basic python tests

python/src/transforms.cpp

@@ -1,5 +1,4 @@
 // Copyright © 2023 Apple Inc.
-
 #include <pybind11/functional.h>
 #include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
@@ -163,6 +162,19 @@ py::object tree_unflatten(
   });
 }
 
+py::object tree_unflatten_none(
+    py::object tree,
+    const std::vector<array>& values,
+    int index = 0) {
+  return tree_map(tree, [&](py::handle obj) {
+    if (py::isinstance<py::none>(obj)) {
+      return py::cast(values[index++]);
+    } else {
+      return py::cast<py::object>(obj);
+    }
+  });
+}
+
 auto validate_argnums_argnames(
     const std::optional<IntOrVec>& argnums,
     const StrOrVec& argnames) {
@@ -438,30 +450,36 @@ auto py_vmap(
 }
 
 auto py_compile(const py::function& fun) {
+  // This map is used to Cache the tree structure of the outputs
+  static std::unordered_map<size_t, py::object> tree_cache;
+
   return [fun](const py::args& args) {
     // Inputs must be array or tree of arrays
     auto inputs = tree_flatten(args, true);
 
-    // py_value_out will hold the output of the python function in order to be
-    // able to reconstruct the python tree of extra return values
-    py::object py_outputs;
+    // TODO, awni, I think this cast is ok??
+    size_t fun_id = reinterpret_cast<size_t>(fun.ptr());
 
-    auto compile_fun =
-        [&fun, &args, &inputs, &py_outputs](const std::vector<array>& a) {
+    auto compile_fun = [fun_id, &fun, &args, &inputs](
+                           const std::vector<array>& a) {
       // Call the python function
-          py_outputs = fun(*tree_unflatten(args, a));
+      py::object py_outputs = fun(*tree_unflatten(args, a));
 
       // Flatten the outputs
-          return tree_flatten(py_outputs, true);
+      auto outputs = tree_flatten(py_outputs, true);
+
+      py_outputs =
+          tree_map(py_outputs, [](const py::handle& x) { return py::none(); });
+      tree_cache.insert({fun_id, py_outputs});
+      return outputs;
     };
 
     // Compile and call
-    // TODO, awni, I think this cast is ok??
-    size_t fun_id = reinterpret_cast<size_t>(fun.ptr());
     auto outputs = detail::compile(compile_fun, fun_id)(inputs);
 
     // Put the outputs back in the container
-    return tree_unflatten(py_outputs, outputs);
+    py::object py_outputs = tree_cache.at(fun_id);
+    return tree_unflatten_none(py_outputs, outputs);
   };
 }
 

python/tests/test_compile.py

@@ -15,7 +15,12 @@ class TestCompile(mlx_tests.MLXTestCase):
         compiled_fn = mx.compile(fun)
         x = mx.array(1.0)
         y = mx.array(1.0)
-        # out = compiled_fn(x, y)
+        out = compiled_fn(x, y)
+        self.assertEqual(out.item(), 2.0)
+
+        # Try again
+        out = compiled_fn(x, y)
+        self.assertEqual(out.item(), 2.0)
 
 
 if __name__ == "__main__":

tests/compile_tests.cpp

@@ -1,6 +1,8 @@
 // Copyright © 2023 Apple Inc.
 
+#include <iostream> // TODO
 #include "doctest/doctest.h"
+#include "mlx/utils.h" // TODO
 
 #include "mlx/mlx.h"
 
@@ -33,17 +35,50 @@ TEST_CASE("test simple compile") {
   CHECK(array_equal(out, array({3.0f, 4.0f})).item<bool>());
 }
 
-std::vector<array> fun1(const std::vector<array>& inputs) {
+std::vector<array> grad_fun(const std::vector<array>& inputs) {
   auto loss = [](std::vector<array> ins) { return exp(ins[0] + ins[1]); };
-  return grad(loss)(inputs);
+  return grad(loss, {0, 1})(inputs);
 }
 
 TEST_CASE("test compile with grad") {
   auto x = array(1.0f);
   auto y = array(1.0f);
-  auto grads_expected = fun1({x, y});
-  auto grads_compile = compile(fun1)({x, y});
+  auto grads_expected = grad_fun({x, y});
+  auto grads_compile = compile(grad_fun)({x, y});
   CHECK_EQ(grads_compile[0].item<float>(), grads_expected[0].item<float>());
+  CHECK_EQ(grads_compile[1].item<float>(), grads_expected[1].item<float>());
 }
 
+TEST_CASE("test compile inputs with primitive") {
+  auto [k1, k2] = random::split(random::key(0));
+  auto x = random::uniform({5, 5}, k1);
+  auto y = random::uniform({5, 5}, k2);
+  auto expected = simple_fun({x, y})[0];
+
+  x = random::uniform({5, 5}, k1);
+  y = random::uniform({5, 5}, k2);
+  auto out = compile(simple_fun)({x, y})[0];
+  CHECK(array_equal(expected, out).item<bool>());
+
+  // Same thing twice
+  out = compile(simple_fun)({x, y})[0];
+  CHECK(array_equal(expected, out).item<bool>());
+}
+
+/*std::vector<array> bigger_fun(const std::vector<array>& inputs) {
+  auto x = inputs[1];
+  for (int i = 1; i < inputs.size(); ++i) {
+    w = inputs[i]
+    x = maximum(matmul(x, w), 0);
+  }
+  return take(x, array(3)) - logsumexp(x);
+}
+
+TEST_CASE("test bigger graph") {
+  std::vector<array> inputs;
+  inputs.push_back(
+  for (int
+  for
+}*/
+
 TEST_CASE("test nested compile") {}