| import math | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from transformers import PretrainedConfig, PreTrainedModel | |
| from transformers.modeling_outputs import CausalLMOutputWithPast | |
| def rotate_half(x): | |
| x1 = x[..., : x.shape[-1] // 2] | |
| x2 = x[..., x.shape[-1] // 2 :] | |
| return torch.cat((-x2, x1), dim=-1) | |
| def apply_rope(q, k): | |
| dim = q.shape[-1] | |
| device = q.device | |
| seq_len = q.shape[-2] | |
| theta = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).float() / dim)) | |
| pos = torch.arange(seq_len, device=device).float() | |
| freqs = torch.einsum('i,j->ij', pos, theta) | |
| emb = torch.cat((freqs, freqs), dim=-1) | |
| cos = emb.cos()[None, None, :, :] | |
| sin = emb.sin()[None, None, :, :] | |
| q = (q * cos) + (rotate_half(q) * sin) | |
| k = (k * cos) + (rotate_half(k) * sin) | |
| return q, k | |
| class ChessConfig(PretrainedConfig): | |
| model_type = "chess_transformer" | |
| def __init__(self, vocab_size=83, n_embd=160, n_layer=8, n_head=8, n_ctx=512, n_inner=480, dropout=0.15, layer_norm_epsilon=1e-5, tie_weights=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, **kwargs): | |
| super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | |
| self.vocab_size = vocab_size | |
| self.n_embd = n_embd | |
| self.n_layer = n_layer | |
| self.n_head = n_head | |
| self.n_ctx = n_ctx | |
| self.n_inner = n_inner | |
| self.dropout = dropout | |
| self.layer_norm_epsilon = layer_norm_epsilon | |
| self.tie_weights = tie_weights | |
| self.tie_word_embeddings = True | |
| class MultiHeadAttention(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| assert config.n_embd % config.n_head == 0 | |
| self.n_head = config.n_head | |
| self.n_embd = config.n_embd | |
| self.head_dim = config.n_embd // config.n_head | |
| self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd) | |
| self.c_proj = nn.Linear(config.n_embd, config.n_embd) | |
| self.dropout = nn.Dropout(config.dropout) | |
| self.register_buffer("bias", torch.tril(torch.ones(config.n_ctx, config.n_ctx)).view(1, 1, config.n_ctx, config.n_ctx), persistent=False) | |
| def forward(self, x, attention_mask=None): | |
| batch_size, seq_len, _ = x.size() | |
| qkv = self.c_attn(x) | |
| q, k, v = qkv.split(self.n_embd, dim=2) | |
| q = q.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2) | |
| k = k.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2) | |
| v = v.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2) | |
| q, k = apply_rope(q, k) | |
| attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim) | |
| causal_mask = self.bias[:, :, :seq_len, :seq_len] | |
| attn_weights = attn_weights.masked_fill(causal_mask == 0, float("-inf")) | |
| if attention_mask is not None: | |
| attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) | |
| attn_weights = attn_weights.masked_fill(attention_mask == 0, float("-inf")) | |
| attn_weights = F.softmax(attn_weights, dim=-1) | |
| attn_weights = self.dropout(attn_weights) | |
| attn_output = torch.matmul(attn_weights, v) | |
| attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len, self.n_embd) | |
| attn_output = self.c_proj(attn_output) | |
| return attn_output | |
| class FeedForward(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.c_fc = nn.Linear(config.n_embd, config.n_inner) | |
| self.c_proj = nn.Linear(config.n_inner, config.n_embd) | |
| self.dropout = nn.Dropout(config.dropout) | |
| def forward(self, x): | |
| x = self.c_fc(x) | |
| x = F.gelu(x) | |
| x = self.c_proj(x) | |
| x = self.dropout(x) | |
| return x | |
| class TransformerBlock(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) | |
| self.attn = MultiHeadAttention(config) | |
| self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) | |
| self.mlp = FeedForward(config) | |
| def forward(self, x, attention_mask=None): | |
| x = x + self.attn(self.ln_1(x), attention_mask=attention_mask) | |
| x = x + self.mlp(self.ln_2(x)) | |
| return x | |
| class ChessForCausalLM(PreTrainedModel): | |
| config_class = ChessConfig | |
| base_model_prefix = "transformer" | |
| supports_gradient_checkpointing = True | |
| keys_to_ignore_on_load_missing = ["lm_head.weight"] | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.wte = nn.Embedding(config.vocab_size, config.n_embd) | |
| self.drop = nn.Dropout(config.dropout) | |
| self.h = nn.ModuleList([TransformerBlock(config) for _ in range(config.n_layer)]) | |
| self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) | |
| self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) | |
| if config.tie_weights: | |
| self._tied_weights_keys = ["lm_head.weight"] | |
| self.post_init() | |
| if config.tie_weights: | |
| self.tie_weights() | |
| def get_input_embeddings(self): | |
| return self.wte | |
| def set_input_embeddings(self, new_embeddings): | |
| self.wte = new_embeddings | |
| if getattr(self.config, "tie_weights", False): | |
| self.tie_weights() | |
| def get_output_embeddings(self): | |
| return self.lm_head | |
| def set_output_embeddings(self, new_embeddings): | |
| self.lm_head = new_embeddings | |
| def tie_weights(self): | |
| if getattr(self.config, "tie_weights", False) or getattr(self.config, "tie_word_embeddings", False): | |
| self._tie_or_clone_weights(self.lm_head, self.wte) | |
| def _init_weights(self, module): | |
| if isinstance(module, nn.Linear): | |
| torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) | |
| if module.bias is not None: | |
| torch.nn.init.zeros_(module.bias) | |
| elif isinstance(module, nn.Embedding): | |
| torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) | |
| elif isinstance(module, nn.LayerNorm): | |
| torch.nn.init.ones_(module.weight) | |
| torch.nn.init.zeros_(module.bias) | |
| def forward(self, input_ids, attention_mask=None, position_ids=None, labels=None, return_dict=None, **kwargs): | |
| return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
| batch_size, seq_len = input_ids.size() | |
| token_embeds = self.wte(input_ids) | |
| hidden_states = self.drop(token_embeds) | |
| for block in self.h: | |
| hidden_states = block(hidden_states, attention_mask=attention_mask) | |
| hidden_states = self.ln_f(hidden_states) | |
| logits = self.lm_head(hidden_states) | |
| loss = None | |
| if labels is not None: | |
| shift_logits = logits[..., :-1, :].contiguous() | |
| shift_labels = labels[..., 1:].contiguous() | |
| loss_fct = nn.CrossEntropyLoss(ignore_index=-100) | |
| loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) | |
| if not return_dict: | |
| output = (logits,) | |
| return ((loss,) + output) if loss is not None else output | |
| return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=None, hidden_states=None, attentions=None) | |