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[Decoding] Update incremental decoding implementation #1583

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[Decoding] Update incremental decoding implementation #1583

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49 changes: 34 additions & 15 deletions src/gluonnlp/models/transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -634,7 +634,7 @@ def init_states(self, batch_size, ctx, dtype='float32'):
self._units // self._num_heads), ctx=ctx, dtype=dtype)
return init_key, init_value

def incremental_decode(self, data, states, mem, mem_valid_length, mem_attn_mask=None):
def incremental_decode(self, data, states, mem, mem_valid_length, mem_attn_mask=None, prev_inter_kv=None):
"""Incrementally generate the output given the decoder input.

Parameters
Expand Down Expand Up @@ -670,6 +670,8 @@ def incremental_decode(self, data, states, mem, mem_valid_length, mem_attn_mask=
mem_attn_mask
The attention mask between data and the memory
Has shape (batch_size, 1, mem_length)
prev_inter_kv
The previously cached inter_attention keys, values

Returns
-------
Expand Down Expand Up @@ -716,13 +718,20 @@ def incremental_decode(self, data, states, mem, mem_valid_length, mem_attn_mask=
data = out
if self._pre_norm:
data = self.ln_inter(data)
out, _ = self.inter_attention(npx.reshape(self.attn_inter_q(data),
(-2, -2, self._num_heads, -1)),
npx.reshape(self.attn_inter_k(mem),
(-2, -2, self._num_heads, -1)),
npx.reshape(self.attn_inter_v(mem),
(-2, -2, self._num_heads, -1)),
mem_attn_mask)
query = npx.reshape(self.attn_inter_q(data), (-2, -2, self._num_heads, -1))
if prev_inter_kv is None:
key = npx.reshape(self.attn_inter_k(mem), (-2, -2, self._num_heads, -1))
value = npx.reshape(self.attn_inter_v(mem), (-2, -2, self._num_heads, -1))
prev_inter_kv = {'prev_key': key, 'prev_value': value}
out, _ = self.inter_attention(query,
key,
value,
mem_attn_mask)
else:
out, _ = self.inter_attention(query,
prev_inter_kv['prev_key'],
prev_inter_kv['prev_value'],
mem_attn_mask)
out = self.proj_inter(out)
out = self.dropout_layer(out)
out = out + data
Expand All @@ -731,7 +740,7 @@ def incremental_decode(self, data, states, mem, mem_valid_length, mem_attn_mask=
# 3. Encode the output via an FFN layer
out = self.ffn(out)
out = npx.reshape(out, (-5, -1))
return out, (new_key, new_value)
return out, (new_key, new_value), prev_inter_kv


@use_np
Expand Down Expand Up @@ -881,7 +890,7 @@ def init_states(self, batch_size, ctx, dtype='float32'):
dtype=dtype))
return states

def incremental_decode(self, data, states, mem, mem_valid_length):
def incremental_decode(self, data, states, mem, mem_valid_length, prev_inter_kvs=None):
"""Incrementally generate the output given the decoder input.

Parameters
Expand Down Expand Up @@ -914,6 +923,9 @@ def incremental_decode(self, data, states, mem, mem_valid_length):
Valid length of the memory
Shape (batch_size,)

prev_inter_kvs
The previously cached inter_attention keys, values

Returns
-------
out
Expand Down Expand Up @@ -946,17 +958,23 @@ def incremental_decode(self, data, states, mem, mem_valid_length):
# TODO(sxjscience) Try with boolean masking
mem_attn_mask = mem_attn_mask.astype(self._dtype)
new_states = []
if prev_inter_kvs is None:
prev_inter_kvs = [None for _ in range(self.num_layers)]
new_inter_kvs = []
for i in range(self.num_layers):
if self.recurrent:
layer = self.layers[0]
else:
layer = self.layers[i]
out, new_state = layer.incremental_decode(out, states[i],
mem, mem_valid_length, mem_attn_mask)
out, new_state, prev_inter_kv = layer.incremental_decode(out, states[i],
mem, mem_valid_length,
mem_attn_mask,
prev_inter_kvs[i])
new_states.append(new_state)
new_inter_kvs.append(prev_inter_kv)
if self._pre_norm:
out = self.ln_final(out)
return out, new_states
return out, new_states, new_inter_kvs


@use_np
Expand Down Expand Up @@ -1364,6 +1382,7 @@ def __init__(self, model):
"""
super().__init__()
self.model = model
self.cached_kvs = None

def initialize(self, **kwargs):
# Manually disable the initialize
Expand Down Expand Up @@ -1464,9 +1483,9 @@ def forward(self, step_data, states):
step_data = step_data * _np.sqrt(self.model.dec_units)
if self.model.pos_embed_type is not None:
step_data = step_data + self.model.tgt_pos_embed_layer(position)
out, new_states =\
out, new_states, self.cached_kvs =\
self.model.decoder.incremental_decode(step_data, dec_states,
mem_data, mem_valid_length)
mem_data, mem_valid_length, self.cached_kvs)
out = self.model.tgt_final_layer(out)
return out, (mem_data, mem_valid_length, position + 1, new_states)

Expand Down