chore: import upstream snapshot with attribution

deploy/slim/quantization/quant.py

@@ -0,0 +1,226 @@
+# 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
+from paddleslim.dygraph.quant import QAT
+
+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 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()
+    train_dataloader = build_dataloader(config, "Train", device, logger)
+    if config["Eval"]:
+        valid_dataloader = build_dataloader(config, "Eval", device, logger)
+    else:
+        valid_dataloader = None
+
+    # build post process
+    post_process_class = build_post_process(config["PostProcess"], global_config)
+
+    # build model
+    # for rec algorithm
+    if hasattr(post_process_class, "character"):
+        char_num = len(getattr(post_process_class, "character"))
+        if config["Architecture"]["algorithm"] in [
+            "Distillation",
+        ]:  # distillation model
+            for key in config["Architecture"]["Models"]:
+                if (
+                    config["Architecture"]["Models"][key]["Head"]["name"] == "MultiHead"
+                ):  # for multi head
+                    if config["PostProcess"]["name"] == "DistillationSARLabelDecode":
+                        char_num = char_num - 2
+                    # update SARLoss params
+                    assert (
+                        list(config["Loss"]["loss_config_list"][-1].keys())[0]
+                        == "DistillationSARLoss"
+                    )
+                    config["Loss"]["loss_config_list"][-1]["DistillationSARLoss"][
+                        "ignore_index"
+                    ] = (char_num + 1)
+                    out_channels_list = {}
+                    out_channels_list["CTCLabelDecode"] = char_num
+                    out_channels_list["SARLabelDecode"] = char_num + 2
+                    config["Architecture"]["Models"][key]["Head"][
+                        "out_channels_list"
+                    ] = out_channels_list
+                else:
+                    config["Architecture"]["Models"][key]["Head"][
+                        "out_channels"
+                    ] = char_num
+        elif config["Architecture"]["Head"]["name"] == "MultiHead":  # for multi head
+            if config["PostProcess"]["name"] == "SARLabelDecode":
+                char_num = char_num - 2
+            # update SARLoss params
+            assert list(config["Loss"]["loss_config_list"][1].keys())[0] == "SARLoss"
+            if config["Loss"]["loss_config_list"][1]["SARLoss"] is None:
+                config["Loss"]["loss_config_list"][1]["SARLoss"] = {
+                    "ignore_index": char_num + 1
+                }
+            else:
+                config["Loss"]["loss_config_list"][1]["SARLoss"]["ignore_index"] = (
+                    char_num + 1
+                )
+            out_channels_list = {}
+            out_channels_list["CTCLabelDecode"] = char_num
+            out_channels_list["SARLabelDecode"] = char_num + 2
+            config["Architecture"]["Head"]["out_channels_list"] = out_channels_list
+        else:  # base rec model
+            config["Architecture"]["Head"]["out_channels"] = char_num
+
+        if config["PostProcess"]["name"] == "SARLabelDecode":  # for SAR model
+            config["Loss"]["ignore_index"] = char_num - 1
+    model = build_model(config["Architecture"])
+
+    pre_best_model_dict = dict()
+    # load fp32 model to begin quantization
+    pre_best_model_dict = load_model(
+        config, model, None, config["Architecture"]["model_type"]
+    )
+
+    freeze_params = False
+    if config["Architecture"]["algorithm"] in ["Distillation"]:
+        for key in config["Architecture"]["Models"]:
+            freeze_params = freeze_params or config["Architecture"]["Models"][key].get(
+                "freeze_params", False
+            )
+    act = None if freeze_params else PACT
+    quanter = QAT(config=quant_config, act_preprocess=act)
+    quanter.quantize(model)
+
+    if config["Global"]["distributed"]:
+        model = paddle.DataParallel(model)
+
+    # build loss
+    loss_class = build_loss(config["Loss"])
+
+    # build optim
+    optimizer, lr_scheduler, _wd_scheduler = build_optimizer(
+        config["Optimizer"],
+        epochs=config["Global"]["epoch_num"],
+        step_each_epoch=len(train_dataloader),
+        model=model,
+    )
+
+    # resume PACT training process
+    pre_best_model_dict = load_model(
+        config, model, optimizer, config["Architecture"]["model_type"]
+    )
+
+    # build metric
+    eval_class = build_metric(config["Metric"])
+
+    logger.info(
+        "train dataloader has {} iters, valid dataloader has {} iters".format(
+            len(train_dataloader), len(valid_dataloader)
+        )
+    )
+
+    # start train
+    program.train(
+        config,
+        train_dataloader,
+        valid_dataloader,
+        device,
+        model,
+        loss_class,
+        optimizer,
+        lr_scheduler,
+        post_process_class,
+        eval_class,
+        pre_best_model_dict,
+        logger,
+        vdl_writer,
+    )
+
+
+if __name__ == "__main__":
+    config, device, logger, vdl_writer = program.preprocess(is_train=True)
+    main(config, device, logger, vdl_writer)