[v1] add accelerator (#9607)

tests_v1/accelerator/test_interface.py

@@ -0,0 +1,24 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# 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
+
+from llamafactory.v1.accelerator.interface import DistributedInterface, DistributedStrategy
+
+
+def test_distributed_interface():
+    DistributedInterface(DistributedStrategy())
+    assert DistributedInterface.rank == int(os.getenv("RANK", "0"))
+    assert DistributedInterface.world_size == int(os.getenv("WORLD_SIZE", "1"))

tests_v1/plugins/data_plugins/test_converter.py

@@ -19,7 +19,7 @@ from datasets import load_dataset
 
 from llamafactory.v1.config.data_args import DataArguments
 from llamafactory.v1.core.data_engine import DataEngine
-from llamafactory.v1.plugins.data_plugins.converter import get_converter
+from llamafactory.v1.plugins.data_plugins.converter import DataConverterPlugin
 
 
 @pytest.mark.parametrize("num_samples", [16])
@@ -49,99 +49,27 @@ def test_alpaca_converter(num_samples: int):
         assert data_engine[index] == {"_dataset_name": "tiny_dataset", **expected_data}
 
 
-def test_sharegpt_converter_invalid():
+def test_sharegpt_converter():
     example = {
         "conversations": [
-            {
-                "from": "system",
-                "value": "Processes historical market data to generate trading signals "
-                "based on specified technical indicators.",
-            },
-            {
-                "from": "human",
-                "value": "I possess a detailed dataset, 'Historical_Market_Data.csv'. "
-                "Could you proceed with these function calls to assist me with the task?",
-            },
-            {
-                "from": "gpt",
-                "value": "```tool_call\n{'arguments': '{\"data_file\": \"Historical_Market_Data.csv\"]}', "
-                "'name': 'backtest_trading_signals'}```\n",
-            },
-            {
-                "from": "tool",
-                "value": '<tool id="D2">\n{"analysis": {"RSI_signals": [{"date": "2025-01-10", '
-                '"symbol": "AAPL", "signal": "Buy"}]}}}\n</tool>\n',
-            },
+            {"from": "system", "value": "System"},
+            {"from": "human", "value": "User"},
+            {"from": "gpt", "value": "Assistant"},
         ]
     }
-    dataset_converter = get_converter("sharegpt")
-    assert dataset_converter(example) == {"messages": []}
-
-
-def test_sharegpt_converter_valid():
-    example = {
-        "conversations": [
-            {
-                "from": "system",
-                "value": "Processes historical market data to generate trading signals based on "
-                "specified technical indicators.",
-            },
-            {
-                "from": "human",
-                "value": "I possess a detailed dataset, 'Historical_Market_Data.csv'. "
-                "Could you proceed with these function calls to assist me with the task?",
-            },
-            {
-                "from": "gpt",
-                "value": "```tool_call\n{'arguments': '{\"data_file\": \"Historical_Market_Data.csv\"]}', "
-                "'name': 'backtest_trading_signals'}```\n",
-            },
-        ]
-    }
-    dataset_converter = get_converter("sharegpt")
     expected_data = {
         "messages": [
-            {
-                "content": [
-                    {
-                        "type": "text",
-                        "value": "Processes historical market data to generate trading signals based on "
-                        "specified technical indicators.",
-                    }
-                ],
-                "loss_weight": 0.0,
-                "role": "system",
-            },
-            {
-                "content": [
-                    {
-                        "type": "text",
-                        "value": "I possess a detailed dataset, 'Historical_Market_Data.csv'. "
-                        "Could you proceed with these function calls to assist me with the task?",
-                    }
-                ],
-                "loss_weight": 0.0,
-                "role": "user",
-            },
-            {
-                "content": [
-                    {
-                        "type": "text",
-                        "value": "```tool_call\n{'arguments': '{\"data_file\": \"Historical_Market_Data.csv\"]}', "
-                        "'name': 'backtest_trading_signals'}```\n",
-                    }
-                ],
-                "loss_weight": 1.0,
-                "role": "assistant",
-            },
+            {"content": [{"type": "text", "value": "System"}], "loss_weight": 0.0, "role": "system"},
+            {"content": [{"type": "text", "value": "User"}], "loss_weight": 0.0, "role": "user"},
+            {"content": [{"type": "text", "value": "Assistant"}], "loss_weight": 1.0, "role": "assistant"},
         ]
     }
-    assert dataset_converter(example) == expected_data
+    assert DataConverterPlugin("sharegpt")(example) == expected_data
 
 
 @pytest.mark.parametrize("num_samples", [16])
 def test_pair_converter(num_samples: int):
-    data_args = DataArguments(dataset="frozenleaves/tiny-dpo/dataset_info.yaml")
+    data_args = DataArguments(dataset="llamafactory/tiny-preference-dataset/dataset_info.yaml")
     data_engine = DataEngine(data_args)
     original_data = load_dataset("HuggingFaceH4/orca_dpo_pairs", split="train_prefs")
     indexes = random.choices(range(len(data_engine)), k=num_samples)
@@ -189,6 +117,5 @@ def test_pair_converter(num_samples: int):
 
 if __name__ == "__main__":
     test_alpaca_converter(1)
-    test_sharegpt_converter_invalid()
-    test_sharegpt_converter_valid()
+    test_sharegpt_converter()
     test_pair_converter(1)

tests_v1/plugins/model_plugins/test_kernel_plugin.py

@@ -17,10 +17,13 @@ from unittest.mock import MagicMock, patch
 
 from transformers import AutoModelForCausalLM
 
+from llamafactory.v1.accelerator.helper import get_current_accelerator
+
 
 class TestKernelPlugin(unittest.TestCase):
     @patch("torch.accelerator.current_accelerator")
     def test_apply_kernel(self, mock_get_accelerator):
+        get_current_accelerator.cache_clear()
         mock_device = MagicMock()
         mock_device.type = "npu"
         mock_get_accelerator.return_value = mock_device
@@ -47,6 +50,7 @@ class TestKernelPlugin(unittest.TestCase):
 class Test_Use_V1_Kernels(unittest.TestCase):
     @patch("torch.accelerator.current_accelerator")
     def test_use_v1_kernels(self, mock_get_accelerator):
+        get_current_accelerator.cache_clear()
         mock_device = MagicMock()
         mock_device.type = "npu"
         mock_get_accelerator.return_value = mock_device