feat : preprocessing 支持 DPO/GRPO 多输出格式

- InputConfig 新增 sources 字段驱动多输出映射 - SectionedMaskBuilder 提取 _process_sections/_build_multi 模板方法 - Pipeline 泛化 accumulate 逻辑处理多 key 结果 - 测试拆分为 config/builder/pipeline 三文件，纯函数风格
2026-06-03 10:32:10 +08:00
parent 9fe2121743
commit 02a7cb9fa0
9 changed files with 1529 additions and 931 deletions
@@ -0,0 +1,349 @@
+import json
+import os
+
+from astrai.config.preprocess_config import (
+    InputConfig,
+    OutputConfig,
+    PipelineConfig,
+    ProcessingConfig,
+)
+from astrai.preprocessing.pipeline import Pipeline, filter_by_length
+from tests.data.conftest import (
+    _CHAT_SECTIONS,
+    _CHAT_TEMPLATE,
+    _INSTRUCTION_SECTIONS,
+    _SPECIAL_TOKENS_CONFIG,
+    _TEXT_SECTIONS,
+    make_dpo_chat_config,
+    make_grpo_no_template_config,
+)
+
+
+def test_filter_by_length():
+    assert filter_by_length("hello world", min_len=5)
+    assert not filter_by_length("hi", min_len=5)
+    assert not filter_by_length("x" * 100, max_len=50)
+    assert filter_by_length("just right", min_len=5, max_len=20)
+
+
+def test_full_chat_pipeline(temp_dir, chat_tokenizer):
+    tokenizer_dir = os.path.join(temp_dir, "tok")
+    os.makedirs(tokenizer_dir, exist_ok=True)
+    chat_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
+    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
+        json.dump(
+            {
+                "special_tokens": _SPECIAL_TOKENS_CONFIG,
+                "chat_template": _CHAT_TEMPLATE,
+            },
+            f,
+        )
+
+    jsonl_path = os.path.join(temp_dir, "chat.jsonl")
+    with open(jsonl_path, "w", encoding="utf-8") as f:
+        f.write(
+            json.dumps(
+                {
+                    "messages": [
+                        {"role": "system", "content": "You are helpful."},
+                        {"role": "user", "content": "Hi."},
+                        {"role": "assistant", "content": "Hello!"},
+                    ]
+                }
+            )
+            + "\n"
+        )
+        f.write(
+            json.dumps(
+                {
+                    "messages": [
+                        {"role": "user", "content": "What is 2+2?"},
+                        {"role": "assistant", "content": "4"},
+                    ]
+                }
+            )
+            + "\n"
+        )
+
+    config = PipelineConfig(
+        input=InputConfig(sections=_CHAT_SECTIONS),
+        mask={"system": "mask", "user": "mask", "assistant": "train"},
+        mask_default="mask",
+        preprocessing=ProcessingConfig(max_seq_len=2048),
+        output=OutputConfig(storage_format="bin", domain_key=None),
+    )
+
+    out_dir = os.path.join(temp_dir, "output")
+    Pipeline(
+        config=config,
+        input_paths=[jsonl_path],
+        output_dir=out_dir,
+        tokenizer_path=tokenizer_dir,
+    ).run()
+
+    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
+    assert os.path.exists(meta_path)
+    with open(meta_path, "r") as f:
+        meta = json.load(f)
+    assert "sequence" in meta
+    assert "loss_mask" in meta
+    assert meta["sequence"]["dtype"] == "int32"
+    assert meta["loss_mask"]["dtype"] == "int32"
+
+
+def test_full_text_pipeline(temp_dir, test_tokenizer):
+    tokenizer_dir = os.path.join(temp_dir, "tok")
+    os.makedirs(tokenizer_dir, exist_ok=True)
+    test_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
+    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
+        json.dump(
+            {
+                "special_tokens": {
+                    "pad_token": "<|_pad_|>",
+                    "unk_token": "<|_unk_|>",
+                }
+            },
+            f,
+        )
+
+    jsonl_path = os.path.join(temp_dir, "text.jsonl")
+    with open(jsonl_path, "w", encoding="utf-8") as f:
+        f.write(
+            json.dumps(
+                {
+                    "text": "Hello world this is a test document with enough characters to pass the minimum length filter."
+                }
+            )
+            + "\n"
+        )
+        f.write(
+            json.dumps(
+                {
+                    "text": "Another document for testing purposes with sufficient length to be processed."
+                }
+            )
+            + "\n"
+        )
+
+    config = PipelineConfig(
+        input=InputConfig(sections=_TEXT_SECTIONS),
+        preprocessing=ProcessingConfig(max_seq_len=2048, min_chars=10),
+        output=OutputConfig(storage_format="bin"),
+    )
+
+    out_dir = os.path.join(temp_dir, "output")
+    Pipeline(
+        config=config,
+        input_paths=[jsonl_path],
+        output_dir=out_dir,
+        tokenizer_path=tokenizer_dir,
+    ).run()
+
+    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
+    assert os.path.exists(meta_path)
+    with open(meta_path, "r") as f:
+        meta = json.load(f)
+    assert "sequence" in meta
+    assert "loss_mask" not in meta
+    assert meta["sequence"]["dtype"] == "int32"
+
+
+def test_full_instruction_pipeline(temp_dir, test_tokenizer):
+    tokenizer_dir = os.path.join(temp_dir, "tok")
+    os.makedirs(tokenizer_dir, exist_ok=True)
+    test_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
+    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
+        json.dump(
+            {
+                "special_tokens": {
+                    "pad_token": "<|_pad_|>",
+                    "unk_token": "<|_unk_|>",
+                }
+            },
+            f,
+        )
+
+    jsonl_path = os.path.join(temp_dir, "instruct.jsonl")
+    with open(jsonl_path, "w", encoding="utf-8") as f:
+        f.write(
+            json.dumps(
+                {
+                    "prompt": "Tell me a joke",
+                    "response": "Why did the chicken cross the road?",
+                }
+            )
+            + "\n"
+        )
+        f.write(
+            json.dumps(
+                {
+                    "prompt": "What is AI?",
+                    "response": "Artificial Intelligence is a field of computer science.",
+                }
+            )
+            + "\n"
+        )
+
+    config = PipelineConfig(
+        input=InputConfig(sections=_INSTRUCTION_SECTIONS),
+        mask={"prompt": "mask", "response": "train"},
+        mask_default="mask",
+        preprocessing=ProcessingConfig(max_seq_len=2048),
+        output=OutputConfig(storage_format="bin"),
+    )
+
+    out_dir = os.path.join(temp_dir, "output")
+    Pipeline(
+        config=config,
+        input_paths=[jsonl_path],
+        output_dir=out_dir,
+        tokenizer_path=tokenizer_dir,
+    ).run()
+
+    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
+    assert os.path.exists(meta_path)
+    with open(meta_path, "r") as f:
+        meta = json.load(f)
+    assert "sequence" in meta
+    assert "loss_mask" in meta
+    assert meta["sequence"]["dtype"] == "int32"
+    assert meta["loss_mask"]["dtype"] == "int32"
+
+
+def test_dtype_override(temp_dir, test_tokenizer):
+    tokenizer_dir = os.path.join(temp_dir, "tok")
+    os.makedirs(tokenizer_dir, exist_ok=True)
+    test_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
+    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
+        json.dump(
+            {
+                "special_tokens": {
+                    "pad_token": "<|_pad_|>",
+                    "unk_token": "<|_unk_|>",
+                }
+            },
+            f,
+        )
+
+    jsonl_path = os.path.join(temp_dir, "data.jsonl")
+    with open(jsonl_path, "w", encoding="utf-8") as f:
+        f.write(json.dumps({"prompt": "Q", "response": "A"}) + "\n")
+
+    config = PipelineConfig(
+        input=InputConfig(sections=_INSTRUCTION_SECTIONS),
+        mask={"prompt": "mask", "response": "train"},
+        mask_default="mask",
+        preprocessing=ProcessingConfig(max_seq_len=2048),
+        output=OutputConfig(storage_format="bin", dtype={"loss_mask": "bool"}),
+    )
+
+    out_dir = os.path.join(temp_dir, "output")
+    Pipeline(
+        config=config,
+        input_paths=[jsonl_path],
+        output_dir=out_dir,
+        tokenizer_path=tokenizer_dir,
+    ).run()
+
+    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
+    with open(meta_path, "r") as f:
+        meta = json.load(f)
+    assert meta["sequence"]["dtype"] == "int32"
+    assert meta["loss_mask"]["dtype"] == "bool"
+
+
+def test_dpo_pipeline(temp_dir, chat_tokenizer):
+    tokenizer_dir = os.path.join(temp_dir, "tok")
+    os.makedirs(tokenizer_dir, exist_ok=True)
+    chat_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
+    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
+        json.dump(
+            {
+                "special_tokens": _SPECIAL_TOKENS_CONFIG,
+                "chat_template": _CHAT_TEMPLATE,
+            },
+            f,
+        )
+
+    jsonl_path = os.path.join(temp_dir, "dpo.jsonl")
+    with open(jsonl_path, "w", encoding="utf-8") as f:
+        f.write(
+            json.dumps(
+                {
+                    "chosen": [
+                        {"role": "user", "content": "Hi."},
+                        {"role": "assistant", "content": "Hello!"},
+                    ],
+                    "rejected": [
+                        {"role": "user", "content": "Hi."},
+                        {"role": "assistant", "content": "Go away."},
+                    ],
+                }
+            )
+            + "\n"
+        )
+
+    out_dir = os.path.join(temp_dir, "output")
+    Pipeline(
+        config=make_dpo_chat_config(),
+        input_paths=[jsonl_path],
+        output_dir=out_dir,
+        tokenizer_path=tokenizer_dir,
+    ).run()
+
+    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
+    assert os.path.exists(meta_path)
+    with open(meta_path, "r") as f:
+        meta = json.load(f)
+    assert "chosen" in meta
+    assert "rejected" in meta
+    assert "chosen_mask" in meta
+    assert "rejected_mask" in meta
+    assert "sequence" not in meta
+
+
+def test_grpo_pipeline(temp_dir, test_tokenizer):
+    tokenizer_dir = os.path.join(temp_dir, "tok")
+    os.makedirs(tokenizer_dir, exist_ok=True)
+    test_tokenizer._tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
+    with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w") as f:
+        json.dump(
+            {
+                "special_tokens": {
+                    "pad_token": "<|_pad_|>",
+                    "unk_token": "<|_unk_|>",
+                }
+            },
+            f,
+        )
+
+    jsonl_path = os.path.join(temp_dir, "grpo.jsonl")
+    with open(jsonl_path, "w", encoding="utf-8") as f:
+        f.write(
+            json.dumps(
+                {
+                    "prompt": "Question?",
+                    "responses": ["Answer A", "Answer B"],
+                    "rewards": [0.8, 0.3],
+                }
+            )
+            + "\n"
+        )
+
+    out_dir = os.path.join(temp_dir, "output")
+    Pipeline(
+        config=make_grpo_no_template_config(),
+        input_paths=[jsonl_path],
+        output_dir=out_dir,
+        tokenizer_path=tokenizer_dir,
+    ).run()
+
+    meta_path = os.path.join(out_dir, "__default__", "shard_0000", "meta.json")
+    assert os.path.exists(meta_path)
+    with open(meta_path, "r") as f:
+        meta = json.load(f)
+    assert "prompts" in meta
+    assert "responses" in meta
+    assert "masks" in meta
+    assert "rewards" in meta
+    assert "sequence" not in meta