[Program Capture] Add pytree support to captured qml.grad and `qml.…

…jacobian` (#6134)

**Context:**
#6120 and #6127 add support to capture `qml.grad` and `qml.jacobian` in
plxpr. Once captured, they dispatch to `jax.grad` and `jax.jacobian`.

**Description of the Change:**
This PR adds support for pytree inputs and outputs of the differentiated
functions, similar to #6081.
For this, it extends the internal class `FlatFn` by the extra
functionality to turn the wrapper into a `*flat_args -> *flat_outputs`
function, instead of a `*pytree_args -> *flat_outputs` function.

**Benefits:**
Pytree support 🌳 

**Possible Drawbacks:**

**Related GitHub Issues:**

[sc-70930]
[sc-71862]

---------

Co-authored-by: Christina Lee <christina@xanadu.ai>
Co-authored-by: Mudit Pandey <mudit.pandey@xanadu.ai>

doc/releases/changelog-dev.md

@@ -29,9 +29,10 @@
 * Differentiation of hybrid programs via `qml.grad` and `qml.jacobian` can now be captured
   into plxpr. When evaluating a captured `qml.grad` (`qml.jacobian`) instruction, it will
   dispatch to `jax.grad` (`jax.jacobian`), which differs from the Autograd implementation
-  without capture.
+  without capture. Pytree inputs and outputs are supported.
   [(#6120)](https://github.com/PennyLaneAI/pennylane/pull/6120)
   [(#6127)](https://github.com/PennyLaneAI/pennylane/pull/6127)
+  [(#6134)](https://github.com/PennyLaneAI/pennylane/pull/6134)
 
 * Improve unit testing for capturing of nested control flows.
   [(#6111)](https://github.com/PennyLaneAI/pennylane/pull/6111)

pennylane/_grad.py

@@ -25,6 +25,7 @@
 
 from pennylane.capture import enabled
 from pennylane.capture.capture_diff import _get_grad_prim, _get_jacobian_prim
+from pennylane.capture.flatfn import FlatFn
 from pennylane.compiler import compiler
 from pennylane.compiler.compiler import CompileError
 
@@ -33,18 +34,53 @@
 
 def _capture_diff(func, argnum=None, diff_prim=None, method=None, h=None):
     """Capture-compatible gradient computation."""
-    import jax  # pylint: disable=import-outside-toplevel
+    # pylint: disable=import-outside-toplevel
+    import jax
+    from jax.tree_util import tree_flatten, tree_leaves, tree_unflatten, treedef_tuple
 
-    if isinstance(argnum, int):
-        argnum = [argnum]
     if argnum is None:
-        argnum = [0]
+        argnum = 0
+    if argnum_is_int := isinstance(argnum, int):
+        argnum = [argnum]
 
     @wraps(func)
     def new_func(*args, **kwargs):
-        jaxpr = jax.make_jaxpr(partial(func, **kwargs))(*args)
-        prim_kwargs = {"argnum": argnum, "jaxpr": jaxpr.jaxpr, "n_consts": len(jaxpr.consts)}
-        return diff_prim.bind(*jaxpr.consts, *args, **prim_kwargs, method=method, h=h)
+        flat_args, in_trees = zip(*(tree_flatten(arg) for arg in args))
+        full_in_tree = treedef_tuple(in_trees)
+
+        # Create a new input tree that only takes inputs marked by argnum into account
+        trainable_in_trees = (in_tree for i, in_tree in enumerate(in_trees) if i in argnum)
+        # If an integer was provided as argnum, unpack the arguments axis of the derivatives
+        if argnum_is_int:
+            trainable_in_tree = list(trainable_in_trees)[0]
+        else:
+            trainable_in_tree = treedef_tuple(trainable_in_trees)
+
+        # Create argnum for the flat list of input arrays. For each flattened argument,
+        # add a list of flat argnums if the argument is trainable and an empty list otherwise.
+        start = 0
+        flat_argnum_gen = (
+            (
+                list(range(start, (start := start + len(flat_arg))))
+                if i in argnum
+                else list(range((start := start + len(flat_arg)), start))
+            )
+            for i, flat_arg in enumerate(flat_args)
+        )
+        flat_argnum = sum(flat_argnum_gen, start=[])
+
+        # Create fully flattened function (flat inputs & outputs)
+        flat_fn = FlatFn(partial(func, **kwargs) if kwargs else func, full_in_tree)
+        flat_args = sum(flat_args, start=[])
+        jaxpr = jax.make_jaxpr(flat_fn)(*flat_args)
+        prim_kwargs = {"argnum": flat_argnum, "jaxpr": jaxpr.jaxpr, "n_consts": len(jaxpr.consts)}
+        out_flat = diff_prim.bind(*jaxpr.consts, *flat_args, **prim_kwargs, method=method, h=h)
+        # flatten once more to go from 2D derivative structure (outputs, args) to flat structure
+        out_flat = tree_leaves(out_flat)
+        assert flat_fn.out_tree is not None, "out_tree should be set after executing flat_fn"
+        # The derivative output tree is the composition of output tree and trainable input trees
+        combined_tree = flat_fn.out_tree.compose(trainable_in_tree)
+        return tree_unflatten(combined_tree, out_flat)
 
     return new_func
 

pennylane/capture/__init__.py

@@ -34,6 +34,7 @@
     ~create_measurement_wires_primitive
     ~create_measurement_mcm_primitive
     ~qnode_call
+    ~FlatFn
 
 
 The ``primitives`` submodule offers easy access to objects with jax dependencies such as
@@ -154,6 +155,7 @@ def _(*args, **kwargs):
     create_measurement_mcm_primitive,
 )
 from .capture_qnode import qnode_call
+from .flatfn import FlatFn
 
 # by defining this here, we avoid
 # E0611: No name 'AbstractOperator' in module 'pennylane.capture' (no-name-in-module)
@@ -196,4 +198,5 @@ def __getattr__(key):
     "AbstractOperator",
     "AbstractMeasurement",
     "qnode_prim",
+    "FlatFn",
 )

pennylane/capture/capture_diff.py

@@ -103,7 +103,7 @@ def _(*args, argnum, jaxpr, n_consts, method, h):
         def func(*inner_args):
             return jax.core.eval_jaxpr(jaxpr, consts, *inner_args)
 
-        return jax.jacobian(func, argnums=argnum)(*args)
+        return jax.tree_util.tree_leaves(jax.jacobian(func, argnums=argnum)(*args))
 
     # pylint: disable=unused-argument
     @jacobian_prim.def_abstract_eval

pennylane/capture/capture_qnode.py

@@ -82,8 +82,12 @@ def _(*args, qnode, shots, device, qnode_kwargs, qfunc_jaxpr, n_consts):
         mps = qfunc_jaxpr.outvars
         return _get_shapes_for(*mps, shots=shots, num_device_wires=len(device.wires))
 
-    def _qnode_jvp(*args_and_tangents, **impl_kwargs):
-        return jax.jvp(partial(qnode_prim.impl, **impl_kwargs), *args_and_tangents)
+    def make_zero(tan, arg):
+        return jax.lax.zeros_like_array(arg) if isinstance(tan, ad.Zero) else tan
+
+    def _qnode_jvp(args, tangents, **impl_kwargs):
+        tangents = tuple(map(make_zero, tangents, args))
+        return jax.jvp(partial(qnode_prim.impl, **impl_kwargs), args, tangents)
 
     ad.primitive_jvps[qnode_prim] = _qnode_jvp
 
@@ -174,7 +178,7 @@ def f(x):
     qnode_kwargs = {"diff_method": qnode.diff_method, **execute_kwargs, **mcm_config}
     qnode_prim = _get_qnode_prim()
 
-    flat_args, _ = jax.tree_util.tree_flatten(args)
+    flat_args = jax.tree_util.tree_leaves(args)
     res = qnode_prim.bind(
         *qfunc_jaxpr.consts,
         *flat_args,

pennylane/capture/explanations.md

@@ -159,12 +159,11 @@ You can also see the const variable `a` as argument `e:i32[]` to the inner neste
 ### Pytree handling
 
 Evaluating a jaxpr requires accepting and returning a flat list of tensor-like inputs and outputs.
-list of tensor-like outputs.  These long lists can be hard to manage and are very
-restrictive on the allowed functions, but we can take advantage of pytrees to allow handling
-arbitrary functions.
+These long lists can be hard to manage and are very restrictive on the allowed functions, but we
+can take advantage of pytrees to allow handling arbitrary functions.
 
 To start, we import the `FlatFn` helper. This class converts a function to one that caches
-the resulting result pytree into `flat_fn.out_tree` when executed. This can be used to repack the
+the result pytree into `flat_fn.out_tree` when executed. This can be used to repack the
 results into the correct shape. It also returns flattened results. This does not particularly
 matter for program capture, as we will only be producing jaxpr from the function, not calling
 it directly.

pennylane/capture/flatfn.py

@@ -29,23 +29,48 @@ class FlatFn:
     property, so that the results can be repacked later. It also returns flattened results
     instead of the original result object.
 
+    If an ``in_tree`` is provided, the function accepts flattened inputs instead of the
+    original inputs with tree structure given by ``in_tree``.
+
+    **Example**
+
+    >>> import jax
+    >>> from pennylane.capture.flatfn import FlatFn
     >>> def f(x):
     ...     return {"y": 2+x["x"]}
     >>> flat_f = FlatFn(f)
-    >>> res = flat_f({"x": 0})
+    >>> arg = {"x": 0.5}
+    >>> res = flat_f(arg)
+    >>> res
+    [2.5]
+    >>> jax.tree_util.tree_unflatten(flat_f.out_tree, res)
+    {'y': 2.5}
+
+    If we want to use a fully flattened function that also takes flat inputs instead of
+    the original inputs with tree structure, we can provide the treedef for this input
+    structure:
+
+    >>> flat_args, in_tree = jax.tree_util.tree_flatten((arg,))
+    >>> flat_f = FlatFn(f, in_tree)
+    >>> res = flat_f(*flat_args)
     >>> res
-    [2]
+    [2.5]
     >>> jax.tree_util.tree_unflatten(flat_f.out_tree, res)
     {'y': 2.5}
 
+    Note that the ``in_tree`` has to be  created by flattening a tuple of all input
+    arguments, even if there is only a single argument.
     """
 
-    def __init__(self, f):
+    def __init__(self, f, in_tree=None):
         self.f = f
+        self.in_tree = in_tree
         self.out_tree = None
         update_wrapper(self, f)
 
     def __call__(self, *args):
+        if self.in_tree is not None:
+            args = jax.tree_util.tree_unflatten(self.in_tree, args)
         out = self.f(*args)
         out_flat, out_tree = jax.tree_util.tree_flatten(out)
         self.out_tree = out_tree