- 12 Mar, 2020 1 commit
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* [CUDA] Op strategy changes for Int8 schedules. * Applying Haichen's suggestions. * Make 4D output work for task extraction. * Make x86 work. * Fix lint. * Lint fixes. * Tests, comments, out channel a multiple of 4. * Topi test. Co-authored-by: Ubuntu <ubuntu@ip-172-31-38-96.us-west-2.compute.internal>
Animesh Jain committed
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- 27 Feb, 2020 1 commit
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* [REFACTOR][PY][API-CHANGE] Remove legacy python files. Remove legacy python files. Use the te namespace for most of the tensor expression primitives. - tvm.create_schedule -> tvm.te.create_schedule - tvm.placeholder -> tvm.te.placeholder - tvm.compute -> tvm.te.compute * Remove top-level exposures.
Tianqi Chen committed
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- 15 Feb, 2020 1 commit
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* add test case for per channel dense * add unit arg in tflite frontend * update qnn legalize test * fix output dim index
masahi committed
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- 14 Feb, 2020 1 commit
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* [QNN] Doc fix on quantize and convolution * update test
masahi committed
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- 12 Feb, 2020 1 commit
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* [REFACTOR][PY][API-CHANGE] establish tvm.ir, migrate corresponding relay files. This PR establishes tvm.ir and migrates the corresponding relay files into the new folder. API Change: - relay.Module -> tvm.IRModule * Update with ADT * Migrate transform * address comments * Migrate module * Migrate json_compact * Migrate attrs * Move LoweredFunc to stmt temporarily * temp migrate container * Finish migrate container
Tianqi Chen committed
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- 03 Jan, 2020 1 commit
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Animesh Jain committed
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- 19 Nov, 2019 1 commit
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Animesh Jain committed
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- 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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- 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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- 30 Aug, 2019 1 commit
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Animesh Jain committed
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