Initial commit

.gitignore

+.idea
+target
+Cargo.lock

Cargo.toml

+[package]
+name = "candle-cublaslt"
+version = "0.2.2"
+edition = "2021"
+
+description = "CUBLASLt gemm for the candle ML framework."
+
+[dependencies]
+#candle = { version = "*", package = "candle-core", features = ["cuda"]}
+candle = { git = "ssh://git@developer.sourcefind.cn:yongshk/candle.git", branch = "update_flash_nodebug_cutlass",  package = "candle-core", features = ["cuda"]}
+cudarc = { git = "ssh://git@developer.sourcefind.cn:yongshk/cudarc.git", branch = "specific-main-nodebug", default-features = false, features = [ "cublaslt", "f16" ]}
+#cudarc = { git = "https://github.com/coreylowman/cudarc", rev = "c388e724af93a3e8fbe484f5ded2d8b3c1badd8e", default-features = false, features = [ "cublaslt", "f16" ]}
+half = { version = "2.3.1", features = ["num-traits"] }
+
+[patch.crates-io]
+#cudarc = { git = "ssh://git@developer.sourcefind.cn:yongshk/cudarc.git", branch = "specific-main-nodebug", default-features = false, features = [ "cublaslt", "f16" ]}
+cudarc = { git = "https://github.com/coreylowman/cudarc", rev = "c388e724af93a3e8fbe484f5ded2d8b3c1badd8e", default-features = false, features = [ "cublaslt", "f16" ]}
+
+[features]
+default = []
+static-linking = ["cudarc/static-linking"]

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+IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.

README.md

+# Candle CublasLt Matmul Layer
+
+CublasLt Matmul operation for the Candle ML framework.
+Allows for bias and Relu/Gelu fusing.
\ No newline at end of file

src/lib.rs

+pub use cudarc::cublaslt::Activation;
+use std::ffi::c_int;
+
+use candle::backend::BackendStorage;
+use candle::cuda_backend::WrapErr;
+use candle::{CpuStorage, Device, Layout, Result, Shape, Storage, Tensor};
+use half::{bf16, f16};
+use std::sync::Arc;
+
+use cudarc::cublaslt::{CudaBlasLT, Matmul, MatmulConfig};
+
+#[derive(Debug, Clone)]
+pub struct CublasLt(Arc<CudaBlasLT>);
+
+impl CublasLt {
+    pub fn new(device: &Device) -> Result<Self> {
+        let dev = match &*device {
+            Device::Cuda(d) => d,
+            _ => candle::bail!("`device` must be a `cuda` device"),
+        };
+
+        let inner = CudaBlasLT::new(dev.cuda_device()).unwrap();
+
+        Ok(Self(Arc::new(inner)))
+    }
+}
+
+pub struct CublasLTMatmul {
+    pub cublaslt: Arc<CudaBlasLT>,
+    pub act: Option<Activation>,
+    pub c: Option<Tensor>,
+    pub alpha: Option<f32>,
+    pub beta: Option<f32>,
+}
+
+impl CublasLTMatmul {
+    pub fn fwd_f16(
+        &self,
+        a: &candle::CudaStorage,
+        a_l: &Layout,
+        b: &candle::CudaStorage,
+        b_l: &Layout,
+        bias: Option<&candle::CudaStorage>,
+        bias_l: Option<&Layout>,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        //println!("CublasLTMatmul fwd_f16 1");
+        let dev = a.device();
+
+        // Assume TN
+        let (m, k) = a_l.shape().dims2()?;
+
+        let (n, b_1) = b_l.shape().dims2()?;
+
+        if b_1 != k {
+            candle::bail!("This layer only supports TN layout");
+        }
+
+        let lda = k;
+        let ldb = k;
+        let ldc = m;
+
+        let out_shape = Shape::from((n, m));
+
+        let a = a.as_cuda_slice::<f16>()?.slice(a_l.start_offset()..);
+        let b = b.as_cuda_slice::<f16>()?.slice(b_l.start_offset()..);
+
+        let bias = if let (Some(bias), Some(bias_l)) = (bias, bias_l) {
+            if bias_l.shape().dims1()? != m {
+                candle::bail!("Bias does not have the correct shape");
+            }
+
+            Some(bias.as_cuda_slice::<f16>()?.slice(bias_l.start_offset()..))
+        } else {
+            None
+        };
+
+        let mut out = if let Some(c) = &self.c {
+            let (c, c_l) = c.storage_and_layout();
+            let c = match &*c {
+                Storage::Cuda(storage) => storage.as_cuda_slice::<f16>()?,
+                _ => candle::bail!("`c` must be a cuda tensor"),
+            };
+            match c_l.contiguous_offsets() {
+                Some((o1, o2)) => {
+                    if o1 != 0 {
+                        candle::bail!("`c` start offset must be 0");
+                    }
+                    if o2 != out_shape.elem_count() {
+                        candle::bail!("`c` end offset must be {}", out_shape.elem_count())
+                    }
+                }
+                None => candle::bail!("`c` has to be contiguous"),
+            };
+            if c_l.shape().dims2()? != (n, m) {
+                candle::bail!("`c` does not have the correct shape");
+            }
+
+            c.clone()
+        } else {
+            // Allocate out tensor
+            unsafe { dev.alloc::<f16>(out_shape.elem_count()).w()? }
+        };
+
+        let config = MatmulConfig {
+            transa: true,
+            transb: false,
+            m: m as u64,
+            n: n as u64,
+            k: k as u64,
+            alpha: self.alpha.unwrap_or(1.0),
+            lda: lda as i64,
+            ldb: ldb as i64,
+            beta: self.beta.unwrap_or(0.0),
+            ldc: ldc as i64,
+            stride_a: None,
+            stride_b: None,
+            stride_c: None,
+            stride_bias: None,
+            batch_size: None,
+        };
+
+        unsafe {
+            self.cublaslt
+                .matmul(config, &a, &b, &mut out, bias.as_ref(), self.act.as_ref())
+                .map_err(|e| candle::Error::Cuda(Box::new(e)))?;
+        }
+
+        let out = candle::CudaStorage::wrap_cuda_slice(out, dev.clone());
+
+        Ok((out, out_shape))
+    }
+
+    pub fn fwd_bf16(
+        &self,
+        a: &candle::CudaStorage,
+        a_l: &Layout,
+        b: &candle::CudaStorage,
+        b_l: &Layout,
+        bias: Option<&candle::CudaStorage>,
+        bias_l: Option<&Layout>,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        let dev = a.device();
+
+        // Assume TN
+        let (m, k) = a_l.shape().dims2()?;
+
+        let (n, b_1) = b_l.shape().dims2()?;
+
+        if b_1 != k {
+            candle::bail!("This layer only supports TN layout");
+        }
+
+        let lda = k;
+        let ldb = k;
+        let ldc = m;
+
+        let out_shape = Shape::from((n, m));
+
+        let a = a.as_cuda_slice::<bf16>()?.slice(a_l.start_offset()..);
+        let b = b.as_cuda_slice::<bf16>()?.slice(b_l.start_offset()..);
+
+        let bias = if let (Some(bias), Some(bias_l)) = (bias, bias_l) {
+            if bias_l.shape().dims1()? != m {
+                candle::bail!("Bias does not have the correct shape");
+            }
+
+            Some(bias.as_cuda_slice::<bf16>()?.slice(bias_l.start_offset()..))
+        } else {
+            None
+        };
+
+        let mut out = if let Some(c) = &self.c {
+            let (c, c_l) = c.storage_and_layout();
+            let c = match &*c {
+                Storage::Cuda(storage) => storage.as_cuda_slice::<bf16>()?,
+                _ => candle::bail!("`c` must be a cuda tensor"),
+            };
+            match c_l.contiguous_offsets() {
+                Some((o1, o2)) => {
+                    if o1 != 0 {
+                        candle::bail!("`c` start offset must be 0");
+                    }
+                    if o2 != out_shape.elem_count() {
+                        candle::bail!("`c` end offset must be {}", out_shape.elem_count())
+                    }
+                }
+                None => candle::bail!("`c` has to be contiguous"),
+            };
+            if c_l.shape().dims2()? != (n, m) {
+                candle::bail!("`c` does not have the correct shape");
+            }
+
+            c.clone()
+        } else {
+            // Allocate out tensor
+            unsafe { dev.alloc::<bf16>(out_shape.elem_count()).w()? }
+        };
+
+        let config = MatmulConfig {
+            transa: true,
+            transb: false,
+            m: m as u64,
+            n: n as u64,
+            k: k as u64,
+            alpha: self.alpha.unwrap_or(1.0),
+            lda: lda as i64,
+            ldb: ldb as i64,
+            beta: self.beta.unwrap_or(0.0),
+            ldc: ldc as i64,
+            stride_a: None,
+            stride_b: None,
+            stride_c: None,
+            stride_bias: None,
+            batch_size: None,
+        };
+
+        unsafe {
+            self.cublaslt
+                .matmul(config, &a, &b, &mut out, bias.as_ref(), self.act.as_ref())
+                .map_err(|e| candle::Error::Cuda(Box::new(e)))?;
+        }
+
+        let out = candle::CudaStorage::wrap_cuda_slice(out, dev.clone());
+
+        Ok((out, out_shape))
+    }
+
+    pub fn fwd_f32(
+        &self,
+        a: &candle::CudaStorage,
+        a_l: &Layout,
+        b: &candle::CudaStorage,
+        b_l: &Layout,
+        bias: Option<&candle::CudaStorage>,
+        bias_l: Option<&Layout>,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        let dev = a.device();
+
+        // Assume TN
+        let (m, k) = a_l.shape().dims2()?;
+
+        let (n, b_1) = b_l.shape().dims2()?;
+
+        if b_1 != k {
+            candle::bail!("This layer only supports TN layout");
+        }
+
+        let lda = k;
+        let ldb = k;
+        let ldc = m;
+
+        let out_shape = Shape::from((n, m));
+
+        let a = a.as_cuda_slice::<f32>()?.slice(a_l.start_offset()..);
+        let b = b.as_cuda_slice::<f32>()?.slice(b_l.start_offset()..);
+
+        let bias = if let (Some(bias), Some(bias_l)) = (bias, bias_l) {
+            if bias_l.shape().dims1()? != m {
+                candle::bail!("Bias does not have the correct shape");
+            }
+
+            Some(bias.as_cuda_slice::<f32>()?.slice(bias_l.start_offset()..))
+        } else {
+            None
+        };
+
+        let mut out = if let Some(c) = &self.c {
+            let (c, c_l) = c.storage_and_layout();
+            let c = match &*c {
+                Storage::Cuda(storage) => storage.as_cuda_slice::<f32>()?,
+                _ => candle::bail!("`c` must be a cuda tensor"),
+            };
+            match c_l.contiguous_offsets() {
+                Some((o1, o2)) => {
+                    if o1 != 0 {
+                        candle::bail!("`c` start offset must be 0");
+                    }
+                    if o2 != out_shape.elem_count() {
+                        candle::bail!("`c` end offset must be {}", out_shape.elem_count())
+                    }
+                }
+                None => candle::bail!("`c` has to be contiguous"),
+            };
+            if c_l.shape().dims2()? != (n, m) {
+                candle::bail!("`c` does not have the correct shape");
+            }
+
+            c.clone()
+        } else {
+            // Allocate out tensor
+            unsafe { dev.alloc::<f32>(out_shape.elem_count()).w()? }
+        };
+
+        let config = MatmulConfig {
+            transa: true,
+            transb: false,
+            m: m as u64,
+            n: n as u64,
+            k: k as u64,
+            alpha: self.alpha.unwrap_or(1.0),
+            lda: lda as i64,
+            ldb: ldb as i64,
+            beta: self.beta.unwrap_or(0.0),
+            ldc: ldc as i64,
+            stride_a: None,
+            stride_b: None,
+            stride_c: None,
+            stride_bias: None,
+            batch_size: None,
+        };
+
+        unsafe {
+            self.cublaslt
+                .matmul(config, &a, &b, &mut out, bias.as_ref(), self.act.as_ref())
+                .map_err(|e| candle::Error::Cuda(Box::new(e)))?;
+        }
+
+        let out = candle::CudaStorage::wrap_cuda_slice(out, dev.clone());
+
+        Ok((out, out_shape))
+    }
+}
+
+impl candle::CustomOp2 for CublasLTMatmul {
+    fn name(&self) -> &'static str {
+        "cublaslt-matmul"
+    }
+
+    fn cpu_fwd(
+        &self,
+        _: &CpuStorage,
+        _: &Layout,
+        _: &CpuStorage,
+        _: &Layout,
+    ) -> Result<(CpuStorage, Shape)> {
+        candle::bail!("no cpu support for cublaslt-matmul")
+    }
+
+    fn cuda_fwd(
+        &self,
+        a: &candle::CudaStorage,
+        a_l: &Layout,
+        b: &candle::CudaStorage,
+        b_l: &Layout,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        //println!("candle-cublast src/lib.rs:345 cuda_fwd11");
+        match a.dtype() {
+            candle::DType::F16 => self.fwd_f16(a, a_l, b, b_l, None, None),
+            candle::DType::BF16 => self.fwd_bf16(a, a_l, b, b_l, None, None),
+            candle::DType::F32 => self.fwd_f32(a, a_l, b, b_l, None, None),
+            dt => candle::bail!("cublaslt-matmul is only supported for f16/bf16/f32 ({dt:?})"),
+        }
+    }
+}
+
+impl candle::CustomOp3 for CublasLTMatmul {
+    fn name(&self) -> &'static str {
+        "cublaslt-matmul-add"
+    }
+
+    fn cpu_fwd(
+        &self,
+        _: &CpuStorage,
+        _: &Layout,
+        _: &CpuStorage,
+        _: &Layout,
+        _: &CpuStorage,
+        _: &Layout,
+    ) -> Result<(CpuStorage, Shape)> {
+        candle::bail!("no cpu support for cublaslt-matmul")
+    }
+
+    fn cuda_fwd(
+        &self,
+        a: &candle::CudaStorage,
+        a_l: &Layout,
+        b: &candle::CudaStorage,
+        b_l: &Layout,
+        bias: &candle::CudaStorage,
+        bias_l: &Layout,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        //println!("candle-cublast src/lib.rs:381 cuda_fwd22");
+        match a.dtype() {
+            candle::DType::F16 => self.fwd_f16(a, a_l, b, b_l, Some(bias), Some(bias_l)),
+            candle::DType::BF16 => self.fwd_bf16(a, a_l, b, b_l, Some(bias), Some(bias_l)),
+            candle::DType::F32 => self.fwd_f32(a, a_l, b, b_l, Some(bias), Some(bias_l)),
+            dt => candle::bail!("cublaslt-matmul is only supported for f16/bf16/f32 ({dt:?})"),
+        }
+    }
+}
+
+/// Fused matmul + add + Relu/Gelu activation using CublasLt
+///
+/// # Arguments
+///
+/// * `a` - Input tensor of size MxK
+/// * `b` - Input tensor of size NxK
+/// * `out` - Optional Output tensor of size NxK.
+///           If set and beta != 0, will be added to the end result of A*B before `act`
+/// * `alpha` - Optional scaling factor for A*B
+/// * `beta` - Optional scaling factor for C
+/// * `bias` - Optional bias tensor of size M
+/// * `act` - Optional Gelu or Relu activation. If set, will be added to the end result
+/// * `cublaslt` - CublasLt handle
+///
+/// The resulting tensor is of shape NxM
+pub fn fused_matmul(
+    a: &Tensor,
+    b: &Tensor,
+    out: Option<&Tensor>,
+    alpha: Option<f32>,
+    beta: Option<f32>,
+    bias: Option<&Tensor>,
+    act: Option<Activation>,
+    cublaslt: CublasLt,
+) -> Result<Tensor> {
+    let op = CublasLTMatmul {
+        act,
+        cublaslt: cublaslt.0,
+        c: out.cloned(),
+        alpha,
+        beta,
+    };
+
+    if let Some(bias) = bias {
+        a.apply_op3(&b, &bias, op)
+    } else {
+        a.apply_op2(&b, op)
+    }
+}
+
+pub struct CublasLTBatchMatmul {
+    pub cublaslt: Arc<CudaBlasLT>,
+    pub act: Option<Activation>,
+    pub c: Option<Tensor>,
+    pub alpha: Option<f32>,
+    pub beta: Option<f32>,
+}
+
+impl CublasLTBatchMatmul {
+    pub fn fwd_f16(
+        &self,
+        a: &candle::CudaStorage,
+        a_l: &Layout,
+        b: &candle::CudaStorage,
+        b_l: &Layout,
+        bias: Option<&candle::CudaStorage>,
+        bias_l: Option<&Layout>,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        //println!("CublasLTBatchMatmul fwd_f16 1");
+        let dev = a.device();
+        //println!("CublasLTBatchMatmul fwd_f16 2");
+        // Assume TN
+        let (batch_size, m, k) = a_l.shape().dims3()?;
+        let (b_0, n, b_2) = b_l.shape().dims3()?;
+        //println!("CublasLTBatchMatmul fwd_f16 3");
+        if b_2 != k {
+            candle::bail!("This layer only supports TN layout");
+        }
+
+        if b_0 != batch_size {
+            candle::bail!("`b` must have the same batch size as `a`")
+        }
+
+        let lda = k;
+        let ldb = k;
+        let ldc = m;
+
+        let out_shape = Shape::from((batch_size, n, m));
+        //println!("CublasLTBatchMatmul fwd_f16 4");
+        let a = a.as_cuda_slice::<f16>()?.slice(a_l.start_offset()..);
+        let b = b.as_cuda_slice::<f16>()?.slice(b_l.start_offset()..);
+        //println!("CublasLTBatchMatmul fwd_f16 5");
+        let bias = if let (Some(bias), Some(bias_l)) = (bias, bias_l) {
+            if bias_l.shape().dims1()? != m {
+                candle::bail!("Bias does not have the correct shape");
+            }
+
+            Some(bias.as_cuda_slice::<f16>()?.slice(bias_l.start_offset()..))
+        } else {
+            None
+        };
+        //println!("CublasLTBatchMatmul fwd_f16 6");
+        let (mut out, stride_c) = if let Some(c) = &self.c {
+            let (c, c_l) = c.storage_and_layout();
+            let c = match &*c {
+                Storage::Cuda(storage) => storage.as_cuda_slice::<f16>()?,
+                _ => candle::bail!("`c` must be a cuda tensor"),
+            };
+            match c_l.contiguous_offsets() {
+                Some((o1, o2)) => {
+                    if o1 != 0 {
+                        candle::bail!("`c` start offset must be 0");
+                    }
+                    if o2 != out_shape.elem_count() {
+                        candle::bail!("`c` end offset must be {}", out_shape.elem_count())
+                    }
+                }
+                None => candle::bail!("`c` has to be contiguous"),
+            };
+
+            if c_l.shape().dims3()? != (batch_size, n, m) {
+                candle::bail!("`c` does not have the correct shape");
+            }
+
+            // Set beta to 0.0 if it is not set
+            (c.clone(), c_l.stride()[0])
+        } else {
+            // Allocate out tensor
+            (
+                unsafe { dev.alloc::<f16>(out_shape.elem_count()).w()? },
+                (n * m),
+            )
+        };
+        //println!("CublasLTBatchMatmul fwd_f16 7");
+        let config = MatmulConfig {
+            transa: true,
+            transb: false,
+            m: m as u64,
+            n: n as u64,
+            k: k as u64,
+            alpha: self.alpha.unwrap_or(1.0),
+            lda: lda as i64,
+            ldb: ldb as i64,
+            beta: self.beta.unwrap_or(0.0),
+            ldc: ldc as i64,
+            stride_a: Some(a_l.stride()[0] as i64),
+            stride_b: Some(b_l.stride()[0] as i64),
+            stride_c: Some(stride_c as i64),
+            stride_bias: None,
+            batch_size: Some(batch_size as c_int),
+        };
+        //println!("CublasLTBatchMatmul fwd_f16 8");
+        unsafe {
+            
+            self.cublaslt
+                .matmul(config, &a, &b, &mut out, bias.as_ref(), self.act.as_ref())
+                .map_err(|e| candle::Error::Cuda(Box::new(e)))?;
+            
+            /*
+            self.cublaslt
+                .matmul(config, &a, &b, &mut out, bias.as_ref(), self.act.as_ref())
+                .map_err(|e| {
+                    // 打印错误
+                    eprintln!("An error occurred during matrix multiplication: {}", e);
+                    // 转换错误类型
+                    candle::Error::Cuda(Box::new(e))
+                });
+            */
+        }
+        //println!("CublasLTBatchMatmul fwd_f16 9");
+        let out = candle::CudaStorage::wrap_cuda_slice(out, dev.clone());
+        //println!("CublasLTBatchMatmul fwd_f16 10");
+        Ok((out, out_shape))
+    }
+
+    pub fn fwd_bf16(
+        &self,
+        a: &candle::CudaStorage,
+        a_l: &Layout,
+        b: &candle::CudaStorage,
+        b_l: &Layout,
+        bias: Option<&candle::CudaStorage>,
+        bias_l: Option<&Layout>,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        let dev = a.device();
+
+        // Assume TN
+        let (batch_size, m, k) = a_l.shape().dims3()?;
+        let (b_0, n, b_2) = b_l.shape().dims3()?;
+
+        if b_2 != k {
+            candle::bail!("This layer only supports TN layout");
+        }
+
+        if b_0 != batch_size {
+            candle::bail!("`b` must have the same batch size as `a`")
+        }
+
+        let lda = k;
+        let ldb = k;
+        let ldc = m;
+
+        let out_shape = Shape::from((batch_size, n, m));
+
+        let a = a.as_cuda_slice::<bf16>()?.slice(a_l.start_offset()..);
+        let b = b.as_cuda_slice::<bf16>()?.slice(b_l.start_offset()..);
+
+        let bias = if let (Some(bias), Some(bias_l)) = (bias, bias_l) {
+            if bias_l.shape().dims1()? != m {
+                candle::bail!("Bias does not have the correct shape");
+            }
+
+            Some(bias.as_cuda_slice::<bf16>()?.slice(bias_l.start_offset()..))
+        } else {
+            None
+        };
+
+        let (mut out, stride_c) = if let Some(c) = &self.c {
+            let (c, c_l) = c.storage_and_layout();
+            let c = match &*c {
+                Storage::Cuda(storage) => storage.as_cuda_slice::<bf16>()?,
+                _ => candle::bail!("`c` must be a cuda tensor"),
+            };
+            match c_l.contiguous_offsets() {
+                Some((o1, o2)) => {
+                    if o1 != 0 {
+                        candle::bail!("`c` start offset must be 0");
+                    }
+                    if o2 != out_shape.elem_count() {
+                        candle::bail!("`c` end offset must be {}", out_shape.elem_count())
+                    }
+                }
+                None => candle::bail!("`c` has to be contiguous"),
+            };
+
+            if c_l.shape().dims3()? != (batch_size, n, m) {
+                candle::bail!("`c` does not have the correct shape");
+            }
+
+            // Set beta to 0.0 if it is not set
+            (c.clone(), c_l.stride()[0])
+        } else {
+            // Allocate out tensor
+            (
+                unsafe { dev.alloc::<bf16>(out_shape.elem_count()).w()? },
+                (n * m),
+            )
+        };
+
+        let config = MatmulConfig {
+            transa: true,
+            transb: false,
+            m: m as u64,
+            n: n as u64,
+            k: k as u64,
+            alpha: self.alpha.unwrap_or(1.0),
+            lda: lda as i64,
+            ldb: ldb as i64,
+            beta: self.beta.unwrap_or(0.0),
+            ldc: ldc as i64,
+            stride_a: Some(a_l.stride()[0] as i64),
+            stride_b: Some(b_l.stride()[0] as i64),
+            stride_c: Some(stride_c as i64),
+            stride_bias: None,
+            batch_size: Some(batch_size as c_int),
+        };
+
+        unsafe {
+            self.cublaslt
+                .matmul(config, &a, &b, &mut out, bias.as_ref(), self.act.as_ref())
+                .map_err(|e| candle::Error::Cuda(Box::new(e)))?;
+        }
+
+        let out = candle::CudaStorage::wrap_cuda_slice(out, dev.clone());
+
+        Ok((out, out_shape))
+    }
+
+    pub fn fwd_f32(
+        &self,
+        a: &candle::CudaStorage,
+        a_l: &Layout,
+        b: &candle::CudaStorage,
+        b_l: &Layout,
+        bias: Option<&candle::CudaStorage>,
+        bias_l: Option<&Layout>,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        //println!("CublasLTBatchMatmul fwd_f32 1");
+        let dev = a.device();
+
+        // Assume TN
+        let (batch_size, m, k) = a_l.shape().dims3()?;
+        let (b_0, n, b_2) = b_l.shape().dims3()?;
+
+        if b_2 != k {
+            candle::bail!("This layer only supports TN layout");
+        }
+
+        if b_0 != batch_size {
+            candle::bail!("`b` must have the same batch size as `a`")
+        }
+        //println!("CublasLTBatchMatmul fwd_f32 2");
+        let lda = k;
+        let ldb = k;
+        let ldc = m;
+
+        let out_shape = Shape::from((batch_size, n, m));
+        //println!("CublasLTBatchMatmul fwd_f32 3");
+        let a = a.as_cuda_slice::<f32>()?.slice(a_l.start_offset()..);
+        let b = b.as_cuda_slice::<f32>()?.slice(b_l.start_offset()..);
+        //println!("CublasLTBatchMatmul fwd_f32 4");
+        let bias = if let (Some(bias), Some(bias_l)) = (bias, bias_l) {
+            if bias_l.shape().dims1()? != m {
+                candle::bail!("Bias does not have the correct shape");
+            }
+
+            Some(bias.as_cuda_slice::<f32>()?.slice(bias_l.start_offset()..))
+        } else {
+            None
+        };
+        //println!("CublasLTBatchMatmul fwd_f32 5");
+        let (mut out, stride_c) = if let Some(c) = &self.c {
+            let (c, c_l) = c.storage_and_layout();
+            let c = match &*c {
+                Storage::Cuda(storage) => storage.as_cuda_slice::<f32>()?,
+                _ => candle::bail!("`c` must be a cuda tensor"),
+            };
+            match c_l.contiguous_offsets() {
+                Some((o1, o2)) => {
+                    if o1 != 0 {
+                        candle::bail!("`c` start offset must be 0");
+                    }
+                    if o2 != out_shape.elem_count() {
+                        candle::bail!("`c` end offset must be {}", out_shape.elem_count())
+                    }
+                }
+                None => candle::bail!("`c` has to be contiguous"),
+            };
+
+            if c_l.shape().dims3()? != (batch_size, n, m) {
+                candle::bail!("`c` does not have the correct shape");
+            }
+
+            // Set beta to 0.0 if it is not set
+            (c.clone(), c_l.stride()[0])
+        } else {
+            // Allocate out tensor
+            (
+                unsafe { dev.alloc::<f32>(out_shape.elem_count()).w()? },
+                (n * m),
+            )
+        };
+        //println!("CublasLTBatchMatmul fwd_f32 6");
+        let config = MatmulConfig {
+            transa: true,
+            transb: false,
+            m: m as u64,
+            n: n as u64,
+            k: k as u64,
+            alpha: self.alpha.unwrap_or(1.0),
+            lda: lda as i64,
+            ldb: ldb as i64,
+            beta: self.beta.unwrap_or(0.0),
+            ldc: ldc as i64,
+            stride_a: Some(a_l.stride()[0] as i64),
+            stride_b: Some(b_l.stride()[0] as i64),
+            stride_c: Some(stride_c as i64),
+            stride_bias: None,
+            batch_size: Some(batch_size as c_int),
+        };
+        //println!("CublasLTBatchMatmul fwd_f32 7");
+        unsafe {
+            self.cublaslt
+                .matmul(config, &a, &b, &mut out, bias.as_ref(), self.act.as_ref())
+                .map_err(|e| candle::Error::Cuda(Box::new(e)))?;
+        }
+
+        let out = candle::CudaStorage::wrap_cuda_slice(out, dev.clone());
+        //println!("CublasLTBatchMatmul fwd_f32 8");
+        Ok((out, out_shape))
+    }
+}
+
+impl candle::CustomOp2 for CublasLTBatchMatmul {
+    fn name(&self) -> &'static str {
+        "cublaslt-batch-matmul"
+    }
+
+    fn cpu_fwd(
+        &self,
+        _: &CpuStorage,
+        _: &Layout,
+        _: &CpuStorage,
+        _: &Layout,
+    ) -> Result<(CpuStorage, Shape)> {
+        candle::bail!("no cpu support for cublaslt-batch-matmul")
+    }
+
+    fn cuda_fwd(
+        &self,
+        a: &candle::CudaStorage,
+        a_l: &Layout,
+        b: &candle::CudaStorage,
+        b_l: &Layout,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        //println!("candle-cublast src/lib.rs:772 cuda_fwd33");
+        match a.dtype() {
+            candle::DType::F16 => self.fwd_f16(a, a_l, b, b_l, None, None),
+            candle::DType::BF16 => self.fwd_bf16(a, a_l, b, b_l, None, None),
+            candle::DType::F32 => self.fwd_f32(a, a_l, b, b_l, None, None),
+            dt => {
+                candle::bail!("cublaslt-batch-matmul is only supported for f16/bf16/f32 ({dt:?})")
+            }
+        }
+    }
+}
+
+impl candle::CustomOp3 for CublasLTBatchMatmul {
+    fn name(&self) -> &'static str {
+        "cublaslt-batch-matmul-add"
+    }
+
+    fn cpu_fwd(
+        &self,
+        _: &CpuStorage,
+        _: &Layout,
+        _: &CpuStorage,
+        _: &Layout,
+        _: &CpuStorage,
+        _: &Layout,
+    ) -> Result<(CpuStorage, Shape)> {
+        candle::bail!("no cpu support for cublaslt-batch-matmul-add")
+    }
+
+    fn cuda_fwd(
+        &self,
+        a: &candle::CudaStorage,
+        a_l: &Layout,
+        b: &candle::CudaStorage,
+        b_l: &Layout,
+        bias: &candle::CudaStorage,
+        bias_l: &Layout,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        //println!("candle-cublast src/lib.rs:810 cuda_fwd44");
+        match a.dtype() {
+            candle::DType::F16 => {
+                //println!("candle-cublast src/lib.rs:813 cuda_fwd f16");
+                self.fwd_f16(a, a_l, b, b_l, Some(bias), Some(bias_l))
+            },
+            candle::DType::BF16 => {
+                //println!("candle-cublast src/lib.rs:817 cuda_fwd bf16");
+                self.fwd_bf16(a, a_l, b, b_l, Some(bias), Some(bias_l))
+            },
+            candle::DType::F32 => {
+                //println!("candle-cublast src/lib.rs:821 cuda_fwd f32");
+                self.fwd_f32(a, a_l, b, b_l, Some(bias), Some(bias_l))
+            },
+            dt => candle::bail!(
+                "cublaslt-batch-matmul-add is only supported for f16/bf16/f32 ({dt:?})"
+            ),
+        }
+    }
+}
+
+/// Fused batch matmul + add + Relu/Gelu activation using CublasLt
+///
+/// # Arguments
+///
+/// * `a` - Input tensor of size BxMxK
+/// * `b` - Input tensor of size BxNxK
+/// * `out` - Optional Output tensor of size BxNxK.
+///           If set and beta != 0, will be added to the end result of A*B before `act`
+/// * `alpha` - Optional scaling factor for A*B
+/// * `beta` - Optional scaling factor for C
+/// * `bias` - Optional bias tensor of size M
+/// * `act` - Optional Gelu or Relu activation. If set, will be added to the end result
+/// * `cublaslt` - CublasLt handle
+///
+/// The resulting tensor is of shape NxM
+pub fn fused_batch_matmul(
+    a: &Tensor,
+    b: &Tensor,
+    out: Option<&Tensor>,
+    alpha: Option<f32>,
+    beta: Option<f32>,
+    bias: Option<&Tensor>,
+    act: Option<Activation>,
+    cublaslt: CublasLt,
+) -> Result<Tensor> {
+    let op = CublasLTBatchMatmul {
+        act,
+        cublaslt: cublaslt.0,
+        c: out.cloned(),
+        alpha,
+        beta,
+    };
+
+    if let Some(bias) = bias {
+        a.apply_op3(&b, &bias, op)
+    } else {
+        a.apply_op2(&b, op)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use candle::{DType, Device};
+
+    fn to_vec2_round(t: Tensor, digits: i32) -> Result<Vec<Vec<f32>>> {
+        let b = 10f32.powi(digits);
+        let t = t.to_vec2::<f32>()?;
+        let t = t
+            .iter()
+            .map(|t| t.iter().map(|t| f32::round(t * b) / b).collect())
+            .collect();
+        Ok(t)
+    }
+
+    fn to_vec3_round(t: Tensor, digits: i32) -> Result<Vec<Vec<Vec<f32>>>> {
+        let b = 10f32.powi(digits);
+        let t = t.to_vec3::<f32>()?;
+        let t = t
+            .iter()
+            .map(|t| {
+                t.iter()
+                    .map(|t| t.iter().map(|t| f32::round(t * b) / b).collect())
+                    .collect()
+            })
+            .collect();
+        Ok(t)
+    }
+
+    #[test]
+    fn test_fused_matmul() -> Result<()> {
+        let device = Device::new_cuda(0)?;
+
+        let a = Tensor::randn(0., 1., (8, 4), &device)?.to_dtype(DType::F32)?;
+        let b = Tensor::randn(0., 1., (2, 4), &device)?.to_dtype(DType::F32)?;
+        let bias = Tensor::randn(0., 1., 8, &device)?.to_dtype(DType::F32)?;
+
+        let cublaslt = CublasLt::new(&device)?;
+
+        let res = fused_matmul(&a, &b, None, None, None, Some(&bias), None, cublaslt)?;
+        let expected = (b.matmul(&a.t()?)? + bias.broadcast_left(2)?)?;
+
+        assert_eq!(
+            to_vec2_round(res.to_dtype(DType::F32)?, 4)?,
+            to_vec2_round(expected.to_dtype(DType::F32)?, 4)?
+        );
+        Ok(())
+    }
+
+    #[test]
+    fn test_fused_batch_matmul() -> Result<()> {
+        let device = Device::new_cuda(0)?;
+
+        let a = Tensor::randn(0., 1., (3, 8, 4), &device)?.to_dtype(DType::F32)?;
+        let b = Tensor::randn(0., 1., (3, 2, 4), &device)?.to_dtype(DType::F32)?;
+        let c = Tensor::randn(0., 1., (3, 2, 8), &device)?.to_dtype(DType::F32)?;
+        let bias = Tensor::randn(0., 1., 8, &device)?.to_dtype(DType::F32)?;
+
+        let cublaslt = CublasLt::new(&device)?;
+
+        let res = fused_batch_matmul(
+            &a,
+            &b,
+            Some(&c),
+            None,
+            Some(1.0),
+            Some(&bias),
+            None,
+            cublaslt,
+        )?;
+        let expected = (b.matmul(&a.t()?)?.add(&c)? + bias.broadcast_left((3, 2))?)?;
+
+        assert_eq!(
+            to_vec3_round(res.to_dtype(DType::F32)?, 4)?,
+            to_vec3_round(expected.to_dtype(DType::F32)?, 4)?
+        );
+        Ok(())
+    }
+}