catch stream errors earlier to avoid aborts (#1801)

mlx/linalg.cpp

@@ -10,6 +10,15 @@
 
 namespace mlx::core::linalg {
 
+void check_cpu_stream(const StreamOrDevice& s, const std::string& prefix) {
+  if (to_stream(s).device == Device::gpu) {
+    throw std::invalid_argument(
+        prefix +
+        " This op is not yet supported on the GPU. "
+        "Explicitly pass a CPU stream to run it.");
+  }
+}
+
 Dtype at_least_float(const Dtype& d) {
   return issubdtype(d, inexact) ? d : promote_types(d, float32);
 }
@@ -174,6 +183,7 @@ array norm(
 }
 
 std::pair<array, array> qr(const array& a, StreamOrDevice s /* = {} */) {
+  check_cpu_stream(s, "[linalg::qr]");
   if (a.dtype() != float32) {
     std::ostringstream msg;
     msg << "[linalg::qr] Arrays must type float32. Received array "
@@ -201,6 +211,7 @@ std::pair<array, array> qr(const array& a, StreamOrDevice s /* = {} */) {
 }
 
 std::vector<array> svd(const array& a, StreamOrDevice s /* = {} */) {
+  check_cpu_stream(s, "[linalg::svd]");
   if (a.dtype() != float32) {
     std::ostringstream msg;
     msg << "[linalg::svd] Input array must have type float32. Received array "
@@ -239,6 +250,7 @@ std::vector<array> svd(const array& a, StreamOrDevice s /* = {} */) {
 }
 
 array inv_impl(const array& a, bool tri, bool upper, StreamOrDevice s) {
+  check_cpu_stream(s, "[linalg::inv]");
   if (a.dtype() != float32) {
     std::ostringstream msg;
     msg << "[linalg::inv] Arrays must type float32. Received array "
@@ -279,6 +291,7 @@ array cholesky(
     const array& a,
     bool upper /* = false */,
     StreamOrDevice s /* = {} */) {
+  check_cpu_stream(s, "[linalg::cholesky]");
   if (a.dtype() != float32) {
     std::ostringstream msg;
     msg << "[linalg::cholesky] Arrays must type float32. Received array "
@@ -307,6 +320,7 @@ array cholesky(
 }
 
 array pinv(const array& a, StreamOrDevice s /* = {} */) {
+  check_cpu_stream(s, "[linalg::pinv]");
   if (a.dtype() != float32) {
     std::ostringstream msg;
     msg << "[linalg::pinv] Arrays must type float32. Received array "
@@ -353,24 +367,25 @@ array cholesky_inv(
     const array& L,
     bool upper /* = false */,
     StreamOrDevice s /* = {} */) {
+  check_cpu_stream(s, "[linalg::cholesky_inv]");
   if (L.dtype() != float32) {
     std::ostringstream msg;
-    msg << "[linalg::cholesky] Arrays must type float32. Received array "
+    msg << "[linalg::cholesky_inv] Arrays must type float32. Received array "
         << "with type " << L.dtype() << ".";
     throw std::invalid_argument(msg.str());
   }
 
   if (L.ndim() < 2) {
     std::ostringstream msg;
-    msg << "[linalg::cholesky] Arrays must have >= 2 dimensions. Received array "
+    msg << "[linalg::cholesky_inv] Arrays must have >= 2 dimensions. Received array "
            "with "
         << L.ndim() << " dimensions.";
     throw std::invalid_argument(msg.str());
   }
 
   if (L.shape(-1) != L.shape(-2)) {
     throw std::invalid_argument(
-        "[linalg::cholesky] Cholesky inverse is only defined for square "
+        "[linalg::cholesky_inv] Cholesky inverse is only defined for square "
         "matrices.");
   }
 
@@ -454,7 +469,11 @@ array cross(
   return concatenate(outputs, axis, s);
 }
 
-void validate_eigh(const array& a, const std::string fname) {
+void validate_eigh(
+    const array& a,
+    const StreamOrDevice& stream,
+    const std::string fname) {
+  check_cpu_stream(stream, fname);
   if (a.dtype() != float32) {
     std::ostringstream msg;
     msg << fname << " Arrays must have type float32. Received array "
@@ -478,7 +497,7 @@ array eigvalsh(
     const array& a,
     std::string UPLO /* = "L" */,
     StreamOrDevice s /* = {} */) {
-  validate_eigh(a, "[linalg::eigvalsh]");
+  validate_eigh(a, s, "[linalg::eigvalsh]");
   Shape out_shape(a.shape().begin(), a.shape().end() - 1);
   return array(
       std::move(out_shape),
@@ -491,7 +510,7 @@ std::pair<array, array> eigh(
     const array& a,
     std::string UPLO /* = "L" */,
     StreamOrDevice s /* = {} */) {
-  validate_eigh(a, "[linalg::eigh]");
+  validate_eigh(a, s, "[linalg::eigh]");
   auto out = array::make_arrays(
       {Shape(a.shape().begin(), a.shape().end() - 1), a.shape()},
       {a.dtype(), a.dtype()},

python/mlx/nn/init.py

@@ -385,7 +385,7 @@ def initializer(a: mx.array) -> mx.array:
             raise ValueError("Only tensors with 2 dimensions are supported")
 
         rows, cols = a.shape
-        num_zeros = int(mx.ceil(sparsity * cols))
+        num_zeros = int(math.ceil(sparsity * cols))
 
         order = mx.argsort(mx.random.uniform(shape=a.shape), axis=1)
         a = mx.random.normal(shape=a.shape, scale=std, loc=mean, dtype=dtype)

tests/random_tests.cpp

@@ -421,6 +421,9 @@ TEST_CASE("test random normal") {
 }
 
 TEST_CASE("test random multivariate_normal") {
+  // Scope switch to the cpu for SVDs
+  StreamContext sc(Device::cpu);
+
   {
     auto mean = zeros({3});
     auto cov = eye(3);