add gradient free scipy interface

Author: refraction-ray

CHANGELOG.md

@@ -2,6 +2,10 @@
 
 ## Unreleased
 
+### Added
+
+- add gradient free scipy interface for optimization
+
 ## 0.0.220311
 
 ### Added

setup.py

@@ -27,7 +27,6 @@
     ],
     classifiers=(
         "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: Apache",
         "Operating System :: OS Independent",
     ),
 )

tensorcircuit/interfaces.py

@@ -133,24 +133,44 @@ def backward(ctx: Any, *grad_y: Any) -> Any:
 
 
 def scipy_optimize_interface(
-    fun: Callable[..., Any], shape: Optional[Tuple[int, ...]] = None, jit: bool = True
+    fun: Callable[..., Any],
+    shape: Optional[Tuple[int, ...]] = None,
+    jit: bool = True,
+    gradient: bool = True,
 ) -> Callable[..., Any]:
-    vag = backend.value_and_grad(fun, argnums=0)
+    if gradient:
+        vag = backend.value_and_grad(fun, argnums=0)
+        if jit:
+            vag = backend.jit(vag)
+
+        def scipy_vag(*args: Any, **kws: Any) -> Tuple[Tensor, Tensor]:
+            scipy_args = numpy_args_to_backend(args, dtype=dtypestr)
+            if shape is not None:
+                scipy_args = list(scipy_args)
+                scipy_args[0] = backend.reshape(scipy_args[0], shape)
+                scipy_args = tuple(scipy_args)
+            vs, gs = vag(*scipy_args, **kws)
+            scipy_vs = general_args_to_numpy(vs)
+            gs = backend.reshape(gs, [-1])
+            scipy_gs = general_args_to_numpy(gs)
+            scipy_vs = scipy_vs.astype(np.float64)
+            scipy_gs = scipy_gs.astype(np.float64)
+            return scipy_vs, scipy_gs
+
+        return scipy_vag
+    # no gradient
     if jit:
-        vag = backend.jit(vag)
+        fun = backend.jit(fun)
 
-    def scipy_vag(*args: Any, **kws: Any) -> Tuple[Tensor, Tensor]:
+    def scipy_v(*args: Any, **kws: Any) -> Tensor:
         scipy_args = numpy_args_to_backend(args, dtype=dtypestr)
         if shape is not None:
             scipy_args = list(scipy_args)
             scipy_args[0] = backend.reshape(scipy_args[0], shape)
             scipy_args = tuple(scipy_args)
-        vs, gs = vag(*scipy_args, **kws)
+        vs = fun(*scipy_args, **kws)
         scipy_vs = general_args_to_numpy(vs)
-        gs = backend.reshape(gs, [-1])
-        scipy_gs = general_args_to_numpy(gs)
         scipy_vs = scipy_vs.astype(np.float64)
-        scipy_gs = scipy_gs.astype(np.float64)
-        return scipy_vs, scipy_gs
+        return scipy_vs
 
-    return scipy_vag
+    return scipy_v

tests/test_interfaces.py

@@ -105,7 +105,7 @@ def f3(x):
     np.testing.assert_allclose(pg, 2 * np.ones([2]).astype(np.complex64), atol=1e-5)
 
 
-@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
+@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
 def test_scipy_interface(backend):
     n = 3
 
@@ -124,8 +124,13 @@ def f(param):
         )
         return tc.backend.real(loss)
 
-    f_scipy = tc.interfaces.scipy_optimize_interface(f, shape=[2, n])
-    r = optimize.minimize(f_scipy, np.zeros([2 * n]), method="L-BFGS-B", jac=True)
-    # L-BFGS-B may has issue with float32
-    # see: https://github.com/scipy/scipy/issues/5832
+    if tc.backend.name != "numpy":
+        f_scipy = tc.interfaces.scipy_optimize_interface(f, shape=[2, n])
+        r = optimize.minimize(f_scipy, np.zeros([2 * n]), method="L-BFGS-B", jac=True)
+        # L-BFGS-B may has issue with float32
+        # see: https://github.com/scipy/scipy/issues/5832
+        np.testing.assert_allclose(r["fun"], -1.0, atol=1e-5)
+
+    f_scipy = tc.interfaces.scipy_optimize_interface(f, shape=[2, n], gradient=False)
+    r = optimize.minimize(f_scipy, np.zeros([2 * n]), method="COBYLA")
     np.testing.assert_allclose(r["fun"], -1.0, atol=1e-5)