fix: torch.concat compatibility with torch1.8

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10885659

modelscope/models/cv/body_3d_keypoints/body_3d_pose.py

@@ -224,8 +224,8 @@ class BodyKeypointsDetection3D(TorchModel):
             lst_pose2d_cannoical.append(pose2d_canonical[:,
                                                          i - pad:i + pad + 1])
 
-        input_pose2d_rr = torch.concat(lst_pose2d_cannoical, axis=0)
-        input_pose2d_cannoical = torch.concat(lst_pose2d_cannoical, axis=0)
+        input_pose2d_rr = torch.cat(lst_pose2d_cannoical, axis=0)
+        input_pose2d_cannoical = torch.cat(lst_pose2d_cannoical, axis=0)
 
         if self.cfg.model.MODEL.USE_CANONICAL_COORDS:
             input_pose2d_abs = input_pose2d_cannoical.clone()

modelscope/models/science/unifold/data/data_ops.py

@@ -730,7 +730,7 @@ def make_msa_feat_v2(batch):
         batch['cluster_profile'],
         deletion_mean_value,
     ]
-    batch['msa_feat'] = torch.concat(msa_feat, dim=-1)
+    batch['msa_feat'] = torch.cat(msa_feat, dim=-1)
     return batch
 
 
@@ -1320,7 +1320,7 @@ def get_contiguous_crop_idx(
                              asym_offset + this_start + csz))
             asym_offset += ll
 
-    return torch.concat(crop_idxs)
+    return torch.cat(crop_idxs)
 
 
 def get_spatial_crop_idx(

modelscope/models/science/unifold/modules/attentions.py

@@ -217,7 +217,7 @@ class MSAAttention(nn.Module):
                 if mask is not None else None)
             outputs.append(
                 self.mha(q=cur_m, k=cur_m, v=cur_m, mask=cur_mask, bias=bias))
-        return torch.concat(outputs, dim=-3)
+        return torch.cat(outputs, dim=-3)
 
     def _attn_forward(self, m, mask, bias: Optional[torch.Tensor] = None):
         m = self.layer_norm_m(m)

modelscope/pipelines/base.py

@@ -233,7 +233,7 @@ class Pipeline(ABC):
                 batch_data[k] = value_list
         for k in batch_data.keys():
             if isinstance(batch_data[k][0], torch.Tensor):
-                batch_data[k] = torch.concat(batch_data[k])
+                batch_data[k] = torch.cat(batch_data[k])
         return batch_data
 
     def _process_batch(self, input: List[Input], batch_size,

modelscope/pipelines/multi_modal/image_captioning_pipeline.py

@@ -46,17 +46,17 @@ class ImageCaptioningPipeline(Pipeline):
                 batch_data['samples'] = [d['samples'][0] for d in data]
                 batch_data['net_input'] = {}
                 for k in data[0]['net_input'].keys():
-                    batch_data['net_input'][k] = torch.concat(
+                    batch_data['net_input'][k] = torch.cat(
                         [d['net_input'][k] for d in data])
 
             return batch_data
         elif isinstance(self.model, MPlugForAllTasks):
             from transformers.tokenization_utils_base import BatchEncoding
             batch_data = dict(train=data[0]['train'])
-            batch_data['image'] = torch.concat([d['image'] for d in data])
+            batch_data['image'] = torch.cat([d['image'] for d in data])
             question = {}
             for k in data[0]['question'].keys():
-                question[k] = torch.concat([d['question'][k] for d in data])
+                question[k] = torch.cat([d['question'][k] for d in data])
             batch_data['question'] = BatchEncoding(question)
             return batch_data
         else: