chess-gpt-eval-contrastive/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
import numpy as np
from functools import partial
import chess
from sklearn.linear_model import LinearRegression
import torch.nn as nn
import torch.optim as optim
import wandb
import math
import json

BASE_DIR = "mamba/"

class MambaPlayer:
    def __init__(self, model_name: str, move_num_in_gamestate: bool=False, update_contrastive: bool=False, update_linear: bool=False, linear_probe_path: str=None):
        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.space_tok = encode(' ')[0]
        self.dot_tok = encode('.')[0]
        self.model = model
        self.ctx = ctx
        self.device = device

        self.move_num = 0
        self.hooks = []
        self.max_seq_len = 1536
        #self.move_buckets = [10, 20, 30, 40, float('inf')]
        self.move_buckets = [float('inf')]
        
        if update_contrastive or update_linear:
            self.activations_sum = {}
            self.activations_count = {}
        if update_linear:
            if linear_probe_path and os.path.exists(linear_probe_path):
                self.linear_probes = torch.load(linear_probe_path)
            else:
                self.linear_probes = {}
        if update_contrastive or update_linear:
            linear_size = self.model.config.d_model * 8 #self.model.config.d_model * self.max_seq_len
            for i, layer in enumerate(self.model.backbone.layers):
                self.activations_sum[i] = {bucket: {"won": np.zeros((1, 8, self.model.config.d_model)),
                                                    "lost": np.zeros((1, 8, self.model.config.d_model)),
                                                    "current": np.zeros((1, 8, self.model.config.d_model))}
                                           for bucket in self.move_buckets}
                self.activations_count[i] = {bucket: {"won": 0, "lost": 0, "current": 0}
                                             for bucket in self.move_buckets}
                
                def hook(module, input, output, layer_idx=i):
                    if isinstance(output, tuple):
                        tensor_output = output[0]
                    else:
                        tensor_output = output
                    seq_len = tensor_output.shape[1]
                    bucket = next(b for b in self.move_buckets if self.move_num <= b)
                    self.activations_sum[layer_idx][bucket]["current"][:, :min(8, self.seq_len), :] += tensor_output.detach().cpu().numpy()[:, :self.seq_len, :][:, -8:, :]
                    self.activations_count[layer_idx][bucket]["current"] += 1
                
                self.hooks.append(layer.register_forward_hook(hook))
                if update_linear:
                    if not linear_probe_path or not os.path.exists(linear_probe_path):
                        self.linear_probes[i] = {
                            'q_value': nn.Linear(linear_size, 1),
                            'q_value_delta': nn.Linear(linear_size, 1),
                            'material_balance': nn.Linear(linear_size, 1)
                        }
            if update_linear:
                self.linear_probe_targets = {i: {bucket: {'q_value': [], 'q_value_delta': [], 'material_balance': []} for bucket in self.move_buckets} for i in self.linear_probes}
                self.linear_optimizers = {
                    layer_idx: {
                        probe_type: optim.Adam(self.linear_probes[layer_idx][probe_type].parameters(), lr=0.01)
                        for probe_type in ['q_value', 'q_value_delta', 'material_balance']
                    }
                    for layer_idx in self.linear_probes
                }
                wandb.init(project="mamba_linear_probes", name=f"mamba_linear_probes")
                self.wandb_step = 0
                self.linear_save_ct = 0

    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.seq_len = input_ids[0].size(dim=0)

        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)
                probs = torch.clamp(probs, min=1e-6, max=1.0)
                probs = probs / probs.sum()
                try:
                    next_token_id = torch.multinomial(probs, num_samples=1)
                except:
                    return None
                if next_token_id == self.space_tok or next_token_id==self.dot_tok:
                    if have_non_space:
                        break
                else:
                    have_non_space = True
                input_ids = torch.cat([input_ids, next_token_id.unsqueeze(0)], dim=1)
                self.seq_len += 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: chess.Board, game_state: str, temperature: float) -> str:
        self.move_num = game_state.count('.')
        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}

    def update_activations(self, result):
        for layer_idx in self.activations_sum:
            if result == "reset":
                self.activations_sum[layer_idx] = {bucket: {"won": np.zeros((1, 8, self.model.config.d_model)),
                                                    "lost": np.zeros((1, 8, self.model.config.d_model)),
                                                    "current": np.zeros((1, 8, self.model.config.d_model))}
                                           for bucket in self.move_buckets}
                self.activations_count[layer_idx] = {bucket: {"won": 0, "lost": 0, "current": 0}
                                             for bucket in self.move_buckets}
            else:
                for bucket in self.move_buckets:
                    self.activations_sum[layer_idx][bucket][result] += self.activations_sum[layer_idx][bucket]["current"]
                    self.activations_count[layer_idx][bucket][result] += 1
    
    def save_activations(self, path):
        if os.path.exists(path):
            with open(path, "rb") as f:
                activations_sum, activations_count = pickle.load(f)
        else:
            activations_sum = {}
            activations_count = {}
        
        for layer_idx in self.activations_sum:
            for bucket in self.move_buckets:
                if self.activations_count[layer_idx][bucket]["current"] == 0:
                    continue
                if layer_idx not in activations_sum:
                    activations_sum[layer_idx] = {}
                    activations_count[layer_idx] = {}
                if bucket not in activations_sum[layer_idx]:
                    activations_sum[layer_idx][bucket] = {}
                    activations_count[layer_idx][bucket] = {}
                for category in ["won", "lost"]:
                    if category not in activations_sum[layer_idx][bucket]:
                        activations_sum[layer_idx][bucket][category] = np.zeros((1, 8, self.model.config.d_model))
                        activations_count[layer_idx][bucket][category] = 0
                    
                    activations_sum[layer_idx][bucket][category] += self.activations_sum[layer_idx][bucket][category]
                    activations_count[layer_idx][bucket][category] += self.activations_count[layer_idx][bucket][category]
        
        with open(path, "wb") as f:
            pickle.dump((activations_sum, activations_count), f)
        
        for layer_idx in self.activations_sum:
            self.activations_sum[layer_idx] = {bucket: {"won": np.zeros((1, 8, self.model.config.d_model)),
                                                "lost": np.zeros((1, 8, self.model.config.d_model)),
                                                "current": np.zeros((1, 8, self.model.config.d_model))}
                                       for bucket in self.move_buckets}
            self.activations_count[layer_idx] = {bucket: {"won": 0, "lost": 0, "current": 0}
                                         for bucket in self.move_buckets}
    
    def apply_contrastive_activations(self, path, weight=1.0):
        if os.path.exists(path):
            with open(path, "rb") as f:
                activations_sum, activations_count = pickle.load(f)
            
            self.contrastive_activations_cache = {}

            def hook(module, input, output, layer_idx):
                if isinstance(output, tuple):
                    tensor_output = output[0]
                else:
                    tensor_output = output
                seq_len = tensor_output.shape[1]
                bucket = next(b for b in self.move_buckets if self.move_num <= b)
                
                # Check cache first
                if layer_idx in self.contrastive_activations_cache and bucket in self.contrastive_activations_cache[layer_idx]:
                    safe_contrastive_activations = self.contrastive_activations_cache[layer_idx][bucket]
                else:
                    won_activations = activations_sum[layer_idx][bucket]["won"] / activations_count[layer_idx][bucket]["won"]
                    lost_activations = activations_sum[layer_idx][bucket]["lost"] / activations_count[layer_idx][bucket]["lost"]
                    contrastive_activations = won_activations - lost_activations
                    contrastive_activations_tensor = torch.from_numpy(contrastive_activations).to(tensor_output.device)
                    valid_activations = torch.isfinite(contrastive_activations_tensor)
                    safe_contrastive_activations = torch.zeros_like(contrastive_activations_tensor)
                    safe_contrastive_activations[valid_activations] = contrastive_activations_tensor[valid_activations]

                    # Cache the safe activations
                    if layer_idx not in self.contrastive_activations_cache:
                        self.contrastive_activations_cache[layer_idx] = {}
                    self.contrastive_activations_cache[layer_idx][bucket] = safe_contrastive_activations

                tensor_output += safe_contrastive_activations[:, :seq_len, :] * weight
                if isinstance(output, tuple):
                    return tensor_output, output[1]
                else:
                    return tensor_output
            
            for layer_idx in activations_sum:
                self.hooks.append(self.model.backbone.layers[layer_idx].register_forward_hook(
                    lambda module, input, output, layer_idx=layer_idx: hook(module, input, output, layer_idx)
                ))

    def update_linear_probe_targets(self, curr_q_value, q_value_delta, material_bal):
        bucket = next(b for b in self.move_buckets if self.move_num <= b)
        for layer_idx in self.linear_probe_targets:
            self.linear_probe_targets[layer_idx][bucket]['q_value'].append(curr_q_value)
            self.linear_probe_targets[layer_idx][bucket]['q_value_delta'].append(q_value_delta)
            self.linear_probe_targets[layer_idx][bucket]['material_balance'].append(material_bal)

    def train_linear_probes(self):
        def get_lr(it):
            warmup_iters = 0 #300 * 43
            lr_decay_iters = 3000 * 43
            learning_rate = 0.0000075
            min_lr = 0.00000075
            # 1) linear warmup for warmup_iters steps
            if it < warmup_iters:
                return learning_rate * it / warmup_iters
            # 2) if it > lr_decay_iters, return min learning rate
            if it > lr_decay_iters:
                return min_lr
            # 3) in between, use cosine decay down to min learning rate
            decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
            assert 0 <= decay_ratio <= 1
            coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff ranges 0..1
            return min_lr + coeff * (learning_rate - min_lr)
        
        criterion = nn.MSELoss()
        self.wandb_step += 1
        lr = get_lr(self.wandb_step)

        for layer_idx in self.linear_probes:
            for bucket in self.move_buckets:
                if self.activations_count[layer_idx][bucket]['current'] > 0:
                    X = torch.from_numpy(self.activations_sum[layer_idx][bucket]['current']).float().flatten(1) #/ self.activations_count[layer_idx][bucket]['current']).float()
                    for probe_type in ['q_value', 'q_value_delta', 'material_balance']:
                        y = torch.tensor(self.linear_probe_targets[layer_idx][bucket][probe_type]).float().unsqueeze(1)
                        if len(y) > 0:
                            y_pred = self.linear_probes[layer_idx][probe_type](X)
                            loss = criterion(y_pred, y)
                            for param_group in self.linear_optimizers[layer_idx][probe_type].param_groups:
                                param_group['lr'] = lr
                            self.linear_optimizers[layer_idx][probe_type].zero_grad()
                            loss.backward()
                            self.linear_optimizers[layer_idx][probe_type].step()
                            #wandb.log({f"{probe_type}/layer_{layer_idx}_{bucket}_loss": loss.item()})
                            wandb.log({
                                "etc/lr": lr,
                                f"{probe_type}/layer_{layer_idx}_loss": loss.item()
                            }, step=self.wandb_step)

        # Reset linear_probe_targets after training
        self.linear_probe_targets = {i: {bucket: {'q_value': [], 'q_value_delta': [], 'material_balance': []} for bucket in self.move_buckets} for i in self.linear_probes}

    def save_linear_probe_data(self, path):
        self.linear_save_ct += 25
        wandb.log({
            "etc/games": self.linear_save_ct
        }, step=self.wandb_step)
        torch.save(self.linear_probes, path)

    def evaluate_linear_probes(self, board: chess.Board):
        self.move_num = board.fullmove_number
        bucket = next(b for b in self.move_buckets if self.move_num <= b)
        
        # Create a dictionary to store the statistics for the current move
        probe_stats = {probe_type: {layer_idx: {self.move_num: None} for layer_idx in self.linear_probes} for probe_type in ['q_value', 'q_value_delta', 'material_balance']}
        
        for layer_idx in self.linear_probes:
            X = torch.from_numpy(self.activations_sum[layer_idx][bucket]['current']).float().flatten(1)
            for probe_type in ['q_value', 'q_value_delta', 'material_balance']:
                target = torch.tensor(self.linear_probe_targets[layer_idx][bucket][probe_type]).float().item()
                probe = self.linear_probes[layer_idx][probe_type]
                prediction = probe(X).item()
                #print(f"Layer {layer_idx}, {probe_type}: {prediction} vs {target}")
                
                # Calculate the percentage accuracy based on the probe type
                if probe_type == 'q_value':
                    accuracy = 1 - abs(prediction - target) / 2  # Q-value range: -1 to 1
                elif probe_type == 'q_value_delta':
                    accuracy = 1 - abs(prediction - target) / 4  # Q-value delta range: -2 to 2
                else:  # material_balance
                    max_range = 35  # Adjust this value based on the expected range of material balance
                    accuracy = 1 - min(abs(prediction - target) / max_range, 1)
                
                # Store the accuracy in the probe_stats dictionary for the current move
                probe_stats[probe_type][layer_idx][self.move_num] = accuracy
        
        self.linear_probe_targets = {i: {bucket: {'q_value': [], 'q_value_delta': [], 'material_balance': []} for bucket in self.move_buckets} for i in self.linear_probes}
        
        # Append the probe_stats to the file
        with open('probe_stats.json', 'a') as f:
            json.dump(probe_stats, f)
            f.write('\n')  # Add a newline separator between moves