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Optimization of Transformer API #30957

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merged 4 commits into from
Feb 23, 2021
Merged

Optimization of Transformer API #30957

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24d26de
Support 'bool' and 'int' for attention mask.
xiemoyuan Feb 8, 2021
ed50a5c
Update docs.
xiemoyuan Feb 8, 2021
222c07a
Add unittest for Transformer.
xiemoyuan Feb 9, 2021
0724c6b
fix bugs.
xiemoyuan Feb 18, 2021
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113 changes: 64 additions & 49 deletions python/paddle/fluid/tests/unittests/test_transformer_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,15 +51,22 @@ def generate_query_key_value_cache(self_attention,
num_heads,
query_length,
embed_dim,
attn_mask_type,
key_length=None,
value_length=None,
kdim=None,
vdim=None,
cache=None):
query = np.random.rand(batch_size, query_length,
embed_dim).astype("float32")
attn_mask = np.zeros((batch_size, num_heads, query_length, key_length))
attn_mask[0][0][0][0] = -1e9
attn_mask = np.ones(
(batch_size, num_heads, query_length, key_length), dtype=attn_mask_type)
if attn_mask_type == 'int64':
attn_mask = np.tril(attn_mask)
elif attn_mask_type == 'float64':
attn_mask = (np.tril(attn_mask) - 1.0) * 1e9
else:
raise ValueError("'attn_mask_type' should be 'int64' or 'float64'.")

head_dim = embed_dim // num_heads
if self_attention:
Expand Down Expand Up @@ -115,6 +122,10 @@ def scaled_dot_product_attention(q, k, v, d_key, attn_mask, multi_head_attn):
k = k.transpose([0, 1, 3, 2])
qkt = batch_matmul(q, k / np.sqrt(d_key, dtype=np.float64))
if attn_mask is not None:
if attn_mask.dtype.name == 'int64':
attn_mask = (attn_mask.astype(qkt.dtype) - 1.0) * 1e9
else:
attn_mask = attn_mask.astype(qkt.dtype)
qkt += attn_mask
weight = softmax(qkt)
attn_heads = batch_matmul(weight, v)
Expand Down Expand Up @@ -219,53 +230,57 @@ def multihead_attention_test_helper(self_attention, cache):
# generate params for multi_head_attention
batch_size, query_length, key_length, value_length, embed_dim, kdim, vdim, num_heads, attn_dropout = generate_basic_params(
"attn", self_attention)
query, key, value, attn_mask, cache_dict = generate_query_key_value_cache(
self_attention, batch_size, num_heads, query_length,
embed_dim, key_length, value_length, kdim, vdim, cache)
if cache and self_attention:
attn_mask = np.concatenate((attn_mask, attn_mask), axis=3)
need_weight, param_attr, bias_attr = False, None, None
# call paddle's function
multi_head_attn = MultiHeadAttention(
embed_dim, num_heads, attn_dropout, kdim, vdim, need_weight,
param_attr, bias_attr)
# construct cache object
cache_obj = None
if cache_dict:
if 'k' and 'v' in cache_dict:
cache_obj = multi_head_attn.Cache(
paddle.to_tensor(cache_dict['k']),
paddle.to_tensor(cache_dict['v']))
elif 'static_k' and 'static_v' in cache_dict:
cache_obj = multi_head_attn.StaticCache(
paddle.to_tensor(cache_dict['static_k']),
paddle.to_tensor(cache_dict['static_v']))
if attn_mask is not None:
attn_output = multi_head_attn(
paddle.to_tensor(query),
paddle.to_tensor(key),
paddle.to_tensor(value),
paddle.to_tensor(attn_mask), cache_obj)
else:
attn_output = multi_head_attn(
paddle.to_tensor(query),
paddle.to_tensor(key),
paddle.to_tensor(value), attn_mask, cache_obj)
attn_output = attn_output[0] if cache_dict else attn_output

# implementation by numpy
# compute q, k, v
q, k, v, _ = prepare_qkv(query, key, value, num_heads,
embed_dim, self_attention,
multi_head_attn, cache_dict)
# scale dot product attention
attn_heads = scaled_dot_product_attention(
q, k, v, embed_dim // num_heads, attn_mask, multi_head_attn)
out_proj_weight = multi_head_attn.out_proj.weight.numpy()
reference = fc(attn_heads, out_proj_weight)

np.testing.assert_allclose(
attn_output.numpy(), reference, atol=1e-6)
for attn_mask_type in ['int64', 'float64']:
query, key, value, attn_mask, cache_dict = generate_query_key_value_cache(
self_attention, batch_size, num_heads, query_length,
embed_dim, attn_mask_type, key_length, value_length,
kdim, vdim, cache)
if cache and self_attention:
attn_mask = np.concatenate(
(attn_mask, attn_mask), axis=3)
need_weight, param_attr, bias_attr = False, None, None
# call paddle's function
multi_head_attn = MultiHeadAttention(
embed_dim, num_heads, attn_dropout, kdim, vdim,
need_weight, param_attr, bias_attr)
# construct cache object
cache_obj = None
if cache_dict:
if 'k' and 'v' in cache_dict:
cache_obj = multi_head_attn.Cache(
paddle.to_tensor(cache_dict['k']),
paddle.to_tensor(cache_dict['v']))
elif 'static_k' and 'static_v' in cache_dict:
cache_obj = multi_head_attn.StaticCache(
paddle.to_tensor(cache_dict['static_k']),
paddle.to_tensor(cache_dict['static_v']))
if attn_mask is not None:
attn_output = multi_head_attn(
paddle.to_tensor(query),
paddle.to_tensor(key),
paddle.to_tensor(value),
paddle.to_tensor(attn_mask), cache_obj)
else:
attn_output = multi_head_attn(
paddle.to_tensor(query),
paddle.to_tensor(key),
paddle.to_tensor(value), attn_mask, cache_obj)
attn_output = attn_output[0] if cache_dict else attn_output

# implementation by numpy
# compute q, k, v
q, k, v, _ = prepare_qkv(query, key, value, num_heads,
embed_dim, self_attention,
multi_head_attn, cache_dict)
# scale dot product attention
attn_heads = scaled_dot_product_attention(
q, k, v, embed_dim // num_heads, attn_mask,
multi_head_attn)
out_proj_weight = multi_head_attn.out_proj.weight.numpy()
reference = fc(attn_heads, out_proj_weight)

np.testing.assert_allclose(
attn_output.numpy(), reference, atol=1e-6)

multihead_attention_test_helper(True, True)
multihead_attention_test_helper(True, False)
Expand Down
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