| /external/pytorch/aten/src/ATen/native/quantized/cpu/qnnpack/ |
| D | README.md | 1 # QNNPACK chapter
2 QNNPACK (Quantized Neural Networks PACKage) is a mobile-optimized library for low-precision high-pe…
4 QNNPACK is not intended to be directly used by machine learning researchers; instead it provides lo…
29 QNNPACK provides standard CMake-based build scripts.
33 Users are recommended to use `scripts/build-local.sh` script to build QNNPACK for the host machine.
46 …bile CPU does not support ARM NEON. Don't set `-DANDROID_ARM_NEON=1` for QNNPACK compilation as it…
63 …QNNPACK, and provides a [pre-trained quantized MobileNet v2 model](https://github.com/caffe2/model…
72 # Optional: update QNNPACK submodule to latest revision
73 git submodule update --remote third_party/QNNPACK
99 # Optional: update QNNPACK submodule to latest revision
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| /external/pytorch/aten/src/ATen/native/quantized/cpu/qnnpack/src/ |
| D | init.c | 21 #include <qnnpack/log.h>
22 #include <qnnpack/params.h>
23 #include <qnnpack/q8avgpool.h>
24 #include <qnnpack/q8conv.h>
25 #include <qnnpack/q8dwconv.h>
26 #include <qnnpack/q8gavgpool.h>
27 #include <qnnpack/q8gemm.h>
28 #include <qnnpack/q8gemm_sparse.h>
29 #include <qnnpack/q8vadd.h>
30 #include <qnnpack/u8clamp.h>
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| D | fully-connected-sparse.c | 17 #include <qnnpack/log.h>
18 #include <qnnpack/math.h>
19 #include <qnnpack/operator.h>
20 #include <qnnpack/pack.h>
21 #include <qnnpack/params.h>
22 #include <qnnpack/requantization.h>
47 …"pytorch_qnnp_create_fully_connected_sparse_dq_nc_q8 failed because QNNPACK is not properly initia… in pytorch_qnnp_create_fully_connected_sparse_dq_nc_q8()
105 "Invalid indices dtype specified for qnnpack fully connected sparse"); in pytorch_qnnp_create_fully_connected_sparse_dq_nc_q8()
148 … "pytorch_qnnp_setup_fully_connected_nc_q8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_setup_fully_connected_sparse_dq_nc_q8()
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| D | fully-connected.c | 17 #include <qnnpack/log.h>
18 #include <qnnpack/math.h>
19 #include <qnnpack/operator.h>
20 #include <qnnpack/pack.h>
21 #include <qnnpack/params.h>
22 #include <qnnpack/requantization.h>
42 … "pytorch_qnnp_create_fully_connected_nc_q8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_create_fully_connected_nc_q8()
139 … "pytorch_qnnp_setup_fully_connected_nc_q8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_setup_fully_connected_nc_q8()
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| D | fc-prepack.cc | 2 #include <qnnpack/log.h>
3 #include <qnnpack/pack.h>
9 namespace qnnpack { namespace
26 assert(false && "QNNPACK Runtime Error."); in PackBMatrix()
43 assert(false && "QNNPACK Runtime Error."); in PackBMatrix()
58 } // namespace qnnpack
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| D | conv-prepack.cc | 2 #include <qnnpack/log.h>
3 #include <qnnpack/operator.h>
4 #include <qnnpack/pack.h>
8 namespace qnnpack { namespace
26 assert(false && "QNNPACK Runtime Error."); in PrePackConvWeights()
41 assert(false && "QNNPACK Runtime Error."); in PrePackConvWeights()
177 assert(false && "QNNPACK Runtime Error."); in PrePackConvWeights()
214 assert(false && "QNNPACK Runtime Error."); in PrePackConvWeights()
283 } // namespace qnnpack in PrePackConvWeights()
284 } // namespace qnnpack
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| D | max-pooling.c | 18 #include <qnnpack/common.h>
19 #include <qnnpack/indirection.h>
20 #include <qnnpack/log.h>
21 #include <qnnpack/math.h>
22 #include <qnnpack/operator.h>
23 #include <qnnpack/params.h>
56 … "pytorch_qnnp_create_max_pooling2d_nhwc_u8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_create_max_pooling2d_nhwc_u8()
155 … "pytorch_qnnp_setup_max_pooling2d_nhwc_u8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_setup_max_pooling2d_nhwc_u8()
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| D | deconvolution.c | 17 #include <qnnpack/indirection.h>
18 #include <qnnpack/log.h>
19 #include <qnnpack/math.h>
20 #include <qnnpack/operator.h>
21 #include <qnnpack/pack.h>
22 #include <qnnpack/params.h>
23 #include <qnnpack/requantization.h>
67 … "pytorch_qnnp_create_deconvolution2d_nhwc_q8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_create_deconvolution2d_nhwc_q8()
232 … "pytorch_qnnp_setup_deconvolution2d_nhwc_q8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_setup_deconvolution2d_nhwc_q8()
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| D | channel-shuffle.c | 16 #include <qnnpack/log.h>
17 #include <qnnpack/operator.h>
18 #include <qnnpack/params.h>
30 … "pytorch_qnnp_create_channel_shuffle_nc_x8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_create_channel_shuffle_nc_x8()
85 … "pytorch_qnnp_setup_channel_shuffle_nc_x8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_setup_channel_shuffle_nc_x8()
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| D | add.c | 16 #include <qnnpack/log.h>
17 #include <qnnpack/operator.h>
18 #include <qnnpack/params.h>
19 #include <qnnpack/requantization.h>
38 "pytorch_qnnp_create_add_nc_q8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_create_add_nc_q8()
142 "pytorch_qnnp_setup_add_nc_q8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_setup_add_nc_q8()
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| D | global-average-pooling.c | 16 #include <qnnpack/log.h>
17 #include <qnnpack/operator.h>
18 #include <qnnpack/params.h>
19 #include <qnnpack/requantization.h>
36 …"pytorch_qnnp_create_global_average_pooling_nwc_q8 failed because QNNPACK is not properly initiali… in pytorch_qnnp_create_global_average_pooling_nwc_q8()
124 …"pytorch_qnnp_setup_global_average_pooling_nwc_q8 failed because QNNPACK is not properly initializ… in pytorch_qnnp_setup_global_average_pooling_nwc_q8()
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| D | average-pooling.c | 18 #include <qnnpack/common.h>
19 #include <qnnpack/indirection.h>
20 #include <qnnpack/log.h>
21 #include <qnnpack/math.h>
22 #include <qnnpack/operator.h>
23 #include <qnnpack/params.h>
53 …"pytorch_qnnp_create_average_pooling2d_nhwc_q8 failed because QNNPACK is not properly initialized"… in pytorch_qnnp_create_average_pooling2d_nhwc_q8()
216 …"pytorch_qnnp_setup_average_pooling2d_nhwc_q8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_setup_average_pooling2d_nhwc_q8()
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| D | deconv-run.cc | 3 #include <qnnpack/indirection.h>
4 #include <qnnpack/log.h>
5 #include <qnnpack/math.h>
6 #include <qnnpack/params.h>
11 namespace qnnpack { namespace
209 } // namespace qnnpack
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| D | clamp.c | 16 #include <qnnpack/log.h>
17 #include <qnnpack/operator.h>
30 "pytorch_qnnp_create_clamp_nc_u8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_create_clamp_nc_u8()
86 "pytorch_qnnp_setup_clamp_nc_u8 failed because QNNPACK is not properly initialized"); in pytorch_qnnp_setup_clamp_nc_u8()
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| D | fc-unpack.cc | 2 #include <qnnpack/log.h>
3 #include <qnnpack/pack.h>
9 namespace qnnpack { namespace
70 } // namespace qnnpack
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| /external/pytorch/aten/src/ATen/native/quantized/cpu/qnnpack/src/qnnpack/ |
| D | log.h | 21 "QNNPACK",
23 CLOG_DEFINE_LOG_INFO(pytorch_qnnp_log_info, "QNNPACK", PYTORCH_QNNP_LOG_LEVEL);
26 "QNNPACK",
30 "QNNPACK",
34 "QNNPACK",
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| /external/pytorch/aten/src/ATen/native/quantized/cpu/qnnpack/include/ |
| D | pack_block_sparse.h | 16 #include <qnnpack/AlignedAllocator.h>
19 #include <qnnpack/common.h>
20 #include <qnnpack/math.h>
26 namespace qnnpack {
86 // into the qnnpack fully connected sparse op
352 const qnnpack::TypedBCSRMatrix<INDICES_DTYPE>* typed_bcsr = \
353 static_cast<const qnnpack::TypedBCSRMatrix<INDICES_DTYPE>*>( \
359 const qnnpack::TypedBCSRMatrix<INDICES_DTYPE>* typed_bcsr = \
360 static_cast<const qnnpack::TypedBCSRMatrix<INDICES_DTYPE>*>( \
366 const qnnpack::TypedBCSRMatrix<INDICES_DTYPE>* typed_bcsr = \
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| /external/pytorch/aten/src/ATen/native/quantized/cpu/qnnpack/test/ |
| D | test_utils.h | 12 namespace qnnpack {
26 _MAKE_TEST(TestClass, test_name##_static, test_body, qnnpack::testing::Mode::Static)
29 _MAKE_TEST(TestClass, test_name##_runtime, test_body, qnnpack::testing::Mode::Runtime)
35 }} // namespace qnnpack::testing
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| /external/pytorch/aten/src/ATen/native/ao_sparse/quantized/cpu/ |
| D | qnnpack_utils.h | 8 // needed for quantized op from the generic qnnpack specific
23 // optional bias does not exist. This is to compy with qnnpack operator that
29 std::unique_ptr<qnnpack::BCSRMatrix> bcsr_matrix_;
50 false, "Static quantized sparse linear unimplemented on QNNPACK"); in apply()
57 false, "Static quantized sparse linear unimplemented on QNNPACK"); in apply_relu()
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| D | qlinear_dynamic.cpp | 33 "supported on qnnpack backend."); in apply_dynamic_impl()
74 // is owned by this module. The pointer is then passed to qnnpack backend. in apply_dynamic_impl()
102 " qnnpack backend."); in apply_dynamic_impl()
139 " qnnpack backend."); in apply_dynamic_impl()
146 " qnnpack backend."); in apply_dynamic_impl()
173 if (ctx.qEngine() == at::QEngine::QNNPACK) { in run()
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| /external/executorch/backends/ |
| D | backends.bzl | 9 "//executorch/backends/fb/qnnpack:qnnpack_backend",
21 "//executorch/backends/fb/qnnpack:qnnpack_preprocess",
22 "//executorch/backends/fb/qnnpack/partition:qnnpack_partitioner",
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| /external/pytorch/aten/src/ATen/native/quantized/ |
| D | qlinear_unpack.cpp | 2 The dispatch registrations at the end of this file applies to fbgemm, qnnpack, and cudnn backends.
6 …lementations for the unpack functions can be found in /cpu/LinearUnpackImpl.cpp, for fbgemm&qnnpack
37 ctx.qEngine() != at::QEngine::QNNPACK, in run()
39 "not supported by QNNPACK"); in run()
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| /external/pytorch/torch/ao/quantization/backend_config/ |
| D | native.py | 110 …Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) with various additional fp1…
171 Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack).
173 # TODO: express this BackendConfig as a union of the FBGEMM and QNNPACK BackendConfigs
221 Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) in dictionary form.
228 Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) with various additional
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| /external/executorch/exir/backend/test/demos/ |
| D | test_xnnpack_qnnpack.py | 12 from executorch.backends.fb.qnnpack.partition.qnnpack_partitioner import (
15 from executorch.backends.fb.qnnpack.qnnpack_preprocess import QnnpackBackend
94 # Step 3.1: Lower dynamic quant linear to qnnpack
112 # The first delegate backend is Qnnpack
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| /external/pytorch/torch/testing/_internal/ |
| D | common_quantized.py | 13 # Note: We currently do not run QNNPACK tests on WINDOWS and MACOS as it is flaky. Issue #29326
14 # QNNPACK is not supported on PPC
15 # QNNPACK throws ASAN heap-buffer-overflow error.
16 if 'qnnpack' in supported_qengines and any([IS_PPC, TEST_WITH_ASAN, TEST_WITH_TSAN, TEST_WITH_UBSAN…
17 supported_qengines.remove('qnnpack')
168 # for fbgemm vs qnnpack.
180 return torch.backends.quantized.engine == 'qnnpack'
|