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Introduce autotuning to
conv2d and conv_transpose2d with a new `i…
…m2col`/`GEMM` algorithm (tracel-ai#2287)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,125 @@ | ||
| use burn_tensor::{ | ||
| ops::{ConvOptions, ConvTransposeOptions}, | ||
| TensorData, | ||
| }; | ||
|
|
||
| use crate::{tensor::JitTensor, FloatElement, IntElement, JitElement, JitRuntime}; | ||
|
|
||
| #[cfg(feature = "autotune")] | ||
| use super::conv2d_autotune; | ||
| use super::{ | ||
| conv2d_direct, conv2d_im2col, conv_transpose2d_autotune, conv_transpose2d_col2im, | ||
| conv_transpose2d_direct, implicit_gemm::conv2d_implicit_gemm, | ||
| }; | ||
|
|
||
| /// The strategy to be used when launching a convolution kernel. | ||
| pub enum Conv2dStrategy { | ||
| /// A simple direct convolution. | ||
| Direct, | ||
| #[cfg(feature = "autotune")] | ||
| /// Using autotune to choose the best kernel based on runtime information. | ||
| Autotune, | ||
| /// GEMM (im2col) based implementation of convolution. Significantly increased memory usage. | ||
| Gemm, | ||
| /// Implicit GEMM implementation of convolution. Lower memory usage but requires CMMA and | ||
| /// has constraints on tensor shape. | ||
| ImplicitGemm, | ||
| } | ||
|
|
||
| impl Default for Conv2dStrategy { | ||
| fn default() -> Self { | ||
| // if autotune is enabled, default to autotune | ||
| #[cfg(feature = "autotune")] | ||
| return Conv2dStrategy::Autotune; | ||
|
|
||
| // if autotune is disabled, default to the more memory-conservative algorithm | ||
| #[cfg(not(feature = "autotune"))] | ||
| Conv2dStrategy::Direct | ||
| } | ||
| } | ||
|
|
||
| /// The strategy to be used when launching a conv_transpose kernel. | ||
| pub enum ConvTranspose2dStrategy { | ||
| /// A simple direct convolution. | ||
| Direct, | ||
| #[cfg(feature = "autotune")] | ||
| /// Using autotune to choose the best kernel based on runtime information. | ||
| Autotune, | ||
| /// GEMM (im2col) based implementation of convolution. Significantly increased memory usage. | ||
| Gemm, | ||
| } | ||
|
|
||
| impl Default for ConvTranspose2dStrategy { | ||
| fn default() -> Self { | ||
| // if autotune is enabled, default to autotune | ||
| #[cfg(feature = "autotune")] | ||
| return ConvTranspose2dStrategy::Autotune; | ||
|
|
||
| // if autotune is disabled, default to the more memory-conservative algorithm | ||
| #[cfg(not(feature = "autotune"))] | ||
| ConvTranspose2dStrategy::Direct | ||
| } | ||
| } | ||
|
|
||
| /// Perform a 2D convolution with the given strategy | ||
| /// | ||
| /// * `input` - The input feature map | ||
| /// * `weight` - The weights (filter) applied to each kernel | ||
| /// * `bias` - The bias added to each channel | ||
| /// * `options` - The options to use for the convolution | ||
| /// * `strategy` - The convolution algorithm to use. Autotune will pick the fastest available option. | ||
| /// | ||
| pub fn conv2d<R: JitRuntime, E: FloatElement, I: IntElement>( | ||
| input: JitTensor<R, E, 4>, | ||
| weight: JitTensor<R, E, 4>, | ||
| bias: Option<JitTensor<R, E, 1>>, | ||
| options: ConvOptions<2>, | ||
| strategy: Conv2dStrategy, | ||
| ) -> JitTensor<R, E, 4> { | ||
| match strategy { | ||
| Conv2dStrategy::Direct => conv2d_direct::<R, E, I>(input, weight, bias, options), | ||
| #[cfg(feature = "autotune")] | ||
| Conv2dStrategy::Autotune => conv2d_autotune::<R, E, I>(input, weight, bias, options), | ||
| Conv2dStrategy::Gemm => conv2d_im2col::<R, E, I>(input, weight, bias, options), | ||
| Conv2dStrategy::ImplicitGemm => { | ||
| conv2d_implicit_gemm::<R, E, I>(input, weight, bias, options) | ||
| } | ||
| } | ||
| } | ||
|
|
||
| /// Perform a 2D convolution with the given strategy | ||
| /// | ||
| /// * `input` - The input feature map | ||
| /// * `weight` - The weights (filter) applied to each kernel | ||
| /// * `bias` - The bias added to each channel | ||
| /// * `options` - The options to use for the convolution | ||
| /// * `strategy` - The convolution algorithm to use. Autotune will pick the fastest available option. | ||
| /// | ||
| pub fn conv_transpose2d<R: JitRuntime, E: FloatElement, I: IntElement>( | ||
| input: JitTensor<R, E, 4>, | ||
| weight: JitTensor<R, E, 4>, | ||
| bias: Option<JitTensor<R, E, 1>>, | ||
| options: ConvTransposeOptions<2>, | ||
| strategy: ConvTranspose2dStrategy, | ||
| ) -> JitTensor<R, E, 4> { | ||
| match strategy { | ||
| ConvTranspose2dStrategy::Direct => { | ||
| conv_transpose2d_direct::<R, E, I>(input, weight, bias, options) | ||
| } | ||
| #[cfg(feature = "autotune")] | ||
| ConvTranspose2dStrategy::Autotune => { | ||
| conv_transpose2d_autotune::<R, E, I>(input, weight, bias, options) | ||
| } | ||
| ConvTranspose2dStrategy::Gemm => { | ||
| conv_transpose2d_col2im::<R, E, I>(input, weight, bias, options) | ||
| } | ||
| } | ||
| } | ||
|
|
||
| #[allow(unused)] | ||
| pub(crate) fn debug_data<R: JitRuntime, E: JitElement, const D: usize>( | ||
| tensor: JitTensor<R, E, D>, | ||
| ) -> TensorData { | ||
| let bytes = tensor.client.read(tensor.handle.binding()); | ||
| TensorData::new(E::from_bytes(&bytes).to_vec(), tensor.shape) | ||
| } |
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