| |
|
| | import torch |
| | import torch.nn as nn |
| | from transformers import PreTrainedModel, GenerationMixin |
| | from transformers.modeling_outputs import CausalLMOutput |
| | from .configuration_chess import ChessConfig |
| |
|
| | class Block(nn.Module): |
| | def __init__(self, config): |
| | super().__init__() |
| | self.ln1 = nn.LayerNorm(config.n_embd) |
| | self.attn = nn.MultiheadAttention(config.n_embd, config.n_head, batch_first=True) |
| | self.ln2 = nn.LayerNorm(config.n_embd) |
| | self.mlp = nn.Sequential( |
| | nn.Linear(config.n_embd, config.n_inner), |
| | nn.GELU(), |
| | nn.Linear(config.n_inner, config.n_embd) |
| | ) |
| | |
| | def forward(self, x, mask=None): |
| | attn_out, _ = self.attn(self.ln1(x), self.ln1(x), self.ln1(x), attn_mask=mask, need_weights=False) |
| | return x + attn_out + self.mlp(self.ln2(x + attn_out)) |
| |
|
| | class ChessForCausalLM(PreTrainedModel, GenerationMixin): |
| | config_class = ChessConfig |
| | |
| | def __init__(self, config): |
| | super().__init__(config) |
| | self.config = config |
| | self.token_emb = nn.Embedding(config.vocab_size, config.n_embd) |
| | self.pos_emb = nn.Embedding(config.n_ctx, config.n_embd) |
| | self.blocks = nn.ModuleList([Block(config) for _ in range(config.n_layer)]) |
| | self.ln_f = nn.LayerNorm(config.n_embd) |
| | self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) |
| | |
| | |
| | self.lm_head.weight = self.token_emb.weight |
| | |
| | self.apply(self._init_weights) |
| | |
| | def _init_weights(self, module): |
| | if isinstance(module, (nn.Linear, nn.Embedding)): |
| | torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) |
| |
|
| | def prepare_inputs_for_generation(self, input_ids, **kwargs): |
| | return {"input_ids": input_ids} |
| | |
| | def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): |
| | B, T = input_ids.shape |
| | x = self.token_emb(input_ids) + self.pos_emb(torch.arange(T, device=input_ids.device)) |
| | mask = torch.triu(torch.ones(T, T, device=input_ids.device) * float('-inf'), diagonal=1) |
| | for block in self.blocks: x = block(x, mask=mask) |
| | logits = self.lm_head(self.ln_f(x)) |
| | loss = None |
| | if labels is not None: |
| | loss = nn.CrossEntropyLoss(ignore_index=-100)(logits.view(-1, logits.size(-1)), labels.view(-1)) |
| | return CausalLMOutput(loss=loss, logits=logits) |
| |
|
