chore: import upstream snapshot with attribution

python/paddle/incubate/optimizer/functional/utils.py

@@ -0,0 +1,120 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+from paddle.base.data_feeder import check_type
+from paddle.base.framework import Variable, in_pir_mode
+
+
+def check_input_type(input, name, op_name):
+    r"""Check whether the input is tensor or variable."""
+    if paddle.in_dynamic_mode():
+        if not isinstance(input, paddle.Tensor):
+            raise ValueError(f"The input: {input} must be tensor.")
+    else:
+        check_type(input, name, (Variable, paddle.pir.Value), op_name)
+
+
+def check_initial_inverse_hessian_estimate(H0):
+    r"""Check whether the specified initial_inverse_hessian_estimate is symmetric and positive definite.
+        Raise errors when precondition not met.
+
+    Note:
+        In static graph can not raise error directly, so use py_func make raise_func as a op,
+        and use paddle.static.nn.cond to decide if put the op in net.
+        cholesky is the fast way to check positive definition, but in static graph can not catch
+        exception to raise value error, so use eigvals rather than cholesky in static graph.
+    """
+    is_symmetric = paddle.all(paddle.equal(H0, H0.t()))
+
+    def raise_func():
+        raise ValueError(
+            "The initial_inverse_hessian_estimate should be symmetric and positive definite, but the specified is not."
+        )
+
+    if paddle.in_dynamic_mode():
+        if not is_symmetric:
+            raise_func()
+        try:
+            paddle.linalg.cholesky(H0)
+        except RuntimeError as error:
+            raise_func()
+    elif in_pir_mode():
+        paddle.static.nn.control_flow.Assert(
+            is_symmetric,
+            None,
+            10,
+            name="The initial_inverse_hessian_estimate should be symmetric and positive definite, but the specified is not.",
+        )
+        eigvals = paddle.linalg.eigvals(H0)
+        is_positive = paddle.bitwise_and(
+            paddle.all(eigvals.real() > 0.0), paddle.all(eigvals.imag() == 0.0)
+        )
+        paddle.static.nn.control_flow.Assert(
+            is_positive,
+            None,
+            10,
+            name="The initial_inverse_hessian_estimate should be symmetric and positive definite, but the specified is not.",
+        )
+
+    else:
+
+        def create_tmp_var(program, name, dtype, shape):
+            return program.current_block().create_var(
+                name=name, dtype=dtype, shape=shape
+            )
+
+        out_var = create_tmp_var(
+            paddle.static.default_main_program(),
+            name='output',
+            dtype='float32',
+            shape=[-1],
+        )
+
+        def false_fn():
+            paddle.static.nn.py_func(
+                func=raise_func, x=is_symmetric, out=out_var
+            )
+
+        paddle.static.nn.cond(is_symmetric, None, false_fn)
+        # eigvals only support cpu
+        paddle.set_device("cpu")
+        eigvals = paddle.linalg.eigvals(H0)
+        is_positive = paddle.all(eigvals.real() > 0.0) and paddle.all(
+            eigvals.imag() == 0.0
+        )
+        paddle.static.nn.cond(is_positive, None, false_fn)
+
+
+def _value_and_gradient(f, x, v=None):
+    r"""Compute function value and gradient of f at x.
+
+    Args:
+        f (Callable): the objective function.
+        x (Tensor): the input tensor.
+    Returns:
+        value: a tensor that holds the function value.
+        gradient: a tensor that holds the function gradients.
+    """
+    # use detach to cut off relation between x and original graph
+    x = x.detach()
+    x.stop_gradient = False
+    value = f(x)
+    if paddle.in_dynamic_mode():
+        # only need to compute first order derivative, and some op dont support high order derivative.
+        gradient = paddle.grad([value], [x], create_graph=False)[0]
+    else:
+        gradient = paddle.static.gradients([value], [x])[0]
+    # use detach to make results real number without grad to avoid assign error
+    return value.detach(), gradient.detach()