def decode(self, th_b, hidden, train):
ht_b = dy.transpose(th_b)
T = ht_b.dim()[0][1]
ht_b = self.proj_to_dsz(ht_b)
mask = subsequent_mask(T)
output = self.transformer(ht_b, mask, train)
output = [out for out in dy.transpose(output)]
return output, None
def decode(self, th_b, hidden, train):
ht_b = dy.transpose(th_b)
T = ht_b.dim()[0][1]
ht_b = self.proj_to_dsz(ht_b)
mask = subsequent_mask(T)
output = self.transformer(ht_b, mask, train)
output = [out for out in dy.transpose(output)]
return output, None
def attn_values_sub_mask(attn, qkv):
q, k, v = qkv
((_, T, H), B) = q.dim()
q = dy.zeros(q.dim()[0], batch_size=q.dim()[1])
mask = subsequent_mask(T)
res = attn(q, k, v, mask=mask).npvalue()
gold = v.npvalue()
for b in range(B):
for h in range(H):
for t in range(T):
np.testing.assert_allclose(res[:, t, h, b], np.mean(gold[:, :t+1, :, :], axis=1)[:, h, b], atol=1e-5)
def __call__(self, encoder_output, dst, train):
embed_out_th_b = self.tgt_embedding.encode(dst)
embed_out_ht_b = dy.transpose(embed_out_th_b)
embed_out_ht_b = self.proj_to_hsz(embed_out_ht_b)
context = dy.concatenate_cols(encoder_output.output)
T = embed_out_ht_b.dim()[0][1]
dst_mask = subsequent_mask(T)
src_mask = encoder_output.src_mask
output = self.transformer_decoder(embed_out_ht_b, context, src_mask, dst_mask, train)
output = self.proj_to_dsz(output)
return self.output(output)
def test_subsequent_mask_valid_loc():
T = np.random.randint(4, 100)
mask = subsequent_mask(T)[0].npvalue().squeeze()
def test(T, mask):
i, j = np.random.randint(0, T, size=2)
if i > j:
assert mask[i, j] == 0
else:
assert mask[i, j] == 1
for _ in range(100):
test(T, mask)
def test_subsequent_mask_valid_loc():
T = np.random.randint(4, 100)
mask = subsequent_mask(T)[0].npvalue().squeeze()
def test(T, mask):
i, j = np.random.randint(0, T, size=2)
if i > j:
assert mask[i, j] == 0
else:
assert mask[i, j] == 1
for _ in range(100):
test(T, mask)
def attn_values_sub_mask(attn, qkv):
q, k, v = qkv
((_, T, H), B) = q.dim()
q = dy.zeros(q.dim()[0], batch_size=q.dim()[1])
mask = subsequent_mask(T)
res = attn(q, k, v, mask=mask).npvalue()
gold = v.npvalue()
for b in range(B):
for h in range(H):
for t in range(T):
np.testing.assert_allclose(res[:, t, h, b],
np.mean(gold[:, :t + 1, :, :],
axis=1)[:, h, b],
atol=1e-5)
def test_subsequent_mask_valid_count():
T = np.random.randint(4, 50)
gold = (T * (T + 1)) / 2
masks = subsequent_mask(T)
mask = masks[0].npvalue()
assert np.sum(mask) == gold
def test_subsequent_mask_shape():
T = np.random.randint(2, 50)
gold = ((T, T, 1), 1)
masks = subsequent_mask(T)
for mask in masks:
assert mask.dim() == gold
def test_subsequent_mask_valid_count():
T = np.random.randint(4, 50)
gold = (T * (T + 1)) / 2
masks = subsequent_mask(T)
mask = masks[0].npvalue()
assert np.sum(mask) == gold
def test_subsequent_mask_shape():
T = np.random.randint(2, 50)
gold = ((T, T, 1), 1)
masks = subsequent_mask(T)
for mask in masks:
assert mask.dim() == gold
