convert to mixed model python api (#43881)

paddle/fluid/pybind/inference_api.cc

@@ -404,6 +404,16 @@ void BindInferenceApi(py::module *m) {
   m->def("get_trt_compile_version", &paddle_infer::GetTrtCompileVersion);
   m->def("get_trt_runtime_version", &paddle_infer::GetTrtRuntimeVersion);
   m->def("get_num_bytes_of_data_type", &paddle_infer::GetNumBytesOfDataType);
+  m->def("convert_to_mixed_precision_bind",
+         &paddle_infer::ConvertToMixedPrecision,
+         py::arg("model_file"),
+         py::arg("params_file"),
+         py::arg("mixed_model_file"),
+         py::arg("mixed_params_file"),
+         py::arg("mixed_precision"),
+         py::arg("backend"),
+         py::arg("keep_io_types") = true,
+         py::arg("black_list") = std::unordered_set<std::string>());
 }
 
 namespace {
@@ -586,6 +596,14 @@ void BindAnalysisConfig(py::module *m) {
       .value("Float32", AnalysisConfig::Precision::kFloat32)
       .value("Int8", AnalysisConfig::Precision::kInt8)
       .value("Half", AnalysisConfig::Precision::kHalf)
+      .value("Bfloat16", AnalysisConfig::Precision::kBf16)
+      .export_values();
+
+  py::enum_<AnalysisConfig::Backend>(analysis_config, "Backend")
+      .value("CPU", AnalysisConfig::Backend::kCPU)
+      .value("GPU", AnalysisConfig::Backend::kGPU)
+      .value("NPU", AnalysisConfig::Backend::kNPU)
+      .value("XPU", AnalysisConfig::Backend::kXPU)
       .export_values();
 
   analysis_config.def(py::init<>())

python/paddle/fluid/inference/__init__.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .wrapper import Config, DataType, PlaceType, PrecisionType, Tensor, Predictor
+from .wrapper import Config, DataType, PlaceType, PrecisionType, BackendType, Tensor, Predictor
+from .wrapper import convert_to_mixed_precision
 
 from ..core import create_predictor, get_version, get_num_bytes_of_data_type, PredictorPool, get_trt_compile_version, get_trt_runtime_version

python/paddle/fluid/inference/wrapper.py

@@ -14,13 +14,17 @@
 
 from ..core import AnalysisConfig, PaddleDType, PaddlePlace
 from ..core import PaddleInferPredictor, PaddleInferTensor
+from ..core import convert_to_mixed_precision_bind
 from .. import core
 
+import os
 import numpy as np
+from typing import Set
 
 DataType = PaddleDType
 PlaceType = PaddlePlace
 PrecisionType = AnalysisConfig.Precision
+BackendType = AnalysisConfig.Backend
 Config = AnalysisConfig
 Tensor = PaddleInferTensor
 Predictor = PaddleInferPredictor
@@ -50,5 +54,37 @@ def tensor_share_external_data(self, data):
             "In share_external_data, we only support LoDTensor data type.")
 
 
+def convert_to_mixed_precision(model_file: str,
+                               params_file: str,
+                               mixed_model_file: str,
+                               mixed_params_file: str,
+                               mixed_precision: PrecisionType,
+                               backend: BackendType,
+                               keep_io_types: bool = True,
+                               black_list: Set = set()):
+    '''
+    Convert a fp32 model to mixed precision model.
+
+    Args:
+        model_file: fp32 model file, e.g. inference.pdmodel.
+        params_file: fp32 params file, e.g. inference.pdiparams.
+        mixed_model_file: The storage path of the converted mixed-precision model.
+        mixed_params_file: The storage path of the converted mixed-precision params.
+        mixed_precision: The precision, e.g. PrecisionType.Half.
+        backend: The backend, e.g. BackendType.GPU.
+        keep_io_types: Whether the model input and output dtype remains unchanged.
+        black_list: Operators that do not convert precision.
+    '''
+    mixed_model_dirname = os.path.dirname(mixed_model_file)
+    mixed_params_dirname = os.path.dirname(mixed_params_file)
+    if not os.path.exists(mixed_model_dirname):
+        os.makedirs(mixed_model_dirname)
+    if not os.path.exists(mixed_params_dirname):
+        os.makedirs(mixed_params_dirname)
+    convert_to_mixed_precision_bind(model_file, params_file, mixed_model_file,
+                                    mixed_params_file, mixed_precision, backend,
+                                    keep_io_types, black_list)
+
+
 Tensor.copy_from_cpu = tensor_copy_from_cpu
 Tensor.share_external_data = tensor_share_external_data

python/paddle/fluid/tests/unittests/ir/CMakeLists.txt

@@ -17,3 +17,4 @@ endforeach()
 add_subdirectory(inference)
 
 set_tests_properties(test_fuse_resnet_unit PROPERTIES TIMEOUT 120)
+set_tests_properties(test_convert_to_mixed_precision PROPERTIES TIMEOUT 300)

python/paddle/fluid/tests/unittests/ir/test_convert_to_mixed_precision.py

+# 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 unittest
+import numpy as np
+import paddle
+
+from paddle.vision.models import resnet50
+from paddle.jit import to_static
+from paddle.static import InputSpec
+
+from paddle.inference import PrecisionType, BackendType
+from paddle.inference import convert_to_mixed_precision
+
+
+@unittest.skipIf(not paddle.is_compiled_with_cuda()
+                 or paddle.get_cudnn_version() < 8000,
+                 'should compile with cuda.')
+class TestConvertToMixedPrecision(unittest.TestCase):
+
+    def test_convert_to_fp16(self):
+        model = resnet50(True)
+        net = to_static(
+            model, input_spec=[InputSpec(shape=[None, 3, 224, 224], name='x')])
+        paddle.jit.save(net, 'resnet50/inference')
+        convert_to_mixed_precision('resnet50/inference.pdmodel',
+                                   'resnet50/inference.pdiparams',
+                                   'mixed/inference.pdmodel',
+                                   'mixed/inference.pdiparams',
+                                   PrecisionType.Half, BackendType.GPU, True)
+
+    def test_convert_to_fp16_with_fp16_input(self):
+        model = resnet50(True)
+        net = to_static(
+            model, input_spec=[InputSpec(shape=[None, 3, 224, 224], name='x')])
+        paddle.jit.save(net, 'resnet50/inference')
+        convert_to_mixed_precision('resnet50/inference.pdmodel',
+                                   'resnet50/inference.pdiparams',
+                                   'mixed1/inference.pdmodel',
+                                   'mixed1/inference.pdiparams',
+                                   PrecisionType.Half, BackendType.GPU, False)
+
+    def test_convert_to_fp16_with_blacklist(self):
+        model = resnet50(True)
+        net = to_static(
+            model, input_spec=[InputSpec(shape=[None, 3, 224, 224], name='x')])
+        paddle.jit.save(net, 'resnet50/inference')
+        convert_to_mixed_precision('resnet50/inference.pdmodel',
+                                   'resnet50/inference.pdiparams',
+                                   'mixed2/inference.pdmodel',
+                                   'mixed2/inference.pdiparams',
+                                   PrecisionType.Half, BackendType.GPU, False,
+                                   set('conv2d'))
+
+    def test_convert_to_bf16(self):
+        model = resnet50(True)
+        net = to_static(
+            model, input_spec=[InputSpec(shape=[None, 3, 224, 224], name='x')])
+        paddle.jit.save(net, 'resnet50/inference')
+        convert_to_mixed_precision('resnet50/inference.pdmodel',
+                                   'resnet50/inference.pdiparams',
+                                   'mixed3/inference.pdmodel',
+                                   'mixed3/inference.pdiparams',
+                                   PrecisionType.Bfloat16, BackendType.GPU,
+                                   True)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/inference/__init__.py

@@ -16,17 +16,20 @@ from ..fluid.inference import Config  # noqa: F401
 from ..fluid.inference import DataType  # noqa: F401
 from ..fluid.inference import PlaceType  # noqa: F401
 from ..fluid.inference import PrecisionType  # noqa: F401
+from ..fluid.inference import BackendType  # noqa: F401
 from ..fluid.inference import Tensor  # noqa: F401
 from ..fluid.inference import Predictor  # noqa: F401
 from ..fluid.inference import create_predictor  # noqa: F401
 from ..fluid.inference import get_version  # noqa: F401
 from ..fluid.inference import get_trt_compile_version  # noqa: F401
 from ..fluid.inference import get_trt_runtime_version  # noqa: F401
+from ..fluid.inference import convert_to_mixed_precision  # noqa: F401
 from ..fluid.inference import get_num_bytes_of_data_type  # noqa: F401
 from ..fluid.inference import PredictorPool  # noqa: F401
 
 __all__ = [  # noqa
-    'Config', 'DataType', 'PlaceType', 'PrecisionType', 'Tensor', 'Predictor',
-    'create_predictor', 'get_version', 'get_trt_compile_version',
-    'get_trt_runtime_version', 'get_num_bytes_of_data_type', 'PredictorPool'
+    'Config', 'DataType', 'PlaceType', 'PrecisionType', 'BackendType', 'Tensor',
+    'Predictor', 'create_predictor', 'get_version', 'get_trt_compile_version',
+    'convert_to_mixed_precision', 'get_trt_runtime_version',
+    'get_num_bytes_of_data_type', 'PredictorPool'
 ]