chess-gpt-eval/mamba_module.py

import os
import pickle
import torch
from mamba_lm import MambaLMConfig, from_pretrained
from mamba_ssm import MambaLMHeadModel
from contextlib import nullcontext

BASE_DIR = "mamba/"

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

        init_from = "resume"  # either 'resume' or a Mamba variant (e.g. 'state-spaces/mamba-1.4b')
        out_dir = "out"  # ignored if init_from is not 'resume'
        device = "cuda" if torch.cuda.is_available() else "cpu"
        #device = "cpu"
        dtype = 'bfloat16' if torch.cuda.is_bf16_supported() else 'float32'
        seed = 1337
        compile = False  # set to True if using PyTorch 2.0 and Mamba supports it
        # -----------------------------------------------------------------------------

        torch.manual_seed(seed)
        torch.cuda.manual_seed(seed)
        
        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 initialization
        if init_from == "resume":
            #ckpt_path = os.path.join(BASE_DIR, out_dir, self.model_name)
            ckpt_path = os.path.normpath(f"../chess-mamba-vs-xformer/out/Mamba/{self.model_name}")
            checkpoint = torch.load(ckpt_path, map_location=device)
            model_config = checkpoint["model_args"]
            model = MambaLMHeadModel(model_config)
            model.load_state_dict(checkpoint['model'])
        elif init_from.startswith('state-spaces'):
            model = from_pretrained(init_from).to(device)
        else:
            raise ValueError("Invalid init_from value")

        model.eval()
        model.to(device)

        if compile and hasattr(torch, 'compile'):
            model = torch.compile(model)

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

    def get_mamba_response(self, game_state: str, temperature: float, max_new_tokens: int, top_k: int):
        game_state = game_state.split("\n\n")[-1].strip()
        #game_state = ";" + game_state

        # Tokenize the game state
        encoded_prompt = self.encode(game_state)
        input_ids = torch.tensor([encoded_prompt], dtype=torch.long, device=self.device)

        self.model.eval()  # Set the model to evaluation mode
        with torch.no_grad():
            have_non_space = False
            for _ in range(max_new_tokens):
                logits = self.model(input_ids).logits[0, -1, :]  # Get logits for the last token

                # Apply temperature scaling and optionally sample from top k tokens
                logits = logits / temperature
                if top_k > 0:
                    indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
                    logits[indices_to_remove] = -float('Inf')

                probs = torch.nn.functional.softmax(logits, dim=-1)
                next_token_id = torch.multinomial(probs, num_samples=1)
                if have_non_space and (next_token_id == 0 or next_token_id==4):
                    break
                else:
                    have_non_space = True
                input_ids = torch.cat([input_ids, next_token_id.unsqueeze(0)], dim=1)

        model_response = self.decode(input_ids[0].tolist())
        model_response = model_response[len(game_state):].split(";")[0]
        return model_response

    #def encode(self, text: str):
        # Implement the appropriate tokenization for MambaLM
        # This could be a simple mapping or a more complex tokenizer
    #    return [stoi[char] for char in text]  # Example

    #def decode(self, token_ids: list):
        # Implement the appropriate decoding for MambaLM
     #   return ''.join([itos[id] for id in token_ids])  # Example
        
    def get_move_from_response(self, response: str) -> str:
        if not response or len(response) == 0:
            return None
        # Parse the response to get only the first move
        try:
            moves = response.split()
            first_move = moves[0]
            first_move = first_move.lstrip('.') # A patch for a weird phase during training ... doesn't seem to be an issue anymore, but don't see the harm.

            return first_move
        except:
            return None

    def get_move(self, board: str, game_state: str, temperature: float) -> str:
        completion = self.get_mamba_response(game_state, temperature, 8, self.vocab_size)
        return self.get_move_from_response(completion)

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