[to #42322933]fix UT error for 830 version

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10142442

modelscope/models/nlp/star3/configuration_star3.py

@@ -18,21 +18,8 @@
 from __future__ import absolute_import, division, print_function
 import copy
 import logging
-import math
-import os
-import shutil
-import tarfile
-import tempfile
-from pathlib import Path
-from typing import Union
 
 import json
-import numpy as np
-import torch
-import torch_scatter
-from icecream import ic
-from torch import nn
-from torch.nn import CrossEntropyLoss
 
 logger = logging.getLogger(__name__)
 

modelscope/models/nlp/star3/modeling_star3.py

@@ -17,21 +17,15 @@
 
 from __future__ import absolute_import, division, print_function
 import copy
-import logging
 import math
 import os
 import shutil
 import tarfile
 import tempfile
-from pathlib import Path
-from typing import Union
 
-import json
 import numpy as np
 import torch
-import torch_scatter
 from torch import nn
-from torch.nn import CrossEntropyLoss
 
 from modelscope.models.nlp.star3.configuration_star3 import Star3Config
 from modelscope.utils.constant import ModelFile
@@ -121,33 +115,17 @@ class BertEmbeddings(nn.Module):
         words_embeddings = self.word_embeddings(input_ids)
         header_embeddings = self.word_embeddings(header_ids)
 
-        # header mean pooling
-        header_flatten_embeddings = self.word_embeddings(header_flatten_tokens)
-        header_flatten_index = header_flatten_index.reshape(
-            (-1, header_flatten_index.shape[1], 1))
-        header_flatten_index = header_flatten_index.repeat(
-            1, 1, header_flatten_embeddings.shape[2])
-        header_flatten_output = header_flatten_output.reshape(
-            (-1, header_flatten_output.shape[1], 1))
-        header_flatten_output = header_flatten_output.repeat(
-            1, 1, header_flatten_embeddings.shape[2])
-        header_embeddings = torch_scatter.scatter_mean(
-            header_flatten_embeddings,
-            header_flatten_index,
-            out=header_flatten_output,
-            dim=1)
-        token_column_id = token_column_id.reshape(
-            (-1, token_column_id.shape[1], 1))
-        token_column_id = token_column_id.repeat(
-            (1, 1, header_embeddings.shape[2]))
-        token_column_mask = token_column_mask.reshape(
-            (-1, token_column_mask.shape[1], 1))
-        token_column_mask = token_column_mask.repeat(
-            (1, 1, header_embeddings.shape[2]))
-        token_header_embeddings = torch.gather(header_embeddings, 1,
-                                               token_column_id)
-        words_embeddings = words_embeddings * (1.0 - token_column_mask) + \
-            token_header_embeddings * token_column_mask
+        if col_dict_list is not None and l_hs is not None:
+            col_dict_list = np.array(col_dict_list)[ids.cpu().numpy()].tolist()
+            header_len = np.array(
+                header_len, dtype=object)[ids.cpu().numpy()].tolist()
+            for bi, col_dict in enumerate(col_dict_list):
+                for ki, vi in col_dict.items():
+                    length = header_len[bi][vi]
+                    if length == 0:
+                        continue
+                    words_embeddings[bi, ki, :] = torch.mean(
+                        header_embeddings[bi, vi, :length, :], dim=0)
 
         position_embeddings = self.position_embeddings(position_ids)
         token_type_embeddings = self.token_type_embeddings(token_type_ids)

modelscope/models/nlp/table_question_answering.py

@@ -1,11 +1,10 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
 import os
-from typing import Dict, Optional
+from typing import Dict
 
 import numpy
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
 from transformers import BertTokenizer
 
@@ -15,7 +14,6 @@ from modelscope.models.builder import MODELS
 from modelscope.models.nlp.star3.configuration_star3 import Star3Config
 from modelscope.models.nlp.star3.modeling_star3 import Seq2SQL, Star3Model
 from modelscope.preprocessors.star3.fields.struct import Constant
-from modelscope.utils.config import Config
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.device import verify_device
 

modelscope/models/nlp/task_models/sequence_classification.py

@@ -48,7 +48,7 @@ class SequenceClassificationModel(SingleBackboneTaskModelBase):
         self.build_backbone(backbone_cfg)
         self.build_head(head_cfg)
 
-    def forward(self, input: Dict[str, Any]) -> Dict[str, np.ndarray]:
+    def forward(self, **input: Dict[str, Any]) -> Dict[str, np.ndarray]:
         outputs = super().forward(input)
         sequence_output, pooled_output = self.extract_backbone_outputs(outputs)
         outputs = self.head.forward(pooled_output)

modelscope/pipelines/nlp/fill_mask_pipeline.py

@@ -101,7 +101,7 @@ class FillMaskPipeline(Pipeline):
     def forward(self, inputs: Dict[str, Any],
                 **forward_params) -> Dict[str, Any]:
         with torch.no_grad():
-            return self.model(inputs, **forward_params)
+            return self.model(**inputs, **forward_params)
 
     def postprocess(self, inputs: Dict[str, Tensor]) -> Dict[str, Tensor]:
         """process the prediction results

modelscope/pipelines/nlp/fill_mask_ponet_pipeline.py

@@ -97,7 +97,7 @@ class FillMaskPonetPipeline(Pipeline):
     def forward(self, inputs: Dict[str, Any],
                 **forward_params) -> Dict[str, Any]:
         with torch.no_grad():
-            return self.model(inputs, **forward_params)
+            return self.model(**inputs, **forward_params)
 
     def postprocess(self, inputs: Dict[str, Tensor]) -> Dict[str, Tensor]:
         """process the prediction results

modelscope/pipelines/nlp/sequence_classification_pipeline_base.py

@@ -35,7 +35,7 @@ class SequenceClassificationPipelineBase(Pipeline):
     def forward(self, inputs: Dict[str, Any],
                 **forward_params) -> Dict[str, Any]:
         with torch.no_grad():
-            return self.model(inputs, **forward_params)
+            return self.model(**inputs, **forward_params)
 
     def postprocess(self,
                     inputs: Dict[str, Any],

modelscope/pipelines/nlp/table_question_answering_pipeline.py

@@ -2,7 +2,6 @@
 import os
 from typing import Any, Dict, Union
 
-import torch
 from transformers import BertTokenizer
 
 from modelscope.metainfo import Pipelines
@@ -88,7 +87,7 @@ class TableQuestionAnsweringPipeline(Pipeline):
             return current_sql
 
         elif action == 'del_focus':
-            pre_final_sql = copy.deepcopy(history_sql)
+            pre_final_sql = history_sql
             pre_sels = []
             pre_aggs = []
             for idx, seli in enumerate(pre_final_sql['sel']):
@@ -151,7 +150,7 @@ class TableQuestionAnsweringPipeline(Pipeline):
             return pre_final_sql
 
         elif action == 'del_cond':
-            pre_final_sql = copy.deepcopy(history_sql)
+            pre_final_sql = history_sql
 
             final_conds = []
 

modelscope/pipelines/nlp/zero_shot_classification_pipeline.py

@@ -85,7 +85,7 @@ class ZeroShotClassificationPipeline(Pipeline):
     def forward(self, inputs: Dict[str, Any],
                 **forward_params) -> Dict[str, Any]:
         with torch.no_grad():
-            return self.model(inputs, **forward_params)
+            return self.model(**inputs, **forward_params)
 
     def postprocess(self,
                     inputs: Dict[str, Any],

modelscope/trainers/easycv/__init__.py

@@ -0,0 +1,19 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .utils import AddLrLogHook, EasyCVMetric
+else:
+    _import_structure = {'utils': ['AddLrLogHook', 'EasyCVMetric']}
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/trainers/nlp/space/dialog_intent_trainer.py

@@ -5,7 +5,7 @@ from typing import Callable, Dict, Optional, Tuple, Union
 import numpy as np
 
 from modelscope.metainfo import Trainers
-from modelscope.models.nlp.space.model.generator import Generator
+from modelscope.models.nlp.space.model.generator import SpaceGenerator
 from modelscope.models.nlp.space.model.model_base import SpaceModelBase
 from modelscope.preprocessors.space.data_loader import \
     get_sequential_data_loader
@@ -90,7 +90,7 @@ class DialogIntentTrainer(BaseTrainer):
             data_type='test')
 
         # set generator
-        generator = Generator.create(self.cfg, reader=bpe)
+        generator = SpaceGenerator.create(self.cfg, reader=bpe)
         # construct model
         self.model = SpaceModelBase.create(
             self.cfg.Model.init_checkpoint,

modelscope/trainers/trainer.py

@@ -542,7 +542,7 @@ class EpochBasedTrainer(BaseTrainer):
                 value = train_outputs.get(key, None)
                 if value is not None:
                     if dist.is_available() and dist.is_initialized():
-                        value = value.data.clone()
+                        value = value.data.clone().to('cuda')
                         dist.all_reduce(value.div_(dist.get_world_size()))
                     log_vars.update({key: value.item()})
             self.log_buffer.update(log_vars)

modelscope/utils/ast_utils.py

@@ -293,6 +293,9 @@ class AstScaning(object):
                     if type(attribute_node).__name__ == 'Str':
                         result.append((getattr(node,
                                                'arg'), attribute_node.s, None))
+                    elif type(attribute_node).__name__ == 'Constant':
+                        result.append(
+                            (getattr(node, 'arg'), attribute_node.value, None))
                     else:
                         result.append((getattr(node, 'arg'), )
                                       + _get_attribute_item(attribute_node))

modelscope/utils/nlp/nlp_utils.py

@@ -1,4 +1,3 @@
-import os.path as osp
 from typing import List
 
 from modelscope.outputs import OutputKeys

tests/pipelines/test_table_question_answering.py

@@ -1,7 +1,6 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import os
 import unittest
-from typing import List
 
 from transformers import BertTokenizer
 

tests/run_config.yaml

@@ -6,6 +6,9 @@ isolated:  # test cases that may require excessive anmount of GPU memory, which
   - test_video_summarization.py
   - test_dialog_modeling.py
   - test_csanmt_translation.py
+  - test_image_super_resolution.py
+  - test_easycv_trainer.py
+  - test_segformer.py
 
 envs:
   default: # default env, case not in other env will in default, pytorch.

tests/trainers/easycv/test_segformer.py

@@ -31,11 +31,11 @@ class EasyCVTrainerTestSegformer(unittest.TestCase):
         shutil.rmtree(self.tmp_dir, ignore_errors=True)
 
     def _train(self):
-        # adapt to distributed mode
-        from easycv.utils.test_util import pseudo_dist_init
-        pseudo_dist_init()
 
-        cfg_options = {'train.max_epochs': 2}
+        cfg_options = {
+            'train.max_epochs': 2,
+            'model.decode_head.norm_cfg.type': 'BN'
+        }
 
         trainer_name = Trainers.easycv
         train_dataset = MsDataset.load(