150 lines
5.6 KiB
Python
150 lines
5.6 KiB
Python
from typing import List
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import torch
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from torch import nn, BoolTensor, FloatTensor, LongTensor
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class GLU(nn.Module):
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def __init__(self, count_in_out, count_middle):
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super().__init__()
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self.gelu = nn.GELU()
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self.ln0 = nn.LayerNorm(count_in_out)
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self.ln1 = nn.LayerNorm(count_middle)
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self.fc0 = nn.Linear(count_in_out, count_middle, bias=False)
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self.fc1 = nn.Linear(count_in_out, count_middle, bias=False)
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self.fc2 = nn.Linear(count_middle, count_in_out, bias=False)
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def forward(self, z: FloatTensor) -> FloatTensor:
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z = self.ln0.forward(z)
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w = self.fc0.forward(z)
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w = self.gelu.forward(w)
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v = self.fc1.forward(z)
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z = self.ln1.forward(w * v)
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z = self.fc2.forward(z)
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return z
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class AttentionBase(nn.Module):
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def __init__(self, head_count: int, embed_count: int):
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super().__init__()
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self.head_count = head_count
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self.embed_count = embed_count
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self.k_proj = nn.Linear(embed_count, embed_count, bias=False)
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self.v_proj = nn.Linear(embed_count, embed_count, bias=False)
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self.q_proj = nn.Linear(embed_count, embed_count, bias=False)
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self.out_proj = nn.Linear(embed_count, embed_count, bias=False)
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self.one = torch.ones((1, 1))
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if torch.cuda.is_available(): self.one = self.one.cuda()
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def forward(
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self,
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keys: FloatTensor,
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values: FloatTensor,
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queries: FloatTensor,
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attention_mask: BoolTensor
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) -> FloatTensor:
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keys = keys.reshape(keys.shape[:2] + (self.head_count, -1))
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values = values.reshape(values.shape[:2] + (self.head_count, -1))
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queries = queries.reshape(queries.shape[:2] + (self.head_count, -1))
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queries /= queries.shape[-1] ** 0.5
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attention_bias = torch.where(
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attention_mask,
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self.one * 0,
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self.one * (-torch.inf),
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)
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attention_weights: FloatTensor = torch.einsum(
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'bqhc,bkhc->bhqk',
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queries,
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keys
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)
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attention_weights += attention_bias[:, None, None, :]
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attention_weights = torch.softmax(attention_weights, -1)
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attention_output: FloatTensor = torch.einsum(
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"bhqk,bkhc->bqhc",
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attention_weights,
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values
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)
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shape = attention_output.shape[:2] + (self.embed_count,)
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attention_output = attention_output.reshape(shape)
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attention_output = self.out_proj.forward(attention_output)
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return attention_output
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class EncoderSelfAttention(AttentionBase):
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def forward(
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self,
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encoder_state: FloatTensor,
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attention_mask: BoolTensor
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) -> FloatTensor:
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keys = self.k_proj.forward(encoder_state)
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values = self.v_proj.forward(encoder_state)
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queries = self.q_proj.forward(encoder_state)
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return super().forward(keys, values, queries, attention_mask)
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class EncoderLayer(nn.Module):
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def __init__(self, embed_count: int, head_count: int, glu_embed_count: int):
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super().__init__()
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self.pre_self_attn_layer_norm = nn.LayerNorm(embed_count)
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self.self_attn = EncoderSelfAttention(head_count, embed_count)
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self.self_attn_layer_norm = nn.LayerNorm(embed_count)
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self.glu = GLU(embed_count, glu_embed_count)
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def forward(
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self,
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encoder_state: FloatTensor,
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attention_mask: BoolTensor
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) -> FloatTensor:
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residual = encoder_state
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encoder_state = self.pre_self_attn_layer_norm.forward(encoder_state)
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encoder_state = self.self_attn.forward(encoder_state, attention_mask)
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encoder_state = self.self_attn_layer_norm.forward(encoder_state)
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encoder_state = residual + encoder_state
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residual = encoder_state
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encoder_state = self.glu.forward(encoder_state)
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encoder_state = residual + encoder_state
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return encoder_state
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class DalleBartEncoder(nn.Module):
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def __init__(
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self,
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layer_count: int,
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embed_count: int,
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attention_head_count: int,
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text_vocab_count: int,
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text_token_count: int,
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glu_embed_count: int
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):
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super().__init__()
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self.text_vocab_count = text_vocab_count
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self.embed_tokens = nn.Embedding(text_vocab_count, embed_count)
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self.embed_positions = nn.Embedding(text_token_count, embed_count)
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self.layers: List[EncoderLayer] = nn.ModuleList([
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EncoderLayer(
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embed_count = embed_count,
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head_count = attention_head_count,
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glu_embed_count = glu_embed_count
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)
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for _ in range(layer_count)
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])
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self.layernorm_embedding = nn.LayerNorm(embed_count)
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self.final_ln = nn.LayerNorm(embed_count)
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self.token_indices = torch.arange(text_token_count).to(torch.long)
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if torch.cuda.is_available():
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self.token_indices = self.token_indices.cuda()
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def forward(self, text_tokens: LongTensor) -> FloatTensor:
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attention_mask = text_tokens.not_equal(1)
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pose_tokens = self.token_indices[None][[0] * text_tokens.shape[0]]
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text_tokens = text_tokens.clamp(0, self.text_vocab_count - 1)
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encoder_state = (
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self.embed_tokens.forward(text_tokens) +
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self.embed_positions.forward(pose_tokens)
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)
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encoder_state = self.layernorm_embedding.forward(encoder_state)
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for layer in self.layers:
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encoder_state = layer.forward(encoder_state, attention_mask)
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encoder_state = self.final_ln.forward(encoder_state)
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return encoder_state |