make Context lifetime more robust dont rely on RVO which seems to fail if createContext is called from eg conditional branches. Remove webgpu from scratch tutorial to avoid having to maintain/update the implementation

Author: austinvhuang

examples/hello_world/run.cpp

@@ -3,9 +3,7 @@
 #include <cstdio>
 #include <future>
 
-using namespace gpu; // createContext, createTensor, createKernel,
-                     // createShader, dispatchKernel, wait, toCPU
-                     // Tensor, Kernel, Context, Shape, kf32
+using namespace gpu;
 
 static const char *kGelu = R"(
 const GELU_SCALING_FACTOR: f32 = 0.7978845608028654; // sqrt(2.0 / PI)
@@ -29,6 +27,7 @@ int main(int argc, char **argv) {
   printf("\nHello gpu.cpp!\n");
   printf("--------------\n\n");
 
+  // std::unique_ptr<Context> ctx = createContext();
   Context ctx = createContext();
   static constexpr size_t N = 10000;
   std::array<float, N> inputArr, outputArr;
@@ -41,7 +40,7 @@ int main(int argc, char **argv) {
   std::future<void> future = promise.get_future();
   Kernel op = createKernel(ctx, {kGelu, 256, kf32},
                            Bindings{input, output},
-                           /* nWorkgroups */ {cdiv(N, 256), 1, 1});
+                           {cdiv(N, 256), 1, 1});
   dispatchKernel(ctx, op, promise);
   wait(ctx, future);
   toCPU(ctx, output, outputArr.data(), sizeof(outputArr));
@@ -50,5 +49,4 @@ int main(int argc, char **argv) {
   }
   printf("  ...\n\n");
   printf("Computed %zu values of GELU(x)\n\n", N);
-  return 0;
 }

examples/matmul/run.cpp

@@ -792,13 +792,40 @@ void runTest(int version, size_t M, size_t K, size_t N,
   }
 
   // Allocate GPU buffers and copy data
-  Context ctx = createContext(
-      {}, {},
-      /*device descriptor, enabling f16 in WGSL*/
-      {
+  WGPUDeviceDescriptor devDescriptor = {};
+  devDescriptor.requiredFeatureCount = 1;
+  devDescriptor.requiredFeatures = std::array{WGPUFeatureName_ShaderF16}.data();
+
+  Context ctx;
+  if (numtype == kf16) {
+    ctx = createContext(
+        {}, {},
+        /*device descriptor, enabling f16 in WGSL*/
+        {
           .requiredFeatureCount = 1,
-          .requiredFeatures = std::array{WGPUFeatureName_ShaderF16}.data(),
-      });
+          .requiredFeatures = std::array{WGPUFeatureName_ShaderF16}.data()
+        });
+    if (ctx.adapterStatus != WGPURequestAdapterStatus_Success) {
+      LOG(kDefLog, kError, "Failed to create adapter with f16 support, try running an f32 test instead (`export MATMUL_VERSION=9).");
+      exit(1);
+    }
+    if (ctx.deviceStatus != WGPURequestDeviceStatus_Success) {
+      LOG(kDefLog, kError, "Failed to create device with f16 support, try running an f32 test instead. (`export MATMUL_VERSION=9)");
+      exit(1);
+    }
+  }
+
+  if (numtype == kf32) {
+    ctx = createContext({}, {}, {});
+    if (ctx.adapterStatus != WGPURequestAdapterStatus_Success ||
+        ctx.deviceStatus != WGPURequestDeviceStatus_Success) {
+      LOG(kDefLog, kError, "Failed to create adapter or device");
+      // stop execution
+      exit(1);
+    } else {
+      LOG(kDefLog, kInfo, "Successfully created adapter and device");
+    }
+  } 
 
   Tensor input = createTensor(ctx, Shape{M, K}, numtype, inputPtr.get());
   Tensor weights = createTensor(ctx, Shape{N, K}, numtype, weightsPtr.get()); // column-major
@@ -810,8 +837,6 @@ void runTest(int version, size_t M, size_t K, size_t N,
 #endif
 
   // Initialize Kernel and bind GPU buffers
-
-
   // pre-allocate for async dispatch
   std::array<std::promise<void>, nIter> promises;
   std::array<std::future<void>, nIter> futures;
@@ -823,10 +848,6 @@ void runTest(int version, size_t M, size_t K, size_t N,
     kernels[i] = selectMatmul(ctx, version, {input, weights, outputs[i]}, M, K, N, numtype);
   }
 
-#ifndef METAL_PROFILER
-  printf("[ Press enter to start tests ... ]\n");
-  getchar();
-#endif
   LOG(kDefLog, kInfo, "Dispatching Kernel version %d: %s, %d iterations ...",
       version, versionToStr(version).c_str(), nIter);