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92
python/autoencoder.py
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92
python/autoencoder.py
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import pathlib
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import save
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from save import *
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from tinygrad.nn import Conv2d
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def save_resnet_block(resnet_block, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_group_norm(resnet_block.norm1, pathlib.Path(path, 'norm1'))
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save_conv2d(resnet_block.conv1, pathlib.Path(path, 'conv1'))
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save_group_norm(resnet_block.norm2, pathlib.Path(path, 'norm2'))
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save_conv2d(resnet_block.conv2, pathlib.Path(path, 'conv2'))
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if isinstance(resnet_block.nin_shortcut, Conv2d):
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save_conv2d(resnet_block.nin_shortcut, pathlib.Path(path, 'nin_shortcut'))
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def save_attn_block(attn_block, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_group_norm(attn_block.norm, pathlib.Path(path, 'norm'))
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save_conv2d(attn_block.q, pathlib.Path(path, 'q'))
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save_conv2d(attn_block.k, pathlib.Path(path, 'k'))
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save_conv2d(attn_block.v, pathlib.Path(path, 'v'))
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save_conv2d(attn_block.proj_out, pathlib.Path(path, 'proj_out'))
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def save_mid(mid, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_resnet_block(mid.block_1, pathlib.Path(path, 'block_1'))
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save_attn_block(mid.attn_1, pathlib.Path(path, 'attn'))
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save_resnet_block(mid.block_2, pathlib.Path(path, 'block_2'))
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def save_decoder_block(decoder_block, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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if 'block' in decoder_block:
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save_resnet_block(decoder_block["block"][0], pathlib.Path(path, 'res1'))
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save_resnet_block(decoder_block["block"][1], pathlib.Path(path, 'res2'))
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save_resnet_block(decoder_block["block"][2], pathlib.Path(path, 'res3'))
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if 'upsample' in decoder_block:
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save_conv2d(decoder_block['upsample']['conv'], pathlib.Path(path, 'upsampler'))
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def save_decoder(decoder, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_conv2d(decoder.conv_in, pathlib.Path(path, 'conv_in'))
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save_mid(decoder.mid, pathlib.Path(path, 'mid'))
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for i, block in enumerate(decoder.up[::-1]):
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print(i)
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if isinstance(block['block'][0].nin_shortcut, Conv2d):
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print(block['block'][0].nin_shortcut.weight.shape)
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save_decoder_block(block, pathlib.Path(path, f'blocks/{i}'))
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save_scalar(len(decoder.up), "n_block", path)
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save_group_norm(decoder.norm_out, pathlib.Path(path, 'norm_out'))
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save_conv2d(decoder.conv_out, pathlib.Path(path, 'conv_out'))
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def save_encoder_block(encoder_block, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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if 'block' in encoder_block:
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save_resnet_block(encoder_block["block"][0], pathlib.Path(path, 'res1'))
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save_resnet_block(encoder_block["block"][1], pathlib.Path(path, 'res2'))
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if 'downsample' in encoder_block:
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save_padded_conv2d(encoder_block['downsample']['conv'], pathlib.Path(path, 'downsampler'))
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def save_encoder(encoder, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_conv2d(encoder.conv_in, pathlib.Path(path, 'conv_in'))
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save_mid(encoder.mid, pathlib.Path(path, 'mid'))
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for i, block in enumerate(encoder.down):
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save_encoder_block(block, pathlib.Path(path, f'blocks/{i}'))
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save_scalar(len(encoder.down), "n_block", path)
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save_group_norm(encoder.norm_out, pathlib.Path(path, 'norm_out'))
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save_conv2d(encoder.conv_out, pathlib.Path(path, 'conv_out'))
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def save_autoencoder(autoencoder, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_encoder(autoencoder.encoder, pathlib.Path(path, 'encoder'))
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save_decoder(autoencoder.decoder, pathlib.Path(path, 'decoder'))
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save_conv2d(autoencoder.quant_conv, pathlib.Path(path, 'quant_conv'))
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save_conv2d(autoencoder.post_quant_conv, pathlib.Path(path, 'post_quant_conv'))
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BIN
python/bpe_simple_vocab_16e6.txt.gz
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BIN
python/bpe_simple_vocab_16e6.txt.gz
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40
python/clip.py
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40
python/clip.py
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import pathlib
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import save
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from save import *
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def save_clipmlp(clip_mlp, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_linear(clip_mlp.fc1, pathlib.Path(path, 'fc1'))
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save_linear(clip_mlp.fc2, pathlib.Path(path, 'fc2'))
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def save_clip_attention(clip_attention, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_linear(clip_attention.k_proj, pathlib.Path(path, 'key'))
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save_linear(clip_attention.v_proj, pathlib.Path(path, 'value'))
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save_linear(clip_attention.q_proj, pathlib.Path(path, 'query'))
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save_linear(clip_attention.out_proj, pathlib.Path(path, 'out'))
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save_scalar(clip_attention.num_heads, 'n_head', path)
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def save_clip_encoder_layer(clip_encoder_layer, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_clip_attention(clip_encoder_layer.self_attn, pathlib.Path(path, 'attn'))
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save_layer_norm(clip_encoder_layer.layer_norm1, pathlib.Path(path, 'attn_ln'))
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save_clipmlp(clip_encoder_layer.mlp, pathlib.Path(path, 'mlp'))
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save_layer_norm(clip_encoder_layer.layer_norm2, pathlib.Path(path, 'mlp_ln'))
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def save_clip_encoder(clip_encoder, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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for i, layer in enumerate(clip_encoder.layers):
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save_clip_encoder_layer(layer, pathlib.Path(path, f'blocks/{i}'))
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save_scalar(len(clip_encoder.layers), "n_layer", path)
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def save_clip_text_embeddings(clip_text_embeddings, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_embedding(clip_text_embeddings.token_embedding, pathlib.Path(path, 'token_embedding'))
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save_embedding(clip_text_embeddings.position_embedding, pathlib.Path(path, 'position_embedding'))
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def save_clip_text_transformer(clip_text_transformer, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_clip_text_embeddings(clip_text_transformer.embeddings, path)
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save_clip_encoder(clip_text_transformer.encoder, path)
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save_layer_norm(clip_text_transformer.final_layer_norm, pathlib.Path(path, 'layer_norm'))
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100
python/save.py
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100
python/save.py
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import pathlib
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import numpy as np
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from tinygrad.tensor import Tensor
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def save_scalar(s, name, path):
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s = np.array([1.0, float(s)]).astype(np.float32)
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np.save(pathlib.Path(path, f'{name}.npy'), s)
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def save_tensor(tensor, name, path):
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tensor_numpy = tensor.numpy()
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tensor_dims = np.array(tensor_numpy.shape)
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tensor_values = tensor_numpy.flatten()
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tensor_to_save = np.concatenate((tensor_dims, tensor_values)).astype(np.float32)
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np.save(pathlib.Path(path, f'{name}.npy'), tensor_to_save)
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def save_linear(linear, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_tensor(linear.weight.transpose(), 'weight', path) # PyTorch and Tinygrad strangely transpose linear weights so reverse that
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if linear.bias is not None:
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save_tensor(linear.bias, 'bias', path)
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def save_layer_norm(layer_norm, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_tensor(layer_norm.weight, 'weight', path)
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save_tensor(layer_norm.bias, 'bias', path)
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save_scalar(layer_norm.eps, 'eps', path)
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def save_group_norm(layer_norm, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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if layer_norm.weight is not None:
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save_tensor(layer_norm.weight, 'weight', path)
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if layer_norm.bias is not None:
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save_tensor(layer_norm.bias, 'bias', path)
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save_scalar(layer_norm.eps, 'eps', path)
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save_scalar(layer_norm.num_groups, 'n_group', path)
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save_scalar(layer_norm.num_channels, 'n_channel', path)
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def to_tuple_tensor(val):
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if isinstance(val, tuple):
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# Convert tuple to Tensor
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if len(val) == 1:
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return Tensor([val[0], val[0]])
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elif len(val) == 2:
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return Tensor([val[0], val[1]])
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else:
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raise ValueError('Tuple should be of length 1 or 2 only.')
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else:
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# Treat as scalar and convert to Tensor
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return Tensor([val, val])
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def save_conv2d(conv2d, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_tensor(conv2d.weight, 'weight', path)
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if conv2d.bias is not None:
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save_tensor(conv2d.bias, 'bias', path)
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save_tensor(to_tuple_tensor(conv2d.stride), 'stride', path)
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save_tensor(to_tuple_tensor(conv2d.padding), 'padding', path)
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save_tensor(to_tuple_tensor(conv2d.dilation), 'dilation', path)
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save_scalar(conv2d.groups, "n_group", path)
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save_tensor(to_tuple_tensor(conv2d.kernel_size), 'kernel_size', path)
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assert conv2d.groups == 1
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in_channels = conv2d.weight.shape[1]
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out_channels = conv2d.weight.shape[0]
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save_scalar(in_channels, "n_channels_in", path)
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save_scalar(out_channels, "n_channels_out", path)
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def save_padded_conv2d(padded_conv2d, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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# Store conv2d layer weights
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orig_padding = padded_conv2d.padding
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padded_conv2d.padding = (0, 0)
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save_conv2d(padded_conv2d, f"{path}/conv")
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padded_conv2d.padding = orig_padding
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# Dimensions: in-channels and out-channels
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assert padded_conv2d.groups == 1
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channels = (padded_conv2d.weight.shape[1], padded_conv2d.weight.shape[0])
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save_tensor(to_tuple_tensor(channels), 'channels', path)
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assert len(padded_conv2d.kernel_size) == 1 or padded_conv2d.kernel_size[0] == padded_conv2d.kernel_size[1]
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save_scalar(padded_conv2d.kernel_size[0], 'kernel_size', path)
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# Stride
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assert not isinstance(padded_conv2d.stride, tuple) or len(padded_conv2d.stride) == 1
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save_scalar(padded_conv2d.stride, 'stride', path)
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# Padding
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padding = [padded_conv2d.padding[0], padded_conv2d.padding[1],
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padded_conv2d.padding[2], padded_conv2d.padding[3]]
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save_tensor(Tensor(padding), 'padding', path)
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def save_embedding(embedding, path):
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pathlib.Path(path).mkdir(parents=True, exist_ok=True)
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save_tensor(embedding.weight, 'weight', path)
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736
python/stable_diffusion.py
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736
python/stable_diffusion.py
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# https://arxiv.org/pdf/2112.10752.pdf
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# https://github.com/ekagra-ranjan/huggingface-blog/blob/main/stable_diffusion.md
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import os
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import tempfile
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from pathlib import Path
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import gzip, argparse, math, re
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from functools import lru_cache
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from collections import namedtuple
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from tqdm import tqdm
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from tinygrad.tensor import Tensor
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from tinygrad.helpers import dtypes, GlobalCounters
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from tinygrad.nn import Conv2d, Linear, GroupNorm, LayerNorm, Embedding
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from extra.utils import download_file
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from tinygrad.state import torch_load, load_state_dict
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# TODO: refactor AttnBlock, CrossAttention, CLIPAttention to share code
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class AttnBlock:
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def __init__(self, in_channels):
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self.norm = GroupNorm(32, in_channels)
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self.q = Conv2d(in_channels, in_channels, 1)
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self.k = Conv2d(in_channels, in_channels, 1)
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self.v = Conv2d(in_channels, in_channels, 1)
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self.proj_out = Conv2d(in_channels, in_channels, 1)
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# copied from AttnBlock in ldm repo
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def __call__(self, x):
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h_ = self.norm(x)
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q,k,v = self.q(h_), self.k(h_), self.v(h_)
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# compute attention
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b,c,h,w = q.shape
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q = q.reshape(b,c,h*w)
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q = q.permute(0,2,1) # b,hw,c
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k = k.reshape(b,c,h*w) # b,c,hw
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w_ = q @ k
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w_ = w_ * (c**(-0.5))
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w_ = w_.softmax()
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# attend to values
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v = v.reshape(b,c,h*w)
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w_ = w_.permute(0,2,1)
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h_ = v @ w_
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h_ = h_.reshape(b,c,h,w)
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return x + self.proj_out(h_)
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class ResnetBlock:
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def __init__(self, in_channels, out_channels=None):
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self.norm1 = GroupNorm(32, in_channels)
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self.conv1 = Conv2d(in_channels, out_channels, 3, padding=1)
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self.norm2 = GroupNorm(32, out_channels)
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self.conv2 = Conv2d(out_channels, out_channels, 3, padding=1)
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self.nin_shortcut = Conv2d(in_channels, out_channels, 1) if in_channels != out_channels else lambda x: x
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def __call__(self, x):
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h = self.conv1(self.norm1(x).swish())
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'''v = h
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print(v.shape)
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print(v[0, 0:10, :, :].numpy())'''
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h = self.conv2(self.norm2(h).swish())
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return self.nin_shortcut(x) + h
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class Mid:
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def __init__(self, block_in):
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self.block_1 = ResnetBlock(block_in, block_in)
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self.attn_1 = AttnBlock(block_in)
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self.block_2 = ResnetBlock(block_in, block_in)
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def __call__(self, x):
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return x.sequential([self.block_1, self.attn_1, self.block_2])
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class Decoder:
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def __init__(self):
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sz = [(128, 256), (256, 512), (512, 512), (512, 512)]
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self.conv_in = Conv2d(4,512,3, padding=1)
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self.mid = Mid(512)
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arr = []
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for i,s in enumerate(sz):
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arr.append({"block":
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[ResnetBlock(s[1], s[0]),
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ResnetBlock(s[0], s[0]),
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ResnetBlock(s[0], s[0])]})
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if i != 0: arr[-1]['upsample'] = {"conv": Conv2d(s[0], s[0], 3, padding=1)}
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self.up = arr
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self.norm_out = GroupNorm(32, 128)
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self.conv_out = Conv2d(128, 3, 3, padding=1)
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def __call__(self, x):
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x = self.conv_in(x)
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x = self.mid(x)
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for l in self.up[::-1]:
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print("decode", x.shape)
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for b in l['block']:
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x = b(x)
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if 'upsample' in l:
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# https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html ?
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bs,c,py,px = x.shape
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x = x.reshape(bs, c, py, 1, px, 1).expand(bs, c, py, 2, px, 2).reshape(bs, c, py*2, px*2)
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x = l['upsample']['conv'](x)
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x.realize()
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return self.conv_out(self.norm_out(x).swish())
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class Encoder:
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def __init__(self):
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sz = [(128, 128), (128, 256), (256, 512), (512, 512)]
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self.conv_in = Conv2d(3,128,3, padding=1)
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arr = []
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for i,s in enumerate(sz):
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arr.append({"block":
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[ResnetBlock(s[0], s[1]),
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ResnetBlock(s[1], s[1])]})
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if i != 3: arr[-1]['downsample'] = {"conv": Conv2d(s[1], s[1], 3, stride=2, padding=(0,1,0,1))}
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self.down = arr
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self.mid = Mid(512)
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self.norm_out = GroupNorm(32, 512)
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self.conv_out = Conv2d(512, 8, 3, padding=1)
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def __call__(self, x):
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x = self.conv_in(x)
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for i, l in enumerate(self.down):
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print("encode", x.shape)
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for b in l['block']: x = b(x)
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if 'downsample' in l: x = l['downsample']['conv'](x)
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x = self.mid(x)
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return self.conv_out(self.norm_out(x).swish())
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class AutoencoderKL:
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def __init__(self):
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self.encoder = Encoder()
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self.decoder = Decoder()
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self.quant_conv = Conv2d(8, 8, 1)
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self.post_quant_conv = Conv2d(4, 4, 1)
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def __call__(self, x):
|
||||
latent = self.encoder(x)
|
||||
latent = self.quant_conv(latent)
|
||||
latent = latent[:, 0:4] # only the means
|
||||
print("latent", latent.shape)
|
||||
latent = self.post_quant_conv(latent)
|
||||
return self.decoder(latent)
|
||||
|
||||
# not to be confused with ResnetBlock
|
||||
class ResBlock:
|
||||
def __init__(self, channels, emb_channels, out_channels):
|
||||
self.in_layers = [
|
||||
GroupNorm(32, channels),
|
||||
Tensor.silu,
|
||||
Conv2d(channels, out_channels, 3, padding=1)
|
||||
]
|
||||
self.emb_layers = [
|
||||
Tensor.silu,
|
||||
Linear(emb_channels, out_channels)
|
||||
]
|
||||
self.out_layers = [
|
||||
GroupNorm(32, out_channels),
|
||||
Tensor.silu,
|
||||
lambda x: x, # needed for weights loading code to work
|
||||
Conv2d(out_channels, out_channels, 3, padding=1)
|
||||
]
|
||||
self.skip_connection = Conv2d(channels, out_channels, 1) if channels != out_channels else lambda x: x
|
||||
|
||||
def __call__(self, x, emb):
|
||||
h = x.sequential(self.in_layers)
|
||||
emb_out = emb.sequential(self.emb_layers)
|
||||
h = h + emb_out.reshape(*emb_out.shape, 1, 1)
|
||||
h = h.sequential(self.out_layers)
|
||||
ret = self.skip_connection(x) + h
|
||||
return ret
|
||||
|
||||
class CrossAttention:
|
||||
def __init__(self, query_dim, context_dim, n_heads, d_head):
|
||||
self.to_q = Linear(query_dim, n_heads*d_head, bias=False)
|
||||
self.to_k = Linear(context_dim, n_heads*d_head, bias=False)
|
||||
self.to_v = Linear(context_dim, n_heads*d_head, bias=False)
|
||||
self.scale = d_head ** -0.5
|
||||
self.num_heads = n_heads
|
||||
self.head_size = d_head
|
||||
self.to_out = [Linear(n_heads*d_head, query_dim)]
|
||||
|
||||
def __call__(self, x, context=None):
|
||||
context = x if context is None else context
|
||||
q,k,v = self.to_q(x), self.to_k(context), self.to_v(context)
|
||||
q = q.reshape(x.shape[0], -1, self.num_heads, self.head_size).permute(0,2,1,3) # (bs, num_heads, time, head_size)
|
||||
k = k.reshape(x.shape[0], -1, self.num_heads, self.head_size).permute(0,2,3,1) # (bs, num_heads, head_size, time)
|
||||
v = v.reshape(x.shape[0], -1, self.num_heads, self.head_size).permute(0,2,1,3) # (bs, num_heads, time, head_size)
|
||||
|
||||
score = q.dot(k) * self.scale
|
||||
weights = score.softmax() # (bs, num_heads, time, time)
|
||||
attention = weights.dot(v).permute(0,2,1,3) # (bs, time, num_heads, head_size)
|
||||
|
||||
h_ = attention.reshape(shape=(x.shape[0], -1, self.num_heads * self.head_size))
|
||||
return h_.sequential(self.to_out)
|
||||
|
||||
class GEGLU:
|
||||
def __init__(self, dim_in, dim_out):
|
||||
self.proj = Linear(dim_in, dim_out * 2)
|
||||
self.dim_out = dim_out
|
||||
|
||||
def __call__(self, x):
|
||||
x, gate = self.proj(x).chunk(2, dim=-1)
|
||||
return x * gate.gelu()
|
||||
|
||||
class FeedForward:
|
||||
def __init__(self, dim, mult=4):
|
||||
self.net = [
|
||||
GEGLU(dim, dim*mult),
|
||||
lambda x: x, # needed for weights loading code to work
|
||||
Linear(dim*mult, dim)
|
||||
]
|
||||
|
||||
def __call__(self, x):
|
||||
return x.sequential(self.net)
|
||||
|
||||
class BasicTransformerBlock:
|
||||
def __init__(self, dim, context_dim, n_heads, d_head):
|
||||
self.attn1 = CrossAttention(dim, dim, n_heads, d_head)
|
||||
self.ff = FeedForward(dim)
|
||||
self.attn2 = CrossAttention(dim, context_dim, n_heads, d_head)
|
||||
self.norm1 = LayerNorm(dim)
|
||||
self.norm2 = LayerNorm(dim)
|
||||
self.norm3 = LayerNorm(dim)
|
||||
|
||||
def __call__(self, x, context=None):
|
||||
x = self.attn1(self.norm1(x)) + x
|
||||
x = self.attn2(self.norm2(x), context=context) + x
|
||||
x = self.ff(self.norm3(x)) + x
|
||||
return x
|
||||
|
||||
class SpatialTransformer:
|
||||
def __init__(self, channels, context_dim, n_heads, d_head):
|
||||
self.norm = GroupNorm(32, channels)
|
||||
assert channels == n_heads * d_head
|
||||
self.proj_in = Conv2d(channels, n_heads * d_head, 1)
|
||||
self.transformer_blocks = [BasicTransformerBlock(channels, context_dim, n_heads, d_head)]
|
||||
self.proj_out = Conv2d(n_heads * d_head, channels, 1)
|
||||
|
||||
def __call__(self, x, context=None):
|
||||
b, c, h, w = x.shape
|
||||
x_in = x
|
||||
x = self.norm(x)
|
||||
x = self.proj_in(x)
|
||||
x = x.reshape(b, c, h*w).permute(0,2,1)
|
||||
for block in self.transformer_blocks:
|
||||
x = block(x, context=context)
|
||||
x = x.permute(0,2,1).reshape(b, c, h, w)
|
||||
ret = self.proj_out(x) + x_in
|
||||
return ret
|
||||
|
||||
class Downsample:
|
||||
def __init__(self, channels):
|
||||
self.op = Conv2d(channels, channels, 3, stride=2, padding=1)
|
||||
|
||||
def __call__(self, x):
|
||||
return self.op(x)
|
||||
|
||||
class Upsample:
|
||||
def __init__(self, channels):
|
||||
self.conv = Conv2d(channels, channels, 3, padding=1)
|
||||
|
||||
def __call__(self, x):
|
||||
bs,c,py,px = x.shape
|
||||
x = x.reshape(bs, c, py, 1, px, 1).expand(bs, c, py, 2, px, 2).reshape(bs, c, py*2, px*2)
|
||||
return self.conv(x)
|
||||
|
||||
def timestep_embedding(timesteps, dim, max_period=10000):
|
||||
half = dim // 2
|
||||
freqs = (-math.log(max_period) * Tensor.arange(half) / half).exp()
|
||||
args = timesteps * freqs
|
||||
return Tensor.cat(args.cos(), args.sin()).reshape(1, -1)
|
||||
|
||||
class UNetModel:
|
||||
def __init__(self):
|
||||
self.time_embed = [
|
||||
Linear(320, 1280),
|
||||
Tensor.silu,
|
||||
Linear(1280, 1280),
|
||||
]
|
||||
self.input_blocks = [
|
||||
[Conv2d(4, 320, kernel_size=3, padding=1)],
|
||||
[ResBlock(320, 1280, 320), SpatialTransformer(320, 768, 8, 40)],
|
||||
[ResBlock(320, 1280, 320), SpatialTransformer(320, 768, 8, 40)],
|
||||
[Downsample(320)],
|
||||
[ResBlock(320, 1280, 640), SpatialTransformer(640, 768, 8, 80)],
|
||||
[ResBlock(640, 1280, 640), SpatialTransformer(640, 768, 8, 80)],
|
||||
[Downsample(640)],
|
||||
[ResBlock(640, 1280, 1280), SpatialTransformer(1280, 768, 8, 160)],
|
||||
[ResBlock(1280, 1280, 1280), SpatialTransformer(1280, 768, 8, 160)],
|
||||
[Downsample(1280)],
|
||||
[ResBlock(1280, 1280, 1280)],
|
||||
[ResBlock(1280, 1280, 1280)]
|
||||
]
|
||||
self.middle_block = [
|
||||
ResBlock(1280, 1280, 1280),
|
||||
SpatialTransformer(1280, 768, 8, 160),
|
||||
ResBlock(1280, 1280, 1280)
|
||||
]
|
||||
self.output_blocks = [
|
||||
[ResBlock(2560, 1280, 1280)],
|
||||
[ResBlock(2560, 1280, 1280)],
|
||||
[ResBlock(2560, 1280, 1280), Upsample(1280)],
|
||||
[ResBlock(2560, 1280, 1280), SpatialTransformer(1280, 768, 8, 160)],
|
||||
[ResBlock(2560, 1280, 1280), SpatialTransformer(1280, 768, 8, 160)],
|
||||
[ResBlock(1920, 1280, 1280), SpatialTransformer(1280, 768, 8, 160), Upsample(1280)],
|
||||
[ResBlock(1920, 1280, 640), SpatialTransformer(640, 768, 8, 80)], # 6
|
||||
[ResBlock(1280, 1280, 640), SpatialTransformer(640, 768, 8, 80)],
|
||||
[ResBlock(960, 1280, 640), SpatialTransformer(640, 768, 8, 80), Upsample(640)],
|
||||
[ResBlock(960, 1280, 320), SpatialTransformer(320, 768, 8, 40)],
|
||||
[ResBlock(640, 1280, 320), SpatialTransformer(320, 768, 8, 40)],
|
||||
[ResBlock(640, 1280, 320), SpatialTransformer(320, 768, 8, 40)],
|
||||
]
|
||||
self.out = [
|
||||
GroupNorm(32, 320),
|
||||
Tensor.silu,
|
||||
Conv2d(320, 4, kernel_size=3, padding=1)
|
||||
]
|
||||
|
||||
def __call__(self, x, timesteps=None, context=None):
|
||||
# TODO: real time embedding
|
||||
t_emb = timestep_embedding(timesteps, 320)
|
||||
emb = t_emb.sequential(self.time_embed)
|
||||
|
||||
|
||||
|
||||
def run(x, bb):
|
||||
if isinstance(bb, ResBlock): x = bb(x, emb)
|
||||
elif isinstance(bb, SpatialTransformer): x = bb(x, context)
|
||||
else: x = bb(x)
|
||||
return x
|
||||
|
||||
saved_inputs = []
|
||||
for i,b in enumerate(self.input_blocks):
|
||||
#print("input block", i)
|
||||
print(x.numpy())
|
||||
for bb in b:
|
||||
x = run(x, bb)
|
||||
saved_inputs.append(x)
|
||||
for bb in self.middle_block:
|
||||
x = run(x, bb)
|
||||
for i,b in enumerate(self.output_blocks):
|
||||
#print("output block", i)
|
||||
x = x.cat(saved_inputs.pop(), dim=1)
|
||||
for bb in b:
|
||||
x = run(x, bb)
|
||||
return x.sequential(self.out)
|
||||
|
||||
class CLIPMLP:
|
||||
def __init__(self):
|
||||
self.fc1 = Linear(768, 3072)
|
||||
self.fc2 = Linear(3072, 768)
|
||||
|
||||
def __call__(self, hidden_states):
|
||||
hidden_states = self.fc1(hidden_states)
|
||||
hidden_states = hidden_states.quick_gelu()
|
||||
hidden_states = self.fc2(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
class CLIPAttention:
|
||||
def __init__(self):
|
||||
self.embed_dim = 768
|
||||
self.num_heads = 12
|
||||
self.head_dim = self.embed_dim // self.num_heads
|
||||
self.scale = self.head_dim**-0.5
|
||||
self.k_proj = Linear(self.embed_dim, self.embed_dim)
|
||||
self.v_proj = Linear(self.embed_dim, self.embed_dim)
|
||||
self.q_proj = Linear(self.embed_dim, self.embed_dim)
|
||||
self.out_proj = Linear(self.embed_dim, self.embed_dim)
|
||||
|
||||
def _shape(self, tensor, seq_len: int, bsz: int):
|
||||
return tensor.reshape(bsz, seq_len, self.num_heads, self.head_dim).permute(0,2,1,3)
|
||||
|
||||
def __call__(self, hidden_states, causal_attention_mask):
|
||||
bsz, tgt_len, embed_dim = hidden_states.shape
|
||||
|
||||
query_states = self.q_proj(hidden_states) * self.scale
|
||||
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
|
||||
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
|
||||
|
||||
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
|
||||
query_states = self._shape(query_states, tgt_len, bsz).reshape(*proj_shape)
|
||||
key_states = key_states.reshape(*proj_shape)
|
||||
src_len = key_states.shape[1]
|
||||
value_states = value_states.reshape(*proj_shape)
|
||||
|
||||
attn_weights = query_states @ key_states.permute(0,2,1)
|
||||
|
||||
attn_weights = attn_weights.reshape(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
|
||||
attn_weights = attn_weights.reshape(bsz * self.num_heads, tgt_len, src_len)
|
||||
|
||||
attn_weights = attn_weights.softmax()
|
||||
|
||||
attn_output = attn_weights @ value_states
|
||||
|
||||
attn_output = attn_output.reshape(bsz, self.num_heads, tgt_len, self.head_dim)
|
||||
attn_output = attn_output.permute(0,2,1,3)
|
||||
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
|
||||
|
||||
attn_output = self.out_proj(attn_output)
|
||||
return attn_output
|
||||
|
||||
class CLIPEncoderLayer:
|
||||
def __init__(self):
|
||||
self.self_attn = CLIPAttention()
|
||||
self.layer_norm1 = LayerNorm(768)
|
||||
self.mlp = CLIPMLP()
|
||||
self.layer_norm2 = LayerNorm(768)
|
||||
|
||||
def __call__(self, hidden_states, causal_attention_mask):
|
||||
residual = hidden_states
|
||||
hidden_states = self.layer_norm1(hidden_states)
|
||||
hidden_states = self.self_attn(hidden_states, causal_attention_mask)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
residual = hidden_states
|
||||
hidden_states = self.layer_norm2(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
return hidden_states
|
||||
|
||||
class CLIPEncoder:
|
||||
def __init__(self):
|
||||
self.layers = [CLIPEncoderLayer() for i in range(12)]
|
||||
|
||||
def __call__(self, hidden_states, causal_attention_mask):
|
||||
for l in self.layers:
|
||||
hidden_states = l(hidden_states, causal_attention_mask)
|
||||
return hidden_states
|
||||
|
||||
class CLIPTextEmbeddings:
|
||||
def __init__(self):
|
||||
self.token_embedding = Embedding(49408, 768)
|
||||
self.position_embedding = Embedding(77, 768)
|
||||
|
||||
def __call__(self, input_ids, position_ids):
|
||||
return self.token_embedding(input_ids) + self.position_embedding(position_ids)
|
||||
|
||||
class CLIPTextTransformer:
|
||||
def __init__(self):
|
||||
self.embeddings = CLIPTextEmbeddings()
|
||||
self.encoder = CLIPEncoder()
|
||||
self.final_layer_norm = LayerNorm(768)
|
||||
|
||||
def __call__(self, input_ids):
|
||||
seq_len = input_ids.shape[1]
|
||||
x = self.embeddings(input_ids, Tensor.arange(seq_len).reshape(1, -1))
|
||||
mask = Tensor.full((1, 1, seq_len, seq_len), float("-inf")).triu(1)
|
||||
x = self.encoder(x, mask)
|
||||
return self.final_layer_norm(x)
|
||||
|
||||
# Clip tokenizer, taken from https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py (MIT license)
|
||||
@lru_cache()
|
||||
def default_bpe():
|
||||
return Path(__file__).parent.parent / "weights/bpe_simple_vocab_16e6.txt.gz"
|
||||
|
||||
def get_pairs(word):
|
||||
"""Return set of symbol pairs in a word.
|
||||
Word is represented as tuple of symbols (symbols being variable-length strings).
|
||||
"""
|
||||
pairs = set()
|
||||
prev_char = word[0]
|
||||
for char in word[1:]:
|
||||
pairs.add((prev_char, char))
|
||||
prev_char = char
|
||||
return pairs
|
||||
|
||||
def whitespace_clean(text):
|
||||
text = re.sub(r'\s+', ' ', text)
|
||||
text = text.strip()
|
||||
return text
|
||||
|
||||
def bytes_to_unicode():
|
||||
"""
|
||||
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
||||
The reversible bpe codes work on unicode strings.
|
||||
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
||||
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
||||
This is a signficant percentage of your normal, say, 32K bpe vocab.
|
||||
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
||||
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
||||
"""
|
||||
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
|
||||
cs = bs[:]
|
||||
n = 0
|
||||
for b in range(2**8):
|
||||
if b not in bs:
|
||||
bs.append(b)
|
||||
cs.append(2**8+n)
|
||||
n += 1
|
||||
cs = [chr(n) for n in cs]
|
||||
return dict(zip(bs, cs))
|
||||
|
||||
class ClipTokenizer:
|
||||
def __init__(self, bpe_path: str = default_bpe()):
|
||||
self.byte_encoder = bytes_to_unicode()
|
||||
merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
|
||||
merges = merges[1:49152-256-2+1]
|
||||
merges = [tuple(merge.split()) for merge in merges]
|
||||
vocab = list(bytes_to_unicode().values())
|
||||
vocab = vocab + [v+'</w>' for v in vocab]
|
||||
for merge in merges:
|
||||
vocab.append(''.join(merge))
|
||||
vocab.extend(['<|startoftext|>', '<|endoftext|>'])
|
||||
self.encoder = dict(zip(vocab, range(len(vocab))))
|
||||
self.bpe_ranks = dict(zip(merges, range(len(merges))))
|
||||
self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
|
||||
self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[^\s]+""", re.IGNORECASE)
|
||||
|
||||
def bpe(self, token):
|
||||
if token in self.cache:
|
||||
return self.cache[token]
|
||||
word = tuple(token[:-1]) + ( token[-1] + '</w>',)
|
||||
pairs = get_pairs(word)
|
||||
|
||||
if not pairs:
|
||||
return token+'</w>'
|
||||
|
||||
while True:
|
||||
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
|
||||
if bigram not in self.bpe_ranks:
|
||||
break
|
||||
first, second = bigram
|
||||
new_word = []
|
||||
i = 0
|
||||
while i < len(word):
|
||||
try:
|
||||
j = word.index(first, i)
|
||||
new_word.extend(word[i:j])
|
||||
i = j
|
||||
except Exception:
|
||||
new_word.extend(word[i:])
|
||||
break
|
||||
|
||||
if word[i] == first and i < len(word)-1 and word[i+1] == second:
|
||||
new_word.append(first+second)
|
||||
i += 2
|
||||
else:
|
||||
new_word.append(word[i])
|
||||
i += 1
|
||||
new_word = tuple(new_word)
|
||||
word = new_word
|
||||
if len(word) == 1:
|
||||
break
|
||||
pairs = get_pairs(word)
|
||||
word = ' '.join(word)
|
||||
self.cache[token] = word
|
||||
return word
|
||||
|
||||
def encode(self, text):
|
||||
bpe_tokens = []
|
||||
text = whitespace_clean(text.strip()).lower()
|
||||
for token in re.findall(self.pat, text):
|
||||
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
|
||||
bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
|
||||
# Truncation, keeping two slots for start and end tokens.
|
||||
if len(bpe_tokens) > 75:
|
||||
bpe_tokens = bpe_tokens[:75]
|
||||
return [49406] + bpe_tokens + [49407] * (77 - len(bpe_tokens) - 1)
|
||||
|
||||
class StableDiffusion:
|
||||
def __init__(self):
|
||||
self.alphas_cumprod = Tensor.empty(1000)
|
||||
self.model = namedtuple("DiffusionModel", ["diffusion_model"])(diffusion_model = UNetModel())
|
||||
self.first_stage_model = AutoencoderKL()
|
||||
self.cond_stage_model = namedtuple("CondStageModel", ["transformer"])(transformer = namedtuple("Transformer", ["text_model"])(text_model = CLIPTextTransformer()))
|
||||
|
||||
# TODO: make __call__ run the model
|
||||
|
||||
# ** ldm.models.autoencoder.AutoencoderKL (done!)
|
||||
# 3x512x512 <--> 4x64x64 (16384)
|
||||
# decode torch.Size([1, 4, 64, 64]) torch.Size([1, 3, 512, 512])
|
||||
# section 4.3 of paper
|
||||
# first_stage_model.encoder, first_stage_model.decoder
|
||||
|
||||
# ** ldm.modules.diffusionmodules.openaimodel.UNetModel
|
||||
# this is what runs each time to sample. is this the LDM?
|
||||
# input: 4x64x64
|
||||
# output: 4x64x64
|
||||
# model.diffusion_model
|
||||
# it has attention?
|
||||
|
||||
# ** ldm.modules.encoders.modules.FrozenCLIPEmbedder
|
||||
# cond_stage_model.transformer.text_model
|
||||
|
||||
# this is sd-v1-4.ckpt
|
||||
FILENAME = Path(__file__).parent.parent / "weights/sd-v1-4.ckpt"
|
||||
|
||||
import clip as clipsave
|
||||
import autoencoder as autoencodersave
|
||||
import unet as unetsave
|
||||
import stablediffusion as sdsave
|
||||
|
||||
import numpy as np
|
||||
|
||||
if __name__ == "__main__":
|
||||
Tensor.no_grad = True
|
||||
'''clip = CLIPTextTransformer()
|
||||
|
||||
print('Saving model...')
|
||||
clipsave.save_clip_text_transformer(clip, "params")
|
||||
|
||||
input = Tensor([3, 1])
|
||||
output = clip(input.unsqueeze(0))
|
||||
|
||||
print(output[0, 0:2, 0:10].numpy())'''
|
||||
|
||||
'''autoencoder = AutoencoderKL()
|
||||
print('Saving model...')
|
||||
autoencodersave.save_autoencoder(autoencoder, "params")
|
||||
input = Tensor.zeros((1, 3, 10, 10))
|
||||
output = autoencoder(input)
|
||||
print(output.shape)
|
||||
print(output.numpy())'''
|
||||
|
||||
'''unet = UNetModel()
|
||||
print('Saving model...')
|
||||
unetsave.save_unet_model(unet, 'params')
|
||||
input = Tensor.zeros([1, 4, 64, 64])
|
||||
|
||||
context = np.array([0.5, 1.3], dtype=np.float32) # specify dtype when defining the array
|
||||
context = np.repeat(context, 768 // 2)
|
||||
context = np.expand_dims(context, axis=0)
|
||||
context = Tensor(context)
|
||||
|
||||
timesteps = Tensor([1.0])
|
||||
|
||||
output = unet(input, timesteps, context)
|
||||
#print(output.numpy())'''
|
||||
|
||||
|
||||
Tensor.no_grad = True
|
||||
model = StableDiffusion()
|
||||
|
||||
# load in weights
|
||||
download_file('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', FILENAME)
|
||||
load_state_dict(model, torch_load(FILENAME)['state_dict'], strict=False)
|
||||
|
||||
sdsave.save_stable_diffusion(model, "params")
|
||||
|
||||
|
||||
'''parser = argparse.ArgumentParser(description='Run Stable Diffusion', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--steps', type=int, default=5, help="Number of steps in diffusion")
|
||||
parser.add_argument('--prompt', type=str, default="a horse sized cat eating a bagel", help="Phrase to render")
|
||||
parser.add_argument('--out', type=str, default=os.path.join(tempfile.gettempdir(), "rendered.png"), help="Output filename")
|
||||
args = parser.parse_args()
|
||||
|
||||
Tensor.no_grad = True
|
||||
model = StableDiffusion()
|
||||
|
||||
# load in weights
|
||||
download_file('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', FILENAME)
|
||||
load_state_dict(model, torch_load(FILENAME)['state_dict'], strict=False)
|
||||
|
||||
# run through CLIP to get context
|
||||
tokenizer = ClipTokenizer()
|
||||
prompt = Tensor([tokenizer.encode(args.prompt)])
|
||||
context = model.cond_stage_model.transformer.text_model(prompt).realize()
|
||||
print("got CLIP context", context.shape)
|
||||
|
||||
prompt = Tensor([tokenizer.encode("")])
|
||||
unconditional_context = model.cond_stage_model.transformer.text_model(prompt).realize()
|
||||
print("got unconditional CLIP context", unconditional_context.shape)
|
||||
|
||||
# done with clip model
|
||||
del model.cond_stage_model
|
||||
|
||||
def get_model_output(latent, timestep):
|
||||
# put into diffuser
|
||||
latents = model.model.diffusion_model(latent.expand(2, *latent.shape[1:]), timestep.expand(2, *timestep.shape[1:]), unconditional_context.cat(context, dim=0))
|
||||
unconditional_latent, latent = latents[0:1], latents[1:2]
|
||||
|
||||
unconditional_guidance_scale = 7.5
|
||||
e_t = unconditional_latent + unconditional_guidance_scale * (latent - unconditional_latent)
|
||||
return e_t
|
||||
|
||||
timesteps = list(range(1, 1000, 1000//args.steps))
|
||||
print(f"running for {timesteps} timesteps")
|
||||
alphas = [model.alphas_cumprod.numpy()[t] for t in timesteps]
|
||||
alphas_prev = [1.0] + alphas[:-1]
|
||||
|
||||
def get_x_prev_and_pred_x0(x, e_t, index):
|
||||
temperature = 1
|
||||
a_t, a_prev = alphas[index], alphas_prev[index]
|
||||
sigma_t = 0
|
||||
sqrt_one_minus_at = math.sqrt(1-a_t)
|
||||
#print(a_t, a_prev, sigma_t, sqrt_one_minus_at)
|
||||
|
||||
pred_x0 = (x - sqrt_one_minus_at * e_t) / math.sqrt(a_t)
|
||||
|
||||
# direction pointing to x_t
|
||||
dir_xt = math.sqrt(1. - a_prev - sigma_t**2) * e_t
|
||||
noise = sigma_t * Tensor.randn(*x.shape) * temperature
|
||||
|
||||
x_prev = math.sqrt(a_prev) * pred_x0 + dir_xt #+ noise
|
||||
return x_prev, pred_x0
|
||||
|
||||
# start with random noise
|
||||
latent = Tensor.randn(1,4,64,64)
|
||||
|
||||
# this is diffusion
|
||||
for index, timestep in (t:=tqdm(list(enumerate(timesteps))[::-1])):
|
||||
GlobalCounters.reset()
|
||||
t.set_description("%3d %3d" % (index, timestep))
|
||||
e_t = get_model_output(latent, Tensor([timestep]))
|
||||
x_prev, pred_x0 = get_x_prev_and_pred_x0(latent, e_t, index)
|
||||
#e_t_next = get_model_output(x_prev)
|
||||
#e_t_prime = (e_t + e_t_next) / 2
|
||||
#x_prev, pred_x0 = get_x_prev_and_pred_x0(latent, e_t_prime, index)
|
||||
latent = x_prev
|
||||
latent.realize()
|
||||
|
||||
# upsample latent space to image with autoencoder
|
||||
x = model.first_stage_model.post_quant_conv(1/0.18215 * latent)
|
||||
x = model.first_stage_model.decoder(x)
|
||||
|
||||
# make image correct size and scale
|
||||
x = (x + 1.0) / 2.0
|
||||
x = (x.reshape(3,512,512).permute(1,2,0).clip(0,1)*255).cast(dtypes.uint8)
|
||||
print(x.shape)
|
||||
|
||||
# save image
|
||||
from PIL import Image
|
||||
im = Image.fromarray(x.numpy())
|
||||
print(f"saving {args.out}")
|
||||
im.save(args.out)
|
||||
# Open image.
|
||||
im.show()'''
|
||||
12
python/stablediffusion.py
Normal file
12
python/stablediffusion.py
Normal file
@@ -0,0 +1,12 @@
|
||||
from autoencoder import save_autoencoder
|
||||
from unet import save_unet_model
|
||||
from clip import save_clip_text_transformer
|
||||
|
||||
from save import save_scalar, save_tensor
|
||||
|
||||
def save_stable_diffusion(stable_diffusion, path):
|
||||
save_scalar(stable_diffusion.alphas_cumprod.shape[0], "n_steps", path)
|
||||
save_tensor(stable_diffusion.alphas_cumprod, 'alphas_cumprod', path)
|
||||
save_autoencoder(stable_diffusion.autoencoder, 'autoencoder', path)
|
||||
save_unet_model(stable_diffusion.diffusion, 'unet', path)
|
||||
save_clip_text_transformer(stable_diffusion.clip, 'clip', path)
|
||||
54
python/test.py
Normal file
54
python/test.py
Normal file
@@ -0,0 +1,54 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch import Tensor
|
||||
import math
|
||||
|
||||
'''import torch
|
||||
import torch.nn as nn
|
||||
|
||||
import torch
|
||||
|
||||
norm = torch.nn.LayerNorm(3)
|
||||
|
||||
tensor = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).reshape((2, 3))
|
||||
|
||||
out = norm(tensor)
|
||||
|
||||
print(out)'''
|
||||
|
||||
'''n_channel = 6
|
||||
norm = nn.LayerNorm(10)
|
||||
|
||||
height = 10
|
||||
width = 10
|
||||
n_elements = height * width * n_channel
|
||||
|
||||
t = torch.arange(0, n_elements, dtype=torch.float32).mul_(10.0 / n_elements).sin().reshape(1, n_channel, height, width)
|
||||
|
||||
out = norm(t)
|
||||
print(out)'''
|
||||
|
||||
def timestep_embedding(timesteps, dim, max_period=10000):
|
||||
half = dim // 2
|
||||
freqs = (-math.log(max_period) * torch.arange(half) / half).exp()
|
||||
args = timesteps * freqs
|
||||
return torch.cat( (args.cos(), args.sin()) ).reshape(1, -1)
|
||||
|
||||
timesteps = Tensor([1, 2, 3]).reshape((3, 1))
|
||||
dim = 10
|
||||
res = timestep_embedding(timesteps, dim)
|
||||
|
||||
print(res)
|
||||
|
||||
'''n_group = 3
|
||||
n_channel = 6
|
||||
norm = nn.GroupNorm(n_group, n_channel)
|
||||
|
||||
height = 10
|
||||
width = 10
|
||||
n_elements = height * width * n_channel
|
||||
|
||||
t = torch.arange(0, n_elements, dtype=torch.float32).mul_(10.0 / n_elements).sin().reshape(1, n_channel, height, width)
|
||||
|
||||
out = norm(t)
|
||||
print(out.flatten())'''
|
||||
41
python/test_tiny.py
Normal file
41
python/test_tiny.py
Normal file
@@ -0,0 +1,41 @@
|
||||
from tinygrad.tensor import Tensor
|
||||
from tinygrad.nn import Conv2d, Linear, GroupNorm, LayerNorm, Embedding
|
||||
import math
|
||||
|
||||
'''import torch
|
||||
import torch.nn as nn
|
||||
|
||||
import torch
|
||||
|
||||
norm = torch.nn.LayerNorm(3)
|
||||
|
||||
tensor = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).reshape((2, 3))
|
||||
|
||||
out = norm(tensor)
|
||||
|
||||
print(out)'''
|
||||
|
||||
n_channel = 6
|
||||
norm = LayerNorm(10)
|
||||
|
||||
height = 10
|
||||
width = 10
|
||||
n_elements = height * width * n_channel
|
||||
|
||||
t = Tensor.arange(n_elements).mul(10.0 / n_elements).sin().reshape(1, n_channel, height, width)
|
||||
|
||||
out = norm(t)
|
||||
print(out.numpy())
|
||||
|
||||
'''n_group = 3
|
||||
n_channel = 6
|
||||
norm = nn.GroupNorm(n_group, n_channel)
|
||||
|
||||
height = 10
|
||||
width = 10
|
||||
n_elements = height * width * n_channel
|
||||
|
||||
t = torch.arange(0, n_elements, dtype=torch.float32).mul_(10.0 / n_elements).sin().reshape(1, n_channel, height, width)
|
||||
|
||||
out = norm(t)
|
||||
print(out.flatten())'''
|
||||
145
python/tokenizer.py
Normal file
145
python/tokenizer.py
Normal file
@@ -0,0 +1,145 @@
|
||||
import gzip
|
||||
import html
|
||||
import os
|
||||
from functools import lru_cache
|
||||
|
||||
import ftfy
|
||||
import regex as re
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def default_bpe():
|
||||
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def bytes_to_unicode():
|
||||
"""
|
||||
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
||||
The reversible bpe codes work on unicode strings.
|
||||
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
||||
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
||||
This is a signficant percentage of your normal, say, 32K bpe vocab.
|
||||
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
||||
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
||||
"""
|
||||
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
|
||||
cs = bs[:]
|
||||
n = 0
|
||||
for b in range(2**8):
|
||||
if b not in bs:
|
||||
bs.append(b)
|
||||
cs.append(2**8+n)
|
||||
n += 1
|
||||
cs = [chr(n) for n in cs]
|
||||
return dict(zip(bs, cs))
|
||||
|
||||
|
||||
def get_pairs(word):
|
||||
"""Return set of symbol pairs in a word.
|
||||
Word is represented as tuple of symbols (symbols being variable-length strings).
|
||||
"""
|
||||
pairs = set()
|
||||
prev_char = word[0]
|
||||
for char in word[1:]:
|
||||
pairs.add((prev_char, char))
|
||||
prev_char = char
|
||||
return pairs
|
||||
|
||||
|
||||
def basic_clean(text):
|
||||
text = ftfy.fix_text(text)
|
||||
text = html.unescape(html.unescape(text))
|
||||
return text.strip()
|
||||
|
||||
|
||||
def whitespace_clean(text):
|
||||
text = re.sub(r'\s+', ' ', text)
|
||||
text = text.strip()
|
||||
return text
|
||||
|
||||
|
||||
class SimpleTokenizer(object):
|
||||
def __init__(self, bpe_path: str = default_bpe()):
|
||||
self.byte_encoder = bytes_to_unicode()
|
||||
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
|
||||
merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
|
||||
merges = merges[1:49152-256-2+1]
|
||||
merges = [tuple(merge.split()) for merge in merges]
|
||||
vocab = list(bytes_to_unicode().values())
|
||||
vocab = vocab + [v+'</w>' for v in vocab]
|
||||
for merge in merges:
|
||||
vocab.append(''.join(merge))
|
||||
vocab.extend(['<|startoftext|>', '<|endoftext|>'])
|
||||
self.encoder = dict(zip(vocab, range(len(vocab))))
|
||||
self.decoder = {v: k for k, v in self.encoder.items()}
|
||||
self.bpe_ranks = dict(zip(merges, range(len(merges))))
|
||||
self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|startoftext|>'}
|
||||
self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
|
||||
|
||||
def bpe(self, token):
|
||||
if token in self.cache:
|
||||
return self.cache[token]
|
||||
word = tuple(token[:-1]) + ( token[-1] + '</w>',)
|
||||
pairs = get_pairs(word)
|
||||
|
||||
if not pairs:
|
||||
return token+'</w>'
|
||||
|
||||
while True:
|
||||
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
|
||||
if bigram not in self.bpe_ranks:
|
||||
break
|
||||
first, second = bigram
|
||||
new_word = []
|
||||
i = 0
|
||||
while i < len(word):
|
||||
try:
|
||||
j = word.index(first, i)
|
||||
new_word.extend(word[i:j])
|
||||
i = j
|
||||
except:
|
||||
new_word.extend(word[i:])
|
||||
break
|
||||
|
||||
if word[i] == first and i < len(word)-1 and word[i+1] == second:
|
||||
new_word.append(first+second)
|
||||
i += 2
|
||||
else:
|
||||
new_word.append(word[i])
|
||||
i += 1
|
||||
new_word = tuple(new_word)
|
||||
word = new_word
|
||||
if len(word) == 1:
|
||||
break
|
||||
else:
|
||||
pairs = get_pairs(word)
|
||||
word = ' '.join(word)
|
||||
self.cache[token] = word
|
||||
return word
|
||||
|
||||
def encode(self, text):
|
||||
bpe_tokens = []
|
||||
text = whitespace_clean(basic_clean(text)).lower()
|
||||
for token in re.findall(self.pat, text):
|
||||
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
|
||||
bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
|
||||
return bpe_tokens
|
||||
|
||||
def decode(self, tokens):
|
||||
text = ''.join([self.decoder[token] for token in tokens])
|
||||
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
|
||||
return text
|
||||
|
||||
if __name__ == "__main__":
|
||||
simple_tokenizer = SimpleTokenizer()
|
||||
#print([simple_tokenizer.byte_encoder[0], simple_tokenizer.byte_encoder[1]])
|
||||
tokens = [0, 1, 59, 67, 23]
|
||||
text = simple_tokenizer.decode(tokens)
|
||||
print(f"Text: {text}")
|
||||
|
||||
text = "Hello world! <|startoftext|>asdf<|startoftext|>"
|
||||
encoded = simple_tokenizer.encode(text)
|
||||
decoded = simple_tokenizer.decode(encoded)
|
||||
print(f"Encoded: {encoded}")
|
||||
print(f"Decoded: {decoded}")
|
||||
153
python/unet.py
Normal file
153
python/unet.py
Normal file
@@ -0,0 +1,153 @@
|
||||
import pathlib
|
||||
import os
|
||||
import save
|
||||
from save import *
|
||||
|
||||
from tinygrad.nn import Conv2d
|
||||
|
||||
def save_res_block(res_block, path):
|
||||
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
|
||||
# We can't directly save activation functions, but as they are just attribute of the block,
|
||||
# we don't need to save them separately, they will be recreated along with the block.
|
||||
|
||||
# saving group normalization layer
|
||||
save_group_norm(res_block.in_layers[0], os.path.join(path, 'norm_in'))
|
||||
|
||||
# saving the convolutional layer
|
||||
save_conv2d(res_block.in_layers[2], os.path.join(path, 'conv_in'))
|
||||
|
||||
# saving the linear layer
|
||||
save_linear(res_block.emb_layers[1], os.path.join(path, 'lin_embed'))
|
||||
|
||||
# saving group normalization in out_layers
|
||||
save_group_norm(res_block.out_layers[0], os.path.join(path, 'norm_out'))
|
||||
|
||||
# saving the convolutional layer in out_layers
|
||||
save_conv2d(res_block.out_layers[3], os.path.join(path, 'conv_out'))
|
||||
|
||||
# save skip_connection based on the object type
|
||||
if isinstance(res_block.skip_connection, Conv2d):
|
||||
save_conv2d(res_block.skip_connection, os.path.join(path, 'skip_connection'))
|
||||
|
||||
def save_cross_attention(cross_attention, path):
|
||||
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save Linear layers
|
||||
save_linear(cross_attention.to_q, os.path.join(path, 'query'))
|
||||
save_linear(cross_attention.to_k, os.path.join(path, 'key'))
|
||||
save_linear(cross_attention.to_v, os.path.join(path, 'value'))
|
||||
save_linear(cross_attention.to_out[0], os.path.join(path, 'out'))
|
||||
|
||||
# Save parameters
|
||||
save_scalar(cross_attention.num_heads, 'n_head', path)
|
||||
|
||||
def save_geglu(geglu, path):
|
||||
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save Linear layers
|
||||
save_linear(geglu.proj, os.path.join(path, 'proj'))
|
||||
|
||||
def save_feed_forward(feed_forward, path):
|
||||
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save GEGLU module
|
||||
save_geglu(feed_forward.net[0], os.path.join(path, 'geglu'))
|
||||
|
||||
# Save Linear layer
|
||||
save_linear(feed_forward.net[2], os.path.join(path, 'lin'))
|
||||
|
||||
|
||||
def save_basic_transformer_block(basic_transformer_block, path):
|
||||
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save CrossAttention, FeedForward and LayerNorm instances
|
||||
save_cross_attention(basic_transformer_block.attn1, os.path.join(path, 'attn1'))
|
||||
save_feed_forward(basic_transformer_block.ff, os.path.join(path, 'mlp'))
|
||||
save_cross_attention(basic_transformer_block.attn2, os.path.join(path, 'attn2'))
|
||||
|
||||
save_layer_norm(basic_transformer_block.norm1, os.path.join(path, 'norm1'))
|
||||
save_layer_norm(basic_transformer_block.norm2, os.path.join(path, 'norm2'))
|
||||
save_layer_norm(basic_transformer_block.norm3, os.path.join(path, 'norm3'))
|
||||
|
||||
|
||||
|
||||
def save_spatial_transformer(spatial_transformer, path):
|
||||
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save GroupNorm, Conv2d, BasicTransformerBlock instances
|
||||
save_group_norm(spatial_transformer.norm, os.path.join(path, 'norm'))
|
||||
save_conv2d(spatial_transformer.proj_in, os.path.join(path, 'proj_in'))
|
||||
save_basic_transformer_block(spatial_transformer.transformer_blocks[0], os.path.join(path, 'transformer'))
|
||||
save_conv2d(spatial_transformer.proj_out, os.path.join(path, 'proj_out'))
|
||||
|
||||
|
||||
def save_downsample(downsample, path):
|
||||
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save Conv2d instance
|
||||
save_conv2d(downsample.op, path)
|
||||
|
||||
def save_upsample(upsample, path):
|
||||
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save Conv2d instance
|
||||
save_conv2d(upsample.conv, os.path.join(path, 'conv'))
|
||||
|
||||
|
||||
def save_res_transformer_res(block, path):
|
||||
save_res_block(block[0], pathlib.Path(path, 'res1'))
|
||||
save_spatial_transformer(block[1], pathlib.Path(path, 'transformer'))
|
||||
save_res_block(block[2], pathlib.Path(path, 'res2'))
|
||||
|
||||
def save_res_upsample(block, path):
|
||||
save_res_block(block[0], pathlib.Path(path, 'res'))
|
||||
save_upsample(block[1], pathlib.Path(path, 'upsample'))
|
||||
|
||||
def save_res_transformer(block, path):
|
||||
save_res_block(block[0], pathlib.Path(path, 'res'))
|
||||
save_spatial_transformer(block[1], pathlib.Path(path, 'transformer'))
|
||||
|
||||
def save_res_transformer_upsample(block, path):
|
||||
save_res_block(block[0], pathlib.Path(path, 'res'))
|
||||
save_spatial_transformer(block[1], pathlib.Path(path, 'transformer'))
|
||||
save_upsample(block[2], pathlib.Path(path, 'upsample'))
|
||||
|
||||
|
||||
def save_unet_input_blocks(input_blocks, path):
|
||||
save_conv2d(input_blocks[0][0], pathlib.Path(path, 'conv'))
|
||||
save_res_transformer(input_blocks[1], pathlib.Path(path, 'rt1'))
|
||||
save_res_transformer(input_blocks[2], pathlib.Path(path, 'rt2'))
|
||||
save_downsample(input_blocks[3][0], pathlib.Path(path, 'd1'))
|
||||
save_res_transformer(input_blocks[4], pathlib.Path(path, 'rt3'))
|
||||
save_res_transformer(input_blocks[5], pathlib.Path(path, 'rt4'))
|
||||
save_downsample(input_blocks[6][0], pathlib.Path(path, 'd2'))
|
||||
save_res_transformer(input_blocks[7], pathlib.Path(path, 'rt5'))
|
||||
save_res_transformer(input_blocks[8], pathlib.Path(path, 'rt6'))
|
||||
save_downsample(input_blocks[9][0], pathlib.Path(path, 'd3'))
|
||||
save_res_block(input_blocks[10][0], pathlib.Path(path, 'r1'))
|
||||
save_res_block(input_blocks[11][0], pathlib.Path(path, 'r2'))
|
||||
|
||||
def save_unet_output_blocks(output_blocks, path):
|
||||
save_res_block(output_blocks[0][0], pathlib.Path(path, 'r1'))
|
||||
save_res_block(output_blocks[1][0], pathlib.Path(path, 'r2'))
|
||||
save_res_upsample(output_blocks[2], pathlib.Path(path, 'ru'))
|
||||
save_res_transformer(output_blocks[3], pathlib.Path(path, 'rt1'))
|
||||
save_res_transformer(output_blocks[4], pathlib.Path(path, 'rt2'))
|
||||
save_res_transformer_upsample(output_blocks[5], pathlib.Path(path, 'rtu1'))
|
||||
save_res_transformer(output_blocks[6], pathlib.Path(path, 'rt3'))
|
||||
save_res_transformer(output_blocks[7], pathlib.Path(path, 'rt4'))
|
||||
save_res_transformer_upsample(output_blocks[8], pathlib.Path(path, 'rtu2'))
|
||||
save_res_transformer(output_blocks[9], pathlib.Path(path, 'rt5'))
|
||||
save_res_transformer(output_blocks[10], pathlib.Path(path, 'rt6'))
|
||||
save_res_transformer(output_blocks[11], pathlib.Path(path, 'rt7'))
|
||||
|
||||
def save_unet_model(model, path):
|
||||
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
|
||||
save_linear(model.time_embed[0], pathlib.Path(path, 'lin1_time_embed'))
|
||||
save_linear(model.time_embed[2], pathlib.Path(path, 'lin2_time_embed'))
|
||||
save_unet_input_blocks(model.input_blocks, pathlib.Path(path, 'input_blocks'))
|
||||
save_res_transformer_res(model.middle_block, pathlib.Path(path, 'middle_block'))
|
||||
save_unet_output_blocks(model.output_blocks, pathlib.Path(path, 'output_blocks'))
|
||||
save_group_norm(model.out[0], pathlib.Path(path, 'norm_out'))
|
||||
save_conv2d(model.out[2], pathlib.Path(path, 'conv_out'))
|
||||
|
||||
Reference in New Issue
Block a user