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MambaMate-Micro / mamba_module.py

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	import os
	import pickle
	import torch
	from mamba_lm import MambaLM, MambaLMConfig, from_pretrained
	from contextlib import nullcontext

	BASE_DIR = "mamba/"

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

	init_from = "resume" # either 'resume' or a Mamba variant (e.g. 'state-spaces/mamba-1.4b')
	move_num_in_gamestate = True
	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"../../mamba.py/out/{self.model_name}")
	checkpoint = torch.load(ckpt_path, map_location=device)
	model_config = checkpoint["model_args"]
	model = MambaLM(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]).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_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)[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:
	return None
	# Parse the response to get only the first move
	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

	def get_move(self, board: str, game_state: str, temperature: float) -> str:
	completion = self.get_mamba_response(game_state, temperature, 8, 32)
	return self.get_move_from_response(completion)

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