mirror of https://github.com/tracel-ai/burn.git
bb5e6faff2
* Add int_random to int tensor ops * Int random for tch backend * Int random for burn-fusion * int random for autodiff * Int random for candle backend * Int random for ndarray backend * Int random for wgpu backend * Merge imports * Typo * Shader file for int uniform distribution * Create AutotuneOperationSet and public int_sum_dim_autotune * Adjust bounds to 0..10 * Create uniform_int_kernel, unit tests, use new kernel * Reduction kernels for regular and shared memory sum_dim int operations * Macro that accomadates wgpu IntElement * Add autotuning to int_mean_dim * Use correct macro for Int autotuning * Add int_mean_dim_shared_memory * Add int_mean_dim and unit test * Create autotunables for mean_dim * Run fmt * Remove comment * Finish resolving merge conflict, fix doc * Make the element trait bound a parameter to reduce_tune_ops macro * Update book * Fix requested change * Change range to [0, 255] and update test accordingly * Forgot to include candle in last commit * Fix comment * Use correct int autotune for mean dim * Fix typo- not sure how this passed earlier * Resolve syntax issues from merge * Fix cast_float * Saving here * Continue fixing merge conflicts, all tests pass locally * Run fmt * Change cast_float to cast_u32_to_float * Make uniform_int_inner_loop safer * Be even more explicit about u32 casts * Skip an intermediate step and cast directly to u32 * Replace JitElement + Element with IntElement * Run fmt * This should fix the CI * This time for sure |
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README.md
Burn Candle Backend
This crate provides a backend for Burn based on the Candle framework.
It is still in alpha stage, not all operations are supported. It is usable for some use cases, like for inference.
It can be used with CPU or CUDA. On macOS computations can be accelerated by using the Accelerate framework.
Feature Flags
The following features are supported:
cuda- Cuda GPU device (NVIDIA only)accelerate- Accelerate framework (macOS only)