mirror of https://github.com/razor-ai/soup.git
Merge branch 'main' of https://github.com/MakazhanAlpamys/Soup
This commit is contained in:
Alpamys
commit
9df0ee573e
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@ -489,7 +489,13 @@ class GRPOTrainerWrapper:
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model_kwargs["quantization_config"] = quant_config_obj
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self.model = AutoModelForCausalLM.from_pretrained(cfg.base, **model_kwargs)
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from soup_cli.utils.data_pipeline import apply_vocab_expansion
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apply_vocab_expansion(
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self.tokenizer,
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self.model,
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cfg.data,
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)
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if tcfg.quantization in ("4bit", "8bit", "mxfp4"):
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self.model = prepare_model_for_kbit_training(self.model)
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@ -197,7 +197,13 @@ class ORPOTrainerWrapper:
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model_kwargs["quantization_config"] = quant_config_obj
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self.model = AutoModelForCausalLM.from_pretrained(cfg.base, **model_kwargs)
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from soup_cli.utils.data_pipeline import apply_vocab_expansion
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apply_vocab_expansion(
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self.tokenizer,
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self.model,
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cfg.data,
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)
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if tcfg.quantization in ("4bit", "8bit", "mxfp4"):
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self.model = prepare_model_for_kbit_training(self.model)
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@ -200,7 +200,13 @@ class SimPOTrainerWrapper:
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model_kwargs["quantization_config"] = quant_config_obj
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self.model = AutoModelForCausalLM.from_pretrained(cfg.base, **model_kwargs)
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from soup_cli.utils.data_pipeline import apply_vocab_expansion
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apply_vocab_expansion(
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self.tokenizer,
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self.model,
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cfg.data,
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)
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if tcfg.quantization in ("4bit", "8bit", "mxfp4"):
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self.model = prepare_model_for_kbit_training(self.model)
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@ -499,6 +499,114 @@ class TestGRPOTrainerInit:
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assert wrapper.model is None
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assert wrapper.tokenizer is None
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def test_grpo_vocab_expansion_adds_tokens_and_resizes(self, monkeypatch):
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"""GRPO should apply configured vocabulary expansion."""
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import sys
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import types
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from types import SimpleNamespace
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from soup_cli.trainer.grpo import GRPOTrainerWrapper
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cfg = _make_config(
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task="grpo",
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data={
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"train": "./data.jsonl",
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"format": "chatml",
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"add_new_tokens": ["<grpo_new>", "<old>"],
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"new_special_tokens": ["<grpo_special>"],
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"resize_vocab": True,
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},
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training={"quantization": "none"},
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)
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calls = {
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"add_tokens": None,
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"add_special_tokens": None,
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"resize": None,
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}
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class _Tokenizer:
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pad_token = None
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eos_token = "<eos>"
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def __init__(self):
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self.vocab = {"<old>": 0}
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def get_vocab(self):
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return self.vocab
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def add_tokens(self, tokens):
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calls["add_tokens"] = list(tokens)
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added = 0
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for token in tokens:
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if token not in self.vocab:
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self.vocab[token] = len(self.vocab)
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added += 1
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return added
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def add_special_tokens(self, data):
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tokens = list(data["additional_special_tokens"])
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calls["add_special_tokens"] = tokens
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added = 0
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for token in tokens:
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if token not in self.vocab:
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self.vocab[token] = len(self.vocab)
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added += 1
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return added
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def __len__(self):
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return len(self.vocab)
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class _Model:
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config = SimpleNamespace()
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def resize_token_embeddings(self, size):
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calls["resize"] = size
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def parameters(self):
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return []
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tokenizer = _Tokenizer()
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model = _Model()
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fake_transformers = types.SimpleNamespace(
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AutoTokenizer=types.SimpleNamespace(
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from_pretrained=lambda *args, **kwargs: tokenizer
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),
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AutoModelForCausalLM=types.SimpleNamespace(
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from_pretrained=lambda *args, **kwargs: model
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),
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)
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fake_peft = types.SimpleNamespace(
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LoraConfig=lambda **kwargs: SimpleNamespace(**kwargs),
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TaskType=SimpleNamespace(CAUSAL_LM="CAUSAL_LM"),
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get_peft_model=lambda model_obj, _cfg: model_obj,
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prepare_model_for_kbit_training=lambda model_obj: model_obj,
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)
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monkeypatch.setitem(sys.modules, "transformers", fake_transformers)
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monkeypatch.setitem(sys.modules, "peft", fake_peft)
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monkeypatch.setattr(
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"soup_cli.utils.quant_menu.build_quantization_config_for_loader",
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lambda **kwargs: None,
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)
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wrapper = object.__new__(GRPOTrainerWrapper)
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wrapper.config = cfg
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wrapper.device = "cpu"
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wrapper._trust_remote_code = False
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wrapper.model = None
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wrapper.tokenizer = None
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wrapper._setup_transformers(cfg, cfg.training)
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assert calls["add_tokens"] == ["<grpo_new>"]
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assert calls["add_special_tokens"] == ["<grpo_special>"]
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assert calls["resize"] == len(tokenizer)
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class TestRewardModelTrainerInit:
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"""Test RewardModelTrainerWrapper constructor."""
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@ -853,6 +961,224 @@ class TestBCOTrainerInit:
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assert calls["add_special_tokens"] == ["<bco_special>"]
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assert calls["resize"] == len(tokenizer)
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class TestORPOTrainerInit:
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def test_orpo_vocab_expansion_adds_tokens_and_resizes(self, monkeypatch):
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"""ORPO should apply configured vocabulary expansion."""
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import sys
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import types
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from types import SimpleNamespace
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from soup_cli.trainer.orpo import ORPOTrainerWrapper
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cfg = _make_config(
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task="orpo",
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data={
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"train": "./data.jsonl",
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"format": "chatml",
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"add_new_tokens": ["<orpo_new>", "<old>"],
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"new_special_tokens": ["<orpo_special>"],
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"resize_vocab": True,
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},
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training={"quantization": "none"},
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)
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calls = {
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"add_tokens": None,
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"add_special_tokens": None,
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"resize": None,
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}
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class _Tokenizer:
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pad_token = None
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eos_token = "<eos>"
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def __init__(self):
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self.vocab = {"<old>": 0}
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def get_vocab(self):
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return self.vocab
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def add_tokens(self, tokens):
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calls["add_tokens"] = list(tokens)
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added = 0
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for token in tokens:
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if token not in self.vocab:
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self.vocab[token] = len(self.vocab)
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added += 1
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return added
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def add_special_tokens(self, data):
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tokens = list(data["additional_special_tokens"])
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calls["add_special_tokens"] = tokens
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added = 0
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for token in tokens:
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if token not in self.vocab:
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self.vocab[token] = len(self.vocab)
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added += 1
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return added
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def __len__(self):
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return len(self.vocab)
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class _Model:
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config = SimpleNamespace()
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def resize_token_embeddings(self, size):
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calls["resize"] = size
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def parameters(self):
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return []
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tokenizer = _Tokenizer()
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model = _Model()
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fake_transformers = types.SimpleNamespace(
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AutoTokenizer=types.SimpleNamespace(
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from_pretrained=lambda *args, **kwargs: tokenizer
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),
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AutoModelForCausalLM=types.SimpleNamespace(
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from_pretrained=lambda *args, **kwargs: model
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),
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)
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fake_peft = types.SimpleNamespace(
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LoraConfig=lambda **kwargs: SimpleNamespace(**kwargs),
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TaskType=SimpleNamespace(CAUSAL_LM="CAUSAL_LM"),
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get_peft_model=lambda model_obj, _cfg: model_obj,
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prepare_model_for_kbit_training=lambda model_obj: model_obj,
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)
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monkeypatch.setitem(sys.modules, "transformers", fake_transformers)
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monkeypatch.setitem(sys.modules, "peft", fake_peft)
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monkeypatch.setattr(
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"soup_cli.utils.quant_menu.build_quantization_config_for_loader",
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lambda **kwargs: None,
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)
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wrapper = object.__new__(ORPOTrainerWrapper)
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wrapper.config = cfg
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wrapper.device = "cpu"
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wrapper._trust_remote_code = False
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wrapper.model = None
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wrapper.tokenizer = None
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wrapper._setup_transformers(cfg, cfg.training)
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assert calls["add_tokens"] == ["<orpo_new>"]
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assert calls["add_special_tokens"] == ["<orpo_special>"]
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assert calls["resize"] == len(tokenizer)
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class TestSIMPOTrainerInit:
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def test_simpo_vocab_expansion_adds_tokens_and_resizes(self, monkeypatch):
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"""SIMPO should apply configured vocabulary expansion."""
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import sys
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import types
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from types import SimpleNamespace
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from soup_cli.trainer.simpo import SimPOTrainerWrapper
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cfg = _make_config(
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task="simpo",
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data={
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"train": "./data.jsonl",
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"format": "chatml",
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"add_new_tokens": ["<simpo_new>", "<old>"],
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"new_special_tokens": ["<simpo_special>"],
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"resize_vocab": True,
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},
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training={"quantization": "none"},
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)
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calls = {
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"add_tokens": None,
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"add_special_tokens": None,
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"resize": None,
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}
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class _Tokenizer:
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pad_token = None
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eos_token = "<eos>"
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def __init__(self):
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self.vocab = {"<old>": 0}
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def get_vocab(self):
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return self.vocab
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def add_tokens(self, tokens):
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calls["add_tokens"] = list(tokens)
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added = 0
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for token in tokens:
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if token not in self.vocab:
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self.vocab[token] = len(self.vocab)
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added += 1
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return added
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def add_special_tokens(self, data):
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tokens = list(data["additional_special_tokens"])
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calls["add_special_tokens"] = tokens
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added = 0
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for token in tokens:
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if token not in self.vocab:
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self.vocab[token] = len(self.vocab)
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added += 1
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return added
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def __len__(self):
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return len(self.vocab)
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class _Model:
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config = SimpleNamespace()
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def resize_token_embeddings(self, size):
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calls["resize"] = size
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def parameters(self):
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return []
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tokenizer = _Tokenizer()
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model = _Model()
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fake_transformers = types.SimpleNamespace(
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AutoTokenizer=types.SimpleNamespace(
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from_pretrained=lambda *args, **kwargs: tokenizer
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),
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AutoModelForCausalLM=types.SimpleNamespace(
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from_pretrained=lambda *args, **kwargs: model
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),
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)
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fake_peft = types.SimpleNamespace(
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LoraConfig=lambda **kwargs: SimpleNamespace(**kwargs),
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TaskType=SimpleNamespace(CAUSAL_LM="CAUSAL_LM"),
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get_peft_model=lambda model_obj, _cfg: model_obj,
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prepare_model_for_kbit_training=lambda model_obj: model_obj,
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)
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monkeypatch.setitem(sys.modules, "transformers", fake_transformers)
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monkeypatch.setitem(sys.modules, "peft", fake_peft)
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monkeypatch.setattr(
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"soup_cli.utils.quant_menu.build_quantization_config_for_loader",
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lambda **kwargs: None,
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)
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wrapper = object.__new__(SimPOTrainerWrapper)
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wrapper.config = cfg
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wrapper.device = "cpu"
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wrapper._trust_remote_code = False
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wrapper.model = None
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wrapper.tokenizer = None
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wrapper._setup_transformers(cfg, cfg.training)
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assert calls["add_tokens"] == ["<simpo_new>"]
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assert calls["add_special_tokens"] == ["<simpo_special>"]
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assert calls["resize"] == len(tokenizer)
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class TestTrainTaskRouting:
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"""Test that train command routes to correct trainer based on task."""
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