akhaliq3
spaces demo
58597f0
import tensorflow as tf
def embedding_lookup(lookup_table, x):
return tf.compat.v1.nn.embedding_lookup(lookup_table, x)
def normal_embedding_lookup(x, n_token, d_embed, d_proj, initializer,
proj_initializer, scope='normal_embed', **kwargs):
emb_scale = d_proj ** 0.5
with tf.compat.v1.variable_scope(scope):
lookup_table = tf.compat.v1.get_variable('lookup_table', [n_token, d_embed], initializer=initializer)
y = embedding_lookup(lookup_table, x)
if d_proj != d_embed:
proj_W = tf.compat.v1.get_variable('proj_W', [d_embed, d_proj], initializer=proj_initializer)
y = tf.einsum('ibe,ed->ibd', y, proj_W)
else:
proj_W = None
ret_params = [lookup_table, proj_W]
y *= emb_scale
return y, ret_params
def normal_softmax(hidden, target, n_token, params, scope='normal_softmax', **kwargs):
def _logit(x, W, b, proj):
y = x
if proj is not None:
y = tf.einsum('ibd,ed->ibe', y, proj)
return tf.einsum('ibd,nd->ibn', y, W) + b
params_W, params_projs = params[0], params[1]
with tf.compat.v1.variable_scope(scope):
softmax_b = tf.compat.v1.get_variable('bias', [n_token], initializer=tf.zeros_initializer())
output = _logit(hidden, params_W, softmax_b, params_projs)
nll = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output)
return nll, output
def positional_embedding(pos_seq, inv_freq, bsz=None):
sinusoid_inp = tf.einsum('i,j->ij', pos_seq, inv_freq)
pos_emb = tf.concat([tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)], -1)
if bsz is not None:
return tf.tile(pos_emb[:, None, :], [1, bsz, 1])
else:
return pos_emb[:, None, :]
def positionwise_FF(inp, d_model, d_inner, dropout, kernel_initializer,
scope='ff', is_training=True):
output = inp
with tf.compat.v1.variable_scope(scope):
output = tf.keras.layers.Dense(d_inner, activation=tf.nn.relu,
kernel_initializer=kernel_initializer, name='layer_1')(inp)
output = tf.keras.layers.Dropout(dropout, name='drop_1')(output, training=is_training)
output = tf.keras.layers.Dense(d_model, activation=tf.nn.relu,
kernel_initializer=kernel_initializer, name='layer_2')(output)
output = tf.keras.layers.Dropout(dropout, name='drop_2')(output, training=is_training)
output = tf.keras.layers.LayerNormalization(axis=-1)(output + inp)
return output
def _create_mask(qlen, mlen, same_length=False):
attn_mask = tf.ones([qlen, qlen])
mask_u = tf.linalg.band_part(attn_mask, 0, -1)
mask_dia = tf.linalg.band_part(attn_mask, 0, 0)
attn_mask_pad = tf.zeros([qlen, mlen])
ret = tf.concat([attn_mask_pad, mask_u - mask_dia], 1)
if same_length:
mask_l = tf.matrix_band_part(attn_mask, -1, 0)
ret = tf.concat([ret[:, :qlen] + mask_l - mask_dia, ret[:, qlen:]], 1)
return ret
def _cache_mem(curr_out, prev_mem, mem_len=None):
if mem_len is None or prev_mem is None:
new_mem = curr_out
elif mem_len == 0:
return prev_mem
else:
new_mem = tf.concat([prev_mem, curr_out], 0)[-mem_len:]
return tf.stop_gradient(new_mem)
def rel_shift(x):
x_size = tf.shape(x)
x = tf.pad(x, [[0, 0], [1, 0], [0, 0], [0, 0]])
x = tf.reshape(x, [x_size[1] + 1, x_size[0], x_size[2], x_size[3]])
x = tf.slice(x, [1, 0, 0, 0], [-1, -1, -1, -1])
x = tf.reshape(x, x_size)
return x
def rel_multihead_attn(w, r, r_w_bias, r_r_bias, attn_mask, mems, d_model,
n_head, d_head, dropout, dropatt, is_training,
kernel_initializer, scope='rel_attn'):
scale = 1 / (d_head ** 0.5)
with tf.compat.v1.variable_scope(scope):
qlen = tf.shape(w)[0]
rlen = tf.shape(r)[0]
bsz = tf.shape(w)[1]
cat = tf.concat([mems, w], 0) if mems is not None and mems.shape.ndims > 1 else w
w_heads = tf.keras.layers.Dense(3 * n_head * d_head, use_bias=False,
kernel_initializer=kernel_initializer, name='qkv')(cat)
r_head_k = tf.keras.layers.Dense(n_head * d_head, use_bias=False,
kernel_initializer=kernel_initializer, name='r')(r)
w_head_q, w_head_k, w_head_v = tf.split(w_heads, 3, -1)
w_head_q = w_head_q[-qlen:]
klen = tf.shape(w_head_k)[0]
w_head_q = tf.reshape(w_head_q, [qlen, bsz, n_head, d_head])
w_head_k = tf.reshape(w_head_k, [klen, bsz, n_head, d_head])
w_head_v = tf.reshape(w_head_v, [klen, bsz, n_head, d_head])
r_head_k = tf.reshape(r_head_k, [rlen, n_head, d_head])
rw_head_q = w_head_q + r_w_bias
rr_head_q = w_head_q + r_r_bias
AC = tf.einsum('ibnd,jbnd->ijbn', rw_head_q, w_head_k)
BD = tf.einsum('ibnd,jnd->ijbn', rr_head_q, r_head_k)
BD = rel_shift(BD)
attn_score = (AC + BD) * scale
attn_mask_t = attn_mask[:, :, None, None]
attn_score = attn_score * (1 - attn_mask_t) - 1e30 * attn_mask_t
attn_prob = tf.nn.softmax(attn_score, 1)
attn_prob = tf.keras.layers.Dropout(dropatt)(attn_prob, training=is_training)
attn_vec = tf.einsum('ijbn,jbnd->ibnd', attn_prob, w_head_v)
size_t = tf.shape(attn_vec)
attn_vec = tf.reshape(attn_vec, [size_t[0], size_t[1], n_head * d_head])
attn_out = tf.keras.layers.Dense(d_model, use_bias=False,
kernel_initializer=kernel_initializer, name='o')(attn_vec)
attn_out = tf.keras.layers.Dropout(dropout)(attn_out, training=is_training)
output = tf.keras.layers.LayerNormalization(axis=-1)(attn_out + w)
return output
def transformer(dec_inp, target, mems, n_token, n_layer, d_model, d_embed,
n_head, d_head, d_inner, dropout, dropatt,
initializer, is_training, proj_initializer=None,
mem_len=None, cutoffs=[], div_val=1, tie_projs=[],
same_length=False, clamp_len=-1,
input_perms=None, target_perms=None, head_target=None,
untie_r=False, proj_same_dim=True,
scope='transformer'):
"""
cutoffs: a list of python int. Cutoffs for adaptive softmax.
tie_projs: a list of python bools. Whether to tie the projections.
perms: a list of tensors. Each tensor should of size [len, bsz, bin_size].
Only used in the adaptive setting.
"""
new_mems = []
with tf.compat.v1.variable_scope(scope):
if untie_r:
r_w_bias = tf.compat.v1.get_variable('r_w_bias', [n_layer, n_head, d_head], initializer=initializer)
r_r_bias = tf.compat.v1.get_variable('r_r_bias', [n_layer, n_head, d_head], initializer=initializer)
else:
r_w_bias = tf.compat.v1.get_variable('r_w_bias', [n_head, d_head], initializer=initializer)
r_r_bias = tf.compat.v1.get_variable('r_r_bias', [n_head, d_head], initializer=initializer)
qlen = tf.shape(dec_inp)[0]
mlen = tf.shape(mems[0])[0] if mems is not None else 0
klen = qlen + mlen
if proj_initializer is None:
proj_initializer = initializer
embeddings, shared_params = normal_embedding_lookup(
x=dec_inp,
n_token=n_token,
d_embed=d_embed,
d_proj=d_model,
initializer=initializer,
proj_initializer=proj_initializer)
attn_mask = _create_mask(qlen, mlen, same_length)
pos_seq = tf.range(klen - 1, -1, -1.0)
if clamp_len > 0:
pos_seq = tf.minimum(pos_seq, clamp_len)
inv_freq = 1 / (10000 ** (tf.range(0, d_model, 2.0) / d_model))
pos_emb = positional_embedding(pos_seq, inv_freq)
output = tf.keras.layers.Dropout(rate=dropout)(embeddings, training=is_training)
pos_emb = tf.keras.layers.Dropout(rate=dropout)(pos_emb, training=is_training)
if mems is None:
mems = [None] * n_layer
for i in range(n_layer):
# cache new mems
new_mems.append(_cache_mem(output, mems[i], mem_len))
with tf.compat.v1.variable_scope('layer_{}'.format(i)):
output = rel_multihead_attn(
w=output,
r=pos_emb,
r_w_bias=r_w_bias if not untie_r else r_w_bias[i],
r_r_bias=r_r_bias if not untie_r else r_r_bias[i],
attn_mask=attn_mask,
mems=mems[i],
d_model=d_model,
n_head=n_head,
d_head=d_head,
dropout=dropout,
dropatt=dropatt,
is_training=is_training,
kernel_initializer=initializer)
output = positionwise_FF(
inp=output,
d_model=d_model,
d_inner=d_inner,
dropout=dropout,
kernel_initializer=initializer,
is_training=is_training)
output = tf.keras.layers.Dropout(dropout)(output, training=is_training)
loss, logits = normal_softmax(
hidden=output,
target=target,
n_token=n_token,
params=shared_params)
return loss, logits, new_mems