root:[./]
|--convbert_base_outputs
|      |--args.json
|      |--best.pdparams
|      |      |--model_config.json
|      |      |--model_state.pdparams
|      |      |--tokenizer_config.json
|      |      |--vocab.txt
|--convbert_infer
|      |--inference.pdiparams
|      |--inference.pdiparams.info
|      |--inference.pdmodel
|      |--tokenizer_config.json
|      |--vocab.txt
|--deploy
|      |--inference_python
|      |      |--infer.py
|      |      |--README.md
|      |--serving_python
|      |      |--config.yml
|      |      |--convbert_client
|      |      |      |--serving_client_conf.prototxt
|      |      |      |--serving_client_conf.stream.prototxt
|      |      |--convbert_server
|      |      |      |--inference.pdiparams
|      |      |      |--inference.pdmodel
|      |      |      |--serving_server_conf.prototxt
|      |      |      |--serving_server_conf.stream.prototxt
|      |      |--PipelineServingLogs
|      |      |      |--pipeline.log
|      |      |      |--pipeline.log.wf
|      |      |      |--pipeline.tracer
|      |      |--pipeline_http_client.py
|      |      |--ProcessInfo.json
|      |      |--README.md
|      |      |--web_service.py
|--images
|      |--convbert_framework.jpg
|      |--py_serving_client_results.jpg
|      |--py_serving_startup_visualization.jpg
|--LICENSE
|--output_inference_engine.npy
|--output_predict_engine.npy
|--paddlenlp
|--print_project_tree.py
|--README.md
|--requirements.txt
|--shell
|      |--export.sh
|      |--inference_python.sh
|      |--predict.sh
|      |--train.sh
|      |--train_dist.sh
|--test_tipc
|      |--common_func.sh
|      |--configs
|      |      |--ConvBERT
|      |      |      |--model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
|      |      |      |--train_infer_python.txt
|      |--docs
|      |      |--test_serving.md
|      |      |--test_train_inference_python.md
|      |      |--tipc_guide.png
|      |      |--tipc_serving.png
|      |      |--tipc_train_inference.png
|      |--output
|      |      |--python_infer_cpu_usemkldnn_False_threads_null_precision_null_batchsize_null.log
|      |      |--python_infer_gpu_usetrt_null_precision_null_batchsize_null.log
|      |      |--results_python.log
|      |      |--results_serving.log
|      |      |--server_infer_gpu_pipeline_http_usetrt_null_precision_null_batchsize_1.log
|      |--README.md
|      |--test_serving.sh
|      |--test_train_inference_python.sh
|--tools
|      |--export_model.py
|      |--predict.py
|--train.log
|--train.py

git clone https://github.com/junnyu/ConvBERT-Prod.git

# 需要安装2.2及以上版本的Paddle，如果
# 安装GPU版本的Paddle
pip install paddlepaddle-gpu==2.2.0
# 安装CPU版本的Paddle
pip install paddlepaddle==2.2.0

pip install -r requirements.txt

export CUDA_VISIBLE_DEVICES=0
python -m paddle.distributed.launch --gpus "0" train.py \
    --model_type convbert \
    --model_name_or_path convbert-base \
    --task_name sst-2 \
    --max_seq_length 128 \
    --learning_rate 1e-4 \
    --num_train_epochs 3 \
    --output_dir ./convbert_base_outputs/ \
    --logging_steps 100 \
    --save_steps 400 \
    --batch_size 32   \
    --warmup_proportion 0.1

====================================================================================================
global step 2500/6315, epoch: 1, batch: 394, rank_id: 0, loss: 0.140546, lr: 0.0000671182, speed: 3.7691 step/s
global step 2600/6315, epoch: 1, batch: 494, rank_id: 0, loss: 0.062813, lr: 0.0000653589, speed: 4.1413 step/s
global step 2700/6315, epoch: 1, batch: 594, rank_id: 0, loss: 0.051268, lr: 0.0000635996, speed: 4.1867 step/s
global step 2800/6315, epoch: 1, batch: 694, rank_id: 0, loss: 0.133289, lr: 0.0000618403, speed: 4.1769 step/s
eval loss: 0.342346, acc: 0.9461009174311926,
eval done total : 1.9056718349456787 s
====================================================================================================

export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m paddle.distributed.launch --gpus "0,1,2,3" train.py \
    --model_type convbert \
    --model_name_or_path convbert-base \
    --task_name sst-2 \
    --max_seq_length 128 \
    --learning_rate 1e-4 \
    --num_train_epochs 3 \
    --output_dir ./convbert_base_outputs/ \
    --logging_steps 100 \
    --save_steps 400 \
    --batch_size 32   \
    --warmup_proportion 0.1

python tools/predict.py --model_path=./convbert_base_outputs/best.pdparams

python tools/predict.py --model_path=./convbert_base_outputs/best.pdparams --device=cpu

bash test_tipc/test_train_inference_python.sh test_tipc/configs/ConvBERT/train_infer_python.txt whole_train_whole_infer

�[33m Run successfully with command - python train.py --save_steps 400      --max_steps=6315           !  �[0m
�[33m Run successfully with command - python tools/export_model.py --model_path=./convbert_base_outputs/best.pdparams --save_inference_dir ./convbert_infer      !  �[0m
�[33m Run successfully with command - python deploy/inference_python/infer.py --model_dir ./convbert_infer --use_gpu=True               > ./test_tipc/output/python_infer_gpu_usetrt_null_precision_null_batchsize_null.log 2>&1 !  �[0m
�[33m Run successfully with command - python deploy/inference_python/infer.py --model_dir ./convbert_infer --use_gpu=False --benchmark=False               > ./test_tipc/output/python_infer_cpu_usemkldnn_False_threads_null_precision_null_batchsize_null.log 2>&1 !  �[0m

if self.conv_type == "sdconv":
    bs = paddle.shape(q)[0]
    seqlen = paddle.shape(q)[1]
    mixed_key_conv_attn_layer = self.key_conv_attn_layer(query)
    conv_attn_layer = mixed_key_conv_attn_layer * q

    # conv_kernel_layer
    conv_kernel_layer = self.conv_kernel_layer(conv_attn_layer)
    conv_kernel_layer = tensor.reshape(
        conv_kernel_layer, shape=[-1, self.conv_kernel_size, 1])
    conv_kernel_layer = F.softmax(conv_kernel_layer, axis=1)
    conv_out_layer = self.conv_out_layer(query)
    conv_out_layer = paddle.stack(
        [
            paddle.slice(F.pad(conv_out_layer, pad=[
                            self.padding, self.padding], data_format="NLC"), [1], starts=[i], ends=[i+seqlen])
            for i in range(self.conv_kernel_size)
        ],
        axis=-1,
    )
    conv_out_layer = tensor.reshape(
        conv_out_layer,
        shape=[-1, self.head_dim, self.conv_kernel_size])
    conv_out_layer = tensor.matmul(conv_out_layer, conv_kernel_layer)
    conv_out = tensor.reshape(
        conv_out_layer,
        shape=[bs, seqlen, self.num_heads, self.head_dim])