chore: import upstream snapshot with attribution

deploy/slim/quantization/quant_kl.py

@@ -0,0 +1,172 @@
+# 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
+#
+#     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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, "..", "..", "..")))
+sys.path.append(os.path.abspath(os.path.join(__dir__, "..", "..", "..", "tools")))
+
+import yaml
+import paddle
+import paddle.distributed as dist
+
+paddle.seed(2)
+
+from ppocr.data import build_dataloader, set_signal_handlers
+from ppocr.modeling.architectures import build_model
+from ppocr.losses import build_loss
+from ppocr.optimizer import build_optimizer
+from ppocr.postprocess import build_post_process
+from ppocr.metrics import build_metric
+from ppocr.utils.save_load import load_model
+import tools.program as program
+import paddleslim
+from paddleslim.dygraph.quant import QAT
+import numpy as np
+
+dist.get_world_size()
+
+
+class PACT(paddle.nn.Layer):
+    def __init__(self):
+        super(PACT, self).__init__()
+        alpha_attr = paddle.ParamAttr(
+            name=self.full_name() + ".pact",
+            initializer=paddle.nn.initializer.Constant(value=20),
+            learning_rate=1.0,
+            regularizer=paddle.regularizer.L2Decay(2e-5),
+        )
+
+        self.alpha = self.create_parameter(shape=[1], attr=alpha_attr, dtype="float32")
+
+    def forward(self, x):
+        out_left = paddle.nn.functional.relu(x - self.alpha)
+        out_right = paddle.nn.functional.relu(-self.alpha - x)
+        x = x - out_left + out_right
+        return x
+
+
+quant_config = {
+    # weight preprocess type, default is None and no preprocessing is performed.
+    "weight_preprocess_type": None,
+    # activation preprocess type, default is None and no preprocessing is performed.
+    "activation_preprocess_type": None,
+    # weight quantize type, default is 'channel_wise_abs_max'
+    "weight_quantize_type": "channel_wise_abs_max",
+    # activation quantize type, default is 'moving_average_abs_max'
+    "activation_quantize_type": "moving_average_abs_max",
+    # weight quantize bit num, default is 8
+    "weight_bits": 8,
+    # activation quantize bit num, default is 8
+    "activation_bits": 8,
+    # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
+    "dtype": "int8",
+    # window size for 'range_abs_max' quantization. default is 10000
+    "window_size": 10000,
+    # The decay coefficient of moving average, default is 0.9
+    "moving_rate": 0.9,
+    # for dygraph quantization, layers of type in quantizable_layer_type will be quantized
+    "quantizable_layer_type": ["Conv2D", "Linear"],
+}
+
+
+def sample_generator(loader):
+    def __reader__():
+        for _, data in enumerate(loader):
+            images = np.array(data[0])
+            yield images
+
+    return __reader__
+
+
+def sample_generator_layoutxlm_ser(loader):
+    def __reader__():
+        for _, data in enumerate(loader):
+            input_ids = np.array(data[0])
+            bbox = np.array(data[1])
+            attention_mask = np.array(data[2])
+            token_type_ids = np.array(data[3])
+            images = np.array(data[4])
+            yield [input_ids, bbox, attention_mask, token_type_ids, images]
+
+    return __reader__
+
+
+def main(config, device, logger, vdl_writer):
+    # init dist environment
+    if config["Global"]["distributed"]:
+        dist.init_parallel_env()
+
+    global_config = config["Global"]
+
+    # build dataloader
+    set_signal_handlers()
+    config["Train"]["loader"]["num_workers"] = 0
+    is_layoutxlm_ser = (
+        config["Architecture"]["model_type"] == "kie"
+        and config["Architecture"]["Backbone"]["name"] == "LayoutXLMForSer"
+    )
+    train_dataloader = build_dataloader(config, "Train", device, logger)
+    if config["Eval"]:
+        config["Eval"]["loader"]["num_workers"] = 0
+        valid_dataloader = build_dataloader(config, "Eval", device, logger)
+        if is_layoutxlm_ser:
+            train_dataloader = valid_dataloader
+    else:
+        valid_dataloader = None
+
+    paddle.enable_static()
+    exe = paddle.static.Executor(device)
+
+    if "inference_model" in global_config.keys():  # , 'inference_model'):
+        inference_model_dir = global_config["inference_model"]
+    else:
+        inference_model_dir = os.path.dirname(global_config["pretrained_model"])
+        if not (
+            os.path.exists(os.path.join(inference_model_dir, "inference.pdmodel"))
+            and os.path.exists(os.path.join(inference_model_dir, "inference.pdiparams"))
+        ):
+            raise ValueError(
+                "Please set inference model dir in Global.inference_model or Global.pretrained_model for post-quantization"
+            )
+
+    if is_layoutxlm_ser:
+        generator = sample_generator_layoutxlm_ser(train_dataloader)
+    else:
+        generator = sample_generator(train_dataloader)
+
+    paddleslim.quant.quant_post_static(
+        executor=exe,
+        model_dir=inference_model_dir,
+        model_filename="inference.pdmodel",
+        params_filename="inference.pdiparams",
+        quantize_model_path=global_config["save_inference_dir"],
+        sample_generator=generator,
+        save_model_filename="inference.pdmodel",
+        save_params_filename="inference.pdiparams",
+        batch_size=1,
+        batch_nums=None,
+    )
+
+
+if __name__ == "__main__":
+    config, device, logger, vdl_writer = program.preprocess(is_train=True)
+    main(config, device, logger, vdl_writer)