls ************ // - -template -void THNN_(QuantizedLSTM_forw_ind_wrap)( - THCState *state, - THCTensor *input, - THCTensor *hidden, - THCTensor *bias1, - THCTensor *bias2, - THCTensor *cx, - THCTensor *hy, - THCTensor *cy, - THCTensor *quantizationBitWidth) -{ - bool has_bias = (bias1!=NULL); - - int maxDim; - if (has_bias) { - THCUNN_assertSameGPU(state, 7, input, hidden, bias1, bias2, hy, cy, cx, quantizationBitWidth); - maxDim = THNN_(minIndexType) - (state, 7, input, hidden, bias1, bias2, hy, cy, cx); - } else { - THCUNN_assertSameGPU(state, 5, input, hidden, hy, cy, cx, quantizationBitWidth); - maxDim = THNN_(minIndexType) - (state, 5, input, hidden, hy, cy, cx); - } - - ptrdiff_t totalElements = TensorUtils::getNumElements(state, cx); - - const dim3 block = getApplyBlock(); - dim3 grid; - THAssertMsg(getApplyGrid(state, totalElements, grid), - "Could not get grid size for pointwise apply."); - - TensorInfo inputI = - getTensorInfo(state, input); - TensorInfo hiddenI = - getTensorInfo(state, hidden); - TensorInfo cxI = - getTensorInfo(state, cx); - TensorInfo hyI = - getTensorInfo(state, hy); - TensorInfo cyI = - getTensorInfo(state, cy); - TensorInfo quantizationBitWidthI = - getTensorInfo(state, quantizationBitWidth); - - INDTYPE hid_size = cxI.sizes[cxI.dims-1]; - if(has_bias){ - THAssertMsg( hid_size*4 == THCTensor_(nElement)(state, bias1) && - hid_size*4 == THCTensor_(nElement)(state, bias2), - "Bias in pointwise operation is an incorrect size, must be 4 x feature size."); - } - - if(maxDim == -2){ - inputI.collapseDims(); - hiddenI.collapseDims(); - cxI.collapseDims(); - hyI.collapseDims(); - cyI.collapseDims(); - quantizationBitWidthI.collapseDims(); - } - - INDTYPE zero[1] = {0}; - TensorInfo nullinfo = - TensorInfo(NULL, 1, zero, zero); - TensorInfo bias1I = nullinfo; - TensorInfo bias2I = nullinfo; - - if(has_bias){ - bias1I = getTensorInfo(state, bias1); - bias2I = getTensorInfo(state, bias2); - if(maxDim == -2){ - bias1I.collapseDims(); - bias2I.collapseDims(); - } - } - - FILL_DIM(INDTYPE, maxDim, QUANTIZED_LSTM_FORWARD); - -} - -template -void THNN_(QuantizedLSTM_back_ind_wrap)( - THCState *state, - THCTensor *storage, - THCTensor *gradInGates, - THCTensor *cx, - THCTensor *cy, - THCTensor *gradOutput, - THCTensor *gradOutputCell, - THCTensor *gradInputCx) -{ - int maxDim = THNN_(minIndexType) - (state, 7, storage, gradInGates, cx, cy, - gradOutput, gradOutputCell, gradInputCx); - ptrdiff_t totalElements = TensorUtils::getNumElements(state, gradOutput); - - const dim3 block = getApplyBlock(); - dim3 grid; - THAssertMsg(getApplyGrid(state, totalElements, grid), - "Could not get grid size for pointwise apply"); - - TensorInfo storageI = - getTensorInfo(state, storage); - TensorInfo gradingatesI = - getTensorInfo(state, gradInGates); - TensorInfo cxI = - getTensorInfo(state, cx); - TensorInfo cyI = - getTensorInfo(state, cy); - TensorInfo gradoutI = - getTensorInfo(state, gradOutput); - TensorInfo gradoutcI = - getTensorInfo(state, gradOutputCell); - TensorInfo gradincxI = - getTensorInfo(state, gradInputCx); - - INDTYPE hid_size = gradoutI.sizes[gradoutI.dims-1]; - - if(maxDim == -2){ - storageI.collapseDims(); - gradingatesI.collapseDims(); - cxI.collapseDims(); - cyI.collapseDims(); - gradoutI.collapseDims(); - gradoutcI.collapseDims(); - gradincxI.collapseDims(); - } - FILL_DIM(INDTYPE, maxDim, QUANTIZED_LSTM_BACKWARD); - -} - -template -void THNN_(QuantizedGRU_forw_ind_wrap)( - THCState *state, - THCTensor *input, - THCTensor *hidden, - THCTensor *bias1, - THCTensor *bias2, - THCTensor *hx, - THCTensor *hy, - THCTensor *storage) -{ - bool has_bias = (bias1!=NULL); - int maxDim; - - if(has_bias){ - THCUNN_assertSameGPU - (state, 7, input, hidden, hx, hy, bias1, bias2, storage); - maxDim = THNN_(minIndexType) - (state, 7, input, hidden, hx, hy, bias1, bias2, storage); - }else{ - THCUNN_assertSameGPU - (state, 5, input, hidden, hx, hy, storage); - maxDim = THNN_(minIndexType) - (state, 5, input, hidden, hx, hy, storage); - } - - ptrdiff_t totalElements = TensorUtils::getNumElements(state, hx); - - const dim3 block = getApplyBlock(); - dim3 grid; - THAssertMsg(getApplyGrid(state, totalElements, grid), - "Could not get grid size for pointwise apply."); - - TensorInfo inputI = - getTensorInfo(state, input); - TensorInfo hiddenI = - getTensorInfo(state, hidden); - TensorInfo hxI = - getTensorInfo(state, hx); - TensorInfo hyI = - getTensorInfo(state, hy); - TensorInfo storageI = - getTensorInfo(state, storage); - - INDTYPE hid_size = hxI.sizes[hxI.dims-1]; - if(has_bias){ - THAssertMsg( hid_size*3 == THCTensor_(nElement)(state, bias1) && - hid_size*3 == THCTensor_(nElement)(state, bias2), - "Bias in pointwise operation is an incorrect size, must be 3 x feature size."); - } - - if(maxDim == -2){ - inputI.collapseDims(); - hiddenI.collapseDims(); - hyI.collapseDims(); - hxI.collapseDims(); - storageI.collapseDims(); - } - - INDTYPE zero[1] = {0}; - TensorInfo nullinfo = - TensorInfo(NULL, 1, zero, zero); - TensorInfo bias1I = nullinfo; - TensorInfo bias2I = nullinfo; - - if(has_bias){ - bias1I = getTensorInfo(state, bias1); - bias2I = getTensorInfo(state, bias2); - if(maxDim == -2){ - bias1I.collapseDims(); - bias2I.collapseDims(); - } - } - - FILL_DIM(INDTYPE, maxDim, QUANTIZED_GRU_FORWARD); - -} - -template -void THNN_(QuantizedGRU_back_ind_wrap)( - THCState *state, - THCTensor *gradInInput, - THCTensor *gradInHidden, - THCTensor *gradOutput, - THCTensor *gradInputHx, - THCTensor *storage) -{ - - int maxDim = THNN_(minIndexType)(state, 5, gradInInput, gradInHidden, gradOutput, - gradInputHx, storage); - ptrdiff_t totalElements = TensorUtils::getNumElements(state, gradOutput); - - const dim3 block = getApplyBlock(); - dim3 grid; - THAssertMsg(getApplyGrid(state, totalElements, grid), - "Could not get grid size for pointwise apply"); - - TensorInfo gradininputI = - getTensorInfo(state, gradInInput); - TensorInfo gradinhiddenI = - getTensorInfo(state, gradInHidden); - TensorInfo gradoutI = - getTensorInfo(state, gradOutput); - TensorInfo gradinhxI = - getTensorInfo(state, gradInputHx); - TensorInfo storageI = - getTensorInfo(state, storage); - - INDTYPE hid_size = gradoutI.sizes[gradoutI.dims-1]; - - if(maxDim == -2){ - gradininputI.collapseDims(); - gradinhiddenI.collapseDims(); - gradoutI.collapseDims(); - gradinhxI.collapseDims(); - storageI.collapseDims(); - } - FILL_DIM(INDTYPE, maxDim, QUANTIZED_GRU_BACKWARD); -} - diff --git a/quantization/kernel/quantized_fused_rnn_kernel.cu b/quantization/kernel/quantized_fused_rnn_kernel.cu deleted file mode 100644 index a4dcb5904..000000000 --- a/quantization/kernel/quantized_fused_rnn_kernel.cu +++ /dev/null @@ -1,124 +0,0 @@ -/* -Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#include -#include - -#include -#include - -#include "quantized_fused_rnn_kernel.h" - -template -struct TensorSigmoidOp { - __device__ __forceinline__ void operator()(T* out, T* in) const { - T one = (T) 1.0; - *out = one / (one + math_generics::exp(- *in)); - } - - __device__ __forceinline__ void operator()(T* v) const { - T one = (T) 1.0; - *v = one / (one + math_generics::exp(- *v)); - } -}; - -template -struct TensorTanhOp { - __device__ __forceinline__ void operator()(T* out, T* in) const { - *out = math_generics::tanh(*in); - } - - __device__ __forceinline__ void operator()(T* v) const { - *v = math_generics::tanh(*v); - } -}; - -template -struct FixedPointQuantizationOp { - __device__ __forceinline__ void operator()(T* out, T* in, T *min_val, T *max_val, T *pre_scale, T *post_scale) const { - T clipped_value = math_generics::max(math_generics::min(*in, *max_val), *min_val); - T rounded_value = math_generics::round(clipped_value * (*pre_scale)); - *out = rounded_value * (*post_scale); - } -}; - -template -struct QuantizedTensorSigmoidOp { - __device__ __forceinline__ void operator()(T* out, T* in, T *quantization_bit_width) const { - if (*quantization_bit_width == (T) 32.0) { - TensorSigmoidOp()(out, in); - } else { - T one = (T) 1.0; - T two = (T) 2.0; - T pre_scale = math_generics::pow(two, *quantization_bit_width); - T post_scale = one / pre_scale; - T min_val = (T) 0.0; - T max_val = one - post_scale; - TensorSigmoidOp()(out, in); - FixedPointQuantizationOp()(out, out, &min_val, &max_val, &pre_scale, &post_scale); - } - } -}; - -template -struct QuantizedTensorTanhOp { - __device__ __forceinline__ void operator()(T* out, T* in, T *quantization_bit_width) const { - if (*quantization_bit_width == (T) 32.0) { - TensorTanhOp()(out, in); - } else { - T one = (T) 1.0; - T two = (T) 2.0; - T pre_scale = math_generics::pow(two, *quantization_bit_width - one); - T post_scale = math_generics::pow(two, - *quantization_bit_width + one); - T min_val = (T) -1.0; - T max_val = one - post_scale; - TensorTanhOp()(out, in); - FixedPointQuantizationOp()(out, out, &min_val, &max_val, &pre_scale, &post_scale); - } - } -}; - -#include "generic/quantized_generic_fused_rnn_kernel.cu" -#include - -#include "generic/quantized_generic_fused_rnn_kernel.cu" -#include diff --git a/quantization/kernel/quantized_fused_rnn_kernel.h b/quantization/kernel/quantized_fused_rnn_kernel.h deleted file mode 100644 index 38fc87f4e..000000000 --- a/quantization/kernel/quantized_fused_rnn_kernel.h +++ /dev/null @@ -1,59 +0,0 @@ -/* -Copyright (c) 2018- Xilinx, Inc (Alessandro Pappalardo) -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#include - -#ifdef __cplusplus -extern "C" { -#endif - -#include - -#include "generic/quantized_generic_fused_rnn_kernel.h" -#include - -#include "generic/quantized_generic_fused_rnn_kernel.h" -#include - -#ifdef __cplusplus -} -#endif \ No newline at end of file diff --git a/quantization/kernel/thcunn_include/THCApply.cuh b/quantization/kernel/thcunn_include/THCApply.cuh deleted file mode 100644 index 2d1710061..000000000 --- a/quantization/kernel/thcunn_include/THCApply.cuh +++ /dev/null @@ -1,701 +0,0 @@ -/* -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef THC_APPLY_INC -#define THC_APPLY_INC - -#include "THCTensorCopy.h" -#include "THCReduceApplyUtils.cuh" -#include "THCTensorTypeUtils.cuh" - -// -// This file contains pointwise operation functions and kernels that -// work on both contiguous and non-contiguous tensor arguments of -// arbitrary (up to MAX_CUTORCH_DIMS) dimensioned arguments without -// copying or temporary storage. -// - -// Threads per block for our apply kernel -// FIXME: use occupancy calculator instead -#define THC_APPLY_THREADS_PER_BLOCK 32 * 16 -#define THC_APPLY_BLOCKS_PER_SM 4 -template -#if __CUDA_ARCH__ >= 350 -__launch_bounds__(THC_APPLY_THREADS_PER_BLOCK, THC_APPLY_BLOCKS_PER_SM) -#endif -__global__ void -kernelPointwiseApply1(TensorInfo a, - IndexType totalElements, - Op op) { - for (IndexType linearIndex = blockIdx.x * blockDim.x + threadIdx.x; - linearIndex < totalElements; - linearIndex += gridDim.x * blockDim.x) { - // Convert `linearIndex` into an offset of `a` - const IndexType aOffset = - IndexToOffset::get(linearIndex, a); - - op(&a.data[aOffset]); - } -} - -template -#if __CUDA_ARCH__ >= 350 -__launch_bounds__(THC_APPLY_THREADS_PER_BLOCK, THC_APPLY_BLOCKS_PER_SM) -#endif -__global__ void -kernelPointwiseApply2(TensorInfo a, - TensorInfo b, - IndexType totalElements, - Op op) { - for (IndexType linearIndex = blockIdx.x * blockDim.x + threadIdx.x; - linearIndex < totalElements; - linearIndex += gridDim.x * blockDim.x) { - // Convert `linearIndex` into an offset of `a` - const IndexType aOffset = - IndexToOffset::get(linearIndex, a); - - // Convert `linearIndex` into an offset of `b` - const IndexType bOffset = - IndexToOffset::get(linearIndex, b); - - op(&a.data[aOffset], &b.data[bOffset]); - } -} - -template -#if __CUDA_ARCH__ >= 350 -__launch_bounds__(THC_APPLY_THREADS_PER_BLOCK, THC_APPLY_BLOCKS_PER_SM) -#endif -__global__ void -kernelPointwiseApply3(TensorInfo a, - TensorInfo b, - TensorInfo c, - IndexType totalElements, - Op op) { - for (IndexType linearIndex = blockIdx.x * blockDim.x + threadIdx.x; - linearIndex < totalElements; - linearIndex += gridDim.x * blockDim.x) { - // Convert `linearIndex` into an offset of `a` - const IndexType aOffset = - IndexToOffset::get(linearIndex, a); - - // Convert `linearIndex` into an offset of `b` - const IndexType bOffset = - IndexToOffset::get(linearIndex, b); - - // Convert `linearIndex` into an offset of `c` - const IndexType cOffset = - IndexToOffset::get(linearIndex, c); - - op(&a.data[aOffset], &b.data[bOffset], &c.data[cOffset]); - } -} - -inline dim3 getApplyBlock() { - return dim3(THC_APPLY_THREADS_PER_BLOCK); -} - -inline bool getApplyGrid(THCState* state, uint64_t totalElements, dim3& grid) { - int curDevice = -1; - cudaGetDevice(&curDevice); - if (curDevice == -1) return false; - - uint64_t numBlocks = THCCeilDiv(totalElements, static_cast(THC_APPLY_THREADS_PER_BLOCK)); - uint64_t maxGridX = THCState_getCurrentDeviceProperties(state)->maxGridSize[0]; - if (numBlocks > maxGridX) - numBlocks = maxGridX; - grid = dim3(numBlocks); - return true; -} - -template -bool THC_pointwiseApply1(THCState* state, - TensorTypeA* a, - const Op& op, - TensorArgType aType = ReadWrite) { - if (TensorUtils::getDims(state, a) > MAX_CUTORCH_DIMS) { - return false; - } - - if (TensorUtils::getDims(state, a) == 0) { - // Zero-dim tensor; do nothing - return true; - } - - const dim3 block = getApplyBlock(); - - dim3 grid; - ptrdiff_t totalElements = TensorUtils::getNumElements(state, a); - - if (!getApplyGrid(state, totalElements, grid)) { - return false; - } - - // If tensor args have overlapping indices and are read/write, then - // we must expand the tensor to a contiguous form first, since - // otherwise there are conflicting writes. Upon copying back to the - // non-contiguous form, there will be conflicting writes, but at - // least with copy, one of the updaters will win atomically. This is - // a sketchy property of the old system as well (writing into all - // indices of a tensor with overlapping indices should probably be - // an error, since it is unclear which one should win), but we will - // preserve this last-writer-wins (in arbitrary copy order) behavior. - TensorTypeA* oldA = NULL; - - if (aType == ReadWrite && - TensorUtils::overlappingIndices(state, a)) { - // Must perform in contiguous space - oldA = a; - a = TensorUtils::newContiguous(state, a); - } - - // It is possible that the tensor dimensions are able to be collapsed, - // and thus we can reduce the actual code complexity of the copy by - // exploiting this knowledge statically, since the div/mod is the - // most expensive part of the operation, more so than memory accesses. - // For instance, when copying a non-contiguous to a contiguous tensor - // (or vice versa), the contiguous tensor can be collapsed to one - // dimension, and the loop to translate the linear index to the array - // index can be similarly collapsed. That is what this unrolling is for. -#define HANDLE_CASE(TYPE, A) \ - kernelPointwiseApply1::DataType, \ - TYPE, A> \ - <<>>( \ - aInfo, (TYPE) totalElements, op); - -#define HANDLE_A_CASE(TYPE, A) \ - { \ - if (aInfo.isContiguous()) { \ - HANDLE_CASE(TYPE, -2); \ - } else { \ - switch (A) { \ - case 1: \ - HANDLE_CASE(TYPE, 1); \ - break; \ - case 2: \ - HANDLE_CASE(TYPE, 2); \ - break; \ - default: \ - HANDLE_CASE(TYPE, -1); \ - break; \ - } \ - } \ - } - - // Can we use 32-bit integer math in the kernel (the linear ID for the copy - // and the resulting non-linear offset is all computable using 32-bit math?) - // We also use unsigned index math in the kernel, as signed div/mod has - // additional overhead. - if (TensorUtils::canUse32BitIndexMath(state, a)) { - TensorInfo::DataType, unsigned int> aInfo = - getTensorInfo(state, a); - aInfo.collapseDims(); -#if CUDA_VERSION < 9000 - if (!aInfo.isContiguous()) - grid.x = min(THCState_getCurrentDeviceProperties(state)->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); -#endif - HANDLE_A_CASE(unsigned int, aInfo.dims); - } else { - TensorInfo::DataType, uint64_t> aInfo = - getTensorInfo(state, a); - aInfo.collapseDims(); - - // For large tensors, we only compile the completely contiguous - // version and the completely generic version, to reduce - // compilation time. - if (aInfo.isContiguous()) { - kernelPointwiseApply1::DataType, - uint64_t, -2> - <<>>( - aInfo, (uint64_t) totalElements, op); - } else { - -#if CUDA_VERSION < 9000 - grid.x = min(THCState_getCurrentDeviceProperties(state)->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); -#endif - kernelPointwiseApply1::DataType, - uint64_t, -1> - <<>>( - aInfo, (uint64_t) totalElements, op); - } - } -#undef HANDLE_CASE -#undef HANDLE_A_CASE - - if (oldA) { - // Ignore overlaps when copying back; if we use THCTensor_copy - // instead, it will recursively try and invoke ourselves to make - // oldA contiguous. - TensorUtils::copyIgnoringOverlaps(state, oldA, a); - TensorUtils::free(state, a); - a = oldA; - } - - return true; -} - -template -bool THC_pointwiseApply2(THCState* state, - TensorTypeA* a, - TensorTypeB* b, - const Op& op, - TensorArgType aType = ReadWrite, - TensorArgType bType = ReadOnly) { - ptrdiff_t totalElements = TensorUtils::getNumElements(state, a); - - if (totalElements != TensorUtils::getNumElements(state, b)) { - return false; - } - - if (TensorUtils::getDims(state, a) > MAX_CUTORCH_DIMS || - TensorUtils::getDims(state, b) > MAX_CUTORCH_DIMS) { - return false; - } - - if (TensorUtils::getDims(state, a) == 0) { - // Zero-dim tensor; do nothing - return true; - } - - const dim3 block = getApplyBlock(); - - dim3 grid; - if (!getApplyGrid(state, totalElements, grid)) { - return false; - } - - // If tensor args have overlapping indices and are read/write, then - // we must expand the tensor to a contiguous form first, since - // otherwise there are conflicting writes. Upon copying back to the - // non-contiguous form, there will be conflicting writes, but at - // least with copy, one of the updaters will win atomically. This is - // a sketchy property of the old system as well (writing into all - // indices of a tensor with overlapping indices should probably be - // an error, since it is unclear which one should win), but we will - // preserve this last-writer-wins (in arbitrary copy order) behavior. - TensorTypeA* oldA = NULL; - TensorTypeB* oldB = NULL; - - if (aType == ReadWrite && - TensorUtils::overlappingIndices(state, a)) { - // Must perform in contiguous space - oldA = a; - a = TensorUtils::newContiguous(state, a); - } - if (bType == ReadWrite && - TensorUtils::overlappingIndices(state, b)) { - // Must perform in contiguous space - oldB = b; - b = TensorUtils::newContiguous(state, b); - } - - // It is possible that the tensor dimensions are able to be collapsed, - // and thus we can reduce the actual code complexity of the copy by - // exploiting this knowledge statically, since the div/mod is the - // most expensive part of the operation, more so than memory accesses. - // For instance, when copying a non-contiguous to a contiguous tensor - // (or vice versa), the contiguous tensor can be collapsed to one - // dimension, and the loop to translate the linear index to the array - // index can be similarly collapsed. That is what this unrolling is for. -#define HANDLE_CASE(TYPE, A, B) \ - kernelPointwiseApply2::DataType, \ - typename TensorUtils::DataType, \ - TYPE, A, B> \ - <<>>( \ - aInfo, bInfo, (TYPE) totalElements, op); - -#define HANDLE_B_CASE(TYPE, A, B) \ - { \ - if (bInfo.isContiguous()) { \ - HANDLE_CASE(TYPE, A, -2); \ - } else { \ - switch (B) { \ - case 1: \ - HANDLE_CASE(TYPE, A, 1); \ - break; \ - case 2: \ - HANDLE_CASE(TYPE, A, 2); \ - break; \ - default: \ - HANDLE_CASE(TYPE, A, -1); \ - break; \ - } \ - } \ - } - -#define HANDLE_A_CASE(TYPE, A, B) \ - { \ - if (aInfo.isContiguous()) { \ - HANDLE_B_CASE(TYPE, -2, B); \ - } else { \ - switch (A) { \ - case 1: \ - HANDLE_B_CASE(TYPE, 1, B); \ - break; \ - case 2: \ - HANDLE_B_CASE(TYPE, 2, B); \ - break; \ - default: \ - HANDLE_B_CASE(TYPE, -1, B); \ - break; \ - } \ - } \ - } - - if (TensorUtils::canUse32BitIndexMath(state, a) && - TensorUtils::canUse32BitIndexMath(state, b)) { - TensorInfo::DataType, unsigned int> aInfo = - getTensorInfo(state, a); - aInfo.collapseDims(); - - TensorInfo::DataType, unsigned int> bInfo = - getTensorInfo(state, b); - bInfo.collapseDims(); -#if CUDA_VERSION < 9000 - if (!(aInfo.isContiguous() && bInfo.isContiguous())) - grid.x = min(THCState_getCurrentDeviceProperties(state)->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); -#endif - - HANDLE_A_CASE(unsigned int, aInfo.dims, bInfo.dims); - } else { - TensorInfo::DataType, uint64_t> aInfo = - getTensorInfo(state, a); - aInfo.collapseDims(); - - TensorInfo::DataType, uint64_t> bInfo = - getTensorInfo(state, b); - bInfo.collapseDims(); - - // For large tensors, we only compile the completely contiguous - // version and the completely generic version, to reduce - // compilation time. - if (aInfo.isContiguous() && bInfo.isContiguous()) { - kernelPointwiseApply2::DataType, - typename TensorUtils::DataType, - uint64_t, -2, -2> - <<>>( - aInfo, bInfo, (uint64_t) totalElements, op); - } else { -#if CUDA_VERSION < 9000 - grid.x = min(THCState_getCurrentDeviceProperties(state)->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); -#endif - kernelPointwiseApply2::DataType, - typename TensorUtils::DataType, - uint64_t, -1, -1> - <<>>( - aInfo, bInfo, (uint64_t) totalElements, op); - } - } -#undef HANDLE_CASE -#undef HANDLE_B_CASE -#undef HANDLE_A_CASE - - if (oldA) { - // Ignore overlaps when copying back; if we use THCTensor_copy - // instead, it will recursively try and invoke ourselves to make - // oldA contiguous. - TensorUtils::copyIgnoringOverlaps(state, oldA, a); - TensorUtils::free(state, a); - a = oldA; - } - - if (oldB) { - // Ignore overlaps when copying back; if we use THCTensor_copy - // instead, it will recursively try and invoke ourselves to make - // oldB contiguous. - TensorUtils::copyIgnoringOverlaps(state, oldB, b); - TensorUtils::free(state, b); - b = oldB; - } - - return true; -} - -template -bool THC_pointwiseApply3(THCState* state, - TensorTypeA* a, - TensorTypeB* b, - TensorTypeC* c, - const Op& op, - TensorArgType aType = ReadWrite, - TensorArgType bType = ReadOnly, - TensorArgType cType = ReadOnly) { - ptrdiff_t totalElements = TensorUtils::getNumElements(state, a); - - if (totalElements != TensorUtils::getNumElements(state, b) || - totalElements != TensorUtils::getNumElements(state, c)) { - return false; - } - - if (TensorUtils::getDims(state, a) > MAX_CUTORCH_DIMS || - TensorUtils::getDims(state, b) > MAX_CUTORCH_DIMS || - TensorUtils::getDims(state, c) > MAX_CUTORCH_DIMS) { - return false; - } - - if (TensorUtils::getDims(state, a) == 0) { - // Zero-dim tensor; do nothing - return true; - } - - const dim3 block = getApplyBlock(); - - dim3 grid; - if (!getApplyGrid(state, totalElements, grid)) { - return false; - } - - // If tensor args have overlapping indices and are read/write, then - // we must expand the tensor to a contiguous form first, since - // otherwise there are conflicting writes. Upon copying back to the - // non-contiguous form, there will be conflicting writes, but at - // least with copy, one of the updaters will win atomically. This is - // a sketchy property of the old system as well (writing into all - // indices of a tensor with overlapping indices should probably be - // an error, since it is unclear which one should win), but we will - // preserve this last-writer-wins (in arbitrary copy order) behavior. - TensorTypeA* oldA = NULL; - TensorTypeB* oldB = NULL; - TensorTypeC* oldC = NULL; - - if (aType == ReadWrite && - TensorUtils::overlappingIndices(state, a)) { - // Must perform in contiguous space - oldA = a; - a = TensorUtils::newContiguous(state, a); - } - if (bType == ReadWrite && - TensorUtils::overlappingIndices(state, b)) { - // Must perform in contiguous space - oldB = b; - b = TensorUtils::newContiguous(state, b); - } - if (cType == ReadWrite && - TensorUtils::overlappingIndices(state, c)) { - // Must perform in contiguous space - oldC = c; - c = TensorUtils::newContiguous(state, c); - } - -#define HANDLE_CASE(TYPE, A, B, C) \ - kernelPointwiseApply3::DataType, \ - typename TensorUtils::DataType, \ - typename TensorUtils::DataType, \ - TYPE, A, B, C> \ - <<>>( \ - aInfo, bInfo, cInfo, (TYPE) totalElements, op); - -#define HANDLE_C_CASE(TYPE, A, B, C) \ - { \ - if (cInfo.isContiguous()) { \ - HANDLE_CASE(TYPE, A, B, -2); \ - } else { \ - switch (C) { \ - case 1: \ - HANDLE_CASE(TYPE, A, B, 1); \ - break; \ - case 2: \ - HANDLE_CASE(TYPE, A, B, 2); \ - break; \ - default: \ - HANDLE_CASE(TYPE, A, B, -1); \ - break; \ - } \ - } \ - } - -#define HANDLE_B_CASE(TYPE, A, B, C) \ - { \ - if (bInfo.isContiguous()) { \ - HANDLE_C_CASE(TYPE, A, -2, C); \ - } else { \ - switch (B) { \ - case 1: \ - HANDLE_C_CASE(TYPE, A, 1, C); \ - break; \ - case 2: \ - HANDLE_C_CASE(TYPE, A, 2, C); \ - break; \ - default: \ - HANDLE_C_CASE(TYPE, A, -1, C); \ - break; \ - } \ - } \ - } - -#define HANDLE_A_CASE(TYPE, A, B, C) \ - { \ - if (aInfo.isContiguous()) { \ - HANDLE_B_CASE(TYPE, -2, B, C); \ - } else { \ - switch (A) { \ - case 1: \ - HANDLE_B_CASE(TYPE, 1, B, C); \ - break; \ - case 2: \ - HANDLE_B_CASE(TYPE, 2, B, C); \ - break; \ - default: \ - HANDLE_B_CASE(TYPE, -1, B, C); \ - break; \ - } \ - } \ - } - - if (TensorUtils::canUse32BitIndexMath(state, a) && - TensorUtils::canUse32BitIndexMath(state, b) && - TensorUtils::canUse32BitIndexMath(state, c)) { - TensorInfo::DataType, unsigned int> aInfo = - getTensorInfo(state, a); - aInfo.collapseDims(); - - TensorInfo::DataType, unsigned int> bInfo = - getTensorInfo(state, b); - bInfo.collapseDims(); - - TensorInfo::DataType, unsigned int> cInfo = - getTensorInfo(state, c); - cInfo.collapseDims(); - -#if CUDA_VERSION < 9000 - if (!(aInfo.isContiguous() && bInfo.isContiguous() && cInfo.isContiguous())) - grid.x = min(THCState_getCurrentDeviceProperties(state)->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); -#endif - HANDLE_A_CASE(unsigned int, aInfo.dims, bInfo.dims, cInfo.dims); - } else { - TensorInfo::DataType, uint64_t> aInfo = - getTensorInfo(state, a); - aInfo.collapseDims(); - - TensorInfo::DataType, uint64_t> bInfo = - getTensorInfo(state, b); - bInfo.collapseDims(); - - TensorInfo::DataType, uint64_t> cInfo = - getTensorInfo(state, c); - cInfo.collapseDims(); - - // For large tensors, we only compile the completely contiguous - // version and the completely generic version, to reduce - // compilation time. - if (aInfo.isContiguous() && bInfo.isContiguous() && cInfo.isContiguous()) { - kernelPointwiseApply3::DataType, - typename TensorUtils::DataType, - typename TensorUtils::DataType, - uint64_t, -2, -2, -2> - <<>>( - aInfo, bInfo, cInfo, (uint64_t) totalElements, op); - } else { -#if CUDA_VERSION < 9000 - grid.x = min(THCState_getCurrentDeviceProperties(state)->multiProcessorCount * THC_APPLY_BLOCKS_PER_SM , grid.x); -#endif - - kernelPointwiseApply3::DataType, - typename TensorUtils::DataType, - typename TensorUtils::DataType, - uint64_t, -1, -1, -1> - <<>>( - aInfo, bInfo, cInfo, (uint64_t) totalElements, op); - } - } -#undef HANDLE_CASE -#undef HANDLE_C_CASE -#undef HANDLE_B_CASE -#undef HANDLE_A_CASE - - if (oldA) { - // Ignore overlaps when copying back; if we use THCTensor_copy - // instead, it will recursively try and invoke ourselves to make - // oldA contiguous. - TensorUtils::copyIgnoringOverlaps(state, oldA, a); - TensorUtils::free(state, a); - a = oldA; - } - - if (oldB) { - // Ignore overlaps when copying back; if we use THCTensor_copy - // instead, it will recursively try and invoke ourselves to make - // oldB contiguous. - TensorUtils::copyIgnoringOverlaps(state, oldB, b); - TensorUtils::free(state, b); - b = oldB; - } - - if (oldC) { - // Ignore overlaps when copying back; if we use THCTensor_copy - // instead, it will recursively try and invoke ourselves to make - // oldC contiguous. - TensorUtils::copyIgnoringOverlaps(state, oldC, c); - TensorUtils::free(state, c); - c = oldC; - } - - return true; -} - -#undef THC_APPLY_THREADS_PER_BLOCK -#undef THC_APPLY_BLOCKS_PER_SM - -#endif // THC_APPLY_INC diff --git a/quantization/kernel/thcunn_include/THCApplyReduced.cuh b/quantization/kernel/thcunn_include/THCApplyReduced.cuh deleted file mode 100644 index 27ecf0165..000000000 --- a/quantization/kernel/thcunn_include/THCApplyReduced.cuh +++ /dev/null @@ -1,135 +0,0 @@ -/* -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef THC_APPLY_REDUCED_INC -#define THC_APPLY_REDUCED_INC - -// -// Stripped down version of THCApply.cuh -// - -// Threads per block for our apply kernel -// FIXME: use occupancy calculator instead -#define THC_APPLY_THREADS_PER_BLOCK 32 * 16 -#define THC_APPLY_BLOCKS_PER_SM 4 - -template -#if __CUDA_ARCH__ >= 350 -__launch_bounds__(THC_APPLY_THREADS_PER_BLOCK, THC_APPLY_BLOCKS_PER_SM) -#endif -__global__ void -kernelPointwiseApply1(TensorInfo a, - IndexType totalElements, - Op op) { - for (IndexType linearIndex = blockIdx.x * blockDim.x + threadIdx.x; - linearIndex < totalElements; - linearIndex += gridDim.x * blockDim.x) { - // Convert `linearIndex` into an offset of `a` - const IndexType aOffset = - IndexToOffset::get(linearIndex, a); - - op(&a.data[aOffset]); - } -} - -template -#if __CUDA_ARCH__ >= 350 -__launch_bounds__(THC_APPLY_THREADS_PER_BLOCK, THC_APPLY_BLOCKS_PER_SM) -#endif -__global__ void -kernelPointwiseApply2(TensorInfo a, - TensorInfo b, - IndexType totalElements, - Op op) { - for (IndexType linearIndex = blockIdx.x * blockDim.x + threadIdx.x; - linearIndex < totalElements; - linearIndex += gridDim.x * blockDim.x) { - // Convert `linearIndex` into an offset of `a` - const IndexType aOffset = - IndexToOffset::get(linearIndex, a); - - // Convert `linearIndex` into an offset of `b` - const IndexType bOffset = - IndexToOffset::get(linearIndex, b); - - op(&a.data[aOffset], &b.data[bOffset]); - } -} - -template -#if __CUDA_ARCH__ >= 350 -__launch_bounds__(THC_APPLY_THREADS_PER_BLOCK, THC_APPLY_BLOCKS_PER_SM) -#endif -__global__ void -kernelPointwiseApply3(TensorInfo a, - TensorInfo b, - TensorInfo c, - IndexType totalElements, - Op op) { - for (IndexType linearIndex = blockIdx.x * blockDim.x + threadIdx.x; - linearIndex < totalElements; - linearIndex += gridDim.x * blockDim.x) { - // Convert `linearIndex` into an offset of `a` - const IndexType aOffset = - IndexToOffset::get(linearIndex, a); - - // Convert `linearIndex` into an offset of `b` - const IndexType bOffset = - IndexToOffset::get(linearIndex, b); - - // Convert `linearIndex` into an offset of `c` - const IndexType cOffset = - IndexToOffset::get(linearIndex, c); - - op(&a.data[aOffset], &b.data[bOffset], &c.data[cOffset]); - } -} - -#endif // THC_APPLY_REDUCED_INC diff --git a/quantization/kernel/thcunn_include/THCDeviceUtils.cuh b/quantization/kernel/thcunn_include/THCDeviceUtils.cuh deleted file mode 100644 index 2fdc6939d..000000000 --- a/quantization/kernel/thcunn_include/THCDeviceUtils.cuh +++ /dev/null @@ -1,137 +0,0 @@ -/* -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef THC_DEVICE_UTILS_INC -#define THC_DEVICE_UTILS_INC - -/* The largest consecutive integer representable in float32 (2^24) */ -#define FLOAT32_MAX_CONSECUTIVE_INT 16777216.0f - -/** - Computes ceil(a / b) -*/ -template -__host__ __device__ __forceinline__ T THCCeilDiv(T a, T b) { - return (a + b - 1) / b; -} - -/** - Computes ceil(a / b) * b; i.e., rounds up `a` to the next highest - multiple of b -*/ -template -__host__ __device__ __forceinline__ T THCRoundUp(T a, T b) { - return THCCeilDiv(a, b) * b; -} - -/** - * For CC 3.5+, perform a load using __ldg - */ -template -__device__ __forceinline__ T doLdg(const T* p) { -#if __CUDA_ARCH__ >= 350 - return __ldg(p); -#else - return *p; -#endif -} - -__device__ __forceinline__ unsigned int ACTIVE_MASK() -{ -#if CUDA_VERSION >= 9000 - return __activemask(); -#else -// will be ignored anyway - return 0xffffffff; -#endif -} - -__device__ __forceinline__ int WARP_BALLOT(int predicate, unsigned int mask = 0xffffffff) -{ -#if CUDA_VERSION >= 9000 - return __ballot_sync(mask, predicate); -#else - return __ballot(predicate); -#endif -} - -template -__device__ __forceinline__ T WARP_SHFL_XOR(T value, int laneMask, int width = warpSize, unsigned int mask = 0xffffffff) -{ -#if CUDA_VERSION >= 9000 - return __shfl_xor_sync(mask, value, laneMask, width); -#else - return __shfl_xor(value, laneMask, width); -#endif -} - -template -__device__ __forceinline__ T WARP_SHFL(T value, int srcLane, int width = warpSize, unsigned int mask = 0xffffffff) -{ -#if CUDA_VERSION >= 9000 - return __shfl_sync(mask, value, srcLane, width); -#else - return __shfl(value, srcLane, width); -#endif -} - -template -__device__ __forceinline__ T WARP_SHFL_UP(T value, unsigned int delta, int width = warpSize, unsigned int mask = 0xffffffff) -{ -#if CUDA_VERSION >= 9000 - return __shfl_up_sync(mask, value, delta, width); -#else - return __shfl_up(value, delta, width); -#endif -} - -template -__device__ __forceinline__ T WARP_SHFL_DOWN(T value, unsigned int delta, int width = warpSize, unsigned int mask = 0xffffffff) -{ -#if CUDA_VERSION >= 9000 - return __shfl_down_sync(mask, value, delta, width); -#else - return __shfl_down(value, delta, width); -#endif -} - - -#endif // THC_DEVICE_UTILS_INC diff --git a/quantization/kernel/thcunn_include/THCReduceApplyUtils.cuh b/quantization/kernel/thcunn_include/THCReduceApplyUtils.cuh deleted file mode 100644 index 4b26c2c9f..000000000 --- a/quantization/kernel/thcunn_include/THCReduceApplyUtils.cuh +++ /dev/null @@ -1,178 +0,0 @@ -/* -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef THC_REDUCE_APPLY_UTILS_INC -#define THC_REDUCE_APPLY_UTILS_INC - -#include -#include -#include "THCGeneral.h" -#include "THCTensor.h" -#include "THCDeviceUtils.cuh" -#include "THCTensorInfo.cuh" - -// Enum that indicates whether tensor arguments are read/write or -// read-only -enum TensorArgType { ReadWrite, ReadOnly }; - -template -__device__ __forceinline__ IndexType getLinearBlockId() { - return blockIdx.z * gridDim.y * gridDim.x + - blockIdx.y * gridDim.x + - blockIdx.x; -} - -// Reduce N values concurrently, i.e. suppose N = 2, and there are 4 threads: -// (1, 2), (3, 4), (5, 6), (7, 8), then the return in threadVals for thread 0 -// is (1 + 3 + 5 + 7, 2 + 4 + 6 + 8) = (16, 20) -template -__device__ void reduceNValuesInBlock(T *smem, - T threadVals[N], - int numVals, - ReduceOp reduceOp, - T init) { - if (numVals == 0) { -#pragma unroll - for (int i = 0; i < N; ++i) { - threadVals[i] = init; - } - return; - } - - // We store each of the N values contiguously, so if N = 2, all values for - // the first threadVal for each thread in the block are stored followed by - // all of the values for the second threadVal for each thread in the block - if (threadIdx.x < numVals) { -#pragma unroll - for (int i = 0; i < N; ++i) { - smem[i * numVals + threadIdx.x] = threadVals[i]; - } - } - __syncthreads(); - - // Number of lanes in the final reduction --> this is used to determine - // where to put the outputs of each of the n things we are reducing. If - // nLP = 32, then we have the 32 outputs for the first threadVal, - // followed by the 32 outputs for the second threadVal, etc. - int numLanesParticipating = min(numVals, warpSize); - - if (numVals > warpSize && ((threadIdx.x / warpSize) == 0 )) { -#pragma unroll - for (int i = 0; i < N; ++i) { - threadVals[i] = threadIdx.x < numVals ? threadVals[i] : init; - } - - for (int i = warpSize + threadIdx.x; i < numVals; i += warpSize) { -#pragma unroll - for (int j = 0; j < N; ++j) { - threadVals[j] = reduceOp(threadVals[j], smem[j * numVals + i]); - } - } - -#pragma unroll - for (int i = 0; i < N; ++i) { - smem[i * numLanesParticipating + threadIdx.x] = threadVals[i]; - } - } - __syncthreads(); - - if (threadIdx.x == 0) { - if (numLanesParticipating == 32) { -#pragma unroll - for (int i = 0; i < N; ++i) { -#pragma unroll - for (int j = 1; j < 32; ++j) { - threadVals[i] = reduceOp(threadVals[i], smem[i * 32 + j]); - } - } - } else { -#pragma unroll - for (int i = 0; i < N; ++i) { - for (int j = 1; j < numLanesParticipating; ++j) { - threadVals[i] = reduceOp(threadVals[i], smem[i * numVals + j]); - } - } - } - } -} - -// Block-wide reduction in shared memory helper; only threadIdx.x == 0 will -// return the reduced value -template -__device__ T reduceBlock(T* smem, - int numVals, - T threadVal, - ReduceOp reduceOp, - T init) { - reduceNValuesInBlock(smem, &threadVal, numVals, reduceOp, init); - return threadVal; -} - - -// Block-wide reduction where each thread locally reduces N -// values before letting a single warp take over - assumes -// threadVals is in registers, not shared memory -template -__device__ T reduceBlockWithNThreadLocalReductions(T *smem, - T threadVals[N], - int numVals, - ReduceOp reduceOp, - T init) { - int offset = threadIdx.x * N; - T local = offset < numVals ? threadVals[0] : init; - -#pragma unroll - for (int i = 1; i < N; ++i) { - ++offset; - T next = offset < numVals ? threadVals[i] : init; - local = reduceOp(local, next); - } - - return reduceBlock(smem, blockDim.x < numVals ? blockDim.x : numVals, local, reduceOp, init); -} - -// Make sure the given tensor doesn't have too many dimensions -void THCCheckTensorDims(THCState* state, THCudaTensor* tensor, int arg); - -// Produces a grid with at least one point per tile -THC_API bool THC_getGridFromTiles(ptrdiff_t gridTiles, dim3& grid); - -#endif // THC_REDUCE_APPLY_UTILS_INC diff --git a/quantization/kernel/thcunn_include/THCTensorInfo.cuh b/quantization/kernel/thcunn_include/THCTensorInfo.cuh deleted file mode 100644 index b0bc45945..000000000 --- a/quantization/kernel/thcunn_include/THCTensorInfo.cuh +++ /dev/null @@ -1,321 +0,0 @@ -/* -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef THC_TENSOR_INFO_INC -#define THC_TENSOR_INFO_INC - -#include -#include -#include "THCGeneral.h" -#include "THCTensor.h" - -// Maximum number of dimensions allowed for cutorch -#define MAX_CUTORCH_DIMS 25 - -// Warning string for tensor arguments that are too large or have too -// many dimensions -#define CUTORCH_STR(X) #X -#define CUTORCH_DIM_WARNING "tensor too large or too many (>" \ - CUTORCH_STR(MAX_CUTORCH_DIMS) ") dimensions" - -// CUDA kernel argument that defines tensor layout -template -struct TensorInfo { - TensorInfo(T* p, - int dim, - IndexType sz[MAX_CUTORCH_DIMS], - IndexType st[MAX_CUTORCH_DIMS]); - - // Set the size of the given dimension to 1, as if it were a - // reduction dim (allows you to calculate offsets of the reduction - // slice) - void reduceDim(int dim); - - // Collapses all runs of successive dimensions if the size/strides - // match up within the run and there are no holes between the - // dimensions. - // If excludeDim is set (not -1), then excludeDim will not be - // collapsed with any other dimension. - // Function returns the new dimension index that excludeDim maps to, - // since the collapsed dimensions are <= the input dimensions. - int collapseDims(int excludeDim = -1); - - // Contiguous tensors of more than one dimension are collapsed down - // to one tensor - __host__ __device__ inline bool isContiguous() const { - return (dims == 1 && strides[0] == 1); - } - - T* data; - IndexType sizes[MAX_CUTORCH_DIMS]; - IndexType strides[MAX_CUTORCH_DIMS]; - int dims; -}; - -template -TensorInfo::TensorInfo(T* p, - int dim, - IndexType sz[MAX_CUTORCH_DIMS], - IndexType st[MAX_CUTORCH_DIMS]) { - data = p; - dims = dim; - assert(dims > 0 && dims < MAX_CUTORCH_DIMS); - - for (int i = 0; i < dim; ++i) { - sizes[i] = sz[i]; - strides[i] = st[i]; - } -} - -template -void -TensorInfo::reduceDim(int dim) { - assert(dim < dims && dim >= 0); - sizes[dim] = 1; -} - -template -int -TensorInfo::collapseDims(int excludeDim) { - // Find the innermost dimension not of size 1, since dimensions of size 1 are - // collapsible. - int firstNonOneDim = -1; - - for (int i = dims - 1; i >= 0; --i) { - if (i == excludeDim) { - // We cannot collapse this dimension, even if it is size 1 - firstNonOneDim = i; - break; - } - - if (sizes[i] != 1) { - firstNonOneDim = i; - break; - } - } - - // Special case: if all dimensions are of size 1, then this is a - // single-point tensor that we still have to operate on. Reduce to a - // single point. - if (firstNonOneDim == -1) { - assert(excludeDim == -1); - - dims = 1; - sizes[0] = 1; - strides[0] = 1; - - // Everything effectively got collapsed into this dimension - return 0; - } - - // Count the number of successive dimensions that can be collapsed, from - // innermost to outermost. - int numCollapsed = 0; - - // Skip the leading size 1 dims - numCollapsed += dims - 1 - firstNonOneDim; - - // We perform one pass through to determine how many dimensions we - // can collapse, before calculating the actual size of the collapsed - // dimensions. - // size/strideInner are the size/strides of the previous inner - // non-collapsible dim we encounter. - int64_t sizeInner = sizes[firstNonOneDim]; - int64_t strideInner = strides[firstNonOneDim]; - - for (int i = firstNonOneDim - 1; i >= 0; --i) { - int64_t sizeOuter = sizes[i]; - int64_t strideOuter = strides[i]; - - // Don't collapse this dimension if we want to exclude it from - // collapsing. - // Since this code is attempting to collapse a subsequent - // dimension (i) with the preceding dimension (i + 1), we can only - // perform collapsing if the preceding dimension can be collapsed - // (i.e., not excludeDim) - if ((excludeDim != i) && (excludeDim != i + 1)) { - // The next outermost dimension can be skipped if size 1 - if (sizeOuter == 1) { - ++numCollapsed; - continue; - } - - // If the next outermost dimension is contiguous with the - // previous non-collapsed one, collapse it - if (strideOuter == strideInner * sizeInner) { - ++numCollapsed; - - // This is the run of collapsed dimensions' size - sizeInner = sizeInner * sizeOuter; - continue; - } - } - - // Otherwise, this new outer dimension at `i` cannot be collapsed - // because it is excluded from collapsing, or it is not contiguous - // with the previous inner dimension. - sizeInner = sizeOuter; - strideInner = strideOuter; - } - - // This will be our new size/stride and dimension. - IndexType newSizes[MAX_CUTORCH_DIMS]; - IndexType newStrides[MAX_CUTORCH_DIMS]; - - assert(numCollapsed < dims); - int newDims = dims - numCollapsed; - - // We return the index of the excluded dimension that is excluded - // from being collapsed here. - int returnDim = -1; - - // We perform a second pass through the dimensions to actually - // calculate the size of the collapsed dimensions. - int collapsedIndex = dims - numCollapsed - 1; - newSizes[collapsedIndex] = sizes[firstNonOneDim]; - newStrides[collapsedIndex] = strides[firstNonOneDim]; - - if (firstNonOneDim == excludeDim) { - returnDim = collapsedIndex; - } - - for (int i = firstNonOneDim - 1; i >= 0; --i) { - IndexType sizeOuter = sizes[i]; - IndexType strideOuter = strides[i]; - - if ((excludeDim != i) && (excludeDim != i + 1)) { - if (sizeOuter == 1) { - // skip - continue; - } - - if (strideOuter == newSizes[collapsedIndex] * newStrides[collapsedIndex]) { - // collapse - newSizes[collapsedIndex] *= sizeOuter; - continue; - } - } - - // Otherwise, strides don't match, or dim `i` is excluded from - // collapsing. - --collapsedIndex; - assert(collapsedIndex >= 0); - assert(collapsedIndex < newDims); - newSizes[collapsedIndex] = sizeOuter; - newStrides[collapsedIndex] = strideOuter; - - if (excludeDim == i) { - returnDim = collapsedIndex; - } - } - - // We must have filled all the dimensions we're looking for - assert(collapsedIndex == 0); - assert((excludeDim == -1) || (returnDim != -1)); - - dims = newDims; - - for (int i = 0; i < dims; ++i) { - sizes[i] = newSizes[i]; - strides[i] = newStrides[i]; - } - - // After collapsing, the original `excludeDim` may have been - // renumbered to this new `returnDim`, since some dimensions could - // have been collapsed. - return returnDim; -} - -// Translate a linear index for the apply to a T* offset; -// specialized on `Dims` to reduce nvcc compilation time -template -struct IndexToOffset { - static __host__ __device__ IndexType get( - IndexType linearId, - const TensorInfo& info) { - IndexType offset = 0; - - // Use static dims - for (int i = Dims - 1; i >= 0; --i) { - IndexType curDimIndex = linearId % info.sizes[i]; - IndexType curDimOffset = curDimIndex * info.strides[i]; - offset += curDimOffset; - - if (i > 0) { - linearId /= info.sizes[i]; - } - } - - return offset; - } -}; - -// For contiguous tensors, the offset = index -template -struct IndexToOffset { - static inline __host__ __device__ IndexType - get(IndexType linearId, const TensorInfo& info) { - return linearId; - } -}; - -template -struct IndexToOffset { - static inline __host__ __device__ IndexType get( - IndexType linearId, - const TensorInfo& info) { - - IndexType offset = 0; - - // Use dynamic dims - for (int i = info.dims - 1; i >= 0; --i) { - IndexType curDimIndex = linearId % info.sizes[i]; - IndexType curDimOffset = curDimIndex * info.strides[i]; - offset += curDimOffset; - - linearId /= info.sizes[i]; - } - - return offset; - } -}; - -#endif // THC_TENSOR_INFO_INC diff --git a/quantization/kernel/thcunn_include/THCTensorTypeUtils.cuh b/quantization/kernel/thcunn_include/THCTensorTypeUtils.cuh deleted file mode 100644 index 8f79d5df0..000000000 --- a/quantization/kernel/thcunn_include/THCTensorTypeUtils.cuh +++ /dev/null @@ -1,241 +0,0 @@ -/* -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef THC_TENSOR_TYPE_UTILS_INC -#define THC_TENSOR_TYPE_UTILS_INC - -#include -#include -#include "THCGeneral.h" -#include "THCHalf.h" -#include "THCTensor.h" -#include "THCTensorInfo.cuh" - -/// A utility for accessing THCuda*Tensor types in a generic manner - -/// Equivalent to C++11's type_traits std::is_same; used for comparing -/// equality of types. Don't assume the existence of C++11 -template -struct SameType { - static const bool same = false; -}; - -template -struct SameType { - static const bool same = true; -}; - -template -bool isSameType() { - return SameType::same; -} - -template -struct TensorUtils { -}; - -#define TENSOR_UTILS(TENSOR_TYPE, DATA_TYPE, ACC_DATA_TYPE) \ - template <> \ - struct THC_CLASS TensorUtils { \ - typedef DATA_TYPE DataType; \ - typedef ACC_DATA_TYPE AccDataType; \ - \ - static TENSOR_TYPE* newTensor(THCState* state); \ - static TENSOR_TYPE* newContiguous(THCState* state, TENSOR_TYPE* t); \ - static THLongStorage* newSizeOf(THCState* state, TENSOR_TYPE* t); \ - static void retain(THCState* state, TENSOR_TYPE* t); \ - static void free(THCState* state, TENSOR_TYPE* t); \ - static void freeCopyTo(THCState* state, TENSOR_TYPE* src, \ - TENSOR_TYPE* dst); \ - static void resize(THCState* state, TENSOR_TYPE* out, \ - THLongStorage* sizes, \ - THLongStorage* strides); \ - static void resizeAs(THCState* state, TENSOR_TYPE* dst, \ - TENSOR_TYPE* src); \ - static void squeeze1d(THCState *state, TENSOR_TYPE *dst, \ - TENSOR_TYPE *src, int dimension); \ - static DATA_TYPE* getData(THCState* state, TENSOR_TYPE* t); \ - static ptrdiff_t getNumElements(THCState* state, TENSOR_TYPE* t); \ - static int64_t getSize(THCState* state, TENSOR_TYPE* t, int dim); \ - static int64_t getStride(THCState* state, TENSOR_TYPE* t, int dim); \ - static int getDims(THCState* state, TENSOR_TYPE* t); \ - static bool isContiguous(THCState* state, TENSOR_TYPE* t); \ - static bool allContiguous(THCState* state, TENSOR_TYPE** inputs, int numInputs); \ - static int getDevice(THCState* state, TENSOR_TYPE* t); \ - static bool allSameDevice(THCState* state, TENSOR_TYPE** inputs, int numInputs); \ - static void copyIgnoringOverlaps(THCState* state, \ - TENSOR_TYPE* dst, TENSOR_TYPE* src); \ - /* Determines if the given tensor has overlapping data points (i.e., */ \ - /* is there more than one index into the tensor that references */ \ - /* the same piece of data)? */ \ - static bool overlappingIndices(THCState* state, TENSOR_TYPE* t); \ - /* Can we use 32 bit math for indexing? */ \ - static bool canUse32BitIndexMath(THCState* state, TENSOR_TYPE* t, ptrdiff_t max_elem=UINT32_MAX); \ - /* Are all tensors 32-bit indexable? */ \ - static bool all32BitIndexable(THCState* state, TENSOR_TYPE** inputs, int numInputs); \ - } - -TENSOR_UTILS(THCudaByteTensor, uint8_t, int64_t); -TENSOR_UTILS(THCudaCharTensor, int8_t, int64_t); -TENSOR_UTILS(THCudaShortTensor, int16_t, int64_t); -TENSOR_UTILS(THCudaIntTensor, int32_t, int64_t); -TENSOR_UTILS(THCudaLongTensor, int64_t, int64_t); -TENSOR_UTILS(THCudaTensor, float, float); -TENSOR_UTILS(THCudaDoubleTensor, double, double); - -#ifdef CUDA_HALF_TENSOR -TENSOR_UTILS(THCudaHalfTensor, half, float); -#endif - -#undef TENSOR_UTILS - -// Utility function for constructing TensorInfo structs. In this case, the -// two template parameters are: -// -// 1. The TensorType, e.g. THCTensor in generic functions, or THCudaTensor, -// THCudaLongTensor etc. -// -// 2. The IndexType. This is always going to be an unsigned integral value, -// but depending on the size of the Tensor you may select uint16_t -// uint32_t, uint64_t etc. -// -// Internally we use the TensorUtils static functions to get the necessary -// dims, sizes, stride etc. -// -// For example, suppose we have a THCudaTensor t, with dim = 2, size = [3, 4], -// stride = [4, 1], offset = 8, and we set our index type to be unsigned int. -// Then we yield a TensorInfo struct templatized with float, unsigned int and -// the following fields: -// -// data is a float* to the underlying storage at position 8 -// dims is 2 -// sizes is a MAX_CUTORCH_DIMS element array with [3, 4] in its first two positions -// strides is a MAX_CUTORCH_DIMS element array with [4, 1] in its first two positions -// -// TensorInfos can then be passed to CUDA kernels, but we can use the static functions -// defined above to perform Tensor Operations that are appropriate for each -// TensorType. -template -TensorInfo::DataType, IndexType> -getTensorInfo(THCState* state, TensorType* t) { - IndexType sz[MAX_CUTORCH_DIMS]; - IndexType st[MAX_CUTORCH_DIMS]; - - int dims = TensorUtils::getDims(state, t); - for (int i = 0; i < dims; ++i) { - sz[i] = TensorUtils::getSize(state, t, i); - st[i] = TensorUtils::getStride(state, t, i); - } - - return TensorInfo::DataType, IndexType>( - TensorUtils::getData(state, t), dims, sz, st); -} - -template -struct ScalarNegate { - static __host__ __device__ T to(const T v) { return -v; } -}; - -template -struct ScalarInv { - static __host__ __device__ T to(const T v) { return ((T) 1) / v; } -}; - -#ifdef CUDA_HALF_TENSOR -template <> -struct ScalarNegate { - static __host__ __device__ half to(const half v) { -#ifdef __CUDA_ARCH__ -#ifdef CUDA_HALF_INSTRUCTIONS - return __hneg(v); -#else - return __float2half(-__half2float(v)); -#endif -#else -#if CUDA_VERSION < 9000 - half out = v; -#else - __half_raw out = __half_raw(v); -#endif - out.x ^= 0x8000; // toggle sign bit - return out; -#endif - } -}; - -template <> -struct ScalarInv { - static __host__ __device__ half to(const half v) { -#ifdef __CUDA_ARCH__ - return __float2half(1.0f / __half2float(v)); -#else - float fv = THC_half2float(v); - fv = 1.0f / fv; - return THC_float2half(fv); -#endif - } -}; - -inline bool operator==(half a, half b) { -#if CUDA_VERSION < 9000 - return a.x == b.x; -#else - __half_raw araw, braw; - araw = __half_raw(a); - braw = __half_raw(b); - return araw.x == braw.x; -#endif -} - -inline bool operator!=(half a, half b) { -#if CUDA_VERSION < 9000 - return a.x != b.x; -#else - __half_raw araw, braw; - araw = __half_raw(a); - braw = __half_raw(b); - return araw.x != braw.x; -#endif -} - -#endif // CUDA_HALF_TENSOR - -#endif // THC_TENSOR_TYPE_UTILS_INC diff --git a/quantization/kernel/thcunn_include/common.h b/quantization/kernel/thcunn_include/common.h deleted file mode 100644 index 6a4dbb6c4..000000000 --- a/quantization/kernel/thcunn_include/common.h +++ /dev/null @@ -1,126 +0,0 @@ -/* -Copyright (c) 2016- Facebook, Inc (Adam Paszke) -Copyright (c) 2014- Facebook, Inc (Soumith Chintala) -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) -Copyright (c) 2011-2013 NYU (Clement Farabet) -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) -Copyright (c) 2006 Idiap Research Institute (Samy Bengio) -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the names of Facebook, Deepmind Technologies, NYU, - NEC Laboratories America and IDIAP Research Institute nor the names - of its contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef THCUNN_COMMON_H -#define THCUNN_COMMON_H - -// CUDA: grid stride looping -#define CUDA_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x) - -#define THCUNN_assertSameGPU(...) THAssertMsg(THCTensor_(checkGPU)(__VA_ARGS__), \ - "Some of weight/gradient/input tensors are located on different GPUs. Please move them to a single one.") - -// Use 1024 threads per block, which requires cuda sm_2x or above -const int CUDA_NUM_THREADS = 1024; - -// CUDA: number of blocks for threads. -inline int GET_BLOCKS(const int N) -{ - return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; -} - -#define THCUNN_resizeAs_indices(STATE, I1, I2) \ - THLongStorage *size2 = THCTensor_(newSizeOf)(STATE, I2); \ - if (!THCIndexTensor_(isSize)(STATE, I1, size2)) \ - { \ - THCudaLongTensor_resize(STATE, I1, size2, NULL); \ - } \ - THLongStorage_free(size2); - -#define THCUNN_check_shape(STATE, I1, I2) \ - if (I1 != NULL && I2 != NULL && !THCTensor_(isSameSizeAs)(STATE, I1, I2)) \ - { \ - THCDescBuff s1 = THCTensor_(sizeDesc)(STATE, I1); \ - THCDescBuff s2 = THCTensor_(sizeDesc)(STATE, I2); \ - THError(#I1 " and " #I2 " shapes do not match: " \ - #I1 " %s, " #I2 " %s", s1.str, s2.str); \ - } - - -#define THCUNN_check_shape_indices(STATE, I1, I2) \ - THLongStorage *size2 = THCTensor_(newSizeOf)(STATE, I2); \ - if (!THCIndexTensor_(isSize)(STATE, I1, size2)) \ - { \ - THCDescBuff s1 = THCIndexTensor_(sizeDesc)(STATE, I1); \ - THCDescBuff s2 = THCTensor_(sizeDesc)(STATE, I2); \ - THError(#I1 " and " #I2 " shapes do not match: " \ - #I1 " %s, " #I2 " %s", s1.str, s2.str); \ - } \ - THLongStorage_free(size2); - -#define THCUNN_check_nElement(STATE, I1, I2) \ - if (I1 != NULL && I2 != NULL ) { \ - ptrdiff_t n1 = THCTensor_(nElement)(STATE, I1); \ - ptrdiff_t n2 = THCTensor_(nElement)(STATE, I2); \ - if (n1 != n2) \ - { \ - THCDescBuff s1 = THCTensor_(sizeDesc)(state, I1); \ - THCDescBuff s2 = THCTensor_(sizeDesc)(state, I2); \ - THError(#I1 " and " #I2 " have different number of elements: " \ - #I1 "%s has %ld elements, while " \ - #I2 "%s has %ld elements", s1.str, n1, s2.str, n2); \ - } \ - } - -#define THCUNN_check_dim_size(STATE, T, DIM, DIM_SIZE, SIZE) \ - if (THCTensor_(nDimension)(STATE, T) != DIM || \ - THCTensor_(size)(STATE, T, DIM_SIZE) != SIZE) { \ - THCDescBuff s1 = THCTensor_(sizeDesc)(state, T); \ - THError("Need " #T " of dimension %d and " #T ".size[%d] == %d" \ - " but got " #T " to be of shape: %s", DIM, DIM_SIZE, SIZE, s1.str); \ - } - -#define THCUNN_check_dim_size_indices(STATE, T, DIM, DIM_SIZE, SIZE) \ - if (THCIndexTensor_(nDimension)(STATE, T) != DIM || \ - THCIndexTensor_(size)(STATE, T, DIM_SIZE) != SIZE) { \ - THCDescBuff s1 = THCIndexTensor_(sizeDesc)(state, T); \ - THError("Need " #T " of dimension %d and " #T ".size[%d] == %d" \ - " but got " #T " to be of shape: %s", DIM, DIM_SIZE, SIZE, s1.str); \ - } - -#define THCUNN_argCheck(STATE, COND, ARG, T, FORMAT) \ - if (!(COND)) { \ - THCDescBuff s1 = THCTensor_(sizeDesc)(state, T); \ - THArgCheck(COND, ARG, FORMAT, s1.str); \ - } - -#endif diff --git a/quantization/lib/CMakeCache.txt b/quantization/lib/CMakeCache.txt deleted file mode 100644 index faf9d812e..000000000 --- a/quantization/lib/CMakeCache.txt +++ /dev/null @@ -1,606 +0,0 @@ -# This is the CMakeCache file. -# For build in directory: /pytorch_quantization/quantization/lib -# It was generated by CMake: /opt/conda/bin/cmake -# You can edit this file to change values found and used by cmake. -# If you do not want to change any of the values, simply exit the editor. -# If you do want to change a value, simply edit, save, and exit the editor. -# The syntax for the file is as follows: -# KEY:TYPE=VALUE -# KEY is the name of a variable in the cache. -# TYPE is a hint to GUIs for the type of VALUE, DO NOT EDIT TYPE!. -# VALUE is the current value for the KEY. - 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Store it in a pointer rather than an array - because some compilers will just produce instructions to fill the - array rather than assigning a pointer to a static array. */ -char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]"; -#ifdef SIMULATE_ID -char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]"; -#endif - -#ifdef __QNXNTO__ -char const* qnxnto = "INFO" ":" "qnxnto[]"; -#endif - -#if defined(__CRAYXE) || defined(__CRAYXC) -char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]"; -#endif - -#define STRINGIFY_HELPER(X) #X -#define STRINGIFY(X) STRINGIFY_HELPER(X) - -/* Identify known platforms by name. */ -#if defined(__linux) || defined(__linux__) || defined(linux) -# define PLATFORM_ID "Linux" - -#elif defined(__CYGWIN__) -# define PLATFORM_ID "Cygwin" - -#elif defined(__MINGW32__) -# define PLATFORM_ID "MinGW" - -#elif defined(__APPLE__) -# define PLATFORM_ID "Darwin" - -#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32) -# define PLATFORM_ID "Windows" - -#elif defined(__FreeBSD__) || defined(__FreeBSD) -# define PLATFORM_ID "FreeBSD" - -#elif defined(__NetBSD__) || defined(__NetBSD) -# define PLATFORM_ID "NetBSD" - -#elif defined(__OpenBSD__) || defined(__OPENBSD) -# define PLATFORM_ID "OpenBSD" - -#elif defined(__sun) || defined(sun) -# define PLATFORM_ID "SunOS" - -#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__) -# define PLATFORM_ID "AIX" - -#elif defined(__sgi) || defined(__sgi__) || defined(_SGI) -# define PLATFORM_ID "IRIX" - -#elif defined(__hpux) || defined(__hpux__) -# define PLATFORM_ID "HP-UX" - -#elif defined(__HAIKU__) -# define PLATFORM_ID "Haiku" - -#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS) -# define PLATFORM_ID "BeOS" - -#elif defined(__QNX__) || defined(__QNXNTO__) -# define PLATFORM_ID "QNX" - -#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__) -# define PLATFORM_ID "Tru64" - -#elif defined(__riscos) || defined(__riscos__) -# define PLATFORM_ID "RISCos" - -#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__) -# define PLATFORM_ID "SINIX" - -#elif defined(__UNIX_SV__) -# define PLATFORM_ID "UNIX_SV" - -#elif defined(__bsdos__) -# define PLATFORM_ID "BSDOS" - -#elif defined(_MPRAS) || defined(MPRAS) -# define PLATFORM_ID "MP-RAS" - -#elif defined(__osf) || defined(__osf__) -# define PLATFORM_ID "OSF1" - -#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv) -# define PLATFORM_ID "SCO_SV" - -#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX) -# define PLATFORM_ID "ULTRIX" - -#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX) -# define PLATFORM_ID "Xenix" - -#elif defined(__WATCOMC__) -# if defined(__LINUX__) -# define PLATFORM_ID "Linux" - -# elif defined(__DOS__) -# define PLATFORM_ID "DOS" - -# elif defined(__OS2__) -# define PLATFORM_ID "OS2" - -# elif defined(__WINDOWS__) -# define PLATFORM_ID "Windows3x" - -# else /* unknown platform */ -# define PLATFORM_ID -# endif - -#else /* unknown platform */ -# define PLATFORM_ID - -#endif - -/* For windows compilers MSVC and Intel we can determine - the architecture of the compiler being used. This is because - the compilers do not have flags that can change the architecture, - but rather depend on which compiler is being used -*/ -#if defined(_WIN32) && defined(_MSC_VER) -# if defined(_M_IA64) -# define ARCHITECTURE_ID "IA64" - -# elif defined(_M_X64) || defined(_M_AMD64) -# define ARCHITECTURE_ID "x64" - -# elif defined(_M_IX86) -# define ARCHITECTURE_ID "X86" - -# elif defined(_M_ARM64) -# define ARCHITECTURE_ID "ARM64" - -# elif defined(_M_ARM) -# if _M_ARM == 4 -# define ARCHITECTURE_ID "ARMV4I" -# elif _M_ARM == 5 -# define ARCHITECTURE_ID "ARMV5I" -# else -# define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) -# endif - -# elif defined(_M_MIPS) -# define ARCHITECTURE_ID "MIPS" - -# elif defined(_M_SH) -# define ARCHITECTURE_ID "SHx" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -#elif defined(__WATCOMC__) -# if defined(_M_I86) -# define ARCHITECTURE_ID "I86" - -# elif defined(_M_IX86) -# define ARCHITECTURE_ID "X86" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) -# if defined(__ICCARM__) -# define ARCHITECTURE_ID "ARM" - -# elif defined(__ICCAVR__) -# define ARCHITECTURE_ID "AVR" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif -#else -# define ARCHITECTURE_ID -#endif - -/* Convert integer to decimal digit literals. */ -#define DEC(n) \ - ('0' + (((n) / 10000000)%10)), \ - ('0' + (((n) / 1000000)%10)), \ - ('0' + (((n) / 100000)%10)), \ - ('0' + (((n) / 10000)%10)), \ - ('0' + (((n) / 1000)%10)), \ - ('0' + (((n) / 100)%10)), \ - ('0' + (((n) / 10)%10)), \ - ('0' + ((n) % 10)) - -/* Convert integer to hex digit literals. */ -#define HEX(n) \ - ('0' + ((n)>>28 & 0xF)), \ - ('0' + ((n)>>24 & 0xF)), \ - ('0' + ((n)>>20 & 0xF)), \ - ('0' + ((n)>>16 & 0xF)), \ - ('0' + ((n)>>12 & 0xF)), \ - ('0' + ((n)>>8 & 0xF)), \ - ('0' + ((n)>>4 & 0xF)), \ - ('0' + ((n) & 0xF)) - -/* Construct a string literal encoding the version number components. */ -#ifdef COMPILER_VERSION_MAJOR -char const info_version[] = { - 'I', 'N', 'F', 'O', ':', - 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', - COMPILER_VERSION_MAJOR, -# ifdef COMPILER_VERSION_MINOR - '.', COMPILER_VERSION_MINOR, -# ifdef COMPILER_VERSION_PATCH - '.', COMPILER_VERSION_PATCH, -# ifdef COMPILER_VERSION_TWEAK - '.', COMPILER_VERSION_TWEAK, -# endif -# endif -# endif - ']','\0'}; -#endif - -/* Construct a string literal encoding the internal version number. */ -#ifdef COMPILER_VERSION_INTERNAL -char const info_version_internal[] = { - 'I', 'N', 'F', 'O', ':', - 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_', - 'i','n','t','e','r','n','a','l','[', - COMPILER_VERSION_INTERNAL,']','\0'}; -#endif - -/* Construct a string literal encoding the version number components. */ -#ifdef SIMULATE_VERSION_MAJOR -char const info_simulate_version[] = { - 'I', 'N', 'F', 'O', ':', - 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', - SIMULATE_VERSION_MAJOR, -# ifdef SIMULATE_VERSION_MINOR - '.', SIMULATE_VERSION_MINOR, -# ifdef SIMULATE_VERSION_PATCH - '.', SIMULATE_VERSION_PATCH, -# ifdef SIMULATE_VERSION_TWEAK - '.', SIMULATE_VERSION_TWEAK, -# endif -# endif -# endif - ']','\0'}; -#endif - -/* Construct the string literal in pieces to prevent the source from - getting matched. Store it in a pointer rather than an array - because some compilers will just produce instructions to fill the - array rather than assigning a pointer to a static array. */ -char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]"; -char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]"; - - - - -#if !defined(__STDC__) -# if (defined(_MSC_VER) && !defined(__clang__)) \ - || (defined(__ibmxl__) || defined(__IBMC__)) -# define C_DIALECT "90" -# else -# define C_DIALECT -# endif -#elif __STDC_VERSION__ >= 201000L -# define C_DIALECT "11" -#elif __STDC_VERSION__ >= 199901L -# define C_DIALECT "99" -#else -# define C_DIALECT "90" -#endif -const char* info_language_dialect_default = - "INFO" ":" "dialect_default[" C_DIALECT "]"; - -/*--------------------------------------------------------------------------*/ - -#ifdef ID_VOID_MAIN -void main() {} -#else -# if defined(__CLASSIC_C__) -int main(argc, argv) int argc; char *argv[]; -# else -int main(int argc, char* argv[]) -# endif -{ - int require = 0; - require += info_compiler[argc]; - require += info_platform[argc]; - require += info_arch[argc]; -#ifdef COMPILER_VERSION_MAJOR - require += info_version[argc]; -#endif -#ifdef COMPILER_VERSION_INTERNAL - require += info_version_internal[argc]; -#endif -#ifdef SIMULATE_ID - require += info_simulate[argc]; -#endif -#ifdef SIMULATE_VERSION_MAJOR - require += info_simulate_version[argc]; -#endif -#if defined(__CRAYXE) || defined(__CRAYXC) - require += info_cray[argc]; -#endif - require += info_language_dialect_default[argc]; - (void)argv; - return require; -} -#endif diff --git a/quantization/lib/CMakeFiles/3.11.1/CompilerIdC/a.out b/quantization/lib/CMakeFiles/3.11.1/CompilerIdC/a.out deleted file mode 100644 index afc42a94bbc7371ac5a573a3b9eb6b0812ecca21..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8800 zcmeHMZ){W76~DIgkAIFs%Nn2?j}D}?TOLjpQqyYXIZnbea)1SgZp+NeOY9_G9UIxt zETC>@7#0l`JHp_pLgFo_kFu3((87)1Q)k>N+7r3(WJ<}3vqsvWJwkftAr-D zh=)ZTNNVBIWD8Qe#`G?kX4*h{9-uXF1>CkE;MQb?>BF`dJlKUJM2h}OMWd=se_3y{ zOodRS0C5_y!gaL8*&M7O+` zDC%N4aLtNi{Y7*tsa8QdBqsz@#(fEJ1R;(8OnlVbPyUu0m)q1oro@UJspN2H$BtCI zHI+=~CtD{2ovoc6YA&O;%YLJM)ZMdhKzibFRDi>nAA}3n0JcB=>b3K4fA!Sc=QqE! z_12-E95nuL>M6wX1=UYn)_~^vD%zh0nNmeApdTX`cBe25tQ$GY%v#2TnM{LZY$B5; zi6Qnx!d)SwU2Ru)f+c%mT@WS4$>W*dlw5^vpC?~;8;4{K=Z2PoRX5<_l;I!)c0L#D?MmC_g7%#O5%HQI+}Ev3)@Q&OfAqOZPosKrh_ZFJ0fQ zyDsZ*-nTX^T3f@`8n=kpobCU_7k|A*5hC9-0Kn^M2rPEF_QxP{eJDL$RJ<72ZjrBj z@4J9oH1+`kiu18|j{^qlg>T-}3;BgBJ@;hvr#A$_%ITh)`Z?V--}stne0|o{b5B3L zp=}xY)@pKTpz*a6N}X1+P+VQb^68#?vAOBs)4@Z*LNPcHj7H9Ef4M<{!8y~4^kBGf zCtP?d^5``==a)YATrYlHEb7nSvXr(z@_0rHHzI{Q-C&{k;GguVIhVfcz5ESa5QDMc z*MhO&;h-@$T^j!nVVy{Ov}9Zrt4>Z?#Vx$AcmES3nTaFGR3bYFPh+g2SyZ$eM-$mx zGLs(k+hwOpl@6}>V>aSQ%Ctr^*@?jj?hYh0vm@h!RF5akRAR(3;)zi+pRxwq+G4`B z+4H0y+~fYf4Tj=bpi!U^pnLBZi$4T<73dp4yJ42s!h?hR2o~4ze&L$*xi+t@Z5dcDni8`pfJ;e^<|>B+~wuA#lkU*EI`?zn*=~!2xB=o!V0)L>_maEI+kDp3<5kYoba`7|tku2#lXbc`Fje2@ z)w13|(CZI+Te`do=z)Hhw?X#n=K%MEo$`GHO|F!c5m*_4l@VAOft3;XUyr~Hd=Fxo zCMp@Y@i!{dwbU@}(PV8M@mJxq63a$;$9!u@);1H*`4Oj+&D1X6)u6-e|GHnyAmp4| zLsH85$RB=BW4Ykgkcidt4*4We%mc7A+eB<2$^>%{S>SUO3%_GopT86E`y9(dHW8EL zpXEcubKH!FBtRDC;WaM%qme#*zDV9hdbf!WP&}Rey)t=6}>~P}==%o&HY$E~jrN?}619aBEI6 z$TPbgX^9Ze6B@LpuasP7*ukbncQM8Qm0}i=La_7F=E$x@$#5k}Vgv(WBJdcAM z?aw&upOpGb=jBHruhy^S`g=jzSubYlHK>)!WvEsg2h^x$|92Alz>@XwS7~SIyx$~! zexFDdZuW#MU7zcr1FkCTp#|jYVSMlu1Pkx`0gx+FQh9q;%9pO6D9C;AmUFJ3c95?Z zOP{YnDX+AR+;OtwT%V^vt}Im--v@b>le9c3%XaU7iXTbI%Qt@OKeU)_UtAX0_)v3Bx<=eA8SznUW+F_N`%R(^C; z9f9M)y`g?15_gfWuJq!T&Nh-ZwkRA$&r8F4F<%^7BXQs8k* zDq$t!>f<|ibyQa}Mw98JVP>=DF(Z+-vd6?|)|^Nf@%+TZF@ThD254Fps-ZsfnM5c= zr#j)djJ3Bv*wV}Eb&!JeoQ4R%F((1Sco9m(ZoPlOSU_Q7Efk5RNQgd-F+hf@h*B;uxJGNg3= zs%hmXt}vFMWgo_ZZpFzgwB<6!xS5Wlx$u6_iYL=XK9`7#WO_7X@OczF!MLzf^jO{;gTb#nv=YYNqeef?dMIV)a&pR{Z#>M{>8f3pcIhW_+(tD$c>x1aiPYK zrNN#}t*qU`PNf<#2DD`pDH8!yJCd@5Do03#sydbd)=Esm9Tz4jW#pWxiE&zr<8d&; zoDH?tq>bSE*GE7N+?W%|5eO(_p?Q$t0#IS_g$k1~0W03|y#Kc>zHi~tFYWVg4Y<;O zC(b*h5N@bjUOo#9e(S?_AWPJ(5tU@g-o*Q^-3sULP$|drcd11Nx3MBoC%zlxcOW-l z|L5GA)I3K5j=xq)OZgMAVjom67h?Pcw+0;MO_B)RCh{K__MP7k@FgY7A-H3%#CYM+ zq+AN27O=U+yL0>wLks3QjOXv>03~2`gyi^HkLfeej=2-_8u`}pV{1D-}pX}e+|K|wbMFuAcKUoEjww(C0Aj7!0pVNe&COqfCWM0O~b)@)yEXU6f ze#X&%#_)eArr&em=LtVgc#fCxPW}fD`~u+@2>(wEuB@X{wiGPpYfc>@c-2ghrZMQMF)PiPLqgP>d;3HymS11 z1MT?hn1*XHFXI2LPJJi;7GN98@SHz2qfsbMeJB5C(82z*f6h~sPl*3JV6m%==X{L+ zhtH#NC_KOHpVz@TXe;;6dD~s=6cpCCxf0Lv_o1O2znI^V1BHP^8OQXl1JB>t3gP|B z;8}bhDrk%Q&)=H|2+w|4pN4~3;Q;~4oPHROf0D}!@e>1|8HvZeBuBA diff --git a/quantization/lib/CMakeFiles/3.11.1/CompilerIdCXX/CMakeCXXCompilerId.cpp b/quantization/lib/CMakeFiles/3.11.1/CompilerIdCXX/CMakeCXXCompilerId.cpp deleted file mode 100644 index 1313e7ed3..000000000 --- a/quantization/lib/CMakeFiles/3.11.1/CompilerIdCXX/CMakeCXXCompilerId.cpp +++ /dev/null @@ -1,600 +0,0 @@ -/* This source file must have a .cpp extension so that all C++ compilers - recognize the extension without flags. 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