|
|
| import torch |
| import torch.nn as nn |
| from transformers import PreTrainedModel, PretrainedConfig |
| from transformers.modeling_outputs import CausalLMOutputWithPast |
|
|
| class ChessConfig(PretrainedConfig): |
| model_type = "chess_lm" |
| |
| def __init__(self, vocab_size=124, n_positions=256, n_embd=128, n_layer=6, |
| n_head=4, n_ctx=256, tie_word_embeddings=True, **kwargs): |
| self.vocab_size = vocab_size |
| self.n_positions = n_positions |
| self.n_embd = n_embd |
| self.n_layer = n_layer |
| self.n_head = n_head |
| self.n_ctx = n_ctx |
| self.tie_word_embeddings = tie_word_embeddings |
| super().__init__(**kwargs) |
|
|
| class ChessForCausalLM(PreTrainedModel): |
| config_class = ChessConfig |
| |
| def __init__(self, config): |
| super().__init__(config) |
| self.config = config |
| self.token_embedding = nn.Embedding(config.vocab_size, config.n_embd) |
| self.position_embedding = nn.Embedding(config.n_positions, config.n_embd) |
| encoder_layer = nn.TransformerEncoderLayer( |
| d_model=config.n_embd, |
| nhead=config.n_head, |
| dim_feedforward=config.n_embd * 4, |
| batch_first=True, |
| norm_first=True |
| ) |
| self.blocks = nn.TransformerEncoder(encoder_layer, num_layers=config.n_layer) |
| self.ln_f = nn.LayerNorm(config.n_embd) |
| self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False) |
| |
| if config.tie_word_embeddings: |
| self.head.weight = self.token_embedding.weight |
| |
| self.post_init() |
| |
| def get_input_embeddings(self): |
| return self.token_embedding |
| |
| def set_input_embeddings(self, value): |
| self.token_embedding = value |
| |
| def forward(self, input_ids, labels=None, **kwargs): |
| B, T = input_ids.shape |
| tok_emb = self.token_embedding(input_ids) |
| pos_emb = self.position_embedding(torch.arange(T, device=input_ids.device)) |
| x = tok_emb + pos_emb |
| mask = torch.triu(torch.ones(T, T, device=input_ids.device) * float('-inf'), diagonal=1) |
| x = self.blocks(x, mask=mask, is_causal=True) |
| x = self.ln_f(x) |
| logits = self.head(x) |
| |
| loss = None |
| if labels is not None: |
| shift_logits = logits[..., :-1, :].contiguous() |
| shift_labels = labels[..., 1:].contiguous() |
| loss_fct = nn.CrossEntropyLoss() |
| loss = loss_fct( |
| shift_logits.view(-1, self.config.vocab_size), |
| shift_labels.view(-1) |
| ) |
| |
| return CausalLMOutputWithPast(loss=loss, logits=logits) |
|
|
