text: import numpy as np
  from tinygrad.tensor import Tensor
  
  def layernorm(x, sz, eps=1e-5):
    in_shape = x.shape
    x = x.reshape(shape=(-1, sz))
    layer_mean = x.mean(axis=(1,))
    y = (x - layer_mean.reshape(shape=[-1, 1]))
    layer_var = (y*y).mean(axis=(1,))
    ret = y.div(layer_var.add(eps).reshape(shape=[-1, 1]).sqrt())
    return ret.reshape(shape=in_shape)
  
  class TransformerBlock:
    def __init__(self, embed_dim, num_heads):
      # Multi-Head Attention
      self.num_heads = num_heads
      self.head_size = embed_dim // num_heads
      assert self.head_size * self.num_heads == embed_dim
  
      # looks like bias is useless
      self.query_dense = Tensor.uniform(embed_dim, embed_dim)
      self.key_dense = Tensor.uniform(embed_dim, embed_dim)
      self.value_dense = Tensor.uniform(embed_dim, embed_dim)
  
      self.final = Tensor.uniform(embed_dim, embed_dim)
  
      self.ff1 = Tensor.uniform(embed_dim, embed_dim)
      self.ff2 = Tensor.uniform(embed_dim, embed_dim)
  
    def __call__(self, x):
      # bs x T x embed_dim
      bs = x.shape[0]
      embed_dim = self.num_heads * self.head_size
      inputs = x.reshape(shape=(-1, embed_dim))
  
      # run multi head attention (bs, T, num_heads, head_size)
      query, key, value = [inputs.dot(y) \
        .reshape(shape=(bs, -1, self.num_heads, self.head_size)) \
        for y in [self.query_dense, self.key_dense, self.value_dense]]
  
      query = query.transpose(order=(0,2,1,3))  # (bs, num_heads, T, head_size)
      key = key.transpose(order=(0,2,3,1))      # (bs, num_heads, head_size, T)
      value = value.transpose(order=(0,2,1,3))  # (bs, num_heads, T, head_size)
  
      score = query.dot(key) * (1 / np.sqrt(self.head_size))
      weights = score.softmax()                                   # (bs, num_heads, T, T)
      attention = weights.dot(value).transpose(order=(0,2,1,3))   # (bs, T, num_heads, head_size)
  
      x = inputs + attention.reshape(shape=(-1, embed_dim)).dot(self.final).dropout(0.1)
      x = layernorm(x, embed_dim)
      x = x + x.dot(self.ff1).relu().dot(self.ff2).dropout(0.1)
      x = layernorm(x, embed_dim)
      return x.reshape(shape=(bs, -1, embed_dim))
  
  class Transformer:
    # L = cnt, H = embed_dim, A = num_heads
    def __init__(self, syms, maxlen, cnt, embed_dim, num_heads):
      self.maxlen, self.syms = maxlen, syms
      self.embed = Tensor.uniform(maxlen+syms, embed_dim, requires_grad=False)
      self.tbs = []
      for i in range(cnt):
        self.tbs.append(TransformerBlock(embed_dim, num_heads))
      self.final = Tensor.uniform(embed_dim, syms)
  
    def forward(self, x):
      bs = x.shape[0]
      xnp = x.cpu().data.astype(np.int32)
      onehot = np.zeros((bs, x.shape[1], self.maxlen+self.syms), dtype=np.float32)
      for i in range(x.shape[1]):
        onehot[range(bs), i, i] = 1
        onehot[range(bs), i, self.maxlen + xnp[:, i]] = 1
      onehot = onehot.reshape(bs*x.shape[1], self.maxlen+self.syms)
  
      x = Tensor(onehot, device=x.device).dot(self.embed).reshape(shape=(bs, x.shape[1], -1))
      for t in self.tbs:
        x = t(x)
      x = x.reshape(shape=(-1, x.shape[-1])).dot(self.final).logsoftmax()
      return x.reshape(shape=(bs, -1, x.shape[-1]))
  
  