* 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>
* 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
* Element already implements One
* Add element module
* Add our own traits for Zero, One and ToPrimitive to support bool Element
* Fix typo
* Add basic tests for ToPrimitive with expected values
* The most important change of all
* Remove One + Zero identities
* Move zero/one outside mapv + refactor ToPrimitive -> ToElement trait
* Add num-traits to NOTICES.md
* Updated documentation for unfold4d
Added links between the struct and the config. Added a link to the related burn_tensor function in the documentation for the forward function.
* Changing nn relu module documentation to functional api
Removing the formula for relu from the module API to the functional API,
citing a paper relevant to relu
and mentionning the functional API in the module API
* Linking gelu module API documentation to functional API documentation
* Linear module : adding documentation
Adding documentation to the Linear module
mentionning that LinearConfig struct
should be used when creating a Linear Layer
Also adding links to the documentation that points people toward
the right path
* Updated documentation for dropout
Added links between the struct and the config. Added a link to the struct in the forward function for more info.
* embedding + swiglu
* RotaryEncodying : adding documentation
Adding documentation stating the RotaryEncoding should be created using a RotaryEncodingConfig
* prelu: adding documentation
Adding documentation to the prelu module:
- Linking forward function documentation to the functional API
- Citing the first paper to mention prelu
- Adding documentation saying that prelu layer should be created using PReluConfig
* pos_encoding: adding documentation
* Updated documentation for mha
Added links for more info. Added shape info at some places.
* docs: Add documentation for Gru module
Provide documentation for the Gru module, including its configuration and usage. Include a link to the paper that introduced the Gated Recurrent Unit (GRU) and specify that the module should be created using GruConfig. Also, mention that the forward function returns a state tensor with specific dimensions.
* burn-core-nn-transformers: adding documentation
Adding documentation:
- Says to use config to create the layers
- Add mathematical formula to the pwff forward pass
- Add citation in the pwff to the "Attention is all you need" paper
* Updated documentation: ConvTranspose1d and ConvTranspose2d
* docs: Add documentation for Lstm and BiLstm modules
Provide documentation for the Lstm and BiLstm modules, including their configurations and usage. Include links to the papers that introduced Long Short-Term Memory (LSTM) and Bidirectional LSTM. Specify that the modules should be created using LstmConfig and BiLstmConfig respectively.
* docs: Update documentation for ConvTranspose1d and ConvTranspose2d modules
* loss: Adding documenntation to the loss layers
Adding documentation stating to use the config to create the layer
* chore: Refactor Conv1d module imports and update documentation
* docs: Add documentation for AdaptiveAvgPool1d and AdaptiveAvgPool2d modules
Added references to the burn_tensor associated functions. Added links between the struct and the config.
* Refactor Conv1d module imports and update documentation
* chore: Refactor Conv2d module imports and update documentation
* Add documentation for AvgPool1d and AvgPool2d modules
Added references to the burn_tensor associated functions. Added links between the struct and the config.
* Add documentation for MaxPool1d and MaxPool2d modules
Added references to the burn_tensor associated functions. Added links between the struct and the config.
* Add documentation for leaky_relu and removed Config generic
Added references to the burn_tensor associated functions. Added links between the struct and the config. Removed the backend generic from the config since it's not needed (might be a breaking change).
* refactor: Update BatchNormConfig initialization and add documentation.
* Added link to config in embedding struct documentation
* refactor: Update GroupNormConfig initialization and add documentation
* refactor: Update InstanceNormConfig initialization and add documentation
* feat: Update LayerNormConfig initialization and add documentation
* refactor: Update RmsNormConfig initialization and add documentation
* fixed: removed #derive accidentally
* Added missing backticks in pools' shapes
* Format nn doc
* Make config fields public in nn modules
* Update import statements in nn modules
Changed burn_tensor imports to crate::tensor
* Update import statements in nn modules' tests
Changed burn_tensor imports to crate::tensor
* breaking change refactor: Update GroupNormConfig and InstanceNormConfig initialization
* Make SwiGlu fields public
* grammar
* slashes
* input tensors grouping
* copy-pasta mistake
* a not an >:I
* Capitalization
* better desc
* math 'n ticks
* group_norm functional implementation
* removed the ... struct
* decoder typo
* fmt
* referring to private fn in docs
---------
Co-authored-by: Thierry Cantin-Demers <piertcd@gmail.com>
Co-authored-by: mepatrick73 <pameu17@ulaval.ca>
* #1747
Upgrade Rust dependencies
* Revert upgrade for tch
The update of tch on windows gives an error:
INTEL MKL ERROR: The specified module could not be found. mkl_vml_avx2.1.dll.
Intel MKL FATAL ERROR: cannot load mkl_vml_avx2.1.dll or mkl_vml_def.1.dll.
* Keep only .cargo/config.toml file which works with rust > 1.75
---------
Co-authored-by: Sylvain Benner <sylvain@benner.online>
* Move HandlerContainer and Tensor Ops description to burn-tensor
Move HandleContainer and Tensor operations descriptions to burn-tensor crate.
Removed the FusionDevice and replaced it with a DeviceOps trait bound to Backend::Device.
For now added modules to burn-tensor are excluded from no-std as they rely on Arc.
* [burn-tensor] Flatten module hierarchy for tensor representation
+ Add new repr feature to cargo file.
* Remove prefix on dosctring
* [burn-fusion] Require default features of burn-tensor
* Add remainder_scalar op to numeric trait and associated int/float functions
* Update burn-tch crate
* Update ndarray crate
* Update jit crate
* Update candle crate
* Update fusion crate
* Update autodiff crate
* Forgot float.rs for fusion
* Add burn-tensor tests
* Redirect to the pre-existing modulus op
* Fix sign
* Remove mut from burn-tch
* Use sign trick to make wgpu backend work
* Add more unit tests in to cover bases
* Naming fix for burn-fusion
* Update tests w/PyTorch link
* Use different WGSL instructions for remainder
* Redirect to remainder Operator instead of modulo
* Revert Modulo in instruction.rs
* Implement LeakyReLu
* Cargo fmt
* Apply suggestions
* cargo fmt
* Use float_mul_scalar
* Should be grad
* Add to books module
* Move test files
* Update leaky relu to use activation function
* Update tensor.md
* Fix failing test due to approx
* Add back the function comment
* Fix comment per PR feedback
---------
Co-authored-by: Dilshod Tadjibaev <939125+antimora@users.noreply.github.com>
* Initial padding approach
Create padding implementation for the last two dimensions of Float and Int Tensors.
Create PadMode Enum, allowing Constant padding.
Create Padding Struct with Uniform, Asymmetric, height, and width implementations.
Create tests for the padding implementation.
* Update padding.rs
remove unneeded import
* Update from Merge
Use crate Element
Swap from old from_data() to new from_data_devauto()
* Formatting Changes
Formatting changes from cargo fmt --all
* Additional Format Change
One more format change that cargo fmt didn't get the first time.
* Changes to Example
Modify Example to ensure it works.
* modify naming
better names for impl / input variables.
* Modify API
- Change Padding to PadSize.
- integrate padding value into PadMode.
- update tests and examples.
* Comments and print
Improve comments+naming and remove println
* Pad Fixes
Moved pad to numeric
Simplified PadMode Element
updated tensor creations
fixed doc example
* Fix test location
* Simplified pad API
* Fix for failed unit tests
* Remove bool_full
* Rename `pads` to `padding`
---------
Co-authored-by: Dilshod Tadjibaev <939125+antimora@users.noreply.github.com>
Dilshod Tadjibaev