min-dalle-test/min_dalle/models/dalle_bart_encoder_torch.py

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2022-06-27 15:57:56 +00:00
from typing import List
import torch
from torch import nn, BoolTensor, FloatTensor, LongTensor
class GLUTorch(nn.Module):
def __init__(self, count_in_out, count_middle):
super().__init__()
self.gelu = nn.GELU()
self.ln0 = nn.LayerNorm(count_in_out)
self.ln1 = nn.LayerNorm(count_middle)
self.fc0 = nn.Linear(count_in_out, count_middle, bias=False)
self.fc1 = nn.Linear(count_in_out, count_middle, bias=False)
self.fc2 = nn.Linear(count_middle, count_in_out, bias=False)
def forward(self, z: FloatTensor) -> FloatTensor:
z = self.ln0.forward(z)
w = self.fc0.forward(z)
w = self.gelu.forward(w)
v = self.fc1.forward(z)
z = self.ln1.forward(w * v)
z = self.fc2.forward(z)
return z
class AttentionTorch(nn.Module):
def __init__(self, head_count: int, embed_count: int):
super().__init__()
self.head_count = head_count
self.embed_count = embed_count
self.head_dim = embed_count // head_count
self.k_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False)
self.v_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False)
self.q_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False)
self.out_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False)
def forward(self,
keys: FloatTensor,
values: FloatTensor,
queries: FloatTensor,
attention_mask: BoolTensor
) -> FloatTensor:
batch_count = keys.shape[0]
# b(hc)1q -> bqhc
# print(keys.shape, "keys", values.shape, "values", queries.shape, "queries")
keys = keys.transpose(1, 3)
keys = keys.reshape(keys.shape[:2] + (self.head_count, -1))
# b(hc)1q -> bchq
shape = (batch_count, self.head_count, self.head_dim, -1)
values = values.reshape(shape)
values = values.transpose(1, 2)
queries = queries.reshape(shape)
queries = queries.transpose(1, 2)
# print(keys.shape, "keys", values.shape, "values", queries.shape, "queries")
attention_bias = torch.where(
attention_mask,
torch.zeros([1, 1]),
torch.ones([1, 1]) * (-torch.inf),
)
attention_weights: FloatTensor = torch.einsum(
'bchq,bkhc->bkhq',
queries / self.head_dim ** 0.5,
keys
)
attention_weights += attention_bias[:, :, None, None]
attention_weights = torch.softmax(attention_weights, 1)
# print(attention_weights.shape, "attention_weights")
hidden_state: FloatTensor = torch.einsum(
"bkhq,bchk->bchq",
attention_weights,
values
)
# bchq -> b(hc)1q
# print(hidden_state.shape, "hidden_state")
hidden_state = hidden_state.transpose(1, 2)
hidden_state = hidden_state.reshape(batch_count, self.embed_count, 1, -1)
hidden_state = self.out_proj.forward(hidden_state)
# print(hidden_state.shape, "hidden_state")
return hidden_state
class EncoderSelfAttentionTorch(AttentionTorch):
def forward(
self,
encoder_state: FloatTensor,
attention_mask: BoolTensor
) -> FloatTensor:
encoder_state = encoder_state.transpose(1, 2).unsqueeze(2)
# print(encoder_state.shape, "encoder_state")
keys = self.k_proj.forward(encoder_state)
values = self.v_proj.forward(encoder_state)
queries = self.q_proj.forward(encoder_state)
return super().forward(keys, values, queries, attention_mask)
class EncoderLayerTorch(nn.Module):
def __init__(self, embed_count: int, head_count: int, glu_embed_count: int):
super().__init__()
self.pre_self_attn_layer_norm = nn.LayerNorm(embed_count)
self.self_attn = EncoderSelfAttentionTorch(head_count, embed_count)
self.self_attn_layer_norm = nn.LayerNorm(embed_count)
self.glu = GLUTorch(embed_count, glu_embed_count)
def forward(
self,
encoder_state: FloatTensor,
attention_mask: BoolTensor
) -> FloatTensor:
residual = encoder_state
encoder_state = self.pre_self_attn_layer_norm.forward(encoder_state)
encoder_state = self.self_attn.forward(encoder_state, attention_mask)
encoder_state = encoder_state.transpose(1, 3).squeeze(2)
encoder_state = self.self_attn_layer_norm.forward(encoder_state)
encoder_state = residual + encoder_state
residual = encoder_state
encoder_state = self.glu.forward(encoder_state)
encoder_state = residual + encoder_state
return encoder_state
class DalleBartEncoderTorch(nn.Module):
def __init__(self,
layer_count: int,
embed_count: int,
attention_head_count: int,
text_vocab_count: int,
text_token_count: int,
glu_embed_count: int
):
super().__init__()
self.embed_tokens = nn.Embedding(text_vocab_count, embed_count)
self.embed_positions = nn.Embedding(text_token_count, embed_count)
self.layers: List[EncoderLayerTorch] = nn.ModuleList([
EncoderLayerTorch(
embed_count = embed_count,
head_count = attention_head_count,
glu_embed_count = glu_embed_count
)
for _ in range(layer_count)
])
self.layernorm_embedding = nn.LayerNorm(embed_count)
self.final_ln = nn.LayerNorm(embed_count)
def forward(self, text_tokens: LongTensor) -> FloatTensor:
attention_mask = text_tokens.not_equal(1)
batch_count, token_count = text_tokens.shape
pose_tokens = torch.stack([torch.arange(token_count)] * batch_count)
encoder_state = (
self.embed_tokens.forward(text_tokens) +
self.embed_positions.forward(pose_tokens)
)
encoder_state = self.layernorm_embedding.forward(encoder_state)
for layer in self.layers:
encoder_state = layer.forward(encoder_state, attention_mask)
encoder_state = self.final_ln.forward(encoder_state)
return encoder_state