Correct types for vjp + tests (#418)

* correct types for vjp + tests

* fix build + comment

mlx/io/gguf.cpp

@@ -1,5 +1,7 @@
 // Copyright © 2023 Apple Inc.
 
+#include <cstring>
+
 #include "mlx/ops.h"
 #include "mlx/primitives.h"
 #include "mlx/utils.h"

mlx/transforms.cpp

@@ -396,7 +396,10 @@ std::pair<std::vector<array>, std::vector<array>> vjp(
   // products for each primitive
   std::unordered_map<std::uintptr_t, array> cotan_map;
   for (auto [out_idx, cotan_idx] : output_cotan_pairs) {
-    cotan_map.insert({outputs[out_idx].id(), cotans[cotan_idx]});
+    auto& o = outputs[out_idx];
+    auto s = o.has_primitive() ? o.primitive().stream()
+                               : default_stream(default_device());
+    cotan_map.insert({o.id(), astype(cotans[cotan_idx], o.dtype(), s)});
   }
   for (auto it = tape.rbegin(); it != tape.rend(); ++it) {
     auto& a = *it;
@@ -636,9 +639,8 @@ ValueAndGradFn value_and_grad(
     for (auto arg : args) {
       ginputs.push_back(inputs[arg]);
     }
-    // Set the incoming gradient as int32 so that it will be promoted to the
+    // Set the incoming gradient to int32, vjp will cast it to the output type
-    // appropriate floating point type op(int, floatXX) -> floatXX for most ops
+    auto [outputs, grads] = vjp(gfun, ginputs, {array(1.0f)});
-    auto [outputs, grads] = vjp(gfun, ginputs, {array(1)});
     return std::make_pair(outputs, grads);
   };
 }

python/tests/test_autograd.py

@@ -274,6 +274,47 @@ class TestAutograd(mlx_tests.MLXTestCase):
         mx.eval(state)
         self.assertTrue(mx.allclose(state, mx.ones((2,))))
 
+    def test_scatter_vjp(self):
+        def fun(x, idx):
+            x[idx] = 2.0
+            return x.sum()
+
+        dfdx = mx.grad(fun)(mx.array([1.0, 2.0, 3.0]), mx.array([1]))
+        self.assertTrue(mx.array_equal(dfdx, mx.array([1.0, 0.0, 1.0])))
+        self.assertEqual(dfdx.dtype, mx.float32)
+
+        y = mx.array([0.0, 1.0, 2.0])
+
+        def fun(x, idx):
+            y[idx] = x
+            return y.sum()
+
+        dfdx = mx.grad(fun)(mx.array([2.0]), mx.array([1]))
+        self.assertTrue(mx.array_equal(dfdx, mx.array([1.0])))
+        self.assertEqual(dfdx.dtype, mx.float32)
+
+    def test_vjp_types(self):
+        def fun(x):
+            return x
+
+        for t in [mx.float16, mx.bfloat16, mx.float32]:
+            out = mx.grad(fun)(mx.array(1.0, t))
+            self.assertEqual(out.dtype, t)
+
+        def fun(x):
+            return x.sum()
+
+        for t in [mx.float16, mx.bfloat16, mx.float32]:
+            out = mx.grad(fun)(mx.array(1.0, t))
+            self.assertEqual(out.dtype, t)
+
+        def fun(x, y):
+            return (x + y).sum()
+
+        for t in [mx.float16, mx.bfloat16, mx.float32]:
+            out = mx.grad(fun)(mx.array(1.0, t), mx.array(1.0, t))
+            self.assertEqual(out.dtype, t)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/autograd_tests.cpp

@@ -1202,3 +1202,29 @@ TEST_CASE("test update state") {
   CHECK(state.is_evaled());
   CHECK(array_equal(state, array({1.0, 1.0})).item<bool>());
 }
+
+TEST_CASE("test grad types") {
+  {
+    auto fn = [](array x) { return sum(x); };
+
+    for (auto t : {float16, bfloat16, float32}) {
+      auto x = array(1.0, t);
+      auto dfdx = grad(fn)(x);
+      CHECK_EQ(dfdx.dtype(), t);
+    }
+  }
+
+  {
+    // Check for multi-input grad
+    auto fn = [](std::vector<array> inputs) {
+      return sum(inputs[0] + inputs[1]);
+    };
+
+    for (auto t : {float16, bfloat16, float32}) {
+      auto x = array(1.0, t);
+      auto y = array(1.0, t);
+      auto out = grad(fn)({x, y});
+      CHECK_EQ(out[0].dtype(), t);
+    }
+  }
+}