Create train-dora-alpaca.py

train-dora-alpaca.py

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+import torch
+torch.backends.cuda.matmul.allow_tf32 = True
+import random
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline, AutoConfig
+from datasets import load_dataset
+from transformers import TrainingArguments
+from trl import SFTTrainer
+from peft import LoraConfig
+# from accelerate import infer_auto_device_map, init_empty_weights, dispatch_model
+from torch.nn import CrossEntropyLoss
+
+import time
+random_seed = 42
+torch.manual_seed(random_seed)
+random.seed(random_seed)
+
+dataset = load_dataset("Vezora/Tested-22k-Python-Alpaca", split="train")
+
+def chatml_format(example):
+    """Format the dataset for training, accounting for empty columns."""
+    return {
+        "instruction": example['instruction'] if 'instruction' in example else " \n",
+        "input": example['input'] if 'input' in example else " \n",
+        "system": example['system'] if 'system' in example else " \n",
+        "output": example['output'] if 'output' in example else " \n",
+    }
+
+# Format dataset
+dataset = dataset.map(chatml_format, remove_columns=dataset.column_names)
+
+n_ahead_talk_global = 4
+n_passes_global = 2
+n_ahead_global = 8
+n_examples = 0
+
+def model_init(params):
+    original = False
+    if params is None:
+        params = {}
+    else:
+        params = params.params
+    # save params to file
+    n_ahead = params.get("n_ahead", n_ahead_global if not original else 1)
+    n_ahead_talk = params.get("n_ahead_talk", n_ahead_talk_global if not original else 1)
+    n_passes = params.get("n_passes", n_passes_global if not original else 1)
+    gumbel_temperature = params.get("gumbel_temperature", 1)
+    use_start_thought_token = params.get("use_start_thought_token", True)
+    use_end_thought_token = params.get("use_end_thought_token", True)
+    include_policy_loss = params.get("include_policy_loss", True)
+    gumbel_detach = params.get("gumbel_detach", True)
+    merged_talk_heads = params.get("merged_talk_heads", True)
+    residual_think_head = params.get("residual_think_head", False)
+    optimize_lm_head_only_at_start = params.get("optimize_lm_head_only_at_start", False)
+
+    model_id = "Crystalcareai/Quiet-Star-Custom"
+    tokenizer_id = model_id
+    print("Loading model")
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+        max_thoughts=n_ahead + n_ahead_talk + 1,
+        merged_talk_heads=merged_talk_heads,
+        merged_lm_and_talk_heads=False,
+        merged_lm_and_think_heads=True,
+        use_concat_talk_head=True,
+        use_shallow_think=True,
+        use_shallow_talk=False,
+        use_complex_think_head=False,
+        use_complex_talk_head=True,
+        use_weighted_talk_head=True,
+        trust_remote_code=True,
+        device_map="auto",
+    )
+    print("Loaded model")
+
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, truncation=True, padding_side="right")
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+
+    special_tokens_to_add = []
+    if model.use_start_thought_token:
+        special_tokens_to_add.append("<|startthought|>")
+    if model.use_end_thought_token:
+        special_tokens_to_add.append("<|endthought|>")
+    if special_tokens_to_add:
+        tokenizer.add_special_tokens({"additional_special_tokens": special_tokens_to_add})
+        model.resize_token_embeddings(len(tokenizer))
+    model.tokenizer = tokenizer
+    for name, module in model.named_modules():
+        if "embed" in name:
+            print(module, flush=True)
+
+    model.gumbel_detach = gumbel_detach
+    model.include_policy_loss = include_policy_loss
+    model.use_end_thought_token = use_end_thought_token
+    model.use_start_thought_token = use_start_thought_token
+    model.n_ahead = n_ahead
+    model.n_ahead_talk = n_ahead_talk
+    model.n_passes = n_passes
+    model.residual_think_head = residual_think_head
+    model.optimize_lm_head_only_at_start = optimize_lm_head_only_at_start
+    model.gumbel_temperature = gumbel_temperature
+    model.original_mode = original
+    model.config_params = params
+    model.run_start = int(time.time())
+    model.train()
+    return model
+
+max_seq_length = 1024
+run_id = int(time.time())
+training_args = TrainingArguments(
+    output_dir="./out",
+    num_train_epochs=3,
+    per_device_train_batch_size=1,
+    gradient_checkpointing=False,
+    gradient_accumulation_steps=8,
+    optim="lion_32bit",
+    logging_steps=1,
+    save_strategy="steps",
+    save_steps=300,
+    max_steps=1000,
+    bf16=True,
+    tf32=False,
+    learning_rate=6e-05,
+    max_grad_norm=0.3,
+    warmup_ratio=0.06,
+    lr_scheduler_type="cosine",
+    push_to_hub=False,
+    report_to="wandb"
+)
+
+peft_config = LoraConfig(
+          r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    target_modules = ["q_proj", "k_proj"],
+    lora_alpha = 16,
+    lora_dropout = 0, # Supports any, but = 0 is optimized
+    bias = "none", # Enable Dora method
+    use_dora=True,
+)
+
+
+torch.autograd.set_detect_anomaly(True)
+
+# Set the device for each process
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# torch.cuda.set_device(device)
+
+model = model_init(None)  # Initialize the model
+
+tokenizer = model.tokenizer
+
+trainer = SFTTrainer(
+    args=training_args,
+    train_dataset=dataset,
+    model=model,
+    tokenizer=tokenizer,
+    max_seq_length=max_seq_length,
+    dataset_text_field="output",
+    peft_config=peft_config,
+)
+
+trainer.train()