chess-gpt-eval-contrastive/nanogpt/nanogpt_module.py

"""
Sample from a trained model
"""
import os
import pickle
from contextlib import nullcontext
import torch
import tiktoken
from nanogpt.model import GPTConfig, GPT

BASE_DIR = "nanogpt/"


class NanoGptPlayer:
    def __init__(self, model_name: str, move_num_in_gamestate: bool=False):
        self.model_name = model_name
        # -----------------------------------------------------------------------------

        init_from = "resume"  # either 'resume' (from an out_dir) or a gpt2 variant (e.g. 'gpt2-xl')
        out_dir = "out"  # ignored if init_from is not 'resume'
        input_dir = "addition"
        test_name = "test.txt"
        start = "12+44="  # or "<|endoftext|>" or etc. Can also specify a file, use as: "FILE:prompt.txt"
        num_samples = 1  # number of samples to draw
        max_new_tokens = 6  # number of tokens generated in each sample
        temperature = 0.01  # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions
        top_k = 200  # retain only the top_k most likely tokens, clamp others to have 0 probability
        seed = 1337
        device = "cuda"  # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc.
        #device = "cpu"
        dtype = "float16"  # 'float32' or 'bfloat16' or 'float16'
        compile = False  # use PyTorch 2.0 to compile the model to be faster
        exec(
            open(f"{BASE_DIR}configurator.py").read()
        )  # overrides from command line or config file
        # -----------------------------------------------------------------------------

        torch.manual_seed(seed)
        torch.cuda.manual_seed(seed)
        torch.backends.cuda.matmul.allow_tf32 = True  # allow tf32 on matmul
        torch.backends.cudnn.allow_tf32 = True  # allow tf32 on cudnn
        device_type = (
            "cuda" if "cuda" in device else "cpu"
        )  # for later use in torch.autocast
        ptdtype = {
            "float32": torch.float32,
            "bfloat16": torch.bfloat16,
            "float16": torch.float16,
        }[dtype]
        ctx = (
            nullcontext()
            if device_type == "cpu"
            else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
        )

        # model
        if init_from == "resume":
            # init from a model saved in a specific directory
            #ckpt_path = os.path.join(BASE_DIR, out_dir, self.model_name)
            ckpt_path = os.path.normpath(f"../chess-mamba-vs-xformer/out/Xformer/{self.model_name}")
            checkpoint = torch.load(ckpt_path, map_location=device)
            gptconf = GPTConfig(**checkpoint["model_args"])
            model = GPT(gptconf)
            #model = GPT(checkpoint["model_args"])
            state_dict = checkpoint["model"]
            unwanted_prefix = "_orig_mod."
            for k, v in list(state_dict.items()):
                if k.startswith(unwanted_prefix):
                    state_dict[k[len(unwanted_prefix) :]] = state_dict.pop(k)
            model.load_state_dict(state_dict)
        elif init_from.startswith("gpt2"):
            # init from a given GPT-2 model
            model = GPT.from_pretrained(init_from, dict(dropout=0.0))

        model.eval()
        model.to(device)
        if compile:
            model = torch.compile(model)  # requires PyTorch 2.0 (optional)

        # look for the meta pickle in case it is available in the dataset folder
        meta_path = os.path.join(BASE_DIR, "out", "meta.pkl")
        load_meta = os.path.exists(meta_path)
        if move_num_in_gamestate and load_meta:
            with open(meta_path, "rb") as f:
                meta = pickle.load(f)
            stoi, itos = meta["stoi"], meta["itos"]
            vocab_size = meta['vocab_size']
            encode = lambda s: [stoi[c] for c in s]
            decode = lambda l: "".join([itos[i] for i in l])
        else:
            stoi = {' ': 0, '.': 1, 'a': 2, 'b': 3, 'c': 4, 'd': 5, 'e': 6, 'f': 7, 'g': 8, 'h': 9, '1': 10, '2': 11, '3': 12, '4': 13, '5': 14, '6': 15, '7': 16, '8': 17, 'B': 18, 'N': 19, 'R': 20, 'Q': 21, 'K': 22, 'O': 23, 'x': 24, '+': 25, '#': 26, '=': 27}
            itos = {0: ' ', 1: '.', 2: 'a', 3: 'b', 4: 'c', 5: 'd', 6: 'e', 7: 'f', 8: 'g', 9: 'h', 10: '1', 11: '2', 12: '3', 13: '4', 14: '5', 15: '6', 16: '7', 17: '8', 18: 'B', 19: 'N', 20: 'R', 21: 'Q', 22: 'K', 23: 'O', 24: 'x', 25: '+', 26: '#', 27: '='}
            for s in stoi:
                assert itos[stoi[s]] == s
            vocab_size = len(stoi)
            print(f"Vocab size {vocab_size}")
            encode = lambda s: [stoi[c] for c in s.replace('-', '')]
            decode = lambda l: "".join([itos[i] for i in l if i < vocab_size]).replace("OOO", "O-O-O").replace("OO", "O-O")

        self.encode = encode
        self.decode = decode
        self.model = model
        self.ctx = ctx
        self.device = device

    def get_nanogpt_response(self, game_state: str, temperature: float) -> str:
        num_samples = 1  # number of samples to draw
        top_k = 200  # retain only the top_k most likely tokens, clamp others to have 0 probability
        max_new_tokens = 8

        # Remove ["stockfish elo xxx"]\n["stockfish elo xxx"]\n\n from game_state
        # nanogpt was trained only on pgn transcripts
        game_state = game_state.split("\n\n")[-1].strip()

        # print("game_state", game_state)

        #game_state = ";" + game_state

        start_ids = self.encode(game_state)

        x = torch.tensor(start_ids, dtype=torch.long, device=self.device)[None, ...]
        with torch.no_grad():
            with self.ctx:
                for k in range(num_samples):
                    y = self.model.generate(
                        x, max_new_tokens, temperature=temperature, top_k=top_k
                    )

                    model_response = self.decode(y[0].tolist())

        # print("model_response", model_response)
        # model_response includes the input string
        model_response = model_response[len(game_state):].split(";")[0]
        return model_response

    def get_move_from_response(self, response: str) -> str:
        try:
            # Parse the response to get only the first move
            moves = response.split()
            first_move = moves[0]
            return first_move
        except:
            return None


    def get_move(self, board: str, game_state: str, temperature: float) -> str:
        completion = self.get_nanogpt_response(game_state, temperature)
        return self.get_move_from_response(completion)

    def get_config(self) -> dict:
        return {"model": self.model_name}