mod
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l.gabrysiak
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2cceeb31c8
commit
02aa12d24e
161
hft.py
161
hft.py
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@ -110,16 +110,13 @@ def custom_collate_fn(batch):
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input_ids = torch.stack([torch.tensor(b["input_ids"]) for b in batch])
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attention_mask = torch.stack([torch.tensor(b["attention_mask"]) for b in batch])
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labels = torch.stack([torch.tensor(b["labels"]) for b in batch])
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source_idx = torch.tensor([b.get("source_idx", -1) for b in batch], dtype=torch.long)
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#print("source_idx shape:", source_idx.shape) # Debugowanie
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return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels, "source_idx": source_idx}
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# Zmodyfikowana klasa CustomModel
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class CustomModel(AutoModelForCausalLM): # 🔵 Zmiana dziedziczenia
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class CustomModel(nn.Module):
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def __init__(self, model_name, config):
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super().__init__(config) # 🔵 Inicjalizacja klasy bazowej
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self.model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
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super().__init__()
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self.base_model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
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self.source_embedding = nn.Embedding(
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num_embeddings=1000,
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embedding_dim=config.hidden_size,
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@ -130,21 +127,24 @@ class CustomModel(AutoModelForCausalLM): # 🔵 Zmiana dziedziczenia
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if source_idx is not None:
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source_idx = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings - 1)
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source_embeds = self.source_embedding(source_idx).unsqueeze(1).expand(-1, input_ids.size(1), -1)
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inputs_embeds = self.model.get_input_embeddings()(input_ids) + source_embeds
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return self.model(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels, **kwargs)
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return self.model(input_ids=input_ids, attention_mask=attention_mask, labels=labels, **kwargs)
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inputs_embeds = self.base_model.get_input_embeddings()(input_ids) + source_embeds
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outputs = self.base_model(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels, **kwargs)
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else:
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outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask, labels=labels, **kwargs)
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return outputs
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# 🔵 Dodanie metody generate
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def generate(self, *args, **kwargs):
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return self.model.generate(*args, **kwargs)
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return self.base_model.generate(*args, **kwargs)
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class CustomTrainer(Trainer):
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def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
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labels = inputs.pop("labels")
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source_idx = inputs.pop("source_idx", None)
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outputs = model(**inputs, labels=labels, source_idx=source_idx)
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loss = outputs.loss
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return (loss, outputs) if return_outputs else loss
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outputs = model(input_ids=inputs["input_ids"],
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attention_mask=inputs["attention_mask"],
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labels=labels,
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source_idx=source_idx)
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return (outputs.loss, outputs) if return_outputs else outputs.loss
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# Inicjalizacja komponentów
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source_mapper = SourceMapper()
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@ -160,9 +160,9 @@ tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=8)
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# Inicjalizacja modelu
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config = AutoModelForCausalLM.from_pretrained(model_name).config
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#print("Vocabulary size:", config.vocab_size)
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model = CustomModel(model_name, config)
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#model.to("cpu") # Zmienione na CPU dla debugowania
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = model.to(device)
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# Konfiguracja treningu
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training_args = TrainingArguments(
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@ -171,13 +171,12 @@ training_args = TrainingArguments(
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per_device_train_batch_size=2,
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gradient_accumulation_steps=4,
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learning_rate=2e-5,
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fp16=False, # Wyłączone dla CPU
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fp16=torch.cuda.is_available(),
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logging_steps=1,
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logging_dir="./logs",
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save_strategy="steps",
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save_steps=1000,
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logging_strategy="no",
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report_to="none",
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report_to="none"
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)
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# Trening
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@ -189,91 +188,9 @@ trainer = CustomTrainer(
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)
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trainer.train()
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# Utwórz katalog do zapisu modelu
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save_directory = "./trained_model/ably.do/hse"
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os.makedirs(save_directory, exist_ok=True)
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# 1. Zapisz wagę modelu
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torch.save(model.state_dict(), os.path.join(save_directory, "hse-nano-mistral.bin"))
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# 2. Zapisz tokenizer
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tokenizer.save_pretrained(save_directory)
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# 3. Zapisz mapowanie źródeł
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source_mapper_data = {
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"source_to_idx": dict(source_mapper.source_to_idx),
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"idx_to_source": source_mapper.idx_to_source
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}
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with open(os.path.join(save_directory, "source_mapper.json"), 'w') as f:
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json.dump(source_mapper_data, f)
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# 4. Zapisz konfigurację modelu (opcjonalnie, ale zalecane)
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model.base_model.config.save_pretrained(save_directory)
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# Funkcja generująca odpowiedź
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# Funkcja testująca
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def generate_answer_with_source(question, model, tokenizer, source_mapper, max_length=200):
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device = next(model.parameters()).device
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inputs = tokenizer(
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question,
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return_tensors="pt",
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truncation=True,
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max_length=512
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).to(device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_length=max_length,
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num_return_sequences=1,
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return_dict_in_generate=True,
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temperature=0.7,
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top_p=0.9,
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)
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answer = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
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# Ekstrakcja informacji o źródłach
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article_matches = re.finditer(r'Art\.\s+\d+', answer)
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sources = set()
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for match in article_matches:
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article_ref = match.group(0).strip()
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for idx, source in source_mapper.idx_to_source.items():
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if article_ref in source:
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sources.add(source)
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break
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return {
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"question": question,
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"answer": answer,
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"sources": list(sources) if sources else ["Opracowanie własne"],
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"num_tokens": len(outputs.sequences[0])
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}
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# Przykładowe testy
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test_cases = [
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"Jaki jest wymiar urlopu wypoczynkowego?",
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"Jakie są zasady bezpieczeństwa na budowie?",
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"Wyjaśnij procedurę zwolnienia grupowego",
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"Co reguluje ustawa o ochronie danych osobowych?",
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"Jakie dokumenty są potrzebne do zawarcia umowy o pracę?"
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]
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print("\n\n🔴 🔴 🔴 ROZPOCZĘCIE TESTOWANIA MODELU 🔴 🔴 🔴")
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for case in test_cases:
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result = generate_answer_with_source(case, model, tokenizer, source_mapper)
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print(f"\n🔷 Pytanie: {result['question']}")
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print(f"🔷 Odpowiedź ({result['num_tokens']} tokenów):")
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print(result['answer'])
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print(f"🔷 Źródła: {', '.join(result['sources'])}")
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print("-"*80)
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# Funkcja generująca odpowiedź
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def generate_answer(question, max_length=200):
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model.eval()
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inputs = tokenizer(question, return_tensors="pt", truncation=True, max_length=512).to(device)
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with torch.no_grad():
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@ -281,12 +198,42 @@ def generate_answer(question, max_length=200):
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**inputs,
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max_length=max_length,
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num_return_sequences=1,
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return_dict_in_generate=True
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temperature=0.7,
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top_p=0.9,
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pad_token_id=tokenizer.eos_token_id
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)
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answer = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
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return answer
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answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
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# Utwórz katalog do zapisu modelu
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save_directory = "./trained_model/ably.do/hse"
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# Wyszukiwanie źródeł
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sources = set()
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for idx in source_mapper.idx_to_source:
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if source_mapper.idx_to_source[idx] in answer:
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sources.add(source_mapper.idx_to_source[idx])
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return {
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"question": question,
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"answer": answer,
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"sources": list(sources) if sources else ["Opracowanie własne"]
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}
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# Testowanie
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test_questions = [
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"Jaki jest wymiar urlopu wypoczynkowego?",
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"Jakie są zasady bezpieczeństwa na budowie?",
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"Wyjaśnij procedurę zwolnienia grupowego"
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]
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print("\n=== TEST MODELU ===")
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for question in test_questions:
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result = generate_answer_with_source(question, model, tokenizer, source_mapper)
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print(f"\nPytanie: {result['question']}")
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print(f"Odpowiedź: {result['answer']}")
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print(f"Źródła: {', '.join(result['sources'])}")
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print("="*80)
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# Zapis modelu
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save_directory = "./trained_model"
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os.makedirs(save_directory, exist_ok=True)
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torch.save(model.state_dict(), os.path.join(save_directory, "model.bin"))
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tokenizer.save_pretrained(save_directory)
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