Set the algo.

Cargo.toml

@@ -20,7 +20,7 @@ exclude = [
 resolver = "2"
 
 [workspace.package]
-version = "0.9.0-alpha.3"
+version = "0.9.0-alpha.2"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"
@@ -33,14 +33,14 @@ ab_glyph = "0.2.23"
 accelerate-src = { version = "0.3.2" }
 anyhow = { version = "1", features = ["backtrace"] }
 byteorder = "1.4.3"
-candle = { path = "./candle-core", package = "candle-core", version = "0.9.0-alpha.3" }
+candle = { path = "./candle-core", package = "candle-core", version = "0.9.0-alpha.2" }
-candle-datasets = { path = "./candle-datasets", version = "0.9.0-alpha.3" }
+candle-datasets = { path = "./candle-datasets", version = "0.9.0-alpha.2" }
-candle-flash-attn = { path = "./candle-flash-attn", version = "0.9.0-alpha.3" }
+candle-flash-attn = { path = "./candle-flash-attn", version = "0.9.0-alpha.2" }
-candle-kernels = { path = "./candle-kernels", version = "0.9.0-alpha.3" }
+candle-kernels = { path = "./candle-kernels", version = "0.9.0-alpha.2" }
-candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.9.0-alpha.3" }
+candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.9.0-alpha.2" }
-candle-nn = { path = "./candle-nn", version = "0.9.0-alpha.3" }
+candle-nn = { path = "./candle-nn", version = "0.9.0-alpha.2" }
-candle-onnx = { path = "./candle-onnx", version = "0.9.0-alpha.3" }
+candle-onnx = { path = "./candle-onnx", version = "0.9.0-alpha.2" }
-candle-transformers = { path = "./candle-transformers", version = "0.9.0-alpha.3" }
+candle-transformers = { path = "./candle-transformers", version = "0.9.0-alpha.2" }
 clap = { version = "4.2.4", features = ["derive"] }
 criterion = { version = "0.5.1", default-features=false }
 cudarc = { version = "0.15.1", features = ["std", "cublas", "cublaslt", "curand", "driver", "nvrtc", "f16", "cuda-version-from-build-system", "dynamic-linking"], default-features=false }

candle-core/examples/cuda_basics.rs

@@ -6,18 +6,28 @@ 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, (1024, 64, 1924), &device)?;
+    let x = Tensor::randn(0f32, 1.0, (8 * 4096, 8 * 4096), &device)?
-    let c = Tensor::randn(0f32, 1.0, (128, 64, 8), &device)?;
+        .to_dtype(candle_core::DType::BF16)?;
-    let _x1 = x.conv1d(&c, 0, 4, 1, 1)?;
+    candle_core::cuda::set_gemm_reduced_precision_f32(false);
+    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, Copy, PartialEq, Eq, Hash)]
+#[derive(Debug, Clone, PartialEq, Eq, Hash)]
 pub enum CudnnFwdAlgo {
     ImplicitGemm,
     ImplicitPrecompGemm,
@@ -152,19 +152,6 @@ 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()?;
@@ -188,7 +175,7 @@ impl Tensor {
             padding,
             stride,
             dilation,
-            cudnn_fwd_algo,
+            cudnn_fwd_algo: Some(CudnnFwdAlgo::ImplicitGemm),
         };
         if groups == 1 {
             self.conv1d_single_group(kernel, &params)
@@ -293,18 +280,6 @@ 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()?;
@@ -324,7 +299,7 @@ impl Tensor {
             padding,
             stride,
             dilation,
-            cudnn_fwd_algo,
+            cudnn_fwd_algo: None,
         };
         if groups == 1 {
             self.conv2d_single_group(kernel, &params)

candle-examples/examples/helium/main.rs

@@ -46,7 +46,7 @@ impl TextGeneration {
                 Sampling::ArgMax
             } else {
                 match (top_k, top_p) {
-                    (None, None) => Sampling::GumbelSoftmax { temperature },
+                    (None, None) => Sampling::All { temperature },
                     (Some(k), None) => Sampling::TopK { k, temperature },
                     (None, Some(p)) => Sampling::TopP { p, temperature },
                     (Some(k), Some(p)) => Sampling::TopKThenTopP { k, p, temperature },

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

@@ -133,7 +133,6 @@ 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

@@ -92,7 +92,6 @@ 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-flash-attn/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-flash-attn"
-version = "0.9.0-alpha.3"
+version = "0.9.0-alpha.2"
 edition = "2021"
 
 description = "Flash attention layer for the candle ML framework."
@@ -11,7 +11,7 @@ license = "MIT OR Apache-2.0"
 readme = "README.md"
 
 [dependencies]
-candle = { path = "../candle-core", features = ["cuda"], package = "candle-core", version = "0.9.0-alpha.3" }
+candle = { path = "../candle-core", features = ["cuda"], package = "candle-core", version = "0.9.0-alpha.2" }
 half = { version = "2.3.1", features = ["num-traits"] }
 
 [build-dependencies]

candle-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-kernels"
-version = "0.9.0-alpha.3"
+version = "0.9.0-alpha.2"
 edition = "2021"
 
 description = "CUDA kernels for Candle"

candle-metal-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-metal-kernels"
-version = "0.9.0-alpha.3"
+version = "0.9.0-alpha.2"
 edition = "2021"
 
 description = "Metal kernels for Candle"

candle-nn/src/conv.rs

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

@@ -31,7 +31,6 @@ pub mod ops;
 pub mod optim;
 pub mod rnn;
 pub mod rotary_emb;
-pub mod sampling;
 pub mod sequential;
 pub mod var_builder;
 pub mod var_map;

candle-nn/src/sampling.rs

@@ -1,20 +0,0 @@
-use candle::{Result, Tensor};
-
-/// Sample according to the Gumbel-Softmax distribution.
-pub fn gumbel_softmax<D: candle::shape::Dim>(
-    logits: &Tensor,
-    temperature: f64,
-    dim: D,
-) -> Result<Tensor> {
-    if temperature <= 0.0 {
-        logits.argmax(dim)
-    } else if temperature == 1.0 {
-        let minus_g = logits.rand_like(1e-7, 0.999)?.log()?.neg()?.log()?;
-        let sampled = (logits - minus_g)?.argmax(dim)?;
-        Ok(sampled)
-    } else {
-        let minus_g = logits.rand_like(1e-7, 0.999)?.log()?.neg()?.log()?;
-        let sampled = (logits + minus_g * (-temperature))?.argmax(dim)?;
-        Ok(sampled)
-    }
-}

candle-onnx/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-onnx"
-version = "0.9.0-alpha.3"
+version = "0.9.0-alpha.2"
 edition = "2021"
 
 description = "ONNX support for Candle"
@@ -10,8 +10,8 @@ categories = ["science"]
 license = "MIT OR Apache-2.0"
 
 [dependencies]
-candle = { path = "../candle-core", package = "candle-core", version = "0.9.0-alpha.3" }
+candle = { path = "../candle-core", package = "candle-core", version = "0.9.0-alpha.2" }
-candle-nn = { path = "../candle-nn", version = "0.9.0-alpha.3" }
+candle-nn = { path = "../candle-nn", version = "0.9.0-alpha.2" }
 prost = "0.12.1"
 
 [build-dependencies]

candle-transformers/src/generation/mod.rs

@@ -13,8 +13,6 @@ pub enum Sampling {
     TopK { k: usize, temperature: f64 },
     TopP { p: f64, temperature: f64 },
     TopKThenTopP { k: usize, p: f64, temperature: f64 },
-    // Note that the rng is not used for the Gumbel-Softmax sampling.
-    GumbelSoftmax { temperature: f64 },
 }
 
 pub struct LogitsProcessor {
@@ -51,11 +49,6 @@ impl LogitsProcessor {
         Ok(next_token)
     }
 
-    fn sample_gumbel_softmax(&mut self, logits: &Tensor, temperature: f64) -> Result<u32> {
-        let sampled = candle_nn::sampling::gumbel_softmax(logits, temperature, candle::D::Minus1)?;
-        sampled.to_vec0::<u32>()
-    }
-
     fn sample_multinomial(&mut self, prs: &Vec<f32>) -> Result<u32> {
         let distr = rand::distr::weighted::WeightedIndex::new(prs).map_err(Error::wrap)?;
         let next_token = distr.sample(&mut self.rng) as u32;
@@ -134,9 +127,6 @@ impl LogitsProcessor {
 
         let next_token = match &self.sampling {
             Sampling::ArgMax => self.sample_argmax(logits)?,
-            Sampling::GumbelSoftmax { temperature } => {
-                self.sample_gumbel_softmax(&logits, *temperature)?
-            }
             Sampling::All { temperature } => {
                 let prs = prs(*temperature)?;
                 self.sample_multinomial(&prs)?

candle-transformers/src/models/depth_anything_v2.rs

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

candle-transformers/src/models/encodec.rs

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

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

@@ -267,7 +267,6 @@ 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

@@ -68,7 +68,6 @@ 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"))?;
@@ -84,7 +83,6 @@ impl ResnetBlock2D {
                 padding: 0,
                 groups: 1,
                 dilation: 1,
-                cudnn_fwd_algo: None,
             };
             Some(conv2d(
                 in_channels,

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

@@ -248,14 +248,12 @@ 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

@@ -244,14 +244,12 @@ 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/tests/generation_tests.rs

@@ -54,25 +54,3 @@ fn sample_with_top_k() -> Result<()> {
     assert_eq!(token, 2);
     Ok(())
 }
-
-#[test]
-fn sample_gumbel() -> Result<()> {
-    let mut logits_process = LogitsProcessor::from_sampling(
-        42,
-        candle_transformers::generation::Sampling::GumbelSoftmax { temperature: 1.0 },
-    );
-    let logits = Tensor::new(&[-1.0, 0.0, 0.2, 1.0], &Device::Cpu)?;
-    let sm = candle_nn::ops::softmax(&logits, 0)?.to_vec1::<f64>()?;
-    let mut counts = vec![0f64; 4];
-    let samples = 100000;
-    for _ in 0..samples {
-        let token = logits_process.sample(&logits)?;
-        counts[token as usize] += 1f64 / samples as f64;
-    }
-    for i in 0..4 {
-        if (counts[i] - sm[i]).abs() > 0.05 {
-            panic!("pr mismatch {counts:?} {sm:?}");
-        }
-    }
-    Ok(())
-}

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

@@ -98,7 +98,6 @@ 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"))?;