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0ee9aa1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutput
from .configuration_duchifat_v2 import DuchifatConfig
class DuchifatBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.ln1 = nn.LayerNorm(config.hidden_size)
self.qkv = nn.Linear(config.hidden_size, 3 * config.hidden_size)
self.wo = nn.Linear(config.hidden_size, config.hidden_size)
self.ln2 = nn.LayerNorm(config.hidden_size)
self.mlp = nn.Sequential(
nn.Linear(config.hidden_size, 4 * config.hidden_size),
nn.GELU(approximate='tanh'),
nn.Linear(4 * config.hidden_size, config.hidden_size)
)
self.n_head = config.nhead
self.head_dim = config.hidden_size // config.nhead
def forward(self, x):
norm_x = self.ln1(x)
B, T, C = norm_x.size()
qkv = self.qkv(norm_x).view(B, T, 3, self.n_head, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
# Flash Attention (SDPA)
attn_out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
attn_out = attn_out.transpose(1, 2).contiguous().view(B, T, C)
x = x + self.wo(attn_out)
x = x + self.mlp(self.ln2(x))
return x
class DuchifatPreTrainedModel(PreTrainedModel):
config_class = DuchifatConfig
base_model_prefix = "model"
_no_split_modules = ["DuchifatBlock"]
class DuchifatCore(DuchifatPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
self.wpe = nn.Embedding(config.max_seq, config.hidden_size)
self.blocks = nn.ModuleList([DuchifatBlock(config) for _ in range(config.num_layers)])
self.ln_f = nn.LayerNorm(config.hidden_size)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights
self.post_init()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, value):
self.wte = value
def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
# ืืืคืื ืืืงืจื ืฉืื input_ids ืื ื ืฉืื ืืจืืื
if input_ids is None:
raise ValueError("You must specify input_ids")
B, T = input_ids.size()
device = input_ids.device
# ืื ืืืช ืคืืืืฆืืืช (Absolute Positional Embeddings)
pos = torch.arange(0, T, dtype=torch.long, device=device)
x = self.wte(input_ids) + self.wpe(pos)
for block in self.blocks:
x = block(x)
logits = self.lm_head(self.ln_f(x))
loss = None
if labels is not None:
# Shift logits/labels ืขืืืจ Causal Language Modeling (ืืืื ืฉื 1 ืืืื ื)
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss = F.cross_entropy(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
return CausalLMOutput(
loss=loss,
logits=logits
)
# ืคืื ืงืฆืื ืืืื ืืช ืฉืืืคืฉืจืช ื-generate ืืขืืื
def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **kwargs):
return {
"input_ids": input_ids,
"attention_mask": attention_mask
}
# ืชืืืื ื-Beam Search ืืืืืงืืช ืงืืฉ ืืกืืกืืืช
def _reorder_cache(self, past_key_values, beam_idx):
return past_key_values |