fix: fix optree compatibility for multi-tree-map with None values

.github/workflows/lint.yml

@@ -29,10 +29,10 @@ jobs:
           submodules: "recursive"
           fetch-depth: 1
 
-      - name: Set up Python 3.8
+      - name: Set up Python 3.9
         uses: actions/setup-python@v4
         with:
-          python-version: "3.8"
+          python-version: "3.9"
           update-environment: true
 
       - name: Setup CUDA Toolkit

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ ci:
 default_stages: [commit, push, manual]
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
       - id: check-symlinks
       - id: destroyed-symlinks
@@ -26,11 +26,11 @@ repos:
       - id: debug-statements
       - id: double-quote-string-fixer
   - repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v16.0.6
+    rev: v17.0.4
     hooks:
       - id: clang-format
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.287
+    rev: v0.1.4
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]
@@ -39,11 +39,11 @@ repos:
     hooks:
       - id: isort
   - repo: https://github.com/psf/black
-    rev: 23.7.0
+    rev: 23.11.0
     hooks:
       - id: black-jupyter
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.10.1
+    rev: v3.15.0
     hooks:
       - id: pyupgrade
         args: [--py38-plus] # sync with requires-python
@@ -68,7 +68,7 @@ repos:
             ^docs/source/conf.py$
           )
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.5
+    rev: v2.2.6
     hooks:
       - id: codespell
         additional_dependencies: [".[toml]"]

CHANGELOG.md

@@ -17,11 +17,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
--
+- Set minimal C++ standard to C++17 by [@XuehaiPan](https://github.com/XuehaiPan) in [#195](https://github.com/metaopt/torchopt/pull/195).
 
 ### Fixed
 
--
+- Fix `optree` compatibility for multi-tree-map with `None` values by [@XuehaiPan](https://github.com/XuehaiPan) in [#195](https://github.com/metaopt/torchopt/pull/195).
 
 ### Removed
 

CMakeLists.txt

@@ -17,13 +17,13 @@ cmake_minimum_required(VERSION 3.11)  # for FetchContent
 project(torchopt LANGUAGES CXX)
 
 include(FetchContent)
-set(PYBIND11_VERSION v2.10.3)
+set(PYBIND11_VERSION v2.11.1)
 
 if(NOT CMAKE_BUILD_TYPE)
     set(CMAKE_BUILD_TYPE Release)
 endif()
 
-set(CMAKE_CXX_STANDARD 14)
+set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 
 find_package(Threads REQUIRED)           # -pthread

docs/requirements.txt

@@ -5,16 +5,17 @@ torch >= 1.13
 --requirement ../requirements.txt
 
 sphinx >= 5.2.1
+sphinxcontrib-bibtex >= 2.4
+sphinx-autodoc-typehints >= 1.20
+myst-nb >= 0.15
+
 sphinx-autoapi
 sphinx-autobuild
 sphinx-copybutton
 sphinx-rtd-theme
 sphinxcontrib-katex
-sphinxcontrib-bibtex
-sphinx-autodoc-typehints >= 1.19.2
 IPython
 ipykernel
 pandoc
-myst-nb
 docutils
 matplotlib

torchopt/alias/sgd.py

@@ -44,6 +44,7 @@
 __all__ = ['sgd']
 
 
+# pylint: disable-next=too-many-arguments
 def sgd(
     lr: ScalarOrSchedule,
     momentum: float = 0.0,

torchopt/alias/utils.py

@@ -108,19 +108,21 @@ def update_fn(
 
             if inplace:
 
-                def f(g: torch.Tensor, p: torch.Tensor) -> torch.Tensor:
+                def f(p: torch.Tensor, g: torch.Tensor | None) -> torch.Tensor | None:
+                    if g is None:
+                        return g
                     if g.requires_grad:
                         return g.add_(p, alpha=weight_decay)
                     return g.add_(p.data, alpha=weight_decay)
 
-                updates = tree_map_(f, updates, params)
+                tree_map_(f, params, updates)
 
             else:
 
-                def f(g: torch.Tensor, p: torch.Tensor) -> torch.Tensor:
-                    return g.add(p, alpha=weight_decay)
+                def f(p: torch.Tensor, g: torch.Tensor | None) -> torch.Tensor | None:
+                    return g.add(p, alpha=weight_decay) if g is not None else g
 
-                updates = tree_map(f, updates, params)
+                updates = tree_map(f, params, updates)
 
             return updates, state
 
@@ -139,7 +141,7 @@ def update_fn(
                     def f(g: torch.Tensor) -> torch.Tensor:
                         return g.neg_()
 
-                    updates = tree_map_(f, updates)
+                    tree_map_(f, updates)
 
                 else:
 
@@ -166,19 +168,21 @@ def update_fn(
 
                 if inplace:
 
-                    def f(g: torch.Tensor, p: torch.Tensor) -> torch.Tensor:
+                    def f(p: torch.Tensor, g: torch.Tensor | None) -> torch.Tensor | None:
+                        if g is None:
+                            return g
                         if g.requires_grad:
                             return g.neg_().add_(p, alpha=weight_decay)
                         return g.neg_().add_(p.data, alpha=weight_decay)
 
-                    updates = tree_map_(f, updates, params)
+                    tree_map_(f, params, updates)
 
                 else:
 
-                    def f(g: torch.Tensor, p: torch.Tensor) -> torch.Tensor:
-                        return g.neg().add_(p, alpha=weight_decay)
+                    def f(p: torch.Tensor, g: torch.Tensor | None) -> torch.Tensor | None:
+                        return g.neg().add_(p, alpha=weight_decay) if g is not None else g
 
-                    updates = tree_map(f, updates, params)
+                    updates = tree_map(f, params, updates)
 
                 return updates, state
 

torchopt/distributed/api.py

@@ -271,6 +271,7 @@ def sum_reducer(results: Iterable[torch.Tensor]) -> torch.Tensor:
     return torch.sum(torch.stack(tuple(results), dim=0), dim=0)
 
 
+# pylint: disable-next=too-many-arguments
 def remote_async_call(
     func: Callable[..., T],
     *,
@@ -328,6 +329,7 @@ def remote_async_call(
     return future
 
 
+# pylint: disable-next=too-many-arguments
 def remote_sync_call(
     func: Callable[..., T],
     *,

torchopt/linalg/cg.py

@@ -53,7 +53,7 @@ def _identity(x: TensorTree) -> TensorTree:
     return x
 
 
-# pylint: disable-next=too-many-locals
+# pylint: disable-next=too-many-arguments,too-many-locals
 def _cg_solve(
     A: Callable[[TensorTree], TensorTree],
     b: TensorTree,
@@ -102,6 +102,7 @@ def body_fn(
     return x_final
 
 
+# pylint: disable-next=too-many-arguments
 def _isolve(
     _isolve_solve: Callable,
     A: TensorTree | Callable[[TensorTree], TensorTree],
@@ -134,6 +135,7 @@ def _isolve(
     return isolve_solve(A, b)
 
 
+# pylint: disable-next=too-many-arguments
 def cg(
     A: TensorTree | Callable[[TensorTree], TensorTree],
     b: TensorTree,

torchopt/linalg/ns.py

@@ -123,12 +123,14 @@ def _ns_inv(A: torch.Tensor, maxiter: int, alpha: float | None = None) -> torch.
         # A^{-1} = a [I - (I - a A)]^{-1} = a [I + (I - a A) + (I - a A)^2 + (I - a A)^3 + ...]
         M = I - alpha * A
         for rank in range(maxiter):
+            # pylint: disable-next=not-callable
             inv_A_hat = inv_A_hat + torch.linalg.matrix_power(M, rank)
         inv_A_hat = alpha * inv_A_hat
     else:
         # A^{-1} = [I - (I - A)]^{-1} = I + (I - A) + (I - A)^2 + (I - A)^3 + ...
         M = I - A
         for rank in range(maxiter):
+            # pylint: disable-next=not-callable
             inv_A_hat = inv_A_hat + torch.linalg.matrix_power(M, rank)
     return inv_A_hat
 

torchopt/nn/stateless.py

@@ -66,8 +66,8 @@
             mod._parameters[attr] = value  # type: ignore[assignment]
         elif hasattr(mod, '_buffers') and attr in mod._buffers:
             mod._buffers[attr] = value
-        elif hasattr(mod, '_meta_parameters') and attr in mod._meta_parameters:  # type: ignore[operator]
-            mod._meta_parameters[attr] = value  # type: ignore[operator,index]
+        elif hasattr(mod, '_meta_parameters') and attr in mod._meta_parameters:
+            mod._meta_parameters[attr] = value
         else:
             setattr(mod, attr, value)
         # pylint: enable=protected-access

torchopt/transform/add_decayed_weights.py

@@ -226,19 +226,21 @@ def update_fn(
 
         if inplace:
 
-            def f(g: torch.Tensor, p: torch.Tensor) -> torch.Tensor:
+            def f(p: torch.Tensor, g: torch.Tensor | None) -> torch.Tensor | None:
+                if g is None:
+                    return g
                 if g.requires_grad:
                     return g.add_(p, alpha=weight_decay)
                 return g.add_(p.data, alpha=weight_decay)
 
-            updates = tree_map_(f, updates, params)
+            tree_map_(f, params, updates)
 
         else:
 
-            def f(g: torch.Tensor, p: torch.Tensor) -> torch.Tensor:
-                return g.add(p, alpha=weight_decay)
+            def f(p: torch.Tensor, g: torch.Tensor | None) -> torch.Tensor | None:
+                return g.add(p, alpha=weight_decay) if g is not None else g
 
-            updates = tree_map(f, updates, params)
+            updates = tree_map(f, params, updates)
 
         return updates, state
 

torchopt/transform/scale_by_adadelta.py

@@ -129,23 +129,15 @@ def update_fn(
 
         if inplace:
 
-            def f(
-                g: torch.Tensor,  # pylint: disable=unused-argument
-                m: torch.Tensor,
-                v: torch.Tensor,
-            ) -> torch.Tensor:
-                return g.mul_(v.add(eps).div_(m.add(eps)).sqrt_())
+            def f(m: torch.Tensor, v: torch.Tensor, g: torch.Tensor | None) -> torch.Tensor | None:
+                return g.mul_(v.add(eps).div_(m.add(eps)).sqrt_()) if g is not None else g
 
         else:
 
-            def f(
-                g: torch.Tensor,  # pylint: disable=unused-argument
-                m: torch.Tensor,
-                v: torch.Tensor,
-            ) -> torch.Tensor:
-                return g.mul(v.add(eps).div_(m.add(eps)).sqrt_())
+            def f(m: torch.Tensor, v: torch.Tensor, g: torch.Tensor | None) -> torch.Tensor | None:
+                return g.mul(v.add(eps).div_(m.add(eps)).sqrt_()) if g is not None else g
 
-        updates = tree_map(f, updates, mu, state.nu)
+        updates = tree_map(f, mu, state.nu, updates)
 
         nu = update_moment.impl(  # type: ignore[attr-defined]
             updates,

torchopt/transform/scale_by_adam.py

@@ -132,6 +132,7 @@ def _scale_by_adam_flat(
     )
 
 
+# pylint: disable-next=too-many-arguments
 def _scale_by_adam(
     b1: float = 0.9,
     b2: float = 0.999,
@@ -200,23 +201,15 @@ def update_fn(
 
         if inplace:
 
-            def f(
-                g: torch.Tensor,  # pylint: disable=unused-argument
-                m: torch.Tensor,
-                v: torch.Tensor,
-            ) -> torch.Tensor:
-                return m.div_(v.add_(eps_root).sqrt_().add(eps))
+            def f(m: torch.Tensor, v: torch.Tensor, g: torch.Tensor | None) -> torch.Tensor | None:
+                return m.div_(v.add_(eps_root).sqrt_().add(eps)) if g is not None else g
 
         else:
 
-            def f(
-                g: torch.Tensor,  # pylint: disable=unused-argument
-                m: torch.Tensor,
-                v: torch.Tensor,
-            ) -> torch.Tensor:
-                return m.div(v.add(eps_root).sqrt_().add(eps))
+            def f(m: torch.Tensor, v: torch.Tensor, g: torch.Tensor | None) -> torch.Tensor | None:
+                return m.div(v.add(eps_root).sqrt_().add(eps)) if g is not None else g
 
-        updates = tree_map(f, updates, mu_hat, nu_hat)
+        updates = tree_map(f, mu_hat, nu_hat, updates)
         return updates, ScaleByAdamState(mu=mu, nu=nu, count=count_inc)
 
     return GradientTransformation(init_fn, update_fn)
@@ -283,6 +276,7 @@ def _scale_by_accelerated_adam_flat(
     )
 
 
+# pylint: disable-next=too-many-arguments
 def _scale_by_accelerated_adam(
     b1: float = 0.9,
     b2: float = 0.999,

torchopt/transform/scale_by_adamax.py

@@ -137,23 +137,17 @@ def update_fn(
             already_flattened=already_flattened,
         )
 
-        def update_nu(
-            g: torch.Tensor,
-            n: torch.Tensor,
-        ) -> torch.Tensor:
-            return torch.max(n.mul(b2), g.abs().add_(eps))
+        def update_nu(n: torch.Tensor, g: torch.Tensor | None) -> torch.Tensor | None:
+            return torch.max(n.mul(b2), g.abs().add_(eps)) if g is not None else g
 
-        nu = tree_map(update_nu, updates, state.nu)
+        nu = tree_map(update_nu, state.nu, updates)
 
         one_minus_b1_pow_t = 1 - b1**state.t
 
-        def f(
-            n: torch.Tensor,
-            m: torch.Tensor,
-        ) -> torch.Tensor:
-            return m.div(n).div_(one_minus_b1_pow_t)
+        def f(m: torch.Tensor, n: torch.Tensor | None) -> torch.Tensor:
+            return m.div(n).div_(one_minus_b1_pow_t) if n is not None else m
 
-        updates = tree_map(f, nu, mu)
+        updates = tree_map(f, mu, nu)
 
         return updates, ScaleByAdamaxState(mu=mu, nu=nu, t=state.t + 1)