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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)
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