【pir_save_load】modify pir ci for test_paddle_save_load,py and test_jit_save_load.py (#65446)

xiaoguoguo626807 · web-flow · commit 4d12653fa332 · 2024-06-26T14:19:21.000+08:00
* modify test_jit_save_load

* modify test_jit_save_load

* modify ci save_load
diff --git a/python/paddle/pir/core.py b/python/paddle/pir/core.py
@@ -15,6 +15,7 @@
 
 import numpy as np
 
+import paddle
 from paddle.base.core import Place, VarDesc
 from paddle.base.libpaddle import DataType
 from paddle.base.libpaddle.pir import (
@@ -476,3 +477,77 @@ def static_op_arg_cast_guard(hook):
         yield
     finally:
         set_static_op_arg_pre_cast_hook(original_callback)
+
+
+def set_state_dict(program, state_dict, scope=None):
+    """
+    Set parameters and persistable buffers in state_dict to program.
+    An exception will throw if shape or dtype of the parameters is not match.
+
+    .. note::
+        This function MUST called after run start_up_program
+
+    Args:
+        state_dict(dict): the dict store parameters and persistable buffers.
+            The key is the name of the parameter or the name of the buffer.
+            The value is the tensor of this variable in the given scope.
+        scope(Scope, optional) : If scope is None, state_dict will be set to global scope
+            obtained through 'paddle.static.global_scope()'. Otherwise, value will be set to scope.
+            Default: None
+
+    Returns:
+        None
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+            >>> import paddle.static as static
+
+            >>> paddle.enable_static()
+
+            >>> x = static.data(name="x", shape=[10, 10], dtype='float32')
+            >>> y = static.nn.fc(x, 10)
+            >>> z = static.nn.fc(y, 10)
+
+            >>> place = paddle.CPUPlace()
+            >>> exe = static.Executor(place)
+            >>> exe.run(static.default_startup_program())
+            >>> prog = static.default_main_program()
+
+            >>> path = "./temp/model.pdparams"
+            >>> paddle.save(prog.state_dict(), path)
+            >>> state_dict_load = paddle.load(path)
+            >>> prog.set_state_dict(state_dict_load)
+    """
+    if not isinstance(state_dict, dict):
+        raise TypeError(
+            f"Type of `state_dict` should be dict, but received {type(state_dict)}."
+        )
+
+    condition = True if "StructuredToParameterName@@" in state_dict else False
+    if condition:
+        clear_state_dict = {}
+        for name, value in state_dict.items():
+            if name == "StructuredToParameterName@@":
+                continue
+            if name in state_dict["StructuredToParameterName@@"]:
+                name = state_dict["StructuredToParameterName@@"][name]
+                clear_state_dict[name] = value
+            else:
+                clear_state_dict[name] = value
+    else:
+        clear_state_dict = state_dict
+
+    for name, value in clear_state_dict.items():
+        if isinstance(value, paddle.base.libpaddle.Tensor):
+            continue
+        elif isinstance(value, np.ndarray):
+            clear_state_dict[name] = paddle.to_tensor(value)
+        else:
+            raise TypeError(
+                f"The type of `{name}` should be Tensor, ndarray, but received {type(value)}."
+            )
+    if scope is None:
+        scope = paddle.static.global_scope()
+    program.set_state_dict(clear_state_dict, scope)
diff --git a/test/deprecated/legacy_test/test_save_load_deprecated.py b/test/deprecated/legacy_test/test_save_load_deprecated.py
@@ -0,0 +1,201 @@
+# Copyright (c) 2020 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
+#
+#     https://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 os
+import pickle
+import tempfile
+import unittest
+from io import BytesIO
+
+import numpy as np
+from test_imperative_base import new_program_scope
+
+import paddle
+from paddle import base, nn
+from paddle.jit.api import to_static
+from paddle.jit.translated_layer import INFER_PARAMS_INFO_SUFFIX
+from paddle.nn import Linear
+from paddle.static import InputSpec
+
+IMAGE_SIZE = 784
+CLASS_NUM = 10
+
+SEED = 10
+
+
+class LinearNet(nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
+
+    def forward(self, x):
+        return self._linear(x)
+
+
+class LinearNetReturnHidden(paddle.nn.Layer):
+    def __init__(self, in_size, out_size):
+        super().__init__()
+        self._linear_1 = Linear(in_size, out_size)
+        self._linear_2 = Linear(in_size, out_size)
+
+    @to_static
+    def forward(self, x):
+        y = self._linear_1(x)
+        z = self._linear_2(y)
+        loss = paddle.mean(z)
+        return y, loss
+
+
+class TestSaveLoadProgram(unittest.TestCase):
+    def test_save_load_program(self):
+        paddle.enable_static()
+        temp_dir = tempfile.TemporaryDirectory()
+
+        with new_program_scope():
+            layer = LinearNet()
+            data = paddle.static.data(
+                name='x_static_save', shape=(None, IMAGE_SIZE), dtype='float32'
+            )
+            y_static = layer(data)
+            main_program = paddle.static.default_main_program()
+            startup_program = paddle.static.default_startup_program()
+            origin_main = main_program.desc.serialize_to_string()
+            origin_startup = startup_program.desc.serialize_to_string()
+            path1 = os.path.join(
+                temp_dir.name,
+                "test_paddle_save_load_program/main_program.pdmodel",
+            )
+            path2 = os.path.join(
+                temp_dir.name,
+                "test_paddle_save_load_program/startup_program.pdmodel",
+            )
+            paddle.save(main_program, path1)
+            paddle.save(startup_program, path2)
+
+        with new_program_scope():
+            load_main = paddle.load(path1).desc.serialize_to_string()
+            load_startup = paddle.load(path2).desc.serialize_to_string()
+            self.assertTrue(origin_main == load_main)
+            self.assertTrue(origin_startup == load_startup)
+        temp_dir.cleanup()
+
+
+class TestJitPruneModelAndLoad(unittest.TestCase):
+    def setUp(self):
+        self.linear_size = 4
+        self.temp_dir = tempfile.TemporaryDirectory()
+        self.model_path = os.path.join(
+            self.temp_dir.name, "jit_prune_model_and_load/model"
+        )
+        # enable dygraph mode
+        base.enable_dygraph()
+        # config seed
+        paddle.seed(SEED)
+        paddle.framework.random._manual_program_seed(SEED)
+
+    def tearDown(self):
+        self.temp_dir.cleanup()
+
+    def train_and_save(self):
+        train_layer = LinearNetReturnHidden(8, 8)
+        train_layer = to_static(
+            train_layer,
+            input_spec=[InputSpec([None, 8], name='x')],
+            full_graph=True,
+        )
+        adam = paddle.optimizer.Adam(
+            learning_rate=0.1, parameters=train_layer.parameters()
+        )
+        x = paddle.to_tensor(np.random.random((4, 8)).astype('float32'))
+        for i in range(10):
+            hidden, loss = train_layer(x)
+            loss.backward()
+            adam.minimize(loss)
+            train_layer.clear_gradients()
+
+        output_spec = train_layer.forward.outputs[:1]
+        paddle.jit.save(
+            layer=train_layer,
+            path=self.model_path,
+            input_spec=[x],
+            output_spec=output_spec,
+        )
+
+        return train_layer
+
+    # pir has no need to save extra var info, param always saved with program,
+    # and trainable info saved in program's op attr
+    def test_load_var_not_in_extra_var_info(self):
+        self.train_and_save()
+
+        # chage extra var info
+        var_info_path = self.model_path + INFER_PARAMS_INFO_SUFFIX
+        with open(var_info_path, 'rb') as f:
+            extra_var_info = pickle.load(f)
+            extra_var_info.clear()
+        with open(var_info_path, 'wb') as f:
+            pickle.dump(extra_var_info, f, protocol=2)
+
+        with self.assertRaises(RuntimeError):
+            paddle.jit.load(self.model_path)
+
+
+class TestSaveLoadToMemory(unittest.TestCase):
+    def test_static_save_to_memory(self):
+        paddle.enable_static()
+        with new_program_scope():
+            # create network
+            x = paddle.static.data(
+                name="x", shape=[None, IMAGE_SIZE], dtype='float32'
+            )
+            z = paddle.static.nn.fc(x, 10, bias_attr=False)
+            z = paddle.static.nn.fc(z, 128, bias_attr=False)
+            loss = paddle.mean(z)
+            place = (
+                base.CPUPlace()
+                if not paddle.base.core.is_compiled_with_cuda()
+                else base.CUDAPlace(0)
+            )
+            prog = paddle.static.default_main_program()
+            exe = paddle.static.Executor(place)
+            exe.run(paddle.static.default_startup_program())
+
+            state_dict = prog.state_dict()
+            keys = list(state_dict.keys())
+            tensor = state_dict[keys[0]]
+
+            byio = BytesIO()
+            byio2 = BytesIO()
+            paddle.save(prog, byio2)
+            paddle.save(tensor, byio)
+            paddle.save(state_dict, byio)
+            byio.seek(0)
+            byio2.seek(0)
+
+            prog_load = paddle.load(byio2)
+            self.assertTrue(
+                prog.desc.serialize_to_string()
+                == prog_load.desc.serialize_to_string()
+            )
+
+            tensor_load = paddle.load(byio, return_numpy=True)
+            np.testing.assert_array_equal(tensor_load, np.array(tensor))
+
+            state_dict_load = paddle.load(byio, return_numpy=True)
+            for k, v in state_dict.items():
+                np.testing.assert_array_equal(np.array(v), state_dict_load[k])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/legacy_test/test_jit_save_load.py b/test/legacy_test/test_jit_save_load.py
@@ -15,7 +15,6 @@
 
 import copy
 import os
-import pickle
 import shutil
 import tempfile
 import unittest
@@ -27,7 +26,6 @@
 from paddle import base
 from paddle.base import unique_name
 from paddle.jit.api import to_static
-from paddle.jit.translated_layer import INFER_PARAMS_INFO_SUFFIX
 from paddle.nn import Linear
 from paddle.pir_utils import test_with_dygraph_pir
 from paddle.static import InputSpec
@@ -1112,22 +1110,6 @@ def test_load_pruned_model(self):
             train_layer(x)[0].numpy(), infer_layer(x).numpy()
         )
 
-    # pir has no need to save extra var info, param always saved with program,
-    # and trainable info saved in program's op attr
-    def test_load_var_not_in_extra_var_info(self):
-        self.train_and_save()
-
-        # chage extra var info
-        var_info_path = self.model_path + INFER_PARAMS_INFO_SUFFIX
-        with open(var_info_path, 'rb') as f:
-            extra_var_info = pickle.load(f)
-            extra_var_info.clear()
-        with open(var_info_path, 'wb') as f:
-            pickle.dump(extra_var_info, f, protocol=2)
-
-        with self.assertRaises(RuntimeError):
-            paddle.jit.load(self.model_path)
-
 
 class TestJitSaveMultiCases(unittest.TestCase):
     def setUp(self):
diff --git a/test/legacy_test/test_paddle_save_load.py b/test/legacy_test/test_paddle_save_load.py
