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

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from typing import List, Tuple
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import torch
from torch import LongTensor, nn, FloatTensor, BoolTensor
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torch.no_grad()
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from .dalle_bart_encoder_torch import GLUTorch, AttentionTorch
class DecoderCrossAttentionTorch(AttentionTorch):
def forward(
self,
decoder_state: FloatTensor,
encoder_state: FloatTensor,
attention_mask: BoolTensor
) -> FloatTensor:
keys = self.k_proj.forward(encoder_state)
values = self.v_proj.forward(encoder_state)
queries = self.q_proj.forward(decoder_state)
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query_shape = queries.shape[:2] + (self.head_count, -1)
key_value_shape = keys.shape[:2] + (self.head_count, -1)
keys = keys.reshape(key_value_shape)
values = values.reshape(key_value_shape)
queries = queries.reshape(query_shape)
queries /= queries.shape[-1] ** 0.5
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return super().forward(keys, values, queries, attention_mask)
class DecoderSelfAttentionTorch(AttentionTorch):
def forward(self,
decoder_state: FloatTensor,
keys_values: FloatTensor,
attention_mask: BoolTensor,
token_index: LongTensor
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) -> Tuple[FloatTensor, FloatTensor]:
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batch_count = decoder_state.shape[0]
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token_count = keys_values.shape[1]
shape = (batch_count, 1) + keys_values.shape[2:]
keys = self.k_proj.forward(decoder_state).view(shape)
values = self.v_proj.forward(decoder_state).view(shape)
token_mask = torch.arange(token_count) == token_index
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keys_values = torch.where(
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token_mask[None, :, None, None],
torch.cat([keys, values]),
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keys_values
)
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queries = self.q_proj.forward(decoder_state).reshape(shape)
queries /= queries.shape[-1] ** 0.5
keys, values = keys_values[:batch_count], keys_values[batch_count:]
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decoder_state = super().forward(keys, values, queries, attention_mask)
return decoder_state, keys_values
class DecoderLayerTorch(nn.Module):
def __init__(self,
image_token_count: int,
head_count: int,
embed_count: int,
glu_embed_count: int
):
super().__init__()
self.image_token_count = image_token_count
self.pre_self_attn_layer_norm = nn.LayerNorm(embed_count)
self.self_attn = DecoderSelfAttentionTorch(head_count, embed_count)
self.self_attn_layer_norm = nn.LayerNorm(embed_count)
self.pre_encoder_attn_layer_norm = nn.LayerNorm(embed_count)
self.encoder_attn = DecoderCrossAttentionTorch(head_count, embed_count)
self.encoder_attn_layer_norm = nn.LayerNorm(embed_count)
self.glu = GLUTorch(embed_count, glu_embed_count)
def forward(self,
decoder_state: FloatTensor,
encoder_state: FloatTensor,
keys_values_state: FloatTensor,
attention_mask: BoolTensor,
token_index: LongTensor
) -> Tuple[FloatTensor, FloatTensor]:
# Self Attention
residual = decoder_state
decoder_state = self.pre_self_attn_layer_norm.forward(decoder_state)
self_attn_mask = torch.arange(self.image_token_count) < token_index + 1
self_attn_mask = torch.stack([self_attn_mask] * decoder_state.shape[0])
decoder_state, keys_values_state = self.self_attn.forward(
decoder_state,
keys_values_state,
self_attn_mask,
token_index
)
decoder_state = self.self_attn_layer_norm.forward(decoder_state)
decoder_state = residual + decoder_state
# Cross Attention
residual = decoder_state
decoder_state = self.pre_encoder_attn_layer_norm.forward(decoder_state)
decoder_state = self.encoder_attn.forward(
decoder_state,
encoder_state,
attention_mask
)
decoder_state = self.encoder_attn_layer_norm.forward(decoder_state)
decoder_state = residual + decoder_state
# Feed forward
residual = decoder_state
decoder_state = self.glu.forward(decoder_state)
decoder_state = residual + decoder_state
return decoder_state, keys_values_state
class DalleBartDecoderTorch(nn.Module):
def __init__(self,
image_vocab_size: int,
image_token_count: int,
sample_token_count: int,
embed_count: int,
attention_head_count: int,
glu_embed_count: int,
layer_count: int,
batch_count: int,
start_token: int,
is_verbose: bool
):
super().__init__()
self.is_verbose = is_verbose
self.layer_count = layer_count
self.sample_token_count = sample_token_count
self.start_token = torch.tensor([start_token]).to(torch.long)
self.pad_token = torch.tensor([1]).to(torch.long)
self.condition_factor = torch.tensor([10]).to(torch.float)
self.image_token_count = image_token_count
self.embed_tokens = nn.Embedding(image_vocab_size + 1, embed_count)
self.embed_positions = nn.Embedding(image_token_count, embed_count)
self.layers: List[DecoderLayerTorch] = nn.ModuleList([
DecoderLayerTorch(
image_token_count,
attention_head_count,
embed_count,
glu_embed_count
)
for _ in range(layer_count)
])
self.layernorm_embedding = nn.LayerNorm(embed_count)
self.final_ln = nn.LayerNorm(embed_count)
self.lm_head = nn.Linear(embed_count, image_vocab_size + 1, bias=False)
self.keys_values_state_shape = (
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layer_count * 2 * batch_count,
image_token_count,
attention_head_count,
embed_count // attention_head_count
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)
def decode_step(self,
text_tokens: LongTensor,
encoder_state: FloatTensor,
keys_values_state: FloatTensor,
prev_token_and_index: LongTensor
) -> Tuple[LongTensor, FloatTensor]:
attention_mask = text_tokens.not_equal(self.pad_token)
batch_count = encoder_state.shape[0]
prev_token = torch.cat([prev_token_and_index[:1]] * batch_count)
token_index = torch.cat([prev_token_and_index[1:]] * batch_count)
decoder_state = self.embed_tokens.forward(prev_token)
decoder_state += self.embed_positions.forward(token_index)
decoder_state = self.layernorm_embedding.forward(decoder_state)
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decoder_state = decoder_state[:, None]
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keys_values = []
for i, layer in enumerate(self.layers):
j1, j2 = i * 2 * batch_count, (i + 1) * 2 * batch_count
decoder_state, keys_values_layer = layer.forward(
decoder_state,
encoder_state,
keys_values_state[j1:j2],
attention_mask,
token_index[:1]
)
keys_values.append(keys_values_layer)
keys_values = torch.cat(keys_values, dim=0)
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decoder_state = self.final_ln(decoder_state)
logits = self.lm_head(decoder_state)
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a = self.condition_factor
logits: FloatTensor = a * logits[0, -1] + (1 - a) * logits[1, -1]
top_logits = logits.sort(descending=True)[0][:50]
probs = torch.where(
logits < top_logits[-1],
torch.zeros([1]),
torch.exp(logits - top_logits[0])
)
return probs, keys_values
def forward(self,
text_tokens: LongTensor,
encoder_state: FloatTensor
) -> LongTensor:
image_tokens: List[LongTensor] = []
keys_values_state = torch.zeros(self.keys_values_state_shape)
image_token = self.start_token
for i in range(self.sample_token_count):
token_index = torch.tensor([i]).to(torch.long)
probs, keys_values_state = self.decode_step(
text_tokens = text_tokens,
encoder_state = encoder_state,
keys_values_state = keys_values_state,
prev_token_and_index = torch.cat([image_token, token_index])
)
image_token = torch.multinomial(probs, 1)
image_tokens += [image_token]
if self.is_verbose:
token = int(image_token.detach().numpy())
print("image token {} is {}".format(i, token))
return torch.cat(image_tokens)