- 16 Nov, 2019 1 commit
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* Add qnn conv2d attributes for input_tensor_scale and kernel_tensor_scale. The lowering in the tflite frontend loses the input_tensor_scale and the kernel_tensor_scale by multiplying it and putting it into the Requantize operation. This means that any graph partitioning passes or other passes that need to access this information no longer have it available in the qnn dialect. regards Ramana * Store input tensor scale and Weight tensor scale for Dense as well As for conv2d, the tflite frontend drops the input tensor scale and the weight tensor scale from the relay op. Store it as separate fields in there. * Fix unintentional tab * Rename input_tensor_scale to input_scale and kernel_tensor_scale to kernel_scale for conv2d. * input_tensor_scale -> input_scale weight_tensor_scale->weight_scale * Rework dense testcase And use input_scale and kernel_scale * Be consistent in use of input_scale and kernel_scale values * Fixup qnn conv2d tests for input_scale and kernel_scale * Make pydoc identical between conv2d and dense for weight_tensor * Fix up conv2d parameters to be in the same order between C++ and python * Fix ordering of parameters for dense. * Add input_scale and output_scale to try and satisfy ci gods * Delete input_scale and kernel_scale. nn.conv2d does not contain input_scale and kernel_scale. We need to delete it when lowering it to nn.conv2d. * Add input_scale and kernel_scale for qnn.conv2d
Ramana Radhakrishnan committed
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- 13 Nov, 2019 1 commit
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Animesh Jain committed
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- 30 Oct, 2019 1 commit
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* [QNN] Improving Dense lowering. * - Moving get_shape method to util - Finalizing the test cases and the code structure for optimized dense computation. * - Fixing cpplint. * - Addressing review comments. * - Renaming the variables correctly. * - Renaming the variables correctly.
shoubhik committed
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- 21 Oct, 2019 1 commit
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This patch adds multiply operator for quantized tensors. The details of the quantized multiplication are outlined in the code. This builds on pull request 3927 and includes the changes Animesh mentions in the comments on that request. Change-Id: I555715b53d0266a91d5c03dc3dfe8fc31e7ce4e1
ekalda committed
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- 17 Oct, 2019 1 commit
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* [TOPI][x86] Cascade lake support. * Jenkins test debug 1. * Testing cascade lake alone.
Animesh Jain committed
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- 16 Oct, 2019 1 commit
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Animesh Jain committed
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- 30 Sep, 2019 1 commit
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Animesh Jain committed
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- 22 Sep, 2019 1 commit
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* Qnn Dense layer. * Reformatting code. * Reformatting code and making the test case more readable. * Fixing lint issues. * Fixing test method names to pass the nose related configurations. * Aligning the code for code style.
shoubhik committed
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- 16 Sep, 2019 1 commit
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* QNNLegalize for conv2d * [QNN] Legalization for Intel x86 QNN Conv2D
Animesh Jain committed
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- 05 Sep, 2019 1 commit
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Animesh Jain committed
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- 04 Sep, 2019 1 commit
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Rebasing. Empty commit. Clang-format styling.
Animesh Jain committed
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- 31 Aug, 2019 1 commit
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Animesh Jain committed
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- 30 Aug, 2019 1 commit
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Animesh Jain committed
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- 16 Aug, 2019 1 commit
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* QNN quantize and dequantize operators. * addressing review comments. * addressing review comments. * Adding new line at the end of the file. * Adhering to styling guidelines. * Adding name to contributors. * Fixing lint issue. * Fixing file name. * Removing unnecessary code.
shoubhik committed
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- 15 Aug, 2019 1 commit
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Animesh Jain committed
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- 14 Aug, 2019 1 commit
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Animesh Jain committed
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- 08 Aug, 2019 1 commit
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* [Relay] [Quantization] WIP - Common files for the qauntization work. * [Relay] [Quantization] WIP - Prototyping requantize op. * Requantize operator implementation. Requantize converts one quantized tensor representation to another quantized representation. The PR has following implementation features - Requantize operator defined in qnn namespace - relay.qnn.requantize - Lowering of the requantize to exisiting Relay operators - Integer fixed point implementation of requantize - Two rounding modes - FE_UPWARDS (round towards infinity) and FE_AWAY_FROM_ZERO (std::round behavior) - Floating point implementation as well, that can act as reference or can be used for devices when FP32 computation is not used. - Unit test cases Relevant Issue - https://github.com/dmlc/tvm/issues/2351 Credit to TFLite and GemmLowp to provide reference implementations. * Typo and lint fixes. * Doc fix. * Uncommenting the lint script (fixing mistake). * Modifying the unit tests. * Moving C++ files into src/relay/qnn * Moving python files to python/tvm/relay/qnn. Some minor fixes. * Moving the attrs.h inside the include directory. * Pushing files that I forgot earlier. Changing util location. * Incorporating comments. API change. Lint fixes. * Modifying the GetFixedPointMultiplierShift API as per comments. * Forgot the dialect change. * Changing rewrite to qnn_lower. * Renaming Quantize to Qnn for clarity. * Remove use_int_domain. * Incorportaing review comments. * Adding API doc for QNN dialect. * Move the qnn_lower pass to transform namespace. * Moving from expr to module. Adding namespace in C++. * Minor sentence rewrites. Added qnn namespace. * Added the API doc. * Chanding default out_dtype to int8. Adding a test with in/out_dtype as uint8. * Style fixes. Better error messages. * Adding documentation. * More documentation fixes. * Adding out dtype check for requantize. * Adding corner case for FP32 to fixed point conversion. * Adding extra line. * Documentation fix. * Adding static inline. * Incorporating jackwish comment. Removed idtype from requantize lowering. * Removing Quantize/Dequantize code. Restricting Requantize to (u)int8/int32. * Style fixes. * Fix the docs. * Move to Legalize API.
Animesh Jain committed
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