From 57cbafa80b269aff1a7a302066c16ee2c9b11e34 Mon Sep 17 00:00:00 2001 From: Malte Rasch <158572058+maljoras-sony@users.noreply.github.com> Date: Wed, 18 Jun 2025 12:29:13 +0200 Subject: [PATCH 01/33] Fix chopper bug (#732) * fix: debug mode, chopper trans bug, memory issue, maximizer trans * fix memeory error with valgrind * changelog * fix UMH test * CUDA arch Signed-off-by: Pablo Carmona Gonzalez --- CHANGELOG.md | 1 + CMakeLists.txt | 37 ++- cmake/dependencies_test.cmake | 2 + examples/04_lenet5_training.py | 13 +- examples/23_using_analog_tile_as_matrix.py | 3 +- examples/26_correlation_detection.py | 3 +- examples/35_half_precision_training.py | 4 +- src/aihwkit/extension/__init__.py | 2 +- src/aihwkit/linalg/matrix.py | 3 +- src/aihwkit/nn/low_precision_conversion.py | 2 +- src/aihwkit/nn/modules/conv.py | 1 - src/aihwkit/nn/modules/rnn/cells.py | 2 +- src/aihwkit/nn/modules/rnn/layers.py | 2 +- src/aihwkit/nn/modules/rnn/rnn.py | 2 +- .../rpu_base_src/rpu_base_tiles_cuda.cpp | 1 - src/rpucuda/cuda/bit_line_maker.cu | 27 ++- src/rpucuda/cuda/chopped_weight_output.cu | 10 +- src/rpucuda/cuda/chopped_weight_output.h | 7 +- src/rpucuda/cuda/cuda_buffer.cu | 18 +- src/rpucuda/cuda/cuda_buffer.h | 6 +- src/rpucuda/cuda/cuda_math_util.cu | 13 ++ src/rpucuda/cuda/cuda_util.cu | 156 ++++++++----- src/rpucuda/cuda/cuda_util.h | 20 +- src/rpucuda/cuda/forward_backward_pass.h | 9 +- src/rpucuda/cuda/io_iterator.h | 19 +- src/rpucuda/cuda/io_iterator_test.cpp | 220 ++++++++++++++++++ src/rpucuda/cuda/io_manager.cu | 46 ++-- src/rpucuda/cuda/maximizer.cu | 51 ++-- src/rpucuda/cuda/pwu_kernel.h | 32 +-- src/rpucuda/cuda/pwu_kernel_parameter.h | 52 +++-- src/rpucuda/cuda/pwu_kernel_parameter_base.h | 4 +- src/rpucuda/cuda/rpucuda.h | 2 +- .../cuda/rpucuda_buffered_transfer_device.h | 4 +- .../cuda/rpucuda_chopped_transfer_device.cu | 133 ++++++++--- .../cuda/rpucuda_chopped_transfer_device.h | 4 +- .../rpucuda_chopped_transfer_device_test.cpp | 168 ++++++------- .../cuda/rpucuda_constantstep_device.cu | 29 ++- .../cuda/rpucuda_dynamic_transfer_device.h | 4 +- src/rpucuda/cuda/rpucuda_expstep_device.cu | 50 ++-- src/rpucuda/cuda/rpucuda_hidden_device.cu | 4 +- src/rpucuda/cuda/rpucuda_linearstep_device.cu | 12 +- src/rpucuda/cuda/rpucuda_mixedprec_device.h | 4 +- .../cuda/rpucuda_mixedprec_device_base.h | 4 +- .../cuda/rpucuda_mixedprec_device_test.cpp | 2 +- .../cuda/rpucuda_mixedprec_int_device.h | 4 +- .../rpucuda_mixedprec_int_device_test.cpp | 2 +- src/rpucuda/cuda/rpucuda_onesided_device.h | 6 +- .../cuda/rpucuda_powstep_reference_device.cu | 6 +- src/rpucuda/cuda/rpucuda_pulsed.h | 2 +- src/rpucuda/cuda/rpucuda_pulsed_device.h | 39 ++-- .../cuda/rpucuda_pulsed_device_test.cpp | 2 +- src/rpucuda/cuda/rpucuda_simple_device.cu | 5 +- src/rpucuda/cuda/rpucuda_simple_device.h | 10 +- .../rpucuda_softbounds_reference_device.cu | 27 ++- src/rpucuda/cuda/rpucuda_transfer_device.h | 6 +- src/rpucuda/cuda/rpucuda_vector_device.h | 4 +- src/rpucuda/cuda/update_management_helper.cu | 68 +++--- src/rpucuda/cuda/update_management_helper.h | 2 + .../cuda/update_management_helper_test.cpp | 26 ++- src/rpucuda/cuda/weight_clipper_cuda.cu | 2 +- src/rpucuda/cuda/weight_clipper_cuda.h | 6 +- src/rpucuda/cuda/weight_drifter_cuda.h | 2 +- src/rpucuda/cuda/weight_modifier_cuda.cu | 16 +- src/rpucuda/cuda/weight_modifier_cuda.h | 2 +- src/rpucuda/cuda/weight_remapper_cuda.h | 6 +- src/rpucuda/dense_bit_line_maker.h | 6 +- src/rpucuda/math_util.cpp | 16 +- src/rpucuda/rng.h | 4 +- src/rpucuda/rpu.cpp | 12 +- src/rpucuda/rpu.h | 18 +- src/rpucuda/rpu_buffered_transfer_device.h | 2 +- src/rpucuda/rpu_chopped_transfer_device.cpp | 9 +- src/rpucuda/rpu_chopped_transfer_device.h | 6 +- src/rpucuda/rpu_constantstep_device.h | 23 +- src/rpucuda/rpu_dynamic_transfer_device.h | 2 +- src/rpucuda/rpu_forward_backward_pass.h | 8 +- src/rpucuda/rpu_mixedprec_device_base.h | 4 +- src/rpucuda/rpu_mixedprec_int_device.h | 2 +- src/rpucuda/rpu_onesided_device.h | 8 +- src/rpucuda/rpu_pulsed.h | 2 +- src/rpucuda/rpu_pulsed_device.cpp | 10 +- src/rpucuda/rpu_pulsed_device.h | 18 +- src/rpucuda/rpu_simple_device.h | 14 +- src/rpucuda/rpu_transfer_device.cpp | 5 +- src/rpucuda/rpu_transfer_device.h | 10 +- src/rpucuda/rpu_vector_device.cpp | 5 +- src/rpucuda/rpu_vector_device.h | 4 +- src/rpucuda/rpu_weight_updater.h | 10 +- src/rpucuda/sparse_bit_line_maker.h | 6 +- src/rpucuda/utility_functions.h | 25 +- src/rpucuda/weight_clipper.h | 6 +- src/rpucuda/weight_drifter.h | 2 +- src/rpucuda/weight_modifier.h | 2 +- src/rpucuda/weight_remapper.h | 6 +- tests/test_calibration.py | 2 +- tests/test_quantized_tile.py | 6 +- tests/test_torch_tiles.py | 2 +- 97 files changed, 1095 insertions(+), 591 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index a546b4ee..6df886fd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -27,6 +27,7 @@ The format is based on [Keep a Changelog], and this project adheres to * Fix Hardware-Aware training tutorial notebooks (\#700) * Fix Post-Training Input Range Calibration notebook (\#716) +* Fix memory issues and bugs in analog training for CUDA (\#732) ## Changed diff --git a/CMakeLists.txt b/CMakeLists.txt index 9150cfbc..22592ca5 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -23,7 +23,7 @@ option(RPU_USE_TORCH_BUFFERS "Use torch buffers for RPUCuda" ON) set(RPU_BLAS "OpenBLAS" CACHE STRING "BLAS backend of choice (OpenBLAS, MKL)") -set(RPU_CUDA_ARCHITECTURES "70;75;80" CACHE STRING "Target CUDA architectures") +set(RPU_CUDA_ARCHITECTURES "75;80;89" CACHE STRING "Target CUDA architectures") # Internal variables. set(CUDA_TARGET_PROPERTIES POSITION_INDEPENDENT_CODE ON @@ -110,12 +110,13 @@ if(USE_CUDA) if (RPU_USE_TORCH_BUFFERS) if (BUILD_TEST) # we could just link torch to the tests in principle - message(FATAL_ERROR "Cannot use torch buffers when BUILD_TEST=ON. Set RPU_USE_TORCH_BUFFERS=OFF") + message(STATUS "Cannot use torch buffers when BUILD_TEST=ON. Set RPU_USE_TORCH_BUFFERS=OFF") + set(RPU_USE_TORCH_BUFFERS OFF) + else (BUILD_TEST) + add_compile_definitions(RPU_TORCH_CUDA_BUFFERS) + set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") + set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Xcudafe --diag_suppress=186") endif(BUILD_TEST) - - add_compile_definitions(RPU_TORCH_CUDA_BUFFERS) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Xcudafe --diag_suppress=186") endif(RPU_USE_TORCH_BUFFERS) if(${CUDAToolkit_VERSION_MAJOR} LESS 11) @@ -140,7 +141,7 @@ if (BUILD_EXTENSION) target_link_libraries(AIHWKIT_EXTENSION_OPS torch_python c10 torch_cpu) target_include_directories(AIHWKIT_EXTENSION_OPS PRIVATE src/aihwkit/extension/extension_src) - + if(WIN32) target_link_libraries(AIHWKIT_EXTENSION_OPS c10.lib torch_cpu.lib) endif() @@ -149,7 +150,7 @@ if (BUILD_EXTENSION) add_library(AIHWKIT_EXTENSION_OPS_GPU ${AIHWKIT_EXTENSION_OPS_GPU_SRCS}) target_link_libraries(AIHWKIT_EXTENSION_OPS_GPU AIHWKIT_EXTENSION_OPS c10_cuda torch_cuda cudart) target_include_directories(AIHWKIT_EXTENSION_OPS_GPU PRIVATE src/aihwkit/extension/extension_src) - + set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Xcudafe --diag_suppress=186") set_target_properties(AIHWKIT_EXTENSION_OPS_GPU PROPERTIES ${CUDA_TARGET_PROPERTIES}) @@ -187,20 +188,34 @@ endif(BUILD_EXTENSION) # Add tests. if(BUILD_TEST) + enable_testing() foreach(test_src ${RPU_CPU_TEST_SRCS} ${RPU_GPU_TEST_SRCS}) get_filename_component(test_name ${test_src} NAME_WE) add_executable(${test_name} ${test_src}) target_link_libraries(${test_name} gtest gmock) + target_link_libraries(${test_name} torch_python c10 torch_cpu) + set_target_properties(${test_name} PROPERTIES CXX_STANDARD 17 + POSITION_INDEPENDENT_CODE ON) + + if(WIN32) + target_link_libraries(${test_name} c10.lib torch_cpu.lib) + endif() # Link to main library. - if(${test_src} IN_LIST RPU_CPU_TEST_SRCS) - target_link_libraries(${test_name} RPU_CPU ${RPU_DEPENDENCY_LIBS}) - else() + target_link_libraries(${test_name} RPU_CPU ${RPU_DEPENDENCY_LIBS}) + + if(${test_src} IN_LIST RPU_GPU_TEST_SRCS) + target_link_libraries(${test_name} torch_cuda c10_cuda cudart) target_link_libraries(${test_name} RPU_GPU RPU_CPU cublas curand ${RPU_DEPENDENCY_LIBS}) set_target_properties(${test_name} PROPERTIES ${CUDA_TARGET_PROPERTIES}) set_property(TARGET ${test_name} PROPERTY CUDA_ARCHITECTURES ${RPU_CUDA_ARCHITECTURES}) + + if(WIN32) + target_link_libraries(${test_name} c10_cuda.lib torch_cuda.lib) + endif(WIN32) + endif() add_test(NAME ${test_name} COMMAND $) diff --git a/cmake/dependencies_test.cmake b/cmake/dependencies_test.cmake index 6745ad2f..0d45ff54 100644 --- a/cmake/dependencies_test.cmake +++ b/cmake/dependencies_test.cmake @@ -11,6 +11,7 @@ if(BUILD_TEST) URL_HASH MD5=52943a59cefce0ae0491d4d2412c120b CMAKE_ARGS "-DCMAKE_CXX_FLAGS=-D_GLIBCXX_USE_CXX11_ABI\=0" INSTALL_COMMAND "" + DOWNLOAD_EXTRACT_TIMESTAMP true ) ExternalProject_Get_Property(GTest source_dir) @@ -20,4 +21,5 @@ if(BUILD_TEST) include_directories(SYSTEM ${GTest_INCLUDE_DIR}) link_directories(SYSTEM ${GTest_LIBRARY_DIR}) + endif() diff --git a/examples/04_lenet5_training.py b/examples/04_lenet5_training.py index 4809385b..8ddd4bc8 100644 --- a/examples/04_lenet5_training.py +++ b/examples/04_lenet5_training.py @@ -29,14 +29,13 @@ from aihwkit.nn import AnalogConv2d, AnalogLinear, AnalogSequential from aihwkit.optim import AnalogSGD from aihwkit.simulator.configs import ( - SingleRPUConfig, FloatingPointRPUConfig, - ConstantStepDevice, + SoftBoundsReferenceDevice, FloatingPointDevice, + build_config, ) from aihwkit.simulator.rpu_base import cuda - # Check device USE_CUDA = 0 if cuda.is_compiled(): @@ -59,13 +58,17 @@ # Select the device model to use in the training. # * If `SingleRPUConfig(device=ConstantStepDevice())` then analog tiles with # constant step devices will be used, +# * One can use `build_config` to build a config for different +# specialized analog gradient algorithms # * If `FloatingPointRPUConfig(device=FloatingPointDevice())` then standard # floating point devices will be used -USE_ANALOG_TRAINING = False +USE_ANALOG_TRAINING = True if USE_ANALOG_TRAINING: - RPU_CONFIG = SingleRPUConfig(device=ConstantStepDevice()) + algo = "agad" # or e.g. ttv2 + RPU_CONFIG = build_config(algo, device=SoftBoundsReferenceDevice(dw_min=0.05)) else: RPU_CONFIG = FloatingPointRPUConfig(device=FloatingPointDevice()) +print(RPU_CONFIG) def load_images(): diff --git a/examples/23_using_analog_tile_as_matrix.py b/examples/23_using_analog_tile_as_matrix.py index 0a64f700..eae63683 100644 --- a/examples/23_using_analog_tile_as_matrix.py +++ b/examples/23_using_analog_tile_as_matrix.py @@ -4,8 +4,7 @@ # # Licensed under the MIT license. See LICENSE file in the project root for details. -"""aihwkit example 22: Simple example of how to use an analog tile as a matrix -""" +"""aihwkit example 22: Simple example of how to use an analog tile as a matrix""" # pylint: disable=invalid-name # pylint: disable=too-many-locals diff --git a/examples/26_correlation_detection.py b/examples/26_correlation_detection.py index 2f971d7a..b67dd452 100644 --- a/examples/26_correlation_detection.py +++ b/examples/26_correlation_detection.py @@ -4,8 +4,7 @@ # # Licensed under the MIT license. See LICENSE file in the project root for details. -"""aihwkit example 25: Simple correlation detection with analog optimizers. -""" +"""aihwkit example 25: Simple correlation detection with analog optimizers.""" # pylint: disable=invalid-name, too-many-locals, too-many-statements from typing import Union, Tuple, Optional, List, Dict diff --git a/examples/35_half_precision_training.py b/examples/35_half_precision_training.py index 203a8157..13955667 100644 --- a/examples/35_half_precision_training.py +++ b/examples/35_half_precision_training.py @@ -70,9 +70,7 @@ def forward(self, x): pbar = tqdm.tqdm(enumerate(train_loader)) for batch_idx, (data, target) in pbar: - data, target = data.to(device=device, dtype=torch.bfloat16), target.to( - device=device - ) + data, target = data.to(device=device, dtype=torch.bfloat16), target.to(device=device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output.float(), target) diff --git a/src/aihwkit/extension/__init__.py b/src/aihwkit/extension/__init__.py index a5af0f21..b4ba6935 100644 --- a/src/aihwkit/extension/__init__.py +++ b/src/aihwkit/extension/__init__.py @@ -6,7 +6,7 @@ # pylint: disable=import-error, no-name-in-module, invalid-name -"""AIHWKIT extension """ +"""AIHWKIT extension""" from importlib.util import find_spec diff --git a/src/aihwkit/linalg/matrix.py b/src/aihwkit/linalg/matrix.py index 764d81ef..02e74369 100644 --- a/src/aihwkit/linalg/matrix.py +++ b/src/aihwkit/linalg/matrix.py @@ -4,8 +4,7 @@ # # Licensed under the MIT license. See LICENSE file in the project root for details. -""" Defines an analog matrix -""" +"""Defines an analog matrix""" from typing import Any, Union, Tuple from scipy.sparse.linalg import LinearOperator diff --git a/src/aihwkit/nn/low_precision_conversion.py b/src/aihwkit/nn/low_precision_conversion.py index f76e63f6..b35fdd0a 100644 --- a/src/aihwkit/nn/low_precision_conversion.py +++ b/src/aihwkit/nn/low_precision_conversion.py @@ -4,7 +4,7 @@ # # Licensed under the MIT license. See LICENSE file in the project root for details. -""" Functions to convert a given model to a quantized counterpart """ +"""Functions to convert a given model to a quantized counterpart""" from copy import deepcopy diff --git a/src/aihwkit/nn/modules/conv.py b/src/aihwkit/nn/modules/conv.py index 67348872..ae9a7d81 100644 --- a/src/aihwkit/nn/modules/conv.py +++ b/src/aihwkit/nn/modules/conv.py @@ -163,7 +163,6 @@ def forward(self, x_input: Tensor) -> Tensor: input_size = x_input.numel() / x_input.size(0) if self.input_size != input_size or not self.analog_module.is_indexed(): self._recalculate_indexes(x_input) - return self.analog_module(x_input, tensor_view=self.tensor_view) # Brute-force unfold. diff --git a/src/aihwkit/nn/modules/rnn/cells.py b/src/aihwkit/nn/modules/rnn/cells.py index 02011246..68fe8db7 100644 --- a/src/aihwkit/nn/modules/rnn/cells.py +++ b/src/aihwkit/nn/modules/rnn/cells.py @@ -4,7 +4,7 @@ # # Licensed under the MIT license. See LICENSE file in the project root for details. -""" Analog cells for RNNs. """ +"""Analog cells for RNNs.""" from typing import Optional, Tuple, Type from collections import namedtuple diff --git a/src/aihwkit/nn/modules/rnn/layers.py b/src/aihwkit/nn/modules/rnn/layers.py index 5e41ce28..06d1fa91 100644 --- a/src/aihwkit/nn/modules/rnn/layers.py +++ b/src/aihwkit/nn/modules/rnn/layers.py @@ -4,7 +4,7 @@ # # Licensed under the MIT license. See LICENSE file in the project root for details. -""" Analog RNN layers """ +"""Analog RNN layers""" from typing import Any, List, Tuple, Type, Union from torch import Tensor, stack, jit, cat diff --git a/src/aihwkit/nn/modules/rnn/rnn.py b/src/aihwkit/nn/modules/rnn/rnn.py index e723a37a..c4ff6d19 100644 --- a/src/aihwkit/nn/modules/rnn/rnn.py +++ b/src/aihwkit/nn/modules/rnn/rnn.py @@ -4,7 +4,7 @@ # # Licensed under the MIT license. See LICENSE file in the project root for details. -""" Analog RNN modules. """ +"""Analog RNN modules.""" import warnings import math diff --git a/src/aihwkit/simulator/rpu_base_src/rpu_base_tiles_cuda.cpp b/src/aihwkit/simulator/rpu_base_src/rpu_base_tiles_cuda.cpp index a6bf9bc5..1b1a93fa 100644 --- a/src/aihwkit/simulator/rpu_base_src/rpu_base_tiles_cuda.cpp +++ b/src/aihwkit/simulator/rpu_base_src/rpu_base_tiles_cuda.cpp @@ -171,7 +171,6 @@ void declare_rpu_tiles_cuda(py::module &m, std::string type_name_add, bool add_u int expected_in_size = self.getXSize() - (bias ? 1 : 0); int m_batch = x_input.numel() / in_size; int out_size = self.getDSize(); - // Validate the x_input dimensions. if (in_size != expected_in_size) { std::string shape_str = x_trans ? ("[*, " + std::to_string(expected_in_size) + "]") diff --git a/src/rpucuda/cuda/bit_line_maker.cu b/src/rpucuda/cuda/bit_line_maker.cu index 1c7ee352..b8c162a5 100644 --- a/src/rpucuda/cuda/bit_line_maker.cu +++ b/src/rpucuda/cuda/bit_line_maker.cu @@ -156,7 +156,7 @@ namespace RPU { \ int batch_idx = m_batch - 1; \ for (int j = 0; j < size * nK32; j++) { \ - counts[j] = tmp32[(batch_idx)*size * nK32 + j]; \ + counts[j] = tmp32[(batch_idx) * size * nK32 + j]; \ } \ \ CUDA_CALL(cudaDeviceSynchronize()); \ @@ -2127,15 +2127,13 @@ void BitLineMaker::makeCounts( Kc_values = umh_->getKcValueData(); Kn = umh_->getKnData(current_ublm_, m_batch); } - + auto *random_states = context_->getRandomStates(nthreads_ * nblocks); RPU_BLM_SWITCH_TRANS_TEMPLATE_UM( x_trans, d_trans, out_trans, current_um_, current_ublm_, kernelUpdateGetCountsBatch_SimpleLoop2, (x_in, x_size_, B, dev_x_counts_bo64_->getData(), d_in, d_size_, A, - dev_d_counts_bo64_->getData(), dev_d_noz, current_BL_ + 1, m_batch, - context_->getRandomStates(nthreads_ * nblocks), res, sr, scale_values, K_values, - lr / weight_granularity, Kc_values, Kn)); - + dev_d_counts_bo64_->getData(), dev_d_noz, current_BL_ + 1, m_batch, random_states, + res, sr, scale_values, K_values, lr / weight_granularity, Kc_values, Kn)); } else { // need to set buffers to zero for zero short-cut @@ -2169,14 +2167,15 @@ void BitLineMaker::makeCounts( dev_d_counts_->getData(), m_batch, current_BL_, current_ublm_); } - DEBUG_CALL(context_->synchronizeDevice(); CudaArray dev_x(context_, x_size_); - CudaArray dev_d(context_, d_size_); - RPU::math::copyWithIterator(context_, dev_x.getData(), x_in, x_size_); - RPU::math::copyWithIterator(context_, dev_d.getData(), d_in, d_size_); - context_->synchronizeDevice(); test_helper::checkCounts( - dev_x.getData(), x_size_, dev_d.getData(), d_size_, current_BL_, A, B, - &*dev_x_counts_, &*dev_d_counts_); - context_->synchronizeDevice();); + // TODO: check this debug code + // DEBUG_CALL(context_->synchronizeDevice(); CudaArray dev_x(context_, x_size_); + // CudaArray dev_d(context_, d_size_); + // RPU::math::copyWithIterator(context_, dev_x.getData(), x_in, x_size_); + // RPU::math::copyWithIterator(context_, dev_d.getData(), d_in, d_size_); + // context_->synchronizeDevice(); test_helper::checkCounts( + // dev_x.getData(), x_size_, dev_d.getData(), d_size_, current_BL_, A, B, + // &*dev_x_counts_, &*dev_d_counts_); + // context_->synchronizeDevice();); } break; default: diff --git a/src/rpucuda/cuda/chopped_weight_output.cu b/src/rpucuda/cuda/chopped_weight_output.cu index d5dff5dd..1583e359 100644 --- a/src/rpucuda/cuda/chopped_weight_output.cu +++ b/src/rpucuda/cuda/chopped_weight_output.cu @@ -290,6 +290,8 @@ void ChoppedWeightOutput::makeWeightOutputChoppers(const BitLineMaker *blm dev_x_chopper_buffer_2_->setConst(1); dev_d_chopper_buffer_2_->setConst(1); + this->context_->synchronize(); + x_chopper_in_ = dev_x_chopper_buffer_1_->getData(); x_chopper_out_ = dev_x_chopper_buffer_2_->getData(); d_chopper_in_ = dev_d_chopper_buffer_1_->getData(); @@ -338,8 +340,12 @@ void ChoppedWeightOutput::makeWeightOutputChoppers(const BitLineMaker *blm if (n_weight_outputs > 0) { nwo_counter_ += n_weight_outputs; // BEFORE applying RPU_GET_CUDA_BUFFER( - context_, chop_t, dev_weight_output_out_chopper_, n_weight_outputs * getOutSize()); - RPU_GET_CUDA_BUFFER(context_, chop_t, dev_weight_output_in_chopper_, n_weight_outputs); + context_, chop_t, dev_weight_output_out_chopper_, max_weight_outputs * getOutSize()); + RPU_GET_CUDA_BUFFER(context_, chop_t, dev_weight_output_in_chopper_, max_weight_outputs); + dev_weight_output_out_chopper_->setConst( + 1); // should not be needed, but otherwise has some random issues + dev_weight_output_in_chopper_->setConst( + 1); // should not be needed, but otherwise has some random issues if (par_.in_chop_random || par_.out_chop_prob > (T)0.0) { context_->randUniform(dev_switching_probs_->getData(), sw_size); diff --git a/src/rpucuda/cuda/chopped_weight_output.h b/src/rpucuda/cuda/chopped_weight_output.h index 9af118f6..c0f076bf 100644 --- a/src/rpucuda/cuda/chopped_weight_output.h +++ b/src/rpucuda/cuda/chopped_weight_output.h @@ -48,7 +48,7 @@ template class ChoppedWeightOutput { public: explicit ChoppedWeightOutput(CudaContextPtr c, int x_size, int d_size); - ChoppedWeightOutput(){}; + ChoppedWeightOutput() {}; ~ChoppedWeightOutput() = default; ChoppedWeightOutput(const ChoppedWeightOutput &); @@ -112,8 +112,9 @@ template class ChoppedWeightOutput { inline int getOutSize() const { return par_.use_columns ? d_size_ : x_size_; }; inline int getInSize() const { return par_.use_columns ? x_size_ : d_size_; }; inline int getWODataSize() const { - return flexible_in_size_ ? getNumWeightOutputs() * getOutSize() - : (getNumWeightOutputs() / getInSize() + 1) * x_size_ * d_size_; + return flexible_in_size_ + ? getNumWeightOutputs() * getOutSize() + : ((getNumWeightOutputs() + getInSize() - 1) / getInSize() + 1) * x_size_ * d_size_; }; int getBatchStart() const; int getValStart() const; diff --git a/src/rpucuda/cuda/cuda_buffer.cu b/src/rpucuda/cuda/cuda_buffer.cu index 0e844439..176b0010 100644 --- a/src/rpucuda/cuda/cuda_buffer.cu +++ b/src/rpucuda/cuda/cuda_buffer.cu @@ -11,11 +11,11 @@ namespace RPU { #if defined(RPU_TORCH_CUDA_BUFFERS) -template void CudaBuffer::print(int size) const { +template void CudaBuffer::print(size_t size) const { auto values = buffer_.cpu(); - int n = values.numel() > size ? size : values.numel(); - for (int i = 0; i < n; ++i) { + size_t n = values.numel() > size ? size : values.numel(); + for (size_t i = 0; i < n; ++i) { std::cout << "[" << i << "]:" << values[i] << ", "; } if (n < values.numel()) { @@ -24,12 +24,12 @@ template void CudaBuffer::print(int size) const { std::cout << std::endl; } -template T *CudaBuffer::get(CudaContextPtr c, int size) { +template T *CudaBuffer::get(CudaContextPtr c, size_t size) { mutex_.lock(); // need to be explicitely released to avoid multi-threading issues if (buffer_.numel() < size || c->getGPUId() != buffer_.device().index()) { // Build the buffers. - std::vector dims{size}; + std::vector dims{(signed long)size}; auto options = at::TensorOptions().device(at::kCUDA, c->getGPUId()).requires_grad(false); #ifdef RPU_DEFINE_CUDA_HALF_ARRAY @@ -83,13 +83,13 @@ template CudaBuffer &CudaBuffer::operator=(const CudaBuffer & #else -template void CudaBuffer::print(int size) const { +template void CudaBuffer::print(size_t size) const { if (buffer_ != nullptr) { buffer_->printValues(size); } } -template T *CudaBuffer::get(CudaContextPtr c, int size) { +template T *CudaBuffer::get(CudaContextPtr c, size_t size) { mutex_.lock(); // need to be explicitely released to avoid multi-threading issues if (buffer_ == nullptr || buffer_->getSize() < size || &*(buffer_->getContext()) != &*c) { if (buffer_ != nullptr) { @@ -125,7 +125,9 @@ template CudaBuffer &CudaBuffer::operator=(const CudaBuffer & // move constructor template CudaBuffer::CudaBuffer(CudaBuffer &&other) { - { const std::lock_guard lock(other.mutex_); } + { + const std::lock_guard lock(other.mutex_); + } *this = std::move(other); } diff --git a/src/rpucuda/cuda/cuda_buffer.h b/src/rpucuda/cuda/cuda_buffer.h index fc8e4bcb..306ef33d 100644 --- a/src/rpucuda/cuda/cuda_buffer.h +++ b/src/rpucuda/cuda/cuda_buffer.h @@ -21,7 +21,7 @@ template class CudaArray; template class CudaBuffer { public: - CudaBuffer(){}; + CudaBuffer() {}; CudaBuffer(const CudaBuffer &); CudaBuffer &operator=(const CudaBuffer &); CudaBuffer(CudaBuffer &&); @@ -38,10 +38,10 @@ template class CudaBuffer { #endif } - T *get(CudaContextPtr context, int size); + T *get(CudaContextPtr context, size_t size); void release(); - void print(int size) const; + void print(size_t size) const; private: #if defined RPU_TORCH_CUDA_BUFFERS diff --git a/src/rpucuda/cuda/cuda_math_util.cu b/src/rpucuda/cuda/cuda_math_util.cu index 4eadeae3..ff4ddbc1 100644 --- a/src/rpucuda/cuda/cuda_math_util.cu +++ b/src/rpucuda/cuda/cuda_math_util.cu @@ -1778,6 +1778,7 @@ void copyWithIterator( RPU_CMU_DEFINE_CWI(float *, const float *); RPU_CMU_DEFINE_CWI(float *, float *); +RPU_CMU_DEFINE_CWI(chop_t *, chop_t *); RPU_CMU_DEFINE_CWI(float *, IndexReaderInputIterator); RPU_CMU_DEFINE_CWI(float *, IndexReaderTransInputIterator); RPU_CMU_DEFINE_CWI(float *, PermuterTransInputIterator); @@ -1786,7 +1787,11 @@ RPU_CMU_DEFINE_CWI(float *, IndexReaderSliceInputIterator); RPU_CMU_DEFINE_CWI(float *, SliceInputIterator); RPU_CMU_DEFINE_CWI(float *, SliceInputIterator); RPU_CMU_DEFINE_CWI(float *, DiagInputIterator); +RPU_CMU_DEFINE_CWI(float *, DiagInputIterator); RPU_CMU_DEFINE_CWI(float *, EyeInputIterator); +RPU_CMU_DEFINE_CWI(float *, IndicatorInputIterator); +RPU_CMU_DEFINE_CWI(float *, LogInputIterator); +RPU_CMU_DEFINE_CWI(float *, NegateInputIterator); RPU_CMU_DEFINE_CWI(PermuterTransOutputIterator, const float *); RPU_CMU_DEFINE_CWI(IndexReaderOutputIterator, const float *); RPU_CMU_DEFINE_CWI(IndexReaderTransOutputIterator, const float *); @@ -1809,7 +1814,11 @@ RPU_CMU_DEFINE_CWI(double *, IndexReaderSliceInputIterator) RPU_CMU_DEFINE_CWI(double *, SliceInputIterator); RPU_CMU_DEFINE_CWI(double *, SliceInputIterator); RPU_CMU_DEFINE_CWI(double *, DiagInputIterator); +RPU_CMU_DEFINE_CWI(double *, DiagInputIterator); RPU_CMU_DEFINE_CWI(double *, EyeInputIterator); +RPU_CMU_DEFINE_CWI(double *, IndicatorInputIterator); +RPU_CMU_DEFINE_CWI(double *, LogInputIterator); +RPU_CMU_DEFINE_CWI(double *, NegateInputIterator); RPU_CMU_DEFINE_CWI(PermuterTransOutputIterator, const double *); RPU_CMU_DEFINE_CWI(IndexReaderOutputIterator, const double *); RPU_CMU_DEFINE_CWI(IndexReaderTransOutputIterator, const double *); @@ -1833,7 +1842,11 @@ RPU_CMU_DEFINE_CWI(half_t *, IndexReaderSliceInputIterator); RPU_CMU_DEFINE_CWI(half_t *, SliceInputIterator); RPU_CMU_DEFINE_CWI(half_t *, SliceInputIterator); RPU_CMU_DEFINE_CWI(half_t *, DiagInputIterator); +RPU_CMU_DEFINE_CWI(half_t *, DiagInputIterator); RPU_CMU_DEFINE_CWI(half_t *, EyeInputIterator); +RPU_CMU_DEFINE_CWI(half_t *, IndicatorInputIterator); +RPU_CMU_DEFINE_CWI(half_t *, LogInputIterator); +RPU_CMU_DEFINE_CWI(half_t *, NegateInputIterator); RPU_CMU_DEFINE_CWI(PermuterTransOutputIterator, const half_t *); RPU_CMU_DEFINE_CWI(IndexReaderOutputIterator, const half_t *); RPU_CMU_DEFINE_CWI(IndexReaderTransOutputIterator, const half_t *); diff --git a/src/rpucuda/cuda/cuda_util.cu b/src/rpucuda/cuda/cuda_util.cu index b1c45a9a..faf1ab81 100644 --- a/src/rpucuda/cuda/cuda_util.cu +++ b/src/rpucuda/cuda/cuda_util.cu @@ -14,7 +14,7 @@ #define DISABLE_SHARED_MUTEX 1 -#define IDX2F(i, j, ld) ((((j)-1) * (ld)) + ((i)-1)) +#define IDX2F(i, j, ld) ((((j) - 1) * (ld)) + ((i) - 1)) // this should be not necesary, because device id is set individually // per thread. However, if one would want to use 2 GPUs within one @@ -212,23 +212,23 @@ void curandSetup( CublasEnvironment::~CublasEnvironment() { - DEBUG_OUT("Destroy BLAS env."); - // DEBUG_OUT("handle : " <handle_); + DEBUG_ALL_OUT("Destroy BLAS env."); + DEBUG_ALL_OUT("handle : " << this->handle_); // destroy device // destroy host if (handle_ != nullptr) { cublasDestroy(handle_); - DEBUG_OUT("CUBLAS destroyed"); + DEBUG_ALL_OUT("CUBLAS destroyed"); } #ifdef RPU_WITH_CUBLAS_DEVICE if (device_handle_created_) { - DEBUG_OUT("destroy device handle"); + DEBUG_ALL_OUT("destroy device handle"); kernelCublasDestroy<<<1, 1>>>(device_handle_); CUDA_CALL(cudaDeviceSynchronize()); CUDA_CALL(cudaFree(device_handle_)); - DEBUG_OUT("CUBLAS device destroyed"); + DEBUG_ALL_OUT("CUBLAS device destroyed"); } #endif // cudaDeviceReset(); @@ -236,7 +236,7 @@ CublasEnvironment::~CublasEnvironment() { CublasEnvironment::CublasEnvironment(int gpu_id) { - DEBUG_OUT("GET BLAS env."); + DEBUG_ALL_OUT("GET BLAS env."); if (gpu_id >= 0) { CUDA_CALL(cudaSetDevice(gpu_id)); } @@ -250,7 +250,7 @@ CublasEnvironment::CublasEnvironment(int gpu_id) { if (stat != CUBLAS_STATUS_SUCCESS) { RPU_FATAL("CUBLAS initialization failed"); } else - DEBUG_OUT("CUBLAS Host initialized."); + DEBUG_ALL_OUT("CUBLAS Host initialized."); #ifdef RPU_WITH_CUBLAS_DEVICE device_handle_created_ = false; @@ -362,7 +362,7 @@ void CublasEnvironment::createDeviceHandle() { kernelCublasCreateDevice<<<1, 1>>>(device_handle_); CUDA_CALL(cudaDeviceSynchronize()); - DEBUG_OUT("Created device handle"); + DEBUG_ALL_OUT("Created device handle"); device_handle_created_ = true; } @@ -417,7 +417,7 @@ int CublasEnvironment::runTestDevice() { //**********************************************************************// void CudaContext::init() { - DEBUG_OUT("Init context..."); + DEBUG_ALL_OUT("Init context..."); if (gpu_id_ >= 0) { CUDA_CALL(cudaSetDevice(gpu_id_)); @@ -425,7 +425,7 @@ void CudaContext::init() { CUDA_CALL(cudaGetDevice(&gpu_id_)); } CUDA_CALL(cudaDeviceSynchronize()); - DEBUG_OUT("Create context on GPU " << gpu_id_); + DEBUG_ALL_OUT("Create context on GPU " << gpu_id_); env_ = new CublasEnvironment(gpu_id_); stream_id_ = 0; rng_created_ = false; @@ -446,7 +446,7 @@ CudaContext::CudaContext(int gpu_id, bool non_blocking) } CudaContext::CudaContext(cudaStream_t shared_stream, int gpu_id) : gpu_id_(gpu_id) { - DEBUG_OUT("Create context on GPU " << gpu_id << " with shared stream (on id 0)\n"); + DEBUG_ALL_OUT("Create context on GPU " << gpu_id << " with shared stream (on id 0)\n"); this->init(); shared_ = true; @@ -502,7 +502,7 @@ CudaContext::~CudaContext() { delete env_; env_ = nullptr; } - DEBUG_OUT("Destroyed CudaContext."); + DEBUG_ALL_OUT("Destroyed CudaContext."); } // copy constructor @@ -534,7 +534,8 @@ CudaContext::CudaContext(const CudaContext &other) { // random states and buffers won't be copied. They will be created a new - DEBUG_OUT("CudaContext copy constructed [but only first stream shared. New streams and event!]."); + DEBUG_ALL_OUT( + "CudaContext copy constructed [but only first stream shared. New streams and event!]."); } // copy assignment @@ -610,6 +611,7 @@ void CudaContext::enforceDeviceId() const { } void CudaContext::synchronizeDevice() const { + DEBUG_ALL_OUT("Synchronize device!"); enforceDeviceId(); CUDA_CALL(cudaDeviceSynchronize()); } @@ -635,20 +637,20 @@ void CudaContext::synchronizeWith(CudaContextPtr ca, CudaContextPtr cb) const { } void CudaContext::synchronizeStream(int idx) const { - DEBUG_OUT("Synchronize stream idx " << idx); + DEBUG_ALL_OUT("Synchronize stream idx " << idx); enforceDeviceId(); if ((idx >= 0) && (idx < streams_.size())) { CUDA_CALL(cudaStreamSynchronize(streams_[idx])); } } void CudaContext::synchronizeStream() const { - DEBUG_OUT("Synchronize stream id " << stream_id_); + DEBUG_ALL_OUT("Synchronize stream id " << stream_id_); enforceDeviceId(); CUDA_CALL(cudaStreamSynchronize(streams_[stream_id_])); } int CudaContext::getNStrideBlocks(int size, int nthreads) const { - DEBUG_OUT("get N Stride Blocks for size " << size); + DEBUG_ALL_OUT("get N Stride Blocks for size " << size); nthreads = MIN(maxThreadsPerBlock(), nthreads); int max_blocks = getSMCount() * maxThreadsPerBlock() / nthreads; return MIN(getNBlocks(size, nthreads), max_blocks); @@ -658,7 +660,7 @@ cudaStream_t CudaContext::getStream(int idx) { enforceDeviceId(); - DEBUG_OUT("Try to get streams " << idx); + DEBUG_ALL_OUT("Try to get streams " << idx); if ((idx >= 0) && (idx < streams_.size())) { if (stream_id_ != idx) { stream_id_ = idx; @@ -678,7 +680,7 @@ cudaStream_t CudaContext::getStream(int idx) { stream_id_ = idx; CUBLAS_CALL(cublasSetStream(this->getBlasHandle(), streams_[idx])); - DEBUG_OUT("Created stream id " << idx << " at : " << streams_[idx] << " ( s: " << s << ")"); + DEBUG_ALL_OUT("Created stream id " << idx << " at : " << streams_[idx] << " ( s: " << s << ")"); return streams_[idx]; } else { RPU_FATAL("Requested stream size mismatch."); @@ -793,20 +795,23 @@ curandState_t *CudaContext::getRandomStates(int size) { } if (!(*rs)[stream_id] || (n > (*rs)[stream_id]->getSize())) { curandSetup(this, (*rs)[stream_id], n, 0, false); + this->synchronizeDevice(); } + return (*rs)[stream_id]->getData(); } -template <> float *CudaContext::getSharedBuffer(int id, int size) { +template <> float *CudaContext::getSharedBuffer(int id, size_t size) { auto *buffer = &float_buffer_; auto stream_id = stream_id_; if (shared_ && stream_id_ == 0) { buffer = &shared_float_buffer_; stream_id = shared_stream_id_; - DEBUG_OUT("Get SHARED float buffer ID " << id << ", size " << size << ", stream " << stream_id); + DEBUG_ALL_OUT( + "Get SHARED float buffer ID " << id << ", size " << size << ", stream " << stream_id); } else { - DEBUG_OUT("Get float buffer ID " << id << ", size " << size << ", stream " << stream_id); + DEBUG_ALL_OUT("Get float buffer ID " << id << ", size " << size << ", stream " << stream_id); } while (buffer->size() <= stream_id) { @@ -822,15 +827,15 @@ template <> void CudaContext::releaseSharedBuffer(int id) { if (shared_ && stream_id_ == 0) { buffer = &shared_float_buffer_; stream_id = shared_stream_id_; - DEBUG_OUT("Release SHARED float buffer ID " << id << ", stream " << stream_id); + DEBUG_ALL_OUT("Release SHARED float buffer ID " << id << ", stream " << stream_id); } else { - DEBUG_OUT("Release float buffer ID " << id << ", stream " << stream_id); + DEBUG_ALL_OUT("Release float buffer ID " << id << ", stream " << stream_id); } (*buffer)[stream_id][id].release(); } -template <> void CudaContext::printSharedBuffer(int id, int size) { +template <> void CudaContext::printSharedBuffer(int id, size_t size) { auto *buffer = &float_buffer_; auto stream_id = stream_id_; @@ -843,7 +848,7 @@ template <> void CudaContext::printSharedBuffer(int id, int size) { } #ifdef RPU_USE_DOUBLE -template <> double *CudaContext::getSharedBuffer(int id, int size) { +template <> double *CudaContext::getSharedBuffer(int id, size_t size) { // somehow this needs to be a MAX_BUFFER vector to avoid dynamical // resizing. Not sure why, but dynamical allocation of the // CudaBuffer vector elements does not work without uniptr (which @@ -874,7 +879,7 @@ template <> void CudaContext::releaseSharedBuffer(int id) { (*buffer)[stream_id][id].release(); } -template <> void CudaContext::printSharedBuffer(int id, int size) { +template <> void CudaContext::printSharedBuffer(int id, size_t size) { auto *buffer = &double_buffer_; auto stream_id = stream_id_; @@ -890,7 +895,7 @@ template <> void CudaContext::printSharedBuffer(int id, int size) { #endif #ifdef RPU_USE_FP16 -template <> half_t *CudaContext::getSharedBuffer(int id, int size) { +template <> half_t *CudaContext::getSharedBuffer(int id, size_t size) { // somehow this needs to be a MAX_BUFFER vector to avoid dynamical // resizing. Not sure why, but dynamical allocation of the // CudaBuffer vector elements does not work without uniptr (which @@ -921,7 +926,7 @@ template <> void CudaContext::releaseSharedBuffer(int id) { (*buffer)[stream_id][id].release(); } -template <> void CudaContext::printSharedBuffer(int id, int size) { +template <> void CudaContext::printSharedBuffer(int id, size_t size) { auto *buffer = &half_t_buffer_; auto stream_id = stream_id_; @@ -969,10 +974,9 @@ template CudaArray::CudaArray(CudaContextPtr c, int n) : CudaArr height_ = 1; // this needs to be one! No height>1 supported yet if (n > 0) { context_->enforceDeviceId(); - int mem_size = size_ * sizeof(T); - mem_size = (mem_size + 3) / 4 * 4; // align on 32-bit word - CUDA_CALL(cudaMallocPitch(&values_, &pitch_, mem_size, height_)); - ADDTOMEMCOUNTER(mem_size); + mem_size_ = (size_ * sizeof(T) + 3) / 4 * 4; // align on 32-bit word + CUDA_CALL(cudaMallocPitch(&values_, &pitch_, mem_size_, height_)); + ADDTOMEMCOUNTER(mem_size_); } } @@ -1001,11 +1005,12 @@ template CudaArray::~CudaArray() { if ((size_ > 0) && (values_ != nullptr) && (!shared_if_)) { // cudaDeviceSynchronize(); // too much? - SUBTRACTMEMCOUNTER(size_ * sizeof(T)); + SUBTRACTMEMCOUNTER(mem_size_); cudaFree(values_); values_ = nullptr; size_ = 0; width_ = 0; + mem_size_ = 0; } values_ = nullptr; @@ -1017,6 +1022,7 @@ template CudaArray::CudaArray(const CudaArray &other) { width_ = other.width_; height_ = other.height_; pitch_ = other.pitch_; + mem_size_ = other.mem_size_; context_ = other.context_; values_ = nullptr; @@ -1026,7 +1032,7 @@ template CudaArray::CudaArray(const CudaArray &other) { if (other.shared_if_) { this->setShared(other.values_); } else { - CUDA_CALL(cudaMallocPitch(&values_, &pitch_, size_ * sizeof(T), height_)); + CUDA_CALL(cudaMallocPitch(&values_, &pitch_, mem_size_, height_)); this->assign(other); } context_->synchronize(); // better synchronize. Constructing is slow anyway @@ -1063,6 +1069,9 @@ template CudaArray &CudaArray::operator=(CudaArray &&other pitch_ = other.pitch_; other.pitch_ = 0; + mem_size_ = other.mem_size_; + other.mem_size_ = 0; + context_ = other.context_; other.context_ = nullptr; @@ -1076,8 +1085,8 @@ template CudaArray &CudaArray::operator=(CudaArray &&other template void CudaArray::setConst(T set_value) { - DEBUG_OUT( - "Set (hsize,P,W,H): " << size_ << ", " << pitch_ << ", " << width_ * sizeof(T) << ", " + DEBUG_ALL_OUT( + "Set (hsize,P,W,H): " << size_ << ", " << pitch_ << ", " << this->getWidthBytes() << ", " / << height_); if (size_ > 0) { context_->enforceDeviceId(); @@ -1123,6 +1132,20 @@ template void CudaArray::printValues(int nmax) const { delete[] values; } +template void CudaArray::printMatrixValues(int first_size) const { + T *values = new T[size_]; + this->copyTo(values); // will synchronize + int n = size_; + for (int i = 0; i < n; ++i) { + if (i % first_size == 0) { + std::cout << std::endl << "[" << i << "]:\t"; + } + std::cout << values[i] << "\t"; + } + std::cout << std::endl; + delete[] values; +} + template void CudaArray::printNZValues(int nmax) const { T *values = new T[size_]; this->copyTo(values); // will synchronize @@ -1145,6 +1168,9 @@ template <> void CudaArray::printValues(int nmax) const { template <> void CudaArray::printNZValues(int nmax) const { RPU_FATAL("Cannot print curandstates."); } +template <> void CudaArray::printMatrixValues(int first_size) const { + RPU_FATAL("Cannot print curandstates."); +} template <> void CudaArray::printValues(int nmax) const { int8_t *values = new int8_t[size_]; @@ -1176,6 +1202,20 @@ template <> void CudaArray::printNZValues(int nmax) const { delete[] values; } +template <> void CudaArray::printMatrixValues(int first_size) const { + int8_t *values = new int8_t[size_]; + this->copyTo(values); // will synchronize + int n = size_; + for (int i = 0; i < n; ++i) { + if (i % first_size == 0) { + std::cout << std::endl << "[" << i << "]:\t"; + } + std::cout << static_cast(values[i]) << "\t"; + } + std::cout << std::endl; + delete[] values; +} + #ifdef RPU_DEFINE_CUDA_HALF_ARRAY template <> void CudaArray::printValues(int nmax) const { half_t *values = new half_t[size_]; @@ -1207,18 +1247,26 @@ template <> void CudaArray::printNZValues(int nmax) const { delete[] values; } -template <> void CudaArray::printValues(int nmax) const { - RPU_FATAL("Cannot print half_t* values."); -} -template <> void CudaArray::printNZValues(int nmax) const { - RPU_FATAL("Cannot print half_t* values."); +template <> void CudaArray::printMatrixValues(int first_size) const { + half_t *values = new half_t[size_]; + this->copyTo(values); // will synchronize + int n = size_; + for (int i = 0; i < n; ++i) { + if (i % first_size == 0) { + std::cout << std::endl << "[" << i << "]:\t"; + } + std::cout << static_cast(values[i]) << "\t"; + } + std::cout << std::endl; + delete[] values; } + #endif template void CudaArray::assign(const T *host_array) { int sz = size_ * sizeof(T); - DEBUG_OUT( - "Assign host (hsize,P,W,H): " << sz << ", " << pitch_ << ", " << width_ * sizeof(T) << ", " + DEBUG_ALL_OUT( + "Assign host (hsize,P,W,H): " << sz << ", " << pitch_ << ", " << this->getWidthBytes() << ", " << height_); if (size_ > 0) { context_->enforceDeviceId(); @@ -1246,8 +1294,8 @@ void CudaArray::assignTranspose(const T *host_array, const int m, const int n } context_->enforceDeviceId(); int sz = size_ * sizeof(T); - DEBUG_OUT( - "Assign host (hsize,P,W,H): " << sz << ", " << pitch_ << ", " << width_ * sizeof(T) << ", " + DEBUG_ALL_OUT( + "Assign host (hsize,P,W,H): " << sz << ", " << pitch_ << ", " << this->getWidthBytes() << ", " << height_); context_->synchronize(); CUDA_CALL(cudaMemcpy2D( @@ -1256,9 +1304,9 @@ void CudaArray::assignTranspose(const T *host_array, const int m, const int n } template void CudaArray::assign(const CudaArray &source) { - DEBUG_OUT( - "Assign from CudaArray (S,P,W,H): " << size_ << ", " << pitch_ << ", " << width_ * sizeof(T) - << ", " << height_); + DEBUG_ALL_OUT( + "Assign from CudaArray (S,P,W,H): " << size_ << ", " << pitch_ << ", " + << this->getWidthBytes() << ", " << height_); if (source.getSize() != size_) { RPU_FATAL("Assignment of Cuda Array failed. Size mismatch."); } @@ -1266,13 +1314,13 @@ template void CudaArray::assign(const CudaArray &source) { cudaStream_t s = context_->getStream(); context_->synchronizeWith(source.getContext()); CUDA_CALL(cudaMemcpy2DAsync( - values_, pitch_, source.getDataConst(), source.getPitch(), source.getWidthBytes(), 1, + values_, pitch_, source.getDataConst(), pitch_, this->getWidthBytes(), height_, cudaMemcpyDeviceToDevice, s)); } } template void CudaArray::assignFromDevice(const T *device_array) { - DEBUG_OUT( + DEBUG_ALL_OUT( "Assign device (S, P,W,H): " << size_ << ", " << pitch_ << ", " << width_ * sizeof(T) << ", " << height_); if ((size_ > 0)) { @@ -1306,9 +1354,9 @@ template void CudaArray::setShared(T *device_array) { template void CudaArray::copyTo(T *host_array) const { int sz = size_ * sizeof(T); - DEBUG_OUT( - "Copy to host (hsize,P,W,H): " << sz << ", " << pitch_ << ", " << width_ * sizeof(T) << ", " - << height_); + DEBUG_ALL_OUT( + "Copy to host (hsize,P,W,H): " << sz << ", " << pitch_ << ", " << this->getWidthBytes() + << ", " << height_); if (size_ > 0) { context_->enforceDeviceId(); diff --git a/src/rpucuda/cuda/cuda_util.h b/src/rpucuda/cuda/cuda_util.h index f6eeea5b..d8468170 100644 --- a/src/rpucuda/cuda/cuda_util.h +++ b/src/rpucuda/cuda/cuda_util.h @@ -230,8 +230,9 @@ typedef int8_t chop_t; // chopper type #ifdef RPU_PARAM_FP16 typedef half_t param_t; typedef half2_t param2_t; -typedef struct __align__(8) { half_t x, y, z, w; } -param4_t; +typedef struct __align__(8) { + half_t x, y, z, w; +} param4_t; #else typedef float param_t; typedef float2 param2_t; @@ -241,7 +242,7 @@ typedef float4 param4_t; class CublasEnvironment { public: - explicit CublasEnvironment() : CublasEnvironment(-1){}; + explicit CublasEnvironment() : CublasEnvironment(-1) {}; explicit CublasEnvironment(int gpu_id); ~CublasEnvironment(); @@ -274,7 +275,7 @@ typedef CudaContext *CudaContextPtr; class CudaContext : public std::enable_shared_from_this, public Context { public: - explicit CudaContext() : CudaContext(-1){}; + explicit CudaContext() : CudaContext(-1) {}; // NOTE: not tested on gpu_id (does a streams implicitely specifies a GPU id?) explicit CudaContext(int gpu_id, bool non_blocking = true); explicit CudaContext(cudaStream_t shared_stream, int gpu_id = -1); @@ -368,9 +369,9 @@ class CudaContext : public std::enable_shared_from_this, public Con void randUniform(float *dev_array, int size); void setRandomSeed(unsigned long long rseed); - template T *getSharedBuffer(int id, int size); + template T *getSharedBuffer(int id, size_t size); template void releaseSharedBuffer(int id); - template void printSharedBuffer(int id, int size); + template void printSharedBuffer(int id, size_t size); void recordEvent(); void waitEvent(CudaContextPtr wait_on_context); @@ -424,7 +425,7 @@ class CudaContext : public std::enable_shared_from_this, public Con template class CudaArray { public: - CudaArray(){}; + CudaArray() {}; explicit CudaArray(CudaContextPtr c); explicit CudaArray(CudaContextPtr c, int n); explicit CudaArray(CudaContextPtr c, int n, const T *host_array); @@ -447,6 +448,7 @@ template class CudaArray { swap(a.height_, b.height_); swap(a.context_, b.context_); swap(a.shared_if_, b.shared_if_); + swap(a.mem_size_, b.mem_size_); } void assign(const T *host_array); @@ -476,9 +478,8 @@ template class CudaArray { inline T *getData() { return values_; }; const T *getDataConst() const { return values_; }; - int getLD() const { return (((int)this->getPitch()) / sizeof(T)); } - void printValues(int nmax = 0) const; + void printMatrixValues(int first_size) const; void printNZValues(int nmax = 0) const; private: @@ -487,6 +488,7 @@ template class CudaArray { int size_ = 0; size_t pitch_ = 0; int width_ = 0; + int mem_size_ = 0; int height_ = 0; CudaContextPtr context_ = nullptr; diff --git a/src/rpucuda/cuda/forward_backward_pass.h b/src/rpucuda/cuda/forward_backward_pass.h index 95e9d691..c3a191b4 100644 --- a/src/rpucuda/cuda/forward_backward_pass.h +++ b/src/rpucuda/cuda/forward_backward_pass.h @@ -91,7 +91,7 @@ void backwardMatrix( template class MVParameterCuda { public: - MVParameterCuda(){}; + MVParameterCuda() {}; CudaArray out_noise_values; CudaArray v_offset; CudaArray w_asymmetry; @@ -110,7 +110,7 @@ template class MVParameterCuda { template class FBParameterCuda { public: - FBParameterCuda(){}; + FBParameterCuda() {}; MVParameterCuda fwd; MVParameterCuda bwd; @@ -125,8 +125,8 @@ template class ForwardBackwardPassIOManagedCuda { public: explicit ForwardBackwardPassIOManagedCuda(CudaContextPtr context, int x_size, int d_size) - : x_size_(x_size), d_size_(d_size), context_(context){}; - ForwardBackwardPassIOManagedCuda(){}; + : x_size_(x_size), d_size_(d_size), context_(context) {}; + ForwardBackwardPassIOManagedCuda() {}; ~ForwardBackwardPassIOManagedCuda() = default; ForwardBackwardPassIOManagedCuda(const ForwardBackwardPassIOManagedCuda &); @@ -188,6 +188,7 @@ template class ForwardBackwardPassIOManagedCuda { } // init IO + DEBUG_OUT("in_size " << in_size << " batch " << m_batch); f_iom.initWithInput(X_input, f_io, in_size, m_batch, x_trans, alpha, is_test); bool bound_test_passed = false; diff --git a/src/rpucuda/cuda/io_iterator.h b/src/rpucuda/cuda/io_iterator.h index b99992c5..121379ee 100644 --- a/src/rpucuda/cuda/io_iterator.h +++ b/src/rpucuda/cuda/io_iterator.h @@ -12,7 +12,7 @@ namespace RPU { struct BatchSkipper { - explicit BatchSkipper(int skip) : skip_(skip){}; + explicit BatchSkipper(int skip) : skip_(skip) {}; __device__ __forceinline__ int operator()(const int &a) const { return int(a * skip_); } int skip_ = 1; @@ -102,24 +102,31 @@ template class DiagInputIterator { typedef T reference; typedef std::input_iterator_tag iterator_category; - __host__ __device__ __forceinline__ DiagInputIterator(const DataT *data, int dim, int offset) { + __host__ __device__ __forceinline__ + DiagInputIterator(const DataT *data, int dim, int offset, int diag_offset = 0) { data_ = data; dim_ = dim; + dim2_ = dim * dim; offset_ = offset; + diag_offset_ = diag_offset; } __host__ __device__ __forceinline__ T operator[](int idx) const { - int i = idx + offset_; - return (i % dim_ == i / dim_) ? static_cast(data_[idx / dim_]) : static_cast(0); + int i = (idx + offset_) % dim2_; + int j = (diag_offset_ != 0) ? (((idx + offset_ + diag_offset_ * dim_) % dim2_) / dim_) % dim_ + : (i / dim_) % dim_; + + return (i % dim_ == i / dim_) ? static_cast(data_[j]) : static_cast(0); } __host__ __device__ __forceinline__ self_type operator+(int shift_n) const { - self_type retval(data_, dim_, shift_n + offset_); + self_type retval(data_, dim_, shift_n + offset_, diag_offset_); return retval; } const DataT *data_; - int dim_; + int dim_, dim2_; int offset_; + int diag_offset_; }; template class EyeInputIterator { diff --git a/src/rpucuda/cuda/io_iterator_test.cpp b/src/rpucuda/cuda/io_iterator_test.cpp index 064f02e5..d0476547 100644 --- a/src/rpucuda/cuda/io_iterator_test.cpp +++ b/src/rpucuda/cuda/io_iterator_test.cpp @@ -11,6 +11,7 @@ #include "gtest/gtest.h" #include #include +#include #include #include #include @@ -130,6 +131,225 @@ TYPED_TEST(IteratorTestFixture, copyWithIteratorNoIterator) { CUDA_TIMING_DESTROY; } +TYPED_TEST(IteratorTestFixture, IndicatorInputIterator) { + CUDA_TIMING_INIT; + + TypeParam scale = (TypeParam)2; + IndicatorInputIterator in_iter( + this->dev_orig_vector->getDataConst(), this->orig_vector[this->N], scale); + this->context->synchronizeDevice(); + + CUDA_TIMING_START(this->context); + math::copyWithIterator( + this->context, this->dev_unfolded_vector->getData(), in_iter, + this->orig_matrix_size * this->N); + + CUDA_TIMING_STOP(this->context, "Copy with IndicatorInputIterator"); + + this->dev_unfolded_vector->copyTo(this->unfolded_vector); + + // compare to reference + for (int i = 0; i < this->orig_matrix_size * this->N; i++) { + ASSERT_FLOAT_EQ( + this->unfolded_vector[i], + (TypeParam)(this->orig_vector[this->N] == this->orig_vector[i]) * scale); + } + + CUDA_TIMING_DESTROY; +} + +TYPED_TEST(IteratorTestFixture, NegateInputIterator) { + CUDA_TIMING_INIT; + + NegateInputIterator in_iter(this->dev_orig_vector->getDataConst()); + this->context->synchronizeDevice(); + + CUDA_TIMING_START(this->context); + math::copyWithIterator( + this->context, this->dev_unfolded_vector->getData(), in_iter, + this->orig_matrix_size * this->N); + + CUDA_TIMING_STOP(this->context, "Copy with NegateInputIterator"); + + this->dev_unfolded_vector->copyTo(this->unfolded_vector); + + // compare to reference + for (int i = 0; i < this->orig_matrix_size * this->N; i++) { + ASSERT_FLOAT_EQ(this->unfolded_vector[i], -this->orig_vector[i]); + } + + CUDA_TIMING_DESTROY; +} + +TYPED_TEST(IteratorTestFixture, LogInputIterator) { + CUDA_TIMING_INIT; + + LogInputIterator in_iter(this->dev_orig_vector->getDataConst()); + this->context->synchronizeDevice(); + + CUDA_TIMING_START(this->context); + math::copyWithIterator( + this->context, this->dev_unfolded_vector->getData(), in_iter, + this->orig_matrix_size * this->N); + + CUDA_TIMING_STOP(this->context, "Copy with LogInputIterator"); + + this->dev_unfolded_vector->copyTo(this->unfolded_vector); + + // compare to reference + for (int i = 0; i < this->orig_matrix_size * this->N; i++) { + ASSERT_NEAR(this->unfolded_vector[i], (float)std::log((float)this->orig_vector[i]), 1e-5); + } + + CUDA_TIMING_DESTROY; +} + +TYPED_TEST(IteratorTestFixture, DiagInputIterator) { + CUDA_TIMING_INIT; + + DiagInputIterator in_iter( + this->dev_orig_vector->getDataConst(), this->orig_matrix_size, (int)0); + this->context->synchronizeDevice(); + + CUDA_TIMING_START(this->context); + math::copyWithIterator( + this->context, this->dev_unfolded_vector->getData(), in_iter, + this->orig_matrix_size * this->N); + + CUDA_TIMING_STOP(this->context, "Copy with DiagInputIterator"); + + this->dev_unfolded_vector->copyTo(this->unfolded_vector); + + // compare to reference + int j = 0; + for (int i = 0; i < this->orig_matrix_size * this->N; i++) { + if (i % (this->orig_matrix_size + 1) == 0) { + ASSERT_FLOAT_EQ(this->unfolded_vector[i], this->orig_vector[j++]); + } else { + ASSERT_FLOAT_EQ(this->unfolded_vector[i], (TypeParam)0.0); + }; + }; + + CUDA_TIMING_DESTROY; +} + +TYPED_TEST(IteratorTestFixture, DiagInputIteratorOffset) { + CUDA_TIMING_INIT; + + const int offset = 10; + DiagInputIterator in_iter( + this->dev_orig_vector->getDataConst(), this->orig_matrix_size, offset); + this->context->synchronizeDevice(); + + CUDA_TIMING_START(this->context); + math::copyWithIterator( + this->context, this->dev_unfolded_vector->getData(), in_iter, + this->orig_matrix_size * this->N - offset); + + CUDA_TIMING_STOP(this->context, "Copy with DiagInputIterator w/Offset"); + + this->dev_unfolded_vector->copyTo(this->unfolded_vector); + + // compare to reference + + for (int i = offset; i < this->orig_matrix_size * this->N; i++) { + if (i % (this->orig_matrix_size + 1) == 0) { + ASSERT_FLOAT_EQ( + this->unfolded_vector[i - offset], this->orig_vector[i / (this->orig_matrix_size + 1)]); + } else { + ASSERT_FLOAT_EQ(this->unfolded_vector[i - offset], (TypeParam)0.0); + } + } + + CUDA_TIMING_DESTROY; +} + +TYPED_TEST(IteratorTestFixture, DiagInputIteratorDiagOffset) { + CUDA_TIMING_INIT; + + const int diag_offset = 2; + DiagInputIterator in_iter( + this->dev_orig_vector->getDataConst(), this->N, 0, diag_offset); + this->context->synchronizeDevice(); + + CUDA_TIMING_START(this->context); + math::copyWithIterator( + this->context, this->dev_unfolded_vector->getData(), in_iter, + this->orig_matrix_size * this->N); + + CUDA_TIMING_STOP(this->context, "Copy with DiagInputIterator w/DiagOffset"); + + this->dev_unfolded_vector->copyTo(this->unfolded_vector); + + // compare to reference + int j = 0; + for (int i = 0; i < this->orig_matrix_size * this->N; i++) { + if ((i % (this->N * this->N)) % (this->N + 1) == 0) { + std::cout << i << std::endl; + ASSERT_FLOAT_EQ(this->unfolded_vector[i], this->orig_vector[(diag_offset + j++) % this->N]); + } else { + ASSERT_FLOAT_EQ(this->unfolded_vector[i], (TypeParam)0.0); + } + } + + CUDA_TIMING_DESTROY; +} + +TYPED_TEST(IteratorTestFixture, EyeInputIterator) { + CUDA_TIMING_INIT; + + EyeInputIterator in_iter(this->orig_matrix_size, (int)0); + this->context->synchronizeDevice(); + + CUDA_TIMING_START(this->context); + math::copyWithIterator( + this->context, this->dev_unfolded_vector->getData(), in_iter, + this->orig_matrix_size * this->N); + + CUDA_TIMING_STOP(this->context, "Copy with EyeInputIterator"); + + this->dev_unfolded_vector->copyTo(this->unfolded_vector); + + // compare to reference + for (int i = 0; i < this->orig_matrix_size * this->N; i++) { + if (i % (this->orig_matrix_size + 1) == 0) { + ASSERT_FLOAT_EQ(this->unfolded_vector[i], (TypeParam)1.0); + } else { + ASSERT_FLOAT_EQ(this->unfolded_vector[i], (TypeParam)0.0); + }; + } + + CUDA_TIMING_DESTROY; +} + +TYPED_TEST(IteratorTestFixture, EyeInputIteratorOffset) { + CUDA_TIMING_INIT; + + int offset = 10; + EyeInputIterator in_iter(this->orig_matrix_size, offset); + this->context->synchronizeDevice(); + + CUDA_TIMING_START(this->context); + math::copyWithIterator( + this->context, this->dev_unfolded_vector->getData(), in_iter, + this->orig_matrix_size * this->N - offset); + + CUDA_TIMING_STOP(this->context, "Copy with EyeInputIterator w/Offset"); + + this->dev_unfolded_vector->copyTo(this->unfolded_vector); + + // compare to reference + for (int i = offset; i < this->orig_matrix_size * this->N; i++) { + if (i % (this->orig_matrix_size + 1) == 0) { + ASSERT_FLOAT_EQ(this->unfolded_vector[i - offset], (TypeParam)1.0); + } else { + ASSERT_FLOAT_EQ(this->unfolded_vector[i - offset], (TypeParam)0.0); + }; + } + + CUDA_TIMING_DESTROY; +} + TYPED_TEST(IteratorTestFixture, IndexReaderInputIterator) { CUDA_TIMING_INIT; diff --git a/src/rpucuda/cuda/io_manager.cu b/src/rpucuda/cuda/io_manager.cu index d402c392..b3505c9c 100644 --- a/src/rpucuda/cuda/io_manager.cu +++ b/src/rpucuda/cuda/io_manager.cu @@ -81,10 +81,14 @@ // if LOCAL_NM_SCALE is zero no need to scale up, since value is zero #define APPLY_INPUT_NOISE_MANAGMENT(LOCAL_NM_SCALE) \ - { value = LOCAL_NM_SCALE > (T)0.0 ? value / LOCAL_NM_SCALE : value; } + { \ + value = LOCAL_NM_SCALE > (T)0.0 ? value / LOCAL_NM_SCALE : value; \ + } #define APPLY_OUTPUT_NOISE_MANAGMENT(LOCAL_NM_SCALE) \ - { value = (LOCAL_NM_SCALE > (T)0.0) ? value * LOCAL_NM_SCALE : (T)0.0; } + { \ + value = (LOCAL_NM_SCALE > (T)0.0) ? value * LOCAL_NM_SCALE : (T)0.0; \ + } namespace RPU { @@ -260,17 +264,18 @@ __global__ void kernelInputBoundManagement( local_scale *= bms; - STRIDE_LOOP(size, value, + STRIDE_LOOP( + size, value, - APPLY_INPUT_NOISE_MANAGMENT(local_scale); + APPLY_INPUT_NOISE_MANAGMENT(local_scale); - DISCRETIZE_VALUE_STOCH; + DISCRETIZE_VALUE_STOCH; - ADD_NOISE; + ADD_NOISE; - BOUND_CHECK; + BOUND_CHECK; - APPLY_ASYMMETRY; + APPLY_ASYMMETRY; ); @@ -351,20 +356,21 @@ __global__ void kernelInputBoundManagementBatch( STOCH_DEFINITIONS(stoch_if, total_size); - STRIDE_LOOP(total_size, value, + STRIDE_LOOP( + total_size, value, - int sidx = trans ? (idx % m_batch) : (idx / size); - T svalue = scale_values[sidx]; + int sidx = trans ? (idx % m_batch) : (idx / size); + T svalue = scale_values[sidx]; - APPLY_INPUT_NOISE_MANAGMENT(svalue); + APPLY_INPUT_NOISE_MANAGMENT(svalue); - DISCRETIZE_VALUE_STOCH; + DISCRETIZE_VALUE_STOCH; - ADD_NOISE; + ADD_NOISE; - BOUND_CHECK; + BOUND_CHECK; - APPLY_ASYMMETRY;); + APPLY_ASYMMETRY;); STOCH_FINALIZE(stoch_if); } @@ -639,6 +645,7 @@ template void InputOutputManager::initializeBatchBuffer(int m_ba dev_scale_values_ = RPU::make_unique>(context_, m_batch); dev_bound_exceeded_ = RPU::make_unique>(context_, m_batch); + context_->synchronizeDevice(); } } @@ -679,7 +686,7 @@ void InputOutputManager::initWithInput( temp_out_scale_ = add_out_scale * io.out_scale; temp_is_test_ = is_test; temp_in_size_ = in_size; - + DEBUG_OUT("Init with in size " << in_size << " and batch " << m_batch); // in_size can be changed momentarily but only when noise management etc. is turned off. if (in_size != in_size_ && (io.noise_management != NoiseManagementType::None || io.bound_management != BoundManagementType::None)) { @@ -691,8 +698,9 @@ void InputOutputManager::initWithInput( this->initializeBatchBuffer(m_batch); } temp_input_applied_ = - context_->template getSharedBuffer(RPU_BUFFER_IN, m_batch * temp_in_size_); - temp_output_applied_ = context_->template getSharedBuffer(RPU_BUFFER_OUT, m_batch * out_size_); + context_->template getSharedBuffer(RPU_BUFFER_IN, (size_t)m_batch * (size_t)temp_in_size_); + temp_output_applied_ = + context_->template getSharedBuffer(RPU_BUFFER_OUT, (size_t)m_batch * (size_t)out_size_); // noise management this->noise_manager_->compute(dev_input, io.noise_management, io, m_batch, input_trans, is_test); diff --git a/src/rpucuda/cuda/maximizer.cu b/src/rpucuda/cuda/maximizer.cu index 442580c9..3c2e09a1 100644 --- a/src/rpucuda/cuda/maximizer.cu +++ b/src/rpucuda/cuda/maximizer.cu @@ -24,54 +24,51 @@ namespace { template __forceinline__ __device__ T atomicMaxFP(T *addr, T value); template <> __forceinline__ __device__ float atomicMaxFP(float *addr, float value) { - float old = *addr, assumed; - if (old >= value) - return old; + int *address_as_i = (int *)addr; + int old = *address_as_i, assumed; do { assumed = old; - old = __int_as_float(atomicCAS((int *)addr, __float_as_int(assumed), __float_as_int(value))); - } while (old != assumed || old < value); - return old; + old = + ::atomicCAS(address_as_i, assumed, __float_as_int(::fmaxf(value, __int_as_float(assumed)))); + } while (assumed != old); + return __int_as_float(old); } #ifdef RPU_USE_DOUBLE template <> __forceinline__ __device__ double atomicMaxFP(double *addr, double value) { - double old = *addr, assumed; - if (old >= value) - return old; + longlong *address_as_i = (longlong *)addr; + longlong old = *address_as_i, assumed; do { assumed = old; - old = __longlong_as_double(atomicCAS( - (long long int *)addr, __double_as_longlong(assumed), __double_as_longlong(value))); - } while (old != assumed || old < value); - return old; + old = ::atomicCAS( + address_as_i, assumed, __double_as_longlong(::fmaxf(value, __longlong_as_double(assumed)))); + } while (assumed != old); + return __longlong_as_double(old); } #endif #ifdef RPU_USE_FP16 #ifdef RPU_BFLOAT_AS_FP16 template <> __forceinline__ __device__ half_t atomicMaxFP(half_t *addr, half_t value) { - half_t old = *addr, assumed; - if (old >= value) - return old; + short *address_as_i = (short *)addr; + short old = *address_as_i, assumed; do { assumed = old; - old = __short_as_bfloat16(atomicCAS( - (unsigned short *)addr, __bfloat16_as_short(assumed), __bfloat16_as_short(value))); - } while (old != assumed || old < value); - return old; + old = ::atomicCAS( + address_as_i, assumed, __bfloat16_as_short(::fmaxf(value, __short_as_bfloat16(assumed)))); + } while (assumed != old); + return __short_as_bfloat16(old); } #else template <> __forceinline__ __device__ half_t atomicMaxFP(half_t *addr, half_t value) { - half_t old = *addr, assumed; - if (old >= value) - return old; + short *address_as_i = (short *)addr; + short old = *address_as_i, assumed; do { assumed = old; - old = __short_as_half( - atomicCAS((unsigned short *)addr, __half_as_short(assumed), __half_as_short(value))); - } while (old != assumed || old < value); - return old; + old = ::atomicCAS( + address_as_i, assumed, __half_as_short(::fmaxf(value, __short_as_half(assumed)))); + } while (assumed != old); + return __short_as_half(old); } #endif #endif diff --git a/src/rpucuda/cuda/pwu_kernel.h b/src/rpucuda/cuda/pwu_kernel.h index 5879198a..9e1271de 100644 --- a/src/rpucuda/cuda/pwu_kernel.h +++ b/src/rpucuda/cuda/pwu_kernel.h @@ -84,7 +84,7 @@ getIdxToLoad(int batch_index, int count_index, int sz, int m_batch, int c #define DEFINE_GETNFROMCOUNT32(ONE_SIDED, OS_ADD) \ template <> \ __device__ __forceinline__ void getNfromCount( \ - uint32_t & n, uint32_t & negative, bool &mixed, uint32_t *x_ptr, uint32_t *d_ptr, int nK32, \ + uint32_t &n, uint32_t &negative, bool &mixed, uint32_t *x_ptr, uint32_t *d_ptr, int nK32, \ int shared_x_offset, int shared_d_offset, bool enforce_mixed) { \ uint32_t x = *x_ptr; \ uint32_t d = *d_ptr; \ @@ -97,7 +97,7 @@ getIdxToLoad(int batch_index, int count_index, int sz, int m_batch, int c \ uint32_t x_and_d = x & d; \ n = __popc(x_and_d); \ - n -= ((x_and_d)&1); \ + n -= ((x_and_d) & 1); \ \ if (nK32 > 1) { \ int i_d = 0; \ @@ -129,7 +129,7 @@ DEFINE_GETNFROMCOUNT32( #define DEFINE_GETNFROMCOUNTFP(FPTYPE, ONE_SIDED, OS_ADD) \ template <> \ __device__ __forceinline__ void getNfromCount( \ - uint32_t & n, uint32_t & negative, bool &mixed, FPTYPE *x_ptr, FPTYPE *d_ptr, int nK32, \ + uint32_t &n, uint32_t &negative, bool &mixed, FPTYPE *x_ptr, FPTYPE *d_ptr, int nK32, \ int shared_x_offset, int shared_d_offset, bool enforce_mixed) { \ FPTYPE x = *x_ptr; \ FPTYPE d = *d_ptr; \ @@ -197,7 +197,7 @@ DEFINE_GETNFROMCOUNTFP( #define DEFINE_GETNFROMCOUNT64(ONE_SIDED, OS_ADD) \ template <> \ __device__ __forceinline__ void getNfromCount( \ - uint32_t & n, uint32_t & negative, bool &mixed, uint64_t *x_ptr, uint64_t *d_ptr, int nK32, \ + uint32_t &n, uint32_t &negative, bool &mixed, uint64_t *x_ptr, uint64_t *d_ptr, int nK32, \ int shared_x_offset, int shared_d_offset, bool enforce_mixed) { \ /* -- nK32 is ignored (assumed 1). larger K will be in put into the batch order*/ \ /* -- this is the bit-wise negative version */ \ @@ -445,8 +445,8 @@ __global__ void kernelUpdateWFunctor( } sum_n = 0; \ last_negative = 0; \ \ - int pos_n = __popc((~negative) & n); int neg_n = __popc((negative)&n); T dw_pos = (T)pos_n; \ - T dw_neg = (T)neg_n; \ + int pos_n = __popc((~negative) & n); int neg_n = __popc((negative) & n); \ + T dw_pos = (T)pos_n; T dw_neg = (T)neg_n; \ \ if (noise_std_dw > (T)0.0) { \ if (pos_n > 0) { \ @@ -931,7 +931,7 @@ __global__ void kernelUpdateWBatchSharedSum( } } // within range - } // batch strides + } // batch strides if (within_range) { weights[idx] = w; } @@ -1004,7 +1004,7 @@ __global__ void kernelUpdateWBatchSharedSumBoundCheck( } } // within range - } // batch strides + } // batch strides if (within_range) { weights[idx] = w; @@ -1105,7 +1105,7 @@ __global__ void kernelUpdateWBatchSharedFunctor( } // batch } // within range - } // batch strides + } // batch strides if (within_range) { weights[idx] = w; @@ -1221,11 +1221,13 @@ __device__ __forceinline__ int getWeightOutputIdx( // is used if (wo_column) { + // out size is d-size int val_wo = (val_start + i_weight_output) % x_size; return d_index + d_size * (val_wo + i_weight_output / x_size * x_size); } else { + // out size is x-size int val_wo = (val_start + i_weight_output) % d_size; - return val_wo + d_size * x_index + i_weight_output / d_size * d_size * x_size; + return val_wo + d_size * (x_index + i_weight_output / d_size * x_size); } } } @@ -1296,11 +1298,13 @@ __device__ __forceinline__ void updateChopper( x_index, d_index, i_weight_output, wo_column, xsz, dsz, n_wo, wo_val_start, \ wo_flexible_in_size); \ weight_output[wo_idx] = w; \ - weight_output_out_chopper[wo_idx] = wo_column ? d_chop : x_chop; \ - if (0 == (wo_column ? d_index : x_index)) { \ + int out_index = (wo_column ? d_index : x_index); \ + int out_size = (wo_column ? dsz : xsz); \ + weight_output_out_chopper[out_index + i_weight_output * out_size] = \ + wo_column ? d_chop : x_chop; \ + if (0 == out_index) { \ weight_output_in_chopper[i_weight_output] = wo_column ? x_chop : d_chop; \ } \ - /* //printf("X %d, D %d, B %d: WO\n", x_index, d_index, current_batch); */ \ } \ updateChopper( \ current_chop_neg, i_weight_output, x_chop, d_chop, x_switching_probs, d_switching_probs, \ @@ -1499,7 +1503,7 @@ __global__ void kernelUpdateWBatchSharedWeightOutputFunctor( } // batch } // within range - } // batch strides + } // batch strides if (within_range) { weights[idx] = w; diff --git a/src/rpucuda/cuda/pwu_kernel_parameter.h b/src/rpucuda/cuda/pwu_kernel_parameter.h index ccc78e00..d540c6d0 100644 --- a/src/rpucuda/cuda/pwu_kernel_parameter.h +++ b/src/rpucuda/cuda/pwu_kernel_parameter.h @@ -217,14 +217,14 @@ DEFINE_PWU_KERNEL_BASE( /******************************************************************************** * PWUKernelParameterBatchBaseInf // no limit on size *********************************************************************************/ -DEFINE_PWU_KERNEL_BASE(BatchBaseInf, - /*ctor*/ - this->nthreads = MIN(RPU_THREADS_PER_BLOCK_UPDATE, this->size); - this->nthreads = (this->nthreads + 31) / 32 * 32; - this->nblocks = - MIN(this->max_block_count, - construction_context->getNBlocks(this->size, this->nthreads)); - this->nstates = this->nthreads * this->nblocks;); +DEFINE_PWU_KERNEL_BASE( + BatchBaseInf, + /*ctor*/ + this->nthreads = MIN(RPU_THREADS_PER_BLOCK_UPDATE, this->size); + this->nthreads = (this->nthreads + 31) / 32 * 32; + this->nblocks = + MIN(this->max_block_count, construction_context->getNBlocks(this->size, this->nthreads)); + this->nstates = this->nthreads * this->nblocks;); /******************************************************************************** * PWUKernelParameterBatchFunctor @@ -294,16 +294,18 @@ DEFINE_PWU_KERNEL_PARAMETER( } template -DEFINE_PWU_KERNEL_PARAMETER(BatchSum, - BatchBase, - /*run*/ - RPU_PWU_START_BATCH_KERNEL(kernelUpdateWBatchSum);); +DEFINE_PWU_KERNEL_PARAMETER( + BatchSum, + BatchBase, + /*run*/ + RPU_PWU_START_BATCH_KERNEL(kernelUpdateWBatchSum);); template -DEFINE_PWU_KERNEL_PARAMETER(BatchSumBoundCheck, - BatchBase, - /*run*/ - RPU_PWU_START_BATCH_KERNEL(kernelUpdateWBatchSumBoundCheck);); +DEFINE_PWU_KERNEL_PARAMETER( + BatchSumBoundCheck, + BatchBase, + /*run*/ + RPU_PWU_START_BATCH_KERNEL(kernelUpdateWBatchSumBoundCheck);); #undef RPU_PWU_START_BATCH_KERNEL @@ -411,10 +413,11 @@ DEFINE_PWU_KERNEL_PARAMETER( } template -DEFINE_PWU_KERNEL_PARAMETER(BatchSharedSum, - BatchSharedBase, - /*run*/ - RPU_PWU_START_BATCH_SHARED_KERNEL(kernelUpdateWBatchSharedSum);); +DEFINE_PWU_KERNEL_PARAMETER( + BatchSharedSum, + BatchSharedBase, + /*run*/ + RPU_PWU_START_BATCH_SHARED_KERNEL(kernelUpdateWBatchSharedSum);); template DEFINE_PWU_KERNEL_PARAMETER( @@ -590,10 +593,11 @@ DEFINE_PWU_KERNEL_PARAMETER( } template -DEFINE_PWU_KERNEL_PARAMETER(PulseCounter, - BatchBaseInf, - /*run*/ - RPU_PWU_COUNTER_KERNEL;); +DEFINE_PWU_KERNEL_PARAMETER( + PulseCounter, + BatchBaseInf, + /*run*/ + RPU_PWU_COUNTER_KERNEL;); #undef RPU_PWU_COUNTER_KERNEL diff --git a/src/rpucuda/cuda/pwu_kernel_parameter_base.h b/src/rpucuda/cuda/pwu_kernel_parameter_base.h index 268be129..01e9564d 100644 --- a/src/rpucuda/cuda/pwu_kernel_parameter_base.h +++ b/src/rpucuda/cuda/pwu_kernel_parameter_base.h @@ -19,7 +19,7 @@ template class ChoppedWeightOutput; template class PWUKernelParameterBase { public: - PWUKernelParameterBase(){}; // default + PWUKernelParameterBase() {}; // default PWUKernelParameterBase( CudaContextPtr construction_context, int x_size_in, @@ -121,7 +121,7 @@ template class PWUKernelParameterBase { if (this->use_bo64 == 1) { this->use_bo64 = 2; } - }; // debug hack + }; // debug hack inline void forceNonTrans() { this->out_trans = false; }; // debug hack inline void force32() { this->use_bo64 = 0; }; // debug hack diff --git a/src/rpucuda/cuda/rpucuda.h b/src/rpucuda/cuda/rpucuda.h index 0d572b13..bca8f419 100644 --- a/src/rpucuda/cuda/rpucuda.h +++ b/src/rpucuda/cuda/rpucuda.h @@ -24,7 +24,7 @@ namespace RPU { template class RPUCudaSimple : public RPUSimple { public: - RPUCudaSimple(){}; + RPUCudaSimple() {}; explicit RPUCudaSimple(CudaContextPtr c, int x_size, int d_size); explicit RPUCudaSimple(CudaContextPtr c, RPUSimple &o); explicit RPUCudaSimple(cudaStream_t s, int x_size, int d_size); diff --git a/src/rpucuda/cuda/rpucuda_buffered_transfer_device.h b/src/rpucuda/cuda/rpucuda_buffered_transfer_device.h index aebbeaee..19392c5e 100644 --- a/src/rpucuda/cuda/rpucuda_buffered_transfer_device.h +++ b/src/rpucuda/cuda/rpucuda_buffered_transfer_device.h @@ -16,11 +16,11 @@ namespace RPU { template class BufferedTransferRPUDeviceCuda : public TransferRPUDeviceCuda { public: - explicit BufferedTransferRPUDeviceCuda(){}; + explicit BufferedTransferRPUDeviceCuda() {}; explicit BufferedTransferRPUDeviceCuda( CudaContextPtr c, const BufferedTransferRPUDevice &other); - ~BufferedTransferRPUDeviceCuda(){}; + ~BufferedTransferRPUDeviceCuda() {}; BufferedTransferRPUDeviceCuda(const BufferedTransferRPUDeviceCuda &other); BufferedTransferRPUDeviceCuda &operator=(const BufferedTransferRPUDeviceCuda &other); BufferedTransferRPUDeviceCuda(BufferedTransferRPUDeviceCuda &&other); diff --git a/src/rpucuda/cuda/rpucuda_chopped_transfer_device.cu b/src/rpucuda/cuda/rpucuda_chopped_transfer_device.cu index b609ede3..bfb62500 100644 --- a/src/rpucuda/cuda/rpucuda_chopped_transfer_device.cu +++ b/src/rpucuda/cuda/rpucuda_chopped_transfer_device.cu @@ -106,7 +106,12 @@ void ChoppedTransferRPUDeviceCuda::populateFrom(const AbstractRPUDevice &r cwo_par.in_chop_random = par.in_chop_random; cwo_->setPar(cwo_par); cwo_->setCounter(this->current_update_idx_); - cwo_->setFlexibleInSize(this->transfer_fb_pass_->checkFlexibleInSize(par.transfer_io)); + + if (par.transfer_flexible_insize) { + cwo_->setFlexibleInSize(this->transfer_fb_pass_->checkFlexibleInSize(par.transfer_io)); + } else { + cwo_->setFlexibleInSize(false); + } if (par.usesAutoTransferEvery()) { if (par.units_in_mbatch) { @@ -158,7 +163,7 @@ int ChoppedTransferRPUDeviceCuda::getTransferEvery( template void ChoppedTransferRPUDeviceCuda::readMatrix( - int device_idx, const T *in_vec, T *out_vec, int m_batch, T alpha) { + int device_idx, const T *in_vec, T *out_vec, int n_vec, T alpha) { const auto &par = getPar(); if (device_idx != 0) { @@ -168,40 +173,52 @@ void ChoppedTransferRPUDeviceCuda::readMatrix( if (in_vec != nullptr) { RPU_FATAL("only one-hot transfer vectors supported."); } - if (m_batch != cwo_->getNumWeightOutputs()) { - RPU_FATAL("m_batch mismatch!"); + if (n_vec != cwo_->getNumWeightOutputs()) { + RPU_FATAL("n_vec mismatch!"); } - if (m_batch == 0) { + if (n_vec == 0) { return; } bool in_size_flexible = this->cwo_->getFlexibleInSize(); + DEBUG_CALL(cwo_->print()); if (in_size_flexible) { // in case no input dependence, we can read out in one go + DEBUG_CALL(cwo_->printWeightOutputInChopper()); T *output_weights = cwo_->getWeightOutputData(); chop_t *wo_chopper_data = cwo_->getWeightOutputInChopperData(); - DiagInputIterator diag_iter(wo_chopper_data, m_batch, 0); - - if (par.transfer_columns) { - this->transfer_fb_pass_->forwardMatrixIterator( - output_weights, diag_iter, m_batch, false, out_vec, this->d_size_, false, m_batch, alpha, - *this->transfer_iom_, par.transfer_io, false); - - } else { - // backward with transfer vectors. - this->transfer_fb_pass_->backwardMatrixIterator( - output_weights, diag_iter, m_batch, false, out_vec, this->x_size_, false, m_batch, alpha, - *this->transfer_iom_, par.transfer_io); + size_t max_n_vec_per_chunk = (par.transfer_max_vec_chunk_size + n_vec - 1) / n_vec; + size_t n_chunks = (n_vec + max_n_vec_per_chunk - 1) / max_n_vec_per_chunk; + + for (int i_chunk = 0; i_chunk < n_chunks; i_chunk++) { + + size_t n_done_vec = i_chunk * max_n_vec_per_chunk; + size_t offset = n_done_vec * n_vec; + + DiagInputIterator diag_iter(wo_chopper_data, n_vec, offset); + size_t m_chunk = i_chunk == n_chunks - 1 ? n_vec - n_done_vec : max_n_vec_per_chunk; + + if (par.transfer_columns) { + this->transfer_fb_pass_->forwardMatrixIterator( + output_weights, diag_iter, n_vec, false, out_vec + n_done_vec * this->d_size_, + this->d_size_, false, m_chunk, alpha, *this->transfer_iom_, par.transfer_io, false); + + } else { + // backward with transfer vectors. + this->transfer_fb_pass_->backwardMatrixIterator( + output_weights, diag_iter, n_vec, false, out_vec + n_done_vec * this->x_size_, + this->x_size_, false, m_chunk, alpha, *this->transfer_iom_, par.transfer_io); + } } } else { int out_size = cwo_->getOutSize(); int in_size = cwo_->getInSize(); - int n_pass = m_batch / in_size + 1; + int n_pass = (n_vec + in_size - 1) / in_size; int size = in_size * out_size; for (int i_pass = 0; i_pass < n_pass; i_pass++) { // we potentially need to run multiple passes in case a @@ -211,26 +228,52 @@ void ChoppedTransferRPUDeviceCuda::readMatrix( // NOTE: the non-read out weights might be arbitrary value. Should // not matter though as input is 0 for those. + DEBUG_OUT("Chopper output: "); + DEBUG_CALL(cwo_->printWeightOutputInChopper()); int wo_offset = i_pass * size; - chop_t *wo_chopper_data = cwo_->getWeightOutputInChopperData() + i_pass * in_size; DiagInputIterator diag_iter( - wo_chopper_data, in_size, cwo_->getValStart() * in_size); + wo_chopper_data, in_size, cwo_->getValStart() * in_size, -cwo_->getValStart()); T *output_weights = cwo_->getWeightOutputData() + wo_offset; - int sub_m_batch = (i_pass < n_pass - 1) ? in_size : (m_batch - in_size * (n_pass - 1)); + + DEBUG_OUT("Iterator content: "); + DEBUG_CALL( + CudaArray dev_a(this->context_, in_size * in_size); + math::copyWithIterator(this->context_, dev_a.getData(), diag_iter, in_size * in_size); + this->context_->synchronize(); dev_a.printMatrixValues(in_size);); + + DEBUG_OUT("Weight output content: "); + DEBUG_CALL( + CudaArray dev_a(this->context_, in_size * out_size); math::copyWithIterator( + this->context_, dev_a.getData(), output_weights, in_size * out_size); + this->context_->synchronize(); dev_a.printMatrixValues(this->d_size_);); + + int sub_n_vec = (i_pass < n_pass - 1) ? in_size : (n_vec - in_size * i_pass); + + DEBUG_OUT("sub_n_vec: " << sub_n_vec); + DEBUG_OUT("n_pass: " << n_pass); + DEBUG_OUT("i_pass: " << i_pass); + DEBUG_OUT("in_size: " << in_size); + DEBUG_OUT("n_vec: " << n_vec); if (par.transfer_columns) { this->transfer_fb_pass_->forwardMatrixIterator( - output_weights, diag_iter, this->x_size_, false, out_vec + wo_offset, this->d_size_, - false, sub_m_batch, alpha, *this->transfer_iom_, par.transfer_io, false); + output_weights, diag_iter, in_size, false, out_vec + wo_offset, out_size, false, + sub_n_vec, alpha, *this->transfer_iom_, par.transfer_io, false); } else { // backward with transfer vectors. this->transfer_fb_pass_->backwardMatrixIterator( - output_weights, diag_iter, this->d_size_, false, out_vec + wo_offset, this->x_size_, - false, sub_m_batch, alpha, *this->transfer_iom_, par.transfer_io); + output_weights, diag_iter, in_size, false, out_vec + wo_offset, out_size, false, + sub_n_vec, alpha, *this->transfer_iom_, par.transfer_io); } + + DEBUG_OUT("Transfer output content: "); + DEBUG_CALL( + CudaArray dev_a(this->context_, in_size * out_size); math::copyWithIterator( + this->context_, dev_a.getData(), out_vec + wo_offset, in_size * out_size); + this->context_->synchronize(); dev_a.printMatrixValues(out_size);); } } } @@ -277,6 +320,12 @@ void ChoppedTransferRPUDeviceCuda::writeMatrix( out_vec, eye_iter, W, &*this->rpucuda_device_vec_[device_idx], up, fabsf(lr), m_batch, false, false); } + + DEBUG_OUT("Updated W out: "); + DEBUG_CALL( + CudaArray dev_a(this->context_, this->x_size_ * this->d_size_); + math::copyWithIterator(this->context_, dev_a.getData(), W, this->x_size_ * this->d_size_); + this->context_->synchronize(); dev_a.printMatrixValues(this->d_size_);); } /*********************************************************************************/ @@ -288,12 +337,12 @@ template __global__ void kernelChoppedTransfer( T *transfer_out, T *W_buffer, - const T *transfer_in, - const chop_t *in_chopper, // size n_wo NOTE: is already applied to transfer_in - const chop_t *out_chopper, // size n_wo * out_size + const T *transfer_in, // size: out_size * n_wo [starts with start_read_idx] + const chop_t *in_chopper, // size: n_wo NOTE: is already applied to transfer_in + const chop_t *out_chopper, // size: n_wo * out_size const int out_size, const int in_size, - const int m_batch, + const int n_vec, const int start_read_idx, const T lr_scale_in, const T sub_momentum, @@ -303,7 +352,7 @@ __global__ void kernelChoppedTransfer( const T max_steps = (T)max_steps_in; const int w_size = out_size * in_size; - const int t_size = out_size * m_batch; + const int t_size = out_size * n_vec; const T momentum = -sub_momentum + (T)1.0; const bool forget_buffer = forget_buffer_in; const T lr_scale = lr_scale_in; @@ -311,7 +360,7 @@ __global__ void kernelChoppedTransfer( // CAUTION: n_vec might have mulitple wraps around in_size, we need // to thus make sure that the same threads are working on the same // repeat. - int n_repeats = (m_batch + in_size - 1) / in_size; + int n_repeats = (n_vec + in_size - 1) / in_size; RPU_CUDA_1D_KERNEL_LOOP(idx, w_size) { @@ -399,6 +448,23 @@ void ChoppedTransferRPUDeviceCuda::readAndUpdate( cwo_->getWeightOutputOutChopperData(), out_size, in_size, n_vec, i_slice_start, lr_scale, sub_momentum, up.desired_BL, par.forget_buffer, par.no_buffer); + DEBUG_OUT("Out chopper: "); + DEBUG_CALL( + CudaArray dev_a(this->context_, out_size * n_vec); math::copyWithIterator( + this->context_, dev_a.getData(), cwo_->getWeightOutputOutChopperData(), out_size * n_vec); + this->context_->synchronize(); dev_a.printMatrixValues(out_size);); + + DEBUG_OUT("Transfer out: "); + DEBUG_CALL( + CudaArray dev_a(this->context_, out_size * n_vec); + math::copyWithIterator(this->context_, dev_a.getData(), transfer_out, out_size * n_vec); + this->context_->synchronize(); dev_a.printMatrixValues(out_size);); + DEBUG_OUT("Buffer out: "); + DEBUG_CALL( + CudaArray dev_a(this->context_, out_size * in_size); + math::copyWithIterator(this->context_, dev_a.getData(), B, out_size * in_size); + this->context_->synchronize(); dev_a.printMatrixValues(out_size);); + // update according to device T write_lr = par.getWriteLR(to_weight_granularity); this->writeMatrix(to_device_idx, nullptr, transfer_out, n_vec, write_lr, up); @@ -489,7 +555,6 @@ void ChoppedTransferRPUDeviceCuda::runUpdateKernel( const ChoppedWeightOutput *cwo) { // calling kpars->run(..,this,..) directly should cause error because derived from abstract // device.. - DEBUG_OUT("start run update kernel."); if (x_counts_chunk != nullptr || d_counts_chunk != nullptr) { RPU_FATAL("Chunking not allowed here."); @@ -524,9 +589,9 @@ void ChoppedTransferRPUDeviceCuda::runUpdateKernel( nullptr, &*cwo_); if (up._currently_tuning) { + cwo_->releaseBuffers(); return; } - const auto &par = getPar(); if (par.auto_scale) { T abs_m_x; @@ -552,7 +617,7 @@ void ChoppedTransferRPUDeviceCuda::runUpdateKernel( // always fully hidden, reduce is no-op anyway this->reduceToWeights(up_context, dev_weights); } - + // will sync cwo_->releaseBuffers(); } diff --git a/src/rpucuda/cuda/rpucuda_chopped_transfer_device.h b/src/rpucuda/cuda/rpucuda_chopped_transfer_device.h index fc768ddf..c0cd63e1 100644 --- a/src/rpucuda/cuda/rpucuda_chopped_transfer_device.h +++ b/src/rpucuda/cuda/rpucuda_chopped_transfer_device.h @@ -17,10 +17,10 @@ namespace RPU { template class ChoppedTransferRPUDeviceCuda : public BufferedTransferRPUDeviceCuda { public: - explicit ChoppedTransferRPUDeviceCuda(){}; + explicit ChoppedTransferRPUDeviceCuda() {}; explicit ChoppedTransferRPUDeviceCuda(CudaContextPtr c, const ChoppedTransferRPUDevice &other); - ~ChoppedTransferRPUDeviceCuda(){}; + ~ChoppedTransferRPUDeviceCuda() {}; ChoppedTransferRPUDeviceCuda(const ChoppedTransferRPUDeviceCuda &other); ChoppedTransferRPUDeviceCuda &operator=(const ChoppedTransferRPUDeviceCuda &other); ChoppedTransferRPUDeviceCuda(ChoppedTransferRPUDeviceCuda &&other); diff --git a/src/rpucuda/cuda/rpucuda_chopped_transfer_device_test.cpp b/src/rpucuda/cuda/rpucuda_chopped_transfer_device_test.cpp index aa1c4bc2..e94ad817 100644 --- a/src/rpucuda/cuda/rpucuda_chopped_transfer_device_test.cpp +++ b/src/rpucuda/cuda/rpucuda_chopped_transfer_device_test.cpp @@ -21,12 +21,12 @@ namespace { using namespace RPU; -class RPUDeviceCudaTestFixture : public ::testing::TestWithParam { +class RPUDeviceCudaTestFixture : public ::testing::TestWithParam { public: void SetUp() { - x_size = 3; - d_size = 3; - m_batch = 1; + x_size = 4; + d_size = 5; + m_batch = 6; context = &context_container; w_ref = Array_2D_Get(d_size, x_size); @@ -51,8 +51,8 @@ class RPUDeviceCudaTestFixture : public ::testing::TestWithParam { dp_cs.dw_min_dtod = 0.0; dp_cs.dw_min_std = 0.0; dp_cs.up_down_dtod = 0.0; - dp_cs.w_max = 100; - dp_cs.w_min = -100; + dp_cs.w_max = 1000; + dp_cs.w_min = -1000; dp_cs.w_max_dtod = 0; dp_cs.w_min_dtod = 0; dp_cs.lifetime = 0.0; @@ -60,9 +60,9 @@ class RPUDeviceCudaTestFixture : public ::testing::TestWithParam { dp = new ChoppedTransferRPUDeviceMetaParameter(dp_cs, 2); dp->gamma = 0.0; - dp->thres_scale = (num_t)0.5 / dp_cs.dw_min; - dp->transfer_columns = GetParam(); - dp->transfer_every = GetParam() ? x_size : d_size; + dp->thres_scale = (num_t)1.0 / dp_cs.dw_min; + dp->transfer_columns = GetParam() / 2; + dp->transfer_every = dp->transfer_columns ? d_size : x_size; dp->n_reads_per_transfer = 1; dp->units_in_mbatch = false; dp->forget_buffer = true; @@ -82,6 +82,10 @@ class RPUDeviceCudaTestFixture : public ::testing::TestWithParam { dp->transfer_lr = 1.0; dp->scale_transfer_lr = false; // do not scale with current_lr dp->random_selection = false; + dp->in_chop_random = false; + dp->in_chop_prob = 0; + dp->transfer_flexible_insize = GetParam() % 2; + dp->transfer_max_vec_chunk_size = 100; rx.resize(x_size * m_batch); rd.resize(d_size * m_batch); @@ -92,11 +96,13 @@ class RPUDeviceCudaTestFixture : public ::testing::TestWithParam { auto urnd = std::bind(udist, generator); // just assign some numbers from the weight matrix - for (int i = 0; i < x_size * m_batch; i++) - rx[i] = (num_t)urnd(); + float tol = 0.01; + for (int i = 0; i < x_size * m_batch; i++) { + rx[i] = (num_t)(std::round(urnd() / tol) * tol); + } for (int j = 0; j < d_size * m_batch; j++) { - rd[j] = (num_t)urnd(); + rd[j] = (num_t)(std::round(urnd() / tol) * tol); } up_pwu = RPU::make_unique>(context, x_size, d_size); @@ -119,7 +125,7 @@ class RPUDeviceCudaTestFixture : public ::testing::TestWithParam { num_t lifetime; num_t **weights; num_t **w_ref; - std::vector rx, rd, w, w2; + std::vector rx, rd, d, x, w, w2; PulsedUpdateMetaParameter up; ChoppedTransferRPUDeviceMetaParameter *dp; ConstantStepRPUDeviceMetaParameter dp_cs; @@ -134,7 +140,7 @@ class RPUDeviceCudaTestFixture : public ::testing::TestWithParam { }; // define the tests -INSTANTIATE_TEST_CASE_P(RowColumn, RPUDeviceCudaTestFixture, ::testing::Values(true, false)); +INSTANTIATE_TEST_CASE_P(RowColumn, RPUDeviceCudaTestFixture, ::testing::Values(0, 1, 2, 3)); TEST_P(RPUDeviceCudaTestFixture, createDevice) { @@ -143,8 +149,9 @@ TEST_P(RPUDeviceCudaTestFixture, createDevice) { TEST_P(RPUDeviceCudaTestFixture, Update) { + dp->thres_scale = (num_t)1.0 / dp_cs.dw_min; dp->transfer_lr = 0; // no transfer here - // just newly create from paramerers + // just newly create from parameters rpu_device = dp->createDeviceUnique(this->x_size, this->d_size, &this->rw_rng); rpucuda_device = AbstractRPUDeviceCuda::createFromUnique(context, *rpu_device); @@ -180,7 +187,7 @@ TEST_P(RPUDeviceCudaTestFixture, Update) { // should actually be exactly one s += w_vec[i]; } - // std::cout << "Average weight " << s / size << " (Expected is 0.1)" << std::endl; + DEBUG_OUT("Average weight " << s / size << " (Expected is 0.1)"); // visible weights not for (int i = 0; i < size; i++) { @@ -209,7 +216,7 @@ TEST_P(RPUDeviceCudaTestFixture, UpdateAndTransfer) { rpucuda_device->populateFrom(*rpu_device); // device pars have changed (due to onSetWeights) } context->synchronize(); - int max_size = (GetParam() ? this->x_size : this->d_size); + int max_size = (dp->transfer_columns ? this->d_size : this->x_size); for (int k = 0; k < max_size; k++) { up_pwu->update( @@ -225,31 +232,40 @@ TEST_P(RPUDeviceCudaTestFixture, UpdateAndTransfer) { context->synchronize(); auto w_vec = static_cast *>(&*rpucuda_device)->getHiddenWeights(); + + DEBUG_CALL(dev_weights->printMatrixValues(this->d_size)); dev_weights->copyTo(weights[0]); dev_weights->assignTranspose(weights[0], x_size, d_size); dev_weights->copyTo(weights[0]); int size = this->d_size * this->x_size; - // for (int k = 0; k < 3; k++) { - // switch (k) { - // case 0: std::cout << " A " << k << ":" << std::endl; break; - // case 1: std::cout << " C " << k << ":" << std::endl; break; - // case 2: std::cout << " Buffer " << k << ":" << std::endl; break; - // } - // for (int i_x = 0; i_x < x_size; i_x++) { - // for (int i_d = 0; i_d < d_size; i_d++) { - // int i = i_x + x_size * i_d + k*size; - - // if (k == 1) {// fully hidden, thus take weight here - // std::cout << "\t" << weights[0][i]; - // } else { - // std::cout << "\t" << w_vec[i]; - // } - // } - // std::cout << std::endl; - // } - // std::cout << std::endl; - // } + + DEBUG_CALL(for (int k = 0; k < 3; k++) { + switch (k) { + case 0: + std::cout << " A " << k << ":" << std::endl; + break; + case 1: + std::cout << " C " << k << ":" << std::endl; + break; + case 2: + std::cout << " Buffer " << k << ":" << std::endl; + break; + } + for (int i_x = 0; i_x < x_size; i_x++) { + for (int i_d = 0; i_d < d_size; i_d++) { + int i = i_x + x_size * i_d + k * size; + + if (k == 1) { + std::cout << "\t" << weights[0][i - size]; + } else { + std::cout << "\t" << w_vec[i]; + } + } + std::cout << std::endl; + } + std::cout << std::endl; + }); // update only on fast [nothing to transfer for first row] // hidden weights updated @@ -270,9 +286,8 @@ TEST_P(RPUDeviceCudaTestFixture, UpdateAndTransfer) { // always fully hidden this not A in C for (int i = 0; i < size; i++) { - // std::cout << "[" << i / x_size << "," << i % x_size << "]: " << this->weights[0][i] << - // std::endl; - if (GetParam()) { + DEBUG_OUT("[" << i / x_size << "," << i % x_size << "]: " << this->weights[0][i]) + if (dp->transfer_columns) { ASSERT_FLOAT_EQ(this->weights[0][i], i % x_size ? (num_t)0.0 : dp_cs.dw_min * rw[1]); } else { ASSERT_FLOAT_EQ(this->weights[0][i], i >= x_size ? (num_t)0.0 : dp_cs.dw_min * rw[1]); @@ -282,8 +297,9 @@ TEST_P(RPUDeviceCudaTestFixture, UpdateAndTransfer) { TEST_P(RPUDeviceCudaTestFixture, UpdateAndTransferBatch) { - int max_size = GetParam() ? this->x_size : this->d_size; + int max_size = this->x_size * this->d_size; + // cuda CudaArray dev_x(context, this->x_size * max_size); dev_x.setConst(1.0); CudaArray dev_d(context, this->d_size * max_size); @@ -302,25 +318,26 @@ TEST_P(RPUDeviceCudaTestFixture, UpdateAndTransferBatch) { max_size, // batch false, // trans false); - // weight values of the hidden weights should be x_size and first - // col should be transfered once (that is set to dw_min) + + // weight values of the hidden weights should be x_size and all + // weights should be transfered exactly once (that is set to dw_min) context->synchronize(); auto w_vec = static_cast *>(&*rpucuda_device)->getHiddenWeights(); dev_weights->copyTo(weights[0]); dev_weights->assignTranspose(weights[0], x_size, d_size); dev_weights->copyTo(weights[0]); + context->synchronize(); // update only on fast [nothing to transfer for first row] int size = this->d_size * this->x_size; // hidden weights updated num_t s = 0; for (int i = 0; i < size; i++) { - ASSERT_FLOAT_EQ(w_vec[i], (num_t)max_size); + DEBUG_OUT("[" << i / x_size << "," << i % x_size << "]: " << w_vec[i]); + ASSERT_TRUE(w_vec[i] != 0); s += w_vec[i]; } - // only first col of weights should be transferred - for (int i = 0; i < size; i++) { ASSERT_FLOAT_EQ(w_vec[i + size], 0.0); // should not be used } @@ -331,13 +348,7 @@ TEST_P(RPUDeviceCudaTestFixture, UpdateAndTransferBatch) { // always fully hidden this not A in C for (int i = 0; i < size; i++) { - // std::cout << "[" << i / x_size << "," << i % x_size << "]: " << this->weights[0][i] << - // std::endl; - if (GetParam()) { - ASSERT_FLOAT_EQ(this->weights[0][i], i % x_size ? (num_t)0.0 : dp_cs.dw_min * rw[1]); - } else { - ASSERT_FLOAT_EQ(this->weights[0][i], i >= x_size ? (num_t)0.0 : dp_cs.dw_min * rw[1]); - } + ASSERT_FLOAT_EQ(this->weights[0][i], dp_cs.dw_min * rw[1]); } } @@ -345,40 +356,40 @@ TEST_P(RPUDeviceCudaTestFixture, CUDAvsCPU) { PulsedMetaParameter p; p.up = up; - p.up.x_res_implicit = 0.01; - p.up.d_res_implicit = 0.01; + p.up.x_res_implicit = 0.1; + p.up.d_res_implicit = 0.1; + p.up.desired_BL = 100; + p.up.update_bl_management = true; + p.up.update_management = true; p.up.pulse_type = PulseType::DeterministicImplicit; - p.up.desired_BL = 10; dp->in_chop_prob = 1; - dp->out_chop_prob = 1; - - dp->transfer_up.pulse_type = PulseType::DeterministicImplicit; - dp->transfer_up.x_res_implicit = 0.01; - dp->transfer_up.d_res_implicit = 0.01; + dp->out_chop_prob = 0; + dp->in_chop_random = true; dp->transfer_up.desired_BL = 1; + dp->transfer_up.pulse_type = PulseType::None; dp->transfer_io.is_perfect = true; - CudaArray dev_x(context, x_size * m_batch, rx.data()); - CudaArray dev_d(context, d_size * m_batch, rd.data()); + CudaArray dev_x(context, x_size * m_batch, this->rx.data()); + CudaArray dev_d(context, d_size * m_batch, this->rd.data()); context->synchronize(); - auto *rpu = new RPUPulsed(x_size, d_size); - rpu->populateParameter(&p, dp); - rpu->setWeights(this->weights[0]); + RPUPulsed rpu(x_size, d_size); + rpu.populateParameter(&p, dp); + rpu.setWeights(this->weights[0]); + rpu.setLearningRate(1.0); - rpu->setLearningRate(1.0); - auto *rpucuda = new RPUCudaPulsed(context->getStream(), *rpu); - rpucuda->setLearningRate(1.0); + RPUCudaPulsed rpucuda(context->getStream(), rpu); + rpucuda.setLearningRate(1.0); context->synchronize(); int size = this->d_size * this->x_size; // double check whether weights are correct w.resize(x_size * d_size); - rpu->getWeights(w.data()); + rpu.getWeights(w.data()); w2.resize(x_size * d_size); - rpucuda->getWeights(w2.data()); + rpucuda.getWeights(w2.data()); for (int i = 0; i < size; i++) { ASSERT_NEAR(w[i], w2[i], 1.0e-5); @@ -386,21 +397,20 @@ TEST_P(RPUDeviceCudaTestFixture, CUDAvsCPU) { context->synchronize(); for (int k = 0; k < this->d_size * this->x_size; k++) { - rpu->update(rx.data(), rd.data(), false, m_batch, false, false); - rpucuda->update(dev_x.getData(), dev_d.getData(), false, m_batch, false, false); + rpu.update(rx.data(), rd.data(), false, m_batch, false, false); + rpucuda.update(dev_x.getData(), dev_d.getData(), false, m_batch, false, false); } context->synchronize(); w.resize(x_size * d_size); - rpu->getWeights(w.data()); + rpu.getWeights(w.data()); w2.resize(x_size * d_size); - rpucuda->getWeights(w2.data()); + rpucuda.getWeights(w2.data()); - std::cout << "CUDA vs. CPU:" << std::endl; + DEBUG_OUT("CUDA vs. CPU:"); for (int i = 0; i < size; i++) { - std::cout << "[" << i / x_size << "," << i % x_size << "]: " << w2[i] << " \tvs. \t" << w[i] - << std::endl; - ASSERT_NEAR(w[i], w2[i], 1.0e-5); + DEBUG_OUT("[" << i / x_size << "," << i % x_size << "]: " << w2[i] << " \tvs. \t" << w[i]); + EXPECT_NEAR(w[i], w2[i], 0.10001); // rounding differences between CPU and CUDA? } } diff --git a/src/rpucuda/cuda/rpucuda_constantstep_device.cu b/src/rpucuda/cuda/rpucuda_constantstep_device.cu index ae978911..3c062371 100644 --- a/src/rpucuda/cuda/rpucuda_constantstep_device.cu +++ b/src/rpucuda/cuda/rpucuda_constantstep_device.cu @@ -69,14 +69,21 @@ pwukpvec_t ConstantStepRPUDeviceCuda::getUpdateKernels( pwukpvec_t v; if (getPar().dw_min_std > (T)0.33) { // 3 sigma - v.push_back(RPU::make_unique, 1>> ARGS(FunctorLargeNoise)); - v.push_back(RPU::make_unique, 1>> ARGS(FunctorLargeNoise)); - v.push_back(RPU::make_unique, 1>> ARGS(FunctorLargeNoise)); - v.push_back(RPU::make_unique, 1>> ARGS(FunctorLargeNoise)); + v.push_back( + RPU::make_unique< + PWUKernelParameterSingleFunctor, 1>> + ARGS(FunctorLargeNoise)); + v.push_back( + RPU::make_unique< + PWUKernelParameterBatchFunctor, 1>> + ARGS(FunctorLargeNoise)); + v.push_back( + RPU::make_unique< + PWUKernelParameterBatchSharedFunctor, 1>> + ARGS(FunctorLargeNoise)); + v.push_back( + RPU::make_unique, 1>> ARGS(FunctorLargeNoise)); } else { // use summing approximation is save in this case @@ -84,8 +91,10 @@ pwukpvec_t ConstantStepRPUDeviceCuda::getUpdateKernels( v.push_back( RPU::make_unique, 1>> ARGS(Functor)); - v.push_back(RPU::make_unique, 1>> ARGS(Functor)); + v.push_back( + RPU::make_unique< + PWUKernelParameterBatchSharedWeightOutputFunctor, 1>> + ARGS(Functor)); v.push_back( RPU::make_unique, 1>> ARGS( Functor)); diff --git a/src/rpucuda/cuda/rpucuda_dynamic_transfer_device.h b/src/rpucuda/cuda/rpucuda_dynamic_transfer_device.h index db1366ab..76d91648 100644 --- a/src/rpucuda/cuda/rpucuda_dynamic_transfer_device.h +++ b/src/rpucuda/cuda/rpucuda_dynamic_transfer_device.h @@ -14,10 +14,10 @@ namespace RPU { template class DynamicTransferRPUDeviceCuda : public ChoppedTransferRPUDeviceCuda { public: - explicit DynamicTransferRPUDeviceCuda(){}; + explicit DynamicTransferRPUDeviceCuda() {}; explicit DynamicTransferRPUDeviceCuda(CudaContextPtr c, const DynamicTransferRPUDevice &other); - ~DynamicTransferRPUDeviceCuda(){}; + ~DynamicTransferRPUDeviceCuda() {}; DynamicTransferRPUDeviceCuda(const DynamicTransferRPUDeviceCuda &other); DynamicTransferRPUDeviceCuda &operator=(const DynamicTransferRPUDeviceCuda &other); DynamicTransferRPUDeviceCuda(DynamicTransferRPUDeviceCuda &&other); diff --git a/src/rpucuda/cuda/rpucuda_expstep_device.cu b/src/rpucuda/cuda/rpucuda_expstep_device.cu index 15b010e3..e80468e2 100644 --- a/src/rpucuda/cuda/rpucuda_expstep_device.cu +++ b/src/rpucuda/cuda/rpucuda_expstep_device.cu @@ -159,44 +159,50 @@ pwukpvec_t ExpStepRPUDeviceCuda::getUpdateKernels( const auto &pars = getPar(); if (pars.hasComplexNoise()) { - v.push_back(RPU::make_unique< - PWUKernelParameterSingleFunctor, 9>>( - this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, - pars.getName())); + v.push_back( + RPU::make_unique< + PWUKernelParameterSingleFunctor, 9>>( + this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, + pars.getName())); v.push_back( RPU::make_unique, 9>>( this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, pars.getName())); - v.push_back(RPU::make_unique< - PWUKernelParameterBatchSharedFunctor, 9>>( - this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, - pars.getName())); + v.push_back( + RPU::make_unique< + PWUKernelParameterBatchSharedFunctor, 9>>( + this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, + pars.getName())); - v.push_back(RPU::make_unique, 9>>( - this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, - pars.getName())); + v.push_back( + RPU::make_unique, 9>>( + this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, + pars.getName())); } else { - v.push_back(RPU::make_unique, 7>>( - this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, - pars.getName())); + v.push_back( + RPU::make_unique, 7>>( + this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, + pars.getName())); - v.push_back(RPU::make_unique, 7>>( - this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, - pars.getName())); + v.push_back( + RPU::make_unique, 7>>( + this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, + pars.getName())); v.push_back( RPU::make_unique, 7>>( this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, pars.getName())); - v.push_back(RPU::make_unique< - PWUKernelParameterBatchSharedWeightOutputFunctor, 7>>( - this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, - pars.getName())); + v.push_back( + RPU::make_unique< + PWUKernelParameterBatchSharedWeightOutputFunctor, 7>>( + this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, + pars.getName())); } return v; } diff --git a/src/rpucuda/cuda/rpucuda_hidden_device.cu b/src/rpucuda/cuda/rpucuda_hidden_device.cu index 297027b4..68f35d1c 100644 --- a/src/rpucuda/cuda/rpucuda_hidden_device.cu +++ b/src/rpucuda/cuda/rpucuda_hidden_device.cu @@ -19,9 +19,9 @@ namespace RPU { } else { \ hw += hs_dw; \ } \ - if (hw > (T)1.0 || hw < (T)-1.0) { \ + if (hw > (T)1.0 || hw < (T) - 1.0) { \ \ - T dw = (hw > (T)1) ? ((T)par_4.w) : ((T)-par_4.y); \ + T dw = (hw > (T)1) ? ((T)par_4.w) : ((T) - par_4.y); \ hw = (T)0.0; \ if (noise_std_dw > (T)0.0) { \ T stoch_value = curand_normal(&local_state); \ diff --git a/src/rpucuda/cuda/rpucuda_linearstep_device.cu b/src/rpucuda/cuda/rpucuda_linearstep_device.cu index 84983c39..d70c4e73 100644 --- a/src/rpucuda/cuda/rpucuda_linearstep_device.cu +++ b/src/rpucuda/cuda/rpucuda_linearstep_device.cu @@ -153,8 +153,10 @@ pwukpvec_t LinearStepRPUDeviceCuda::getUpdateKernels( v.push_back( RPU::make_unique, 1>> ARGS); - v.push_back(RPU::make_unique, 1>> ARGS); + v.push_back( + RPU::make_unique< + PWUKernelParameterBatchSharedWeightOutputFunctor, 1>> + ARGS); } else { @@ -166,8 +168,10 @@ pwukpvec_t LinearStepRPUDeviceCuda::getUpdateKernels( v.push_back( RPU::make_unique, 1>> ARGS); - v.push_back(RPU::make_unique, 1>> ARGS); + v.push_back( + RPU::make_unique< + PWUKernelParameterBatchSharedWeightOutputFunctor, 1>> + ARGS); } return v; } diff --git a/src/rpucuda/cuda/rpucuda_mixedprec_device.h b/src/rpucuda/cuda/rpucuda_mixedprec_device.h index d8d3b44f..fa836c34 100644 --- a/src/rpucuda/cuda/rpucuda_mixedprec_device.h +++ b/src/rpucuda/cuda/rpucuda_mixedprec_device.h @@ -14,11 +14,11 @@ namespace RPU { template class MixedPrecRPUDeviceCuda : public MixedPrecRPUDeviceBaseCuda { public: - explicit MixedPrecRPUDeviceCuda(){}; + explicit MixedPrecRPUDeviceCuda() {}; explicit MixedPrecRPUDeviceCuda(CudaContextPtr c, int x_size, int d_size); explicit MixedPrecRPUDeviceCuda(CudaContextPtr c, const MixedPrecRPUDevice &other); - ~MixedPrecRPUDeviceCuda(){}; + ~MixedPrecRPUDeviceCuda() {}; MixedPrecRPUDeviceCuda(const MixedPrecRPUDeviceCuda &other); MixedPrecRPUDeviceCuda &operator=(const MixedPrecRPUDeviceCuda &other); MixedPrecRPUDeviceCuda(MixedPrecRPUDeviceCuda &&other); diff --git a/src/rpucuda/cuda/rpucuda_mixedprec_device_base.h b/src/rpucuda/cuda/rpucuda_mixedprec_device_base.h index 54b2cc07..6b9e630a 100644 --- a/src/rpucuda/cuda/rpucuda_mixedprec_device_base.h +++ b/src/rpucuda/cuda/rpucuda_mixedprec_device_base.h @@ -16,11 +16,11 @@ namespace RPU { template class MixedPrecRPUDeviceBaseCuda : public SimpleRPUDeviceCuda { public: - explicit MixedPrecRPUDeviceBaseCuda(){}; + explicit MixedPrecRPUDeviceBaseCuda() {}; explicit MixedPrecRPUDeviceBaseCuda(CudaContextPtr c, int x_size, int d_size); explicit MixedPrecRPUDeviceBaseCuda(CudaContextPtr c, const MixedPrecRPUDeviceBase &other); - virtual ~MixedPrecRPUDeviceBaseCuda(){}; + virtual ~MixedPrecRPUDeviceBaseCuda() {}; MixedPrecRPUDeviceBaseCuda(const MixedPrecRPUDeviceBaseCuda &other); MixedPrecRPUDeviceBaseCuda &operator=(const MixedPrecRPUDeviceBaseCuda &other); MixedPrecRPUDeviceBaseCuda(MixedPrecRPUDeviceBaseCuda &&other); diff --git a/src/rpucuda/cuda/rpucuda_mixedprec_device_test.cpp b/src/rpucuda/cuda/rpucuda_mixedprec_device_test.cpp index 4ade5196..6daa24ad 100644 --- a/src/rpucuda/cuda/rpucuda_mixedprec_device_test.cpp +++ b/src/rpucuda/cuda/rpucuda_mixedprec_device_test.cpp @@ -38,7 +38,7 @@ class MixedPrecRPUDeviceCudaTestFixtureSmall : public ::testing::Test { dp.setDevicePar(dp_cs); }; - void TearDown(){}; + void TearDown() {}; int x_size, d_size; MixedPrecRPUDeviceMetaParameter dp; diff --git a/src/rpucuda/cuda/rpucuda_mixedprec_int_device.h b/src/rpucuda/cuda/rpucuda_mixedprec_int_device.h index 832f2615..200b7a8f 100644 --- a/src/rpucuda/cuda/rpucuda_mixedprec_int_device.h +++ b/src/rpucuda/cuda/rpucuda_mixedprec_int_device.h @@ -15,11 +15,11 @@ namespace RPU { template class MixedPrecIntRPUDeviceCuda : public MixedPrecRPUDeviceBaseCuda { public: - explicit MixedPrecIntRPUDeviceCuda(){}; + explicit MixedPrecIntRPUDeviceCuda() {}; explicit MixedPrecIntRPUDeviceCuda(CudaContextPtr c, int x_size, int d_size); explicit MixedPrecIntRPUDeviceCuda(CudaContextPtr c, const MixedPrecIntRPUDevice &other); - ~MixedPrecIntRPUDeviceCuda(){}; + ~MixedPrecIntRPUDeviceCuda() {}; MixedPrecIntRPUDeviceCuda(const MixedPrecIntRPUDeviceCuda &other); MixedPrecIntRPUDeviceCuda &operator=(const MixedPrecIntRPUDeviceCuda &other); MixedPrecIntRPUDeviceCuda(MixedPrecIntRPUDeviceCuda &&other); diff --git a/src/rpucuda/cuda/rpucuda_mixedprec_int_device_test.cpp b/src/rpucuda/cuda/rpucuda_mixedprec_int_device_test.cpp index fd21888d..3a770728 100644 --- a/src/rpucuda/cuda/rpucuda_mixedprec_int_device_test.cpp +++ b/src/rpucuda/cuda/rpucuda_mixedprec_int_device_test.cpp @@ -28,7 +28,7 @@ class MixedPrecIntRPUDeviceCudaTestFixtureSmall : public ::testing::Test { dp.setDevicePar(dp_cs); }; - void TearDown(){}; + void TearDown() {}; int x_size, d_size; MixedPrecIntRPUDeviceMetaParameter dp; diff --git a/src/rpucuda/cuda/rpucuda_onesided_device.h b/src/rpucuda/cuda/rpucuda_onesided_device.h index 24203418..ec569b82 100644 --- a/src/rpucuda/cuda/rpucuda_onesided_device.h +++ b/src/rpucuda/cuda/rpucuda_onesided_device.h @@ -18,10 +18,10 @@ namespace RPU { template class OneSidedRPUDeviceCuda : public VectorRPUDeviceCuda { public: - explicit OneSidedRPUDeviceCuda(){}; + explicit OneSidedRPUDeviceCuda() {}; explicit OneSidedRPUDeviceCuda(CudaContextPtr c, const OneSidedRPUDevice &other); - ~OneSidedRPUDeviceCuda(){}; + ~OneSidedRPUDeviceCuda() {}; OneSidedRPUDeviceCuda(const OneSidedRPUDeviceCuda &other); OneSidedRPUDeviceCuda &operator=(const OneSidedRPUDeviceCuda &other); OneSidedRPUDeviceCuda(OneSidedRPUDeviceCuda &&other); @@ -47,7 +47,7 @@ template class OneSidedRPUDeviceCuda : public VectorRPUDeviceCuda *clone() const override { return new OneSidedRPUDeviceCuda(*this); }; - void setHiddenUpdateIdx(int idx) override{}; + void setHiddenUpdateIdx(int idx) override {}; void runUpdateKernel( pwukp_t kpars, diff --git a/src/rpucuda/cuda/rpucuda_powstep_reference_device.cu b/src/rpucuda/cuda/rpucuda_powstep_reference_device.cu index e653b84e..8de88c8b 100644 --- a/src/rpucuda/cuda/rpucuda_powstep_reference_device.cu +++ b/src/rpucuda/cuda/rpucuda_powstep_reference_device.cu @@ -108,8 +108,10 @@ pwukpvec_t PowStepReferenceRPUDeviceCuda::getUpdateKernels( v.push_back( RPU::make_unique, 1>> ARGS); - v.push_back(RPU::make_unique, 1>> ARGS); + v.push_back( + RPU::make_unique< + PWUKernelParameterBatchSharedWeightOutputFunctor, 1>> + ARGS); return v; } diff --git a/src/rpucuda/cuda/rpucuda_pulsed.h b/src/rpucuda/cuda/rpucuda_pulsed.h index b4738890..31bced3c 100644 --- a/src/rpucuda/cuda/rpucuda_pulsed.h +++ b/src/rpucuda/cuda/rpucuda_pulsed.h @@ -24,7 +24,7 @@ namespace RPU { template class RPUCudaPulsed : public RPUCudaSimple { public: - explicit RPUCudaPulsed(){}; // dummy + explicit RPUCudaPulsed() {}; // dummy explicit RPUCudaPulsed(CudaContextPtr c, int x_size, int d_size); explicit RPUCudaPulsed(CudaContextPtr c, RPUPulsed &o); explicit RPUCudaPulsed(cudaStream_t s, int x_size, int d_size); diff --git a/src/rpucuda/cuda/rpucuda_pulsed_device.h b/src/rpucuda/cuda/rpucuda_pulsed_device.h index 93039855..f2c0c43d 100644 --- a/src/rpucuda/cuda/rpucuda_pulsed_device.h +++ b/src/rpucuda/cuda/rpucuda_pulsed_device.h @@ -21,7 +21,7 @@ template class PulsedRPUDeviceCudaBase : public SimpleRPUDeviceCuda public: explicit PulsedRPUDeviceCudaBase() = default; explicit PulsedRPUDeviceCudaBase(CudaContextPtr c, int x_size, int d_size) - : SimpleRPUDeviceCuda(c, x_size, d_size){}; + : SimpleRPUDeviceCuda(c, x_size, d_size) {}; ~PulsedRPUDeviceCudaBase() = default; PulsedRPUDeviceCudaBase(const PulsedRPUDeviceCudaBase &other) = default; @@ -135,11 +135,11 @@ template class PulsedRPUDeviceCudaBase : public SimpleRPUDeviceCuda template class PulsedRPUDeviceCuda : public PulsedRPUDeviceCudaBase { public: - explicit PulsedRPUDeviceCuda(){}; + explicit PulsedRPUDeviceCuda() {}; explicit PulsedRPUDeviceCuda(CudaContextPtr c, int x_size, int d_size); // explicit PulsedRPUDeviceCuda(CudaContextPtr c, const PulsedRPUDevice * other); - ~PulsedRPUDeviceCuda(){}; + ~PulsedRPUDeviceCuda() {}; PulsedRPUDeviceCuda(const PulsedRPUDeviceCuda &other); PulsedRPUDeviceCuda &operator=(const PulsedRPUDeviceCuda &other); PulsedRPUDeviceCuda(PulsedRPUDeviceCuda &&other); @@ -238,14 +238,18 @@ public: CUDACLASS(const CUDACLASS &other) : PulsedRPUDeviceCuda(other) { \ \ initialize(); \ - { COPY_BODY; } \ + { \ + COPY_BODY; \ + } \ this->context_->synchronize(); \ }; \ \ friend void swap(CUDACLASS &a, CUDACLASS &b) noexcept { \ using std::swap; \ swap(static_cast &>(a), static_cast &>(b)); \ - { SWAP_BODY; } \ + { \ + SWAP_BODY; \ + } \ }; \ \ CUDACLASS &operator=(const CUDACLASS &other) { \ @@ -270,7 +274,9 @@ public: RPU_FATAL("populateFrom expects " << #CPUCLASS << "."); \ } \ PulsedRPUDeviceCuda::populateFrom(rpu_device); \ - { HOST_COPY_BODY; } \ + { \ + HOST_COPY_BODY; \ + } \ this->context_->synchronize(); \ } \ \ @@ -295,17 +301,20 @@ public: \ pwukpvec_t v; \ \ - v.push_back(RPU::make_unique>( \ - this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, \ - getPar().getName())); \ + v.push_back( \ + RPU::make_unique>( \ + this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, \ + getPar().getName())); \ \ - v.push_back(RPU::make_unique>( \ - this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, \ - getPar().getName())); \ + v.push_back( \ + RPU::make_unique>( \ + this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, \ + getPar().getName())); \ \ - v.push_back(RPU::make_unique>( \ - this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, \ - getPar().getName())); \ + v.push_back( \ + RPU::make_unique>( \ + this->context_, this->x_size_, this->d_size_, m_batch, nK32, use_bo64, out_trans, up, \ + getPar().getName())); \ \ return v; \ } diff --git a/src/rpucuda/cuda/rpucuda_pulsed_device_test.cpp b/src/rpucuda/cuda/rpucuda_pulsed_device_test.cpp index 45bb0ea0..047a7a49 100644 --- a/src/rpucuda/cuda/rpucuda_pulsed_device_test.cpp +++ b/src/rpucuda/cuda/rpucuda_pulsed_device_test.cpp @@ -31,7 +31,7 @@ namespace { using namespace RPU; -template void specific_settings(DeviceParT &par){}; +template void specific_settings(DeviceParT &par) {}; template <> void specific_settings(PiecewiseStepRPUDeviceMetaParameter &par) { par.piecewise_up_vec = std::vector{0.1, 0.5, 1.0, 0.3, 0.1}; diff --git a/src/rpucuda/cuda/rpucuda_simple_device.cu b/src/rpucuda/cuda/rpucuda_simple_device.cu index 28919e7c..b9af374a 100644 --- a/src/rpucuda/cuda/rpucuda_simple_device.cu +++ b/src/rpucuda/cuda/rpucuda_simple_device.cu @@ -82,8 +82,9 @@ AbstractRPUDeviceCuda::createFrom(CudaContextPtr c, const AbstractRPUDevice( c, static_cast &>(rpu_device)); default: - RPU_FATAL("Pulsed device type not implemented in CUDA. Maybe not added to createFrom in " - "rpucuda_simple_device.cu?"); + RPU_FATAL( + "Pulsed device type not implemented in CUDA. Maybe not added to createFrom in " + "rpucuda_simple_device.cu?"); } } diff --git a/src/rpucuda/cuda/rpucuda_simple_device.h b/src/rpucuda/cuda/rpucuda_simple_device.h index 6e0e6172..2c933b91 100644 --- a/src/rpucuda/cuda/rpucuda_simple_device.h +++ b/src/rpucuda/cuda/rpucuda_simple_device.h @@ -15,8 +15,8 @@ namespace RPU { template class AbstractRPUDeviceCuda { public: - explicit AbstractRPUDeviceCuda(){}; - virtual ~AbstractRPUDeviceCuda(){}; + explicit AbstractRPUDeviceCuda() {}; + virtual ~AbstractRPUDeviceCuda() {}; virtual void decayWeights(T *dev_weights, bool bias_no_decay) = 0; virtual void decayWeights(T *dev_weights, T alpha, bool bias_no_decay) = 0; @@ -43,7 +43,7 @@ template class AbstractRPUDeviceCuda { T *d_buffer) = 0; virtual bool hasDirectUpdate() const = 0; virtual int getHiddenUpdateIdx() const { return 0; }; - virtual void setHiddenUpdateIdx(int idx){}; + virtual void setHiddenUpdateIdx(int idx) {}; virtual void dumpExtra(RPU::state_t &extra, const std::string prefix) = 0; virtual void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict) = 0; virtual void populateFrom(const AbstractRPUDevice &rpu_device) = 0; @@ -65,11 +65,11 @@ template class AbstractRPUDeviceCuda { template class SimpleRPUDeviceCuda : public AbstractRPUDeviceCuda { public: - explicit SimpleRPUDeviceCuda(){}; + explicit SimpleRPUDeviceCuda() {}; explicit SimpleRPUDeviceCuda(CudaContextPtr c, int x_size, int d_size); explicit SimpleRPUDeviceCuda(CudaContextPtr c, const SimpleRPUDevice &other); - ~SimpleRPUDeviceCuda(){}; + ~SimpleRPUDeviceCuda() {}; SimpleRPUDeviceCuda(const SimpleRPUDeviceCuda &other); SimpleRPUDeviceCuda &operator=(const SimpleRPUDeviceCuda &other); SimpleRPUDeviceCuda(SimpleRPUDeviceCuda &&other); diff --git a/src/rpucuda/cuda/rpucuda_softbounds_reference_device.cu b/src/rpucuda/cuda/rpucuda_softbounds_reference_device.cu index c41807c8..44410ca2 100644 --- a/src/rpucuda/cuda/rpucuda_softbounds_reference_device.cu +++ b/src/rpucuda/cuda/rpucuda_softbounds_reference_device.cu @@ -182,22 +182,29 @@ pwukpvec_t SoftBoundsReferenceRPUDeviceCuda::getUpdateKernels( v.push_back( RPU::make_unique< PWUKernelParameterBatchFunctor, 1>> ARGS); - v.push_back(RPU::make_unique, 1>> ARGS); - v.push_back(RPU::make_unique, 1>> ARGS); + v.push_back( + RPU::make_unique< + PWUKernelParameterBatchSharedFunctor, 1>> + ARGS); + v.push_back( + RPU::make_unique, 1>> ARGS); } else { v.push_back( RPU::make_unique< PWUKernelParameterSingleFunctor, 1>> ARGS); - v.push_back(RPU::make_unique< - PWUKernelParameterBatchFunctor, 1>> ARGS); - v.push_back(RPU::make_unique, 1>> ARGS); - v.push_back(RPU::make_unique, 1>> ARGS); + v.push_back( + RPU::make_unique< + PWUKernelParameterBatchFunctor, 1>> ARGS); + v.push_back( + RPU::make_unique< + PWUKernelParameterBatchSharedFunctor, 1>> + ARGS); + v.push_back( + RPU::make_unique, 1>> ARGS); } return v; } diff --git a/src/rpucuda/cuda/rpucuda_transfer_device.h b/src/rpucuda/cuda/rpucuda_transfer_device.h index c60270fb..721bdfb6 100644 --- a/src/rpucuda/cuda/rpucuda_transfer_device.h +++ b/src/rpucuda/cuda/rpucuda_transfer_device.h @@ -18,11 +18,11 @@ namespace RPU { template class TransferRPUDeviceCuda : public VectorRPUDeviceCuda { public: - explicit TransferRPUDeviceCuda(){}; + explicit TransferRPUDeviceCuda() {}; // explicit TransferRPUDeviceCuda(CudaContextPtr c, int x_size, int d_size); explicit TransferRPUDeviceCuda(CudaContextPtr c, const TransferRPUDevice &other); - ~TransferRPUDeviceCuda(){}; + ~TransferRPUDeviceCuda() {}; TransferRPUDeviceCuda(const TransferRPUDeviceCuda &other); TransferRPUDeviceCuda &operator=(const TransferRPUDeviceCuda &other); TransferRPUDeviceCuda(TransferRPUDeviceCuda &&other); @@ -46,7 +46,7 @@ template class TransferRPUDeviceCuda : public VectorRPUDeviceCuda *clone() const override { return new TransferRPUDeviceCuda(*this); }; - void setHiddenUpdateIdx(int idx) override{}; + void setHiddenUpdateIdx(int idx) override {}; void dumpExtra(RPU::state_t &extra, const std::string prefix) override; void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict) override; diff --git a/src/rpucuda/cuda/rpucuda_vector_device.h b/src/rpucuda/cuda/rpucuda_vector_device.h index 842bd76d..a2d9b56e 100644 --- a/src/rpucuda/cuda/rpucuda_vector_device.h +++ b/src/rpucuda/cuda/rpucuda_vector_device.h @@ -14,11 +14,11 @@ namespace RPU { template class VectorRPUDeviceCuda : public PulsedRPUDeviceCudaBase { public: - explicit VectorRPUDeviceCuda(){}; + explicit VectorRPUDeviceCuda() {}; // explicit VectorRPUDeviceCuda(CudaContextPtr c, int x_size, int d_size); explicit VectorRPUDeviceCuda(CudaContextPtr c, const VectorRPUDevice &other); - ~VectorRPUDeviceCuda(){}; + ~VectorRPUDeviceCuda() {}; VectorRPUDeviceCuda(const VectorRPUDeviceCuda &other); VectorRPUDeviceCuda &operator=(const VectorRPUDeviceCuda &other); // = default; VectorRPUDeviceCuda(VectorRPUDeviceCuda &&other); // = default; diff --git a/src/rpucuda/cuda/update_management_helper.cu b/src/rpucuda/cuda/update_management_helper.cu index 524489ac..cc41e043 100644 --- a/src/rpucuda/cuda/update_management_helper.cu +++ b/src/rpucuda/cuda/update_management_helper.cu @@ -357,7 +357,7 @@ int debugKernelTranslateTransFormatToBatchOrder64Format( for (int i = 0; i < k; i++) { // k is smaller than 32 because nK32==1 kagg_t current_cK = Kc + i; kagg_t iB = (current_cK) >> 5; - int ibit = (current_cK)&0x1f; + int ibit = (current_cK) & 0x1f; if ((c & (one << i)) > 0) { counts_out_ref[iB + idx * nBref] |= ((uint64_t)1) << ibit; } @@ -497,6 +497,7 @@ UpdateManagementHelper::UpdateManagementHelper(CudaContextPtr c, int x_size, x_maximizer_ = RPU::make_unique>(c, x_size_, true); d_maximizer_ = RPU::make_unique>(c, d_size_, true); dev_sumabsmax_value_ = RPU::make_unique>(context_, 2); + context_->synchronize(); } template void UpdateManagementHelper::initializeBuffers(int m_batch) { @@ -519,18 +520,20 @@ template void UpdateManagementHelper::initializeBuffers(int m_ba size_t temp_storage_bytes = 0; auto s = context_->getStream(); - CUDA_CALL(RPU_CUB_NS_QUALIFIER DeviceScan::InclusiveSum( - temp_storage, temp_storage_bytes, dev_K_values_->getData(), dev_Kc_values_->getData() + 1, - m_batch, s)); + CUDA_CALL( + RPU_CUB_NS_QUALIFIER DeviceScan::InclusiveSum( + temp_storage, temp_storage_bytes, dev_K_values_->getData(), dev_Kc_values_->getData() + 1, + m_batch, s)); context_->synchronize(); dev_Kc_temp_storage_ = RPU::make_unique>(context_, (int)temp_storage_bytes); // average max sum - CUDA_CALL(RPU_CUB_NS_QUALIFIER DeviceReduce::Sum( - nullptr, temp_storage_bytes, x_maximizer_->getMaxValues(), dev_sumabsmax_value_->getData(), - m_batch, s)); + CUDA_CALL( + RPU_CUB_NS_QUALIFIER DeviceReduce::Sum( + nullptr, temp_storage_bytes, x_maximizer_->getMaxValues(), + dev_sumabsmax_value_->getData(), m_batch, s)); dev_sumabsmax_temp_storage_ = RPU::make_unique>(context_, temp_storage_bytes); - context_->synchronize(); + context_->synchronizeDevice(); } template void UpdateManagementHelper::computeKcBlock(int m_batch) { @@ -548,9 +551,10 @@ template void UpdateManagementHelper::computeKc(int m_batch) { // CAUTION: needs K_values to be already computed !! size_t temp_storage_bytes = dev_Kc_temp_storage_->getSize(); - CUDA_CALL(RPU_CUB_NS_QUALIFIER DeviceScan::InclusiveSum( - (void *)dev_Kc_temp_storage_->getData(), temp_storage_bytes, dev_K_values_->getData(), - dev_Kc_values_->getData() + 1, m_batch, context_->getStream())); + CUDA_CALL( + RPU_CUB_NS_QUALIFIER DeviceScan::InclusiveSum( + (void *)dev_Kc_temp_storage_->getData(), temp_storage_bytes, dev_K_values_->getData(), + dev_Kc_values_->getData() + 1, m_batch, context_->getStream())); } template @@ -580,12 +584,14 @@ void UpdateManagementHelper::getAverageAbsMax(T &m_x, T &m_d, int m_batch) co // first compute the average of the max over batch size_t ssz = dev_sumabsmax_temp_storage_->getSize(); - CUDA_CALL(RPU_CUB_NS_QUALIFIER DeviceReduce::Sum( - (void *)dev_sumabsmax_temp_storage_->getData(), ssz, x_maximizer_->getMaxValues(), - dev_sumabsmax_value_->getData(), m_batch, context_->getStream())); - CUDA_CALL(RPU_CUB_NS_QUALIFIER DeviceReduce::Sum( - (void *)dev_sumabsmax_temp_storage_->getData(), ssz, d_maximizer_->getMaxValues(), - dev_sumabsmax_value_->getData() + 1, m_batch, context_->getStream())); + CUDA_CALL( + RPU_CUB_NS_QUALIFIER DeviceReduce::Sum( + (void *)dev_sumabsmax_temp_storage_->getData(), ssz, x_maximizer_->getMaxValues(), + dev_sumabsmax_value_->getData(), m_batch, context_->getStream())); + CUDA_CALL( + RPU_CUB_NS_QUALIFIER DeviceReduce::Sum( + (void *)dev_sumabsmax_temp_storage_->getData(), ssz, d_maximizer_->getMaxValues(), + dev_sumabsmax_value_->getData() + 1, m_batch, context_->getStream())); T result[2]; dev_sumabsmax_value_->copyTo(result); m_x = result[0] / (T)m_batch; @@ -607,12 +613,14 @@ void UpdateManagementHelper::getAverageLogAbsMax(T &m_x, T &m_d, int m_batch) LogInputIterator x_input_iter(x_maximizer_->getMaxValues()); LogInputIterator d_input_iter(d_maximizer_->getMaxValues()); - CUDA_CALL(RPU_CUB_NS_QUALIFIER DeviceReduce::Sum( - (void *)dev_sumabsmax_temp_storage_->getData(), ssz, x_input_iter, - dev_sumabsmax_value_->getData(), m_batch, context_->getStream())); - CUDA_CALL(RPU_CUB_NS_QUALIFIER DeviceReduce::Sum( - (void *)dev_sumabsmax_temp_storage_->getData(), ssz, d_input_iter, - dev_sumabsmax_value_->getData() + 1, m_batch, context_->getStream())); + CUDA_CALL( + RPU_CUB_NS_QUALIFIER DeviceReduce::Sum( + (void *)dev_sumabsmax_temp_storage_->getData(), ssz, x_input_iter, + dev_sumabsmax_value_->getData(), m_batch, context_->getStream())); + CUDA_CALL( + RPU_CUB_NS_QUALIFIER DeviceReduce::Sum( + (void *)dev_sumabsmax_temp_storage_->getData(), ssz, d_input_iter, + dev_sumabsmax_value_->getData() + 1, m_batch, context_->getStream())); T result[2]; dev_sumabsmax_value_->copyTo(result); m_x = expf(result[0] / (T)m_batch); @@ -630,12 +638,14 @@ template void UpdateManagementHelper::getAbsMax(T &m_x, T &m_d, // first compute the average of the max over batch size_t ssz = dev_sumabsmax_temp_storage_->getSize(); - CUDA_CALL(RPU_CUB_NS_QUALIFIER DeviceReduce::Max( - (void *)dev_sumabsmax_temp_storage_->getData(), ssz, x_maximizer_->getMaxValues(), - dev_sumabsmax_value_->getData(), m_batch, context_->getStream())); - CUDA_CALL(RPU_CUB_NS_QUALIFIER DeviceReduce::Max( - (void *)dev_sumabsmax_temp_storage_->getData(), ssz, d_maximizer_->getMaxValues(), - dev_sumabsmax_value_->getData() + 1, m_batch, context_->getStream())); + CUDA_CALL( + RPU_CUB_NS_QUALIFIER DeviceReduce::Max( + (void *)dev_sumabsmax_temp_storage_->getData(), ssz, x_maximizer_->getMaxValues(), + dev_sumabsmax_value_->getData(), m_batch, context_->getStream())); + CUDA_CALL( + RPU_CUB_NS_QUALIFIER DeviceReduce::Max( + (void *)dev_sumabsmax_temp_storage_->getData(), ssz, d_maximizer_->getMaxValues(), + dev_sumabsmax_value_->getData() + 1, m_batch, context_->getStream())); T result[2]; dev_sumabsmax_value_->copyTo(&result[0]); m_x = result[0]; diff --git a/src/rpucuda/cuda/update_management_helper.h b/src/rpucuda/cuda/update_management_helper.h index d823857b..209420d0 100644 --- a/src/rpucuda/cuda/update_management_helper.h +++ b/src/rpucuda/cuda/update_management_helper.h @@ -65,6 +65,8 @@ template class UpdateManagementHelper { inline const CudaArray &getKValues() const { return *dev_K_values_; }; inline void getScaleValues(T *dest) const { dev_scale_values_->copyTo(dest); }; inline void getKValues(int *dest) const { dev_K_values_->copyTo(dest); }; + inline void getXMaxValues(T *dest) const { x_maximizer_->copyMaxValuesToHost(dest); }; + inline void getDMaxValues(T *dest) const { d_maximizer_->copyMaxValuesToHost(dest); }; void getAverageAbsMax(T &m_x, T &m_d, int m_batch) const; void getAverageLogAbsMax(T &m_x, T &m_d, int m_batch) const; diff --git a/src/rpucuda/cuda/update_management_helper_test.cpp b/src/rpucuda/cuda/update_management_helper_test.cpp index 56455a90..2a3b1991 100644 --- a/src/rpucuda/cuda/update_management_helper_test.cpp +++ b/src/rpucuda/cuda/update_management_helper_test.cpp @@ -96,7 +96,11 @@ TYPED_TEST(UMHTestFixture, computeScaleAndK) { c->synchronize(); TypeParam *scale_val = new TypeParam[this->m_batch]; int *K_val = new int[this->m_batch]; + TypeParam *x_max_vals = new TypeParam[this->m_batch]; + TypeParam *d_max_vals = new TypeParam[this->m_batch]; + umh.getXMaxValues(x_max_vals); + umh.getDMaxValues(d_max_vals); umh.getScaleValues(scale_val); umh.getKValues(K_val); @@ -109,21 +113,31 @@ TYPED_TEST(UMHTestFixture, computeScaleAndK) { TypeParam d_abs_max_value = Find_Absolute_Max(this->d1 + this->size * i_batch, this->size); - int bl = ceilf(lr * x_abs_max_value * d_abs_max_value / dw_min); + ASSERT_FLOAT_EQ(x_abs_max_value, x_max_vals[i_batch]); + ASSERT_FLOAT_EQ(d_abs_max_value, d_max_vals[i_batch]); + + TypeParam d_val = d_abs_max_value; + TypeParam x_val = x_abs_max_value; + + TypeParam k_val = lr * x_val * d_val / dw_min; + if (k_val > (TypeParam)BL) { + d_val *= (TypeParam)BL / k_val; + } + TypeParam scale = sqrtf(x_val / d_val); + + int bl = ceilf(k_val); if (bl > BL) { bl = BL; } - TypeParam reg = dw_min; - - TypeParam scale = sqrtf((float)MAX(x_abs_max_value, reg) / (float)MAX(d_abs_max_value, reg)); - - EXPECT_FLOAT_EQ(scale, scale_val[i_batch]); // large error ? + ASSERT_NEAR(scale, scale_val[i_batch], 1e-4); ASSERT_EQ(bl, K_val[i_batch]); } delete[] scale_val; delete[] K_val; + delete[] d_max_vals; + delete[] x_max_vals; } } // namespace diff --git a/src/rpucuda/cuda/weight_clipper_cuda.cu b/src/rpucuda/cuda/weight_clipper_cuda.cu index 5f6427ee..e1b659a3 100644 --- a/src/rpucuda/cuda/weight_clipper_cuda.cu +++ b/src/rpucuda/cuda/weight_clipper_cuda.cu @@ -13,7 +13,7 @@ namespace RPU { template struct StdFunctor { - StdFunctor(T size, T *sum) : size_(size), sum_(sum){}; + StdFunctor(T size, T *sum) : size_(size), sum_(sum) {}; __device__ __forceinline__ T operator()(const T &a) const { T m = *sum_ / size_; diff --git a/src/rpucuda/cuda/weight_clipper_cuda.h b/src/rpucuda/cuda/weight_clipper_cuda.h index 8e1feb5c..ffb392d3 100644 --- a/src/rpucuda/cuda/weight_clipper_cuda.h +++ b/src/rpucuda/cuda/weight_clipper_cuda.h @@ -16,12 +16,12 @@ template class WeightClipperCuda { public: explicit WeightClipperCuda(CudaContextPtr context, int x_size, int d_size); - WeightClipperCuda(){}; + WeightClipperCuda() {}; void apply(T *weights, const WeightClipParameter &wclpar); - void dumpExtra(RPU::state_t &extra, const std::string prefix){}; - void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict){}; + void dumpExtra(RPU::state_t &extra, const std::string prefix) {}; + void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict) {}; private: CudaContextPtr context_ = nullptr; diff --git a/src/rpucuda/cuda/weight_drifter_cuda.h b/src/rpucuda/cuda/weight_drifter_cuda.h index da7197c4..806dfff3 100644 --- a/src/rpucuda/cuda/weight_drifter_cuda.h +++ b/src/rpucuda/cuda/weight_drifter_cuda.h @@ -16,7 +16,7 @@ template class WeightDrifterCuda { public: explicit WeightDrifterCuda(CudaContextPtr context, int size); explicit WeightDrifterCuda(CudaContextPtr, const WeightDrifter &wd, int x_size, int d_size); - WeightDrifterCuda(){}; + WeightDrifterCuda() {}; virtual ~WeightDrifterCuda() = default; WeightDrifterCuda(const WeightDrifterCuda &); // = default; diff --git a/src/rpucuda/cuda/weight_modifier_cuda.cu b/src/rpucuda/cuda/weight_modifier_cuda.cu index 99905828..a37ccfde 100644 --- a/src/rpucuda/cuda/weight_modifier_cuda.cu +++ b/src/rpucuda/cuda/weight_modifier_cuda.cu @@ -151,10 +151,11 @@ __global__ void kernelModifyWeightsAddNormal( const T stddev = amax * stddev_in; - RPU_WM_KERNEL_LOOP(true, + RPU_WM_KERNEL_LOOP( + true, - T stoch_value = curand_normal(&local_state); - new_weights[i] = weights[i] + stddev * stoch_value;); + T stoch_value = curand_normal(&local_state); + new_weights[i] = weights[i] + stddev * stoch_value;); } template @@ -173,12 +174,13 @@ __global__ void kernelModifyWeightsMultNormal( const T stddev = stddev_in * amax; - RPU_WM_KERNEL_LOOP(true, + RPU_WM_KERNEL_LOOP( + true, - T w = weights[i]; - T stoch_value = curand_normal(&local_state); + T w = weights[i]; + T stoch_value = curand_normal(&local_state); - new_weights[i] = w * ((T)1.0 + stddev * stoch_value);); + new_weights[i] = w * ((T)1.0 + stddev * stoch_value);); } template diff --git a/src/rpucuda/cuda/weight_modifier_cuda.h b/src/rpucuda/cuda/weight_modifier_cuda.h index 36de5897..332dfd67 100644 --- a/src/rpucuda/cuda/weight_modifier_cuda.h +++ b/src/rpucuda/cuda/weight_modifier_cuda.h @@ -16,7 +16,7 @@ template class WeightModifierCuda { public: explicit WeightModifierCuda(CudaContextPtr context, int x_size, int d_size); - WeightModifierCuda(){}; + WeightModifierCuda() {}; void apply(T *new_weights, const T *weights, const WeightModifierParameter &wmpar); diff --git a/src/rpucuda/cuda/weight_remapper_cuda.h b/src/rpucuda/cuda/weight_remapper_cuda.h index c222b79a..283c11a1 100644 --- a/src/rpucuda/cuda/weight_remapper_cuda.h +++ b/src/rpucuda/cuda/weight_remapper_cuda.h @@ -16,7 +16,7 @@ template class WeightRemapperCuda { public: explicit WeightRemapperCuda(CudaContextPtr context, int x_size, int d_size); - WeightRemapperCuda(){}; + WeightRemapperCuda() {}; void apply( T *weights, @@ -36,8 +36,8 @@ template class WeightRemapperCuda { T *biases = nullptr, int *channel_exceded = nullptr); - void dumpExtra(RPU::state_t &extra, const std::string prefix){}; - void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict){}; + void dumpExtra(RPU::state_t &extra, const std::string prefix) {}; + void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict) {}; private: CudaContextPtr context_ = nullptr; diff --git a/src/rpucuda/dense_bit_line_maker.h b/src/rpucuda/dense_bit_line_maker.h index 87315ae0..5ea6f073 100644 --- a/src/rpucuda/dense_bit_line_maker.h +++ b/src/rpucuda/dense_bit_line_maker.h @@ -16,7 +16,7 @@ template class DenseBitLineMaker { public: explicit DenseBitLineMaker(int x_size, int d_size); - DenseBitLineMaker(){}; + DenseBitLineMaker() {}; virtual ~DenseBitLineMaker(); DenseBitLineMaker(const DenseBitLineMaker &); DenseBitLineMaker &operator=(const DenseBitLineMaker &); @@ -52,8 +52,8 @@ template class DenseBitLineMaker { bool supports(RPU::PulseType pulse_type) const; /* Ignore the buffer / counts, as they will be generated anew each sample.*/ - void dumpExtra(RPU::state_t &extra, const std::string prefix){}; - void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict){}; + void dumpExtra(RPU::state_t &extra, const std::string prefix) {}; + void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict) {}; private: void freeContainers(); diff --git a/src/rpucuda/math_util.cpp b/src/rpucuda/math_util.cpp index 48b6cbad..fc4781d2 100644 --- a/src/rpucuda/math_util.cpp +++ b/src/rpucuda/math_util.cpp @@ -67,14 +67,14 @@ void gemm( const int ldb, const half_t beta, half_t *C, - const int ldc){ - // TODO: DOES HGEMM JUST NOT work for some reasons? MKL FP16 different from half_t ? - // RPU_INFO("A: " << (float)A[0] << ", B[0] " << B[0] << ", C[0] " << C[0]); - // cblas_hgemm( - // Order, TransA, TransB, M, N, K, alpha, (const unsigned short *) A, lda, - // (const unsigned short *) B, ldb, beta, (unsigned short *) C, ldc); - - // just use sgemm for now (quite slow to copy) + const int ldc) { + // TODO: DOES HGEMM JUST NOT work for some reasons? MKL FP16 different from half_t ? + // RPU_INFO("A: " << (float)A[0] << ", B[0] " << B[0] << ", C[0] " << C[0]); + // cblas_hgemm( + // Order, TransA, TransB, M, N, K, alpha, (const unsigned short *) A, lda, + // (const unsigned short *) B, ldb, beta, (unsigned short *) C, ldc); + + // just use sgemm for now (quite slow to copy) }; #endif diff --git a/src/rpucuda/rng.h b/src/rpucuda/rng.h index 15599a54..787cbfb1 100644 --- a/src/rpucuda/rng.h +++ b/src/rpucuda/rng.h @@ -45,7 +45,7 @@ inline randomint_t fastrand() { template class RealWorldRNG { public: explicit RealWorldRNG(unsigned int seed); - RealWorldRNG() : RealWorldRNG(0){}; + RealWorldRNG() : RealWorldRNG(0) {}; void setSeed(unsigned int seed); @@ -68,7 +68,7 @@ template class RNG { public: explicit RNG(unsigned int seed); - RNG() : RNG(0){}; + RNG() : RNG(0) {}; ~RNG(); RNG(const RNG &); diff --git a/src/rpucuda/rpu.cpp b/src/rpucuda/rpu.cpp index b58dd501..80578034 100644 --- a/src/rpucuda/rpu.cpp +++ b/src/rpucuda/rpu.cpp @@ -267,8 +267,10 @@ template RPUSimple::~RPUSimple() { if (!shared_weights_if_) { Array_2D_Free(weights_); } else { - delete[] weights_; - weights_ = nullptr; + if (weights_ != nullptr) { + delete[] weights_; + weights_ = nullptr; + } } Array_2D_Free(weights_buffer_); @@ -513,6 +515,8 @@ void RPUSimple::forward( bool d_trans, bool is_test) { + DEBUG_OUT("Forward[" << x_trans << ", " << d_trans << "] (m_batch = " << m_batch << ")"); + if ((m_batch == 1) && (!x_trans) && (!d_trans)) { if (bias) { this->forwardVectorBias(X_input, D_output, 1, 1, is_test); @@ -531,6 +535,9 @@ void RPUSimple::forward( template void RPUSimple::backward( const T *D_input, T *X_output, bool bias, int m_batch, bool d_trans, bool x_trans) { + + DEBUG_OUT("Backward[" << d_trans << ", " << x_trans << "] (m_batch = " << m_batch << ")"); + if ((m_batch == 1) && (!x_trans) && (!d_trans)) { if (bias) { this->backwardVectorBias(D_input, X_output); @@ -549,6 +556,7 @@ void RPUSimple::backward( template void RPUSimple::update( const T *X_input, const T *D_input, bool bias, int m_batch, bool x_trans, bool d_trans) { + DEBUG_OUT("Update[" << x_trans << ", " << d_trans << "] (m_batch = " << m_batch << ")"); last_update_m_batch_ = m_batch; // this is mini-batchsize * reuse_factor ! // update weights diff --git a/src/rpucuda/rpu.h b/src/rpucuda/rpu.h index 73ec9498..d22afb64 100644 --- a/src/rpucuda/rpu.h +++ b/src/rpucuda/rpu.h @@ -132,9 +132,9 @@ template class RPUAbstract { T getLearningRate() const { return learning_rate_; }; - virtual void finishUpdateCalculations(){}; - virtual void finishAllCalculations(){}; - virtual void makeUpdateAsync(){}; + virtual void finishUpdateCalculations() {}; + virtual void finishAllCalculations() {}; + virtual void makeUpdateAsync() {}; protected: int x_size_ = 0; @@ -194,7 +194,7 @@ template struct SimpleMetaParameter { template class RPUSimple : public RPUAbstract { public: - RPUSimple(){}; + RPUSimple() {}; RPUSimple(int x_size, int d_size); ~RPUSimple(); @@ -318,8 +318,8 @@ template class RPUSimple : public RPUAbstract { which usually are drawn during instantiation of the RPU object based on parameters defining their probabilty distributions. */ virtual void getDeviceParameterNames(std::vector &names) const { names.clear(); }; - virtual void getDeviceParameter(std::vector &data_ptrs){}; - virtual void setDeviceParameter(const std::vector &data_ptrs){}; + virtual void getDeviceParameter(std::vector &data_ptrs) {}; + virtual void setDeviceParameter(const std::vector &data_ptrs) {}; /* These dumps extra state vectors that are not returned by getDeviuceParameters or getWeights*/ @@ -327,7 +327,7 @@ template class RPUSimple : public RPUAbstract { virtual void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict); virtual int getHiddenUpdateIdx() const { return 0; }; - virtual void setHiddenUpdateIdx(int idx){}; + virtual void setHiddenUpdateIdx(int idx) {}; /* Decaying the weights once. Alpha can be a factor additionally applied to the current decay rate*/ @@ -410,7 +410,7 @@ template class RPUSimple : public RPUAbstract { virtual void setDeltaWeights(T *dw_extern); virtual T *getDeltaWeights() const { return delta_weights_extern_[0]; }; - virtual void setVerbosityLevel(int verbose){}; + virtual void setVerbosityLevel(int verbose) {}; /* public interfaces for forward/backward/update. Format is expected in x-major order. However, the batch dimension comes @@ -547,7 +547,7 @@ template class RPUSimple : public RPUAbstract { virtual T *copyToMatrixBiasBuffer(const T *X_input_without_bias, int m_batch, bool x_trans); virtual void copyFromMatrixBiasBuffer(T *X_input_without_bias, int m_batch, bool x_trans, T *bias_buffer); - virtual void releaseMatrixBiasBuffer(){}; + virtual void releaseMatrixBiasBuffer() {}; virtual T *getMatrixBiasBuffer(int m_batch); void forwardMatrixBias( const T *X_input_without_bias, diff --git a/src/rpucuda/rpu_buffered_transfer_device.h b/src/rpucuda/rpu_buffered_transfer_device.h index 14ef65f2..2ec12412 100644 --- a/src/rpucuda/rpu_buffered_transfer_device.h +++ b/src/rpucuda/rpu_buffered_transfer_device.h @@ -100,7 +100,7 @@ template class BufferedTransferRPUDevice : public TransferRPUDevice public: // constructor / destructor - BufferedTransferRPUDevice(){}; + BufferedTransferRPUDevice() {}; BufferedTransferRPUDevice(int x_size, int d_size); BufferedTransferRPUDevice( int x_size, diff --git a/src/rpucuda/rpu_chopped_transfer_device.cpp b/src/rpucuda/rpu_chopped_transfer_device.cpp index 4a8ce29e..612f189b 100644 --- a/src/rpucuda/rpu_chopped_transfer_device.cpp +++ b/src/rpucuda/rpu_chopped_transfer_device.cpp @@ -42,8 +42,6 @@ void ChoppedTransferRPUDeviceMetaParameter::printToStream(std::stringstream & ss << "\t auto_momentum:\t\t" << auto_momentum << std::endl; - ss << "\t auto_momentum:\t\t" << auto_momentum << std::endl; - if (no_buffer) { ss << "\t buffer not used."; ss << std::endl; @@ -58,6 +56,8 @@ void ChoppedTransferRPUDeviceMetaParameter::printToStream(std::stringstream & ss << std::endl; } } + ss << "\t transfer_flexible_insize:\t\t" << transfer_flexible_insize << std::endl; + ss << "\t transfer_max_vec_chunk_size:\t\t" << transfer_max_vec_chunk_size << std::endl; BufferedTransferRPUDeviceMetaParameter::printToStream(ss); }; @@ -82,8 +82,9 @@ template void ChoppedTransferRPUDeviceMetaParameter::checkSuppor if ((this->n_reads_per_transfer != 1) || (this->random_selection != false) || (this->with_reset_prob > (T)0.0)) { - RPU_FATAL("In / out chopper not implemented the given parameters. \nRequired: " - "n_devices==2, n_reads_per_transfer==1, random_selection=false).\n"); + RPU_FATAL( + "In / out chopper not implemented the given parameters. \nRequired: " + "n_devices==2, n_reads_per_transfer==1, random_selection=false).\n"); } } diff --git a/src/rpucuda/rpu_chopped_transfer_device.h b/src/rpucuda/rpu_chopped_transfer_device.h index dc346738..fe03dd19 100644 --- a/src/rpucuda/rpu_chopped_transfer_device.h +++ b/src/rpucuda/rpu_chopped_transfer_device.h @@ -36,6 +36,10 @@ struct ChoppedTransferRPUDeviceMetaParameter : BufferedTransferRPUDeviceMetaPara (T)1.0; // does REPLACE the thres_scale (and is NOT scaled with weight_granularity) bool no_buffer = false; // turn off buffer (TTv1) + bool transfer_flexible_insize = + true; // whether to enable fast flexible insize weight output if possible + size_t transfer_max_vec_chunk_size = + 10000000; // max in-size to preserve memory (~ m_batch**2 -> chunk_size) ChoppedTransferRPUDeviceMetaParameter() : BufferedTransferRPUDeviceMetaParameter() { initDefaults(); }; @@ -96,7 +100,7 @@ template class ChoppedTransferRPUDevice : public BufferedTransferRP public: // constructor / destructor - ChoppedTransferRPUDevice(){}; + ChoppedTransferRPUDevice() {}; ChoppedTransferRPUDevice(int x_size, int d_size); ChoppedTransferRPUDevice( int x_size, diff --git a/src/rpucuda/rpu_constantstep_device.h b/src/rpucuda/rpu_constantstep_device.h index 82587ec0..55e2c606 100644 --- a/src/rpucuda/rpu_constantstep_device.h +++ b/src/rpucuda/rpu_constantstep_device.h @@ -14,17 +14,18 @@ namespace RPU { template class ConstantStepRPUDevice; -BUILD_PULSED_DEVICE_META_PARAMETER(ConstantStep, - /*implements*/ - DeviceUpdateType::ConstantStep, - /*parameter def*/ - , - /*print body*/ - , - /* calc weight granularity body */ - return this->dw_min; - , - /*add*/ +BUILD_PULSED_DEVICE_META_PARAMETER( + ConstantStep, + /*implements*/ + DeviceUpdateType::ConstantStep, + /*parameter def*/ + , + /*print body*/ + , + /* calc weight granularity body */ + return this->dw_min; + , + /*add*/ ); template class ConstantStepRPUDevice : public PulsedRPUDevice { diff --git a/src/rpucuda/rpu_dynamic_transfer_device.h b/src/rpucuda/rpu_dynamic_transfer_device.h index da567683..5c161388 100644 --- a/src/rpucuda/rpu_dynamic_transfer_device.h +++ b/src/rpucuda/rpu_dynamic_transfer_device.h @@ -86,7 +86,7 @@ template class DynamicTransferRPUDevice : public ChoppedTransferRPU public: // constructor / destructor - DynamicTransferRPUDevice(){}; + DynamicTransferRPUDevice() {}; DynamicTransferRPUDevice(int x_size, int d_size); DynamicTransferRPUDevice( int x_size, diff --git a/src/rpucuda/rpu_forward_backward_pass.h b/src/rpucuda/rpu_forward_backward_pass.h index bfcaa2ff..e4bb9495 100644 --- a/src/rpucuda/rpu_forward_backward_pass.h +++ b/src/rpucuda/rpu_forward_backward_pass.h @@ -32,9 +32,9 @@ template class FBParameter { template class ForwardBackwardPass { public: - explicit ForwardBackwardPass(int x_size, int d_size) : x_size_(x_size), d_size_(d_size){}; - ForwardBackwardPass(){}; - virtual ~ForwardBackwardPass(){}; + explicit ForwardBackwardPass(int x_size, int d_size) : x_size_(x_size), d_size_(d_size) {}; + ForwardBackwardPass() {}; + virtual ~ForwardBackwardPass() {}; ForwardBackwardPass(const ForwardBackwardPass &) = default; ForwardBackwardPass &operator=(const ForwardBackwardPass &) = default; @@ -91,7 +91,7 @@ template class ForwardBackwardPassIOManaged : public ForwardBackwar public: explicit ForwardBackwardPassIOManaged(int x_size, int d_size, std::shared_ptr> rng); - ForwardBackwardPassIOManaged(){}; + ForwardBackwardPassIOManaged() {}; ~ForwardBackwardPassIOManaged(); ForwardBackwardPassIOManaged(const ForwardBackwardPassIOManaged &); diff --git a/src/rpucuda/rpu_mixedprec_device_base.h b/src/rpucuda/rpu_mixedprec_device_base.h index f0da25cd..ed062c03 100644 --- a/src/rpucuda/rpu_mixedprec_device_base.h +++ b/src/rpucuda/rpu_mixedprec_device_base.h @@ -76,9 +76,9 @@ template class MixedPrecRPUDeviceBase : public SimpleRPUDevice { public: // constructor / destructor - MixedPrecRPUDeviceBase(){}; + MixedPrecRPUDeviceBase() {}; MixedPrecRPUDeviceBase(int x_size, int d_size); - virtual ~MixedPrecRPUDeviceBase(){}; + virtual ~MixedPrecRPUDeviceBase() {}; MixedPrecRPUDeviceBase(const MixedPrecRPUDeviceBase &); MixedPrecRPUDeviceBase &operator=(const MixedPrecRPUDeviceBase &); diff --git a/src/rpucuda/rpu_mixedprec_int_device.h b/src/rpucuda/rpu_mixedprec_int_device.h index 3720abea..d7eea7fe 100644 --- a/src/rpucuda/rpu_mixedprec_int_device.h +++ b/src/rpucuda/rpu_mixedprec_int_device.h @@ -84,7 +84,7 @@ template class MixedPrecIntRPUDevice : public MixedPrecRPUDeviceBas public: // constructor / destructor - MixedPrecIntRPUDevice(){}; + MixedPrecIntRPUDevice() {}; MixedPrecIntRPUDevice(int x_size, int d_size); MixedPrecIntRPUDevice( int x_size, diff --git a/src/rpucuda/rpu_onesided_device.h b/src/rpucuda/rpu_onesided_device.h index 5ccbe578..f01ad4b3 100644 --- a/src/rpucuda/rpu_onesided_device.h +++ b/src/rpucuda/rpu_onesided_device.h @@ -27,7 +27,7 @@ template struct OneSidedRPUDeviceMetaParameter : VectorRPUDeviceMet T refresh_lower_thres = 0.25; bool copy_inverted = false; // whether to use copy inverted for second device - OneSidedRPUDeviceMetaParameter(){}; + OneSidedRPUDeviceMetaParameter() {}; OneSidedRPUDeviceMetaParameter(const PulsedRPUDeviceMetaParameterBase &dp, int n_devices = 2) : VectorRPUDeviceMetaParameter(dp, n_devices) { if (n_devices != 2) { @@ -64,11 +64,11 @@ template class OneSidedRPUDevice : public VectorRPUDevice { public: // constructor / destructor - OneSidedRPUDevice(){}; + OneSidedRPUDevice() {}; OneSidedRPUDevice(int x_size, int d_size); OneSidedRPUDevice( int x_size, int d_size, const OneSidedRPUDeviceMetaParameter &par, RealWorldRNG *rng); - ~OneSidedRPUDevice(){}; + ~OneSidedRPUDevice() {}; OneSidedRPUDevice(const OneSidedRPUDevice &); OneSidedRPUDevice &operator=(const OneSidedRPUDevice &); @@ -103,7 +103,7 @@ template class OneSidedRPUDevice : public VectorRPUDevice { void finishUpdateCycle( T **weights, const PulsedUpdateMetaParameter &up, T current_lr, int m_batch_info) override; - void setHiddenUpdateIdx(int idx) override{}; + void setHiddenUpdateIdx(int idx) override {}; void doSparseUpdate( T **weights, int i, const int *x_signed_indices, int x_count, int d_sign, RNG *rng) diff --git a/src/rpucuda/rpu_pulsed.h b/src/rpucuda/rpu_pulsed.h index d9865735..fb8f5e91 100644 --- a/src/rpucuda/rpu_pulsed.h +++ b/src/rpucuda/rpu_pulsed.h @@ -24,7 +24,7 @@ template class RPUPulsed : public RPUSimple { public: // constructor / destructor - RPUPulsed(){}; // for move + RPUPulsed() {}; // for move RPUPulsed(int x_size, int d_size); ~RPUPulsed(); diff --git a/src/rpucuda/rpu_pulsed_device.cpp b/src/rpucuda/rpu_pulsed_device.cpp index f8cf6fc5..bb4ae5c7 100644 --- a/src/rpucuda/rpu_pulsed_device.cpp +++ b/src/rpucuda/rpu_pulsed_device.cpp @@ -243,8 +243,9 @@ template void PulsedRPUDevice::getDPNames(std::vector::setDeviceParameter(T **out_weights, const std::vectorsize_; // need dw_min for update management if ((T)fabsf(dw_min - getPar().dw_min) / getPar().dw_min > (T)2.0 * getPar().dw_min_dtod) { - RPU_WARNING("DW min seems to have changed during hidden parameter set. Will update parameter " - "with estimated value."); + RPU_WARNING( + "DW min seems to have changed during hidden parameter set. Will update parameter " + "with estimated value."); getPar().dw_min = dw_min; //!! update par. Should be possible since unique this->setWeightGranularity(getPar().calcWeightGranularity()); } diff --git a/src/rpucuda/rpu_pulsed_device.h b/src/rpucuda/rpu_pulsed_device.h index 983bc3bc..47a1cc6a 100644 --- a/src/rpucuda/rpu_pulsed_device.h +++ b/src/rpucuda/rpu_pulsed_device.h @@ -118,8 +118,8 @@ template class PulsedRPUDeviceBase : public SimpleRPUDevice { public: // constructor / destructor - PulsedRPUDeviceBase(){}; - explicit PulsedRPUDeviceBase(int x_sz, int d_sz) : SimpleRPUDevice(x_sz, d_sz){}; + PulsedRPUDeviceBase() {}; + explicit PulsedRPUDeviceBase(int x_sz, int d_sz) : SimpleRPUDevice(x_sz, d_sz) {}; virtual ~PulsedRPUDeviceBase() = default; PulsedRPUDeviceBase(const PulsedRPUDeviceBase &other) = default; @@ -161,10 +161,10 @@ template class PulsedRPUDeviceBase : public SimpleRPUDevice { const T *x_input = nullptr, const int x_inc = 1, const T *d_input = nullptr, - const int d_inc = 1){}; + const int d_inc = 1) {}; // called when update completed virtual void finishUpdateCycle( - T **weights, const PulsedUpdateMetaParameter &up, T current_lr, int m_batch_info){}; + T **weights, const PulsedUpdateMetaParameter &up, T current_lr, int m_batch_info) {}; inline T getWeightGranularity() const { return weight_granularity_; }; inline T getNumStates() const { return num_states_; }; @@ -179,7 +179,7 @@ template class PulsedRPUDeviceBase : public SimpleRPUDevice { initUpdateCycle. Can be used to do some additional computation on the input */ virtual void - initWithUpdateInput(const T *x_input, const int x_inc, const T *d_input, const int d_inc){}; + initWithUpdateInput(const T *x_input, const int x_inc, const T *d_input, const int d_inc) {}; void dumpExtra(RPU::state_t &extra, const std::string prefix) override { SimpleRPUDevice::dumpExtra(extra, prefix); @@ -221,7 +221,7 @@ template class PulsedRPUDevice : public PulsedRPUDeviceBase { public: // constructor / destructor - PulsedRPUDevice(){}; + PulsedRPUDevice() {}; /* populate cannot be done through constructor because parameter objects reside in derived. Derived populate method needs to make sure to call the populate of base class */ @@ -434,7 +434,7 @@ public: return new DEVICENAME##RPUDeviceMetaParameter(*this); \ }; \ \ - T calcWeightGranularity() const override{GRANULARITY_BODY}; \ + T calcWeightGranularity() const override { GRANULARITY_BODY }; \ T calcNumStates() const override { \ return (this->w_max - this->w_min) / calcWeightGranularity(); \ }; \ @@ -454,7 +454,9 @@ public: int j_signed = x_signed_indices[jj]; \ int sign = (j_signed < 0) ? -d_sign : d_sign; \ int j = (j_signed < 0) ? -j_signed - 1 : j_signed - 1; \ - { BODY; } \ + { \ + BODY; \ + } \ } #define PULSED_UPDATE_W_LOOP_DENSE(BODY) \ diff --git a/src/rpucuda/rpu_simple_device.h b/src/rpucuda/rpu_simple_device.h index b6ef9a01..bfdb6dca 100644 --- a/src/rpucuda/rpu_simple_device.h +++ b/src/rpucuda/rpu_simple_device.h @@ -117,7 +117,7 @@ template class AbstractRPUDevice { public: // constructor / destructor - AbstractRPUDevice(){}; + AbstractRPUDevice() {}; virtual ~AbstractRPUDevice() = default; virtual AbstractRPUDevice *clone() const = 0; @@ -130,7 +130,7 @@ template class AbstractRPUDevice { virtual int getHiddenWeightsCount() const = 0; virtual void setHiddenWeights(const std::vector &data) = 0; virtual int getHiddenUpdateIdx() const { return 0; }; - virtual void setHiddenUpdateIdx(int idx){}; + virtual void setHiddenUpdateIdx(int idx) {}; virtual void dumpExtra(RPU::state_t &extra, const std::string prefix) = 0; virtual void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict) = 0; @@ -191,7 +191,7 @@ template class AbstractRPUDevice { template class SimpleRPUDevice : public AbstractRPUDevice { public: - SimpleRPUDevice(){}; + SimpleRPUDevice() {}; explicit SimpleRPUDevice(int x_sz, int d_sz); explicit SimpleRPUDevice( int x_sz, int d_sz, const SimpleRPUDeviceMetaParameter &par, RealWorldRNG *rng); @@ -214,11 +214,11 @@ template class SimpleRPUDevice : public AbstractRPUDevice { SimpleRPUDevice *clone() const override { return new SimpleRPUDevice(*this); } void getDPNames(std::vector &names) const override { names.clear(); }; - void getDeviceParameter(T **weights, std::vector &data_ptrs) override{}; - void setDeviceParameter(T **out_weights, const std::vector &data_ptrs) override{}; + void getDeviceParameter(T **weights, std::vector &data_ptrs) override {}; + void setDeviceParameter(T **out_weights, const std::vector &data_ptrs) override {}; int getHiddenWeightsCount() const override { return 0; }; - void setHiddenWeights(const std::vector &data) override{}; - void printDP(int x_count, int d_count) const override{}; + void setHiddenWeights(const std::vector &data) override {}; + void printDP(int x_count, int d_count) const override {}; void printToStream(std::stringstream &ss) const override { this->getPar().printToStream(ss); }; void disp(std::stringstream &ss) const override { ss << "Device " << this->getPar().getName() << " [" << this->x_size_ << "," << this->d_size_ diff --git a/src/rpucuda/rpu_transfer_device.cpp b/src/rpucuda/rpu_transfer_device.cpp index 2809fd49..1a5fe30d 100644 --- a/src/rpucuda/rpu_transfer_device.cpp +++ b/src/rpucuda/rpu_transfer_device.cpp @@ -153,8 +153,9 @@ void TransferRPUDeviceMetaParameter::initializeWithSize(int x_size, int d_siz g += this->gamma_vec[i]; } if (this->gamma_vec[n_devices - 1] == (T)0.0) { - RPU_FATAL("Expect that last device has some constribution to the network weights. [otherwise " - "why transfer?]"); + RPU_FATAL( + "Expect that last device has some constribution to the network weights. [otherwise " + "why transfer?]"); } gamma = g; } diff --git a/src/rpucuda/rpu_transfer_device.h b/src/rpucuda/rpu_transfer_device.h index b78cb379..2232659c 100644 --- a/src/rpucuda/rpu_transfer_device.h +++ b/src/rpucuda/rpu_transfer_device.h @@ -73,9 +73,9 @@ template struct TransferRPUDeviceMetaParameter : VectorRPUDeviceMet IOMetaParameter transfer_io; PulsedUpdateMetaParameter transfer_up; - TransferRPUDeviceMetaParameter(){}; + TransferRPUDeviceMetaParameter() {}; TransferRPUDeviceMetaParameter(const PulsedRPUDeviceMetaParameterBase &dp, int n_devices) - : VectorRPUDeviceMetaParameter(dp, n_devices){}; + : VectorRPUDeviceMetaParameter(dp, n_devices) {}; TransferRPUDeviceMetaParameter( const PulsedRPUDeviceMetaParameterBase &dp_fast, @@ -83,7 +83,7 @@ template struct TransferRPUDeviceMetaParameter : VectorRPUDeviceMet int n_total_devices); virtual void initializeWithSize(int x_size, int d_size); - void initialize() override{/* do nothing */}; + void initialize() override { /* do nothing */ }; inline bool fullyHidden() const { return (!gamma && this->gamma_vec.back() == (T)1.0); }; @@ -134,7 +134,7 @@ template class TransferRPUDevice : public VectorRPUDevice { public: // constructor / destructor - TransferRPUDevice(){}; + TransferRPUDevice() {}; TransferRPUDevice(int x_size, int d_size); TransferRPUDevice( int x_size, int d_size, const TransferRPUDeviceMetaParameter &par, RealWorldRNG *rng); @@ -175,7 +175,7 @@ template class TransferRPUDevice : public VectorRPUDevice { void getDeviceParameter(T **weights, std::vector &data_ptrs) override; void setDeviceParameter(T **out_weights, const std::vector &data_ptrs) override; - void setHiddenUpdateIdx(int idx) override{}; + void setHiddenUpdateIdx(int idx) override {}; void finishUpdateCycle( T **weights, const PulsedUpdateMetaParameter &up, T current_lr, int m_batch_info) override; diff --git a/src/rpucuda/rpu_vector_device.cpp b/src/rpucuda/rpu_vector_device.cpp index db65f50d..fa3c74ae 100644 --- a/src/rpucuda/rpu_vector_device.cpp +++ b/src/rpucuda/rpu_vector_device.cpp @@ -409,8 +409,9 @@ void VectorRPUDevice::populate(const VectorRPUDeviceMetaParameter &p, Real reduce_weightening_.clear(); T weight_granularity = (T)0.0; for (int k = 0; k < n_devices_; k++) { - rpu_device_vec_.push_back(std::unique_ptr>( - par.vec_par[k]->createDevice(this->x_size_, this->d_size_, rng))); + rpu_device_vec_.push_back( + std::unique_ptr>( + par.vec_par[k]->createDevice(this->x_size_, this->d_size_, rng))); weight_granularity += rpu_device_vec_.back()->getWeightGranularity(); reduce_weightening_.push_back((T)1.0 / (T)n_devices_); // average per default diff --git a/src/rpucuda/rpu_vector_device.h b/src/rpucuda/rpu_vector_device.h index 2f307ddc..48b41aad 100644 --- a/src/rpucuda/rpu_vector_device.h +++ b/src/rpucuda/rpu_vector_device.h @@ -25,7 +25,7 @@ template struct VectorRPUDeviceMetaParameter : PulsedRPUDeviceMetaP int first_update_idx = 0; std::vector gamma_vec; - VectorRPUDeviceMetaParameter(){}; + VectorRPUDeviceMetaParameter() {}; explicit VectorRPUDeviceMetaParameter( const PulsedRPUDeviceMetaParameterBase &dp, int n_devices); @@ -131,7 +131,7 @@ template class VectorRPUDevice : public PulsedRPUDeviceBase { public: // constructor / destructor - VectorRPUDevice(){}; + VectorRPUDevice() {}; VectorRPUDevice(int x_size, int d_size); VectorRPUDevice( int x_size, int d_size, const VectorRPUDeviceMetaParameter &p, RealWorldRNG *rng); diff --git a/src/rpucuda/rpu_weight_updater.h b/src/rpucuda/rpu_weight_updater.h index 1ee61b1f..bcf3d9e4 100644 --- a/src/rpucuda/rpu_weight_updater.h +++ b/src/rpucuda/rpu_weight_updater.h @@ -18,8 +18,8 @@ namespace RPU { template class RPUWeightUpdater { public: - explicit RPUWeightUpdater(int x_size, int d_size) : x_size_(x_size), d_size_(d_size){}; - RPUWeightUpdater(){}; + explicit RPUWeightUpdater(int x_size, int d_size) : x_size_(x_size), d_size_(d_size) {}; + RPUWeightUpdater() {}; friend void swap(RPUWeightUpdater &a, RPUWeightUpdater &b) noexcept { using std::swap; @@ -35,8 +35,8 @@ template class RPUWeightUpdater { const int d_inc, const T learning_rate); - virtual void dumpExtra(RPU::state_t &extra, const std::string prefix){}; - virtual void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict){}; + virtual void dumpExtra(RPU::state_t &extra, const std::string prefix) {}; + virtual void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict) {}; protected: int x_size_ = 0; @@ -48,7 +48,7 @@ template class PulsedRPUWeightUpdater : public RPUWeightUpdater public: explicit PulsedRPUWeightUpdater(int x_size, int d_size, std::shared_ptr> rng); - PulsedRPUWeightUpdater(){}; + PulsedRPUWeightUpdater() {}; virtual ~PulsedRPUWeightUpdater(); PulsedRPUWeightUpdater(const PulsedRPUWeightUpdater &); diff --git a/src/rpucuda/sparse_bit_line_maker.h b/src/rpucuda/sparse_bit_line_maker.h index 97ce6b3e..8a6bcfa9 100644 --- a/src/rpucuda/sparse_bit_line_maker.h +++ b/src/rpucuda/sparse_bit_line_maker.h @@ -16,7 +16,7 @@ template class SparseBitLineMaker { public: explicit SparseBitLineMaker(int x_size, int d_size); - SparseBitLineMaker(){}; + SparseBitLineMaker() {}; virtual ~SparseBitLineMaker(); SparseBitLineMaker(const SparseBitLineMaker &); SparseBitLineMaker &operator=(const SparseBitLineMaker &); @@ -65,8 +65,8 @@ template class SparseBitLineMaker { bool supports(RPU::PulseType pulse_type) const; /* Ignore the buffer / indices, as they will be generated anew each sample.*/ - void dumpExtra(RPU::state_t &extra, const std::string prefix){}; - void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict){}; + void dumpExtra(RPU::state_t &extra, const std::string prefix) {}; + void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict) {}; private: void freeContainers(); diff --git a/src/rpucuda/utility_functions.h b/src/rpucuda/utility_functions.h index 9054c5ea..7bd500fc 100644 --- a/src/rpucuda/utility_functions.h +++ b/src/rpucuda/utility_functions.h @@ -94,13 +94,28 @@ typedef half2 half2_t; #ifdef RPU_DEBUG #define DEBUG_OUT(x) std::cout << __FILENAME__ << ":" << __LINE__ << " : " << x << std::endl; #define DEBUG_CALL(x) \ - { x; } + { \ + x; \ + } #else #define DEBUG_OUT(x) #define DEBUG_CALL(x) #endif #endif +#ifndef DEBUG_ALL_OUT +#ifdef RPU_DEBUG_ALL +#define DEBUG_ALL_OUT(x) std::cout << __FILENAME__ << ":" << __LINE__ << " : " << x << std::endl; +#define DEBUG_ALL_CALL(x) \ + { \ + x; \ + } +#else +#define DEBUG_ALL_OUT(x) +#define DEBUG_ALL_CALL(x) +#endif +#endif + // Caution: round() might be the best, but slower on GPU. Also GPU and // CPU have sometimes different rounding behavior for some reasons. In // case of RINT CPU/GPU results are consistent. Note that using rint() @@ -221,10 +236,12 @@ template inline T **Array_2D_Get_Eye(size_t n) { template void Array_2D_Free(T **arr) { if (arr != nullptr) { - delete[] (*arr); - *arr = nullptr; + if (*arr != nullptr) { + delete[] (*arr); + // *arr = nullptr; + } delete[] arr; - arr = nullptr; + // arr = nullptr; } } diff --git a/src/rpucuda/weight_clipper.h b/src/rpucuda/weight_clipper.h index 612c13e7..9f6378bc 100644 --- a/src/rpucuda/weight_clipper.h +++ b/src/rpucuda/weight_clipper.h @@ -65,13 +65,13 @@ template class WeightClipper { public: explicit WeightClipper(int x_size, int d_size); - WeightClipper(){}; + WeightClipper() {}; /* in-place clipping of weights */ void apply(T *weights, const WeightClipParameter &wclpar); - void dumpExtra(RPU::state_t &extra, const std::string prefix){}; - void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict){}; + void dumpExtra(RPU::state_t &extra, const std::string prefix) {}; + void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict) {}; private: void clip(T *weights, T clip_value); diff --git a/src/rpucuda/weight_drifter.h b/src/rpucuda/weight_drifter.h index 098edd7a..9ae1fbf7 100644 --- a/src/rpucuda/weight_drifter.h +++ b/src/rpucuda/weight_drifter.h @@ -95,7 +95,7 @@ template class WeightDrifter { explicit WeightDrifter(int size); explicit WeightDrifter(int size, const DriftParameter &par); // forces SimpleDrift explicit WeightDrifter(int size, const DriftParameter &par, RealWorldRNG *rng); - WeightDrifter(){}; + WeightDrifter() {}; virtual ~WeightDrifter() = default; WeightDrifter(const WeightDrifter &) = default; diff --git a/src/rpucuda/weight_modifier.h b/src/rpucuda/weight_modifier.h index 5c12ffc1..768c795d 100644 --- a/src/rpucuda/weight_modifier.h +++ b/src/rpucuda/weight_modifier.h @@ -149,7 +149,7 @@ template class WeightModifier { public: explicit WeightModifier(int x_size, int d_size); - WeightModifier(){}; + WeightModifier() {}; /* buffers the weight changes and redraws the drop connection*/ void apply(T *new_weights, const T *weights, const WeightModifierParameter &wmpar); diff --git a/src/rpucuda/weight_remapper.h b/src/rpucuda/weight_remapper.h index 5bda4455..9545c9c6 100644 --- a/src/rpucuda/weight_remapper.h +++ b/src/rpucuda/weight_remapper.h @@ -96,7 +96,7 @@ template class WeightRemapper { public: explicit WeightRemapper(int x_size, int d_size); - WeightRemapper(){}; + WeightRemapper() {}; /* in-place remap of weights */ void apply( @@ -112,8 +112,8 @@ template class WeightRemapper { T *scales = nullptr, T *biases = nullptr); - void dumpExtra(RPU::state_t &extra, const std::string prefix){}; - void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict){}; + void dumpExtra(RPU::state_t &extra, const std::string prefix) {}; + void loadExtra(const RPU::state_t &extra, const std::string prefix, bool strict) {}; private: std::vector max_values_; diff --git a/tests/test_calibration.py b/tests/test_calibration.py index 72525b35..c5d4ac26 100644 --- a/tests/test_calibration.py +++ b/tests/test_calibration.py @@ -56,7 +56,7 @@ def create_analog_network(rpu_config): def get_rpu( - rpu: Union[TorchInferenceRPUConfig, InferenceRPUConfig, QuantizedTorchInferenceRPUConfig] + rpu: Union[TorchInferenceRPUConfig, InferenceRPUConfig, QuantizedTorchInferenceRPUConfig], ): """Create test rpu config.""" rpu.forward.out_noise = 0.01 diff --git a/tests/test_quantized_tile.py b/tests/test_quantized_tile.py index 20b4ae3e..0dfe0ce0 100644 --- a/tests/test_quantized_tile.py +++ b/tests/test_quantized_tile.py @@ -25,7 +25,7 @@ def test_output_quantization(n_bits, symmetric, range_estimator): """Test that output quantization works, returning the appropriate number of states""" def set_perfect_rpuconfig( - rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig] + rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig], ): rpu_config.forward.is_perfect = True if isinstance(rpu_config, QuantizedTorchInferenceRPUConfig): @@ -70,7 +70,7 @@ def test_array_module_output_quantization( """Test that when an array is used, output quantization is properly applied""" def set_perfect_rpuconfig( - rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig] + rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig], ): rpu_config.forward.is_perfect = True if isinstance(rpu_config, QuantizedTorchInferenceRPUConfig): @@ -107,7 +107,7 @@ def test_quantized_periphery(n_bits, symmetric, arr_rows, arr_columns): """Test that quantized periphery is properly applied""" def set_perfect_rpuconfig_with_periphery( - rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig] + rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig], ): rpu_config.forward.is_perfect = True rpu_config.mapping.weight_scaling_omega = 1.0 diff --git a/tests/test_torch_tiles.py b/tests/test_torch_tiles.py index d913adeb..765ec2b6 100644 --- a/tests/test_torch_tiles.py +++ b/tests/test_torch_tiles.py @@ -459,7 +459,7 @@ def test_noise_and_bound_management( """ def set_bm_nm( - rpu: Union[TorchInferenceRPUConfig, InferenceRPUConfig] + rpu: Union[TorchInferenceRPUConfig, InferenceRPUConfig], ) -> Union[TorchInferenceRPUConfig, InferenceRPUConfig]: """Set the rpu config.""" rpu.forward.out_noise = 0.0 From cd0f0c86bd70cc3767c4f507a532867eead683b6 Mon Sep 17 00:00:00 2001 From: Corey Lammie Date: Mon, 15 Sep 2025 18:33:11 +0200 Subject: [PATCH 02/33] Add files via upload (#739) Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- notebooks/analog_fusion.ipynb | 27 +++++++++------------------ 1 file changed, 9 insertions(+), 18 deletions(-) diff --git a/notebooks/analog_fusion.ipynb b/notebooks/analog_fusion.ipynb index c758e34c..8065e0f4 100644 --- a/notebooks/analog_fusion.ipynb +++ b/notebooks/analog_fusion.ipynb @@ -37,23 +37,14 @@ "metadata": {}, "outputs": [], "source": [ - "# Install the aihwkit and other needed libraries. \n", - "# You can uncomment this section or parts of it if the libraries are installed in your environment. \n", - "import os\n", - "return_code = os.system(\"which nvidia-smi\")\n", - "#if torch.cuda.is_available():\n", - "if return_code == 0:\n", - " DEVICE = 'cuda'\n", - " !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - " %pip install aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - " USE_CUDA = 1\n", - "else:\n", - " DEVICE = 'cpu'\n", - " %pip install aihwkit\n", - " USE_CUDA = 0\n", - "\n", - "%pip install matplotlib\n", - "%pip install mpl-scatter-density" + "# Install the aihwkit and other needed libraries. Using Python 3.12, only CPU wheels are currently supported.\n", + "!pip install torch==2.6.0 torchvision==0.21.0\n", + "!wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "!pip install aihwkit-1.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl --no-dependencies\n", + "!pip install matplotlib\n", + "!pip install mpl-scatter-density\n", + "DEVICE = 'cpu'\n", + "USE_CUDA = 0" ] }, { @@ -251,7 +242,7 @@ "model = create_analog_lenet5_network()\n", "\n", "# Load the state dictionaries from the checkpoint file\n", - "state_dict = torch.load(WEIGHT_PATH, DEVICE)\n", + "state_dict = torch.load(WEIGHT_PATH, DEVICE, weights_only=False)\n", "\n", "# Convert the legacy checkpoint if the checkpoint was captured with the aihwkit version 0.7.1. Otherwise you do not need to do the next step\n", "state_dict, _ = convert_legacy_checkpoint(state_dict, model)\n", From 3f376c80fe5d8f1c4b5c1038ca4fea75af94040c Mon Sep 17 00:00:00 2001 From: pablocarmona Date: Wed, 24 Sep 2025 13:41:49 +0200 Subject: [PATCH 03/33] update notebooks wheel to 1.0.0 gpu enabled (#741) * update notebooks wheel to 1.0.0 gpu enabled Signed-off-by: PabloCarmona * update analog fusion notebook to use cuda Signed-off-by: PabloCarmona * add ipywidgets top lenet5 notebook Signed-off-by: PabloCarmona * update and run notebooks Signed-off-by: PabloCarmona --------- Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- notebooks/LeNet5.ipynb | 21 +- ...analog_device_nonIdealities_tutorial.ipynb | 285 ++---------------- notebooks/analog_fusion.ipynb | 58 ++-- notebooks/analog_sensitivity_LeNet5.ipynb | 18 +- notebooks/analog_training_LeNet5.ipynb | 14 +- notebooks/analog_training_LeNet5_TT.ipynb | 21 +- notebooks/analog_training_LeNet5_hwa.ipynb | 21 +- notebooks/analog_training_LeNet5_plot.ipynb | 14 +- .../iscas_tutorial/mobilebert_squad.ipynb | 6 +- notebooks/tutorial/analog_training.ipynb | 11 +- .../tutorial/extending_functionality.ipynb | 11 +- notebooks/tutorial/hw_aware_training.ipynb | 11 +- ...ost_training_input_range_calibration.ipynb | 7 +- 13 files changed, 100 insertions(+), 398 deletions(-) diff --git a/notebooks/LeNet5.ipynb b/notebooks/LeNet5.ipynb index 035596c8..ae7c9b95 100644 --- a/notebooks/LeNet5.ipynb +++ b/notebooks/LeNet5.ipynb @@ -67,10 +67,9 @@ "# To install the cpu-only enabled kit, un-comment the line below\n", "# %pip install aihwkit\n", "\n", - "# To install the GPU-enabled wheel go to https://aihwkit.readthedocs.io/en/latest/advanced_install.html#install-the-aihwkit-using-pip\n", - "# and copy the option on GPU options that best suits your enviroment and paste it below and run the cell. For example, Python 3.10 and CUDA 12.1:\n", - "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - "# %pip install aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n" + "# GPU-enabled wheel\n", + "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "# %pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n" ] }, { @@ -536,13 +535,6 @@ "\n", "training_loop(model, criterion, optimizer, train_data, test_data)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -551,11 +543,8 @@ "name": "LeNet5.ipynb", "provenance": [] }, - "interpreter": { - "hash": "8afadb82c8c635d284d204a78cd7f3b56094702ee8f92f25084bfbbc5b27362b" - }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "notebooks_py312", "language": "python", "name": "python3" }, @@ -569,7 +558,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.11" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/analog_device_nonIdealities_tutorial.ipynb b/notebooks/analog_device_nonIdealities_tutorial.ipynb index a8965fb4..0cff14f2 100644 --- a/notebooks/analog_device_nonIdealities_tutorial.ipynb +++ b/notebooks/analog_device_nonIdealities_tutorial.ipynb @@ -32,29 +32,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import os\n", "import numpy as np\n", @@ -85,40 +65,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Word Line Number')" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "PAR_NumWL = 256\n", "PAR_NumBL = 256\n", @@ -180,48 +129,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_4111/3287664176.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " PAR_MatrixRD2D[i][j]= DUM_C*(PAR_t**-DUM_Factor)\n" - ] - }, - { - "data": { - "text/plain": [ - "(0.0, 3500.0)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Get state dependent mean and standard deviation of drift coefficients\n", "PAR_DriftMean=[]\n", @@ -297,50 +207,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_4111/796165106.py:25: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " PAR_MatrixRead2D[i,j]= DUM_A+Factor*np.random.uniform(0,1,1)\n" - ] - }, - { - "data": { - "text/plain": [ - "(0.0, 3500.0)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Get state dependent standard deviation/READ noise\n", "PAR_READStd=[]\n", @@ -409,48 +280,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_4111/4055078216.py:28: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " PAR_MatrixTS2D[i][j]= DUM_E*np.exp(-DUM_Factor/(8.6e-5*PAR_T))\n" - ] - }, - { - "data": { - "text/plain": [ - "(0.0, 3500.0)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Get state dependent mean and standard deviation of Activation Energy\n", "PAR_TempMean=[]\n", @@ -524,48 +356,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_4111/1669556741.py:34: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " PAR_MatrixBP2D[j][i]= DUM_C*np.divide(1,DUM_Factor)\n" - ] - }, - { - "data": { - "text/plain": [ - "(0.0, 3500.0)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Get state dependent mean and standard deviation of α\n", "x=np.arange(0.01,1,0.01)\n", @@ -646,30 +439,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 0.35)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "## Intitialize an Input Vector. Each element in the vector can be +200 mV or -200 mV\n", "\n", @@ -731,30 +503,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 0.35)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# GDC for Temperature fluctuation correction\n", "PAR_FactoTS= np.divide(np.sum([MAC_I[1:20]]),np.sum([MAC_IbarTemp[1:20]]))\n", @@ -861,7 +612,7 @@ ], "metadata": { "kernelspec": { - "display_name": "notebooks-1.0.0", + "display_name": "notebooks_py312", "language": "python", "name": "python3" }, @@ -875,7 +626,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/notebooks/analog_fusion.ipynb b/notebooks/analog_fusion.ipynb index 8065e0f4..8979e6ed 100644 --- a/notebooks/analog_fusion.ipynb +++ b/notebooks/analog_fusion.ipynb @@ -37,19 +37,15 @@ "metadata": {}, "outputs": [], "source": [ - "# Install the aihwkit and other needed libraries. Using Python 3.12, only CPU wheels are currently supported.\n", - "!pip install torch==2.6.0 torchvision==0.21.0\n", - "!wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", - "!pip install aihwkit-1.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl --no-dependencies\n", - "!pip install matplotlib\n", - "!pip install mpl-scatter-density\n", - "DEVICE = 'cpu'\n", - "USE_CUDA = 0" + "# Install the aihwkit and other needed libraries.\n", + "!wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "!pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "!pip install matplotlib mpl-scatter-density scikit-learn" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "28305c3c-2844-47e7-8f2f-4e72c46f6bde", "metadata": {}, "outputs": [], @@ -61,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "86ff4cb0-8437-4df2-a8c2-d66bb15d5841", "metadata": {}, "outputs": [], @@ -81,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "d7f4592b-392f-4507-bc23-8d5be1fdeaba", "metadata": {}, "outputs": [], @@ -106,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "e0d40aa0", "metadata": {}, "outputs": [], @@ -141,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "fb617d05-542b-4879-adaf-0e3e98d561dd", "metadata": {}, "outputs": [], @@ -151,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "1c88b597-ccfe-48ed-a3ef-8b45d2572a94", "metadata": {}, "outputs": [], @@ -159,12 +155,14 @@ "BATCH_SIZE = 8\n", "N_CLASSES = 10\n", "criterion = nn.CrossEntropyLoss()\n", - "PATH_DATASET = './data'" + "PATH_DATASET = './data'\n", + "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "USE_CUDA = torch.cuda.is_available()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "cf1c4762-9fba-497e-8e50-89e5cf228475", "metadata": {}, "outputs": [], @@ -182,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "f318663a-97d9-4606-8094-1e71d415561c", "metadata": {}, "outputs": [], @@ -287,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "4158f0d5-e5c3-4c7e-8840-ad7c220194d3", "metadata": {}, "outputs": [], @@ -303,13 +301,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "8eee95b7-24ed-4d61-ab28-5afb0c8cf6e4", "metadata": {}, "outputs": [], "source": [ "# Display the file by uncommenting out the line below. NOTE: change the file name to your specified file name in the above step.\n", - "#!ls -l data/analog_lenet5.csv" + "!ls -l data/analog_lenet5.csv" ] }, { @@ -352,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "ca96af20-45de-4155-a647-f09395f9c7d3", "metadata": {}, "outputs": [], @@ -372,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "394e3796-39f9-4f24-af0f-08d1b511fe66", "metadata": {}, "outputs": [], @@ -422,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "12e9ebc1-c9db-4d04-8d8c-96925fb8e834", "metadata": {}, "outputs": [], @@ -435,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "3fa6d07f-b766-45d2-a5b4-32e2fc5b25b6", "metadata": {}, "outputs": [], @@ -457,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "4c6ac027-7998-4453-9133-de82d3324896", "metadata": {}, "outputs": [], @@ -471,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "5d1afeb4-6046-4205-9d61-82012e3fd2a7", "metadata": { "scrolled": true @@ -561,7 +559,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "c3b5b21e-c118-46d0-9db5-de6deccb6d51", "metadata": {}, "outputs": [], @@ -574,7 +572,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "88d78208-f9d5-4cbe-b7aa-476121f995c8", "metadata": {}, "outputs": [], @@ -643,7 +641,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "notebooks_py312", "language": "python", "name": "python3" }, @@ -657,7 +655,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/notebooks/analog_sensitivity_LeNet5.ipynb b/notebooks/analog_sensitivity_LeNet5.ipynb index 9f22d6a0..434a0c01 100644 --- a/notebooks/analog_sensitivity_LeNet5.ipynb +++ b/notebooks/analog_sensitivity_LeNet5.ipynb @@ -46,10 +46,9 @@ "# To install the cpu-only enabled kit, un-comment the line below\n", "# %pip install aihwkit\n", "\n", - "# To install the GPU-enabled wheel go to https://aihwkit.readthedocs.io/en/latest/advanced_install.html#install-the-aihwkit-using-pip\n", - "# and copy the option on GPU options that best suits your enviroment and paste it below and run the cell. For example, Python 3.10 and CUDA 12.1:\n", - "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - "# %pip install aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n" + "# GPU-enabled wheel\n", + "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "# %pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n" ] }, { @@ -85,7 +84,9 @@ "%matplotlib notebook\n", "\n", "# if Google colab etc\n", - "# %matplotlib inline" + "# %matplotlib inline\n", + "\n", + "!pip install ipywidgets" ] }, { @@ -931,11 +932,8 @@ "lastKernelId": null }, "celltoolbar": "Slideshow", - "interpreter": { - "hash": "a24e3050b7661a470f98f936da6c79a9df99933256f82e80de72c7fdbcd73071" - }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "notebooks_py312", "language": "python", "name": "python3" }, @@ -949,7 +947,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.11" }, "rise": { "scroll": true, diff --git a/notebooks/analog_training_LeNet5.ipynb b/notebooks/analog_training_LeNet5.ipynb index ccdf3a84..d23a4db9 100644 --- a/notebooks/analog_training_LeNet5.ipynb +++ b/notebooks/analog_training_LeNet5.ipynb @@ -74,10 +74,9 @@ "# To install the cpu-only enabled kit, un-comment the line below\n", "# %pip install aihwkit\n", "\n", - "# To install the GPU-enabled wheel go to https://aihwkit.readthedocs.io/en/latest/advanced_install.html#install-the-aihwkit-using-pip\n", - "# and copy the option on GPU options that best suits your enviroment and paste it below and run the cell. For example, Python 3.10 and CUDA 12.1:\n", - "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - "# %pip install aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n" + "# GPU-enabled wheel\n", + "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "# %pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n" ] }, { @@ -600,11 +599,8 @@ "name": "Copy of LeNet5.ipynb", "provenance": [] }, - "interpreter": { - "hash": "8afadb82c8c635d284d204a78cd7f3b56094702ee8f92f25084bfbbc5b27362b" - }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "notebooks_py312", "language": "python", "name": "python3" }, @@ -618,7 +614,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/notebooks/analog_training_LeNet5_TT.ipynb b/notebooks/analog_training_LeNet5_TT.ipynb index 8cbc272c..73018c5b 100644 --- a/notebooks/analog_training_LeNet5_TT.ipynb +++ b/notebooks/analog_training_LeNet5_TT.ipynb @@ -56,10 +56,9 @@ "# To install the cpu-only enabled kit, un-comment the line below\n", "# %pip install aihwkit\n", "\n", - "# To install the GPU-enabled wheel go to https://aihwkit.readthedocs.io/en/latest/advanced_install.html#install-the-aihwkit-using-pip\n", - "# and copy the option on GPU options that best suits your enviroment and paste it below and run the cell. For example, Python 3.10 and CUDA 12.1:\n", - "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - "# %pip install aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n" + "# GPU-enabled wheel\n", + "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "# %pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n" ] }, { @@ -401,21 +400,11 @@ "\n", "training_loop(model, criterion, optimizer, train_data, test_data)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { - "interpreter": { - "hash": "8afadb82c8c635d284d204a78cd7f3b56094702ee8f92f25084bfbbc5b27362b" - }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "notebooks_py312", "language": "python", "name": "python3" }, @@ -429,7 +418,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/notebooks/analog_training_LeNet5_hwa.ipynb b/notebooks/analog_training_LeNet5_hwa.ipynb index 2e30910e..e5236d41 100644 --- a/notebooks/analog_training_LeNet5_hwa.ipynb +++ b/notebooks/analog_training_LeNet5_hwa.ipynb @@ -61,10 +61,9 @@ "# To install the cpu-only enabled kit, un-comment the line below\n", "# %pip install aihwkit\n", "\n", - "# To install the GPU-enabled wheel go to https://aihwkit.readthedocs.io/en/latest/advanced_install.html#install-the-aihwkit-using-pip\n", - "# and copy the option on GPU options that best suits your enviroment and paste it below and run the cell. For example, Python 3.10 and CUDA 12.1:\n", - "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - "# %pip install aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n" + "# GPU-enabled wheel\n", + "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "# %pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n" ] }, { @@ -467,21 +466,11 @@ "test_inference(model, criterion, test_data)\n", "\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { - "interpreter": { - "hash": "8afadb82c8c635d284d204a78cd7f3b56094702ee8f92f25084bfbbc5b27362b" - }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "notebooks_py312", "language": "python", "name": "python3" }, @@ -495,7 +484,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/notebooks/analog_training_LeNet5_plot.ipynb b/notebooks/analog_training_LeNet5_plot.ipynb index 8bce291f..3de9e4b6 100644 --- a/notebooks/analog_training_LeNet5_plot.ipynb +++ b/notebooks/analog_training_LeNet5_plot.ipynb @@ -76,10 +76,9 @@ "# To install the cpu-only enabled kit, un-comment the line below\n", "# %pip install aihwkit\n", "\n", - "# To install the GPU-enabled wheel go to https://aihwkit.readthedocs.io/en/latest/advanced_install.html#install-the-aihwkit-using-pip\n", - "# and copy the option on GPU options that best suits your enviroment and paste it below and run the cell. For example, Python 3.10 and CUDA 12.1:\n", - "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - "# %pip install aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n" + "# GPU-enabled wheel\n", + "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "# %pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n" ] }, { @@ -655,11 +654,8 @@ "name": "Copy of LeNet5.ipynb", "provenance": [] }, - "interpreter": { - "hash": "8afadb82c8c635d284d204a78cd7f3b56094702ee8f92f25084bfbbc5b27362b" - }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "notebooks_py312", "language": "python", "name": "python3" }, @@ -673,7 +669,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/notebooks/iscas_tutorial/mobilebert_squad.ipynb b/notebooks/iscas_tutorial/mobilebert_squad.ipynb index 1bc60e00..1f3bcebe 100644 --- a/notebooks/iscas_tutorial/mobilebert_squad.ipynb +++ b/notebooks/iscas_tutorial/mobilebert_squad.ipynb @@ -42,9 +42,9 @@ "# To install the cpu-only enabled kit, un-comment the line below\n", "#!pip install aihwkit\n", "\n", - "# To install the GPU-enabled wheel, un-comment the lines below\n", - "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.0+cuda117-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - "# !pip install aihwkit-0.9.0+cuda117-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" + "# GPU-enabled wheel\n", + "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "# !pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl" ] }, { diff --git a/notebooks/tutorial/analog_training.ipynb b/notebooks/tutorial/analog_training.ipynb index b6863b2f..698abeb3 100644 --- a/notebooks/tutorial/analog_training.ipynb +++ b/notebooks/tutorial/analog_training.ipynb @@ -51,10 +51,9 @@ "# To install the cpu-only enabled kit, un-comment the line below\n", "# %pip install aihwkit\n", "\n", - "# To install the GPU-enabled wheel go to https://aihwkit.readthedocs.io/en/latest/advanced_install.html#install-the-aihwkit-using-pip\n", - "# and copy the option on GPU options that best suits your enviroment and paste it below and run the cell. For example, Python 3.10 and CUDA 12.1:\n", - "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - "# %pip install aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n" + "# GPU-enabled wheel\n", + "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "# %pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n" ] }, { @@ -1086,7 +1085,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "update-notebooks", + "display_name": "notebooks_py312", "language": "python", "name": "python3" }, @@ -1100,7 +1099,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.12.11" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/tutorial/extending_functionality.ipynb b/notebooks/tutorial/extending_functionality.ipynb index 3bf56fd4..29477950 100644 --- a/notebooks/tutorial/extending_functionality.ipynb +++ b/notebooks/tutorial/extending_functionality.ipynb @@ -52,10 +52,9 @@ "# To install the cpu-only enabled kit, un-comment the line below\n", "# %pip install aihwkit\n", "\n", - "# To install the GPU-enabled wheel go to https://aihwkit.readthedocs.io/en/latest/advanced_install.html#install-the-aihwkit-using-pip\n", - "# and copy the option on GPU options that best suits your enviroment and paste it below and run the cell. For example, Python 3.10 and CUDA 12.1:\n", - "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - "# %pip install aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", + "# GPU-enabled wheel\n", + "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "# %pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", "\n", "# Install some prerequisites\n", "%pip install pytorch-lightning pandas\n" @@ -693,7 +692,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "update-notebooks", + "display_name": "notebooks_py312", "language": "python", "name": "python3" }, @@ -707,7 +706,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.12.11" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/tutorial/hw_aware_training.ipynb b/notebooks/tutorial/hw_aware_training.ipynb index 1f156ad2..276f40a3 100644 --- a/notebooks/tutorial/hw_aware_training.ipynb +++ b/notebooks/tutorial/hw_aware_training.ipynb @@ -40,10 +40,9 @@ "# To install the cpu-only enabled kit, un-comment the line below\n", "# %pip install aihwkit\n", "\n", - "# To install the GPU-enabled wheel go to https://aihwkit.readthedocs.io/en/latest/advanced_install.html#install-the-aihwkit-using-pip\n", - "# and copy the option on GPU options that best suits your enviroment and paste it below and run the cell. For example, Python 3.10 and CUDA 12.1:\n", - "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - "# %pip install aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n" + "# GPU-enabled wheel\n", + "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "# %pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n" ] }, { @@ -725,7 +724,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "notebooks_py312", "language": "python", "name": "python3" }, @@ -739,7 +738,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/notebooks/tutorial/post_training_input_range_calibration.ipynb b/notebooks/tutorial/post_training_input_range_calibration.ipynb index 36059044..e30ee762 100644 --- a/notebooks/tutorial/post_training_input_range_calibration.ipynb +++ b/notebooks/tutorial/post_training_input_range_calibration.ipynb @@ -40,10 +40,9 @@ "# To install the cpu-only enabled kit, un-comment the line below\n", "# %pip install aihwkit\n", "\n", - "# To install the GPU-enabled wheel go to https://aihwkit.readthedocs.io/en/latest/advanced_install.html#install-the-aihwkit-using-pip\n", - "# and copy the option on GPU options that best suits your enviroment and paste it below and run the cell. For example, Python 3.10 and CUDA 12.1:\n", - "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n", - "# %pip install aihwkit-0.9.2+cuda121-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl" + "# GPU-enabled wheel\n", + "# !wget https://aihwkit-gpu-demo.s3.us-east.cloud-object-storage.appdomain.cloud/aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\n", + "# %pip install aihwkit-1.0.0+cuda121-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl" ] }, { From 0e8507535ae0ba05a3fb57b3f0e22563fd928080 Mon Sep 17 00:00:00 2001 From: pablocarmona Date: Mon, 29 Sep 2025 18:50:29 +0200 Subject: [PATCH 04/33] change online demo link to proper one (#743) Signed-off-by: Pablo Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d22515bc..5b4b67a4 100644 --- a/README.md +++ b/README.md @@ -237,7 +237,7 @@ This project is licensed under [MIT License]. [resistive random-access memory]: https://en.wikipedia.org/wiki/Resistive_random-access_memory [Flash memory]: https://en.wikipedia.org/wiki/Flash_memory [Kirchhoff’s circuits laws]: https://en.wikipedia.org/wiki/Kirchhoff%27s_circuit_laws -[online demo]: https://analog-ai-demo.mybluemix.net/ +[online demo]: https://aihw-composer.draco.res.ibm.com/analog-ai [AIHW Composer]: https://aihw-composer.draco.res.ibm.com [award]: https://conferences.computer.org/services/2023/awards/ [CUDA Dockerfile instructions]: https://github.com/IBM/aihwkit/blob/master/docs/source/advanced_install.rst#cuda-enabled-docker-image From 399bae0ecbbc4263e2a10660033d5bb91e204491 Mon Sep 17 00:00:00 2001 From: PabloCarmona Date: Wed, 12 Nov 2025 18:45:18 +0100 Subject: [PATCH 05/33] initial rc for v1.1.0 Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- CHANGELOG.md | 14 +++++++++++++- LICENSE.txt | 2 +- src/aihwkit/VERSION.txt | 2 +- 3 files changed, 15 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6df886fd..90c67943 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -12,6 +12,19 @@ The format is based on [Keep a Changelog], and this project adheres to * `Fixed` for any bug fixes. * `Security` in case of vulnerabilities. +## [1.1.0] - 2025/11/10 + +### Added +* Add newly uploaded resources for CPU-only wheels (\#739) + +### Changed +* Replace legacy release-build workflow with the updated build process (\#744) +* Point the online demo link to the correct destination (\#743) +* Update bundled notebook wheel to the GPU-enabled 1.0.0 release (\#741) + +### Fixed +* Fix memory issues and bugs in analog training for CUDA (\#732) + ## [1.0.0] - 2025/05/19 ## Added @@ -27,7 +40,6 @@ The format is based on [Keep a Changelog], and this project adheres to * Fix Hardware-Aware training tutorial notebooks (\#700) * Fix Post-Training Input Range Calibration notebook (\#716) -* Fix memory issues and bugs in analog training for CUDA (\#732) ## Changed diff --git a/LICENSE.txt b/LICENSE.txt index 741deca2..5eb1830b 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,6 +1,6 @@ MIT License -Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved. +Copyright 2020, 2021, 2022, 2023, 2024, 2025 IBM. All Rights Reserved. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/src/aihwkit/VERSION.txt b/src/aihwkit/VERSION.txt index 3eefcb9d..9084fa2f 100644 --- a/src/aihwkit/VERSION.txt +++ b/src/aihwkit/VERSION.txt @@ -1 +1 @@ -1.0.0 +1.1.0 From 1deaafb3856d8211671bf2f891fbc5c42f18a004 Mon Sep 17 00:00:00 2001 From: pablocarmona Date: Tue, 7 Oct 2025 19:35:21 +0200 Subject: [PATCH 06/33] remove release-build.yaml to change to new and stablish build process (#744) Signed-off-by: Pablo Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- .github/workflows/release-build.yml | 234 ---------------------------- 1 file changed, 234 deletions(-) delete mode 100644 .github/workflows/release-build.yml diff --git a/.github/workflows/release-build.yml b/.github/workflows/release-build.yml deleted file mode 100644 index b4a129bb..00000000 --- a/.github/workflows/release-build.yml +++ /dev/null @@ -1,234 +0,0 @@ -name: release-build - -on: - workflow_dispatch: - create: - tags: 'v**' - -permissions: - id-token: write - contents: read - -env: - COS_ACCESS_KEY_ID: ${{ secrets.COS_ACCESS_KEY_ID }} - COS_SECRET_ACCESS_KEY: ${{ secrets.COS_SECRET_ACCESS_KEY }} - COS_BUCKET: ${{ secrets.COS_BUCKET }} - COS_ENDPOINT: ${{ secrets.COS_ENDPOINT }} - -jobs: - # ──────────────────────────── - # Test on Python 3.10 - # ──────────────────────────── - test-py310: - name: Test (Python 3.10) - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - - name: Set up Python 3.10 - uses: actions/setup-python@v5 - with: - python-version: 3.10.17 - - - name: Install system dependencies - run: | - sudo apt-get -qq update - sudo apt-get install -y ca-certificates libopenblas-dev gcc-9 g++-9 - sudo update-alternatives \ - --install /usr/bin/gcc gcc /usr/bin/gcc-9 60 \ - --slave /usr/bin/g++ g++ /usr/bin/g++-9 - - - name: Build and install aihwkit wheel - run: | - pip install -r requirements.txt - make build_inplace - - - name: Run pytest - run: | - pip install -r requirements-dev.txt - make pytest - env: - TEST_DATASET: true - - # ──────────────────────────── - # Lint on Python 3.10 - # ──────────────────────────── - lint-py310: - name: Lint (Python 3.10) - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - - name: Set up Python 3.10 - uses: actions/setup-python@v5 - with: - python-version: 3.10.17 - - - name: Install system dependencies - run: | - sudo apt-get -qq update - sudo apt-get install -y ca-certificates libopenblas-dev gcc-9 g++-9 - sudo update-alternatives \ - --install /usr/bin/gcc gcc /usr/bin/gcc-9 60 \ - --slave /usr/bin/g++ g++ /usr/bin/g++-9 - - - name: Build and install aihwkit wheel - run: | - pip install -r requirements.txt - make build_inplace - - - name: Run lint checks - run: | - pip install -r requirements-dev.txt - pip install -r requirements-examples.txt - make pycodestyle - make pylint - make mypy - # ──────────────────────────────────────── - # Stage: Test multiple Python versions - # ──────────────────────────────────────── - test-matrix: - name: Tests (3.8 & 3.9) - runs-on: ubuntu-latest - strategy: - matrix: - python-version: [3.8, 3.9] - steps: - - uses: actions/checkout@v3 - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python-version }} - - - name: Install system deps - run: | - sudo apt-get -qq update - sudo apt-get install -y ca-certificates libopenblas-dev gcc-9 g++-9 - sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 60 \ - --slave /usr/bin/g++ g++ /usr/bin/g++-9 - - - name: Build and install aihwkit wheel - run: | - pip install -r requirements.txt - make build_inplace - - - name: Run pytest - run: | - pip install -r requirements-dev.txt - make pytest - - # ──────────────────────────── - # Stage: Build & Deploy Wheels - # ──────────────────────────── - build-and-deploy-linux: - name: Build & Deploy wheels (manylinux) - runs-on: ubuntu-latest - services: - docker: - image: docker:20.10.16 - options: --privileged - env: - CIBW_ENVIRONMENT: "TORCH_VERSION_SPECIFIER='==2.3.1+cu121'" - CIBW_BEFORE_BUILD: "pip install torch==2.3.1+cu121 torchvision -f https://download.pytorch.org/whl/torch_stable.html && pip install -r requirements.txt" - CIBW_MANYLINUX_X86_64_IMAGE: "aihwkit/manylinux2014_x86_64_aihwkit_cuda" - CIBW_REPAIR_WHEEL_COMMAND: "auditwheel repair -w {dest_dir} {wheel} --exclude libtorch_python.so" - CIBW_BUILD: "cp39-manylinux_x86_64 cp310-manylinux_x86_64" - AIHWKIT_VERSION_SUFFIX: "+cuda121" - steps: - - uses: actions/checkout@v3 - - - name: Pull manylinux image - run: docker pull $CIBW_MANYLINUX_X86_64_IMAGE - - - name: Install cibuildwheel - run: python3 -m pip install cibuildwheel==2.23.3 - - - name: Build wheels - run: python3 -m cibuildwheel --output-dir wheelhouse - - - name: Sync wheels to IBM COS - env: - AWS_ACCESS_KEY_ID: ${{ secrets.COS_ACCESS_KEY_ID }} - AWS_SECRET_ACCESS_KEY: ${{ secrets.COS_SECRET_ACCESS_KEY }} - AWS_DEFAULT_REGION: us-south-1 - run: | - which aws || pip install awscli --upgrade - aws s3 sync wheelhouse/ s3://${{ env.COS_BUCKET }}/ \ - --acl public-read \ - --delete \ - --endpoint-url https://${{ env.COS_ENDPOINT }} - - # build-and-deploy-macos: - # name: Build & Deploy wheels (macOS) - # runs-on: macos-latest - # env: - # CIBW_ENVIRONMENT: "TORCH_VERSION_SPECIFIER='==2.0.1'" - # CIBW_BEFORE_BUILD: "pip install torch==2.0.1 torchvision && pip install ./delocate && pip install -r requirements.txt" - # CIBW_BUILD: "cp38-macosx_x86_64 cp39-macosx_x86_64" - # steps: - # - uses: actions/checkout@v3 - - # - name: Install Homebrew deps - # run: | - # brew update - # brew install openblas - - # - name: Clone delocate - # run: git clone -b aihwkit https://github.com/aihwkit-bot/delocate.git - - # - name: Install cibuildwheel - # run: python3 -m pip install cibuildwheel==2.8.1 - - # - name: Build wheels - # run: python3 -m cibuildwheel --output-dir wheelhouse --platform macos - - # - name: Sync wheels to IBM COS - # env: - # AWS_ACCESS_KEY_ID: ${{ secrets.COS_ACCESS_KEY_ID }} - # AWS_SECRET_ACCESS_KEY: ${{ secrets.COS_SECRET_ACCESS_KEY }} - # AWS_DEFAULT_REGION: us-south-1 - # run: | - # which aws || pip install awscli --upgrade - # aws s3 sync wheelhouse/ s3://${{ env.COS_BUCKET }}/ \ - # --acl public-read \ - # --delete \ - # --endpoint-url https://${{ env.COS_ENDPOINT }} - - # build-and-deploy-windows: - # name: Build & Deploy wheels (Windows) - # runs-on: windows-latest - # env: - # CIBW_ENVIRONMENT: "TORCH_VERSION_SPECIFIER='==2.4.1'" - # CIBW_BEFORE_BUILD: "pip install torch==2.4.1 && pip install -r requirements.txt" - # CIBW_BUILD: "cp38-win_amd64 cp39-win_amd64 cp310-win_amd64" - # OPENBLAS_ROOT: C:\\BLAS - # OPENBLAS_ROOT_DIR: C:\\BLAS - # steps: - # - uses: actions/checkout@v3 - - # - name: Install Python & BLAS - # run: | - # choco install python --version=3.8.6 -y - # SET PATH=C:\Python38;C:\Python38\Scripts;%PATH% - # mkdir C:\BLAS - # Invoke-WebRequest -Uri https://github.com/xianyi/OpenBLAS/releases/download/v0.3.12/OpenBLAS-0.3.12-x64.zip -OutFile openblas.zip - # Expand-Archive openblas.zip -DestinationPath C:\BLAS - - # - name: Install cibuildwheel - # run: python -m pip install cibuildwheel==2.8.1 - - # - name: Build wheels - # run: python -m cibuildwheel --output-dir wheelhouse - - # - name: Sync wheels to IBM COS - # env: - # AWS_ACCESS_KEY_ID: ${{ secrets.COS_ACCESS_KEY_ID }} - # AWS_SECRET_ACCESS_KEY: ${{ secrets.COS_SECRET_ACCESS_KEY }} - # AWS_DEFAULT_REGION: us-south-1 - # run: | - # which aws || pip install awscli --upgrade - # aws s3 sync wheelhouse/ s3://${{ env.COS_BUCKET }}/ \ - # --acl public-read \ - # --delete \ - # --endpoint-url https://${{ env.COS_ENDPOINT }} From ba2e1693a89bf2397c86af27b55330b99570a66b Mon Sep 17 00:00:00 2001 From: pablocarmona Date: Wed, 12 Nov 2025 18:46:06 +0100 Subject: [PATCH 07/33] Extensions fix (#745) * modify CMakeLists to link properly extensions_ops and cuda together Signed-off-by: PabloCarmona * remove strict flag to solve Py reference errors Signed-off-by: PabloCarmona --------- Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- CMakeLists.txt | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 22592ca5..97254ed0 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -139,7 +139,12 @@ if (BUILD_EXTENSION) set_target_properties(AIHWKIT_EXTENSION_OPS PROPERTIES CXX_STANDARD 17 POSITION_INDEPENDENT_CODE ON) - target_link_libraries(AIHWKIT_EXTENSION_OPS torch_python c10 torch_cpu) + target_link_libraries(AIHWKIT_EXTENSION_OPS + PUBLIC + torch_python + c10 + torch_cpu + ) target_include_directories(AIHWKIT_EXTENSION_OPS PRIVATE src/aihwkit/extension/extension_src) if(WIN32) @@ -148,7 +153,13 @@ if (BUILD_EXTENSION) if(USE_CUDA) add_library(AIHWKIT_EXTENSION_OPS_GPU ${AIHWKIT_EXTENSION_OPS_GPU_SRCS}) - target_link_libraries(AIHWKIT_EXTENSION_OPS_GPU AIHWKIT_EXTENSION_OPS c10_cuda torch_cuda cudart) + target_link_libraries(AIHWKIT_EXTENSION_OPS_GPU + PUBLIC + AIHWKIT_EXTENSION_OPS + c10_cuda + torch_cuda + cudart + ) target_include_directories(AIHWKIT_EXTENSION_OPS_GPU PRIVATE src/aihwkit/extension/extension_src) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") @@ -171,7 +182,11 @@ if (BUILD_EXTENSION) POSITION_INDEPENDENT_CODE ON) if (USE_CUDA) - target_link_libraries(${extension_module_name} PRIVATE AIHWKIT_EXTENSION_OPS_GPU) + target_link_libraries(${extension_module_name} + PRIVATE + AIHWKIT_EXTENSION_OPS_GPU + AIHWKIT_EXTENSION_OPS + ) else() target_link_libraries(${extension_module_name} PRIVATE AIHWKIT_EXTENSION_OPS) endif() From 6bf8acc1acbb34a48868c58ecaf0410e2652749d Mon Sep 17 00:00:00 2001 From: PabloCarmona Date: Thu, 13 Nov 2025 13:51:15 +0100 Subject: [PATCH 08/33] fix matplotlib install for Python 3.10 Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- .github/workflows/test-and-lint.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/test-and-lint.yml b/.github/workflows/test-and-lint.yml index 5835de08..495869ed 100644 --- a/.github/workflows/test-and-lint.yml +++ b/.github/workflows/test-and-lint.yml @@ -72,6 +72,7 @@ jobs: run: | pip install -r requirements-dev.txt pip install -r requirements-examples.txt + pip install matplotlib make pycodestyle make pylint make mypy From f8e7f3a935c9529d5b957cf320ff1026c8e31364 Mon Sep 17 00:00:00 2001 From: PabloCarmona Date: Thu, 13 Nov 2025 13:53:31 +0100 Subject: [PATCH 09/33] fix matplotlib install for Python 3.10 Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- .github/workflows/test-and-lint.yml | 1 - setup.cfg | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/test-and-lint.yml b/.github/workflows/test-and-lint.yml index 495869ed..5835de08 100644 --- a/.github/workflows/test-and-lint.yml +++ b/.github/workflows/test-and-lint.yml @@ -72,7 +72,6 @@ jobs: run: | pip install -r requirements-dev.txt pip install -r requirements-examples.txt - pip install matplotlib make pycodestyle make pylint make mypy diff --git a/setup.cfg b/setup.cfg index 74cf3650..5b3c539f 100644 --- a/setup.cfg +++ b/setup.cfg @@ -10,7 +10,7 @@ add_select = D204,D215,D401,D404 match-dir = ^(?!helpers|definitions).* [mypy] -python_version = 3.8 +python_version = 3.10 namespace_packages = True ignore_missing_imports = True warn_redundant_casts = True From 30dc030599b970953cd82aeac10c01d59bf16a28 Mon Sep 17 00:00:00 2001 From: Julian Buechel Date: Thu, 6 Nov 2025 10:11:08 +0100 Subject: [PATCH 10/33] [fix] for https://github.com/IBM/aihwkit/issues/627 Signed-off-by: Julian Buechel Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- src/aihwkit/nn/modules/conv.py | 8 ++++++-- tests/test_calibration.py | 2 +- tests/test_quantized_tile.py | 6 +++--- tests/test_torch_tiles.py | 2 +- 4 files changed, 11 insertions(+), 7 deletions(-) diff --git a/src/aihwkit/nn/modules/conv.py b/src/aihwkit/nn/modules/conv.py index ae9a7d81..6859e953 100644 --- a/src/aihwkit/nn/modules/conv.py +++ b/src/aihwkit/nn/modules/conv.py @@ -73,10 +73,14 @@ def __init__( rpu_config = SingleRPUConfig() - if tile_module_class is None: - tile_module_class = rpu_config.get_default_tile_module_class() self.in_features = self.get_tile_size(in_channels, groups, kernel_size) self.out_features = out_channels + + if tile_module_class is None: + tile_module_class = rpu_config.get_default_tile_module_class( + out_size=self.out_features, in_size=self.in_features + ) + self.analog_module = tile_module_class( self.out_features, self.in_features, rpu_config, bias ) diff --git a/tests/test_calibration.py b/tests/test_calibration.py index c5d4ac26..72525b35 100644 --- a/tests/test_calibration.py +++ b/tests/test_calibration.py @@ -56,7 +56,7 @@ def create_analog_network(rpu_config): def get_rpu( - rpu: Union[TorchInferenceRPUConfig, InferenceRPUConfig, QuantizedTorchInferenceRPUConfig], + rpu: Union[TorchInferenceRPUConfig, InferenceRPUConfig, QuantizedTorchInferenceRPUConfig] ): """Create test rpu config.""" rpu.forward.out_noise = 0.01 diff --git a/tests/test_quantized_tile.py b/tests/test_quantized_tile.py index 0dfe0ce0..20b4ae3e 100644 --- a/tests/test_quantized_tile.py +++ b/tests/test_quantized_tile.py @@ -25,7 +25,7 @@ def test_output_quantization(n_bits, symmetric, range_estimator): """Test that output quantization works, returning the appropriate number of states""" def set_perfect_rpuconfig( - rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig], + rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig] ): rpu_config.forward.is_perfect = True if isinstance(rpu_config, QuantizedTorchInferenceRPUConfig): @@ -70,7 +70,7 @@ def test_array_module_output_quantization( """Test that when an array is used, output quantization is properly applied""" def set_perfect_rpuconfig( - rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig], + rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig] ): rpu_config.forward.is_perfect = True if isinstance(rpu_config, QuantizedTorchInferenceRPUConfig): @@ -107,7 +107,7 @@ def test_quantized_periphery(n_bits, symmetric, arr_rows, arr_columns): """Test that quantized periphery is properly applied""" def set_perfect_rpuconfig_with_periphery( - rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig], + rpu_config: Union[TorchInferenceRPUConfig, QuantizedTorchInferenceRPUConfig] ): rpu_config.forward.is_perfect = True rpu_config.mapping.weight_scaling_omega = 1.0 diff --git a/tests/test_torch_tiles.py b/tests/test_torch_tiles.py index 765ec2b6..d913adeb 100644 --- a/tests/test_torch_tiles.py +++ b/tests/test_torch_tiles.py @@ -459,7 +459,7 @@ def test_noise_and_bound_management( """ def set_bm_nm( - rpu: Union[TorchInferenceRPUConfig, InferenceRPUConfig], + rpu: Union[TorchInferenceRPUConfig, InferenceRPUConfig] ) -> Union[TorchInferenceRPUConfig, InferenceRPUConfig]: """Set the rpu config.""" rpu.forward.out_noise = 0.0 From 9eb6e2d2c927d0855fb65b7ccdf608367c984d5e Mon Sep 17 00:00:00 2001 From: Julian Buechel Date: Fri, 14 Nov 2025 13:53:54 +0100 Subject: [PATCH 11/33] [deprecation] deprecate convert_to_analog_mapped Signed-off-by: Julian Buechel Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- ..._resnet34_imagenet_conversion_to_analog.py | 5 -- examples/19_analog_summary_lenet.py | 4 +- notebooks/tutorial/analog_training.ipynb | 8 +-- .../tutorial/extending_functionality.ipynb | 4 +- src/aihwkit/nn/conversion.py | 71 ------------------- 5 files changed, 8 insertions(+), 84 deletions(-) diff --git a/examples/17_resnet34_imagenet_conversion_to_analog.py b/examples/17_resnet34_imagenet_conversion_to_analog.py index ada972d1..5391ca35 100644 --- a/examples/17_resnet34_imagenet_conversion_to_analog.py +++ b/examples/17_resnet34_imagenet_conversion_to_analog.py @@ -41,9 +41,4 @@ # convolutions) model = convert_to_analog(model, rpu_config) -# Note: One can also use ``convert_to_analog_mapped`` instead to -# convert e.g. ``Conv2d`` to ``AnalogConv2dMapped`` (using a special way to -# unfold over multiple tiles in a more memory efficient way -# for some analog tiles on GPU) - print(model) diff --git a/examples/19_analog_summary_lenet.py b/examples/19_analog_summary_lenet.py index aaf1ba92..70017b49 100644 --- a/examples/19_analog_summary_lenet.py +++ b/examples/19_analog_summary_lenet.py @@ -10,7 +10,7 @@ from torch import nn # Imports from aihwkit. -from aihwkit.nn.conversion import convert_to_analog_mapped +from aihwkit.nn.conversion import convert_to_analog from aihwkit.simulator.configs import SingleRPUConfig, ConstantStepDevice from aihwkit.utils.analog_info import analog_summary @@ -36,6 +36,6 @@ nn.LogSoftmax(dim=1), ) -analog_model = convert_to_analog_mapped(model, rpu_config=rpu_config) +analog_model = convert_to_analog(model, rpu_config=rpu_config) analog_summary(analog_model, (1, 1, 28, 28)) diff --git a/notebooks/tutorial/analog_training.ipynb b/notebooks/tutorial/analog_training.ipynb index 698abeb3..417e2ae8 100644 --- a/notebooks/tutorial/analog_training.ipynb +++ b/notebooks/tutorial/analog_training.ipynb @@ -191,9 +191,9 @@ "outputs": [], "source": [ "from torchvision.models import resnet18\n", - "from aihwkit.nn.conversion import convert_to_analog_mapped\n", + "from aihwkit.nn.conversion import convert_to_analog\n", "\n", - "analog_model = convert_to_analog_mapped(resnet18(), rpu_config=rpu_config)\n", + "analog_model = convert_to_analog(resnet18(), rpu_config=rpu_config)\n", "\n", "print(analog_model)" ] @@ -575,7 +575,7 @@ "from torchmetrics.functional import accuracy\n", "\n", "from aihwkit.optim import AnalogSGD\n", - "from aihwkit.nn.conversion import convert_to_analog_mapped\n", + "from aihwkit.nn.conversion import convert_to_analog\n", "\n", "\n", "class LitAnalogModel(pl.LightningModule):\n", @@ -583,7 +583,7 @@ " super().__init__()\n", "\n", " # We simply convert the given model to analog on-the-fly\n", - " self.analog_model = convert_to_analog_mapped(model, rpu_config)\n", + " self.analog_model = convert_to_analog(model, rpu_config)\n", " self.lr = lr\n", "\n", " def forward(self, x):\n", diff --git a/notebooks/tutorial/extending_functionality.ipynb b/notebooks/tutorial/extending_functionality.ipynb index 29477950..0a06d2c3 100644 --- a/notebooks/tutorial/extending_functionality.ipynb +++ b/notebooks/tutorial/extending_functionality.ipynb @@ -128,7 +128,7 @@ "from torchmetrics.functional import accuracy\n", "\n", "from aihwkit.optim import AnalogSGD\n", - "from aihwkit.nn.conversion import convert_to_analog_mapped\n", + "from aihwkit.nn.conversion import convert_to_analog\n", "\n", "PATH_DATASET = os.path.join('data', 'DATASET')\n", "os.makedirs(PATH_DATASET, exist_ok=True)\n", @@ -163,7 +163,7 @@ " super().__init__()\n", "\n", " # We simply convert the given model to analog on-the-fly\n", - " self.analog_model = convert_to_analog_mapped(model, rpu_config)\n", + " self.analog_model = convert_to_analog(model, rpu_config)\n", " self.lr = lr\n", "\n", " def forward(self, x):\n", diff --git a/src/aihwkit/nn/conversion.py b/src/aihwkit/nn/conversion.py index 41b92f99..b751fdb5 100644 --- a/src/aihwkit/nn/conversion.py +++ b/src/aihwkit/nn/conversion.py @@ -214,77 +214,6 @@ def convert_to_analog( return module -def convert_to_analog_mapped( - module: Module, - rpu_config: RPUConfigGeneric, - tile_module_class: Optional[TileModule] = None, - specific_rpu_config_fun: Optional[Callable] = None, - module_name: str = "", - ensure_analog_root: bool = True, - exclude_modules: Optional[List[str]] = None, - inplace: bool = False, - verbose: bool = False, -) -> Module: - """Convert a given digital model to its analog counterpart with tile - mapping support. - - Note: - The torch device (cuda/cpu) is inferred from the original - models parameters, however, if multiple torch - devices are used in a given module, the corresponding analog - module is not moved to any device. - - Args: - module: The torch module to convert. All layers that are - defined in the ``conversion_map``. - rpu_config: RPU config to apply to all converted tiles. - tile_module_class: Custom tile module class - specific_rpu_config_fun: Function that modifies the generic - RPUConfig for specific modules. See - :func:`~specific_rpu_config_id` as an example how to - specify it. - - module_name: Explicitly given name of the base (root) module, - given to ``specific_rpu_config_fun``. - - ensure_analog_root: Whether to ensure that the root module is - of layer type `AnalogLayerBase` so that custom analog are - methods such as `drift_analog_weigths` are available. If - set, it will wrap the model if `AnalogWrapper` if necessary. - - Note: - - Since the module structure changes when wrapped, the - checkpoint names will also change if this is - enabled (for legacy load this might need to be disabled). - - exclude_modules: List of modules names that are in the - conversion map but should be excluded from the conversion - - inplace: Whether to for in place conversion (without deepcopy) - - verbose: Increase verbosity. Will print converted layers. - - - Returns: - Module where all the digital layers are replaced with analog - mapped layers. - - """ - return convert_to_analog( - module, - rpu_config, - tile_module_class, - _DEFAULT_MAPPED_CONVERSION_MAP, - specific_rpu_config_fun, - module_name, - ensure_analog_root, - exclude_modules, - inplace, - verbose, - ) - - def convert_to_digital( module: Module, conversion_set: Optional[Set] = None, From c56279f2919cff663834952eff6e913d85aa082e Mon Sep 17 00:00:00 2001 From: Julian Buechel Date: Fri, 14 Nov 2025 13:59:46 +0100 Subject: [PATCH 12/33] changelog for convert_to_analog_mapped Signed-off-by: Julian Buechel Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- CHANGELOG.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 90c67943..90944e6d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,9 @@ The format is based on [Keep a Changelog], and this project adheres to ### Added * Add newly uploaded resources for CPU-only wheels (\#739) +### Removed/Deprecated +* Function `convert_to_analog_mapped` in `src/aihwkit/nn/conversion.py` **deprecated and removed**. + ### Changed * Replace legacy release-build workflow with the updated build process (\#744) * Point the online demo link to the correct destination (\#743) @@ -24,6 +27,7 @@ The format is based on [Keep a Changelog], and this project adheres to ### Fixed * Fix memory issues and bugs in analog training for CUDA (\#732) +* Fix `convert_to_analog` for conv layers (\#627) ## [1.0.0] - 2025/05/19 From ba04fd2c92267e8dc29ee8a49d0ab72cfb2ce77c Mon Sep 17 00:00:00 2001 From: Corey Lammie Date: Tue, 18 Nov 2025 18:22:09 +0100 Subject: [PATCH 13/33] Create build-wheels.yml (#738) * Create build-wheels.yml Signed-off-by: PabloCarmona * Update build-wheels.yml Signed-off-by: PabloCarmona * Update build-wheels.yml Signed-off-by: PabloCarmona * Update build-wheels.yml Signed-off-by: PabloCarmona * Update build-wheels.yml Signed-off-by: PabloCarmona * Update build-wheels.yml Signed-off-by: PabloCarmona * Update build-wheels.yml Signed-off-by: PabloCarmona * add build for cuda Signed-off-by: PabloCarmona * remove blas install Signed-off-by: PabloCarmona * change torch version Signed-off-by: PabloCarmona * change torch version Signed-off-by: PabloCarmona * remove torch version for cpu and add openblas-devel for gpu Signed-off-by: PabloCarmona * update before all to match image repo Signed-off-by: PabloCarmona * fix before all command Signed-off-by: PabloCarmona * install only blas and gcc Signed-off-by: PabloCarmona * remove sudo Signed-off-by: PabloCarmona * update before all to setup cuda env Signed-off-by: PabloCarmona * change to one line Signed-off-by: PabloCarmona * modify and add more setup and clean up for optimized space Signed-off-by: PabloCarmona * remove exports Signed-off-by: PabloCarmona * add version specifier and clean up rpm file Signed-off-by: PabloCarmona * remove cuda workflow and fixed latest torch version for cpu on tag push Signed-off-by: PabloCarmona * Extensions fix (#745) * modify CMakeLists to link properly extensions_ops and cuda together Signed-off-by: PabloCarmona * remove strict flag to solve Py reference errors Signed-off-by: PabloCarmona --------- Signed-off-by: PabloCarmona * add matrix of python versions Signed-off-by: PabloCarmona * fix CI command Signed-off-by: PabloCarmona * fix CIBW_BUILD env Signed-off-by: PabloCarmona * add cibw matrix Signed-off-by: PabloCarmona * fix only python 3.10 3.11 and 3.12 Signed-off-by: PabloCarmona * removed fixed version for pytorch Signed-off-by: PabloCarmona * remove matrix Signed-off-by: PabloCarmona * remove matrix Signed-off-by: PabloCarmona * add cleanup on before_build Signed-off-by: PabloCarmona * split versions on jobs Signed-off-by: PabloCarmona * change name of jobs Signed-off-by: PabloCarmona * fix job names for build and update test and lint Signed-off-by: PabloCarmona * update cibw linux image Signed-off-by: PabloCarmona * add trigger to test build Signed-off-by: PabloCarmona * change name for wheels artifact Signed-off-by: PabloCarmona * revert to tag push Signed-off-by: PabloCarmona --------- Signed-off-by: PabloCarmona Co-authored-by: Pablo Carmona Gonzalez Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- .github/workflows/build-wheel-310.yml | 42 +++++++++++++++++ .github/workflows/build-wheel-311.yml | 42 +++++++++++++++++ .github/workflows/build-wheel-312.yml | 42 +++++++++++++++++ .github/workflows/test-and-lint.yml | 67 +++++++-------------------- 4 files changed, 144 insertions(+), 49 deletions(-) create mode 100644 .github/workflows/build-wheel-310.yml create mode 100644 .github/workflows/build-wheel-311.yml create mode 100644 .github/workflows/build-wheel-312.yml diff --git a/.github/workflows/build-wheel-310.yml b/.github/workflows/build-wheel-310.yml new file mode 100644 index 00000000..b90c84ea --- /dev/null +++ b/.github/workflows/build-wheel-310.yml @@ -0,0 +1,42 @@ +name: build-wheel-310 + +on: + push: + tags: + - 'v**' + +jobs: + build-and-deploy-linux: + name: Build wheel 3.10 (manylinux) + runs-on: ubuntu-latest + services: + docker: + image: docker:20.10.16 + options: --privileged + env: + CIBW_BUILD: cp310-manylinux_x86_64 + CIBW_BEFORE_ALL: > + yum install -y openblas-devel + CIBW_BEFORE_BUILD: > + pip install torch==2.9.0 torchvision==0.24.0 torchaudio==2.9.0 --index-url https://download.pytorch.org/whl/cpu + && pip install -r requirements.txt + CIBW_MANYLINUX_X86_64_IMAGE: "quay.io/pypa/manylinux_2_28_x86_64" + CIBW_REPAIR_WHEEL_COMMAND: > + auditwheel repair -w {dest_dir} {wheel} --exclude libtorch_python.so + steps: + - uses: actions/checkout@v3 + + - name: Pull manylinux image + run: docker pull $CIBW_MANYLINUX_X86_64_IMAGE + + - name: Install cibuildwheel + run: python -m pip install cibuildwheel==2.23.3 + + - name: Build wheels + run: python -m cibuildwheel --output-dir wheelhouse + + - name: Upload wheels (per version) + uses: actions/upload-artifact@v4 + with: + name: wheel_py310 + path: wheelhouse/*.whl diff --git a/.github/workflows/build-wheel-311.yml b/.github/workflows/build-wheel-311.yml new file mode 100644 index 00000000..194ac01d --- /dev/null +++ b/.github/workflows/build-wheel-311.yml @@ -0,0 +1,42 @@ +name: build-wheel-311 + +on: + push: + tags: + - 'v**' + +jobs: + build-and-deploy-linux: + name: Build wheel 3.11 (manylinux) + runs-on: ubuntu-latest + services: + docker: + image: docker:20.10.16 + options: --privileged + env: + CIBW_BUILD: cp311-manylinux_x86_64 + CIBW_BEFORE_ALL: > + yum install -y openblas-devel + CIBW_BEFORE_BUILD: > + pip install torch==2.9.0 torchvision==0.24.0 torchaudio==2.9.0 --index-url https://download.pytorch.org/whl/cpu + && pip install -r requirements.txt + CIBW_MANYLINUX_X86_64_IMAGE: "quay.io/pypa/manylinux_2_28_x86_64" + CIBW_REPAIR_WHEEL_COMMAND: > + auditwheel repair -w {dest_dir} {wheel} --exclude libtorch_python.so + steps: + - uses: actions/checkout@v3 + + - name: Pull manylinux image + run: docker pull $CIBW_MANYLINUX_X86_64_IMAGE + + - name: Install cibuildwheel + run: python -m pip install cibuildwheel==2.23.3 + + - name: Build wheels + run: python -m cibuildwheel --output-dir wheelhouse + + - name: Upload wheels (per version) + uses: actions/upload-artifact@v4 + with: + name: wheel_py311 + path: wheelhouse/*.whl diff --git a/.github/workflows/build-wheel-312.yml b/.github/workflows/build-wheel-312.yml new file mode 100644 index 00000000..dab5806d --- /dev/null +++ b/.github/workflows/build-wheel-312.yml @@ -0,0 +1,42 @@ +name: build-wheel-312 + +on: + push: + tags: + - 'v**' + +jobs: + build-and-deploy-linux: + name: Build wheel 3.12 (manylinux) + runs-on: ubuntu-latest + services: + docker: + image: docker:20.10.16 + options: --privileged + env: + CIBW_BUILD: cp312-manylinux_x86_64 + CIBW_BEFORE_ALL: > + yum install -y openblas-devel + CIBW_BEFORE_BUILD: > + pip install torch==2.9.0 torchvision==0.24.0 torchaudio==2.9.0 --index-url https://download.pytorch.org/whl/cpu + && pip install -r requirements.txt + CIBW_MANYLINUX_X86_64_IMAGE: "quay.io/pypa/manylinux_2_28_x86_64" + CIBW_REPAIR_WHEEL_COMMAND: > + auditwheel repair -w {dest_dir} {wheel} --exclude libtorch_python.so + steps: + - uses: actions/checkout@v3 + + - name: Pull manylinux image + run: docker pull $CIBW_MANYLINUX_X86_64_IMAGE + + - name: Install cibuildwheel + run: python -m pip install cibuildwheel==2.23.3 + + - name: Build wheels + run: python -m cibuildwheel --output-dir wheelhouse + + - name: Upload wheels (per version) + uses: actions/upload-artifact@v4 + with: + name: wheel_py312 + path: wheelhouse/*.whl diff --git a/.github/workflows/test-and-lint.yml b/.github/workflows/test-and-lint.yml index 5835de08..b07a696e 100644 --- a/.github/workflows/test-and-lint.yml +++ b/.github/workflows/test-and-lint.yml @@ -2,24 +2,26 @@ name: test-and-lint on: push: - branches: master + branches: + - master pull_request: - branches: master + branches: + - master jobs: - # ──────────────────────────── - # Test on Python 3.10 - # ──────────────────────────── - test-py310: - name: Test (Python 3.10) + test: + name: Test ${{ matrix.python-version }} runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v3 - - name: Set up Python 3.10 + - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: - python-version: 3.10.17 + python-version: ${{ matrix.python-version }} - name: Install system dependencies run: | @@ -41,19 +43,19 @@ jobs: env: TEST_DATASET: true - # ──────────────────────────── - # Lint on Python 3.10 - # ──────────────────────────── - lint-py310: - name: Lint (Python 3.10) + lint: + name: Lint ${{ matrix.python-version }} runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v3 - - name: Set up Python 3.10 + - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: - python-version: 3.10.17 + python-version: ${{ matrix.python-version }} - name: Install system dependencies run: | @@ -75,36 +77,3 @@ jobs: make pycodestyle make pylint make mypy - # ──────────────────────────────────────── - # Stage: Test multiple Python versions - # ──────────────────────────────────────── - test-matrix: - name: Tests (3.8 & 3.9) - runs-on: ubuntu-latest - strategy: - matrix: - python-version: [3.8, 3.9] - steps: - - uses: actions/checkout@v3 - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python-version }} - - - name: Install system deps - run: | - sudo apt-get -qq update - sudo apt-get install -y ca-certificates libopenblas-dev gcc-9 g++-9 - sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 60 \ - --slave /usr/bin/g++ g++ /usr/bin/g++-9 - - - name: Build and install aihwkit wheel - run: | - pip install -r requirements.txt - make build_inplace - - - name: Run pytest - run: | - pip install -r requirements-dev.txt - make pytest \ No newline at end of file From 51411c6a280e0647e66c410bca97da7af40ec91b Mon Sep 17 00:00:00 2001 From: PabloCarmona Date: Tue, 18 Nov 2025 19:06:56 +0100 Subject: [PATCH 14/33] fix pycodestyle errors on python 3.12 Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- examples/04_lenet5_training.py | 8 ++++---- examples/06_lenet5_hardware_aware.py | 16 ++++++++-------- examples/11_vgg8_training.py | 10 +++++----- examples/18_cifar10_on_resnet.py | 8 ++++---- examples/25_torch_tile_lenet5_hardware_aware.py | 16 ++++++++-------- examples/29_linalg_krylov.py | 2 +- examples/35_half_precision_training.py | 2 +- .../experiments/experiments/inferencing.py | 2 +- src/aihwkit/inference/calibration/calibration.py | 2 +- 9 files changed, 33 insertions(+), 33 deletions(-) diff --git a/examples/04_lenet5_training.py b/examples/04_lenet5_training.py index 8ddd4bc8..c1bf1bee 100644 --- a/examples/04_lenet5_training.py +++ b/examples/04_lenet5_training.py @@ -237,10 +237,10 @@ def training_loop(model, criterion, optimizer, train_data, validation_data, epoc print( f"{datetime.now().time().replace(microsecond=0)} --- " f"Epoch: {epoch}\t" - f"Train loss: {train_loss:.4f}\t" - f"Valid loss: {valid_loss:.4f}\t" - f"Test error: {error:.2f}%\t" - f"Accuracy: {accuracy:.2f}%\t" + f"Train loss: {train_loss: .4f}\t" + f"Valid loss: {valid_loss: .4f}\t" + f"Test error: {error: .2f}%\t" + f"Accuracy: {accuracy: .2f}%\t" ) # Save results and plot figures diff --git a/examples/06_lenet5_hardware_aware.py b/examples/06_lenet5_hardware_aware.py index de3a3516..694126a0 100644 --- a/examples/06_lenet5_hardware_aware.py +++ b/examples/06_lenet5_hardware_aware.py @@ -233,10 +233,10 @@ def training_loop(model, criterion, optimizer, train_data, validation_data, epoc print( f"{datetime.now().time().replace(microsecond=0)} --- " f"Epoch: {epoch}\t" - f"Train loss: {train_loss:.4f}\t" - f"Valid loss: {valid_loss:.4f}\t" - f"Test error: {error:.2f}%\t" - f"Accuracy: {accuracy:.2f}%\t" + f"Train loss: {train_loss: .4f}\t" + f"Valid loss: {valid_loss: .4f}\t" + f"Test error: {error: .2f}%\t" + f"Accuracy: {accuracy: .2f}%\t" ) # Save results and plot figures @@ -335,8 +335,8 @@ def inference_phase(t_inference_times, model, criterion, validation_data): _, error_pre, accuracy_pre = test_evaluation(validation_data, model, criterion) print( f"{datetime.now().time().replace(microsecond=0)} --- " - f"Error after training: {error_pre:.2f}%\t" - f"Accuracy after training: {accuracy_pre:.2f}%\t" + f"Error after training: {error_pre: .2f}%\t" + f"Accuracy after training: {accuracy_pre: .2f}%\t" ) error_lst = [] @@ -350,8 +350,8 @@ def inference_phase(t_inference_times, model, criterion, validation_data): print( f"{datetime.now().time().replace(microsecond=0)} --- " - f"Error after inference: {error_post:.2f}%\t" - f"Accuracy after inference: {accuracy_post:.2f}%\t" + f"Error after inference: {error_post: .2f}%\t" + f"Accuracy after inference: {accuracy_post: .2f}%\t" f"Drift t={t_inference: .2e}\t" ) diff --git a/examples/11_vgg8_training.py b/examples/11_vgg8_training.py index 3c862aa4..5190f650 100644 --- a/examples/11_vgg8_training.py +++ b/examples/11_vgg8_training.py @@ -65,7 +65,7 @@ def load_images(): mean = Tensor([0.4377, 0.4438, 0.4728]) std = Tensor([0.1980, 0.2010, 0.1970]) - print(f"Normalization data: ({mean},{std})") + print(f"Normalization data: ({mean}, {std})") transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)]) train_set = datasets.SVHN(PATH_DATASET, download=True, split="train", transform=transform) @@ -274,10 +274,10 @@ def training_loop(model, criterion, optimizer, train_data, validation_data, epoc print( f"{datetime.now().time().replace(microsecond=0)} --- " f"Epoch: {epoch}\t" - f"Train loss: {train_loss:.4f}\t" - f"Valid loss: {valid_loss:.4f}\t" - f"Test error: {error:.2f}%\t" - f"Test accuracy: {accuracy:.2f}%\t" + f"Train loss: {train_loss: .4f}\t" + f"Valid loss: {valid_loss: .4f}\t" + f"Test error: {error: .2f}%\t" + f"Test accuracy: {accuracy: .2f}%\t" ) # Save results and plot figures diff --git a/examples/18_cifar10_on_resnet.py b/examples/18_cifar10_on_resnet.py index 290cd283..5e861471 100644 --- a/examples/18_cifar10_on_resnet.py +++ b/examples/18_cifar10_on_resnet.py @@ -267,10 +267,10 @@ def training_loop(model, criterion, optimizer, train_data, validation_data, epoc print( f"{datetime.now().time().replace(microsecond=0)} --- " f"Epoch: {epoch}\t" - f"Train loss: {train_loss:.4f}\t" - f"Valid loss: {valid_loss:.4f}\t" - f"Test error: {error:.2f}%\t" - f"Test accuracy: {accuracy:.2f}%\t" + f"Train loss: {train_loss: .4f}\t" + f"Valid loss: {valid_loss: .4f}\t" + f"Test error: {error: .2f}%\t" + f"Test accuracy: {accuracy: .2f}%\t" ) return model, optimizer diff --git a/examples/25_torch_tile_lenet5_hardware_aware.py b/examples/25_torch_tile_lenet5_hardware_aware.py index 4aaf2fc1..c6df8524 100644 --- a/examples/25_torch_tile_lenet5_hardware_aware.py +++ b/examples/25_torch_tile_lenet5_hardware_aware.py @@ -230,10 +230,10 @@ def training_loop(model, criterion, optimizer, train_data, validation_data, epoc print( f"{datetime.now().time().replace(microsecond=0)} --- " f"Epoch: {epoch}\t" - f"Train loss: {train_loss:.4f}\t" - f"Valid loss: {valid_loss:.4f}\t" - f"Test error: {error:.2f}%\t" - f"Accuracy: {accuracy:.2f}%\t" + f"Train loss: {train_loss: .4f}\t" + f"Valid loss: {valid_loss: .4f}\t" + f"Test error: {error: .2f}%\t" + f"Accuracy: {accuracy: .2f}%\t" ) # Save results and plot figures @@ -332,8 +332,8 @@ def inference_phase(t_inference_times, model, criterion, validation_data): _, error_pre, accuracy_pre = test_evaluation(validation_data, model, criterion) print( f"{datetime.now().time().replace(microsecond=0)} --- " - f"Error after training: {error_pre:.2f}%\t" - f"Accuracy after training: {accuracy_pre:.2f}%\t" + f"Error after training: {error_pre: .2f}%\t" + f"Accuracy after training: {accuracy_pre: .2f}%\t" ) error_lst = [] @@ -347,8 +347,8 @@ def inference_phase(t_inference_times, model, criterion, validation_data): print( f"{datetime.now().time().replace(microsecond=0)} --- " - f"Error after inference: {error_post:.2f}%\t" - f"Accuracy after inference: {accuracy_post:.2f}%\t" + f"Error after inference: {error_post: .2f}%\t" + f"Accuracy after inference: {accuracy_post: .2f}%\t" f"Drift t={t_inference: .2e}\t" ) diff --git a/examples/29_linalg_krylov.py b/examples/29_linalg_krylov.py index 873fa448..b7ed4d94 100644 --- a/examples/29_linalg_krylov.py +++ b/examples/29_linalg_krylov.py @@ -39,7 +39,7 @@ M = AnalogMatrix(M_fp, rpu_config=rpu_config, realistic=False, device=DEVICE) (x, flag) = fgmres(A, b, M=M, maxiter=6, tol=1e-8) -print(f"{norm(b - A*x):.6}") +print(f"{norm(b - A * x): .6}") plt.clf() diff --git a/examples/35_half_precision_training.py b/examples/35_half_precision_training.py index 13955667..8c17a98e 100644 --- a/examples/35_half_precision_training.py +++ b/examples/35_half_precision_training.py @@ -76,4 +76,4 @@ def forward(self, x): loss = F.nll_loss(output.float(), target) loss.backward() optimizer.step() - pbar.set_description(f"Loss {loss:.4f}") + pbar.set_description(f"Loss {loss: .4f}") diff --git a/src/aihwkit/experiments/experiments/inferencing.py b/src/aihwkit/experiments/experiments/inferencing.py index 90c7d60b..d9128d7d 100644 --- a/src/aihwkit/experiments/experiments/inferencing.py +++ b/src/aihwkit/experiments/experiments/inferencing.py @@ -340,7 +340,7 @@ def _print_rpu_fields(self, model: Module) -> None: if not isinstance(module, AnalogLayerBase): continue - print(f"RPUConfig of module {name}:") + print(f"RPUConfig of module {name}: ") tile = next(module.analog_tiles()) print(tile.rpu_config) print(tile.tile) diff --git a/src/aihwkit/inference/calibration/calibration.py b/src/aihwkit/inference/calibration/calibration.py index 29b0b4b4..432670b5 100644 --- a/src/aihwkit/inference/calibration/calibration.py +++ b/src/aihwkit/inference/calibration/calibration.py @@ -291,7 +291,7 @@ def calibrate_input_ranges( # set the input range tile.set_input_range(input_range) if verbose: - print(f"Calibrated tile {tile_name}: {input_range:.5f}.") + print(f"Calibrated tile {tile_name}: {input_range: .5f}.") # Store calibration info rpu_config.pre_post.input_range.init_value = tile.input_range.item() From c850e7a4fd668ea1c9f394395b0dd5d91b79b128 Mon Sep 17 00:00:00 2001 From: PabloCarmona Date: Tue, 18 Nov 2025 19:17:20 +0100 Subject: [PATCH 15/33] remove python 3.12 Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- .github/workflows/test-and-lint.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test-and-lint.yml b/.github/workflows/test-and-lint.yml index b07a696e..84d9659c 100644 --- a/.github/workflows/test-and-lint.yml +++ b/.github/workflows/test-and-lint.yml @@ -14,7 +14,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.10", "3.11", "3.12"] + python-version: ["3.10", "3.11"] steps: - uses: actions/checkout@v3 From 50d02ecc96a66d2f63a87509c5ef230ae3f8b8bf Mon Sep 17 00:00:00 2001 From: PabloCarmona Date: Tue, 18 Nov 2025 19:28:03 +0100 Subject: [PATCH 16/33] remove python 3.12 from lint Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- .github/workflows/test-and-lint.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test-and-lint.yml b/.github/workflows/test-and-lint.yml index 84d9659c..9048b88c 100644 --- a/.github/workflows/test-and-lint.yml +++ b/.github/workflows/test-and-lint.yml @@ -48,7 +48,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.10", "3.11", "3.12"] + python-version: ["3.10", "3.11"] steps: - uses: actions/checkout@v3 From 201f6c59a194425a096ce8f44c5dc8a2ffdfaf8f Mon Sep 17 00:00:00 2001 From: PabloCarmona Date: Wed, 19 Nov 2025 14:01:14 +0100 Subject: [PATCH 17/33] add cuda build workflow job Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- .github/workflows/build-wheel-cuda-310.yml | 43 ++++++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 .github/workflows/build-wheel-cuda-310.yml diff --git a/.github/workflows/build-wheel-cuda-310.yml b/.github/workflows/build-wheel-cuda-310.yml new file mode 100644 index 00000000..5522cbea --- /dev/null +++ b/.github/workflows/build-wheel-cuda-310.yml @@ -0,0 +1,43 @@ +name: build-wheel-cuda-310 + +on: + push: + tags: + - 'v**' + +jobs: + build-and-deploy-linux: + name: Build wheel CUDA 3.10 (manylinux) + runs-on: ubuntu-latest + services: + docker: + image: docker:20.10.16 + options: --privileged + env: + CIBW_ENVIRONMENT: TORCH_VERSION_SPECIFIER="==2.9.0" MAKEFLAGS="-j4" USE_CUDA=ON + CIBW_BUILD: cp310-manylinux_x86_64 + CIBW_BEFORE_ALL: > + yum install -y openblas-devel + CIBW_BEFORE_BUILD: > + pip install torch==2.9.0 torchvision==0.24.0 torchaudio==2.9.0 --index-url https://download.pytorch.org/whl/cu126 + && pip install -r requirements.txt + CIBW_MANYLINUX_X86_64_IMAGE: "quay.io/pypa/manylinux_2_28_x86_64" + CIBW_REPAIR_WHEEL_COMMAND: > + auditwheel repair -w {dest_dir} {wheel} --exclude libtorch_python.so + steps: + - uses: actions/checkout@v3 + + - name: Pull manylinux image + run: docker pull $CIBW_MANYLINUX_X86_64_IMAGE + + - name: Install cibuildwheel + run: python -m pip install cibuildwheel==2.23.3 + + - name: Build wheels + run: python -m cibuildwheel --output-dir wheelhouse + + - name: Upload wheels (per version) + uses: actions/upload-artifact@v4 + with: + name: wheel_cuda_py310 + path: wheelhouse/*.whl From 41db11d1adb96c9b3f57e78d13370ccf5bfa0df7 Mon Sep 17 00:00:00 2001 From: PabloCarmona Date: Wed, 19 Nov 2025 14:05:49 +0100 Subject: [PATCH 18/33] add trigger for cuda Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- .github/workflows/build-wheel-cuda-310.yml | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build-wheel-cuda-310.yml b/.github/workflows/build-wheel-cuda-310.yml index 5522cbea..d1ce346c 100644 --- a/.github/workflows/build-wheel-cuda-310.yml +++ b/.github/workflows/build-wheel-cuda-310.yml @@ -1,9 +1,8 @@ name: build-wheel-cuda-310 -on: - push: - tags: - - 'v**' +on: [push] + # tags: + # - 'v**' jobs: build-and-deploy-linux: From 2db2d8460e01e4c9c8d7d14de4df574c8730b127 Mon Sep 17 00:00:00 2001 From: PabloCarmona Date: Fri, 21 Nov 2025 12:51:04 +0100 Subject: [PATCH 19/33] remove cuda workflow Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- .github/workflows/build-wheel-cuda-310.yml | 42 ---------------------- 1 file changed, 42 deletions(-) delete mode 100644 .github/workflows/build-wheel-cuda-310.yml diff --git a/.github/workflows/build-wheel-cuda-310.yml b/.github/workflows/build-wheel-cuda-310.yml deleted file mode 100644 index d1ce346c..00000000 --- a/.github/workflows/build-wheel-cuda-310.yml +++ /dev/null @@ -1,42 +0,0 @@ -name: build-wheel-cuda-310 - -on: [push] - # tags: - # - 'v**' - -jobs: - build-and-deploy-linux: - name: Build wheel CUDA 3.10 (manylinux) - runs-on: ubuntu-latest - services: - docker: - image: docker:20.10.16 - options: --privileged - env: - CIBW_ENVIRONMENT: TORCH_VERSION_SPECIFIER="==2.9.0" MAKEFLAGS="-j4" USE_CUDA=ON - CIBW_BUILD: cp310-manylinux_x86_64 - CIBW_BEFORE_ALL: > - yum install -y openblas-devel - CIBW_BEFORE_BUILD: > - pip install torch==2.9.0 torchvision==0.24.0 torchaudio==2.9.0 --index-url https://download.pytorch.org/whl/cu126 - && pip install -r requirements.txt - CIBW_MANYLINUX_X86_64_IMAGE: "quay.io/pypa/manylinux_2_28_x86_64" - CIBW_REPAIR_WHEEL_COMMAND: > - auditwheel repair -w {dest_dir} {wheel} --exclude libtorch_python.so - steps: - - uses: actions/checkout@v3 - - - name: Pull manylinux image - run: docker pull $CIBW_MANYLINUX_X86_64_IMAGE - - - name: Install cibuildwheel - run: python -m pip install cibuildwheel==2.23.3 - - - name: Build wheels - run: python -m cibuildwheel --output-dir wheelhouse - - - name: Upload wheels (per version) - uses: actions/upload-artifact@v4 - with: - name: wheel_cuda_py310 - path: wheelhouse/*.whl From a47d2e474516ce5a65a8fcd0c7d3690ca86b9f11 Mon Sep 17 00:00:00 2001 From: PabloCarmona Date: Fri, 21 Nov 2025 12:53:10 +0100 Subject: [PATCH 20/33] add 3.12 to test and lint Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- .github/workflows/test-and-lint.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test-and-lint.yml b/.github/workflows/test-and-lint.yml index 9048b88c..b07a696e 100644 --- a/.github/workflows/test-and-lint.yml +++ b/.github/workflows/test-and-lint.yml @@ -14,7 +14,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.10", "3.11"] + python-version: ["3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v3 @@ -48,7 +48,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.10", "3.11"] + python-version: ["3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v3 From b7ec1ff71e9ee250e125adc342900cda2b078a69 Mon Sep 17 00:00:00 2001 From: PabloCarmona Date: Fri, 21 Nov 2025 13:58:00 +0100 Subject: [PATCH 21/33] update pylint Signed-off-by: PabloCarmona Signed-off-by: Pablo Carmona Gonzalez --- requirements-dev.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-dev.txt b/requirements-dev.txt index d7716f6a..0ccbc383 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -2,7 +2,7 @@ mypy==0.991 types-dataclasses types-requests==2.26.3 pycodestyle==2.10.0 -pylint==2.15.7 +pylint==4.0.3 pytest==6.2.4 parameterized==0.8.1 black==24.3.0 From 92f0801df32feda941032c1b3c17156ebc6d6661 Mon Sep 17 00:00:00 2001 From: Pablo Carmona Gonzalez Date: Mon, 5 Jan 2026 18:34:14 +0100 Subject: [PATCH 22/33] update project toml and requirements deps versions Signed-off-by: Pablo Carmona Gonzalez --- pyproject.toml | 92 +++++++++++++++++++++++++++++++++------ requirements-dev.txt | 14 +++--- requirements-examples.txt | 3 +- requirements.txt | 15 +++---- 4 files changed, 94 insertions(+), 30 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 26184e86..a6196c4c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,19 +1,85 @@ [build-system] -requires = [ - "setuptools>=66.0.0", - "wheel", - "ninja", - "cmake>=3.18", - "scikit-build>=0.11.1", +requires = ["setuptools", "wheel", "scikit-build >= 0.18.1", "ninja"] +build-backend = "setuptools.build_meta" + +[project] +name = "aihwkit" +version = "1.0.0" +dependencies = [ + "cmake >= 4.2.1", + "scikit-build >= 0.18.1", "scikit-learn", - "pybind11>=2.6.2", - "torch>=1.9", + "pybind11 >= 3.0.1", + "torch >= 2.9.1", "torchvision", "scipy", - "requests>=2.25,<3", - "numpy>=1.22,<2", - "protobuf>=4.21.6", + "requests >= 2.32, <3", + "numpy", + "protobuf", "tqdm", - "mypy==0.991" ] -build-backend = "setuptools.build_meta" +requires-python = ">=3.10" +authors = [ + { name = "IBM Research", email = "aihwkit@us.ibm.com" }, +] +description = "IBM Analog Hardware Acceleration Kit" +readme = "README.md" +license = "MIT" +license-files = ["LICENSE.txt"] +keywords = [ + "ai", + "analog", + "rpu", + "torch", + "memristor", + "pcm", + "reram", + "crossbar", + "in-memory", + "nvm", + "non-von-neumann", + "non-volatile memory", + "phase-change material", +] +classifiers=[ + "Development Status :: 5 - Production/Stable", + "Environment :: Console", + "Environment :: GPU :: NVIDIA CUDA", + "Intended Audience :: Science/Research", + "Operating System :: MacOS", + "Operating System :: Microsoft :: Windows", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python :: 3 :: Only", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Typing :: Typed", +] + +[project.optional-dependencies] +dev = [ + "mypy", + "types-dataclasses", + "types-requests", + "pycodestyle", + "pylint", + "pytest", + "parameterized", + "black", +] + +examples = [ + "matplotlib >= 3.10", + "pyamg", + "jupyter", + "transformers", + "evaluate", + "accelerate", + "lmfit", +] + +[project.urls] +Homepage = "https://github.com/IBM/aihwkit" +Documentation = "https://aihwkit.readthedocs.io/en/latest/" +Repository = "https://github.com/IBM/aihwkit.git" +Issues = "https://github.com/IBM/aihwkit/issues" +Changelog = "https://github.com/IBM/aihwkit/blob/master/CHANGELOG.md" diff --git a/requirements-dev.txt b/requirements-dev.txt index 0ccbc383..5af18d1c 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,8 +1,8 @@ -mypy==0.991 +mypy types-dataclasses -types-requests==2.26.3 -pycodestyle==2.10.0 -pylint==4.0.3 -pytest==6.2.4 -parameterized==0.8.1 -black==24.3.0 +types-requests +pycodestyle +pylint +pytest +parameterized +black diff --git a/requirements-examples.txt b/requirements-examples.txt index beffed4e..99909ebb 100644 --- a/requirements-examples.txt +++ b/requirements-examples.txt @@ -1,5 +1,4 @@ -torchvision>=0.7.0 -matplotlib>=3.0 +matplotlib>=3.10 pyamg jupyter transformers diff --git a/requirements.txt b/requirements.txt index 4ee6881c..4ce196c1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,14 +1,13 @@ # Build dependencies. -cmake>=3.18 -scikit-build>=0.11.1 +cmake>=4.2.1 +scikit-build>=0.18.1 scikit-learn -pybind11>=2.6.2 +pybind11>=3.0.1 # Runtime dependencies. -torch>=1.9 +torch>=2.9.1 torchvision scipy -requests>=2.25,<3 -numpy>=1.22,<2 -protobuf>=4.21.6 +requests>=2.32,<3 +numpy +protobuf tqdm -mypy==0.991 From 55868bccdbcac5915057ea9cc657cd7f39482bda Mon Sep 17 00:00:00 2001 From: Corey Lammie Date: Thu, 18 Dec 2025 09:51:47 +0100 Subject: [PATCH 23/33] Added GlobalDriftCompensationWithExactReference drift compensation class (#674) * Add GlobalDriftCompensationWithExactReference class * Update CHANGELOG.md --------- Signed-off-by: Corey Liam Lammie Signed-off-by: Pablo Carmona Gonzalez --- CHANGELOG.md | 1 + src/aihwkit/inference/compensation/base.py | 7 +- src/aihwkit/inference/compensation/drift.py | 27 +++++++ src/aihwkit/simulator/tiles/inference.py | 81 ++++++++++++++++----- 4 files changed, 97 insertions(+), 19 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 90944e6d..43a7436d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,6 +16,7 @@ The format is based on [Keep a Changelog], and this project adheres to ### Added * Add newly uploaded resources for CPU-only wheels (\#739) +* Add a new drift compensation mechanism which uses an ideal reference readout. In the default global drift compensation mechanism, all non-idealities (as set by the corresponding `rpu_config`) are modeled, potentially resulting in sub-optimal drift compensation scales being computed in some scenarios, e.g., where the output noise is sufficiently large. ### Removed/Deprecated * Function `convert_to_analog_mapped` in `src/aihwkit/nn/conversion.py` **deprecated and removed**. diff --git a/src/aihwkit/inference/compensation/base.py b/src/aihwkit/inference/compensation/base.py index 496c9078..f4387d5d 100644 --- a/src/aihwkit/inference/compensation/base.py +++ b/src/aihwkit/inference/compensation/base.py @@ -11,6 +11,8 @@ from torch import Tensor from torch.autograd import no_grad +from aihwkit.simulator.tiles.inference import InferenceTileWithPeriphery + class BaseDriftCompensation: """Base class for drift compensations.""" @@ -19,17 +21,18 @@ def __init__(self) -> None: pass @no_grad() - def init_baseline(self, forward_output: Tensor) -> Tuple[Tensor, Tensor]: + def init_baseline(self, tile: InferenceTileWithPeriphery) -> Tuple[Tensor, Tensor]: """Initialize the base line for applying the compensation. Uses a all one tensor for read_out. Args: - forward_output: forward output of the read out vector to compensate + tile: forward output of the read out vector to compensate Returns: reference tensor readout """ + forward_output = tile._forward_drift_readout_tensor(True, exact_reference=False) ref_value = self.readout(forward_output) return ref_value diff --git a/src/aihwkit/inference/compensation/drift.py b/src/aihwkit/inference/compensation/drift.py index fdad6068..ae6ab10a 100644 --- a/src/aihwkit/inference/compensation/drift.py +++ b/src/aihwkit/inference/compensation/drift.py @@ -6,11 +6,14 @@ """Global drift compensation for inference.""" +from typing import Tuple + from torch.autograd import no_grad from torch import abs as torch_abs from torch import clamp, Tensor, eye from aihwkit.inference.compensation.base import BaseDriftCompensation +from aihwkit.simulator.tiles.inference import InferenceTileWithPeriphery class GlobalDriftCompensation(BaseDriftCompensation): @@ -36,6 +39,30 @@ def __str__(self) -> str: return "{}()".format(self.__class__.__name__) +class GlobalDriftCompensationWithExactReference(GlobalDriftCompensation): + """Global drift compensation using an exact (ideal) reference readout. + + Uses a constant factor for compensating the drift. + """ + + @no_grad() + def init_baseline(self, tile: InferenceTileWithPeriphery) -> Tuple[Tensor, Tensor]: + """Initialize the base line for applying the compensation. + + Uses a all one tensor for read_out. + + Args: + tile: forward output of the read out vector to compensate + + Returns: + reference tensor readout + """ + forward_output = tile._forward_drift_readout_tensor(True, exact_reference=True) + ref_value = self.readout(forward_output) + + return ref_value + + class PerColumnDriftCompensation(BaseDriftCompensation): """Per column drift compensation. Uses a vector for compensating the drift. diff --git a/src/aihwkit/simulator/tiles/inference.py b/src/aihwkit/simulator/tiles/inference.py index 1cb795d5..f3b4b6c7 100644 --- a/src/aihwkit/simulator/tiles/inference.py +++ b/src/aihwkit/simulator/tiles/inference.py @@ -23,7 +23,11 @@ from aihwkit.simulator.tiles.base import BaseTile from aihwkit.simulator.rpu_base import tiles from aihwkit.simulator.parameters.helpers import parameters_to_bindings -from aihwkit.simulator.parameters.enums import WeightModifierType, WeightClipType, WeightRemapType +from aihwkit.simulator.parameters.enums import ( + WeightModifierType, + WeightClipType, + WeightRemapType, +) from aihwkit.inference.noise.base import BaseNoiseModel if TYPE_CHECKING: @@ -84,19 +88,28 @@ def init_mapping_scales(self) -> None: This method is called from the constructor. """ super().init_mapping_scales() - if hasattr(self.rpu_config, "remap") and self.rpu_config.remap.type != WeightRemapType.NONE: + if ( + hasattr(self.rpu_config, "remap") + and self.rpu_config.remap.type != WeightRemapType.NONE + ): # needs to be always out_size mapping_scales = ones( - (self.out_size,), dtype=self.get_dtype(), device=self.device, requires_grad=False + (self.out_size,), + dtype=self.get_dtype(), + device=self.device, + requires_grad=False, ) self.set_mapping_scales(mapping_scales) @no_grad() - def _forward_drift_readout_tensor(self, reset_if: bool = False) -> Optional[Tensor]: + def _forward_drift_readout_tensor( + self, reset_if: bool = False, exact_reference: bool = False + ) -> Optional[Tensor]: """Perform a forward pass using the drift read-out tensor. Args: reset_if: Will reset the readout tensor, otherwise use the stored one + exact_reference: Whether or not to compute the reference using an "ideal" forward pass. Returns: Readout tensor if drift compensation is on @@ -109,20 +122,41 @@ def _forward_drift_readout_tensor(self, reset_if: bool = False) -> Optional[Tens if self.drift_readout_tensor is None or reset_if: self.drift_readout_tensor = ( - self.rpu_config.drift_compensation.get_readout_tensor(self.tile.get_x_size()) + self.rpu_config.drift_compensation.get_readout_tensor( + self.tile.get_x_size() + ) .detach() .to(self.device) ) if self.in_trans: - self.drift_readout_tensor = self.drift_readout_tensor.tranpose(0, 1).clone() + self.drift_readout_tensor = self.drift_readout_tensor.tranpose( + 0, 1 + ).clone() else: self.drift_readout_tensor = self.drift_readout_tensor.to(self.device) # We need to take the bias as a common column here, also we do # not want to use indexed. - return self.tile.forward( - self.drift_readout_tensor, False, self.in_trans, self.out_trans, True, self.non_blocking - ) + if exact_reference: + input_ = self.drift_readout_tensor + if self.in_trans: + input_ = input_.T + + output = (input_ @ self.reference_combined_weights.T) + if self.out_trans: + output = output.T + + else: + output = self.tile.forward( + self.drift_readout_tensor, + False, + self.in_trans, + self.out_trans, + True, + self.non_blocking, + ) + + return output @no_grad() def program_weights( @@ -160,7 +194,9 @@ def program_weights( if noise_model is not None: if not isinstance(noise_model, BaseNoiseModel): - raise ConfigError("Given noise model has to be of type 'BaseNoiseModel'") + raise ConfigError( + "Given noise model has to be of type 'BaseNoiseModel'" + ) self.rpu_config.noise_model = noise_model @@ -177,8 +213,7 @@ def program_weights( hasattr(self.rpu_config, "drift_compensation") and self.rpu_config.drift_compensation is not None ): - forward_output = self._forward_drift_readout_tensor(True) - self.drift_baseline = self.rpu_config.drift_compensation.init_baseline(forward_output) + self.drift_baseline = self.rpu_config.drift_compensation.init_baseline(self) @no_grad() def drift_weights(self, t_inference: float = 0.0) -> None: @@ -222,7 +257,9 @@ def drift_weights(self, t_inference: float = 0.0) -> None: and self.rpu_config.drift_compensation is not None ): forward_output = self._forward_drift_readout_tensor() - alpha = self.rpu_config.drift_compensation.apply(forward_output, self.drift_baseline) + alpha = self.rpu_config.drift_compensation.apply( + forward_output, self.drift_baseline + ) if isinstance(self, Module): # somehow legacy is incompatible with torch buffers self.__dict__.pop("alpha", None) @@ -267,14 +304,20 @@ def post_update_step(self) -> None: if not hasattr(self, "_tmp"): # pylint: disable=attribute-defined-outside-init self._tmp = {} # type: Dict[str, Any] - if hasattr(self.rpu_config, "clip") and self.rpu_config.clip.type != WeightClipType.NONE: + if ( + hasattr(self.rpu_config, "clip") + and self.rpu_config.clip.type != WeightClipType.NONE + ): if on_the_fly_bindings or "weight_clip_params" not in self._tmp: self._tmp["weight_clip_params"] = parameters_to_bindings( self.rpu_config.clip, data_type ) self.tile.clip_weights(self._tmp["weight_clip_params"]) - if hasattr(self.rpu_config, "remap") and self.rpu_config.remap.type != WeightRemapType.NONE: + if ( + hasattr(self.rpu_config, "remap") + and self.rpu_config.remap.type != WeightRemapType.NONE + ): if on_the_fly_bindings or "weight_remap_params" not in self._tmp: self._tmp["weight_remap_params"] = parameters_to_bindings( self.rpu_config.remap, data_type @@ -305,7 +348,9 @@ def __getstate__(self) -> Dict: state = super().__getstate__() return state - def cuda(self, device: Optional[Union[torch_device, str, int]] = None) -> "BaseTile": + def cuda( + self, device: Optional[Union[torch_device, str, int]] = None + ) -> "BaseTile": self.alpha = self.alpha.cuda(device) ret = super().cuda(device) return ret @@ -316,7 +361,9 @@ def cpu(self) -> "BaseTile": return ret -class InferenceTile(TileModule, InferenceTileWithPeriphery, RPUCudaSimulatorTileWrapper): +class InferenceTile( + TileModule, InferenceTileWithPeriphery, RPUCudaSimulatorTileWrapper +): """Tile used for analog inference and hardware-aware training for inference. Note: From 0a8054c4e24b7735db414239acc85c99f0280145 Mon Sep 17 00:00:00 2001 From: Pablo Carmona Gonzalez Date: Mon, 5 Jan 2026 19:05:05 +0100 Subject: [PATCH 24/33] add temp ignore for type checking Signed-off-by: Pablo Carmona Gonzalez --- src/aihwkit/cloud/client/utils.py | 2 +- src/aihwkit/simulator/parameters/helpers.py | 2 +- src/aihwkit/simulator/tiles/base.py | 2 +- src/aihwkit/simulator/tiles/inference.py | 2 +- src/aihwkit/utils/legacy.py | 2 +- src/aihwkit/utils/visualization.py | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/aihwkit/cloud/client/utils.py b/src/aihwkit/cloud/client/utils.py index f242aa7f..121ab64d 100644 --- a/src/aihwkit/cloud/client/utils.py +++ b/src/aihwkit/cloud/client/utils.py @@ -63,7 +63,7 @@ def parse_config(self) -> Dict: @property def token(self) -> str: """Return the user token.""" - return getenv("AIHW_API_TOKEN", self.stored_config.get("api_token", None)) + return getenv("AIHW_API_TOKEN", self.stored_config.get("api_token", None)) # type: ignore @property def url(self) -> str: diff --git a/src/aihwkit/simulator/parameters/helpers.py b/src/aihwkit/simulator/parameters/helpers.py index 4329dc82..c94f902b 100644 --- a/src/aihwkit/simulator/parameters/helpers.py +++ b/src/aihwkit/simulator/parameters/helpers.py @@ -110,7 +110,7 @@ def parameters_to_bindings(params: Any, data_type: RPUDataType, check_fields: bo else: if HAS_ORIGIN: expected_type = get_origin(dataclass_field.type) or dataclass_field.type - if (not isinstance(value, expected_type)) and not ( + if (not isinstance(value, expected_type)) and not ( # type: ignore expected_type == float and isinstance(value, int) and not isinstance(value, bool) diff --git a/src/aihwkit/simulator/tiles/base.py b/src/aihwkit/simulator/tiles/base.py index d9383d51..dac1a185 100644 --- a/src/aihwkit/simulator/tiles/base.py +++ b/src/aihwkit/simulator/tiles/base.py @@ -618,7 +618,7 @@ def _combine_weights( if not isinstance(bias, Tensor): bias = from_numpy(array(bias)) - bias = unsqueeze(bias.clone().detach().cpu().to(d_type), 1) + bias = unsqueeze(bias.clone().detach().cpu().to(d_type), 1) # type: ignore return cat((weight, bias), dim=1) # Use only the ``[out_size, in_size]`` matrix. return weight diff --git a/src/aihwkit/simulator/tiles/inference.py b/src/aihwkit/simulator/tiles/inference.py index f3b4b6c7..ccc5c4a2 100644 --- a/src/aihwkit/simulator/tiles/inference.py +++ b/src/aihwkit/simulator/tiles/inference.py @@ -142,7 +142,7 @@ def _forward_drift_readout_tensor( if self.in_trans: input_ = input_.T - output = (input_ @ self.reference_combined_weights.T) + output = (input_ @ self.reference_combined_weights.T) # type: ignore if self.out_trans: output = output.T diff --git a/src/aihwkit/utils/legacy.py b/src/aihwkit/utils/legacy.py index 25d5304b..135d037b 100644 --- a/src/aihwkit/utils/legacy.py +++ b/src/aihwkit/utils/legacy.py @@ -79,7 +79,7 @@ def get_key_from_ending(key_name: str, par_name: str, prefix: str) -> str: layer_dic[name] = analog_layer.__class__.__name__ if not has_mapped: - for tile in model.analog_tiles(): + for tile in model.analog_tiles(): # type: ignore tile.rpu_config.mapping.max_input_size = 0 tile.rpu_config.mapping.max_output_size = 0 diff --git a/src/aihwkit/utils/visualization.py b/src/aihwkit/utils/visualization.py index ac215f1c..58405955 100644 --- a/src/aihwkit/utils/visualization.py +++ b/src/aihwkit/utils/visualization.py @@ -668,7 +668,7 @@ def plot_weight_drift( weights.sort() weights = np.tile(weights, [n_repeats, 1]) # type: ignore - analog_tile = InferenceTile(weights.shape[0], weights.shape[1], rpu_config) + analog_tile = InferenceTile(weights.shape[0], weights.shape[1], rpu_config) # type: ignore analog_tile.set_weights(from_numpy(weights)) analog_tile.program_weights() programmed_weights, _ = analog_tile.get_weights() From 4583f91556bfb0ea2720e69e8e6c3d97e55855de Mon Sep 17 00:00:00 2001 From: Pablo Carmona Gonzalez Date: Mon, 5 Jan 2026 20:14:47 +0100 Subject: [PATCH 25/33] remove pylint errors with refactor and temp disabling in some methods Signed-off-by: Pablo Carmona Gonzalez --- .pylintrc | 8 ++------ examples/06_lenet5_hardware_aware.py | 2 +- src/aihwkit/cloud/client/entities.py | 5 ++--- .../nn/low_precision_modules/quantized_base_modules.py | 10 +++++----- src/aihwkit/simulator/parameters/helpers.py | 1 + src/aihwkit/simulator/tiles/periphery.py | 4 ++-- tests/test_extension.py | 2 ++ 7 files changed, 15 insertions(+), 17 deletions(-) diff --git a/.pylintrc b/.pylintrc index 35cb2e7d..4747c63d 100644 --- a/.pylintrc +++ b/.pylintrc @@ -37,10 +37,6 @@ load-plugins=pylint.extensions.docparams # Pickle collected data for later comparisons. persistent=yes -# When enabled, pylint would attempt to guess common misconfiguration and emit -# user-friendly hints instead of false-positive error messages. -suggestion-mode=yes - # Allow loading of arbitrary C extensions. Extensions are imported into the # active Python interpreter and may run arbitrary code. unsafe-load-any-extension=no @@ -527,5 +523,5 @@ preferred-modules= # Exceptions that will emit a warning when being caught. Defaults to # "BaseException, Exception". -overgeneral-exceptions=BaseException, - Exception +overgeneral-exceptions=builtins.BaseException, + builtins.Exception diff --git a/examples/06_lenet5_hardware_aware.py b/examples/06_lenet5_hardware_aware.py index 694126a0..5ad16bef 100644 --- a/examples/06_lenet5_hardware_aware.py +++ b/examples/06_lenet5_hardware_aware.py @@ -8,7 +8,7 @@ Mnist dataset on a LeNet5 inspired network. """ -# pylint: disable=invalid-name +# pylint: disable=invalid-name, possibly-used-before-assignment import os from datetime import datetime diff --git a/src/aihwkit/cloud/client/entities.py b/src/aihwkit/cloud/client/entities.py index b57e03fc..8eff579e 100644 --- a/src/aihwkit/cloud/client/entities.py +++ b/src/aihwkit/cloud/client/entities.py @@ -120,7 +120,7 @@ def get_result(self) -> list: training_output.ParseFromString(output_) converter = BasicTrainingResultConverter() output = converter.from_proto(training_output) - result = output["epochs"] + return output["epochs"] if self.category == CloudExperimentCategory.BASIC_INFERENCE: output_ = self._api_client.output_get(self.job.output_id) # type: ignore # Convert from protobuf. @@ -128,8 +128,7 @@ def get_result(self) -> list: inferencing_output.ParseFromString(output_) iconverter = BasicInferencingResultConverter() i_output = iconverter.result_from_proto(inferencing_output) - result = i_output - return result + return i_output def status(self) -> CloudJobStatus: """Return the status of the experiment.""" diff --git a/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py b/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py index 9ddf0cf5..586401a9 100644 --- a/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py +++ b/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py @@ -13,7 +13,7 @@ from typing import Any, Optional from torch import Tensor, nn -from torch.nn import functional as F +from torch.nn import linear, conv2d, layer_norm, embedding from aihwkit.simulator.digital_low_precision.base_quantized_classes import ( FP32Acts, @@ -29,7 +29,7 @@ class QuantLinear(QuantizationHijacker, nn.Linear): def run_forward( self, x: Tensor, weight: Tensor, bias: Tensor, offsets: Optional[Any] = None ) -> Tensor: - return F.linear(x.contiguous(), weight.contiguous(), bias=bias) + return linear(x.contiguous(), weight.contiguous(), bias=bias) class QuantConv2d(QuantizationHijacker, nn.Conv2d): @@ -38,7 +38,7 @@ class QuantConv2d(QuantizationHijacker, nn.Conv2d): def run_forward( self, x: Tensor, weight: Tensor, bias: Tensor, offsets: Optional[Any] = None ) -> Tensor: - return F.conv2d( + return conv2d( x.contiguous(), weight.contiguous(), bias, @@ -55,7 +55,7 @@ class QuantLayerNorm(QuantizationHijacker, nn.LayerNorm): def run_forward( self, x: Tensor, weight: Tensor, bias: Tensor, offsets: Optional[Any] = None ) -> Tensor: - return F.layer_norm( + return layer_norm( input=x.contiguous(), normalized_shape=self.normalized_shape, weight=weight.contiguous(), @@ -79,7 +79,7 @@ def __init__(self, *args: Any, activation: Optional[Any] = None, **kwargs: Any): def run_forward( self, x: Tensor, weight: Tensor, bias: Tensor, offsets: Optional[Any] = None ) -> Tensor: - return F.embedding( + return embedding( input=x.contiguous(), weight=weight.contiguous(), padding_idx=self.padding_idx, diff --git a/src/aihwkit/simulator/parameters/helpers.py b/src/aihwkit/simulator/parameters/helpers.py index c94f902b..9c6a9a2f 100644 --- a/src/aihwkit/simulator/parameters/helpers.py +++ b/src/aihwkit/simulator/parameters/helpers.py @@ -21,6 +21,7 @@ HAS_ORIGIN = True else: + get_origin = None # type: ignore HAS_ORIGIN = False ALL_SKIP_FIELD = "is_perfect" diff --git a/src/aihwkit/simulator/tiles/periphery.py b/src/aihwkit/simulator/tiles/periphery.py index 3bfe7159..420ff013 100644 --- a/src/aihwkit/simulator/tiles/periphery.py +++ b/src/aihwkit/simulator/tiles/periphery.py @@ -4,7 +4,7 @@ # # Licensed under the MIT license. See LICENSE file in the project root for details. -# pylint: disable=too-many-lines +# pylint: disable=too-many-lines, disable=possibly-used-before-assignment # mypy: disable-error-code=attr-defined """Base tile with added periphery and common utility methods.""" @@ -404,7 +404,7 @@ def read_weights( ones_column = ones(self.in_size * over_sampling, 1, device=self.device, dtype=dtype) x_values = cat([x_values, ones_column], axis=1) - est_weight = lstsq(x_values, y_values).solution.T.cpu() + est_weight = lstsq(x_values, y_values).solution.T.cpu() # pylint: disable=not-callable weight, bias = self._separate_weights(est_weight) if self.digital_bias: diff --git a/tests/test_extension.py b/tests/test_extension.py index 13d211ed..b71f9e47 100644 --- a/tests/test_extension.py +++ b/tests/test_extension.py @@ -16,6 +16,8 @@ if EXTENSION_COMPILED: from aihwkit.extension.aihwkit_extension.ops import float_precision_cast +else: + float_precision_cast = None class FloatPrecisionCastTest(AihwkitTestCase): From 3f178fb25cf87f7f4aaaf9c1ba47272ae23b3262 Mon Sep 17 00:00:00 2001 From: Pablo Carmona Gonzalez Date: Mon, 5 Jan 2026 20:25:09 +0100 Subject: [PATCH 26/33] fix(pyproject.toml): move mypy to build-system req Signed-off-by: Pablo Carmona Gonzalez --- pyproject.toml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index a6196c4c..5ac05cc7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [build-system] -requires = ["setuptools", "wheel", "scikit-build >= 0.18.1", "ninja"] +requires = ["setuptools", "wheel", "scikit-build >= 0.18.1", "ninja", "mypy"] build-backend = "setuptools.build_meta" [project] @@ -57,7 +57,6 @@ classifiers=[ [project.optional-dependencies] dev = [ - "mypy", "types-dataclasses", "types-requests", "pycodestyle", From a05399d92c09d1c7bb54cd312733d7e7be4bae5c Mon Sep 17 00:00:00 2001 From: Pablo Carmona Gonzalez Date: Wed, 7 Jan 2026 13:46:29 +0100 Subject: [PATCH 27/33] change install to editable mode in gh actions test and lint Signed-off-by: Pablo Carmona Gonzalez --- .github/workflows/test-and-lint.yml | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/.github/workflows/test-and-lint.yml b/.github/workflows/test-and-lint.yml index b07a696e..1d7a1fe6 100644 --- a/.github/workflows/test-and-lint.yml +++ b/.github/workflows/test-and-lint.yml @@ -33,12 +33,12 @@ jobs: - name: Build and install aihwkit wheel run: | - pip install -r requirements.txt + pip install -e . make build_inplace - name: Run pytest run: | - pip install -r requirements-dev.txt + pip install -e ".[dev]" make pytest env: TEST_DATASET: true @@ -67,13 +67,12 @@ jobs: - name: Build and install aihwkit wheel run: | - pip install -r requirements.txt + pip install -e . make build_inplace - name: Run lint checks run: | - pip install -r requirements-dev.txt - pip install -r requirements-examples.txt + pip install -e ".[dev, examples]" make pycodestyle - make pylint make mypy + make pylint From f1340317b4d8d9ba186308c5f309371c3c4e4da7 Mon Sep 17 00:00:00 2001 From: Pablo Carmona Gonzalez Date: Wed, 7 Jan 2026 14:09:44 +0100 Subject: [PATCH 28/33] move mypy from build to deps Signed-off-by: Pablo Carmona Gonzalez --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 5ac05cc7..a6f0cfb2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [build-system] -requires = ["setuptools", "wheel", "scikit-build >= 0.18.1", "ninja", "mypy"] +requires = ["setuptools", "wheel", "scikit-build >= 0.18.1", "ninja"] build-backend = "setuptools.build_meta" [project] @@ -17,6 +17,7 @@ dependencies = [ "numpy", "protobuf", "tqdm", + "mypy", ] requires-python = ">=3.10" authors = [ From 3476aee077e59394af743fdf89649b521d0c9e78 Mon Sep 17 00:00:00 2001 From: Pablo Carmona Gonzalez Date: Wed, 7 Jan 2026 14:26:51 +0100 Subject: [PATCH 29/33] add temp flags for pylint to only look for errors Signed-off-by: Pablo Carmona Gonzalez --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index e00fc418..5b3c2475 100644 --- a/Makefile +++ b/Makefile @@ -46,7 +46,7 @@ pycodestyle: pycodestyle src/ tests/ examples/ pylint: - PYTHONPATH=src/ git ls-files | grep -E ".*\.py$$" | grep -v "pb2\.py$$" | xargs pylint -rn + PYTHONPATH=src/ git ls-files | grep -E ".*\.py$$" | grep -v "pb2\.py$$" | xargs pylint -rn --disable=all --enable=E pytest: PYTHONPATH=src/ pytest -v -s tests/ From d267200e0f10bf92bc07fcdae224bd2e899518c5 Mon Sep 17 00:00:00 2001 From: Pablo Carmona Gonzalez Date: Fri, 9 Jan 2026 11:58:45 +0100 Subject: [PATCH 30/33] fix(quantized_base_modules.py): change import from nn to nn.functional to match proper layers Signed-off-by: Pablo Carmona Gonzalez --- src/aihwkit/nn/low_precision_modules/quantized_base_modules.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py b/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py index 586401a9..7e1c0904 100644 --- a/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py +++ b/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py @@ -13,7 +13,7 @@ from typing import Any, Optional from torch import Tensor, nn -from torch.nn import linear, conv2d, layer_norm, embedding +from torch.nn.functional import linear, conv2d, layer_norm, embedding from aihwkit.simulator.digital_low_precision.base_quantized_classes import ( FP32Acts, From 45256ac84b1928f0077e115e9b428d00ead4ec07 Mon Sep 17 00:00:00 2001 From: Pablo Carmona Gonzalez Date: Fri, 9 Jan 2026 11:59:31 +0100 Subject: [PATCH 31/33] fix(pyproject.toml): bump version to 1.1.0 Signed-off-by: Pablo Carmona Gonzalez --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index a6f0cfb2..219912d3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "aihwkit" -version = "1.0.0" +version = "1.1.0" dependencies = [ "cmake >= 4.2.1", "scikit-build >= 0.18.1", From 202feaefd10a71c936cf21051c3f2c35e7d8da5c Mon Sep 17 00:00:00 2001 From: Pablo Carmona Gonzalez Date: Mon, 12 Jan 2026 12:21:26 +0100 Subject: [PATCH 32/33] add temp pylint disable not-callable Signed-off-by: Pablo Carmona Gonzalez --- src/aihwkit/nn/low_precision_modules/quantized_base_modules.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py b/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py index 7e1c0904..1a604ef3 100644 --- a/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py +++ b/src/aihwkit/nn/low_precision_modules/quantized_base_modules.py @@ -8,6 +8,7 @@ # All Rights Reserved. # mypy: disable-error-code=attr-defined +# pylint: disable=not-callable """Basic quantized modules""" From 11b93d146f0704405c6f69aa630e64b3ec6d7af1 Mon Sep 17 00:00:00 2001 From: Pablo Carmona Gonzalez Date: Mon, 12 Jan 2026 14:03:18 +0100 Subject: [PATCH 33/33] fix circular import for InferenceTileWithPeriphery while running examples Signed-off-by: Pablo Carmona Gonzalez --- src/aihwkit/inference/compensation/base.py | 7 ++++--- src/aihwkit/inference/compensation/drift.py | 8 +++++--- src/aihwkit/simulator/tiles/__init__.py | 2 +- 3 files changed, 10 insertions(+), 7 deletions(-) diff --git a/src/aihwkit/inference/compensation/base.py b/src/aihwkit/inference/compensation/base.py index f4387d5d..7e621126 100644 --- a/src/aihwkit/inference/compensation/base.py +++ b/src/aihwkit/inference/compensation/base.py @@ -6,12 +6,13 @@ """Base drift compensation for inference.""" -from typing import Tuple +from typing import Tuple, TYPE_CHECKING from torch import Tensor from torch.autograd import no_grad -from aihwkit.simulator.tiles.inference import InferenceTileWithPeriphery +if TYPE_CHECKING: + from aihwkit.simulator.tiles.inference import InferenceTileWithPeriphery class BaseDriftCompensation: @@ -21,7 +22,7 @@ def __init__(self) -> None: pass @no_grad() - def init_baseline(self, tile: InferenceTileWithPeriphery) -> Tuple[Tensor, Tensor]: + def init_baseline(self, tile: "InferenceTileWithPeriphery") -> Tuple[Tensor, Tensor]: """Initialize the base line for applying the compensation. Uses a all one tensor for read_out. diff --git a/src/aihwkit/inference/compensation/drift.py b/src/aihwkit/inference/compensation/drift.py index ae6ab10a..5254bbb0 100644 --- a/src/aihwkit/inference/compensation/drift.py +++ b/src/aihwkit/inference/compensation/drift.py @@ -6,14 +6,16 @@ """Global drift compensation for inference.""" -from typing import Tuple +from typing import Tuple, TYPE_CHECKING from torch.autograd import no_grad from torch import abs as torch_abs from torch import clamp, Tensor, eye from aihwkit.inference.compensation.base import BaseDriftCompensation -from aihwkit.simulator.tiles.inference import InferenceTileWithPeriphery + +if TYPE_CHECKING: + from aihwkit.simulator.tiles import InferenceTileWithPeriphery class GlobalDriftCompensation(BaseDriftCompensation): @@ -46,7 +48,7 @@ class GlobalDriftCompensationWithExactReference(GlobalDriftCompensation): """ @no_grad() - def init_baseline(self, tile: InferenceTileWithPeriphery) -> Tuple[Tensor, Tensor]: + def init_baseline(self, tile: "InferenceTileWithPeriphery") -> Tuple[Tensor, Tensor]: """Initialize the base line for applying the compensation. Uses a all one tensor for read_out. diff --git a/src/aihwkit/simulator/tiles/__init__.py b/src/aihwkit/simulator/tiles/__init__.py index 5ab9b5db..64d4b7cf 100644 --- a/src/aihwkit/simulator/tiles/__init__.py +++ b/src/aihwkit/simulator/tiles/__init__.py @@ -10,6 +10,6 @@ from aihwkit.simulator.tiles.analog import AnalogTile from aihwkit.simulator.tiles.floating_point import FloatingPointTile -from aihwkit.simulator.tiles.inference import InferenceTile +from aihwkit.simulator.tiles.inference import InferenceTile, InferenceTileWithPeriphery from aihwkit.simulator.tiles.inference_torch import TorchInferenceTile from aihwkit.simulator.tiles.quantized_inference_torch import QuantizedTorchInferenceTile