Add stricter condition to matrix sdpa

docs/src/python/optimizers.rst

@@ -51,14 +51,14 @@ the saved state. Here's a simple example:
    optimizer.update(model, grads)
 
    # Save the state
-   state = tree_flatten(optimizer.state, destination={})
+   state = tree_flatten(optimizer.state)
-   mx.save_safetensors("optimizer.safetensors", state)
+   mx.save_safetensors("optimizer.safetensors", dict(state))
 
    # Later on, for example when loading from a checkpoint,
    # recreate the optimizer and load the state
    optimizer = optim.Adam(learning_rate=1e-2)
 
-   state = tree_unflatten(mx.load("optimizer.safetensors"))
+   state = tree_unflatten(list(mx.load("optimizer.safetensors").items()))
    optimizer.state = state
 
 Note, not every optimizer configuation parameter is saved in the state. For

docs/src/usage/export.rst

@@ -7,17 +7,17 @@ Exporting Functions
 
 MLX has an API to export and import functions to and from a file. This lets you
 run computations written in one MLX front-end (e.g. Python) in another MLX
-front-end (e.g. C++).
+front-end (e.g. C++). 
 
 This guide walks through the basics of the MLX export API with some examples.
 To see the full list of functions check-out the :ref:`API documentation
 <export>`.
 
-Basics of Exporting
+Basics of Exporting 
 -------------------
 
 Let's start with a simple example:
-
+ 
 .. code-block:: python
 
   def fun(x, y):
@@ -67,7 +67,7 @@ specified as variable positional arguments or as a tuple of arrays:
 
   x = mx.array(1.0)
   y = mx.array(1.0)
-
+   
   # Both arguments to fun are positional
   mx.export_function("add.mlxfn", fun, x, y)
 
@@ -133,7 +133,7 @@ parameters are also saved to the ``model.mlxfn`` file.
    For enclosed arrays inside an exported function, be extra careful to ensure
    they are evaluated. The computation graph that gets exported will include
    the computation that produces enclosed inputs.
-
+  
    If the above example was missing ``mx.eval(model.parameters()``, the
    exported function would include the random initialization of the
    :obj:`mlx.nn.Module` parameters.
@@ -150,8 +150,8 @@ parameters, pass them as inputs to the ``call`` wrapper:
      # Set the model's parameters to the input parameters
      model.update(tree_unflatten(list(params.items())))
      return model(x)
-
+ 
-   params = tree_flatten(model.parameters(), destination={})
+   params = dict(tree_flatten(model.parameters()))
    mx.export_function("model.mlxfn", call, (mx.zeros(4),), params)
 
 
@@ -169,8 +169,8 @@ to export a function which can be used for inputs with variable shapes:
 
   # Ok
   out, = imported_abs(mx.array(-1.0))
-
+  
-  # Also ok
+  # Also ok 
   out, = imported_abs(mx.array([-1.0, -2.0]))
 
 With ``shapeless=False`` (which is the default), the second call to
@@ -197,7 +197,7 @@ a single file by creating an exporting context manager with :func:`exporter`:
   def fun(x, y=None):
       constant = mx.array(3.0)
       if y is not None:
-        x += y
+        x += y 
       return x + constant
 
   with mx.exporter("fun.mlxfn", fun) as exporter:
@@ -215,7 +215,7 @@ a single file by creating an exporting context manager with :func:`exporter`:
   print(out)
 
 In the above example the function constant data, (i.e. ``constant``), is only
-saved once.
+saved once. 
 
 Transformations with Imported Functions
 ---------------------------------------
@@ -238,7 +238,7 @@ on imported functions just like regular Python functions:
   # Prints: array(1, dtype=float32)
   print(dfdx(x))
 
-  # Compile the imported function
+  # Compile the imported function 
   mx.compile(imported_fun)
   # Prints: array(0, dtype=float32)
   print(compiled_fun(x)[0])
@@ -275,7 +275,7 @@ Import and run the function in C++ with only a few lines of code:
   // Prints: array(2, dtype=float32)
   std::cout << outputs[0] << std::endl;
 
-Imported functions can be transformed in C++ just like in Python. Use
+Imported functions can be transformed in C++ just like in Python. Use 
 ``std::vector<mx::array>`` for positional arguments and ``std::map<std::string,
 mx::array>`` for keyword arguments when calling imported functions in C++.
 

mlx/backend/cuda/CMakeLists.txt

@@ -39,6 +39,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce/row_reduce.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rms_norm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rope.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/scaled_dot_product_attention.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/scan.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/slicing.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cu

mlx/backend/cuda/primitives.cpp

@@ -6,17 +6,6 @@
 
 namespace mlx::core {
 
-bool fast::ScaledDotProductAttention::use_fallback(
-    const array& q,
-    const array& k,
-    const array& v,
-    bool has_mask,
-    bool has_arr_mask,
-    bool do_causal,
-    Stream s) {
-  return true;
-}
-
 #define NO_GPU_MULTI(func)                                             \
   void func::eval_gpu(                                                 \
       const std::vector<array>& inputs, std::vector<array>& outputs) { \
@@ -53,7 +42,6 @@ NO_GPU_MULTI(Eig)
 NO_GPU_MULTI(Eigh)
 
 namespace fast {
-NO_GPU(ScaledDotProductAttention)
 NO_GPU_MULTI(CustomKernel)
 } // namespace fast
 

mlx/backend/metal/device.h

@@ -104,7 +104,7 @@ struct CommandEncoder {
   };
 
   // Outputs of all kernels in the encoder including temporaries
-  std::unordered_set<const void*>& outputs() {
+  std::unordered_set<const void*> outputs() {
     return all_outputs_;
   };
 

python/mlx/nn/layers/base.py

@@ -178,7 +178,7 @@ class Module(dict):
 
         if strict:
             new_weights = dict(weights)
-            curr_weights = tree_flatten(self.parameters(), destination={})
+            curr_weights = dict(tree_flatten(self.parameters()))
             if extras := (new_weights.keys() - curr_weights.keys()):
                 num_extra = len(extras)
                 extras = ",\n".join(sorted(extras))
@@ -212,7 +212,7 @@ class Module(dict):
         - ``.npz`` will use :func:`mx.savez`
         - ``.safetensors`` will use :func:`mx.save_safetensors`
         """
-        params_dict = tree_flatten(self.parameters(), destination={})
+        params_dict = dict(tree_flatten(self.parameters()))
 
         if file.endswith(".npz"):
             mx.savez(file, **params_dict)

python/mlx/utils.py

@@ -1,7 +1,7 @@
 # Copyright © 2023 Apple Inc.
 from collections import defaultdict
 from itertools import zip_longest
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, List, Optional, Tuple
 
 
 def tree_map(
@@ -114,11 +114,8 @@ def tree_map_with_path(
 
 
 def tree_flatten(
-    tree: Any,
+    tree: Any, prefix: str = "", is_leaf: Optional[Callable] = None
-    prefix: str = "",
+) -> Any:
-    is_leaf: Optional[Callable] = None,
-    destination: Optional[Union[List[Tuple[str, Any]], Dict[str, Any]]] = None,
-) -> Union[List[Tuple[str, Any]], Dict[str, Any]]:
     """Flattens a Python tree to a list of key, value tuples.
 
     The keys are using the dot notation to define trees of arbitrary depth and
@@ -131,12 +128,9 @@ def tree_flatten(
         print(tree_flatten([[[0]]]))
         # [("0.0.0", 0)]
 
-        print(tree_flatten([[[0]]], prefix=".hello"))
+        print(tree_flatten([[[0]]], ".hello"))
         # [("hello.0.0.0", 0)]
 
-        tree_flatten({"a": {"b": 1}}, destination={})
-        {"a.b": 1}
-
     .. note::
        Dictionaries should have keys that are valid Python identifiers.
 
@@ -146,50 +140,26 @@ def tree_flatten(
             always discarded.
         is_leaf (callable): An optional callable that returns True if the
             passed object is considered a leaf or False otherwise.
-        destination (list or dict, optional): A list or dictionary to store the
-            flattened tree. If None an empty list will be used. Default: ``None``.
 
     Returns:
-        Union[List[Tuple[str, Any]], Dict[str, Any]]: The flat representation of
+        List[Tuple[str, Any]]: The flat representation of the Python tree.
-            the Python tree.
     """
-    if destination is None:
+    flat_tree = []
-        destination = []
 
-    # Create the function to update the destination. We are taking advantage of
+    if is_leaf is None or not is_leaf(tree):
-    # the fact that list.extend and dict.update have the same API to simplify
+        if isinstance(tree, (list, tuple)):
-    # the code a bit.
+            for i, t in enumerate(tree):
-    if isinstance(destination, list):
+                flat_tree.extend(tree_flatten(t, f"{prefix}.{i}", is_leaf))
-        _add_to_destination = destination.extend
+            return flat_tree
-    elif isinstance(destination, dict):
+        if isinstance(tree, dict):
-        _add_to_destination = destination.update
+            for k, t in tree.items():
-    else:
+                flat_tree.extend(tree_flatten(t, f"{prefix}.{k}", is_leaf))
-        raise ValueError("Destination should be either a list or a dictionary or None")
+            return flat_tree
 
-    # Leaf identified by is_leaf so add it and return
+    return [(prefix[1:], tree)]
-    if is_leaf is not None and is_leaf(tree):
-        _add_to_destination([(prefix[1:], tree)])
-        return destination
-
-    # List or tuple so recursively add each subtree
-    if isinstance(tree, (list, tuple)):
-        for i, item in enumerate(tree):
-            tree_flatten(item, f"{prefix}.{i}", is_leaf, destination)
-        return destination
-
-    # Dictionary so recursively add each subtree
-    if isinstance(tree, dict):
-        for key, value in tree.items():
-            tree_flatten(value, f"{prefix}.{key}", is_leaf, destination)
-        return destination
-
-    # Leaf so add it and return
-    _add_to_destination([(prefix[1:], tree)])
-
-    return destination
 
 
-def tree_unflatten(tree: Union[List[Tuple[str, Any]], Dict[str, Any]]) -> Any:
+def tree_unflatten(tree: List[Tuple[str, Any]]) -> Any:
     """Recreate a Python tree from its flat representation.
 
     .. code-block:: python
@@ -200,34 +170,31 @@ def tree_unflatten(tree: Union[List[Tuple[str, Any]], Dict[str, Any]]) -> Any:
         print(d)
         # {"hello": {"world": 42}}
 
-        d = tree_unflatten({"hello.world": 42})
-        print(d)
-        # {"hello": {"world": 42}}
-
     Args:
-        tree (list[tuple[str, Any]] or dict[str, Any]): The flat representation of a Python tree.
+        tree (list[tuple[str, Any]]): The flat representation of a Python tree.
            For instance as returned by :meth:`tree_flatten`.
 
     Returns:
         A Python tree.
     """
-    items = tree.items() if isinstance(tree, dict) else tree
+    if len(tree) == 1 and tree[0][0] == "":
+        return tree[0][1]
 
-    # Special case when we have just one element in the tree ie not a tree
+    try:
-    if len(items) == 1:
+        int(tree[0][0].split(".", maxsplit=1)[0])
-        key, value = next(iter(items))
+        is_list = True
-        if key == "":
+    except ValueError:
-            return value
+        is_list = False
 
     # collect children
     children = defaultdict(list)
-    for key, value in items:
+    for key, value in tree:
         current_idx, *next_idx = key.split(".", maxsplit=1)
         next_idx = "" if not next_idx else next_idx[0]
         children[current_idx].append((next_idx, value))
 
-    # Assume they are a list and fail to dict if the keys are not all integers
+    # recursively map them to the original container
-    try:
+    if is_list:
         keys = sorted((int(idx), idx) for idx in children.keys())
         l = []
         for i, k in keys:
@@ -235,7 +202,7 @@ def tree_unflatten(tree: Union[List[Tuple[str, Any]], Dict[str, Any]]) -> Any:
             l.extend([{} for _ in range(i - len(l))])
             l.append(tree_unflatten(children[k]))
         return l
-    except ValueError:
+    else:
         return {k: tree_unflatten(v) for k, v in children.items()}
 
 

python/tests/test_nn.py

@@ -80,7 +80,7 @@ class TestBase(mlx_tests.MLXTestCase):
                 self.weights = {"w1": mx.zeros((2, 2)), "w2": mx.ones((2, 2))}
 
         model = DictModule()
-        params = tree_flatten(model.parameters(), destination={})
+        params = dict(tree_flatten(model.parameters()))
         self.assertEqual(len(params), 2)
         self.assertTrue(mx.array_equal(params["weights.w1"], mx.zeros((2, 2))))
         self.assertTrue(mx.array_equal(params["weights.w2"], mx.ones((2, 2))))