mirror of
https://github.com/huggingface/candle.git
synced 2025-06-17 19:18:50 +00:00
e635f18eda
* Start adding support for reading ggml files. * Compute the proper tensor size. * Print the read tensors. * Fix file reading.
84 lines
2.3 KiB
Rust
84 lines
2.3 KiB
Rust
//! ML framework for Rust
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//!
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//! ```rust
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//! use candle_core::{Tensor, DType, Device};
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//! # use candle_core::Error;
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//! # fn main() -> Result<(), Error>{
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//!
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//! let a = Tensor::arange(0f32, 6f32, &Device::Cpu)?.reshape((2, 3))?;
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//! let b = Tensor::arange(0f32, 12f32, &Device::Cpu)?.reshape((3, 4))?;
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//!
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//! let c = a.matmul(&b)?;
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//! # Ok(())}
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//! ```
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//!
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//! ## Features
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//!
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//! - Simple syntax (looks and like PyTorch)
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//! - CPU and Cuda backends (and M1 support)
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//! - Enable serverless (CPU) small and fast deployments
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//! - Model training
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//! - Distributed computing (NCCL).
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//! - Models out of the box (Llama, Whisper, Falcon, ...)
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//!
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//! ## FAQ
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//!
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//! - Why Candle?
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//!
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//! Candle stems from the need to reduce binary size in order to *enable serverless*
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//! possible by making the whole engine smaller than PyTorch very large library volume
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//!
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//! And simply *removing Python* from production workloads.
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//! Python can really add overhead in more complex workflows and the [GIL](https://www.backblaze.com/blog/the-python-gil-past-present-and-future/) is a notorious source of headaches.
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//!
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//! Rust is cool, and a lot of the HF ecosystem already has Rust crates [safetensors](https://github.com/huggingface/safetensors) and [tokenizers](https://github.com/huggingface/tokenizers)
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pub mod backend;
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pub mod backprop;
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mod conv;
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mod convert;
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pub mod cpu_backend;
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#[cfg(feature = "cuda")]
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pub mod cuda_backend;
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mod device;
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pub mod display;
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mod dtype;
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mod dummy_cuda_backend;
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pub mod error;
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pub mod ggml;
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mod indexer;
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pub mod layout;
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#[cfg(feature = "mkl")]
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mod mkl;
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pub mod npy;
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mod op;
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pub mod safetensors;
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pub mod shape;
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mod storage;
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mod strided_index;
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mod tensor;
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pub mod utils;
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mod variable;
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pub use cpu_backend::CpuStorage;
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pub use device::{Device, DeviceLocation};
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pub use dtype::{DType, FloatDType, IntDType, WithDType};
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pub use error::{Error, Result};
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pub use indexer::IndexOp;
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pub use layout::Layout;
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pub use op::{CustomOp1, CustomOp2, CustomOp3};
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pub use shape::{Shape, D};
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pub use storage::Storage;
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pub use strided_index::{StridedBlocks, StridedIndex};
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pub use tensor::{Tensor, TensorId};
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pub use variable::Var;
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#[cfg(feature = "cuda")]
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pub use cuda_backend::{CudaDevice, CudaStorage};
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#[cfg(not(feature = "cuda"))]
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pub use dummy_cuda_backend::{CudaDevice, CudaStorage};
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#[cfg(feature = "mkl")]
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extern crate intel_mkl_src;
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