diff --git a/MindIE/MindIE-Torch/built-in/multimodal/CLIP/compile_aie.py b/MindIE/MindIE-Torch/built-in/multimodal/CLIP/compile_aie.py
new file mode 100644
index 0000000000000000000000000000000000000000..15f27de077b10f42b74e7789d5cfc86b4c672355
--- /dev/null
+++ b/MindIE/MindIE-Torch/built-in/multimodal/CLIP/compile_aie.py
@@ -0,0 +1,165 @@
+# Copyright 2024 Huawei Technologies Co., Ltd
+#
+# 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.
+import os
+import time
+import json
+import logging
+import argparse
+import torch
+import mindietorch
+import torch.nn as nn
+from torch._export import export, dynamic_dim
+from mindietorch import _enums
+from transformers import AutoConfig
+from transformers.models.auto.modeling_auto import AutoModel
+
+logging.basicConfig(level=logging.INFO)
+
+
+class CLIPWrapper(nn.Module):
+    def __init__(self, clip):
+        super(CLIPWrapper, self).__init__()
+        self.model = clip
+        self.logit_scale = clip.logit_scale.exp().to(self.model.device)
+    
+    def forward(self, input_ids, pixel_values, attention_mask):
+        image_embeds = self.model.get_image_features(pixel_values)
+        text_embeds = self.model.get_text_features(input_ids, attention_mask)
+
+        image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
+        text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
+        
+        logits_per_image = image_embeds @ text_embeds.transpose(1, 0).contiguous() * self.logit_scale
+        logits_per_text = logits_per_image.transpose(1, 0).contiguous()
+
+        return image_embeds, text_embeds, logits_per_text, logits_per_image
+
+
+def compile_clip(args):
+    # 加载Pytorch模型
+    with torch.no_grad():
+        torch_model = AutoModel.from_pretrained(args.hf_model_path).float().eval()
+        torch_model = CLIPWrapper(torch_model)
+    config = AutoConfig.from_pretrained(args.hf_model_path)
+    
+    # 构造模型输入
+    image_width = config.vision_config.image_size
+    pixel_values_shape = (args.img_max_batch, 3, image_width, image_width)
+    input_ids_shape = (args.text_max_batch, args.max_token_len)
+    pixel_values = torch.ones(pixel_values_shape, dtype=torch.float32)
+    input_ids = torch.randint(high=1, size=input_ids_shape, dtype=torch.int32)
+    attention_mask = torch.ones_like(input_ids, dtype=torch.int32)
+
+    # 导出fx格式模型并执行MindIE编译
+    constraints = [
+        # input ids
+        dynamic_dim(input_ids, 0) >= 1,
+        dynamic_dim(input_ids, 0) <= args.text_max_batch,
+        dynamic_dim(input_ids, 1) >= 1,
+        dynamic_dim(input_ids, 1) <= 52,
+        # pixel input
+        dynamic_dim(pixel_values, 0) >= 1,
+        dynamic_dim(pixel_values, 0) <= args.img_max_batch,
+        # input ids attention mask
+        dynamic_dim(attention_mask, 0) == dynamic_dim(input_ids, 0),
+        dynamic_dim(attention_mask, 1) == dynamic_dim(input_ids, 1),
+    ]
+
+    logging.info("Starting to export dynamic clip ...")
+    intermediate_model = export(
+        torch_model, 
+        args=(input_ids, pixel_values, attention_mask,), 
+        constraints=constraints
+    )
+    logging.info("Successfully export dynamic clip!")
+
+    mindietorch.set_device(args.device_id)
+    pixel_values_min_shape = (1, 3, image_width, image_width)
+    pixel_values_max_shape = (args.img_max_batch, 3, image_width, image_width)
+    input_ids_min_shape = (1, 1)
+    input_ids_max_shape = (args.text_max_batch, args.max_token_len)
+    
+    # 执行MindIETorch编译
+    compile_inputs = [
+        mindietorch.Input(min_shape=input_ids_min_shape, max_shape=input_ids_max_shape),
+        mindietorch.Input(min_shape=pixel_values_min_shape, max_shape=pixel_values_max_shape),
+        mindietorch.Input(min_shape=input_ids_min_shape, max_shape=input_ids_max_shape), # attention mask
+    ]
+
+    if args.precision == "fp16":
+        model_precision = _enums.PrecisionPolicy.FP16
+    elif args.precision == "fp32":
+        model_precision = _enums.PrecisionPolicy.FP32
+    else:
+        raise ValueError("Unsupported precision type!")
+    
+    logging.info("Starting to compile mindietorch clip ...")
+    ts = time.time()
+    compiled_model = mindietorch.compile(
+        intermediate_model,
+        inputs=compile_inputs,
+        precision_policy=model_precision,
+        soc_version=args.soc_version,
+    )
+    compile_cost = time.time() - ts
+    logging.info("compile time cost: %f", compile_cost)
+    logging.info("Successfully exported mindietorch clip!")
+    
+    logging.info("Starting to save ...")
+    model_save_dir = f"{args.save_dir}"
+    if not os.path.exists(model_save_dir):
+        os.makedirs(model_save_dir)
+    compiled_file_name = f"CLIP-{args.model_version}-MindIE.pt"
+    torch.save(compiled_model, model_save_dir + compiled_file_name, pickle_protocol=4)
+    logging.info("Saving done!")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Compile Clip model")
+    parser.add_argument("--soc-version", type=str, default="Ascend910B4", help="NPU version")
+    parser.add_argument("--device-id", type=int, default=0)
+    parser.add_argument("--text-max-batch", type=int, default=80)
+    parser.add_argument("--img-max-batch", type=int, default=8)
+    parser.add_argument(
+        "--max-token-len", 
+        type=int, 
+        default=52, 
+        help="The padded length of input text (include [CLS] & [SEP] tokens)."
+    )
+    parser.add_argument(
+        "--model-version",
+        default="ViT-B-16",
+        choices=["ViT-B-16", "ViT-L-14", "ViT-L-14-336", "ViT-H-14", "RN50"],
+        help="Specify the architecture of CLIP model to be converted."
+    )
+    parser.add_argument(
+        "--hf-model-path",
+        default="/Path/to/Huggingface_model_path",
+        type=str,
+        help="Huggingface CLIP Model Path."
+    )
+    parser.add_argument(
+        "--precision",
+        default="fp16",
+        choices=["fp16", "fp32"],
+        help="Specify the precision of CLIP model to be converted."
+    )
+    parser.add_argument("--save-dir", type=str, default="./", help="Path to save the exported model")
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    compile_args = parse_args()
+    compile_clip(compile_args)
\ No newline at end of file
diff --git a/MindIE/MindIE-Torch/built-in/multimodal/CLIP/compile_ts.py b/MindIE/MindIE-Torch/built-in/multimodal/CLIP/compile_ts.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f959ff3bd5ef61bb47db7644a456d908dfc33c2
--- /dev/null
+++ b/MindIE/MindIE-Torch/built-in/multimodal/CLIP/compile_ts.py
@@ -0,0 +1,143 @@
+# Copyright 2024 Huawei Technologies Co., Ltd
+#
+# 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.
+import os
+import time
+import json
+import logging
+import argparse
+import torch
+import mindietorch
+import torch.nn as nn
+from mindietorch import _enums
+from transformers import AutoConfig
+from transformers.models.auto.modeling_auto import AutoModel
+
+logging.basicConfig(level=logging.INFO)
+
+
+class CLIPWrapper(nn.Module):
+    def __init__(self, clip):
+        super(CLIPWrapper, self).__init__()
+        self.model = clip
+        self.logit_scale = clip.logit_scale.exp()
+        self.logit_scale.to(self.model.device)
+    
+    def forward(self, input_ids, pixel_values, attention_mask):
+        image_embeds = self.model.get_image_features(pixel_values)
+        text_embeds = self.model.get_text_features(input_ids, attention_mask)
+
+        image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
+        text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
+
+        logits_per_image = image_embeds @ text_embeds.transpose(1, 0).contiguous() * self.logit_scale
+        logits_per_text = logits_per_image.transpose(1, 0).contiguous()
+
+        return image_embeds, text_embeds, logits_per_text, logits_per_image
+
+
+def compile_clip(args):
+    # 加载Pytorch模型
+    with torch.no_grad():
+        torch_model = AutoModel.from_pretrained(args.hf_model_path).float().eval()
+        torch_model = CLIPWrapper(torch_model)
+    config = AutoConfig.from_pretrained(args.hf_model_path)
+    
+    # 构造模型输入
+    image_width = config.vision_config.image_size
+    pixel_values_shape = (args.img_batch, 3, image_width, image_width)
+    input_ids_shape = (args.text_batch, args.token_len)
+    pixel_values = torch.ones(pixel_values_shape, dtype=torch.float32)
+    input_ids = torch.randint(high=1, size=input_ids_shape, dtype=torch.int32)
+    attention_mask = torch.ones_like(input_ids, dtype=torch.int32)
+
+    # 导出ts格式模型并执行MindIE编译
+    input_data = [input_ids, pixel_values, attention_mask]
+    logging.info("Starting to trace clip ...")
+    intermediate_model = torch.jit.trace(torch_model, input_data)
+    logging.info("Successfully trace clip!")
+
+    mindietorch.set_device(args.device_id)
+ 
+    # 执行MindIETorch编译
+    compile_inputs = [
+        mindietorch.Input(shape=input_ids_shape, dtype=torch.int32), 
+        mindietorch.Input(shape=pixel_values_shape, dtype=torch.float32), 
+        mindietorch.Input(shape=input_ids_shape, dtype=torch.int32)
+    ]
+    if args.precision == "fp16":
+        model_precision = _enums.PrecisionPolicy.FP16
+    elif args.precision == "fp32":
+        model_precision = _enums.PrecisionPolicy.FP32
+    else:
+        raise ValueError("Unsupported precision type!")
+    
+    logging.info("Starting to compile mindietorch clip ...")
+    ts = time.time()
+    compiled_model = mindietorch.compile(
+        intermediate_model,
+        inputs=compile_inputs,
+        precision_policy=model_precision,
+        soc_version=args.soc_version,
+    )
+    compile_cost = time.time() - ts
+    logging.info("compile time cost: %f", compile_cost)
+    logging.info("Successfully exported mindietorch clip!")
+    
+    logging.info("Starting to save ...")
+    model_save_dir = f"{args.save_dir}"
+    if not os.path.exists(model_save_dir):
+        os.makedirs(model_save_dir)
+    compiled_file_name = f"CLIP-{args.model_version}-MindIE.ts"
+    compiled_model.save(model_save_dir + compiled_file_name)
+    logging.info("Saving done!")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Compile Clip model")
+    parser.add_argument("--soc-version", default="Ascend910B4", help="NPU version")
+    parser.add_argument("--device-id", type=int, default=0)
+    parser.add_argument("--text-batch", type=int, default=80)
+    parser.add_argument("--img-batch", type=int, default=1)
+    parser.add_argument(
+        "--token-len", 
+        type=int, 
+        default=52, 
+        help="The padded length of input text (include [CLS] & [SEP] tokens)."
+    )
+    parser.add_argument(
+        "--model-version",
+        default="ViT-L-14",
+        choices=["ViT-B-16", "ViT-L-14", "ViT-L-14-336", "ViT-H-14", "RN50"],
+        help="Specify the architecture of CLIP model to be converted."
+    )
+    parser.add_argument(
+        "--hf-model-path",
+        default="/Path/to/Huggingface_model_path",
+        type=str,
+        help="Huggingface CLIP Model Path."
+    )
+    parser.add_argument(
+        "--precision",
+        default="fp16",
+        choices=["fp16", "fp32"],
+        help="Specify the precision of CLIP model to be converted."
+    )
+    parser.add_argument("--save-dir", type=str, default="./", help="Path to save the exported model")
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    compile_args = parse_args()
+    compile_clip(compile_args)
\ No newline at end of file