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07100cc d8becc1 07100cc d8becc1 07100cc d8becc1 07100cc d8becc1 07100cc d8becc1 07100cc d8becc1 07100cc d8becc1 07100cc d8becc1 07100cc | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | import torch
import torch.nn as nn
from transformers import PreTrainedModel, BertModel, BertTokenizerFast
from transformers.modeling_outputs import MaskedLMOutput
from saute_config import SAUTEConfig
activation_to_class = {
"gelu" : nn.GELU,
"relu" : nn.ReLU,
"sigmoid" : nn.Sigmoid
}
from transformers import AutoModel
class EDUSpeakerAwareMLM(nn.Module):
def __init__(self, config):
super().__init__()
# model_name="sentence-transformers/all-MiniLM-L6-v2"
model_name = "bert-base-uncased"
self.edu_encoder = AutoModel.from_pretrained(model_name)
for param in self.edu_encoder.parameters():
param.requires_grad = False # frozen encoder
self.d_model = config.hidden_size
self.key_proj = nn.Linear(config.hidden_size, config.hidden_size, bias = False)
self.val_proj = nn.Linear(config.hidden_size, config.hidden_size, bias = False)
self.query_proj = nn.Linear(config.hidden_size, config.hidden_size, bias = False)
encoder_layer = nn.TransformerEncoderLayer(d_model=config.hidden_size, nhead=config.num_attention_heads, batch_first=True)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=config.num_hidden_layers)
# self.mlp_proj = nn.Sequential(
# nn.Linear(config.hidden_size, 2048),
# activation_to_class["gelu"](),
# # nn.Dropout(0.1),
# nn.Linear(2048, config.hidden_size),
# # nn.Dropout(0.1),
# )
self.ln1 = nn.LayerNorm(config.hidden_size)
# self.ln2 = nn.LayerNorm(config.hidden_size)
# self.speaker_memory = {} # Will be filled per batch
# self.lm_head = nn.Linear(config.hidden_size, self.edu_encoder.config.vocab_size)
def forward(self, input_ids, attention_mask, speaker_names):
"""
input_ids: (B, T, L)
attention_mask: (B, T, L)
speaker_names: list of list of strings, shape (B, T)
"""
B, T, L = input_ids.shape
# Encode EDUs using frozen encoder
with torch.no_grad():
input_ids_flat = input_ids.view(B * T, L)
attention_mask_flat = attention_mask.view(B * T, L)
outputs = self.edu_encoder(input_ids=input_ids_flat, attention_mask=attention_mask_flat)
token_embeddings = outputs.last_hidden_state # (B*T, L, D)
token_embeddings = token_embeddings.view(B, T, L, self.d_model)
edu_embeddings = token_embeddings.mean(dim=2) # (B, T, D)
query_emb = self.query_proj(token_embeddings)
# Speaker-aware memory
speaker_memories = [{} for _ in range(B)]
speaker_matrices = torch.zeros(B, T, self.d_model, self.d_model, device=edu_embeddings.device)
for b in range(B):
for t in range(T):
speaker = speaker_names[b][t]
e_t = edu_embeddings[b, t] # (D)
if speaker not in speaker_memories[b]:
speaker_memories[b][speaker] = {
'kv_sum': torch.zeros(self.d_model, self.d_model, device=e_t.device),
# 'k_sum': torch.zeros(self.d_model, device=e_t.device),
}
mem = speaker_memories[b][speaker]
k_t = self.key_proj(e_t)
v_t = self.val_proj(e_t)
kv_t = torch.outer(k_t, v_t)
# with torch.no_grad():
mem['kv_sum'] = mem['kv_sum'] + kv_t
# mem['k_sum'] = mem['k_sum'] + k_t
# z = torch.clamp(mem['k_sum'] @ k_t, min=1e-6)
# M_s = mem['kv_sum'] / z # (D, D)
# speaker_matrices[b, t] = M_s
speaker_matrices[b, t] = mem['kv_sum']
# Apply speaker matrix to each token
speaker_matrices_exp = speaker_matrices.unsqueeze(2) # (B, T, 1, D, D)
token_embeddings_exp = query_emb.unsqueeze(-1) # (B, T, L, D, 1)
contextual_tokens = token_embeddings + torch.matmul(speaker_matrices_exp, token_embeddings_exp).squeeze(-1) # (B, T, L, D)
# contextual_tokens = self.ln1(contextual_tokens)
# contextual_tokens = self.ln2(contextual_tokens + self.mlp_proj(contextual_tokens))
# === NEW: EDU-level Transformer ===
edu_tokens = contextual_tokens.view(B * T, L, self.d_model) # (B*T, L, D)
encoded_edu = self.transformer(edu_tokens) # (B*T, L, D)
encoded = encoded_edu.view(B, T, L, self.d_model) # (B, T, L, D)
return encoded, 0
class UtteranceEmbedings(PreTrainedModel):
config_class = SAUTEConfig
def __init__(self, config : SAUTEConfig):
super().__init__(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)
self.saute_unit = EDUSpeakerAwareMLM(config)
self.config : SAUTEConfig = config
self.init_weights()
def forward(
self,
input_ids : torch.Tensor,
speaker_names : list[str],
attention_mask : torch.Tensor = None,
labels : torch.Tensor = None
):
# print(input_ids.shape)
X, flop_penalty = self.saute_unit.forward(
input_ids = input_ids,
speaker_names = speaker_names,
attention_mask = attention_mask,
# hidden_state = None
)
# print(X.shape)
logits = self.lm_head(X)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
# loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) + 1e-3 * flop_penalty
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
return MaskedLMOutput(loss=loss, logits=logits)
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