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2020-09-15[caffe2] Solving bug with disabling all nnpackssandbox/kparichay/v1.6.0-rc1_protobuf_1.12.3Parichay Kapoor2-3/+8
Instead of checking just USE_XNNPACK, check for all NNPACKS Change-Id: Idc566383322b0cad201e7d0e4c32b4da0c91c1ea Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>
2020-07-16[pytorch] Update to version 1.6.0-rc1Parichay Kapoor963-14099/+66329
Update to vesion 1.6.0-rc1 Change-Id: I53e568f805ea7d787c7cc013ed6b858e031160f9 Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>
2019-04-23optimize BatchMatmulOp (#18612)Xiaomeng Yang6-406/+533
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18612 optimize BatchMatmulOp Reviewed By: houseroad Differential Revision: D14681665 fbshipit-source-id: cf5ea4909ace58fd44fe6fa634531102ac84e851
2019-04-23caffe2 | Windows compat fixesOleg Bogdanov2-40/+48
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19531 Reviewed By: hlu1 Differential Revision: D15024541 fbshipit-source-id: cd8249a6d529afb65fa8afd74a05dbfe73eb1fb0
2019-04-23correct comments in group_norm_op (#19621)Huamin Li2-4/+4
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19621 Comments for group_norm_op is not accurate (i.e., the math part), this diff will fix it. Reviewed By: BIT-silence Differential Revision: D15048695 fbshipit-source-id: 27d41d3ae21054257967815254134849944d56ca
2019-04-23Surface the Glow traces to C2 (#19087)Yinghai Lu2-5/+48
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19087 att Reviewed By: jackm321 Differential Revision: D14863112 fbshipit-source-id: 2680161b9f05391e73bb8dac4fbbeabb87a82c05
2019-04-23Allow extracting element-wise loss in softmax (#19579)Priya Goyal2-2/+4
Summary: Often times, we want to experiment with loss per element (image etc.). This changeset allows getting per element loss as well. This output is optional. Pull Request resolved: https://github.com/pytorch/pytorch/pull/19579 Reviewed By: jerryzh168 Differential Revision: D15035797 Pulled By: prigoyal fbshipit-source-id: 562dea514f49c1f2f1cbbc083a1938dc019a75c4
2019-04-23Specify to use Float16UniformFill if necessary in sparse lookup layer (#18499)Jiyan Yang1-2/+7
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18499 If the init op is not fp16 compatible, it should throw. However, in the special case where the original init op is UniformFill, we replace it with Float16UniformFill Reviewed By: kennyhorror Differential Revision: D14627209 fbshipit-source-id: eb427772874a732ca8b3a25d06670d119ce8ac14
2019-04-22Automatic update of fbcode/onnx to 0e8d2bc5e51455c70ef790b9f65aa632ed9bc8a7 ↵Lu Fang1-0/+2
(#19568) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19568 Previous import was 83dd62659fc07d5b7fa93b5d1c1879f93509c7db Included changes: - **[0e8d2bc5](https://github.com/onnx/onnx/commit/0e8d2bc5)**: [Minor need to be in 1.5]Fix an issue in NMS test data which introduce wrong shape. (#1953) <Hector Li> - **[9346dd5d](https://github.com/onnx/onnx/commit/9346dd5d)**: adding modulus operator (#1874) <Jeff Saremi> - **[414dbc73](https://github.com/onnx/onnx/commit/414dbc73)**: Fix shape inference for slice (#1950) <Hariharan Seshadri> - **[6fb0775d](https://github.com/onnx/onnx/commit/6fb0775d)**: Fix shape inference for ConstantOfShape op (#1951) <Ashwini Khade> Reviewed By: bddppq, zrphercule, benoitsteiner Differential Revision: D15033070 fbshipit-source-id: f7eb90b142cbdc9bf1600cfd33e5a8df709045fb
2019-04-22Add back option to not adjust output batch size (#19442)Yinghai Lu6-12/+38
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19442 For cases like CV, some of ops like transpose and tile will mangle the batch size so that we don't know how to adjust output batch size. In this case, the current solution is just fix the input batch statically and do not adjust output batch size. Reviewed By: zrphercule Differential Revision: D15007237 fbshipit-source-id: a21b943a52ee5462d9d7804dfae44360f579f8cf
2019-04-22Add debug logic to c2_ref_test and its helpers (#19359)Michael Antonov3-7/+27
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19359 Even with file IO exception handling, some of the sandcastle c2_ref_tests are still failing in length-check assert, as can be seen here: https://our.intern.facebook.com/intern/test/844424932589974?ref_report_id=0 This is an attempt to add printing logic to debug what's going on. Reviewed By: dzhulgakov Differential Revision: D14966274 fbshipit-source-id: adce6d4780d664c5ef59f9341b6133b0d09324cb
2019-04-22fix variable shadowing issus (#19567)Dehua Cheng1-2/+2
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19567 fix variable shadowing Reviewed By: bddppq, wx1988 Differential Revision: D15032114 fbshipit-source-id: 895ea21f22b87db8c7c8684f54fa186d22f24d10
2019-04-22Automatic update of fbcode/onnx to 83dd62659fc07d5b7fa93b5d1c1879f93509c7db ↵Lu Fang1-0/+5
(#19454) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19454 Previous import was ad7313470a9119d7e1afda7edf1d654497ee80ab Included changes: - **[83dd6265](https://github.com/onnx/onnx/commit/83dd6265)**: Add NonMaxSuppression operator (#1703) <Hector Li> - **[31ca5d6f](https://github.com/onnx/onnx/commit/31ca5d6f)**: add node tests for quantized ops (#1944) <Ashwini Khade> - **[e6076c1d](https://github.com/onnx/onnx/commit/e6076c1d)**: Fix test stat coverage script (#1948) <Raymond Yang> - **[ad036405](https://github.com/onnx/onnx/commit/ad036405)**: Add IsInf to detect infinity values (#1884) <Wei-Sheng Chin> Reviewed By: benoitsteiner Differential Revision: D15010015 fbshipit-source-id: 4b29de21de60f8e6a2db75309809a4e619c92532
2019-04-21Add assertion to make sure init op is always fp16 compatible in fp16 trainingJiyan Yang1-9/+20
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18498 Reviewed By: kennyhorror Differential Revision: D14626755 fbshipit-source-id: d8a0b3c02920ab3835911a21bf05e8956853fcd7
2019-04-20Improve optimizations for DNNLOWP support on MKL-DNN (#18843)Gu, Jinghui2-195/+740
Summary: In this PR, the fusion alogrithms are improved to support DNNLOWP. 1. Enabled conv fusions for DNNLOWP 2. Fused order switch op into following quantize op 3. Improve conv+sum fusion to parse larger scope/window 4. re-org fusion code to fix random crash issue due to changing graph Pull Request resolved: https://github.com/pytorch/pytorch/pull/18843 Differential Revision: D15021030 Pulled By: yinghai fbshipit-source-id: 88d2199d9fc69f392de9bfbe1f291e0ebf78ab08
2019-04-19Support compilation on gcc-7.4.0 (#19470)Sam Leeman-Munk1-0/+1
Summary: There are two corrections in this pull request. The first is specific to gcc-7.4.0. compiled with -std=c++14 gcc-7.4.0 has __cplusplus = 201402L This does not meet the check set in Deprecated.h, which asks for >201402L. The compiler goes down to the __GNUC__ check, which passes and sets C10_DEPRECATED_MESSAGE to a value that c++14 does not appear to support or even recognize, leading to a compile time error. My recommended solution, which worked for my case, was to change the = into a >= The second correction comes in response to this error: caffe2/operators/crash_op.cc: In member function ‘virtual bool caffe2::CrashOp::RunOnDevice()’: caffe2/operators/crash_op.cc:14:11: error: ‘SIGABRT’ was not declared in this scope I am merely committing to the repository the solution suggested here (which worked for me) https://discuss.pytorch.org/t/building-pytorch-from-source-in-conda-fails-in-pytorch-caffe2-operators-crash-op-cc/42859 Pull Request resolved: https://github.com/pytorch/pytorch/pull/19470 Differential Revision: D15019529 Pulled By: ailzhang fbshipit-source-id: 9ce9d713c860ee5fd4266e5c2a7f336a97d7a90d
2019-04-19Improve embedding_bag add kernel (#19329)James Reed1-0/+3
Summary: This was actually getting pretty poor throughput with respect to memory bandwidth. I used this test to measure the memory bandwidth specifically for the AXPY call: https://gist.github.com/jamesr66a/b27ff9ecbe036eed5ec310c0a3cc53c5 And I got ~8 GB/s before this change, but ~14 GB/s after this change. This seems to speed up the operator overall by around 1.3x (benchmark: https://gist.github.com/jamesr66a/c533817c334d0be432720ef5e54a4166): == Before == time_per_iter 0.0001298875093460083 GB/s 3.082544287868467 == After == time_per_iter 0.00010104801654815674 GB/s 3.9623142905451076 The large difference between the local BW increase and the full-op BW increase likely indicates significant time is being spent elsewhere in the op, so I will investigate that. EDIT: I updated this PR to include a call into caffe2/perfkernels. This is the progression: before time_per_iter 8.983819484710693e-05 GB/s 4.456723564864611 After no axpy time_per_iter 7.19951868057251e-05 GB/s 5.56126065872172 AFter perfkernels time_per_iter 5.6699180603027346e-05 GB/s 7.061548257694262 After perfkernels no grad time_per_iter 4.388842582702637e-05 GB/s 9.122769670026413 Pull Request resolved: https://github.com/pytorch/pytorch/pull/19329 Reviewed By: dzhulgakov Differential Revision: D14969630 Pulled By: jamesr66a fbshipit-source-id: 42d1015772c87bedd119e33c0aa2c8105160a738
2019-04-19Fix relu bug for empty tensor (#19451)Xiaomeng Yang2-6/+35
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19451 Fix relu bug for empty tensor Reviewed By: xianjiec Differential Revision: D15009811 fbshipit-source-id: b75e567c3bec08d7d12b950d8f1380c50c138704
2019-04-19Fix out-of-topological-order issue in Nomnigraph (#19458)Yinghai Lu3-14/+32
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19458 The algorithm in https://fburl.com/ggh9iyvc fails to really ensure topological ordering of nodes. The fix is ugly but effective. I think we need a real topological sort to fix this issue more nicely. Mikhail Zolotukhin, Bram Wasti. Differential Revision: D15011893 fbshipit-source-id: 130c3aa442f5d578adfb14fbe5f16aa722434942
2019-04-18Moving at::Tensor into caffe2::Tensor without bumping refcount (#19388)Sebastian Messmer1-2/+2
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19388 The old implementation forced a refcount bump when converting at::Tensor to caffe2::Tensor. Now, it is possible to move it without a refcount bump. Reviewed By: dzhulgakov Differential Revision: D14986815 fbshipit-source-id: 92b4b0a6f323ed38376ffad75f960cad250ecd9b
2019-04-18Use string based schema for exposing caffe2 ops (#19287)Sebastian Messmer8-115/+88
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19287 Since we now have a string-schema-based op registration API, we can also use it when exposing caffe2 operators. Reviewed By: dzhulgakov Differential Revision: D14931925 fbshipit-source-id: ec162469d2d94965e8c99d431c801ae7c43849c8
2019-04-17Add validator for optimizers when parameters are sharedJiyan Yang3-1/+153
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18497 Reviewed By: kennyhorror Differential Revision: D14614738 fbshipit-source-id: beddd8349827dcc8ccae36f21e5d29627056afcd
2019-04-17Remove unused template parameter in OnnxifiOp (#19362)Yinghai Lu2-6/+6
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19362 `float` type is never used in OnnxifiOp.... Reviewed By: bddppq Differential Revision: D14977970 fbshipit-source-id: 8fee02659dbe408e5a3e0ff95d74c04836c5c281
2019-04-17Eliminate AdjustBatch ops (#19083)Yinghai Lu7-384/+202
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19083 As we have discussed, there are too many of AdjustBatch ops and they incur reallocation overhead and affects the performance. We will eliminate these ops by - inling the input adjust batch op into Glow - inling the output adjust batch op into OnnxifiOp and do that only conditionally. This is the C2 part of the change and requires change from Glow side to work e2e. Reviewed By: rdzhabarov Differential Revision: D14860582 fbshipit-source-id: ac2588b894bac25735babb62b1924acc559face6
2019-04-17Delete C10Tensor (#19328)Sebastian Messmer21-72/+49
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19328 Plans changed and we don't want this class anymore. Reviewed By: dzhulgakov Differential Revision: D14966746 fbshipit-source-id: 09ea4c95b352bc1a250834d32f35a94e401f2347
2019-04-16Testing for folded conv_bn_relu (#19298)Jerry Zhang2-0/+47
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19298 Proper testing for conv_bn_relu folding Differential Revision: D13998891 fbshipit-source-id: ceb58ccec19885cbbf38964ee0d0db070e098b4a
2019-04-15Avoid undefined symbol error when building AdIndexer LTO (#19009)Mark Santaniello2-81/+81
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19009 Move the definition of `MulFunctor<>::Backward()` into a header file. Reviewed By: BIT-silence Differential Revision: D14823230 fbshipit-source-id: 1efaec01863fcc02dcbe7e788d376e72f8564501
2019-04-15Add NHWC order support in the cost inference function of 3d conv (#19170)Summer Deng1-9/+19
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19170 As title The quantized resnext3d model in production got the following failures without the fix: ``` Caffe2 operator Int8ConvRelu logging error: [enforce fail at conv_pool_op_base.h:463] order == StorageOrder::NCHW. 1 vs 2. Conv3D only supports NCHW on the production quantized model ``` Reviewed By: jspark1105 Differential Revision: D14894276 fbshipit-source-id: ef97772277f322ed45215e382c3b4a3702e47e59
2019-04-15unit test with multiple op invocations (#19118)Jongsoo Park11-122/+117
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19118 A bug introduced by D14700576 reported by Yufei (fixed by D14778810 and D14785256) was not detected by our units tests. This diff improves unit tests to catch such errors (with this diff and without D14778810, we can reproduce the bug Yufei reported). This improvement also revealed a bug that affects the accuracy when we pre-pack weight and bias together and the pre-packed weight/bias are used by multiple nets. We were modifying the pre-packed bias in-place which was supposed to be constants. Reviewed By: csummersea Differential Revision: D14806077 fbshipit-source-id: aa9049c74b6ea98d21fbd097de306447a662a46d
2019-04-15Fix the return value of ParseFromString (#19262)Gemfield1-0/+1
Summary: Fix the return value of ParseFromString. Pull Request resolved: https://github.com/pytorch/pytorch/pull/19262 Differential Revision: D14937605 Pulled By: ezyang fbshipit-source-id: 3f441086517186a075efb3d74f09160463b696b3
2019-04-12Add more debugging helper to net transformer (#19176)Yinghai Lu3-19/+32
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19176 Add some amenities for debugging. Reviewed By: llyfacebook Differential Revision: D14901740 fbshipit-source-id: 2c4018fdbf7e3aba2a754b6b4103a72893c229c2
2019-04-12use C10_REGISTER for GELU opHuamin Li4-24/+48
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19090 Reviewed By: BIT-silence Differential Revision: D14864737 fbshipit-source-id: 8debd53171f7068726f0ab777a13ca46becbfbdf
2019-04-11Change is_variable() to check existence of AutogradMeta, and remove ↵Will Feng1-2/+1
is_variable_ (#19139) Summary: Currently, a TensorImpl's `is_variable_` is true if and only if the TensorImpl has AutogradMeta. This PR unifies these two concepts by removing `is_variable_` and change `is_variable()` to check existence of AutogradMeta instead. Removing `is_variable_` is part of the work in Variable/Tensor merge. Pull Request resolved: https://github.com/pytorch/pytorch/pull/19139 Differential Revision: D14893339 Pulled By: yf225 fbshipit-source-id: ceb5e22c3c01f79b5d21d5bdbf4a7d1bc397796a
2019-04-11Materialize a non-default device for C2 legacy storage. (#18605)Gregory Chanan1-2/+6
Summary: It's not intended that Storages have 'default' CUDA devices, but this is allowable via the Storage::create_legacy codepath. This also messages with device_caching, because the initial cache is obtained from the Storage, which may have a 'default' device. Instead, we materialize a device by allocating 0 bytes via the allocator. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18605 Differential Revision: D14680620 Pulled By: gchanan fbshipit-source-id: 6d43383d836e90beaf12bfe37c3f0506843f5432
2019-04-11Allow empty net type (#19154)Yinghai Lu1-1/+1
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19154 I recently saw some weird workflow error due to empty but set net_type. Maybe we should just fallback to simple net in this case. Reviewed By: dzhulgakov Differential Revision: D14890072 fbshipit-source-id: 4e9edf8232298000713bebb0bfdec61e9c5df17d
2019-04-11try to enable uncertainty for lr loss (#17236)Xing Wang2-0/+56
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17236 Following the paper in https://papers.nips.cc/paper/7141-what-uncertainties-do-we-need-in-bayesian-deep-learning-for-computer-vision.pdf, approximate the classification case with the regression formulation. For the LRLoss, add penalty based on the variance and regularization on the variance with a tunable parameter lambda. Reviewed By: chocjy Differential Revision: D14077106 fbshipit-source-id: 4405d8995cebdc7275a0dd07857d32a8915d78ef
2019-04-10Optimize SoftmaxOp on CPU (#18635)Xiaomeng Yang8-166/+138
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18635 Optimize SoftmaxOp on CPU Reviewed By: houseroad Differential Revision: D14689516 fbshipit-source-id: d2dcee2476d1a3a21f428e99bce9835f1d229d64
2019-04-10Move ConcatBatchMatMulBatchGatherOp to OSSHao Lu2-0/+156
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19059 Reviewed By: bwasti Differential Revision: D14849735 fbshipit-source-id: fefd1887d38e51151c07a8b187e9c7c50ef02c6e
2019-04-10implement operators for DNNLOWP (#18656)Gu, Jinghui39-87/+1577
Summary: Implement operators for DNNLOWP, including int8_conv, int8_FC, int8_pooling, int8_relu, int8_sum, quantize/dequantize, and order_swtich operators. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18656 Differential Revision: D14767092 Pulled By: yinghai fbshipit-source-id: 1f3e24929a358a42214da333bd304c593ea4468f
2019-04-10Clear input/ouput shape cache for each inference (#19085)Yinghai Lu1-0/+2
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19085 This is a bug where input_shapes_ and output_shapes_ will grow indefinitely. Fix it here. Reviewed By: bertmaher, rdzhabarov Differential Revision: D14861695 fbshipit-source-id: d59116f27c3b54f5cc5a33533de4b9222dbb7afc
2019-04-09amend D14778810 (#18902)Summer Deng5-9/+36
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18902 Fix in D14778810 had an issue that when we fallback to acc32 because the density of outlier is too high W_quantized_ is already modified. In this diff we first just count the number of outliers (without modifying W_quantized_) and only when density is low enough and no need for fallback we modify W_quantized_ and construct an outlier matrix. Reviewed By: jspark1105 Differential Revision: D14785256 fbshipit-source-id: 03933110a4ca7409686a06b18a9bb921f8657950
2019-04-09Fix aten op output assignment (#18581)Wanchao Liang2-28/+41
Summary: Fixes the problem of #18391 The issue is that when we code gen the ATenOp, we always generated static number of outputs for each operator. E.g. If there's operator from a old model that only requires two outputs, in its createOperator it will only allocate two output blobs, while the newer version of the operator (`unique` in this case) requires more output blob to be allocated. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18581 Differential Revision: D14865647 Pulled By: wanchaol fbshipit-source-id: 85f63fe16d6fe408a09eca84798c7e8cab3070e9
2019-04-09Make BlackBoxPredictor handle networks throwing exceptions (#19080)Alexander Sidorov1-0/+9
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19080 OSS: add a tiny unit test utility function to create tensors given shape and data outside of any workspace. I use it in an internal test Reviewed By: dzhulgakov Differential Revision: D14814194 fbshipit-source-id: 6d53b235d99a97da812215f5c7f11fecad363c8c
2019-04-09remove interned_string.h dep (#19061)Lu Fang1-2/+1
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19061 remove the deps on interned_string.h Reviewed By: BIT-silence Differential Revision: D14850078 fbshipit-source-id: 07e6ad72a7de369049ea56f32b72276fb4c59b32
2019-04-09add logging to make the saving action visible (#19042)Liang Xiong1-0/+2
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19042 show the model saving step in the log. Reviewed By: kennyhorror Differential Revision: D14809385 fbshipit-source-id: c7a1e50ff92bb45b16b1c501d9325b304b07fbd3
2019-04-09Convert all tabs to spaces, add CI. (#18959)Edward Yang3-17/+17
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18959 ghimport-source-id: a934163fa34cb2019732d5f49dc7290c376bf156 Differential Revision: D14831246 Pulled By: ezyang fbshipit-source-id: beb92dc4ee8c82f4c8259c081dd72e477fe7a9d0
2019-04-08Add gelu op (#18992)Xiaomeng Yang7-9/+392
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18992 Add gelu op Reviewed By: houseroad Differential Revision: D14814811 fbshipit-source-id: 00f126b8b83763c57ebbf28fbd2de5a8fab6d491
2019-04-08Export C10 operator in PyTorch Model (#18210)Lu Fang2-1/+6
Summary: Almost there, feel free to review. these c10 operators are exported to _caffe2 domain. TODO: - [x] let the onnx checker pass - [x] test tensor list as argument - [x] test caffe2 backend and converter - [x] check the c10 schema can be exported to onnx - [x] refactor the test case to share some code - [x] fix the problem in ONNX_ATEN_FALLBACK Pull Request resolved: https://github.com/pytorch/pytorch/pull/18210 Reviewed By: zrphercule Differential Revision: D14600916 Pulled By: houseroad fbshipit-source-id: 2592a75f21098fb6ceb38c5d00ee40e9e01cd144
2019-04-08caffe2 - Expose tensor filler util to Python (#18886)Duc Ngo9-1/+95
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18886 Expose tensor filler util to Python and add a unit test (both C++/Python) Reviewed By: salexspb Differential Revision: D14784470 fbshipit-source-id: bb8e013d1755c27c166e87d5a8491a97c65d3d8d
2019-04-08Fix a dev mode bug in activation distribution observer (#19004)Summer Deng2-3/+9
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19004 Handling the exception case when the data has min 3.40282e+38 max -3.40282e+38 Reviewed By: jspark1105 Differential Revision: D14822193 fbshipit-source-id: b9771d1584fdf8317f5b8c7f5806be5d27314386
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