Add a cuda kernel for avg-pool2d. (#440)

* Add a cuda kernel for avg-pool2d.

* Avoid running out of bounds.

* Finish wiring the avg pool kernel + add some testing.

* Support for max-pool + testing.

candle-core/src/cuda_backend.rs

@@ -960,6 +960,64 @@ impl<'a> Map2 for Conv2D<'a> {
     }
 }
 
+enum PoolOp {
+    Max,
+    Avg,
+}
+
+struct Pool2D {
+    w_k: usize,
+    h_k: usize,
+    w_stride: usize,
+    h_stride: usize,
+    op: PoolOp,
+}
+
+impl Map1 for Pool2D {
+    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
+        &self,
+        inp: &CudaSlice<T>,
+        dev: &CudaDevice,
+        inp_l: &Layout,
+    ) -> Result<CudaSlice<T>> {
+        // Kernel shape: (c_out, c_in_k, w_k, h_k)
+        let inp = &inp.slice(inp_l.start_offset()..);
+        let shape = inp_l.shape();
+        let dims = shape.dims();
+        let ds = if dims.len() == 4 {
+            [dims, inp_l.stride()].concat()
+        } else {
+            panic!("unexpected input shape for conv1d {dims:?}")
+        };
+        let el = shape.elem_count();
+        let out_w = (dims[2] - self.w_k) / self.w_stride + 1;
+        let out_h = (dims[3] - self.h_k) / self.h_stride + 1;
+        let dst_el = out_w * out_h * dims[0] * dims[1];
+        let cfg = LaunchConfig::for_num_elems(dst_el as u32);
+        let kname = match self.op {
+            PoolOp::Max => "max_pool2d",
+            PoolOp::Avg => "avg_pool2d",
+        };
+        let func = dev.get_or_load_func(&kernel_name::<T>(kname), kernels::CONV)?;
+        // SAFETY: Set later by running the kernel.
+        let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
+        let ds = dev.htod_copy(ds).w()?;
+        let params = (
+            el,
+            self.w_k,
+            self.h_k,
+            self.w_stride,
+            self.h_stride,
+            &ds,
+            inp,
+            &out,
+        );
+        // SAFETY: ffi.
+        unsafe { func.launch(cfg, params) }.w()?;
+        Ok(out)
+    }
+}
+
 struct WhereCond<'a>(&'a CudaStorage, &'a Layout);
 impl<'a> Map2 for WhereCond<'a> {
     fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
@@ -1429,12 +1487,30 @@ impl BackendStorage for CudaStorage {
         Ok(Self { slice, device })
     }
 
-    fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
-        todo!()
+    fn avg_pool2d(&self, l: &Layout, k: (usize, usize), stride: (usize, usize)) -> Result<Self> {
+        let device = self.device().clone();
+        let slice = Pool2D {
+            w_k: k.0,
+            h_k: k.1,
+            w_stride: stride.0,
+            h_stride: stride.1,
+            op: PoolOp::Avg,
+        }
+        .map(&self.slice, &device, l)?;
+        Ok(Self { slice, device })
     }
 
-    fn max_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
-        todo!()
+    fn max_pool2d(&self, l: &Layout, k: (usize, usize), stride: (usize, usize)) -> Result<Self> {
+        let device = self.device().clone();
+        let slice = Pool2D {
+            w_k: k.0,
+            h_k: k.1,
+            w_stride: stride.0,
+            h_stride: stride.1,
+            op: PoolOp::Max,
+        }
+        .map(&self.slice, &device, l)?;
+        Ok(Self { slice, device })
     }
 
     fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self> {