- 11 Oct, 2019 1 commit
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Animesh Jain committed
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- 10 Oct, 2019 1 commit
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* Add FIFO buffer op to enable explicit computation re-use in convolution * Add a test * Add end-to-end test with 1D convolution * Add a stub in MXNet frontend * Address reviewer comments * Add back stub for MXNet frontend
Philip Hyunsu Cho committed
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- 06 Oct, 2019 1 commit
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Animesh Jain committed
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- 05 Oct, 2019 1 commit
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* save save redo max test save address comment fix * address comment * increase rtol * address review comment
雾雨魔理沙 committed
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- 03 Oct, 2019 1 commit
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* [Relay][Op] Add instance norm op * mend [Relay][Op] Add instance norm op
bindog committed
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- 01 Oct, 2019 1 commit
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* Add op argwhere * Move shape func to _algorithm.py * Add lint rule * Raise exception if rank is not supportted * move argwhere to transform * Add argwhere example * Fix lint * Add 1-d support * cleanup * Add more dtype support * CR comment * Improve error message * Docs * raise exception
Wei Chen committed
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- 25 Sep, 2019 1 commit
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* [ARITH] Use explicit div/mod functions instead of operators. * fix pooling case
Tianqi Chen committed
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- 22 Sep, 2019 1 commit
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* Qnn Dense layer. * Reformatting code. * Reformatting code and making the test case more readable. * Fixing lint issues. * Fixing test method names to pass the nose related configurations. * Aligning the code for code style.
shoubhik committed
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- 20 Sep, 2019 1 commit
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MXNet pad is described at: https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.pad Add support for parameter 'None' in MXNet slice operator. MXNet 'slice' is described at https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.slice Add support for MXNet cos, sin, arctan MXNet 'cos' is described at https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.cos MXNet 'sin' is described at https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.sin MXNet arctan is descirbed at https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.arctan Add support for MXNet 1D Convolution and 1D Deconvolution MXNet convolution is described at: https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Convolution MXNet Deconvolution is described at: https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Deconvolution
Alex Gladkov committed
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- 18 Sep, 2019 2 commits
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* [Relay] add shape check for concat * [Relay] add shape check for stack * add test case for shape mismatch * [typo] add the missing assert * fix lint errors. * replace int with size_t. * statically cast param->axis to size_t. * switch to run_infer_type. * fix checking for negative index * add static_cast for param->axis * merge to latest tvm * fix lint error * Fix an error with negative index. * Update transform.h * Update transform.cc
Ligeng Zhu committed -
* Fix upsample layout in keras frontend. * Fixed group conv being used instead of conv when channels=1 * Add new conv2d test to catch bugs when channels=1.
Josh Fromm committed
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- 13 Sep, 2019 1 commit
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Animesh Jain committed
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- 09 Sep, 2019 1 commit
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* add more ops * stop vectorization for erf * x * cleanup * fix * add whitelist for vectorizable intrin * add tf converter * fix dense * fix * add missing intrin * fix mxnet frontend * fix nvptx
Haichen Shen committed
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- 08 Sep, 2019 1 commit
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save fix fix grad
雾雨魔理沙 committed
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- 06 Sep, 2019 1 commit
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* save * init * move type_relations
雾雨魔理沙 committed
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- 05 Sep, 2019 1 commit
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* adding support for graphpack over multiply op * increasing resnet model coverage * fix indentation * lint * moving recursion limit fix into graphpack pass * moving recursionlimit to relay init * pooling on NCHWnc format * adding more models * deploy_resnet_on_vta.py * trailing line * generalizing to vision models * merge conflicts * fix, apply quantization to VTA only * improving comments * trimming models that have runtime issues for the moment * lint * lint * lint
Thierry Moreau committed
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- 01 Sep, 2019 2 commits
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* init shape func in interpreter and vm compiler * Update interpreter * fix * lint * lint * fix * remove hack * update * fix * fix * update * address comments & update for shape_of * fix lint * update * fix hybrid * lint * fix bug & add take shape func * lint * lint * update * fix flaky test * add todo
Haichen Shen committed -
* Added arm_cpu NHWC schedules. * Fixed kernel shape legalization. * Added bitserial ops to relay. * Snapshot and more missing files. * Added dense testing. * Added tests * Added ASF header to new files. * cc lint * Pylint change. * pylint fixes. * Change arm legalize test. * Added assert check to arm legalize. * Added better documentation, fixed some bad style * Reverted arm conv2d nhwc changes.
Josh Fromm committed
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- 30 Aug, 2019 1 commit
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Animesh Jain committed
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- 29 Aug, 2019 2 commits
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* [Relay] Conv2d grad * Fix test * Fix first order gradient
Wuwei Lin committed -
* [TensorFlow] Fix limitation that depth_mult can only be 1 for DepthwiseConv2dNative * Improve code readability
lixiaoquan committed
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- 22 Aug, 2019 2 commits
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* Add one-hot to Relay * topi implementation * Working * add topi test * Add TF test * Fix check * fix linting issues * fix documentation * Fix documentation * Add support for on_value, off_value, axis, dtype * Add full support for axis * Fix compute and update test_forward * Move on_value and off_value to inputs * Add topi test * Update tests * Update docs * Fix style * re-enable tests * Add one_hot to mxnet converter
Jon Soifer committed -
Josh Fromm committed
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- 15 Aug, 2019 1 commit
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* Refactor. * update * update * update * update * update * update
ziheng committed
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- 13 Aug, 2019 1 commit
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* Added relay and topi mirror_pad operator. * Added mirror_padding to tensorflow frontend. * Added mirrorpad testing in tensorflow frontent. * Added space_to_depth in tf frontend. * Added tests for spacetodepth. * spacetodepth bug fix. * Lint fix * Added mirror pad python attrs. * Pad code formatting. * Syntax improvement * Hopefully last lint fix
Josh Fromm committed
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- 12 Aug, 2019 1 commit
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Neo Chien committed
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- 07 Aug, 2019 1 commit
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* Add LayerNorm op * update * fix * Add mean_std and mean_variance * add std and update doc * add license * x * lint * x * fix * fix doc
Haichen Shen committed
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- 06 Aug, 2019 1 commit
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* add build gcn tutorial * add transpose operator for square sparse matrices * remove extra files * change loop tag * comply with lint * comply with lint -- line too long * comply with lint * lint check * lint check * lint check * apply marisa and theirry's reviews
Yulun Yao committed
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- 03 Aug, 2019 1 commit
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* Fix gather_nd in Relay * Add test cases for gather_nd.
Huilin Qu committed
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- 01 Aug, 2019 1 commit
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The patch adds support for Tensorflow operators log1p and cos Tensorflow log1p is described at https://www.tensorflow.org/api_docs/python/tf/math/log1p Tensorflow cos is described at https://www.tensorflow.org/api_docs/python/tf/math/cos Tensorflow sin is described at https://www.tensorflow.org/api_docs/python/tf/math/sin
alexgl-github committed
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- 25 Jul, 2019 1 commit
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Lianmin Zheng committed
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- 24 Jul, 2019 1 commit
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Wuwei Lin committed
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- 23 Jul, 2019 2 commits
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internally and externally, interested in replacing standard dense layers with block-sparse matrix multiplication layers. The motivations are generally: higher performance (due to reduction in FLOPs, memory bandwidth/cache footprint), enabling larger models (e.g. fitting more layers in a given memory budget). Some public work along these lines: * https://openai.com/blog/block-sparse-gpu-kernels/ * https://openai.com/blog/sparse-transformer/ * https://arxiv.org/abs/1802.08435 * https://arxiv.org/abs/1711.02782 Various groups have been able to successfully train models with reasonable levels of sparsity (90%+) with marginal accuracy changes, which suggests substantial speedups are possible (as this implies a >10x reduction in FLOPs). It is fairly straightforward to realize these theoretical speedups, see e.g. TVM benchmarks for Intel CPUs in https://gist.github.com/ajtulloch/e65f90487bceb8848128e8db582fe902, and CUDA results in https://github.com/openai/blocksparse, etc. * https://github.com/openai/blocksparse (CUDA) * https://software.intel.com/en-us/mkl-developer-reference-c-mkl-bsrmm (MKL BSRM) * https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.sparse.bsr_matrix.html (SCIPY BSR representation) This is extracted from an internal patch we've been using internally. There are various extensions possible (int8/fp16/bf16, CUDA/other GPU architectures), but this is a reasonable starting point. This needs more thorough unit test coverage however. We follow the conventions established by scipy.sparse.bsr_matrix and other libraries, see the unit tests for details. For folks interested in experimenting with scheduling/AutoTVM etc, https://gist.github.com/ajtulloch/e65f90487bceb8848128e8db582fe902 is a useful starting point.
Andrew Tulloch committed -
= Motivation It's useful to expose the tvm::reinterpret functionality to Relay/TOPI users, as this allows them to build (fused) operators leveraging the bitwise reinterpretation of an operator. An example is approximate transcendental functions, which can be implemented similar to: ```.py def C(x): return relay.expr.const(x, "float32") def approx_exp(x): x = relay.minimum(relay.maximum(x, C(-88.0)), C(88.0)) x = C(127.0) + x * C(1.44269504) xf = relay.floor(x) i = relay.cast(xf, "int32") x = x - xf Y = C(0.99992522) + x * (C(0.69583354) + x * (C(0.22606716) + x * C(0.078024523))) exponent = relay.left_shift(i, relay.expr.const(23, "int32")) exponent = relay.reinterpret(exponent, "float32") return exponent * Y def approx_sigmoid(x): # <2.0e-5 absolute error over [-5, 5] y = approx_exp(x) return y / (y + C(1.0)) def approx_tanh(x): # <4.0e-5 absolute error over [-5, 5] x = x * C(2.0) y = approx_exp(x) return (y - C(1.0)) / (y + C(1.0)) ``` See unit tests for implementations of these approximate transendentals.
Andrew Tulloch committed
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- 19 Jul, 2019 1 commit
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Yong Wu committed
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- 11 Jul, 2019 1 commit
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* [INFA][IR] Build and Evolve Low-level IR. Remove dep from HalideIR. * Update include/tvm/node/ir_functor.h Co-Authored-By: Jared Roesch <roeschinc@gmail.com> * Update include/tvm/node/ir_functor.h Co-Authored-By: Jared Roesch <roeschinc@gmail.com>
Tianqi Chen committed
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- 10 Jul, 2019 1 commit
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* Implement type checking for Any Remove code generation related changes Remove compile changes Remove more Remove unification hack Add some code back that was needed, and clean up test Refactor test cases WIP Implement TypeHint AST Add test case which should fail Remove unification changes, and fix bug with let rec Restore unification for shapes Improve error reporting while debugging All examples type check All examples type check WIP First version that works with hints, needs clean up Remove dead code Tweaks Remove type hint Remove unecessary type hint stuff Remove more type hints Clean up Expose Any expression node Address CR Fix Fix solver Kill unecessary code Fix PyLint Fix Relocate loops Fix license and test Lint again Lint again Fix loops Fix docstring Fix template error Fix compiler issue Fix compile err Remove more runtime changes Restore buffer Fix segfault Fix Fix arange * Address feedback * Fix typo * Fix arange * Fix op level3 * Fix issue with Python wrapper
Jared Roesch committed
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- 09 Jul, 2019 1 commit
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- Weight dtype can be different than idtype. So, using the weight tensor to set the dtype of weight. - For conv2d NCHWc operator, the weight can be of any dimension. For int8 computation on Intel, it can be 7D. Relaxing the weight type checking.
Animesh Jain committed
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- 28 Jun, 2019 2 commits
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Thierry Moreau committed
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* Add sequence_mask use exactly the same arguments as mxnet fix * fix lint * fix lint * add mxnet conversion + relay * update * update doc * fix pylint * fix doc * address comment * try to address comments * try to enable shape check for valid_length * fix * try to fix * fix bug * try to fix * address comment * address comment
Xingjian Shi committed
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