v0.5.1
Docs | 17.10.2022 |
Highlights
- General
sol.check_version() command can be used to check for new version.- Form to apply for SOL4VE closed beta.
- Improved command-line based installer.
- PyTorch
- Calls to
sol.optimize(...) from PyTorch does no longer require example inputs. Instead it gets parsed the very first time it gets executed.
- Limited support for
torch.einsum(...)
- Support for GANs through MaxUnPooling and TransposedConv layer support.
- Automatically detection when to use
torch.jit.script and when to fall back to torch.jit.trace
- Improved handling of inline-operations
- TensorFlow
Closed Issues
- #775 [OpenSSL] Upgrade to 1.1.1r
- #774 [PyTorch] v1.12.1 breaks VE complex number support
- #772 [PyTorch] SqueezeNet training accuracy problems on VE
- #771 [DFP] Investigate Unvectorizable DType in SqueezeNet Training on VE
- #765 [PyTorch] automatic fallback to jit.trace if model parsing fails with jit.script
- #764 [Docs] add Pytorch lazy optimizer documentation
- #763 [PyTorch] enable kwargs for trace=True cases
- #762 [PyTorch] add lazy optimizer
- #761 [PyTorch] add torch.triu
- #760 [Docs] Update supported layers
- #759 [Core] Disable Progress bar if not running in interactive shell
- #758 [Hugo] Upgrade to v0.103.0
- #756 [VE] find alternative for "constexpr" in RNN implementation
- #754 [DFP] Implement Interpolate autoSqueeze
- #753 [Config] Remove unused config options
- #752 [Docs] Update Docs for v0.5.1
- #751 [NEC-SOL] Add "test access" option to nec-sol
- #750 [NEC-SOL] Device Support Packages that users don't have access to prevent to install the package in the first place.
- #748 [DNNL] Upgrade 2.6.2
- #747 [NEC-SOL] add --verbose flag and report access to urls.
- #745 [TF+RNN] verify RNNSimple results
- #743 Add TF v2.10.0 Support
- #739 [Hugo] upgrade v0.102.1
- #735 [Boost] Upgrade to 1.80.0
- #733 [OMP] Add Heuristics to sol_parallel_for and sol_parallel_simd
- #732 [HLIR] add option to remove unused model parameters
- #731 [HLIR] Make RNN sequence length variable
- #730 [Keras] add tf.ensure_shape to KerasLayer
- #729 [Keras] Support named inputs
- #728 [HLIR] Repair Where 2 min/max transformation
- #726 [DNN/RNN] evaluate not using BLAS
- #725 [TF] Solve Threadblocking Issue on X86
- #723 [HLIR] derive(Slice) == Slice, if it's a reverse-slice
- #720 [TF2RNN] Handle cases where only OH is used without slicing
- #719 [RNN] Move sol.hlir.rnn API to C++ space
- #718 [DNN/RNN] Improve Handling of O and OH
- #717 [RNN] Masking Support
- #716 [RNN] Recurrent Dropout Support
- #714 [HLIR] remove dropout layers from inference executions
- #713 [TF] fix keras (alpha_)dropouts in inference mode
- #712 [TF] enable to change input shapes using sol.optimize(..., shapes={...}, ...)
- #711 [DNNL] Upgrade 2.6.1
- #705 [OpenSSL] Upgrade 1.1.1q
- #704 [NVIDIA] JIT compile 64 and 128 Bit memset functions
- #703 [Numpy] Numpy Runtime can cause SegFault when a NDArray gets freed when SOL already destroyed the handlers during shutdown
- #702 [ONNX] add MaxUnPool
- #701 [PyTorch] add MaxUnPooling
- #700 [HLIR] check if we MaxUnPooling always uses "max" instead of "+=" in fwd Pass
- #699 [HLIR] Add DeSampling optimization to DeConv
- #698 [Core] Fix Conv::Transform::Subsampling when applied in Bwd Pass
- #697 [PyTorch] accuracy problem in RNN with S>1 in v1.12.0
- #695 [PyTorch] add aten::pad
- #694 [PyTorch] add aten::index
- #688 [PyTorch] Upgrade to 1.12.0
- #687 [PyTorch] Add Swin Transformer Tests
- #686 [NEC-SOL] Pure command line mode?
- #685 [DFP] fix unvectorized read loops
- #684 [DFP-NCC] remove struct operator and add restricted keyword.
- #683 [NCC] Improve DFP unrolling
- #682 [PyTorch] Add PyTorch Lightning Support
- #673 [OpenSSL] Upgrade 1.1.1p
- #672 [JIT-NCC] In debug, warn about obstructive functions, and other vectorization problems.
- #671 [VE] Add _Pragma("_NEC always_inline") into DFP-NCC headers
- #670 [API] Add linker script
- #669 [JIT] Add linker script
- #668 [API] Replace all remaining extern "C" with SOL_API
- #665 [CUDNN] Get Bundle from NVIDIA
- #664 [MKL] Switch to PIP MKL Package instead of Bundling
- #663 [Runtime] Add Mutex to runtime::device::Network to prevent parallel executions as in TF.
- #662 [TF/X86] Warn user if tf.config.threading.set_inter_op_parallelism_threads is not set to 1, that it has negative impact on SOL performance
- #661 [Python] Replace any remaining 'print' with 'tungl.info'
- #660 [Web] Add SOL4VE Registration Form
- #658 [PyTorch+TF] Use unified implementation of framework::Handle for all devices
- #657 [TF] Add option to enable grad on inputs
- #654 [OpenSSL] Upgrade 1.1.1o
- #653 [Hugo] Upgrade 0.100.2
- #652 [Installer] Upgrade fails with 'set' object has no attribute 'append'
- #649 [TF] Unable to fetch values for...
- #647 [Core] Lock DB when executing cache::clear
- #646 [Docs] Update TF SDK docs
- #645 [HLIR] Transformation to remove duplicate Permutes is not working correctly.
- #644 [TF] RNN Seq2Seq testcase exposes #3 as vdim
- #643 [Hugo] Upgrade v0.100.1
- #642 [TF] GRU
- #641 [TF] LSTM
- #640 [TF] SimpleRNN
- #638 [TF] Evaluate unified module instead of compiling new modules for each network
- #636 [Profiler] Rework API
- #635 [Installer] Not Showing PyTorch/TensorFlow properly
- #634 [NCC] investigate why OpenMP is not working with -std=c++17
- #633 [MKL] RNN
- #631 [TF] Upgrade 2.9.1
- #630 [Deployment] change binary2obj to objcopy
- #629 [PyTorch] Sum of BOOL needs to be casted into INT DType
- #626 [Python] Evaluate if using CFFI is less verbose compared to CTypes
- #625 [PyTorch] Evaluate if replacing sol.runtime.set_tensor with a CPP function yields in less overhead.
- #621 [HLIR] ZeroCopy Layer, that tries to not duplicate outputs in frameworks.
- #609 [ONNX] GroupNorm: illegal view transformation
- #608 [TF] Add tf.keras.applications testcases
- #607 [TF] Upgrade 2.8.2, 2.7.4, 2.6.5
- #601 [VEDA] VEDA_ERROR_UNKNOWN_CONTEXT thrown when calling vedaDevicePrimaryCtxRetain
- #600 [PyTorch] Missing Primitive: aten::bernoulli
- #597 [Core] Wrong Rendering of Output in Inception
- #593 [X86+VE] IRFFT2D accuracy issues
- #582 [Core] Show Update Message
- #547 [PyTorch] Remove RNN Fix for when PyTorch v1.12.0 is released
- #541 [DFP] improve size-1 write loop removal
- #540 [DFP] Split BatchSize == 1 outer loops if there are multiple inner
- #498 [Python API] Add option to override requires_grad
- #283 [TensorFlow] automatically unload network, when all instances of the network have been destroyed
- #214 [VEBLAS] Better parallelize RNN for small BatchSizes
- #209 [PyTorch] automatically unload network, when all instances of the network have been destroyed
- #123 [Layers] ConvTranspose/Deconvolution
|
v0.5.0rc2
Docs | 23.05.2022 |
This is the 2nd release candidate of SOL v0.5.0 Diadem. It adds brings back
support for ONNX, Numpy (runtime), as well as lots of bugfixes and improvements.
Highlights
- ONNX frontend
- Numpy runtime
- Printing StackTrace when exception occurs in SOL
- Improved printing of model signature
- The installer no longer shows packages you don't have access to
Closed Issues
- #624 [FFT] duplicate symbol "vdims"
- #623 [TF] tf.nn.max_pool_with_argmax indices differ again...
- #619 [Hugo] Upgrade 0.99.0
- #617 [ONNX] "Unable to fetch values for " in ShuffleNet
- #616 [ONNX] "can't 384 / 13 because it's no divider" in X86 Igornet and Layers[Repeat]
- #615 [ONNX] "Unable to find dimension PO1" in X86 MNasNet and EfficientNet
- #614 [ONNX] Cumsum accuracy issues
- #613 [Core] Improve calculation of memory consumption estimation
- #612 [TF] TF casts shape of [1,1,1,1] to [] during training
- #611 [NCC] Segfault in networks using VDims
- #610 [ONNX] CumSum
- #606 [ISPC] Wrong initialization of VDims
- #605 [DB] Verify that we can open multiple instances of SOL using the same DB file
- #604 [X86+FFT] Accuracy Error in PY_IRFFT2D
- #603 [X86+PyTorch] Memory is already allocated
- #602 [X86+FFT] [DNNL ERROR] The operation failed because of incorrect function arguments.
- #599 [PyTorch] Missing Primitive "aten::empty"
- #598 [HLIR] VDims cause promotion to F64 in BatchNorm
- #595 [VE] RNN Segfault in Testcases
- #594 [Tests] Report % of values that exceed threshold
- #592 [PyTorch] Models without parameter can create training context if 'model.training = False' is not set
- #591 [Docs] Update documentation for v0.5.0rc2
- #590 [Core] "axis needs to be between 0 and 0 but found 1" in DeReduce
- #589 [TF] Can't use Keras models with scalar parameters
- #587 [TF] Ordering of Parameters in KerasLayer is not stable
- #586 [VE] Investigate possible Memleak in PyTorch-VE Runtime
- #584 [PyTorch] Add Error Handling when User provides too many/too few arguments to function
- #583 [JsonCPP] Upgrade to 1.9.5
- #581 [ISPC] Upgrade to 1.18.0
- #580 [CMake] Change min GCC to 10.X
- #579 [Hugo] upgrade 0.98.0
- #578 [PyTorch] RNNCell parsing fails in PyTorch 1.11.0
- #577 [PyTorch] "illegal view transformation" when parsing RNN
- #576 [DB] automatically recover from "database image malformed" exceptions
- #575 [VE] RNN does not compile with NCC 3.4.2
- #574 [Runtime] SegFault in Context::Destroy when starting another training context.
- #573 [Core] Print StackTrace when SOL Exceptions are thrown
- #572 [VE] Value2VDim
- #571 [X86] Value2VDim
- #570 [VE] WhereTrue
- #569 [X86] WhereTrue
- #568 [VE] PrefixSum
- #567 [X86] PrefixSum
- #566 [HLIR] return NAN instead of throwing error when dividing by 0
- #563 [HLIR] Remove Unnecessary Permute
- #562 [Runtime] do not reinit Params if the model has been run before
- #561 [PyTorch] add Mobilenet V3 testcases
- #560 [PyTorch] add efficientnet testcases
- #559 [PyTorch] add ConvNext Testcases
- #558 [PyTorch] add RegNet testcases
- #556 [HLIR] show actual input/output shapes, not the "possible"
- #555 [Installer] Cache Password for PIP
- #554 [Installer] hide packages that user does not have access to
- #553 [Runtime] Context needs to distinguish between offloading and framework handle!
- #552 [Numpy] Implement Lazy Allocations in Numpy executor
- #551 [HLIR] Fix Memory Consumption to report Copies not as Outputs
- #550 [NCC/OMP] cannot explicitly instantiate std::tuple in sol parallel simd with NCC 3.3 or newer
- #549 [Compiler/Runtime] Encode/Check framework version, device compute capability, ... in compiled NN library name
- #548 [SQLite] Upgrade to 3.38.2
- #546 [DNNL] Upgrade 2.6
- #545 [TF] SOL's MaxPooling makes other choices than TF's implementation in training
- #544 [TF] implement save/load methods in Keras model
- #543 [TF] fix BatchNorm Assignment
- #539 [DNNL] Illegal Operation in Resnext BWD Pass
- #538 [Python] Don't store size of Tensor in Python but always request from C++ space
- #522 [DNNL] Performance Problem in ConvBwdData Param Reorder
- #514 [PyTorch] Upgrade 1.11.0
- #512 [HLIR] Copy Buffers during Training, not LayerOutputs
- #507 [TF] Which momentum does TF use in BatchNorm?
- #492 [TF/VE] Check if Optimizers and Loss functions are implemented
- #438 [TF] Update 2.8.0
- #429 [ONNX] Add Handler option
- #425 [HLIR] Remove unnecessary Copies in RNN -> Copy -> Output i.e. for Workspace
- #276 [ONNX] set that params require grad, so we can train ONNX models
- #201 [DFP] Double check if we correctly report the scratchpad memory
- #186 [ONNX] use sol.internal.Tensor operators instead of explicit calls
- #166 [PyTorch] torch.nn.InstanceNorm2d missing
- #165 [PyTorch] torch.nn.GroupNorm missing
- #113 [ONNX] PRelu
- #112 [ONNX] Gather
- #108 [ONNX] ERROR getsym_handler
- #74 [DFP] Performance: implement Input Loop Merging
|
v0.5.0rc1
Docs | 23.03.2022 |
The SOL v0.5.0 Diadem is the next major release of SOL, containing already over
160 closed issues! We further switched to a new rolling releases model using
release candidates, to push out fixes + changes more often.
This first release candidate DOES NOT contain support for: NVIDIA devices, ONNX
or Deployment! These features will be reenabled in later release candidates.
Highlights
Breaking Changes
-
We modified the
sol.optimize(model, args, kwargs={}, framework=None, **fwargs)
call. If you have more than one input, you now need to pass it using the
args argument as list or tuple, or using the kwargs
as a dictionary. This was necessary to be more compliant with the AI framework's.
-
sol.optimize(..., batchsize=...) has been removed in favor of the
new variable dimensions system. Please look here
for more details.
Closed Issues
- #538 [Python] Don't store size of Tensor in Python but always request from C++ space
- #537 [OpenSSL] Upgrade to 1.1.1n
- #535 [PyTorch] create OMP Symlink in sol-framework-pytorch-x86
- #534 [DNNL] Upgrade 2.5.3
- #533 [SQLITE] Upgrade 3.38.0
- #531 [Core] Report Peak Memory in Output during Compilation Phase for each Pass
- #530 [Python] increase min Python Version from 3.6 to 3.7 because of TF 2.8.0 requirement
- #529 [PyTorch] Upgrade v1.10.2
- #524 [PyTorch] Check why we get weird JIT accuracy errors in HuggingFace Bert training
- #523 [Python] deprecated "copy_parameters" and just do it always.
- #521 [DNNL] implement memory layout AutoTuning for Conv
- #520 [CMake] Enable Release Candidates
- #519 [VE] If NC++ is not found, we need to disable VE entirely, not just not setting the VE_LD_LIBRARY_PATH
- #518 [VEDA] upgrade 1.2.0
- #517 [Runtime] segfault when an inference Context is still open and get cleaned up during shutdown
- #516 [PyTorch] Find a solution for conflicting GOMP implementation
- #515 [OMP] Limit number of threads to match number of hardware cores, not number of hardware threads
- #511 [Runtime] Store raw pointer in sol_tensor
- #510 [DFP] Unroll Interpolation using TmpVars
- #509 [Runtime] Evaluate if TBB is better than OMP
- #508 [Runtime] Remove sol::runtime::Tensor
- #505 [DFP] Remove Rand from DFP and implement in ISPC/CUDA and VEASL
- #500 [DFP] input loop merge
- #499 [Python API] Change sol.optimize(*args, **kwargs) to use one variable
- #496 [DFP] Ensure that in ISPC each core uses it's own random state
- #494 [DFP] Remove GLoops
- #493 [DFP] Don't allocate stack memory in CORES loops.
- #491 [HLIR] Fix weird TorchVision structure of DenseNet
- #490 [TF] ValueError: Data cardinality is ambiguous: x sizes: 1, 100, 100
- #489 [TF] OperatorNotAllowedInGraphError: iterating over `tf.Tensor` is not allowed in Graph execution. Use Eager execution or decorate this function with @tf.function.
- #488 [HLIR] Add verification for DTypes
- #487 [PyTorch] Testcase "MemberTensors" fails in Training
- #486 [TF] can we pass the sol_ctx as attribute instead of scalar tensor?
- #485 [TF] Enable models with more than 255 model parameter tensors
- #483 [Core] Buffer Chaining fails if there is a view in between the concat operations
- #480 [PyTorch] add strided slice support
- #479 [SQLite] Upgrade to 3.37.2
- #478 [TF] add missing StridedSlice features
- #477 [ISPC] Upgrade to 1.17.0
- #475 [DNNL] Upgrade to v2.5.2
- #471 [X86] correctly detect vector instructions
- #470 [TF] tf.math.cumsum
- #469 [ISPC] migrade dnn_ispc to ispc, as it does not use any dnn components
- #468 [Core] Verify that all layers have properly been assigned an algorithm before generating code
- #466 [PyTorch] add torch.cumsum
- #465 [TF] add tf.Where
- #464 [Core] remove NUMEL through transform if VDIMS get removed
- #463 [Core] add input to NUMEL
- #462 [TF] backport new sol.hlir.reduce API
- #461 [Core] Revise sol_tensor using real shape and separate numel field
- #460 [DFP] BackendRenderer missing DTYPE
- #459 [SQLite] Upgrade to 3.37.1
- #458 [AVEO] Upgrade to 2.10.0
- #457 [PyTorch] Support multivalued constants
- #456 [HLIR] Bugfix GEMM batchHelper function
- #455 [VDIMS] PyTorch AvgPool2D#3 with two different shapes after each other fail because of identical Hash
- #454 [Pytorch] Upgrade 1.10.1
- #453 [Docs] Add TensorFlow Native to VE Chapter
- #452 [DNNL] Upgrade to 2.5.1
- #451 [Core] Remove Tensor Class
- #450 [Docs] Correct VDim case with #2 == 5
- #449 [Deprecated] YaalUpdate and YaalSeed
- #448 [VDIMS] option to enable/disable VDIM usage, disable by default.
- #447 [DFP] Fix BatchNorm BWD Pass
- #446 [DFP] Flag Loops where DataStride() == 1 for ALL data to be used for SIMD
- #445 [PyTorch] torch.reshape missing
- #444 [Core] Print NN Input/Output Signature
- #443 [PyTorch] argmin + argmax missing
- #442 [TF] wrong results in activation layers
- #439 [PIP] Enable manylinux2014 compatible build process
- #437 [DNNL] Upgrade 2.4.4
- #436 nec-sol: download.pytorch.org may also need to be trusted
- #435 nec-sol: codec can't display license agreement
- #434 upon program exit, malloc(): unsorted double linked list corrupted
- #433 [TF] Enable to parse tf.Module and tf.saved_models
- #432 [TF] Upgrade to 2.6.1
- #431 [PyTorch] torch.rand* and torch.randint* missing
- #430 [Pytorch] Enable Models without inputs
- #428 [PyTorch] Add Handler option
- #427 [TensorFlow] Add Handler option
- #426 [HLIR] Add Custom Layer Support
- #423 [PyTorch] Possible Segfault in Py_InfNAN on X86
- #422 [Core] Add Option to register Operations at runtime
- #421 [Core] Missing library libcrypto.so.10 on Ubuntu 20.04
- #420 [PyTorch] Debug v1.10.0 print problems with native-ve
- #419 [PyTorch] Upgrade RNN API to new HLIR version
- #418 [CUDNN] Upgrade to 8.2.4
- #417 [CUDA] Upgrade to 11.3
- #416 [PyTorch] Upgrade to v1.10.0
- #415 [CORE] use SQLite Transactions to lock SOL Cache in multiple processes
- #414 [DNNL] Upgrade to 2.4.2
- #413 [VE] replace sol_ve_copy with VEDAMemset and VEDAMemcopy
- #412 [VEDA] Add device side memset
- #411 [VE] Add ComplexDTypes to Device API
- #410 [TBB] CMake install script fails on first run.
- #408 [VEDA] memset 128
- #407 [VEDA] CMake ASL FFTW
- #401 [DNNL] Upgrade to 2.4.1
- #399 [NCC] Add Struct Functions
- #398 [X86] Check Handle::memset performance!
- #397 [VEDA] improve S8, S16 vedaMemset Performance
- #396 [TBB] Upgrade to 2021.4.0
- #395 [DNNL] Upgrade to 2.4
- #394 [PyTorch] torch.complex missing
- #393 [PyTorch] torch.imag missing
- #391 [PyTorch] sol.optimize(model, torch.Tensor) stopped working?
- #389 [NEC-SOL] automatically detect VENV
- #388 [PyTorch] use torch.jit.trace instead of torch.jit.script to support Transformers
- #387 [HLIR/DFP] Change Dropout to always use input rand
- #386 [NEC-SOL] UTF-8 encoding problem
- #385 [DFP] Bad Loop Merging in multi-path kernels
- #384 [DFP] Wrong vectorization in Bias Sum Case
- #383 [YAAL] Improve Scheduling
- #382 [PyTorch] Upgrade to 1.9.1
- #381 [Tests] Restructure TESTS package, so the PyTorch package does not load TF and vice versa.
- #379 [NNPACK] Deprecate
- #376 [ISPC] Check OpenMP implementation performance for small batchsizes
- #375 [TensorFlow] Upgrade to 2.6.0
- #364 [DFP] Fix Tile backward pass for X86
- #363 [PyTorch] aten::repeat missing
- #362 [PyTorch] aten::tile missing
- #360 [PyTorch] aten::smooth_l1_loss
- #355 [PyTorch] HuggingFace transformers broken
- #332 [TF] Enabled Delayed Allocation in Native-VE
- #329 [PyTorch] Add BatchNorm num_batches_tracked
- #321 [API] change sol_external_malloc to use shapes instead of accumulated sizes
- #307 [HLIR] Input > Dropout > Output produce wrong results in Inference
- #306 [TF] tf.nn.max_pool_with_argmax returns wrong indicies
- #302 [PyTorch] Use VE instead of HIP device
- #299 [PyTorch] Slicing with [0:0] should return empty Tensor
- #293 [HLIR] Constant with more than 1 element
- #289 [TF] Remove sol_model.convert("device")
- #285 [DFP] Remove Nests
- #284 [DFP] Implement Linear
- #271 [TensorFlow] enable lazy allocations
- #267 [Core] Remove OptimizerType
- #259 [IgorNet] Can't run Inference (division by zero)
- #239 [PyTorch] add torch.nn.functional.interpolate
- #225 [DNNL] Upgrade to 2.3.2
- #212 [HLIR] SIGN on Unsigned is 1
- #211 [HLIR] Abs on Unsigned is Noop
- #208 [PyTorch] some networks can't use variable BatchSize
- #203 [DFP] Buffered Dropout >> Narrow allocates too few memory
- #202 [Compiler] Show Progress Bar already during Code generation Phase
- #199 [HLIR] Detect Permutations in front of GEMM and merge them into the GEMM by changing the layout.
- #198 [HLIR] Initialize Gradients with 0 and make all multi-connections to REQURIESACC
- #193 [PyTorch] "Only one dimension for View can use summation." in ShuffleNet when using Variadic BatchSizes
- #187 [DFP] BatchNorm does not support NOT to trace runtime metrics
- #184 [PyTorch] No Gradient for inputs which get disconnected
- #183 [PyTorch] LPPool Backward Pass results do not match
- #182 [DFP] Split BatchNorm into two operations to better fit the actual computations and to remove the workarounds in DFP module
- #177 [VEBLAS] Memory Estimation: RNN
- #163 [PyTorch] torch.Tensor.repeat missing
- #161 [PyTorch] Interpolation Layers missing
- #157 [Autotuning] Device reports out of memory, as SOL does not use the framework's memory allocator during auto-tuning
- #139 [Core] Enable to save/load SOL models
- #135 [HLIR] Pytorchic BERT can't use variable batchsize because of the view?
- #131 [DFP] segfault generating RReLU layer
- #120 [DType] Add Complex DTypes
- #117 [PyTorch] FFT
- #81 [Variable BatchSize] GPT-2 can't use variable batchsize
- #80 [Clusters] Errornous accumulation in GPT-2 BWD Pass
- #71 [DFP] missing layer: MaxUnPooling
- #70 [DFP] LeakyRelu: Remove IF in generated code
- #35 [X86] Does not show correct used memory
|