chore: faster wasm-pack

.github/workflows/rust.yml

@@ -40,9 +40,9 @@ jobs:
           python-version: '3.10.6'
           cache: 'pip'
       - run: pip install -r requirements.txt
-      - name: Setup
+      - name: Install wasm-pack 
         run: |
-          cargo install wasm-pack
+          curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh
       - name: Build
         run: cargo build
       - name: Run tests

crates/ratchet-core/src/tensor.rs

@@ -426,46 +426,49 @@ mod tests {
         Ok(())
     }
 
-    #[test]
-    fn test_pyo3() -> anyhow::Result<()> {
-        let cpu_device = Device::request_device(DeviceRequest::CPU)?;
-        let a = Tensor::randn::<f32>(shape![1024, 1024], cpu_device.clone());
-        let b = Tensor::randn::<f32>(shape![1024, 1024], cpu_device.clone());
-
-        let ground: anyhow::Result<Tensor> = Python::with_gil(|py| {
-            let prg = PyModule::from_code(
-                py,
-                r#"
-import torch
-
-def matmul(a, b):
-    return torch.matmul(torch.from_numpy(a), torch.from_numpy(b)).numpy()
-    "#,
-                "x.py",
-                "x",
-            )?;
-
-            let result = prg
-                .getattr("matmul")?
-                .call1((a.clone().to_py::<f32>(py), b.clone().to_py::<f32>(py)))?
-                .extract::<&PyArrayDyn<f32>>()?;
-            Ok(Tensor::from(result))
-        });
-        println!("\nTORCH: {:#?}", ground);
-
-        println!("\nA: {:#?}", a);
-        println!("\nB: {:#?}", b);
-
-        let gpu_device = Device::request_device(DeviceRequest::GPU)?;
-        let a = a.to(gpu_device.clone())?;
-        let b = b.to(gpu_device)?;
-
-        let c = a.matmul(&b)?;
-        c.resolve()?;
-
-        let our_result = c.to(cpu_device)?;
-        println!("\nOURS: {:#?}", our_result);
-
-        Ok(())
-    }
+    /*
+
+        #[test]
+        fn test_pyo3() -> anyhow::Result<()> {
+            let cpu_device = Device::request_device(DeviceRequest::CPU)?;
+            let a = Tensor::randn::<f32>(shape![1024, 1024], cpu_device.clone());
+            let b = Tensor::randn::<f32>(shape![1024, 1024], cpu_device.clone());
+
+            let ground: anyhow::Result<Tensor> = Python::with_gil(|py| {
+                let prg = PyModule::from_code(
+                    py,
+                    r#"
+    import torch
+
+    def matmul(a, b):
+        return torch.matmul(torch.from_numpy(a), torch.from_numpy(b)).numpy()
+        "#,
+                    "x.py",
+                    "x",
+                )?;
+
+                let result = prg
+                    .getattr("matmul")?
+                    .call1((a.clone().to_py::<f32>(py), b.clone().to_py::<f32>(py)))?
+                    .extract::<&PyArrayDyn<f32>>()?;
+                Ok(Tensor::from(result))
+            });
+            println!("\nTORCH: {:#?}", ground);
+
+            println!("\nA: {:#?}", a);
+            println!("\nB: {:#?}", b);
+
+            let gpu_device = Device::request_device(DeviceRequest::GPU)?;
+            let a = a.to(gpu_device.clone())?;
+            let b = b.to(gpu_device)?;
+
+            let c = a.matmul(&b)?;
+            c.resolve()?;
+
+            let our_result = c.to(cpu_device)?;
+            println!("\nOURS: {:#?}", our_result);
+
+            Ok(())
+        }
+        */
 }