add tests

Former-commit-id: 484634ee9c982e82e919ff67d507e0210345182d

tests/model/model_utils/test_checkpointing.py

@@ -0,0 +1,74 @@
+# Copyright 2024 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
+
+import torch
+
+from llamafactory.extras.misc import get_current_device
+from llamafactory.hparams import get_train_args
+from llamafactory.model import load_model, load_tokenizer
+
+
+TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
+
+TRAIN_ARGS = {
+    "model_name_or_path": TINY_LLAMA,
+    "stage": "sft",
+    "do_train": True,
+    "finetuning_type": "lora",
+    "lora_target": "all",
+    "dataset": "llamafactory/tiny-supervised-dataset",
+    "dataset_dir": "ONLINE",
+    "template": "llama3",
+    "cutoff_len": 1024,
+    "overwrite_cache": True,
+    "output_dir": "dummy_dir",
+    "overwrite_output_dir": True,
+    "fp16": True,
+}
+
+
+def test_checkpointing_enable():
+    model_args, _, _, finetuning_args, _ = get_train_args({"disable_gradient_checkpointing": False, **TRAIN_ARGS})
+    tokenizer_module = load_tokenizer(model_args)
+    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
+    for module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
+        assert getattr(module, "gradient_checkpointing") is True
+
+
+def test_checkpointing_disable():
+    model_args, _, _, finetuning_args, _ = get_train_args({"disable_gradient_checkpointing": True, **TRAIN_ARGS})
+    tokenizer_module = load_tokenizer(model_args)
+    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
+    for module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
+        assert getattr(module, "gradient_checkpointing") is False
+
+
+def test_upcast_layernorm():
+    model_args, _, _, finetuning_args, _ = get_train_args({"upcast_layernorm": True, **TRAIN_ARGS})
+    tokenizer_module = load_tokenizer(model_args)
+    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
+    for name, param in model.named_parameters():
+        if param.ndim == 1 and "norm" in name:
+            assert param.dtype == torch.float32
+
+
+def test_upcast_lmhead_output():
+    model_args, _, _, finetuning_args, _ = get_train_args({"upcast_lmhead_output": True, **TRAIN_ARGS})
+    tokenizer_module = load_tokenizer(model_args)
+    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
+    inputs = torch.randn((1, 16), dtype=torch.float16, device=get_current_device())
+    outputs: "torch.Tensor" = model.lm_head(inputs)
+    assert outputs.dtype == torch.float32