Expose the cudnn algo in the conv ops. (#2892)

* Set the algo.

* Expose the cudnn preferred algo for conv ops.

candle-core/examples/cuda_basics.rs

@@ -6,28 +6,18 @@ extern crate intel_mkl_src;
 
 use anyhow::Result;
 use candle_core::{Device, Tensor};
-
+// xs: [1024, 64, 1924], c Tensor[dims 128, 64, 8; f32, cuda:0] Conv1dConfig { padding: 0, stride: 4, dilation: 1, groups: 1 }
 fn main() -> Result<()> {
     let device = Device::new_cuda(0)?;
-    let x = Tensor::randn(0f32, 1.0, (8 * 4096, 8 * 4096), &device)?
+    let x = Tensor::randn(0f32, 1.0, (1024, 64, 1924), &device)?;
-        .to_dtype(candle_core::DType::BF16)?;
+    let c = Tensor::randn(0f32, 1.0, (128, 64, 8), &device)?;
-    candle_core::cuda::set_gemm_reduced_precision_f32(false);
+    let _x1 = x.conv1d(&c, 0, 4, 1, 1)?;
-    candle_core::cuda::set_gemm_reduced_precision_bf16(false);
-    let _x1 = x.matmul(&x)?;
-    drop(_x1);
-    let start_time = std::time::Instant::now();
-    let _x1 = x.matmul(&x)?;
-    device.synchronize()?;
-    println!("fp32: {:?}", start_time.elapsed());
-    drop(_x1);
-    candle_core::cuda::set_gemm_reduced_precision_f32(true);
-    candle_core::cuda::set_gemm_reduced_precision_bf16(true);
-    let _x1 = x.matmul(&x)?;
-    drop(_x1);
-    let start_time = std::time::Instant::now();
-    let _x1 = x.matmul(&x)?;
-    device.synchronize()?;
-    println!("tf32: {:?}", start_time.elapsed());
     drop(_x1);
+    for _ in 0..20 {
+        let start_time = std::time::Instant::now();
+        let _x1 = x.conv1d(&c, 0, 4, 1, 1)?;
+        device.synchronize()?;
+        println!("conv1d: {:?}", start_time.elapsed());
+    }
     Ok(())
 }

candle-core/src/conv.rs

@@ -55,7 +55,7 @@ impl ParamsConvTranspose1D {
     }
 }
 
-#[derive(Debug, Clone, PartialEq, Eq, Hash)]
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
 pub enum CudnnFwdAlgo {
     ImplicitGemm,
     ImplicitPrecompGemm,
@@ -152,6 +152,19 @@ impl Tensor {
         stride: usize,
         dilation: usize,
         groups: usize,
+    ) -> Result<Self> {
+        self.conv1d_with_algo(kernel, padding, stride, dilation, groups, None)
+    }
+
+    /// Applies a 1D convolution over the input tensor.
+    pub fn conv1d_with_algo(
+        &self,
+        kernel: &Self,
+        padding: usize,
+        stride: usize,
+        dilation: usize,
+        groups: usize,
+        cudnn_fwd_algo: Option<CudnnFwdAlgo>,
     ) -> Result<Self> {
         let (c_out, c_in_k, k_size) = kernel.dims3()?;
         let (b_size, c_in, l_in) = self.dims3()?;
@@ -175,7 +188,7 @@ impl Tensor {
             padding,
             stride,
             dilation,
-            cudnn_fwd_algo: None,
+            cudnn_fwd_algo,
         };
         if groups == 1 {
             self.conv1d_single_group(kernel, &params)
@@ -280,6 +293,18 @@ impl Tensor {
         stride: usize,
         dilation: usize,
         groups: usize,
+    ) -> Result<Self> {
+        self.conv2d_with_algo(kernel, padding, stride, dilation, groups, None)
+    }
+
+    pub fn conv2d_with_algo(
+        &self,
+        kernel: &Self,
+        padding: usize,
+        stride: usize,
+        dilation: usize,
+        groups: usize,
+        cudnn_fwd_algo: Option<CudnnFwdAlgo>,
     ) -> Result<Self> {
         let (b_size, c_in, i_h, i_w) = self.dims4()?;
         let (c_out, c_in_k, k_h, k_w) = kernel.dims4()?;
@@ -299,7 +324,7 @@ impl Tensor {
             padding,
             stride,
             dilation,
-            cudnn_fwd_algo: None,
+            cudnn_fwd_algo,
         };
         if groups == 1 {
             self.conv2d_single_group(kernel, &params)

candle-examples/examples/yolo-v3/darknet.rs

@@ -133,6 +133,7 @@ fn conv(vb: VarBuilder, index: usize, p: usize, b: &Block) -> Result<(usize, Bl)
         padding,
         groups: 1,
         dilation: 1,
+        cudnn_fwd_algo: None,
     };
     let conv = if bias {
         conv2d(p, filters, size, conv_cfg, vb.pp(format!("conv_{index}")))?

candle-examples/examples/yolo-v8/model.rs

@@ -92,6 +92,7 @@ impl ConvBlock {
             stride,
             groups: 1,
             dilation: 1,
+            cudnn_fwd_algo: None,
         };
         let bn = batch_norm(c2, 1e-3, vb.pp("bn"))?;
         let conv = conv2d_no_bias(c1, c2, k, cfg, vb.pp("conv"))?.absorb_bn(&bn)?;

candle-nn/src/conv.rs

@@ -1,6 +1,6 @@
 //! Convolution Layers.
 use crate::BatchNorm;
-use candle::{Result, Tensor};
+use candle::{conv::CudnnFwdAlgo, Result, Tensor};
 
 #[derive(Debug, Clone, Copy, PartialEq, Eq)]
 pub struct Conv1dConfig {
@@ -8,6 +8,7 @@ pub struct Conv1dConfig {
     pub stride: usize,
     pub dilation: usize,
     pub groups: usize,
+    pub cudnn_fwd_algo: Option<CudnnFwdAlgo>,
 }
 
 impl Default for Conv1dConfig {
@@ -17,6 +18,7 @@ impl Default for Conv1dConfig {
             stride: 1,
             dilation: 1,
             groups: 1,
+            cudnn_fwd_algo: None,
         }
     }
 }
@@ -52,12 +54,13 @@ impl Conv1d {
 
 impl crate::Module for Conv1d {
     fn forward(&self, x: &Tensor) -> Result<Tensor> {
-        let x = x.conv1d(
+        let x = x.conv1d_with_algo(
             &self.weight,
             self.config.padding,
             self.config.stride,
             self.config.dilation,
             self.config.groups,
+            self.config.cudnn_fwd_algo,
         )?;
         match &self.bias {
             None => Ok(x),
@@ -147,6 +150,7 @@ pub struct Conv2dConfig {
     pub stride: usize,
     pub dilation: usize,
     pub groups: usize,
+    pub cudnn_fwd_algo: Option<CudnnFwdAlgo>,
 }
 
 impl Default for Conv2dConfig {
@@ -156,6 +160,7 @@ impl Default for Conv2dConfig {
             stride: 1,
             dilation: 1,
             groups: 1,
+            cudnn_fwd_algo: None,
         }
     }
 }
@@ -211,12 +216,13 @@ impl Conv2d {
 
 impl crate::Module for Conv2d {
     fn forward(&self, x: &Tensor) -> Result<Tensor> {
-        let x = x.conv2d(
+        let x = x.conv2d_with_algo(
             &self.weight,
             self.config.padding,
             self.config.stride,
             self.config.dilation,
             self.config.groups,
+            self.config.cudnn_fwd_algo,
         )?;
         match &self.bias {
             None => Ok(x),

candle-transformers/src/models/depth_anything_v2.rs

@@ -124,6 +124,7 @@ impl ResidualConvUnit {
             stride: 1,
             dilation: 1,
             groups: 1,
+            cudnn_fwd_algo: None,
         };
         let conv1 = conv2d(
             conf.num_features,
@@ -208,6 +209,7 @@ impl FeatureFusionBlock {
             stride: 1,
             dilation: 1,
             groups: 1,
+            cudnn_fwd_algo: None,
         };
         let output_conv = conv2d(
             conf.num_features,
@@ -258,6 +260,7 @@ impl Scratch {
             stride: 1,
             dilation: 1,
             groups: 1,
+            cudnn_fwd_algo: None,
         };
 
         let layer1_rn = conv2d_no_bias(
@@ -319,6 +322,7 @@ impl Scratch {
             stride: 1,
             dilation: 1,
             groups: 1,
+            cudnn_fwd_algo: None,
         };
         let output_conv1 = conv2d(
             conf.num_features,
@@ -425,6 +429,7 @@ impl DPTHead {
                     stride: 2,
                     dilation: 1,
                     groups: 1,
+                    cudnn_fwd_algo: None,
                 },
                 vb.pp("resize_layers").pp("3"),
             )?),

candle-transformers/src/models/encodec.rs

@@ -468,6 +468,7 @@ impl EncodecConv1d {
                     stride,
                     groups: 1,
                     dilation: 1,
+                    cudnn_fwd_algo: None,
                 },
                 vb.pp("conv"),
             )?,

candle-transformers/src/models/mimi/conv.rs

@@ -267,6 +267,7 @@ impl StreamableConv1d {
             stride,
             dilation,
             groups,
+            cudnn_fwd_algo: None,
         };
         let conv = NormConv1d::new(in_c, out_c, k_size, causal, norm, bias, cfg, vb)?;
         if k_size < stride {

candle-transformers/src/models/stable_diffusion/resnet.rs

@@ -68,6 +68,7 @@ impl ResnetBlock2D {
             padding: 1,
             groups: 1,
             dilation: 1,
+            cudnn_fwd_algo: None,
         };
         let norm1 = nn::group_norm(config.groups, in_channels, config.eps, vs.pp("norm1"))?;
         let conv1 = conv2d(in_channels, out_channels, 3, conv_cfg, vs.pp("conv1"))?;
@@ -83,6 +84,7 @@ impl ResnetBlock2D {
                 padding: 0,
                 groups: 1,
                 dilation: 1,
+                cudnn_fwd_algo: None,
             };
             Some(conv2d(
                 in_channels,

candle-transformers/src/models/whisper/model.rs

@@ -248,12 +248,14 @@ impl AudioEncoder {
             stride: 1,
             groups: 1,
             dilation: 1,
+            cudnn_fwd_algo: None,
         };
         let cfg2 = Conv1dConfig {
             padding: 1,
             stride: 2,
             groups: 1,
             dilation: 1,
+            cudnn_fwd_algo: None,
         };
         let conv1 = conv1d(cfg.num_mel_bins, n_state, 3, cfg1, vb.pp("conv1"))?;
         let conv2 = conv1d(n_state, n_state, 3, cfg2, vb.pp("conv2"))?;

candle-transformers/src/models/whisper/quantized_model.rs

@@ -244,12 +244,14 @@ impl AudioEncoder {
             stride: 1,
             groups: 1,
             dilation: 1,
+            cudnn_fwd_algo: None,
         };
         let cfg2 = Conv1dConfig {
             padding: 1,
             stride: 2,
             groups: 1,
             dilation: 1,
+            cudnn_fwd_algo: None,
         };
         let conv1 = conv1d(cfg.num_mel_bins, n_state, 3, cfg1, vb.pp("conv1"))?;
         let conv2 = conv1d(n_state, n_state, 3, cfg2, vb.pp("conv2"))?;

candle-wasm-examples/yolo/src/model.rs

@@ -98,6 +98,7 @@ impl ConvBlock {
             stride,
             groups: 1,
             dilation: 1,
+            cudnn_fwd_algo: None,
         };
         let conv = conv2d_no_bias(c1, c2, k, cfg, vb.pp("conv"))?;
         let bn = batch_norm(c2, 1e-3, vb.pp("bn"))?;