[Hackathon 181] Add TVM support for FastDeploy on macOS (#1969)

* update for tvm backend * update third_party * update third_party * update * update * update * update * update * update * update * update --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
2026-04-22 16:07:51 +08:00 · 2023-05-25 19:59:02 +08:00
parent 49c033a828
commit 643730bf5f
20 changed files with 658 additions and 31 deletions
@@ -0,0 +1,58 @@
+import paddle
+import tvm
+from tvm import relay
+from tvm.contrib import graph_executor
+import os
+import argparse
+
+
+def get_config():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_path", default="./picodet_l_320_coco_lcnet/model")
+    parser.add_argument(
+        "--shape_dict",
+        default={"image": [1, 3, 320, 320],
+                 "scale_factor": [1, 2]})
+    parser.add_argument("--tvm_save_name", default="tvm_model")
+    parser.add_argument("--tvm_save_path", default="./tvm_save")
+    args = parser.parse_args()
+    return args
+
+
+def read_model(model_path):
+    return paddle.jit.load(model_path)
+
+
+def paddle_to_tvm(paddle_model,
+                  shape_dict,
+                  tvm_save_name="tvm_model",
+                  tvm_save_path="./tvm_save"):
+    if isinstance(shape_dict, str):
+        shape_dict = eval(shape_dict)
+    mod, params = relay.frontend.from_paddle(paddle_model, shape_dict)
+    # 这里首先在PC的CPU上进行测试 所以使用LLVM进行导出
+    target = tvm.target.Target("llvm", host="llvm")
+    dev = tvm.cpu(0)
+    # 这里利用TVM构建出优化后模型的信息
+    with tvm.transform.PassContext(opt_level=2):
+        base_lib = relay.build_module.build(mod, target, params=params)
+        if not os.path.exists(tvm_save_path):
+            os.mkdir(tvm_save_path)
+        lib_save_path = os.path.join(tvm_save_path, tvm_save_name + ".so")
+        base_lib.export_library(lib_save_path)
+        param_save_path = os.path.join(tvm_save_path,
+                                       tvm_save_name + ".params")
+        with open(param_save_path, 'wb') as fo:
+            fo.write(relay.save_param_dict(base_lib.get_params()))
+        module = graph_executor.GraphModule(base_lib['default'](dev))
+        module.load_params(relay.save_param_dict(base_lib.get_params()))
+        print("export success")
+
+
+if __name__ == "__main__":
+    config = get_config()
+    paddle_model = read_model(config.model_path)
+    shape_dict = config.shape_dict
+    paddle_to_tvm(paddle_model, shape_dict, config.tvm_save_name,
+                  config.tvm_save_path)