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Jul 10, 2023
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Switch tf -> numpy in more docstrings #1123

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10 changes: 3 additions & 7 deletions keras_nlp/models/albert/albert_backbone.py
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Expand Up @@ -76,13 +76,9 @@ class AlbertBackbone(Backbone):
Examples:
```python
input_data = {
"token_ids": tf.ones(shape=(1, 12), dtype="int64"),
"segment_ids": tf.constant(
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)
),
"padding_mask": tf.constant(
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)
),
"token_ids": np.ones(shape=(1, 12), dtype="int32"),
"segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]]),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
}

# Randomly initialized ALBERT encoder
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10 changes: 3 additions & 7 deletions keras_nlp/models/albert/albert_classifier.py
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Expand Up @@ -85,13 +85,9 @@ class AlbertClassifier(Task):
Preprocessed integer data.
```python
features = {
"token_ids": tf.ones(shape=(2, 12), dtype="int64"),
"segment_ids": tf.constant(
[[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2, shape=(2, 12)
),
"padding_mask": tf.constant(
[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2, shape=(2, 12)
),
"token_ids": np.ones(shape=(2, 12), dtype="int32"),
"segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
labels = [0, 3]

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12 changes: 4 additions & 8 deletions keras_nlp/models/albert/albert_masked_lm.py
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Expand Up @@ -81,14 +81,10 @@ class AlbertMaskedLM(Task):
```python
# Create preprocessed batch where 0 is the mask token.
features = {
"token_ids": tf.constant(
[[1, 2, 0, 4, 0, 6, 7, 8]] * 2, shape=(2, 8)
),
"padding_mask": tf.constant(
[[1, 1, 1, 1, 1, 1, 1, 1]] * 2, shape=(2, 8)
),
"mask_positions": tf.constant([[2, 4]] * 2, shape=(2, 2)),
"segment_ids": tf.constant([[0, 0, 0, 0, 0, 0, 0, 0]] * 2, shape=(2, 8))
"token_ids": np.array([[1, 2, 0, 4, 0, 6, 7, 8]] * 2),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1]] * 2),
"mask_positions": np.array([[2, 4]] * 2),
"segment_ids": np.array([[0, 0, 0, 0, 0, 0, 0, 0]] * 2),
}
# Labels are the original masked values.
labels = [[3, 5]] * 2
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12 changes: 6 additions & 6 deletions keras_nlp/models/bart/bart_backbone.py
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Expand Up @@ -65,13 +65,13 @@ class BartBackbone(Backbone):
Examples:
```python
input_data = {
"encoder_token_ids": tf.ones(shape=(1, 12), dtype="int64"),
"encoder_padding_mask": tf.constant(
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)
"encoder_token_ids": np.ones(shape=(1, 12), dtype="int32"),
"encoder_padding_mask": np.array(
[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]
),
"decoder_token_ids": tf.ones(shape=(1, 12), dtype="int64"),
"decoder_padding_mask": tf.constant(
[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0], shape=(1, 12)
"decoder_token_ids": np.ones(shape=(1, 12), dtype="int32"),
"decoder_padding_mask": np.array(
[[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0]]
),
}

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20 changes: 10 additions & 10 deletions keras_nlp/models/bart/bart_seq_2_seq_lm.py
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Expand Up @@ -109,12 +109,12 @@ class BartSeq2SeqLM(GenerativeTask):
# "The quick brown fox", and the decoder inputs to "The fast". Use
# `"padding_mask"` to indicate values that should not be overridden.
prompt = {
"encoder_token_ids": tf.constant([[0, 133, 2119, 6219, 23602, 2, 1, 1]]),
"encoder_padding_mask": tf.constant(
"encoder_token_ids": np.array([[0, 133, 2119, 6219, 23602, 2, 1, 1]]),
"encoder_padding_mask": np.array(
[[True, True, True, True, True, True, False, False]]
),
"decoder_token_ids": tf.constant([[2, 0, 133, 1769, 2, 1, 1]]),
"decoder_padding_mask": tf.constant([[True, True, True, True, False, False]])
"decoder_token_ids": np.array([[2, 0, 133, 1769, 2, 1, 1]]),
"decoder_padding_mask": np.array([[True, True, True, True, False, False]])
}

bart_lm = keras_nlp.models.BartSeq2SeqLM.from_preset(
Expand All @@ -137,13 +137,13 @@ class BartSeq2SeqLM(GenerativeTask):
Call `fit()` without preprocessing.
```python
x = {
"encoder_token_ids": tf.constant([[0, 133, 2119, 2, 1]] * 2),
"encoder_padding_mask": tf.constant([[1, 1, 1, 1, 0]] * 2),
"decoder_token_ids": tf.constant([[2, 0, 133, 1769, 2]] * 2),
"decoder_padding_mask": tf.constant([[1, 1, 1, 1, 1]] * 2),
"encoder_token_ids": np.array([[0, 133, 2119, 2, 1]] * 2),
"encoder_padding_mask": np.array([[1, 1, 1, 1, 0]] * 2),
"decoder_token_ids": np.array([[2, 0, 133, 1769, 2]] * 2),
"decoder_padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = tf.constant([[0, 133, 1769, 2, 1]] * 2)
sw = tf.constant([[1, 1, 1, 1, 0]] * 2)
y = np.array([[0, 133, 1769, 2, 1]] * 2)
sw = np.array([[1, 1, 1, 1, 0]] * 2)

bart_lm = keras_nlp.models.BartSeq2SeqLM.from_preset(
"bart_base_en",
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10 changes: 3 additions & 7 deletions keras_nlp/models/bert/bert_backbone.py
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Expand Up @@ -65,13 +65,9 @@ class BertBackbone(Backbone):
Examples:
```python
input_data = {
"token_ids": tf.ones(shape=(1, 12), dtype="int64"),
"segment_ids": tf.constant(
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)
),
"padding_mask": tf.constant(
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)
),
"token_ids": np.ones(shape=(1, 12), dtype="int32"),
"segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]]),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
}

# Pretrained BERT encoder.
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10 changes: 3 additions & 7 deletions keras_nlp/models/bert/bert_classifier.py
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Expand Up @@ -86,13 +86,9 @@ class BertClassifier(Task):
Preprocessed integer data.
```python
features = {
"token_ids": tf.ones(shape=(2, 12), dtype="int64"),
"segment_ids": tf.constant(
[[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2, shape=(2, 12)
),
"padding_mask": tf.constant(
[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2, shape=(2, 12)
),
"token_ids": np.ones(shape=(2, 12), dtype="int32"),
"segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
labels = [0, 3]

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12 changes: 4 additions & 8 deletions keras_nlp/models/bert/bert_masked_lm.py
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Expand Up @@ -80,14 +80,10 @@ class BertMaskedLM(Task):
```python
# Create preprocessed batch where 0 is the mask token.
features = {
"token_ids": tf.constant(
[[1, 2, 0, 4, 0, 6, 7, 8]] * 2, shape=(2, 8)
),
"padding_mask": tf.constant(
[[1, 1, 1, 1, 1, 1, 1, 1]] * 2, shape=(2, 8)
),
"mask_positions": tf.constant([[2, 4]] * 2, shape=(2, 2)),
"segment_ids": tf.constant([[0, 0, 0, 0, 0, 0, 0, 0]] * 2, shape=(2, 8))
"token_ids": np.array([[1, 2, 0, 4, 0, 6, 7, 8]] * 2),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1]] * 2),
"mask_positions": np.array([[2, 4]] * 2),
"segment_ids": np.array([[0, 0, 0, 0, 0, 0, 0, 0]] * 2)
}
# Labels are the original masked values.
labels = [[3, 5]] * 2
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5 changes: 2 additions & 3 deletions keras_nlp/models/deberta_v3/deberta_v3_backbone.py
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Expand Up @@ -73,9 +73,8 @@ class DebertaV3Backbone(Backbone):
Example usage:
```python
input_data = {
"token_ids": tf.ones(shape=(1, 12), dtype="int64"),
"padding_mask": tf.constant(
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)),
"token_ids": np.ones(shape=(1, 12), dtype="int32"),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
}

# Pretrained DeBERTa encoder.
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6 changes: 2 additions & 4 deletions keras_nlp/models/deberta_v3/deberta_v3_classifier.py
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Expand Up @@ -95,10 +95,8 @@ class DebertaV3Classifier(Task):
Preprocessed integer data.
```python
features = {
"token_ids": tf.ones(shape=(2, 12), dtype="int64"),
"padding_mask": tf.constant(
[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2, shape=(2, 12)
),
"token_ids": np.ones(shape=(2, 12), dtype="int32"),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
labels = [0, 3]

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10 changes: 3 additions & 7 deletions keras_nlp/models/deberta_v3/deberta_v3_masked_lm.py
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Expand Up @@ -84,13 +84,9 @@ class DebertaV3MaskedLM(Task):
```python
# Create preprocessed batch where 0 is the mask token.
features = {
"token_ids": tf.constant(
[[1, 2, 0, 4, 0, 6, 7, 8]] * 2, shape=(2, 8)
),
"padding_mask": tf.constant(
[[1, 1, 1, 1, 1, 1, 1, 1]] * 2, shape=(2, 8)
),
"mask_positions": tf.constant([[2, 4]] * 2, shape=(2, 2)),
"token_ids": np.array([[1, 2, 0, 4, 0, 6, 7, 8]] * 2),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1]] * 2),
"mask_positions": np.array([[2, 4]] * 2),
}
# Labels are the original masked values.
labels = [[3, 5]] * 2
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6 changes: 2 additions & 4 deletions keras_nlp/models/distil_bert/distil_bert_backbone.py
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Expand Up @@ -68,10 +68,8 @@ class DistilBertBackbone(Backbone):
Examples:
```python
input_data = {
"token_ids": tf.ones(shape=(1, 12), dtype="int64"),
"padding_mask": tf.constant(
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)
),
"token_ids": np.ones(shape=(1, 12), dtype="int32"),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
}

# Pretrained DistilBERT encoder.
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6 changes: 2 additions & 4 deletions keras_nlp/models/distil_bert/distil_bert_classifier.py
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Expand Up @@ -97,10 +97,8 @@ class DistilBertClassifier(Task):
Preprocessed integer data.
```python
features = {
"token_ids": tf.ones(shape=(2, 12), dtype="int64"),
"padding_mask": tf.constant(
[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2, shape=(2, 12)
),
"token_ids": np.ones(shape=(2, 12), dtype="int32"),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2)
}
labels = [0, 3]

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10 changes: 3 additions & 7 deletions keras_nlp/models/distil_bert/distil_bert_masked_lm.py
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Expand Up @@ -84,13 +84,9 @@ class DistilBertMaskedLM(Task):
```python
# Create preprocessed batch where 0 is the mask token.
features = {
"token_ids": tf.constant(
[[1, 2, 0, 4, 0, 6, 7, 8]] * 2, shape=(2, 8)
),
"padding_mask": tf.constant(
[[1, 1, 1, 1, 1, 1, 1, 1]] * 2, shape=(2, 8)
),
"mask_positions": tf.constant([[2, 4]] * 2, shape=(2, 2))
"token_ids": np.array([[1, 2, 0, 4, 0, 6, 7, 8]] * 2),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1]] * 2),
"mask_positions": np.array([[2, 4]] * 2)
}
# Labels are the original masked values.
labels = [[3, 5]] * 2
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6 changes: 2 additions & 4 deletions keras_nlp/models/f_net/f_net_backbone.py
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Expand Up @@ -70,10 +70,8 @@ class FNetBackbone(Backbone):
Examples:
```python
input_data = {
"token_ids": tf.ones(shape=(1, 12), dtype="int64"),
"segment_ids": tf.constant(
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)
),
"token_ids": np.ones(shape=(1, 12), dtype="int32"),
"segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]]),
}

# Pretrained BERT encoder.
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6 changes: 2 additions & 4 deletions keras_nlp/models/f_net/f_net_classifier.py
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Expand Up @@ -87,10 +87,8 @@ class FNetClassifier(Task):
Preprocessed integer data.
```python
features = {
"token_ids": tf.ones(shape=(2, 12), dtype="int64"),
"segment_ids": tf.constant(
[[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2, shape=(2, 12)
),
"token_ids": np.ones(shape=(2, 12), dtype="int32"),
"segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
labels = [0, 3]

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10 changes: 3 additions & 7 deletions keras_nlp/models/f_net/f_net_masked_lm.py
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Expand Up @@ -79,13 +79,9 @@ class FNetMaskedLM(Task):
```python
# Create a preprocessed dataset where 0 is the mask token.
features = {
"token_ids": tf.constant(
[[1, 2, 0, 4, 0, 6, 7, 8]] * 2, shape=(2, 8)
),
"segment_ids": tf.constant(
[[0, 0, 0, 1, 1, 1, 0, 0]] * 2, shape=(2, 8)
),
"mask_positions": tf.constant([[2, 4]] * 2, shape=(2, 2))
"token_ids": np.array([[1, 2, 0, 4, 0, 6, 7, 8]] * 2),
"segment_ids": np.array([[0, 0, 0, 1, 1, 1, 0, 0]] * 2),
"mask_positions": np.array([[2, 4]] * 2)
}
# Labels are the original masked values.
labels = [[3, 5]] * 2
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6 changes: 2 additions & 4 deletions keras_nlp/models/gpt2/gpt2_backbone.py
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Expand Up @@ -69,10 +69,8 @@ class GPT2Backbone(Backbone):
Example usage:
```python
input_data = {
"token_ids": tf.ones(shape=(1, 12), dtype="int64"),
"padding_mask": tf.constant(
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)
),
"token_ids": np.ones(shape=(1, 12), dtype="int32"),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
}

# Pretrained GPT-2 decoder.
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12 changes: 6 additions & 6 deletions keras_nlp/models/gpt2/gpt2_causal_lm.py
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Expand Up @@ -99,8 +99,8 @@ class GPT2CausalLM(GenerativeTask):
# Prompt the model with `5338, 318` (the token ids for `"Who is"`).
# Use `"padding_mask"` to indicate values that should not be overridden.
prompt = {
"token_ids": tf.constant([[5338, 318, 0, 0, 0]] * 2),
"padding_mask": tf.constant([[1, 1, 0, 0, 0]] * 2),
"token_ids": np.array([[5338, 318, 0, 0, 0]] * 2),
"padding_mask": np.array([[1, 1, 0, 0, 0]] * 2),
}

gpt2_lm = keras_nlp.models.GPT2CausalLM.from_preset(
Expand All @@ -120,11 +120,11 @@ class GPT2CausalLM(GenerativeTask):
Call `fit()` without preprocessing.
```python
x = {
"token_ids": tf.constant([[50256, 1, 2, 3, 4]] * 2),
"padding_mask": tf.constant([[1, 1, 1, 1, 1]] * 2),
"token_ids": np.array([[50256, 1, 2, 3, 4]] * 2),
"padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = tf.constant([[1, 2, 3, 4, 50256]] * 2)
sw = tf.constant([[1, 1, 1, 1, 1]] * 2)
y = np.array([[1, 2, 3, 4, 50256]] * 2)
sw = np.array([[1, 1, 1, 1, 1]] * 2)

gpt2_lm = keras_nlp.models.GPT2CausalLM.from_preset(
"gpt2_base_en",
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6 changes: 2 additions & 4 deletions keras_nlp/models/opt/opt_backbone.py
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Expand Up @@ -67,10 +67,8 @@ class OPTBackbone(Backbone):
Examples:
```python
input_data = {
"token_ids": tf.ones(shape=(1, 12), dtype="int64"),
"padding_mask": tf.constant(
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)
),
"token_ids": np.ones(shape=(1, 12), dtype="int32"),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
}

# Pretrained OPT decoder
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12 changes: 6 additions & 6 deletions keras_nlp/models/opt/opt_causal_lm.py
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Expand Up @@ -99,8 +99,8 @@ class OPTCausalLM(GenerativeTask):
# Prompt the model with `5338, 318` (the token ids for `"Who is"`).
# Use `"padding_mask"` to indicate values that should not be overridden.
prompt = {
"token_ids": tf.constant([[5338, 318, 0, 0, 0]] * 2),
"padding_mask": tf.constant([[1, 1, 0, 0, 0]] * 2),
"token_ids": np.array([[5338, 318, 0, 0, 0]] * 2),
"padding_mask": np.array([[1, 1, 0, 0, 0]] * 2),
}

opt_lm = keras_nlp.models.OPTCausalLM.from_preset(
Expand All @@ -120,11 +120,11 @@ class OPTCausalLM(GenerativeTask):
Call `fit()` without preprocessing.
```python
x = {
"token_ids": tf.constant([[1, 2, 3, 4, 5]] * 2),
"padding_mask": tf.constant([[1, 1, 1, 1, 1]] * 2),
"token_ids": np.array([[1, 2, 3, 4, 5]] * 2),
"padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = tf.constant([[2, 3, 4, 5, 0]] * 2)
sw = tf.constant([[1, 1, 1, 1, 1]] * 2)
y = np.array([[2, 3, 4, 5, 0]] * 2)
sw = np.array([[1, 1, 1, 1, 1]] * 2)

opt_lm = keras_nlp.models.OPTCausalLM.from_preset(
"opt_base_en",
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4 changes: 2 additions & 2 deletions keras_nlp/models/roberta/roberta_backbone.py
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Expand Up @@ -67,8 +67,8 @@ class RobertaBackbone(Backbone):
Examples:
```python
input_data = {
"token_ids": tf.ones(shape=(1, 12), dtype="int64"),
"padding_mask": tf.constant(
"token_ids": np.ones(shape=(1, 12), dtype="int32"),
"padding_mask": np.array(
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)),
}

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6 changes: 2 additions & 4 deletions keras_nlp/models/roberta/roberta_classifier.py
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Expand Up @@ -87,10 +87,8 @@ class RobertaClassifier(Task):
Preprocessed integer data.
```python
features = {
"token_ids": tf.ones(shape=(2, 12), dtype="int64"),
"padding_mask": tf.constant(
[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2, shape=(2, 12)
),
"token_ids": np.ones(shape=(2, 12), dtype="int32"),
"padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
labels = [0, 3]

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