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cuda_stream_jit
88 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Supriya Rao
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11c32611d7 |
[quant] Support 4-bit embedding_bag operators using the dtype quint4x2 (#45752)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45752 Use the torch.quint4x2 dtype to create 4-bit packed tensors in the previous PR. These packed tensors can be directly consumed by the operator. Serialization of the packed tensors is supported using torchbind custom class. Module support will follow in a later PR. Test Plan: python test/test_quantization.py TestEmbeddingBagOps Imported from OSS Reviewed By: jerryzh168 Differential Revision: D24120996 fbshipit-source-id: 2639353b3343ebc69e058b5ba237d3fc56728e1c |
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Rong Rong
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322855e380 |
type check for torch.quantization.observer (#45630)
Summary: add type checker for observer Pull Request resolved: https://github.com/pytorch/pytorch/pull/45630 Reviewed By: malfet Differential Revision: D24058304 Pulled By: walterddr fbshipit-source-id: ac1c0f5ff0d34b0445bd1364653fc5c9d7571b05 |
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Jerry Zhang
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9d5607fcd9 |
[quant] Use PlaceholderObserver as default dynamic quant observer (#45343)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45343 Current default dynamic quant observer is not correct since we don't accumulate min/max and we don't need to calculate qparams. Test Plan: Imported from OSS Reviewed By: supriyar Differential Revision: D23933995 fbshipit-source-id: 3ff497c9f5f74c687e8e343ab9948d05ccbba09b |
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Supriya Rao
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489af4ddcb |
[quant] Add quant APIs to save/load observer state_dict (#44846)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44846 The save function traverses the model state dict to pick out the observer stats load function traverse the module hierarchy to load the state dict into module attributes depending on observer type Test Plan: python test/test_quantization.py TestQuantizeFx.test_save_observer_state_dict Imported from OSS Reviewed By: raghuramank100 Differential Revision: D23746821 fbshipit-source-id: 05c571b62949a2833602d736a81924d77e7ade55 |
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Vasiliy Kuznetsov
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2163d31016 |
histogram observer: ensure buffer shape consistency (#44956)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44956 Makes buffer shapes for HistogramObserver have the same shapes in uninitialized versus initialized states. This is useful because the detectron2 checkpointer assumes that these states will stay the same, so it removes the need for manual hacks around the shapes changing. Test Plan: ``` python test/test_quantization.py TestObserver.test_histogram_observer_consistent_buffer_shape ``` Imported from OSS Reviewed By: raghuramank100 Differential Revision: D23785382 fbshipit-source-id: 1a83fd4f39b244b00747c368d5d305a07d877c92 |
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Xiang Gao
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20ac736200 |
Remove py2 compatible future imports (#44735)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44735 Reviewed By: mruberry Differential Revision: D23731306 Pulled By: ezyang fbshipit-source-id: 0ba009a99e475ddbe22981be8ac636f8a1c8b02f |
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Supriya Rao
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3f512b0de2 |
[quant][qat] Ensure observers and fq modules are scriptable (#44749)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44749 Ensure fx module is scriptable after calling prepare_qat on it Test Plan: python test/test_quantization.py TestQuantizeFx.test_qat_and_script Imported from OSS Reviewed By: jerryzh168 Differential Revision: D23718380 fbshipit-source-id: abf63ffb21e707f7def8f6c88246877f5aded58c |
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Vasiliy Kuznetsov
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70dfeb44bd |
MinMax based observers: respect device affinity for state_dict (#44537)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44537 Originally, the `min_val`, `max_val`, `min_vals`, `max_vals` attributes of observers were Tensors but not buffers. They had custom state_dict save/load code to ensure their state was saved. At some point, these attributes became buffers, and the custom save/load code remained. This introduced a subtle bug: * create model A, move it to a device (cpu/cuda) and save its state_dict * create model B, load its state dict. * `min_val|min_vals|max_val|max_vals` would always be loaded to model A's device, even if the rest of model B was on a different device * the above is inconsistent with how save/load on different devices is expected to work (see https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-model-across-devices) In practice, the case people would sometimes hit is: * model A is on CPU, state dict is saved * model B is created and moved to GPU, state_dict from model A is loaded * assertions throw when operations are attempted across different devices This PR fixes the behavior by removing the custom save/load where possible and letting the default `nn.Module` save/load code handle device assignment. We special case `PerChannelMinMaxObserver` and its children to allow for loading buffers or different size, which is normal. There are some followups to also enable this for HistogramObserver and FakeQuantize, which can be done in separate PRs due to higher complexity. Test Plan: ``` python test/test_quantization.py TestObserver.test_state_dict_respects_device_affinity ``` Imported from OSS Reviewed By: raghuramank100 Differential Revision: D23644493 fbshipit-source-id: 0dbb6aa309ad569a91a663b9ee7e44644080032e |
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Vasiliy Kuznetsov
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fd8e2064e0 |
quant: switch observers to use min_max (#42957)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42957 Switches observers to use the new min_max function to calculate min and max at the same time. We see around 45-50% speedup on representative input shapes on the microbenchmarks for all observers except `HistogramObserver`. Test Plan: CI for correctness performance: ``` cd benchmarks/operator_benchmark // repeat (before diff, after diff) x (cpu, cuda) python -m pt.qobserver_test --tag_filter all --device cpu /* * before, cpu: https://our.intern.facebook.com/intern/paste/P138633280/ * before, cuda: https://our.intern.facebook.com/intern/paste/P138639473/ * after, cpu: https://our.intern.facebook.com/intern/paste/P138635458/ * after, cuda: https://our.intern.facebook.com/intern/paste/P138636344/ */ ``` Imported from OSS Reviewed By: supriyar Differential Revision: D23093995 fbshipit-source-id: 9f416d144109b5b80baf089eb4bcfabe8fe358d5 |
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Qi Zhou
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f73ba88946 |
Avoid resizing in MinMaxObserver (#43789)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/43789 Since it's single element.. In some cases we may not be able to resize the buffers. Test Plan: unit tests Reviewed By: supriyar Differential Revision: D23393108 fbshipit-source-id: 46cd7f73ed42a05093662213978a01ee726433eb |
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Supriya Rao
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3293fdfa80 |
[quant] Enable from_float for quantized Embedding_Bag (#43176)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/43176 Convert floating point nn.EmbeddingBag module to nn.quantized.dynamic.EmbeddingBag module Test Plan: python test/test_quantization.py TestDynamicQuantizedModule.test_embedding_bag_api python test/test_quantization.py TestPostTrainingDynamic.test_embedding_quantization Imported from OSS Reviewed By: vkuzo Differential Revision: D23200196 fbshipit-source-id: 090f47dbf7aceab9c719cbf282fad20fe3e5a983 |
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Vasiliy Kuznetsov
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57af1ec145 |
observers: use torch.all to check for valid min and max values (#43151)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/43151 Using `torch.all` instead of `torch.sum` and length check. It's unclear whether the increase in perf (~5% for small inputs) is real, but should be a net benefit, especially for larger channel inputs. Test Plan: Imported from OSS Reviewed By: jerryzh168 Differential Revision: D23170426 fbshipit-source-id: ee5c25eb93cee1430661128ac9458a9c525df8e5 |
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Vasiliy Kuznetsov
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3264ba065c |
observers: use clamp instead of min/max in calculate_qparams (#43150)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/43150 The current logic was expensive because it created tensors on CUDA. Switching to clamp since it can work without needing to create tensors. Test Plan: benchmarks Imported from OSS Reviewed By: jerryzh168 Differential Revision: D23170427 fbshipit-source-id: 6fe3a728e737aca9f6c2c4d518c6376738577e21 |
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Vasiliy Kuznetsov
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a5dfba0a6e |
observers: make eps a buffer (#43149)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/43149 This value doesn't change, making it a buffer to only pay the cost of creating a tensor once. Test Plan: Imported from OSS Reviewed By: jerryzh168 Differential Revision: D23170428 fbshipit-source-id: 6b963951a573efcc5b5a57649c814590b448dd72 |
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Paul Shao
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b992a927a9 |
Clearer Semantics and Naming for Customized Quantization Range Initialization in Observer (#42602)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42602 In this diff, clearer semantics and namings for are introduced by splitting the original `init_dynamic_qrange` into 2 separate `Optional[int]` types `qmin` and `qmax` to avoid the confusion of the parameters with dynamic quantization. The `qmin` and `qmax` parameters allow customers to specify their own customary quantization range and enables specific use cases for lower bit quantization. Test Plan: To assert the correctness and compatibility of the changes with existing observers, on a devvm, execute the following command to run the unit tests: `buck test //caffe2/test:quantization -- observer` Reviewed By: vkuzo, raghuramank100 Differential Revision: D22948334 fbshipit-source-id: 275bc8c9b5db4ba76fc2e79ed938376ea4f5a37c |
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Supriya Rao
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816d37b1d8 |
[quant] Make PerChannel Observer work with float qparams (#42690)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42690 Add implementation for new qscheme per_channel_affine_float_qparams in observer Test Plan: python test/test_quantization.py TestObserver.test_per_channel_observers Imported from OSS Reviewed By: vkuzo Differential Revision: D23070633 fbshipit-source-id: 84d348b0ad91e9214770131a72f7adfd3970349c |
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Presley Graham
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7332c21f7a |
Speed up HistogramObserver by vectorizing critical path (#41041)
Summary: 22x speedup over the code this replaces. Tested on ResNet18 on a devvm using CPU only, using default parameters for HistogramObserver (i.e. 2048 bins). Pull Request resolved: https://github.com/pytorch/pytorch/pull/41041 Test Plan: To run the test against the reference (old) implementation, you can use `python test/test_quantization.py TestRecordHistogramObserver.test_histogram_observer_against_reference`. To run the benchmark, while in the folder `benchmarks/operator_benchmark`, you can use `python -m benchmark_all_quantized_test --operators HistogramObserverCalculateQparams`. Benchmark results before speedup: ``` # ---------------------------------------- # PyTorch/Caffe2 Operator Micro-benchmarks # ---------------------------------------- # Tag : short # Benchmarking PyTorch: HistogramObserverCalculateQparams # Mode: Eager # Name: HistogramObserverCalculateQparams_C3_M512_N512_dtypetorch.quint8_cpu_qschemetorch.per_tensor_affine # Input: C: 3, M: 512, N: 512, dtype: torch.quint8, device: cpu, qscheme: torch.per_tensor_affine Forward Execution Time (us) : 185818.566 # Benchmarking PyTorch: HistogramObserverCalculateQparams # Mode: Eager # Name: HistogramObserverCalculateQparams_C3_M512_N512_dtypetorch.quint8_cpu_qschemetorch.per_tensor_symmetric # Input: C: 3, M: 512, N: 512, dtype: torch.quint8, device: cpu, qscheme: torch.per_tensor_symmetric Forward Execution Time (us) : 165325.916 ``` Benchmark results after speedup: ``` # ---------------------------------------- # PyTorch/Caffe2 Operator Micro-benchmarks # ---------------------------------------- # Tag : short # Benchmarking PyTorch: HistogramObserverCalculateQparams # Mode: Eager # Name: HistogramObserverCalculateQparams_C3_M512_N512_dtypetorch.quint8_cpu_qschemetorch.per_tensor_affine # Input: C: 3, M: 512, N: 512, dtype: torch.quint8, device: cpu, qscheme: torch.per_tensor_affine Forward Execution Time (us) : 12242.241 # Benchmarking PyTorch: HistogramObserverCalculateQparams # Mode: Eager # Name: HistogramObserverCalculateQparams_C3_M512_N512_dtypetorch.quint8_cpu_qschemetorch.per_tensor_symmetric # Input: C: 3, M: 512, N: 512, dtype: torch.quint8, device: cpu, qscheme: torch.per_tensor_symmetric Forward Execution Time (us) : 12655.354 ``` Reviewed By: raghuramank100 Differential Revision: D22400755 Pulled By: durumu fbshipit-source-id: 639ac796a554710a33c8a930c1feae95a1148718 |
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Supriya Rao
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38bf5be24f |
[quant] Use PlaceholderObserver instead of Fp16Observer and NoopObserver (#42348)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42348 Use the dtype info in placeholderObserver to decide what ops to insert in the graph In the next PR we can delete NoopObserver Test Plan: python test/test_quantization.py Imported from OSS Reviewed By: jerryzh168 Differential Revision: D22859457 fbshipit-source-id: a5c618f22315534ebd9a2df77b14a0aece196989 |
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Supriya Rao
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8c5bf10264 |
[quant] Add FP16Observer for fp16 quant support (#42221)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42221 Adds a new observer that emits a warning if the range of tensor is beyond fp16 range. This will be further used in graph mode quantization to insert the cast to fp16 ops in the graph Test Plan: python test/test_quantizaton.py TestObserver.test_fp16_observer Imported from OSS Reviewed By: jerryzh168 Differential Revision: D22849222 fbshipit-source-id: a301281ce38ba4d4e7a009308400d34a08c113d2 |
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Supriya Rao
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36fb14b68b |
[quant] Add Graph Mode Passes to quantize EmbeddingBag operators (#41612)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/41612 This change adds preliminary support to quantize the EmbeddingBag operators. We currently support 4-bit and 8-bit quantization+packing of the weights. To quantize these operators, specify the operator name in the `custom_op_name` field of the NoopObserver. Based on the op name (4bit or 8bit) we call the corresponding quantization functions. Refer to the testplan for how to invoke the qconfig for the embedding_bag ops. Future versions of this will support 4-bit and 2-bit qtensors with native support to observe and quantize it. NB - This version assumes that the weights in the EmbeddingBag Module reside on the same device. Test Plan: python test/test_quantization.py TestQuantizeDynamicJitOps.test_embedding_bag Imported from OSS Reviewed By: vkuzo, jerryzh168 Differential Revision: D22609342 fbshipit-source-id: 23e33f44a451c26719e6e283e87fbf09b584c0e6 |
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Paul Shao
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9e0c746b15 |
Augmenting Concrete Observer Constructors to Support Dynamic Quantization Range; Modifying Utility Functions in _LearnableFakeQuantize Module for Better Logging and Baseline Construction. (#41815)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/41815 **All are minor changes to enable better simulations.** The constructors of MinMaxObserver, MovingAverageMinMaxObserver, PerChannelMinMaxObserver, and MovingAveragePerChannelMinMaxObserver are augmented so they can utilize the dynamic quantization range support in the _ObserverBase class. In addition, minor adjustments are made to the enable_static_observation function that allow observer to update parameters but do not fake quantize on the output (for constructing baseline). Test Plan: To ensure this modification is still backward compatible with past usages, numerics are verified by running the quantization unit test suite, which contains various observer tests. The following command executes the test suite, which also verifies the observer numerics: ``` buck test //caffe2/test:quantization -- observer ``` Reviewed By: z-a-f Differential Revision: D22649128 fbshipit-source-id: 32393b706f9b69579dc2f644fb4859924d1f3773 |
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Paul Shao
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16dde6e3a0 |
Augmenting Observers to Support Dynamic Quantization Range (#41113)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/41113 In this diff, the `ObserverBase` class is augmented with 2 additional optional arguments qmin and qmax. Correspondingly the calculation of qmin and qmax and the related quantization parameters are modified to accommodate this additional flexibility should the number of bits for quantization be lower than 8 (the default value). Additional logic in the base class `_calculate_qparams` function has also been modified to provide support for dynamic quantization range. Test Plan: To ensure this modification is still backward compatible with past usages, numerics are verified by running the quantization unit test suite, which contains various observer tests. The following command executes the test suite, which also verifies the observer numerics: `buck test //caffe2/test:quantization -- observer` This modified observer script can be tested within the experiments for lower bit fake quantization. Please see the following diffs for reference. - Single Fake Quantizer: D22337447 - Single Conv Layer: D22338532 Reviewed By: z-a-f Differential Revision: D22427134 fbshipit-source-id: f405e633289322078b0f4a417f54b684adff2549 |
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wudenggang
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9600ed9af3 |
typo fixes (#41632)
Summary: typo fixes Pull Request resolved: https://github.com/pytorch/pytorch/pull/41632 Reviewed By: ezyang Differential Revision: D22617827 Pulled By: mrshenli fbshipit-source-id: c2bfcb7cc36913a8dd32f13fc9adc3aa0a9b682f |
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Edward Leardi
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733b8c23c4 |
Fix several quantization documentation typos (#40567)
Summary: This PR fixes several typos I noticed in the docs here: https://pytorch.org/docs/master/quantization.html. In one case there was a misspelled module [torch.nn.instrinsic.qat](https://pytorch.org/docs/master/quantization.html#torch-nn-instrinsic-qat) which I corrected and am including screenshots of below just in case. <img width="1094" alt="before" src="https://user-images.githubusercontent.com/54918401/85766765-5cdd6280-b6e5-11ea-93e6-4944cf820b71.png"> <img width="1093" alt="after" src="https://user-images.githubusercontent.com/54918401/85766769-5d75f900-b6e5-11ea-8850-0d1f5ed67b16.png"> Pull Request resolved: https://github.com/pytorch/pytorch/pull/40567 Differential Revision: D22311291 Pulled By: ezyang fbshipit-source-id: 65d1f3dd043357e38a584d9e30f31634a5b0995c |
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Supriya Rao
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c04d39aaf2 |
[quant][bug] Histogram observer bug fix with min == max (#40310)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/40310 Test Plan: python test/test_quantization.py test_histogram_observer_same_inputs Imported from OSS Differential Revision: D22145908 fbshipit-source-id: c1646d9ae6738755981fe3d09c8a8e25fcb994d4 |
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Supriya Rao
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9788a74da8 |
[quant][bug] Fix histogram observer with 0 input (#40191)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/40191 When the first couple of inputs passed to histogram observer are all 0's subsequent non-zero inputs cause a div by 0 error Test Plan: python test/test_quantization.py TestHistogramObserver.test_histogram_observer_zero_inputs Imported from OSS Differential Revision: D22119422 fbshipit-source-id: 8bbbba914ba7f343121830c768ca0444439f8e03 |
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Supriya Rao
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67115b226a |
[quant][graphmode] Dynamic Quant Do not depend on input shapes (#39412)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/39412 This PR introduces changes to enable running the weight observer standalone in the graph It extracts the nodes from the graph that correspond to the observed weight value and adds all the related nodes to a new subgraph The subgraph is then executed using GraphFunction Test Plan: python test/test_quantization.py TestGraphMostPostTrainingStatic python test/test_quantization.py TestQuantizeDynamicScript Imported from OSS Differential Revision: D21872940 fbshipit-source-id: 55f1dcc2caef193531e2b807c8e56288b9794520 |
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Vasiliy Kuznetsov
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8d8b586c7a |
fake_quant: make qparams shape consistent (#38587)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/38587 Before this diff, scale+zp were initialized to tensors with a single dimension and 1 element, and then switched to scalar tensors after the first forward. This diff makes the shape stay consistent. This should fix an issue reported when saving/loading models, which crashes on this inconsistent shape. Test Plan: ``` python test/test_quantization.py TestFakeQuantizePerTensor.test_fake_quant_preserves_qparam_shapes_for_activations ``` Imported from OSS Differential Revision: D21605532 fbshipit-source-id: e00cd268d6d3ded1006d18d6c6759c911b3a74ea |
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Jerry Zhang
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86397f6b24 |
[quant] Remove get_qparams in Observers (#38435)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/38435 Test Plan: Imported from OSS Differential Revision: D21597835 Pulled By: jerryzh168 fbshipit-source-id: 88a8dd110db5586509bf98fa6712290f1756c272 |
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Supriya Rao
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97abed7cbe |
[quant] Remove TensorListObserver (#38584)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/38584 All observers will support tensor lists in future PR Test Plan: Imported from OSS Differential Revision: D21623464 fbshipit-source-id: c5c57ecfe14f7c3aa92b7c99d724e846132ae03b |
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Vasiliy Kuznetsov
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b57c8b720e |
[wip] Make quantization modules work with DataParallel (#37032)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37032 DataParallel requires all params and buffers of child modules to be updated in place because of how it implements model replication during the forward pass (see https://github.com/pytorch/pytorch/pull/12671 for context). Any params or buffers not updated in place are lost and not propagated back to the master. This diff updates (some quantized modules) (TBD: all quantized modules? determine a good cut point) to do their parameter update in-place. This will enable static quant and QAT to work correctly with DataParallel. TODO: https://github.com/pytorch/pytorch/pull/32684 needs to land before we can fix the graph mode test failures on this PR. Test Plan: script failed before and passes after the diff: https://gist.github.com/vkuzo/78b06c01f23f98ee2aaaeb37e55f8d40 TODO before land: add integration testing Imported from OSS Differential Revision: D21206454 fbshipit-source-id: df6b4b04d0ae0f7ef582c82d81418163019e96f7 |
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Edward Yang
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4fef3763dd |
Revert "Revert D21337640: [pytorch][PR] Split up documentation into subpages and clean up some warnings" (#37778)
Summary: Original PR: https://github.com/pytorch/pytorch/pull/37419 cc mattip suo Pull Request resolved: https://github.com/pytorch/pytorch/pull/37778 Differential Revision: D21385774 Pulled By: ezyang fbshipit-source-id: 5de532faab8bae132736b6b5189e0ee2ac9935be |
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Michael Suo
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20f7e62b1d |
Revert D21337640: [pytorch][PR] Split up documentation into subpages and clean up some warnings
Test Plan: revert-hammer Differential Revision: D21337640 Original commit changeset: d4ad198780c3 fbshipit-source-id: fa9ba6ac542173a50bdb45bfa12f3fec0ed704fb |
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mattip
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f10fbcc820 |
Split up documentation into subpages and clean up some warnings (#37419)
Summary: xref gh-32838, gh-34032 This is a major refactor of parts of the documentation to split it up using sphinx's `autosummary` feature which will build out `autofuction` and `autoclass` stub files and link to them. The end result is that the top module pages like torch.nn.rst and torch.rst are now more like table-of-contents to the actual single-class or single-function documentations pages. Along the way, I modified many of the docstrings to eliminate sphinx warnings when building. I think the only thing I changed from a non-documentation perspective is to add names to `__all__` when adding them to `globals()` in `torch.__init__.py` I do not know the CI system: are the documentation build artifacts available after the build, so reviewers can preview before merging? Pull Request resolved: https://github.com/pytorch/pytorch/pull/37419 Differential Revision: D21337640 Pulled By: ezyang fbshipit-source-id: d4ad198780c3ae7a96a9f22651e00ff2d31a0c0f |
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Raghuraman Krishnamoorthi
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904949382e |
Ensure that histogram observers have zero-point of zero for post ReLU activations (#37107)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37107 Currently histogram observers relax both the min and max values of the activations for performance speedup reasons. This causes an issue for glow where there is a slow down if the zero-point is not zero for post ReLU activations. ghstack-source-id: 102768017 Test Plan: buck test caffe2/test:quantization -- 'test_histogram_observer_one_sided \(quantization\.test_quantization\.RecordHistogramObserverTest\)' --print-passing-details Differential Revision: D21187636 fbshipit-source-id: 8d616b9e9caf2979a26a215e99434f71025e3d8b |
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Haixin Liu
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8254a63802 |
Speed up calculate Qparams for per-channel observers (#30485)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/30485 Use vectorization to speed up calculate Qparams for per-channel observers. New implementation is about 1000 times faster. Task: https://github.com/pytorch/pytorch/issues/30348#event-2824868602 ghstack-source-id: 102808561 Test Plan: ``` import torch import time import numpy as np from torch.quantization.observer import PerChannelMinMaxObserver obs = PerChannelMinMaxObserver() acc_time = 0 X = torch.randn(1000, 10) obs(X) for i in range(100): start = time.time() obs.calculate_qparams() acc_time = acc_time + time.time()-start print(acc_time) ``` Before change: 20.3 After change: 0.017 Differential Revision: D18711905 fbshipit-source-id: 3ed20a6734c9950773350957aaf0fc5d14827640 |
||
|
Ralf Gommers
|
78d5707041 |
Fix type annotations and make MyPy run on torch/ (#36584)
Summary: This PR fixes a couple of syntax errors in `torch/` that prevent MyPy from running, fixes simple type annotation errors (e.g. missing `from typing import List, Tuple, Optional`), and adds granular ignores for errors in particular modules as well as for missing typing in third party packages. As a result, running `mypy` in the root dir of the repo now runs on: - `torch/` - `aten/src/ATen/function_wrapper.py` (the only file already covered in CI) In CI this runs on GitHub Actions, job Lint, sub-job "quick-checks", task "MyPy typecheck". It should give (right now): `Success: no issues found in 329 source files`. Here are the details of the original 855 errors when running `mypy torch` on current master (after fixing the couple of syntax errors that prevent `mypy` from running through): <details> ``` torch/utils/tensorboard/_proto_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_proto_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_proto_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/backcompat/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/for_onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch.for_onnx.onnx' torch/cuda/nvtx.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/utils/show_pickle.py:59: error: Name 'pickle._Unpickler' is not defined torch/utils/show_pickle.py:113: error: "Type[PrettyPrinter]" has no attribute "_dispatch" torch/utils/tensorboard/_onnx_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.graph_pb2' torch/utils/tensorboard/_onnx_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_onnx_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.versions_pb2' torch/utils/tensorboard/_onnx_graph.py:4: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_onnx_graph.py:5: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/tensorboard/_onnx_graph.py:9: error: Cannot find implementation or library stub for module named 'onnx' torch/contrib/_tensorboard_vis.py:10: error: Cannot find implementation or library stub for module named 'tensorflow.core.util' torch/contrib/_tensorboard_vis.py:11: error: Cannot find implementation or library stub for module named 'tensorflow.core.framework' torch/contrib/_tensorboard_vis.py:12: error: Cannot find implementation or library stub for module named 'tensorflow.python.summary.writer.writer' torch/utils/hipify/hipify_python.py:43: error: Need type annotation for 'CAFFE2_TEMPLATE_MAP' (hint: "CAFFE2_TEMPLATE_MAP: Dict[<type>, <type>] = ...") torch/utils/hipify/hipify_python.py:636: error: "object" has no attribute "items" torch/nn/_reduction.py:27: error: Name 'Optional' is not defined torch/nn/_reduction.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/_reduction.py:47: error: Name 'Optional' is not defined torch/nn/_reduction.py:47: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib.pyplot': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends.backend_agg': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends': found module but no type hints or library stubs torch/nn/modules/utils.py:27: error: Name 'List' is not defined torch/nn/modules/utils.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") caffe2/proto/caffe2_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/caffe2_pb2.py:25: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:31: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:35: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:39: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:47: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:51: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:55: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:59: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:63: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:67: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:71: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:75: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:108: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:112: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:124: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:134: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:138: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:142: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:146: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:150: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:154: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:158: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:162: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:166: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:170: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:174: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:194: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:200: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:204: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:208: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:212: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:224: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:230: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:238: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:242: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:246: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:250: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:267: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:288: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:295: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:302: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:327: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:334: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:341: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:364: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:371: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:378: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:385: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:392: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:399: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:406: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:413: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:420: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:448: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:455: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:462: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:488: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:495: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:502: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:509: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:516: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:523: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:530: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:537: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:544: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:551: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:558: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:565: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:572: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:596: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:603: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:627: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:634: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:641: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:648: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:655: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:662: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:686: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:693: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:717: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:724: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:731: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:738: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:763: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:770: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:777: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:784: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:808: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:815: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:822: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:829: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:836: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:843: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:850: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:857: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:864: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:871: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:878: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:885: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:892: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:916: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:923: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:930: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:937: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:944: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:951: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:958: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:982: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:989: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:996: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1003: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1010: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1017: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1024: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1031: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1038: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1045: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1052: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1059: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1066: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1090: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1097: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1104: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1128: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1135: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1142: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1166: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1173: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1180: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1187: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1194: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1218: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1225: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1232: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1239: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1246: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1253: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1260: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1267: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1305: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1312: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1319: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1326: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1333: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1340: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1347: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1354: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1361: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1368: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1375: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1382: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1389: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1396: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1420: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1465: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1472: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1479: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1486: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1493: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1500: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1507: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1514: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1538: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1545: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1552: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1559: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1566: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1667: error: "GeneratedProtocolMessageType" has no attribute "Segment" torch/multiprocessing/queue.py:4: error: No library stub file for standard library module 'multiprocessing.reduction' caffe2/proto/torch_pb2.py:18: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/torch_pb2.py:27: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/torch_pb2.py:33: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/torch_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:81: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:109: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:116: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:123: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:137: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:144: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:151: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:175: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:189: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:196: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:220: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:227: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:241: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:265: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:272: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:279: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:286: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:293: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:300: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:307: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:314: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:321: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:328: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:335: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:342: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:366: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:373: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:397: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:404: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:411: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:418: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:425: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:432: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/metanet_pb2.py:29: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:36: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:64: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:126: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:133: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:140: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:164: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:171: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:202: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:209: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:216: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:240: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:247: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:261: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:268: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:275: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:282: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:289: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:296: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/__init__.py:13: error: Skipping analyzing 'caffe2.caffe2.fb.session.proto': found module but no type hints or library stubs torch/multiprocessing/pool.py:3: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/pool.py:3: note: (Stub files are from https://github.com/python/typeshed) caffe2/python/scope.py:10: error: Skipping analyzing 'past.builtins': found module but no type hints or library stubs caffe2/python/__init__.py:7: error: Module has no attribute "CPU" caffe2/python/__init__.py:8: error: Module has no attribute "CUDA" caffe2/python/__init__.py:9: error: Module has no attribute "MKLDNN" caffe2/python/__init__.py:10: error: Module has no attribute "OPENGL" caffe2/python/__init__.py:11: error: Module has no attribute "OPENCL" caffe2/python/__init__.py:12: error: Module has no attribute "IDEEP" caffe2/python/__init__.py:13: error: Module has no attribute "HIP" caffe2/python/__init__.py:14: error: Module has no attribute "COMPILE_TIME_MAX_DEVICE_TYPES"; maybe "PROTO_COMPILE_TIME_MAX_DEVICE_TYPES"? caffe2/python/__init__.py:15: error: Module has no attribute "ONLY_FOR_TEST"; maybe "PROTO_ONLY_FOR_TEST"? caffe2/python/__init__.py:34: error: Item "_Loader" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:34: error: Item "None" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:35: error: Module has no attribute "cuda" caffe2/python/__init__.py:37: error: Module has no attribute "cuda" caffe2/python/__init__.py:49: error: Module has no attribute "add_dll_directory" torch/random.py:4: error: Cannot find implementation or library stub for module named 'torch._C' torch/_classes.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/hub.py:21: error: Skipping analyzing 'tqdm.auto': found module but no type hints or library stubs torch/hub.py:24: error: Skipping analyzing 'tqdm': found module but no type hints or library stubs torch/hub.py:27: error: Name 'tqdm' already defined (possibly by an import) torch/_tensor_str.py:164: error: Not all arguments converted during string formatting torch/_ops.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/_linalg_utils.py:26: error: Name 'Optional' is not defined torch/_linalg_utils.py:26: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:26: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Optional' is not defined torch/_linalg_utils.py:63: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Optional' is not defined torch/_linalg_utils.py:70: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Optional' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:88: error: Name 'Tuple' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_jit_internal.py:17: error: Need type annotation for 'boolean_dispatched' torch/_jit_internal.py:474: error: Need type annotation for '_overloaded_fns' (hint: "_overloaded_fns: Dict[<type>, <type>] = ...") torch/_jit_internal.py:512: error: Need type annotation for '_overloaded_methods' (hint: "_overloaded_methods: Dict[<type>, <type>] = ...") torch/_jit_internal.py:648: error: Incompatible types in assignment (expression has type "FinalCls", variable has type "_SpecialForm") torch/sparse/__init__.py:11: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Optional' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/sparse/__init__.py:71: error: Name 'Tuple' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/nn/init.py:109: error: Name 'Tensor' is not defined torch/nn/init.py:126: error: Name 'Tensor' is not defined torch/nn/init.py:142: error: Name 'Tensor' is not defined torch/nn/init.py:165: error: Name 'Tensor' is not defined torch/nn/init.py:180: error: Name 'Tensor' is not defined torch/nn/init.py:194: error: Name 'Tensor' is not defined torch/nn/init.py:287: error: Name 'Tensor' is not defined torch/nn/init.py:315: error: Name 'Tensor' is not defined torch/multiprocessing/reductions.py:8: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/reductions.py:9: error: No library stub file for standard library module 'multiprocessing.reduction' torch/multiprocessing/reductions.py:17: error: No library stub file for standard library module 'multiprocessing.resource_sharer' torch/jit/_builtins.py:72: error: Module has no attribute "_no_grad_embedding_renorm_" torch/jit/_builtins.py:80: error: Module has no attribute "stft" torch/jit/_builtins.py:81: error: Module has no attribute "cdist" torch/jit/_builtins.py:82: error: Module has no attribute "norm" torch/jit/_builtins.py:83: error: Module has no attribute "nuclear_norm" torch/jit/_builtins.py:84: error: Module has no attribute "frobenius_norm" torch/backends/cudnn/__init__.py:8: error: Cannot find implementation or library stub for module named 'torch._C' torch/backends/cudnn/__init__.py:86: error: Need type annotation for '_handles' (hint: "_handles: Dict[<type>, <type>] = ...") torch/autograd/profiler.py:13: error: Name 'ContextDecorator' already defined (possibly by an import) torch/autograd/function.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/autograd/function.py:2: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports torch/autograd/function.py:109: error: Unsupported dynamic base class "with_metaclass" torch/serialization.py:609: error: "Callable[[Any], Any]" has no attribute "cache" torch/_lowrank.py:11: error: Name 'Tensor' is not defined torch/_lowrank.py:13: error: Name 'Optional' is not defined torch/_lowrank.py:13: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Optional' is not defined torch/_lowrank.py:14: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Optional' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:82: error: Name 'Tuple' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:130: error: Name 'Tensor' is not defined torch/_lowrank.py:130: error: Name 'Optional' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:130: error: Name 'Tuple' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:167: error: Name 'Tensor' is not defined torch/_lowrank.py:167: error: Name 'Optional' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:167: error: Name 'Tuple' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:45: error: Variable "torch.quantization.observer.ABC" is not valid as a type torch/quantization/observer.py:45: note: See https://mypy.readthedocs.io/en/latest/common_issues.html#variables-vs-type-aliases torch/quantization/observer.py:45: error: Invalid base class "ABC" torch/quantization/observer.py:127: error: Name 'Tensor' is not defined torch/quantization/observer.py:127: error: Name 'Tuple' is not defined torch/quantization/observer.py:127: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:172: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:172: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:192: error: Name 'Tensor' is not defined torch/quantization/observer.py:192: error: Name 'Tuple' is not defined torch/quantization/observer.py:192: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:233: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:233: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:534: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tuple' is not defined torch/quantization/observer.py:885: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:894: error: Cannot determine type of 'max_val' torch/quantization/observer.py:894: error: Cannot determine type of 'min_val' torch/quantization/observer.py:899: error: Cannot determine type of 'min_val' torch/quantization/observer.py:902: error: Name 'Tensor' is not defined torch/quantization/observer.py:925: error: Name 'Tensor' is not defined torch/quantization/observer.py:928: error: Cannot determine type of 'min_val' torch/quantization/observer.py:929: error: Cannot determine type of 'max_val' torch/quantization/observer.py:946: error: Argument "min" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:946: error: Argument "max" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:1056: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:1058: error: Module has no attribute "per_channel_symmetric" torch/nn/quantized/functional.py:76: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:76: error: Name 'BroadcastingList2' is not defined torch/nn/quantized/functional.py:259: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:259: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:259: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:289: error: Module has no attribute "ops" torch/nn/quantized/functional.py:290: error: Module has no attribute "ops" torch/nn/quantized/functional.py:308: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:326: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:356: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:371: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:400: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:430: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:448: error: Name 'Tensor' is not defined torch/nn/quantized/modules/linear.py:26: error: Module has no attribute "ops" torch/nn/quantized/modules/linear.py:28: error: Module has no attribute "ops" torch/nn/quantized/modules/functional_modules.py:40: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:47: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:54: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:61: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:68: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:68: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:68: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:75: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:140: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:146: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:151: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:157: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:162: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:162: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:162: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:168: error: Name 'Tensor' is not defined torch/multiprocessing/spawn.py:9: error: Module 'torch.multiprocessing' has no attribute '_prctl_pr_set_pdeathsig' torch/multiprocessing/__init__.py:28: error: Module has no attribute "__all__" torch/jit/frontend.py:9: error: Cannot find implementation or library stub for module named 'torch._C._jit_tree_views' torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList2'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList3'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:9: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributions/distribution.py:16: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/distribution.py:74: error: Name 'arg_constraints' already defined on line 16 torch/distributions/distribution.py:84: error: Name 'support' already defined on line 15 torch/functional.py:114: error: Name 'Tuple' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:114: error: Name 'Optional' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:189: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:200: error: Argument 1 to "_indices_product" has incompatible type "Tuple[int, ...]"; expected "List[int]" torch/functional.py:204: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:204: note: Possible overload variants: torch/functional.py:204: note: def __setitem__(self, int, int) -> None torch/functional.py:204: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:204: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:204: note: def __getitem__(self, int) -> int torch/functional.py:204: note: def __getitem__(self, slice) -> List[int] torch/functional.py:207: error: "Tensor" has no attribute "copy_" torch/functional.py:212: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:212: note: Possible overload variants: torch/functional.py:212: note: def __setitem__(self, int, int) -> None torch/functional.py:212: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:212: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:212: note: def __getitem__(self, int) -> int torch/functional.py:212: note: def __getitem__(self, slice) -> List[int] torch/functional.py:215: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:334: error: Name 'Optional' is not defined torch/functional.py:334: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:429: error: Argument 2 to "pad" has incompatible type "Tuple[int, int]"; expected "List[int]" torch/functional.py:431: error: Module has no attribute "stft" torch/functional.py:766: error: Module has no attribute "cdist" torch/functional.py:768: error: Module has no attribute "cdist" torch/functional.py:770: error: Module has no attribute "cdist" torch/functional.py:775: error: Name 'Optional' is not defined torch/functional.py:775: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'Optional' is not defined torch/functional.py:780: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'number' is not defined torch/functional.py:780: error: Name 'norm' already defined on line 775 torch/functional.py:785: error: Name 'Optional' is not defined torch/functional.py:785: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:785: error: Name 'number' is not defined torch/functional.py:785: error: Name 'norm' already defined on line 775 torch/functional.py:790: error: Name 'Optional' is not defined torch/functional.py:790: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:790: error: Name 'norm' already defined on line 775 torch/functional.py:795: error: Name 'norm' already defined on line 775 torch/functional.py:960: error: Name 'Any' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Any") torch/functional.py:960: error: Name 'Tuple' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1036: error: Argument 1 to "len" has incompatible type "int"; expected "Sized" torch/functional.py:1041: error: Name 'Optional' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1041: error: Name 'Tuple' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1056: error: Name 'Optional' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1056: error: Name 'Tuple' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/distributions/von_mises.py:87: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/negative_binomial.py:25: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/multivariate_normal.py:116: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/laplace.py:23: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/independent.py:34: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/cauchy.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/poisson.py:28: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/one_hot_categorical.py:32: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/normal.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/lowrank_multivariate_normal.py:79: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/gamma.py:30: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/exponential.py:23: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/fishersnedecor.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/dirichlet.py:44: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/nn/quantized/dynamic/modules/rnn.py:230: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:232: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:236: error: Incompatible return value type (got "Tuple[Any, Tensor, Any]", expected "Tuple[int, int, int]") torch/nn/quantized/dynamic/modules/rnn.py:351: error: Incompatible types in assignment (expression has type "Type[LSTM]", base class "RNNBase" defined the type as "Type[RNNBase]") torch/nn/quantized/dynamic/modules/rnn.py:381: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:385: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:414: error: Argument 1 to "forward_impl" of "LSTM" has incompatible type "PackedSequence"; expected "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:416: error: Incompatible types in assignment (expression has type "PackedSequence", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:418: error: Incompatible return value type (got "Tuple[Tensor, Tuple[Tensor, Tensor]]", expected "Tuple[PackedSequence, Tuple[Tensor, Tensor]]") torch/nn/quantized/dynamic/modules/rnn.py:420: error: Argument 1 of "permute_hidden" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:420: error: Return type "Tuple[Tensor, Tensor]" of "permute_hidden" incompatible with return type "Tensor" in supertype "RNNBase" torch/nn/quantized/dynamic/modules/rnn.py:426: error: Argument 2 of "check_forward_args" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/intrinsic/qat/modules/conv_fused.py:232: error: Incompatible types in assignment (expression has type "Type[ConvBnReLU2d]", base class "ConvBn2d" defined the type as "Type[ConvBn2d]") torch/distributions/beta.py:27: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/geometric.py:31: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/continuous_bernoulli.py:38: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/bernoulli.py:30: error: Incompatible types in assignment (expression has type "_Boolean", base class "Distribution" defined the type as "None") torch/quantization/fake_quantize.py:126: error: Module has no attribute "per_tensor_symmetric" torch/quantization/fake_quantize.py:132: error: Module has no attribute "per_channel_symmetric" torch/distributions/transformed_distribution.py:41: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/jit/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/jit/__init__.py:15: error: Module 'torch.utils' has no attribute 'set_module' torch/jit/__init__.py:70: error: Name 'Attribute' already defined on line 68 torch/jit/__init__.py:213: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:215: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:1524: error: Unsupported dynamic base class "with_metaclass" torch/jit/__init__.py:1869: error: Name 'ScriptModule' already defined on line 1524 torch/jit/__init__.py:1998: error: Need type annotation for '_jit_caching_layer' torch/jit/__init__.py:1999: error: Need type annotation for '_jit_function_overload_caching' torch/distributions/relaxed_categorical.py:34: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_categorical.py:108: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:31: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:114: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/logistic_normal.py:31: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/log_normal.py:26: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_normal.py:27: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_cauchy.py:28: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/gumbel.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/nn/quantized/modules/conv.py:18: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/quantized/modules/conv.py:209: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:209: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:214: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:321: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:321: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:323: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:447: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:447: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:449: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:513: error: Name 'nn.modules.conv._ConvTransposeNd' is not defined torch/nn/quantized/modules/conv.py:525: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:525: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/conv.py:527: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:527: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/intrinsic/quantized/modules/conv_relu.py:8: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/intrinsic/quantized/modules/conv_relu.py:21: error: Incompatible types in assignment (expression has type "Type[ConvReLU2d]", base class "Conv2d" defined the type as "Type[Conv2d]") torch/nn/intrinsic/quantized/modules/conv_relu.py:62: error: Incompatible types in assignment (expression has type "Type[ConvReLU3d]", base class "Conv3d" defined the type as "Type[Conv3d]") torch/distributions/weibull.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/kl.py:35: error: Need type annotation for '_KL_MEMOIZE' (hint: "_KL_MEMOIZE: Dict[<type>, <type>] = ...") torch/distributions/studentT.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/mixture_same_family.py:48: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/__init__.py:158: error: Name 'transforms' is not defined torch/onnx/utils.py:21: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributed/rendezvous.py:4: error: Cannot find implementation or library stub for module named 'urlparse' torch/distributed/rendezvous.py:4: error: Name 'urlparse' already defined (possibly by an import) torch/distributed/rendezvous.py:4: error: Name 'urlunparse' already defined (possibly by an import) torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'FileStore' torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'TCPStore' torch/distributed/rendezvous.py:65: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceOptions'; maybe "ReduceOptions" or "AllreduceCoalescedOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceCoalescedOptions'; maybe "AllreduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllToAllOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'BroadcastOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'GatherOptions'; maybe "ScatterOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceOptions'; maybe "AllreduceOptions", "ReduceScatterOptions", or "ReduceOp"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceScatterOptions'; maybe "ScatterOptions" or "ReduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ScatterOptions'; maybe "ReduceScatterOptions" or Pull Request resolved: https://github.com/pytorch/pytorch/pull/36584 Reviewed By: seemethere, ailzhang Differential Revision: D21155985 Pulled By: ezyang fbshipit-source-id: f628d4293992576207167e7c417998fad15898d1 |
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Supriya Rao
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0429d2c9b8 |
[quant][graphmode] Add new tensorlist observer for LSTM (#35893)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35893 LSTM operator inputs have tensor list for activations and weights. In graph mode we need a new observer to work with tensor list Test Plan: python test/quantization/test_quantization.py ObserverTest Imported from OSS Differential Revision: D20830389 fbshipit-source-id: 4790f8932ae3d38446c1d942a2b3780aa91e3022 |
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Supriya Rao
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daba68c601 |
[quant][graph] Add a new observer type for dynamic quantization (#35455)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35455 In graph mode we need to observer the activation tensor for dynamic quantization. This observer should behave the same way as the quantization functions called in the dynamic operator. Currently for qlinear_dynamic we call quant_utils::ChooseQuantizationParams which has its own logic for calculating scale and zero_point. We mimic those calculations in the new observer. Test Plan: python test/test_quantization.py ObserverTest Imported from OSS Differential Revision: D20664586 fbshipit-source-id: e987ea71fff777c21e00c498504e6586e92568a2 |
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Supriya Rao
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b4b8b3c0ca |
Revert D20630988: [quant][graph] Add a new observer type for dynamic quantization
Test Plan: revert-hammer Differential Revision: D20630988 Original commit changeset: 7e7aca77590f fbshipit-source-id: 6bc67ca322c1703004e0053f8eba9b8f6a3a5f67 |
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Supriya Rao
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7e24ab8c4a |
[quant][graph] Add a new observer type for dynamic quantization (#35265)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35265 In graph mode we need to observer the activation tensor for dynamic quantization. This observer should behave the same way as the quantization functions called in the dynamic operator. Currently for qlinear_dynamic we call quant_utils::ChooseQuantizationParams which has its own logic for calculating scale and zero_point. We mimic those calculations in the new observer. Test Plan: python test/test_quantization.py ObserverTest Imported from OSS Differential Revision: D20630988 fbshipit-source-id: 7e7aca77590f965dcb423a705e68d030aaf98550 |
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Supriya Rao
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434af5d94a |
[quant] Speed up per-channel min-max observer (#34118)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/34118 Previously calc_per_channel_qparams was using for loops and python primitives, which called `item` many times causing slowdown during training. These changes uses torch primitives on the tensor to speed up the operation over 60x Perf results on MobileNetV2 during training using autograd profiler FP32 forward call - Self CPU time total: 47.222ms CUDA time total: 124.001ms before change FakeQuant Model - Self CPU time total: 19.107s CUDA time total: 27.177s after change FakeQuant Model - Self CPU time total: 404.667ms CUDA time total: 446.344ms Test Plan: python test/test_quantization.py Imported from OSS Differential Revision: D20287841 fbshipit-source-id: 6b706b8206e0d0da3c3c217b014e8da5b71b870d |
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Supriya Rao
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1cf12b7e53 |
[quant] Fix histogram observer to work with QAT on GPU (#34232)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/34232 By default `torch.zeros` creates the tensor on GPU. Need to specify the device argument to get it to work correctly on GPU during QAT. Test Plan: 1. Tested by running QAT on GPU 2. python test/test_quantization.py Imported from OSS Differential Revision: D20286351 fbshipit-source-id: 745723c85d902870c56c1c7492f26cb027ae9dc6 |
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Dmytro Dzhulgakov
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a8fc3d8c2a |
Fix HistogramObserver to not do detach on input (#34114)
Summary: Fixes https://github.com/pytorch/pytorch/issues/33545, added a unittest Pull Request resolved: https://github.com/pytorch/pytorch/pull/34114 Differential Revision: D20224719 Pulled By: dzhulgakov fbshipit-source-id: 053d3b3b0c86340027ba1b95b5f3c247aa151aee |
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Supriya Rao
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996c0adb53 |
[quant] Regsiter fake_quant and observer attributes as buffers (#33626)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33626 For DDP we require the attributes to be registered as buffers. By doing this the value is broadcast from one device to the rest. Test Plan: Tested on actual model on GPU Imported from OSS Differential Revision: D20038839 fbshipit-source-id: 82e829fc3baca0b3262c3894a283c375eb08a4a4 |
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Jerry Zhang
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8ddd5bb0e9 |
Don't serialize None values in observer (#32733)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/32733 Similar to https://github.com/pytorch/pytorch/pull/32318, we should stop serializing None values since they can't be broadcasted Test Plan: Imported from OSS Differential Revision: D19611586 Pulled By: jerryzh168 fbshipit-source-id: 369881de0567ed8eb25bdada892227f49bb5b29d |
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Raghuraman Krishnamoorthi
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eccf42fd15 |
Bug fix: Handle missing keys in observer state dict during load (#30357)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/30357 Fix issue https://github.com/pytorch/pytorch/issues/29032 in loading from state dict for observers and fake quant. ghstack-source-id: 94468814 Test Plan: Ensures that load/save of fake quant and observers with missing keys works correctly. Differential Revision: D18668517 fbshipit-source-id: 0eda6f47c39102e55977fc548b9a03664f123ad7 |
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Jerry Zhang
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661a6c8ef2 |
Add get_qparams and revert the changes to calculate_qparams (#30262)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/30262 `get_qparams` returns all parameters that's needed to call quantize function Test Plan: python test/test_jit.py Imported from OSS Differential Revision: D18645047 fbshipit-source-id: e57c11a66dac2d589778d412a996796ad5b6f86a |
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Raghuraman Krishnamoorthi
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67b77afcdf |
Fast histogram observer
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/29790 Test Plan: import torch import time import numpy as np from torch.quantization.observer import HistogramObserver X = torch.randn(1,1,224,224) obs = HistogramObserver(2048) acc_time = 0 for i in range(100): X = torch.randn(10,1,320,320) start = time.time() obs(X) #obs.forward_new(X) acc_time = acc_time + time.time()-start print(acc_time) Imported from OSS Differential Revision: D18508562 fbshipit-source-id: 456e82360ce1b3f9d8b6e1832d23f1339655011a |
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Jerry Zhang
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f2b851a9e5 |
Returning axis from calculate_qparams (#29494)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/29494 `calculate_qparams` of per channel quantization should return the axis, this PR added this and also added corresponding support in graph mode Test Plan: python test/test_jit.py Imported from OSS Differential Revision: D18580905 fbshipit-source-id: f9691c1f043f8bca39f81716a4d0b10f60a65396 |
