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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import (
MaskedLMOutput,
BaseModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
QuestionAnsweringModelOutput,
)
from .configuration_mybert import MyBertConfig
def _build_rope_cache(head_dim, max_seq_len, base=10000.0):
inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim))
t = torch.arange(max_seq_len, dtype=torch.float32)
freqs = torch.outer(t, inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
return emb.cos(), emb.sin()
def _rotate_half(x):
x1, x2 = x.chunk(2, dim=-1)
return torch.cat((-x2, x1), dim=-1)
def _apply_rope(q, k, cos, sin):
cos = cos.to(q.dtype)[None, None, :, :]
sin = sin.to(q.dtype)[None, None, :, :]
return (q * cos) + (_rotate_half(q) * sin), (k * cos) + (_rotate_half(k) * sin)
class MyBertEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size,
padding_idx=config.pad_token_id)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, input_ids):
return self.dropout(self.LayerNorm(self.word_embeddings(input_ids)))
class MyBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = config.hidden_size // config.num_attention_heads
self.all_head_size = config.hidden_size
b = config.use_bias
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=b)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=b)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=b)
self.dropout_prob = config.attention_probs_dropout_prob
def forward(self, hidden_states, attention_mask=None, cos=None, sin=None):
q, k, v = self.query(hidden_states), self.key(hidden_states), self.value(hidden_states)
shp = q.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
q = q.view(*shp).transpose(1, 2)
k = k.view(*shp).transpose(1, 2)
v = v.view(*shp).transpose(1, 2)
if cos is not None:
q, k = _apply_rope(q, k, cos, sin)
ctx = F.scaled_dot_product_attention(
q, k, v, attn_mask=attention_mask,
dropout_p=self.dropout_prob if self.training else 0.0, is_causal=False,
)
ctx = ctx.transpose(1, 2).contiguous()
return ctx.view(*ctx.size()[:-2], self.all_head_size)
class MyBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=config.use_bias)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states):
return self.dropout(self.dense(hidden_states))
class MyBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = MyBertSelfAttention(config)
self.output = MyBertSelfOutput(config)
def forward(self, hidden_states, attention_mask=None, cos=None, sin=None):
return self.output(self.self(hidden_states, attention_mask, cos, sin))
class MyBertSwiGLU(nn.Module):
def __init__(self, config):
super().__init__()
b = config.use_bias
self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=b)
self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=b)
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=b)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, x):
return self.dropout(self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)))
class MyBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.attention = MyBertAttention(config)
self.ffn_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.mlp = MyBertSwiGLU(config)
def forward(self, hidden_states, attention_mask=None, cos=None, sin=None):
hidden_states = hidden_states + self.attention(
self.attention_layernorm(hidden_states), attention_mask, cos, sin)
hidden_states = hidden_states + self.mlp(self.ffn_layernorm(hidden_states))
return hidden_states
class MyBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.layer = nn.ModuleList([MyBertLayer(config) for _ in range(config.num_hidden_layers)])
self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states, attention_mask=None, cos=None, sin=None):
for lyr in self.layer:
hidden_states = lyr(hidden_states, attention_mask, cos, sin)
return self.final_layernorm(hidden_states)
class MyBertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.transform_act_fn = nn.GELU()
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, h):
return self.LayerNorm(self.transform_act_fn(self.dense(h)))
class MyBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = MyBertPredictionHeadTransform(config)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=True)
def forward(self, h):
return self.decoder(self.transform(h))
class MyBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = MyBertLMPredictionHead(config)
def forward(self, h):
return self.predictions(h)
class MyBertPreTrainedModel(PreTrainedModel):
config_class = MyBertConfig
base_model_prefix = "mybert"
supports_gradient_checkpointing = False
_no_split_modules = ["MyBertLayer"]
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, MyBertModel):
head_dim = self.config.hidden_size // self.config.num_attention_heads
cos, sin = _build_rope_cache(head_dim, self.config.max_position_embeddings,
self.config.rope_theta)
if module.rope_cos.device.type != "meta":
module.rope_cos.copy_(cos)
module.rope_sin.copy_(sin)
class MyBertModel(MyBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = MyBertEmbeddings(config)
self.encoder = MyBertEncoder(config)
head_dim = config.hidden_size // config.num_attention_heads
cos, sin = _build_rope_cache(head_dim, config.max_position_embeddings, config.rope_theta)
self.register_buffer("rope_cos", cos, persistent=False)
self.register_buffer("rope_sin", sin, persistent=False)
self.post_init()
n = config.num_hidden_layers
for lyr in self.encoder.layer:
lyr.attention.output.dense.weight.data.normal_(
0.0, config.initializer_range / math.sqrt(2 * n))
lyr.mlp.down_proj.weight.data.normal_(
0.0, config.initializer_range / math.sqrt(2 * n))
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, v):
self.embeddings.word_embeddings = v
def forward(self, input_ids=None, attention_mask=None, return_dict=True, **kw):
T = input_ids.shape[1]
if T > self.rope_cos.shape[0]:
raise ValueError(f"seq_len {T} > max_position_embeddings {self.rope_cos.shape[0]}")
cos, sin = self.rope_cos[:T], self.rope_sin[:T]
attn_mask = attention_mask.bool()[:, None, None, :] if attention_mask is not None else None
seq = self.encoder(self.embeddings(input_ids), attn_mask, cos, sin)
if not return_dict:
return (seq,)
return BaseModelOutput(last_hidden_state=seq)
class MyBertForMaskedLM(MyBertPreTrainedModel):
_tied_weights_keys = {
"cls.predictions.decoder.weight": "mybert.embeddings.word_embeddings.weight",
}
def __init__(self, config):
super().__init__(config)
self.mybert = MyBertModel(config)
self.cls = MyBertOnlyMLMHead(config)
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new):
self.cls.predictions.decoder = new
def forward(self, input_ids=None, attention_mask=None, labels=None,
label_smoothing=0.0, return_dict=True, **kw):
seq = self.mybert(input_ids=input_ids, attention_mask=attention_mask,
return_dict=True).last_hidden_state
if labels is not None and self.config.sparse_prediction:
flat_h = seq.view(-1, seq.size(-1))
flat_y = labels.view(-1)
idx = (flat_y != -100).nonzero(as_tuple=True)[0]
logits = self.cls(flat_h.index_select(0, idx))
loss = F.cross_entropy(logits.float(), flat_y.index_select(0, idx),
label_smoothing=label_smoothing)
return MaskedLMOutput(loss=loss, logits=None)
logits = self.cls(seq)
loss = None
if labels is not None:
loss = F.cross_entropy(logits.float().view(-1, self.config.vocab_size),
labels.view(-1), ignore_index=-100,
label_smoothing=label_smoothing)
if not return_dict:
return ((loss, logits) if loss is not None else (logits,))
return MaskedLMOutput(loss=loss, logits=logits)
class MyBertForSequenceClassification(MyBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.mybert = MyBertModel(config)
classifier_dropout = (
config.classifier_dropout
if getattr(config, "classifier_dropout", None) is not None
else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.post_init()
def forward(self, input_ids=None, attention_mask=None, labels=None,
return_dict=True, **kw):
seq = self.mybert(input_ids=input_ids, attention_mask=attention_mask,
return_dict=True).last_hidden_state
pooled = seq[:, 0]
logits = self.classifier(self.dropout(pooled))
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and labels.dtype in (torch.long, torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = nn.MSELoss()
loss = (loss_fct(logits.squeeze(), labels.squeeze())
if self.num_labels == 1 else loss_fct(logits, labels))
elif self.config.problem_type == "single_label_classification":
loss = nn.CrossEntropyLoss()(logits.view(-1, self.num_labels),
labels.view(-1))
else:
loss = nn.BCEWithLogitsLoss()(logits, labels)
if not return_dict:
return ((loss, logits) if loss is not None else (logits,))
return SequenceClassifierOutput(loss=loss, logits=logits)
class MyBertForTokenClassification(MyBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mybert = MyBertModel(config)
classifier_dropout = (
config.classifier_dropout
if getattr(config, "classifier_dropout", None) is not None
else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.post_init()
def forward(self, input_ids=None, attention_mask=None, labels=None,
return_dict=True, **kw):
seq = self.mybert(input_ids=input_ids, attention_mask=attention_mask,
return_dict=True).last_hidden_state
logits = self.classifier(self.dropout(seq))
loss = None
if labels is not None:
loss = nn.CrossEntropyLoss()(logits.view(-1, self.num_labels),
labels.view(-1))
if not return_dict:
return ((loss, logits) if loss is not None else (logits,))
return TokenClassifierOutput(loss=loss, logits=logits)
class MyBertForQuestionAnswering(MyBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = 2
self.mybert = MyBertModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
self.post_init()
def forward(self, input_ids=None, attention_mask=None,
start_positions=None, end_positions=None,
return_dict=True, **kw):
seq = self.mybert(input_ids=input_ids, attention_mask=attention_mask,
return_dict=True).last_hidden_state
start_logits, end_logits = self.qa_outputs(seq).split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
if start_positions.dim() > 1:
start_positions = start_positions.squeeze(-1)
if end_positions.dim() > 1:
end_positions = end_positions.squeeze(-1)
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
total_loss = (loss_fct(start_logits, start_positions)
+ loss_fct(end_logits, end_positions)) / 2
if not return_dict:
out = (start_logits, end_logits)
return ((total_loss,) + out) if total_loss is not None else out
return QuestionAnsweringModelOutput(
loss=total_loss, start_logits=start_logits, end_logits=end_logits) |