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works with cuda

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This commit is contained in:
Brett Kuprel 2022-06-28 21:28:36 -04:00
parent 9d6b6dcc92
commit 17c96fe110
6 changed files with 43 additions and 33 deletions
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2
min_dalle/generate_image.py
View File

@ -65,6 +65,8 @@ def generate_image_from_text(
if image_token_count == config['image_length']:
image = detokenize_torch(image_tokens)
return Image.fromarray(image)
else:
print(list(image_tokens.to('cpu').detach().numpy()))
else:
image_tokens = generate_image_tokens_flax(
text_tokens = text_tokens,

4
min_dalle/load_params.py
View File

@ -4,7 +4,7 @@ from copy import deepcopy
from typing import Dict
from flax import traverse_util, serialization
import torch
torch.no_grad()
torch.set_grad_enabled(False)
def load_vqgan_torch_params(path: str) -> Dict[str, torch.Tensor]:
@ -30,7 +30,6 @@ def load_vqgan_torch_params(path: str) -> Dict[str, torch.Tensor]:
for i in P:
P[i] = torch.tensor(P[i])
# if torch.cuda.is_available(): P[i] = P[i].cuda()
P['embedding.weight'] = P.pop('quantize.embedding.embedding')
@ -87,7 +86,6 @@ def convert_dalle_bart_torch_from_flax_params(
for i in P:
P[i] = torch.tensor(P[i])
# if torch.cuda.is_available(): P[i] = P[i].cuda()
for i in list(P):
if 'kernel' in i:

10
min_dalle/min_dalle_torch.py
View File

@ -2,7 +2,7 @@ import numpy
from typing import Dict
from torch import LongTensor, FloatTensor
import torch
torch.no_grad()
torch.set_grad_enabled(False)
from .models.vqgan_detokenizer import VQGanDetokenizer
from .models.dalle_bart_encoder_torch import DalleBartEncoderTorch
@ -35,6 +35,7 @@ def encode_torch(
)
encoder.load_state_dict(encoder_params, strict=False)
del encoder_params
if torch.cuda.is_available(): encoder = encoder.cuda()
print("encoding text tokens")
encoder_state = encoder(text_tokens)
@ -70,6 +71,7 @@ def decode_torch(
)
decoder.load_state_dict(decoder_params, strict=False)
del decoder_params
if torch.cuda.is_available(): decoder = decoder.cuda()
print("sampling image tokens")
torch.manual_seed(seed)
@ -85,7 +87,7 @@ def generate_image_tokens_torch(
image_token_count: int
) -> LongTensor:
text_tokens = torch.tensor(text_tokens).to(torch.long)
# if torch.cuda.is_available(): text_tokens = text_tokens.cuda()
if torch.cuda.is_available(): text_tokens = text_tokens.cuda()
encoder_state = encode_torch(
text_tokens,
config,
@ -108,6 +110,8 @@ def detokenize_torch(image_tokens: LongTensor) -> numpy.ndarray:
params = load_vqgan_torch_params(model_path)
detokenizer = VQGanDetokenizer()
detokenizer.load_state_dict(params)
if torch.cuda.is_available(): detokenizer = detokenizer.cuda()
image = detokenizer.forward(image_tokens).to(torch.uint8)
return image.detach().numpy()
del detokenizer, params
return image.to('cpu').detach().numpy()

43
min_dalle/models/dalle_bart_decoder_torch.py
View File

@ -1,7 +1,7 @@
from typing import List, Tuple
import torch
from torch import LongTensor, nn, FloatTensor, BoolTensor
torch.no_grad()
torch.set_grad_enabled(False)
from .dalle_bart_encoder_torch import GLUTorch, AttentionTorch
@ -30,14 +30,12 @@ class DecoderSelfAttentionTorch(AttentionTorch):
decoder_state: FloatTensor,
keys_values: FloatTensor,
attention_mask: BoolTensor,
token_index: LongTensor
token_mask: BoolTensor
) -> Tuple[FloatTensor, FloatTensor]:
batch_count = decoder_state.shape[0]
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(
token_mask[None, :, None, None],
torch.cat([keys, values]),
@ -67,6 +65,10 @@ class DecoderLayerTorch(nn.Module):
self.encoder_attn_layer_norm = nn.LayerNorm(embed_count)
self.glu = GLUTorch(embed_count, glu_embed_count)
self.token_indices = torch.arange(self.image_token_count)
if torch.cuda.is_available():
self.token_indices = self.token_indices.cuda()
def forward(self,
decoder_state: FloatTensor,
encoder_state: FloatTensor,
@ -77,13 +79,14 @@ class DecoderLayerTorch(nn.Module):
# 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 = self.token_indices < token_index + 1
token_mask = self.token_indices == token_index
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
token_mask
)
decoder_state = self.self_attn_layer_norm.forward(decoder_state)
decoder_state = residual + decoder_state
@ -124,13 +127,7 @@ class DalleBartDecoderTorch(nn.Module):
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)
# if torch.cuda.is_available():
# self.start_token = self.start_token.cuda()
# self.pad_token = self.pad_token.cuda()
# self.condition_factor = self.condition_factor.cuda()
self.condition_factor = 10.0
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)
@ -152,6 +149,13 @@ class DalleBartDecoderTorch(nn.Module):
attention_head_count,
embed_count // attention_head_count
)
self.zero_prob = torch.zeros([1])
self.token_indices = torch.arange(self.sample_token_count)
self.start_token = torch.tensor([start_token]).to(torch.long)
if torch.cuda.is_available():
self.zero_prob = self.zero_prob.cuda()
self.token_indices = self.token_indices.cuda()
self.start_token = self.start_token.cuda()
def decode_step(self,
@ -160,7 +164,7 @@ class DalleBartDecoderTorch(nn.Module):
keys_values_state: FloatTensor,
prev_token_and_index: LongTensor
) -> Tuple[LongTensor, FloatTensor]:
attention_mask = text_tokens.not_equal(self.pad_token)
attention_mask = text_tokens.not_equal(1)
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)
@ -188,7 +192,7 @@ class DalleBartDecoderTorch(nn.Module):
top_logits = logits.sort(descending=True)[0][:50]
probs = torch.where(
logits < top_logits[-1],
torch.zeros([1]),
self.zero_prob,
torch.exp(logits - top_logits[0])
)
return probs, keys_values
@ -200,11 +204,12 @@ class DalleBartDecoderTorch(nn.Module):
) -> LongTensor:
image_tokens: List[LongTensor] = []
keys_values_state = torch.zeros(self.keys_values_state_shape)
if torch.cuda.is_available():
keys_values_state = keys_values_state.cuda()
image_token = self.start_token
for i in range(self.sample_token_count):
token_index = torch.tensor([i]).to(torch.long)
# if torch.cuda.is_available(): token_index = token_index.cuda()
token_index = self.token_indices[i:i+1]
probs, keys_values_state = self.decode_step(
text_tokens = text_tokens,
encoder_state = encoder_state,
@ -214,9 +219,5 @@ class DalleBartDecoderTorch(nn.Module):
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)

15
min_dalle/models/dalle_bart_encoder_torch.py
View File

@ -1,7 +1,7 @@
from typing import List
import torch
from torch import nn, BoolTensor, FloatTensor, LongTensor
torch.no_grad()
torch.set_grad_enabled(False)
class GLUTorch(nn.Module):
@ -34,6 +34,8 @@ class AttentionTorch(nn.Module):
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)
self.one = torch.ones((1, 1))
if torch.cuda.is_available(): self.one = self.one.cuda()
def forward(self,
keys: FloatTensor,
@ -43,8 +45,8 @@ class AttentionTorch(nn.Module):
) -> FloatTensor:
attention_bias = torch.where(
attention_mask,
torch.full(attention_mask.shape, 0.0),
torch.full(attention_mask.shape, -torch.inf),
self.one * 0,
self.one * (-torch.inf),
)
attention_weights: FloatTensor = torch.einsum(
'bqhc,bkhc->bhqk',
@ -124,11 +126,14 @@ class DalleBartEncoderTorch(nn.Module):
])
self.layernorm_embedding = nn.LayerNorm(embed_count)
self.final_ln = nn.LayerNorm(embed_count)
self.token_indices = torch.arange(text_token_count).to(torch.long)
if torch.cuda.is_available():
self.token_indices = self.token_indices.cuda()
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)
batch_count = text_tokens.shape[0]
pose_tokens = torch.stack([self.token_indices] * batch_count)
encoder_state = (
self.embed_tokens.forward(text_tokens) +
self.embed_positions.forward(pose_tokens)

2
min_dalle/models/vqgan_detokenizer.py
View File

@ -1,7 +1,7 @@
import torch
from torch import Tensor
from torch.nn import Module, ModuleList, GroupNorm, Conv2d, Embedding
torch.no_grad()
torch.set_grad_enabled(False)
BATCH_COUNT: int = 1