mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-10 07:28:15 -05:00
80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
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]))
|
|
|