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back to linear attention

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This commit is contained in:
Brett Kuprel 2022-06-27 13:19:03 -04:00
parent 018414a5c3
commit c936d26102
3 changed files with 45 additions and 69 deletions
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4
min_dalle/load_params.py
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@ -102,10 +102,6 @@ def convert_dalle_bart_torch_from_flax_params(
P[k] = P[i][l]
P.pop(i)
for i in list(P):
if '_proj' in i:
P[i] = P[i][:, :, None, None]
P['embed_tokens.weight'] = P.pop('embed_tokens.embedding')
P['embed_positions.weight'] = P.pop('embed_positions.embedding')
return P

44
min_dalle/models/dalle_bart_decoder_torch.py
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@ -15,6 +15,12 @@ class DecoderCrossAttentionTorch(AttentionTorch):
keys = self.k_proj.forward(encoder_state)
values = self.v_proj.forward(encoder_state)
queries = self.q_proj.forward(decoder_state)
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
return super().forward(keys, values, queries, attention_mask)
@ -24,20 +30,21 @@ class DecoderSelfAttentionTorch(AttentionTorch):
keys_values: FloatTensor,
attention_mask: BoolTensor,
token_index: LongTensor
) -> Tuple[FloatTensor, FloatTensor, FloatTensor]:
keys = self.k_proj.forward(decoder_state)
values = self.v_proj.forward(decoder_state)
queries = self.q_proj.forward(decoder_state)
) -> Tuple[FloatTensor, FloatTensor]:
batch_count = decoder_state.shape[0]
token_count = keys_values.shape[-1]
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
keys_values = torch.where(
(torch.arange(token_count) == token_index)[None, None, :],
torch.cat([keys, values]).squeeze(2),
token_mask[None, :, None, None],
torch.cat([keys, values]),
keys_values
)
keys, values = keys_values[:batch_count, :, None], keys_values[batch_count:, :, None]
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:]
decoder_state = super().forward(keys, values, queries, attention_mask)
return decoder_state, keys_values
@ -71,28 +78,23 @@ class DecoderLayerTorch(nn.Module):
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 = decoder_state.transpose(1, 2).unsqueeze(2)
# print("decoder_state", decoder_state.shape)
decoder_state, keys_values_state = self.self_attn.forward(
decoder_state,
keys_values_state,
self_attn_mask,
token_index
)
decoder_state = decoder_state.transpose(1, 3).squeeze(2)
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 = decoder_state.transpose(1, 2).unsqueeze(2)
decoder_state = self.encoder_attn.forward(
decoder_state,
encoder_state,
attention_mask
)
decoder_state = decoder_state.transpose(1, 3).squeeze(2)
decoder_state = self.encoder_attn_layer_norm.forward(decoder_state)
decoder_state = residual + decoder_state
@ -141,8 +143,9 @@ class DalleBartDecoderTorch(nn.Module):
self.lm_head = nn.Linear(embed_count, image_vocab_size + 1, bias=False)
self.keys_values_state_shape = (
layer_count * 2 * batch_count,
embed_count,
image_token_count
image_token_count,
attention_head_count,
embed_count // attention_head_count
)
@ -159,7 +162,7 @@ class DalleBartDecoderTorch(nn.Module):
decoder_state = self.embed_tokens.forward(prev_token)
decoder_state += self.embed_positions.forward(token_index)
decoder_state = self.layernorm_embedding.forward(decoder_state)
decoder_state = decoder_state[:, None] # (batch_count, 1, embed_count)
decoder_state = decoder_state[:, None]
keys_values = []
for i, layer in enumerate(self.layers):
j1, j2 = i * 2 * batch_count, (i + 1) * 2 * batch_count
@ -172,8 +175,8 @@ class DalleBartDecoderTorch(nn.Module):
)
keys_values.append(keys_values_layer)
keys_values = torch.cat(keys_values, dim=0)
decoder_state = self.final_ln(decoder_state) # (batch_count, 1, embed_count)
logits = self.lm_head(decoder_state) # (batch_count, 1, vocab_size)
decoder_state = self.final_ln(decoder_state)
logits = self.lm_head(decoder_state)
a = self.condition_factor
logits: FloatTensor = a * logits[0, -1] + (1 - a) * logits[1, -1]
@ -193,7 +196,6 @@ class DalleBartDecoderTorch(nn.Module):
image_tokens: List[LongTensor] = []
keys_values_state = torch.zeros(self.keys_values_state_shape)
image_token = self.start_token
encoder_state = encoder_state.transpose(1, 2).unsqueeze(2)
for i in range(self.sample_token_count):
token_index = torch.tensor([i]).to(torch.long)

64
min_dalle/models/dalle_bart_encoder_torch.py
View File

@ -26,12 +26,11 @@ class AttentionTorch(nn.Module):
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)
self.k_proj = nn.Linear(embed_count, embed_count, bias=False)
self.v_proj = nn.Linear(embed_count, embed_count, bias=False)
self.q_proj = nn.Linear(embed_count, embed_count, bias=False)
self.out_proj = nn.Linear(embed_count, embed_count, bias=False)
def forward(self,
keys: FloatTensor,
@ -39,47 +38,27 @@ class AttentionTorch(nn.Module):
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),
torch.full(attention_mask.shape, 0.0),
torch.full(attention_mask.shape, -torch.inf),
)
attention_weights: FloatTensor = torch.einsum(
'bchq,bkhc->bkhq',
queries / self.head_dim ** 0.5,
'bqhc,bkhc->bhqk',
queries,
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 += attention_bias[:, None, None, :]
attention_weights = torch.softmax(attention_weights, -1)
attention_output: FloatTensor = torch.einsum(
"bhqk,bkhc->bqhc",
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
shape = attention_output.shape[:2] + (self.embed_count,)
attention_output = attention_output.reshape(shape)
attention_output = self.out_proj.forward(attention_output)
return attention_output
class EncoderSelfAttentionTorch(AttentionTorch):
@ -88,11 +67,11 @@ class EncoderSelfAttentionTorch(AttentionTorch):
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)
shape_split = encoder_state.shape[:2] + (self.head_count, -1)
keys = self.k_proj.forward(encoder_state).reshape(shape_split)
values = self.v_proj.forward(encoder_state).reshape(shape_split)
queries = self.q_proj.forward(encoder_state).reshape(shape_split)
queries /= queries.shape[-1] ** 0.5
return super().forward(keys, values, queries, attention_mask)
@ -112,7 +91,6 @@ class EncoderLayerTorch(nn.Module):
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