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a5fd608 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | from dataclasses import dataclass
from functools import partial
import keras
import tensorflow as tf
from keras import layers
from pipeline.base.generation import generate_with_stateful_model, generate_with_training_model
from pipeline.base.model_builder import ModelArtifact
@dataclass
class RNNModelBuilder:
num_layers: int = 2
embedding_dim: int = 100
hidden_dim: int = 1024
def build_training_artifact(
self,
vocab_size: int,
sequence_length: int
) -> ModelArtifact:
inputs = keras.Input(shape=(None,), dtype="int32", name="inputs")
x = layers.Embedding(
input_dim=vocab_size,
output_dim=self.embedding_dim,
mask_zero=True,
name="embedding"
)(inputs)
for i in range(self.num_layers):
x = layers.LSTM(
self.hidden_dim,
return_sequences=True,
recurrent_dropout=0.1,
name=f"lstm_{i}"
)(x)
x = layers.Dropout(0.1, name=f"dropout_{i}")(x)
outputs = layers.Dense(vocab_size, name="logits")(x)
model = keras.Model(inputs=inputs, outputs=outputs, name="rnn_training")
return ModelArtifact(
model=model,
generate=partial(generate_with_training_model, model)
)
def build_inference_artifact(
self,
training_artifact: ModelArtifact
) -> ModelArtifact:
inference_model = self._build_inference_model_from_training_model(
training_artifact.model
)
return ModelArtifact(
model=inference_model,
generate=partial(
generate_with_stateful_model,
inference_model,
initial_states=self._initial_states(batch_size=1)
)
)
def _build_inference_model_from_training_model(
self,
training_model: keras.Model
) -> keras.Model:
token_input = keras.Input(shape=(None,), dtype="int32", name="token_input")
state_inputs = []
for i in range(self.num_layers):
h_input = keras.Input(shape=(self.hidden_dim,), name=f"h_{i}_input")
c_input = keras.Input(shape=(self.hidden_dim,), name=f"c_{i}_input")
state_inputs.extend([h_input, c_input])
embedding = training_model.get_layer("embedding")
logits_layer = training_model.get_layer("logits")
x = embedding(token_input)
new_states = []
inference_lstm_layers = []
for i in range(self.num_layers):
inference_lstm = layers.LSTM(
self.hidden_dim,
return_sequences=i < self.num_layers - 1,
return_state=True,
recurrent_dropout=0.1,
name=f"lstm_{i}"
)
h_input = state_inputs[i * 2]
c_input = state_inputs[i * 2 + 1]
x, new_h, new_c = inference_lstm(x, initial_state=[h_input, c_input])
new_states.extend([new_h, new_c])
dropout = training_model.get_layer(f"dropout_{i}")
x = dropout(x)
inference_lstm_layers.append(inference_lstm)
logits = logits_layer(x)
inference_model = keras.Model(
[token_input] + state_inputs,
[logits] + new_states,
name="rnn_inference"
)
for i, inference_lstm in enumerate(inference_lstm_layers):
training_lstm = training_model.get_layer(f"lstm_{i}")
inference_lstm.set_weights(training_lstm.get_weights())
return inference_model
def _initial_states(self, batch_size: int) -> list:
states = []
for _ in range(self.num_layers):
states.append(tf.zeros((batch_size, self.hidden_dim)))
states.append(tf.zeros((batch_size, self.hidden_dim)))
return states
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