update to 0.9.2.dev0

tests/data/test_template.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import os
-from typing import TYPE_CHECKING, List, Sequence
+from typing import TYPE_CHECKING, Sequence
 
 import pytest
 from transformers import AutoTokenizer
@@ -42,39 +42,36 @@ MESSAGES = [
 def _check_tokenization(
     tokenizer: "PreTrainedTokenizer", batch_input_ids: Sequence[Sequence[int]], batch_text: Sequence[str]
 ) -> None:
+    r"""
+    Checks token ids and texts.
+
+    encode(text) == token_ids
+    decode(token_ids) == text
+    """
     for input_ids, text in zip(batch_input_ids, batch_text):
-        assert input_ids == tokenizer.encode(text, add_special_tokens=False)
+        assert tokenizer.encode(text, add_special_tokens=False) == input_ids
         assert tokenizer.decode(input_ids) == text
 
 
-def _check_single_template(
-    model_id: str, template_name: str, prompt_str: str, answer_str: str, extra_str: str, use_fast: bool
-) -> List[str]:
-    tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=use_fast, token=HF_TOKEN)
-    content_str = tokenizer.apply_chat_template(MESSAGES, tokenize=False)
-    content_ids = tokenizer.apply_chat_template(MESSAGES, tokenize=True)
-    template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template=template_name))
-    prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
-    assert content_str == prompt_str + answer_str + extra_str
-    assert content_ids == prompt_ids + answer_ids + tokenizer.encode(extra_str, add_special_tokens=False)
-    _check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
-    return content_ids
-
-
-def _check_template(model_id: str, template_name: str, prompt_str: str, answer_str: str, extra_str: str = "") -> None:
-    """
-    Checks template for both the slow tokenizer and the fast tokenizer.
+def _check_template(model_id: str, template_name: str, prompt_str: str, answer_str: str, use_fast: bool) -> None:
+    r"""
+    Checks template.
 
     Args:
         model_id: the model id on hugging face hub.
         template_name: the template name.
         prompt_str: the string corresponding to the prompt part.
         answer_str: the string corresponding to the answer part.
-        extra_str: the extra string in the jinja template of the original tokenizer.
+        use_fast: whether to use fast tokenizer.
     """
-    slow_ids = _check_single_template(model_id, template_name, prompt_str, answer_str, extra_str, use_fast=False)
-    fast_ids = _check_single_template(model_id, template_name, prompt_str, answer_str, extra_str, use_fast=True)
-    assert slow_ids == fast_ids
+    tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=use_fast, token=HF_TOKEN)
+    content_str = tokenizer.apply_chat_template(MESSAGES, tokenize=False)
+    content_ids = tokenizer.apply_chat_template(MESSAGES, tokenize=True)
+    template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template=template_name))
+    prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
+    assert content_str == prompt_str + answer_str
+    assert content_ids == prompt_ids + answer_ids
+    _check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
 
 
 @pytest.mark.parametrize("use_fast", [True, False])
@@ -103,8 +100,7 @@ def test_encode_multiturn(use_fast: bool):
     )
     answer_str_1 = "I am fine!<|eot_id|>"
     prompt_str_2 = (
-        "<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|>"
-        "<|start_header_id|>assistant<|end_header_id|>\n\n"
+        "<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
     )
     answer_str_2 = "很高兴认识你！<|eot_id|>"
     _check_tokenization(
@@ -124,20 +120,28 @@ def test_jinja_template(use_fast: bool):
     assert tokenizer.apply_chat_template(MESSAGES) == ref_tokenizer.apply_chat_template(MESSAGES)
 
 
+def test_get_stop_token_ids():
+    tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
+    template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
+    assert set(template.get_stop_token_ids(tokenizer)) == {128008, 128009}
+
+
 @pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
-def test_gemma_template():
+@pytest.mark.parametrize("use_fast", [True, False])
+def test_gemma_template(use_fast: bool):
     prompt_str = (
         "<bos><start_of_turn>user\nHow are you<end_of_turn>\n"
         "<start_of_turn>model\nI am fine!<end_of_turn>\n"
         "<start_of_turn>user\n你好<end_of_turn>\n"
         "<start_of_turn>model\n"
     )
-    answer_str = "很高兴认识你！"
-    _check_template("google/gemma-2-9b-it", "gemma", prompt_str, answer_str, extra_str="<end_of_turn>\n")
+    answer_str = "很高兴认识你！<end_of_turn>\n"
+    _check_template("google/gemma-2-9b-it", "gemma", prompt_str, answer_str, use_fast)
 
 
 @pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
-def test_llama3_template():
+@pytest.mark.parametrize("use_fast", [True, False])
+def test_llama3_template(use_fast: bool):
     prompt_str = (
         "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
         "<|start_header_id|>assistant<|end_header_id|>\n\nI am fine!<|eot_id|>"
@@ -145,10 +149,25 @@ def test_llama3_template():
         "<|start_header_id|>assistant<|end_header_id|>\n\n"
     )
     answer_str = "很高兴认识你！<|eot_id|>"
-    _check_template("meta-llama/Meta-Llama-3-8B-Instruct", "llama3", prompt_str, answer_str)
+    _check_template("meta-llama/Meta-Llama-3-8B-Instruct", "llama3", prompt_str, answer_str, use_fast)
+
+
+@pytest.mark.parametrize(
+    "use_fast", [True, pytest.param(False, marks=pytest.mark.xfail(reason="Phi-4 slow tokenizer is broken."))]
+)
+def test_phi4_template(use_fast: bool):
+    prompt_str = (
+        "<|im_start|>user<|im_sep|>How are you<|im_end|>"
+        "<|im_start|>assistant<|im_sep|>I am fine!<|im_end|>"
+        "<|im_start|>user<|im_sep|>你好<|im_end|>"
+        "<|im_start|>assistant<|im_sep|>"
+    )
+    answer_str = "很高兴认识你！<|im_end|>"
+    _check_template("microsoft/phi-4", "phi4", prompt_str, answer_str, use_fast)
 
 
-def test_qwen_template():
+@pytest.mark.parametrize("use_fast", [True, False])
+def test_qwen_template(use_fast: bool):
     prompt_str = (
         "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
         "<|im_start|>user\nHow are you<|im_end|>\n"
@@ -156,17 +175,18 @@ def test_qwen_template():
         "<|im_start|>user\n你好<|im_end|>\n"
         "<|im_start|>assistant\n"
     )
-    answer_str = "很高兴认识你！<|im_end|>"
-    _check_template("Qwen/Qwen2-7B-Instruct", "qwen", prompt_str, answer_str, extra_str="\n")
+    answer_str = "很高兴认识你！<|im_end|>\n"
+    _check_template("Qwen/Qwen2-7B-Instruct", "qwen", prompt_str, answer_str, use_fast)
 
 
-@pytest.mark.xfail(reason="The fast tokenizer of Yi model is corrupted.")
-def test_yi_template():
+@pytest.mark.parametrize("use_fast", [True, False])
+@pytest.mark.xfail(reason="Yi tokenizer is broken.")
+def test_yi_template(use_fast: bool):
     prompt_str = (
         "<|im_start|>user\nHow are you<|im_end|>\n"
         "<|im_start|>assistant\nI am fine!<|im_end|>\n"
         "<|im_start|>user\n你好<|im_end|>\n"
         "<|im_start|>assistant\n"
     )
-    answer_str = "很高兴认识你！<|im_end|>"
-    _check_template("01-ai/Yi-1.5-6B-Chat", "yi", prompt_str, answer_str)
+    answer_str = "很高兴认识你！<|im_end|>\n"
+    _check_template("01-ai/Yi-1.5-6B-Chat", "yi", prompt_str, answer_str, use_fast)

tests/model/model_utils/test_checkpointing.py

@@ -14,6 +14,7 @@
 
 import os
 
+import pytest
 import torch
 
 from llamafactory.extras.misc import get_current_device
@@ -39,16 +40,11 @@ TRAIN_ARGS = {
 }
 
 
-def test_checkpointing_enable():
-    model = load_train_model(disable_gradient_checkpointing=False, **TRAIN_ARGS)
+@pytest.mark.parametrize("disable_gradient_checkpointing", [False, True])
+def test_vanilla_checkpointing(disable_gradient_checkpointing: bool):
+    model = load_train_model(disable_gradient_checkpointing=disable_gradient_checkpointing, **TRAIN_ARGS)
     for module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
-        assert getattr(module, "gradient_checkpointing") is True
-
-
-def test_checkpointing_disable():
-    model = load_train_model(disable_gradient_checkpointing=True, **TRAIN_ARGS)
-    for module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
-        assert getattr(module, "gradient_checkpointing") is False
+        assert getattr(module, "gradient_checkpointing") != disable_gradient_checkpointing
 
 
 def test_unsloth_gradient_checkpointing():

tests/model/model_utils/test_visual.py

+# 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 pytest
+import torch
+from transformers import AutoConfig, AutoModelForVision2Seq
+
+from llamafactory.hparams import FinetuningArguments, ModelArguments
+from llamafactory.model.adapter import init_adapter
+
+
+@pytest.mark.parametrize(
+    "freeze_vision_tower,freeze_multi_modal_projector,train_mm_proj_only",
+    [
+        (False, False, False),
+        (False, True, False),
+        (True, False, False),
+        (True, True, False),
+        (True, False, True),
+    ],
+)
+def test_visual_full(freeze_vision_tower: bool, freeze_multi_modal_projector: bool, train_mm_proj_only: bool):
+    model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
+    finetuning_args = FinetuningArguments(
+        finetuning_type="full",
+        freeze_vision_tower=freeze_vision_tower,
+        freeze_multi_modal_projector=freeze_multi_modal_projector,
+        train_mm_proj_only=train_mm_proj_only,
+    )
+    config = AutoConfig.from_pretrained(model_args.model_name_or_path)
+    with torch.device("meta"):
+        model = AutoModelForVision2Seq.from_config(config)
+
+    model = init_adapter(config, model, model_args, finetuning_args, is_trainable=True)
+    for name, param in model.named_parameters():
+        if any(key in name for key in ["visual.patch_embed", "visual.blocks"]):
+            assert param.requires_grad != freeze_vision_tower
+        elif "visual.merger" in name:
+            assert param.requires_grad != freeze_multi_modal_projector
+        else:
+            assert param.requires_grad != train_mm_proj_only
+
+
+@pytest.mark.parametrize("freeze_vision_tower", [False, True])
+def test_visual_lora(freeze_vision_tower: bool):
+    model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
+    finetuning_args = FinetuningArguments(finetuning_type="lora", freeze_vision_tower=freeze_vision_tower)
+    config = AutoConfig.from_pretrained(model_args.model_name_or_path)
+    with torch.device("meta"):
+        model = AutoModelForVision2Seq.from_config(config)
+
+    model = init_adapter(config, model, model_args, finetuning_args, is_trainable=True)
+    trainable_params, frozen_params = set(), set()
+    for name, param in model.named_parameters():
+        if param.requires_grad:
+            trainable_params.add(name)
+        else:
+            frozen_params.add(name)
+
+    if freeze_vision_tower:
+        assert "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight" not in trainable_params
+    else:
+        assert "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight" in trainable_params
+
+    assert "merger" not in trainable_params
+    assert "base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight" in trainable_params

tests/train/test_sft_trainer.py

+# 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
+from dataclasses import dataclass, field
+from typing import Any, Dict, List
+
+import pytest
+from transformers import DataCollatorWithPadding
+
+from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
+from llamafactory.hparams import get_train_args
+from llamafactory.model import load_model, load_tokenizer
+from llamafactory.train.sft.trainer import CustomSeq2SeqTrainer
+
+
+DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
+
+TINY_LLAMA = os.getenv("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
+
+TRAIN_ARGS = {
+    "model_name_or_path": TINY_LLAMA,
+    "stage": "sft",
+    "do_train": True,
+    "finetuning_type": "lora",
+    "dataset": "llamafactory/tiny-supervised-dataset",
+    "dataset_dir": "ONLINE",
+    "template": "llama3",
+    "cutoff_len": 1024,
+    "overwrite_cache": False,
+    "overwrite_output_dir": True,
+    "per_device_train_batch_size": 1,
+    "max_steps": 1,
+}
+
+
+@dataclass
+class DataCollatorWithVerbose(DataCollatorWithPadding):
+    verbose_list: List[Dict[str, Any]] = field(default_factory=list)
+
+    def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
+        self.verbose_list.extend(features)
+        batch = super().__call__(features)
+        return {k: v[:, :1] for k, v in batch.items()}  # truncate input length
+
+
+@pytest.mark.parametrize("disable_shuffling", [False, True])
+def test_shuffle(disable_shuffling: bool):
+    model_args, data_args, training_args, finetuning_args, _ = get_train_args(
+        {
+            "output_dir": os.path.join("output", f"shuffle{str(disable_shuffling).lower()}"),
+            "disable_shuffling": disable_shuffling,
+            **TRAIN_ARGS,
+        }
+    )
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module)
+    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
+    data_collator = DataCollatorWithVerbose(tokenizer=tokenizer)
+    trainer = CustomSeq2SeqTrainer(
+        model=model,
+        args=training_args,
+        finetuning_args=finetuning_args,
+        data_collator=data_collator,
+        **dataset_module,
+        **tokenizer_module,
+    )
+    trainer.train()
+    if disable_shuffling:
+        assert data_collator.verbose_list[0]["input_ids"] == dataset_module["train_dataset"][0]["input_ids"]
+    else:
+        assert data_collator.verbose_list[0]["input_ids"] != dataset_module["train_dataset"][0]["input_ids"]