【PPSCI Export&Infer No.5】lorenz

docs/zh/examples/lorenz.md

@@ -28,6 +28,24 @@
     python train_transformer.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/lorenz/lorenz_transformer_pretrained.pdparams EMBEDDING_MODEL_PATH=https://paddle-org.bj.bcebos.com/paddlescience/models/lorenz/lorenz_pretrained.pdparams
     ```
 
+=== "模型导出命令"
+
+    ``` sh
+    python train_enn.py mode=export
+    ```
+
+=== "模型推理命令"
+
+    ``` sh
+    # linux
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/transformer_physx/lorenz_training_rk.hdf5 -P ./datasets/
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/transformer_physx/lorenz_valid_rk.hdf5 -P ./datasets/
+    # windows
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/transformer_physx/lorenz_training_rk.hdf5 --output ./datasets/lorenz_training_rk.hdf5
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/transformer_physx/lorenz_valid_rk.hdf5 --output ./datasets/lorenz_valid_rk.hdf5
+    python train_transformer.py mode=infer EMBEDDING_MODEL_PATH=https://paddle-org.bj.bcebos.com/paddlescience/models/lorenz/lorenz_pretrained.pdparams
+    ```
+
 | 模型 | MSE |
 | :-- | :-- |
 | [lorenz_transformer_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/lorenz/lorenz_transformer_pretrained.pdparams) | 0.054 |

examples/lorenz/conf/transformer.yaml

@@ -24,6 +24,7 @@ hydra:
 mode: train # running mode: train/eval
 seed: 42
 output_dir: ${hydra:run.dir}
+log_freq: 20
 TRAIN_BLOCK_SIZE: 64
 VALID_BLOCK_SIZE: 256
 TRAIN_FILE_PATH: ./datasets/lorenz_training_rk.hdf5
@@ -63,3 +64,21 @@ TRAIN:
 EVAL:
   batch_size: 16
   pretrained_model_path: null
+
+# inference settings
+INFER:
+  pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/lorenz/lorenz_transformer_pretrained.pdparams
+  export_path: ./inference/lorenz_transformer
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdpiparams_path: ${INFER.export_path}.pdiparams
+  device: gpu
+  engine: native
+  precision: fp32
+  onnx_path: ${INFER.export_path}.onnx
+  ir_optim: false
+  min_subgraph_size: 10
+  gpu_mem: 4000
+  gpu_id: 0
+  max_batch_size: 64
+  num_cpu_threads: 4
+  batch_size: 16

examples/lorenz/train_transformer.py

@@ -245,14 +245,92 @@ def evaluate(cfg: DictConfig):
     solver.visualize()
 
 
+def export(cfg: DictConfig):
+    # set model
+    model = ppsci.arch.PhysformerGPT2(**cfg.MODEL)
+
+    # initialize solver
+    solver = ppsci.solver.Solver(
+        model,
+        pretrained_model_path=cfg.INFER.pretrained_model_path,
+    )
+    # export model
+    from paddle.static import InputSpec
+
+    input_spec = [
+        {
+            key: InputSpec([None, 256, 32], "float32", name=key)
+            for key in model.input_keys
+        },
+    ]
+
+    solver.export(input_spec, cfg.INFER.export_path)
+
+
+def inference(cfg: DictConfig):
+    from deploy.python_infer import pinn_predictor
+
+    predictor = pinn_predictor.PINNPredictor(cfg)
+
+    embedding_model = build_embedding_model(cfg.EMBEDDING_MODEL_PATH)
+    output_transform = OutputTransform(embedding_model)
+    dataset_cfg = {
+        "name": "LorenzDataset",
+        "file_path": cfg.VALID_FILE_PATH,
+        "input_keys": cfg.MODEL.input_keys,
+        "label_keys": cfg.MODEL.output_keys,
+        "block_size": cfg.VALID_BLOCK_SIZE,
+        "stride": 1024,
+        "embedding_model": embedding_model,
+    }
+
+    dataset = ppsci.data.dataset.build_dataset(dataset_cfg)
+
+    input_dict = {
+        "embeds": dataset.embedding_data[: cfg.VIS_DATA_NUMS, :-1, :],
+    }
+
+    output_dict = predictor.predict(
+        {key: input_dict[key] for key in cfg.MODEL.input_keys}, cfg.INFER.batch_size
+    )
+
+    # mapping data to cfg.INFER.output_keys
+    output_dict = {
+        store_key: paddle.to_tensor(output_dict[infer_key])
+        for store_key, infer_key in zip(cfg.MODEL.output_keys, output_dict.keys())
+    }
+
+    input_dict = {
+        "states": dataset.data[: cfg.VIS_DATA_NUMS, 1:, :],
+    }
+
+    output_dict = {
+        "pred_states": output_transform(output_dict).numpy(),
+    }
+
+    data_dict = {**input_dict, **output_dict}
+    for i in range(cfg.VIS_DATA_NUMS):
+        ppsci.visualize.save_plot_from_3d_dict(
+            f"./lorenz_transformer_pred_{i}",
+            {key: value[i] for key, value in data_dict.items()},
+            ("states", "pred_states"),
+        )
+
+
 @hydra.main(version_base=None, config_path="./conf", config_name="transformer.yaml")
 def main(cfg: DictConfig):
     if cfg.mode == "train":
         train(cfg)
     elif cfg.mode == "eval":
         evaluate(cfg)
+    elif cfg.mode == "export":
+        export(cfg)
+    elif cfg.mode == "infer":
+        inference(cfg)
     else:
-        raise ValueError(f"cfg.mode should in ['train', 'eval'], but got '{cfg.mode}'")
+        raise ValueError(
+            f"cfg.mode should in ['train', 'eval', 'export', 'infer'], but got '{cfg.mode}'"
+        )
 
 
 if __name__ == "__main__":