tinygrad/models/vit.py

import numpy as np
from tinygrad.tensor import Tensor
from models.transformer import TransformerBlock

class ViT:
  def __init__(self, layers=12, embed_dim=192, num_heads=3):
    self.embedding = (Tensor.uniform(embed_dim, 3, 16, 16), Tensor.zeros(embed_dim))
    self.embed_dim = embed_dim
    self.cls = Tensor.ones(1, 1, embed_dim)
    self.pos_embedding = Tensor.ones(1, 197, embed_dim)
    self.tbs = [
      TransformerBlock(embed_dim=embed_dim, num_heads=num_heads, ff_dim=embed_dim*4,
        prenorm=True, act=lambda x: x.gelu())
      for i in range(layers)]
    self.encoder_norm = (Tensor.uniform(embed_dim), Tensor.zeros(embed_dim))
    self.head = (Tensor.uniform(embed_dim, 1000), Tensor.zeros(1000))

  def patch_embed(self, x):
    x = x.conv2d(*self.embedding, stride=16)
    x = x.reshape(shape=(x.shape[0], x.shape[1], -1)).permute(order=(0,2,1))
    return x

  def forward(self, x):
    ce = self.cls.add(Tensor.zeros(x.shape[0],1,1))
    pe = self.patch_embed(x)
    x = ce.cat(pe, dim=1)
    x = x.add(self.pos_embedding).sequential(self.tbs)
    x = x.layernorm().linear(*self.encoder_norm)
    return x[:, 0].linear(*self.head)

  def load_from_pretrained(m):
    import io
    from extra.utils import fetch

    # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    if m.embed_dim == 192:
      url = "https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz"
    elif m.embed_dim == 768:
      url = "https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz"
    else:
      raise Exception("no pretrained weights for configuration")
    dat = np.load(io.BytesIO(fetch(url)))

    #for x in dat.keys():
    #  print(x, dat[x].shape, dat[x].dtype)

    m.embedding[0].assign(np.transpose(dat['embedding/kernel'], (3,2,0,1)))
    m.embedding[1].assign(dat['embedding/bias'])

    m.cls.assign(dat['cls'])

    m.head[0].assign(dat['head/kernel'])
    m.head[1].assign(dat['head/bias'])

    m.pos_embedding.assign(dat['Transformer/posembed_input/pos_embedding'])
    m.encoder_norm[0].assign(dat['Transformer/encoder_norm/scale'])
    m.encoder_norm[1].assign(dat['Transformer/encoder_norm/bias'])

    for i in range(12):
      m.tbs[i].query[0].assign(dat[f'Transformer/encoderblock_{i}/MultiHeadDotProductAttention_1/query/kernel'].reshape(m.embed_dim, m.embed_dim))
      m.tbs[i].query[1].assign(dat[f'Transformer/encoderblock_{i}/MultiHeadDotProductAttention_1/query/bias'].reshape(m.embed_dim))
      m.tbs[i].key[0].assign(dat[f'Transformer/encoderblock_{i}/MultiHeadDotProductAttention_1/key/kernel'].reshape(m.embed_dim, m.embed_dim))
      m.tbs[i].key[1].assign(dat[f'Transformer/encoderblock_{i}/MultiHeadDotProductAttention_1/key/bias'].reshape(m.embed_dim))
      m.tbs[i].value[0].assign(dat[f'Transformer/encoderblock_{i}/MultiHeadDotProductAttention_1/value/kernel'].reshape(m.embed_dim, m.embed_dim))
      m.tbs[i].value[1].assign(dat[f'Transformer/encoderblock_{i}/MultiHeadDotProductAttention_1/value/bias'].reshape(m.embed_dim))
      m.tbs[i].out[0].assign(dat[f'Transformer/encoderblock_{i}/MultiHeadDotProductAttention_1/out/kernel'].reshape(m.embed_dim, m.embed_dim))
      m.tbs[i].out[1].assign(dat[f'Transformer/encoderblock_{i}/MultiHeadDotProductAttention_1/out/bias'].reshape(m.embed_dim))
      m.tbs[i].ff1[0].assign(dat[f'Transformer/encoderblock_{i}/MlpBlock_3/Dense_0/kernel'])
      m.tbs[i].ff1[1].assign(dat[f'Transformer/encoderblock_{i}/MlpBlock_3/Dense_0/bias'])
      m.tbs[i].ff2[0].assign(dat[f'Transformer/encoderblock_{i}/MlpBlock_3/Dense_1/kernel'])
      m.tbs[i].ff2[1].assign(dat[f'Transformer/encoderblock_{i}/MlpBlock_3/Dense_1/bias'])
      m.tbs[i].ln1[0].assign(dat[f'Transformer/encoderblock_{i}/LayerNorm_0/scale'])
      m.tbs[i].ln1[1].assign(dat[f'Transformer/encoderblock_{i}/LayerNorm_0/bias'])
      m.tbs[i].ln2[0].assign(dat[f'Transformer/encoderblock_{i}/LayerNorm_2/scale'])
      m.tbs[i].ln2[1].assign(dat[f'Transformer/encoderblock_{i}/LayerNorm_2/bias'])