* Move QuantizationScheme to burn-tensor
* Refactor QuantizedTensorPrimitive to include the quantization strategy
* Fix QFloat tensor data display
* Refactor quantization methods to use scheme and qparams (on backend device)
* Fix clippy
* Fix fmt
* Add qtensor primitive tests
* Added parameter trust_remote_code to hf dataset call.
* Removed test modul as it may break causing false negatives.
Set default trust_remote_code to false.
Added an example that highlights the usecase.
* Feat: burn-import implement ONNX ConstantOfShape
* Introduce shape type and use in ConstantOfShape and Shape
* Add tests for bool and int tensors for ConstantOfShape
* Fix ONNX test generation
* Undo comment
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Co-authored-by: Dilshod Tadjibaev <939125+antimora@users.noreply.github.com>
* Add QuantizationBackend, QTensorOps and QTensor
* Refactor QTensorOps as part of Backend trait
* Add tensor dequantize, QFloat dtype and default affine/symmetric quant
* Add ndarray default quantization implementation
* Fix clippy
* Add rayon parallel iter
* Add quantization operations to book
* Add q_shape and q_device ops to avoid converting the tensor just to get attributes
* Implement autodiff grad ops
* Mark autodiff todo for QAT
* Remove note
* Add q_inner and q_from_inner
* Implement 3D and transposed 3D convolutions.
* Merge changes from onnx-ir #1921 pr
---------
Co-authored-by: Dilshod Tadjibaev <939125+antimora@users.noreply.github.com>
* separating onnx parsing from burn-import
* ran clippy and cargo-fmt
* removed unused deps from onnx-ir
* fixed clippy warnings that were causing run-checks to fail
* removed dead code
* removed unused dependencies from burn-import
* updated contributor-book, updated publish.yml, added readme
* update cargo lock
* formatted md document with prettier, rephrased sentence
* missed the errors with reduce_prod_conversion during merge
* formatted onnx-to-burn-conversion-tool.md, forgot to save
* Preliminary ReduceProd Support
* Add comma to keep formatter happy
* Give test results a 0.001 tolerance to account for floating-point multiplication
* Reformat assersions
* Correctly mark panic conditions in op_configuration
* Move distribution to module
* Add new TensorData with serialization support
* Implement display and from for TensorData
* Add missing Cargo.lock
* Add missing bytemuck feature
* Add zeros, ones, full and random TensorData methods
* Refactor Data -> TensorData usage
* Fix tests
Since TensorData is not generic over the element type anymore no type inference can be done by the compiler. We must explicitly cast the expected results to the expected backend type.
* Remove commented line
* Fix import
* Add record-backward-compat
* Remove dim const generic from TensorData
* Support NestedValue de/serialization with TensorData
* Fix burn-jit tests
* Remove eprinln
* Refactor onnx import to use TensorData
* Fix tch from_data
* Fix nested value serialization for u8
* Fix missing import
* Fix reduce min onnx test
* Fix deprecated attribute
* Remove shape getter
* Remove strict assert in tests
* Add tensor data as_bytes
* Add tensor check for rank mismatch
* Fix typo (dimensions plural)
* Fix error message
* Update book examples with from_data and fix Display impl for TensorData
* Add deprecation note
Nathaniel Simard