fix ci

.github/workflows/tests.yml

@@ -30,10 +30,10 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install .[torch,metrics,quality]
+          python -m pip install .[torch,dev]
       - name: Check quality
         run: |
-            make style && make quality
+          make style && make quality
 
   pytest:
     needs: check_code_quality
@@ -53,7 +53,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install .[torch,metrics,quality]
+          python -m pip install .[torch,dev]
       - name: Test with pytest
         run: |
           make test

setup.py

@@ -33,7 +33,7 @@ extra_require = {
     "aqlm": ["aqlm[gpu]>=1.1.0"],
     "qwen": ["transformers_stream_generator"],
     "modelscope": ["modelscope"],
-    "quality": ["ruff"],
+    "dev": ["ruff", "pytest"],
 }
 
 

tests/model/test_attention.py

@@ -23,13 +23,15 @@ def test_attention():
         "fa2": "LlamaFlashAttention2",
     }
     for requested_attention in attention_available:
-        model_args, _, finetuning_args, _ = get_infer_args({
-            "model_name_or_path": TINY_LLAMA,
-            "template": "llama2",
-            "flash_attn": requested_attention,
-        })
+        model_args, _, finetuning_args, _ = get_infer_args(
+            {
+                "model_name_or_path": TINY_LLAMA,
+                "template": "llama2",
+                "flash_attn": requested_attention,
+            }
+        )
         tokenizer = load_tokenizer(model_args)
         model = load_model(tokenizer["tokenizer"], model_args, finetuning_args)
         for module in model.modules():
             if "Attention" in module.__class__.__name__:
-                assert  module.__class__.__name__ == llama_attention_classes[requested_attention]
+                assert module.__class__.__name__ == llama_attention_classes[requested_attention]