fix hf_space

Author: tastelikefeet

swift/ui/app.py

@@ -53,8 +53,12 @@ def run_ui():
             gr.HTML('<p><center>You have duplicated the space, remember remove the `MODELSCOPE_ENVIRONMENT` '
                     'variable to start an unlimited version</center></p>')
         with gr.Tabs():
-            LLMTrain.build_ui(LLMTrain)
-            LLMInfer.build_ui(LLMInfer)
+            if is_shared_ui:
+                LLMInfer.build_ui(LLMInfer)
+                LLMTrain.build_ui(LLMTrain)
+            else:
+                LLMTrain.build_ui(LLMTrain)
+                LLMInfer.build_ui(LLMInfer)
 
     port = os.environ.get('WEBUI_PORT', None)
     concurrent = {}

swift/ui/llm_train/dataset.py

@@ -122,10 +122,6 @@ def do_build_ui(cls, base_tab: Type['BaseUI']):
         with gr.Row():
             gr.Slider(elem_id='dataset_test_ratio', minimum=0.0, maximum=1.0, step=0.05, scale=20)
             gr.Slider(elem_id='max_length', minimum=32, maximum=8192, step=32, scale=20)
-            if not cls.is_studio:
-                gr.Textbox(elem_id='train_dataset_sample', scale=20)
-                gr.Textbox(elem_id='val_dataset_sample', scale=20)
-            else:
-                gr.Textbox(elem_id='train_dataset_sample', value=500, interactive=False, scale=20)
-                gr.Textbox(elem_id='val_dataset_sample', value=50, interactive=False, scale=20)
+            gr.Textbox(elem_id='train_dataset_sample', scale=20)
+            gr.Textbox(elem_id='val_dataset_sample', scale=20)
             gr.Dropdown(elem_id='truncation_strategy', scale=20)

swift/ui/llm_train/llm_train.py

@@ -30,6 +30,12 @@
 
 logger = get_logger()
 
+is_spaces = True if 'SPACE_ID' in os.environ else False
+if is_spaces:
+    is_shared_ui = True if 'modelscope/swift' in os.environ['SPACE_ID'] else False
+else:
+    is_shared_ui = False
+
 
 class LLMTrain(BaseUI):
 
@@ -214,7 +220,10 @@ def do_build_ui(cls, base_tab: Type['BaseUI']):
                         value=default_device,
                         scale=8)
                     gr.Textbox(elem_id='gpu_memory_fraction', scale=4)
-                    gr.Checkbox(elem_id='dry_run', value=False, scale=4)
+                    if is_shared_ui:
+                        gr.Checkbox(elem_id='dry_run', value=True, interactive=False, scale=4)
+                    else:
+                        gr.Checkbox(elem_id='dry_run', value=False, scale=4)
                     submit = gr.Button(elem_id='submit', scale=4, variant='primary')
 
                 Save.build_ui(base_tab)
@@ -232,7 +241,7 @@ def do_build_ui(cls, base_tab: Type['BaseUI']):
                         [cls.element('runtime_tab'), cls.element('log')]).then(
                             cls.train_studio,
                             [value for value in cls.elements().values() if not isinstance(value, (Tab, Accordion))],
-                            [cls.element('log')] + Runtime.all_plots,
+                            [cls.element('log')] + Runtime.all_plots + [cls.element('running_cmd')],
                             queue=True)
                 else:
                     submit.click(
@@ -342,14 +351,17 @@ def train(cls, *args):
     @classmethod
     def train_studio(cls, *args):
         run_command, sft_args, other_kwargs = cls.train(*args)
-        if cls.is_studio:
+        if not other_kwargs['dry_run']:
             lines = collections.deque(maxlen=int(os.environ.get('MAX_LOG_LINES', 50)))
             process = Popen(run_command, shell=True, stdout=PIPE, stderr=STDOUT)
             with process.stdout:
                 for line in iter(process.stdout.readline, b''):
                     line = line.decode('utf-8')
                     lines.append(line)
-                    yield ['\n'.join(lines)] + Runtime.plot(run_command)
+                    yield ['\n'.join(lines)] + Runtime.plot(run_command) + [run_command]
+        else:
+            yield ['Current is dryrun mode, you can only view the training cmd, please '
+                    'duplicate this space to do training.'] + [None] * len(Runtime.sft_plot) + [run_command]
 
     @classmethod
     def train_local(cls, *args):