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Unverified Commit 9196f48b authored by Joao Gante's avatar Joao Gante Committed by GitHub
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Generate: validate `model_kwargs` on TF (and catch typos in generate arguments) (#18651)

parent c5be7cae
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Showing with 214 additions and 139 deletions
src/transformers/generation_tf_utils.py
View file @ 9196f48b
......@@ -579,6 +579,7 @@ class TFGenerationMixin:
do_sample = do_sample if do_sample is not None else self.config.do_sample
if do_sample is False or num_beams == 1:
seed = model_kwargs.pop("seed", None)
return self._generate(
input_ids=input_ids,
max_length=max_length,
......@@ -601,13 +602,14 @@ class TFGenerationMixin:
attention_mask=attention_mask,
decoder_start_token_id=decoder_start_token_id,
use_cache=use_cache,
seed=model_kwargs.pop("seed", None),
seed=seed,
output_scores=output_scores,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
forced_bos_token_id=forced_bos_token_id,
forced_eos_token_id=forced_eos_token_id,
**model_kwargs,
)
# We cannot generate if the model does not have a LM head
......@@ -1288,6 +1290,29 @@ class TFGenerationMixin:
else:
return logits
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
"""Validates model kwargs for generation. Generate argument typos will also be caught here."""
# Excludes arguments that are handled before calling any model function
if self.config.is_encoder_decoder:
for key in ["decoder_input_ids"]:
model_kwargs.pop(key, None)
unused_model_args = []
model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
# `kwargs` if often used to handle optional forward pass inputs like `attention_mask`. If
# `prepare_inputs_for_generation` doesn't accept `kwargs`, then a stricter check can be made ;)
if "kwargs" in model_args:
model_args |= set(inspect.signature(self.call).parameters)
for key, value in model_kwargs.items():
if value is not None and key not in model_args:
unused_model_args.append(key)
if unused_model_args:
raise ValueError(
f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
" generate arguments will also show up in this list)"
)
def _generate(
self,
input_ids=None,
......@@ -1483,6 +1508,9 @@ class TFGenerationMixin:
# generate sequences without allowing bad_words to be generated
outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids)
```"""
# 0. Validate model kwargs
self._validate_model_kwargs(model_kwargs.copy())
# 1. Set generation parameters if not already defined
length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
......
tests/generation/test_generation_tf_utils.py 0 → 100644
View file @ 9196f48b
# coding=utf-8
# Copyright 2022 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM, TFAutoModelForSeq2SeqLM, tf_top_k_top_p_filtering
@require_tf
class UtilsFunctionsTest(unittest.TestCase):
# tests whether the top_k_top_p_filtering function behaves as expected
def test_top_k_top_p_filtering(self):
logits = tf.convert_to_tensor(
[
[
8.2220991, # 3rd highest value; idx. 0
-0.5620044,
5.23229752,
4.0386393,
-6.8798378,
-0.54785802,
-3.2012153,
2.92777176,
1.88171953,
7.35341276, # 5th highest value; idx. 9
8.43207833, # 2nd highest value; idx. 10
-9.85711836,
-5.96209236,
-1.13039161,
-7.1115294,
-0.8369633,
-5.3186408,
7.06427407,
0.81369344,
-0.82023817,
-5.9179796,
0.58813443,
-6.99778438,
4.71551189,
-0.18771637,
7.44020759, # 4th highest value; idx. 25
9.38450987, # 1st highest value; idx. 26
2.12662941,
-9.32562038,
2.35652522,
], # cummulative prob of 5 highest values <= 0.6
[
0.58425518,
4.53139238,
-5.57510464,
-6.28030699,
-7.19529503,
-4.02122551,
1.39337037,
-6.06707057,
1.59480517,
-9.643119,
0.03907799,
0.67231762,
-8.88206726,
6.27115922, # 4th highest value; idx. 13
2.28520723,
4.82767506,
4.30421368,
8.8275313, # 2nd highest value; idx. 17
5.44029958, # 5th highest value; idx. 18
-4.4735794,
7.38579536, # 3rd highest value; idx. 20
-2.91051663,
2.61946077,
-2.5674762,
-9.48959302,
-4.02922645,
-1.35416918,
9.67702323, # 1st highest value; idx. 27
-5.89478553,
1.85370467,
], # cummulative prob of 5 highest values <= 0.6
],
dtype=tf.float32,
)
non_inf_expected_idx = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
dtype=tf.int32,
) # expected non filtered idx as noted above
non_inf_expected_output = tf.convert_to_tensor(
[8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],
dtype=tf.float32,
) # expected non filtered values as noted above
output = tf_top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
non_inf_output = output[output != -float("inf")]
non_inf_idx = tf.cast(
tf.where(tf.not_equal(output, tf.constant(-float("inf"), dtype=tf.float32))),
dtype=tf.int32,
)
tf.debugging.assert_near(non_inf_output, non_inf_expected_output, rtol=1e-12)
tf.debugging.assert_equal(non_inf_idx, non_inf_expected_idx)
@require_tf
class TFGenerationIntegrationTests(unittest.TestCase):
@slow
def test_generate_tf_function_export(self):
test_model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
max_length = 2
class DummyModel(tf.Module):
def __init__(self, model):
super(DummyModel, self).__init__()
self.model = model
@tf.function(
input_signature=(
tf.TensorSpec((None, max_length), tf.int32, name="input_ids"),
tf.TensorSpec((None, max_length), tf.int32, name="attention_mask"),
),
jit_compile=True,
)
def serving(self, input_ids, attention_mask):
outputs = self.model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_new_tokens=max_length,
return_dict_in_generate=True,
)
return {"sequences": outputs["sequences"]}
dummy_input_ids = [[2, 0], [102, 103]]
dummy_attention_masks = [[1, 0], [1, 1]]
dummy_model = DummyModel(model=test_model)
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(dummy_model, tmp_dir, signatures={"serving_default": dummy_model.serving})
serving_func = tf.saved_model.load(tmp_dir).signatures["serving_default"]
for batch_size in range(1, len(dummy_input_ids) + 1):
inputs = {
"input_ids": tf.constant(dummy_input_ids[:batch_size]),
"attention_mask": tf.constant(dummy_attention_masks[:batch_size]),
}
tf_func_outputs = serving_func(**inputs)["sequences"]
tf_model_outputs = test_model.generate(**inputs, max_new_tokens=max_length)
tf.debugging.assert_equal(tf_func_outputs, tf_model_outputs)
def test_validate_generation_inputs(self):
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
model = TFAutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-t5")
encoder_input_str = "Hello world"
input_ids = tokenizer(encoder_input_str, return_tensors="tf").input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(ValueError, "do_samples"):
model.generate(input_ids, do_samples=True)
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(ValueError, "foo"):
fake_model_kwargs = {"foo": "bar"}
model.generate(input_ids, **fake_model_kwargs)
tests/generation/test_generation_utils.py
View file @ 9196f48b
......@@ -2704,8 +2704,8 @@ class GenerationIntegrationTests(unittest.TestCase):
model.generate(input_ids, force_words_ids=[[[-1]]])
def test_validate_generation_inputs(self):
tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/t5-tiny-random")
model = AutoModelForSeq2SeqLM.from_pretrained("patrickvonplaten/t5-tiny-random")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
model = AutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-t5")
encoder_input_str = "Hello world"
input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
......
tests/test_modeling_tf_common.py
View file @ 9196f48b
......@@ -75,11 +75,9 @@ if is_tf_available():
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
BertConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForSequenceClassification,
TFBertModel,
TFSharedEmbeddings,
tf_top_k_top_p_filtering,
)
from transformers.generation_tf_utils import (
TFBeamSampleDecoderOnlyOutput,
......@@ -1824,100 +1822,6 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None, dtype=None):
@require_tf
class UtilsFunctionsTest(unittest.TestCase):
# tests whether the top_k_top_p_filtering function behaves as expected
def test_top_k_top_p_filtering(self):
logits = tf.convert_to_tensor(
[
[
8.2220991, # 3rd highest value; idx. 0
-0.5620044,
5.23229752,
4.0386393,
-6.8798378,
-0.54785802,
-3.2012153,
2.92777176,
1.88171953,
7.35341276, # 5th highest value; idx. 9
8.43207833, # 2nd highest value; idx. 10
-9.85711836,
-5.96209236,
-1.13039161,
-7.1115294,
-0.8369633,
-5.3186408,
7.06427407,
0.81369344,
-0.82023817,
-5.9179796,
0.58813443,
-6.99778438,
4.71551189,
-0.18771637,
7.44020759, # 4th highest value; idx. 25
9.38450987, # 1st highest value; idx. 26
2.12662941,
-9.32562038,
2.35652522,
], # cummulative prob of 5 highest values <= 0.6
[
0.58425518,
4.53139238,
-5.57510464,
-6.28030699,
-7.19529503,
-4.02122551,
1.39337037,
-6.06707057,
1.59480517,
-9.643119,
0.03907799,
0.67231762,
-8.88206726,
6.27115922, # 4th highest value; idx. 13
2.28520723,
4.82767506,
4.30421368,
8.8275313, # 2nd highest value; idx. 17
5.44029958, # 5th highest value; idx. 18
-4.4735794,
7.38579536, # 3rd highest value; idx. 20
-2.91051663,
2.61946077,
-2.5674762,
-9.48959302,
-4.02922645,
-1.35416918,
9.67702323, # 1st highest value; idx. 27
-5.89478553,
1.85370467,
], # cummulative prob of 5 highest values <= 0.6
],
dtype=tf.float32,
)
non_inf_expected_idx = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
dtype=tf.int32,
) # expected non filtered idx as noted above
non_inf_expected_output = tf.convert_to_tensor(
[8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],
dtype=tf.float32,
) # expected non filtered values as noted above
output = tf_top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
non_inf_output = output[output != -float("inf")]
non_inf_idx = tf.cast(
tf.where(tf.not_equal(output, tf.constant(-float("inf"), dtype=tf.float32))),
dtype=tf.int32,
)
tf.debugging.assert_near(non_inf_output, non_inf_expected_output, rtol=1e-12)
tf.debugging.assert_equal(non_inf_idx, non_inf_expected_idx)
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
......@@ -2179,46 +2083,6 @@ class UtilsFunctionsTest(unittest.TestCase):
for p1, p2 in zip(model.weights, new_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
def test_generate_tf_function_export(self):
test_model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
max_length = 2
class DummyModel(tf.Module):
def __init__(self, model):
super(DummyModel, self).__init__()
self.model = model
@tf.function(
input_signature=(
tf.TensorSpec((None, max_length), tf.int32, name="input_ids"),
tf.TensorSpec((None, max_length), tf.int32, name="attention_mask"),
),
jit_compile=True,
)
def serving(self, input_ids, attention_mask):
outputs = self.model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_new_tokens=max_length,
return_dict_in_generate=True,
)
return {"sequences": outputs["sequences"]}
dummy_input_ids = [[2, 0], [102, 103]]
dummy_attention_masks = [[1, 0], [1, 1]]
dummy_model = DummyModel(model=test_model)
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(dummy_model, tmp_dir, signatures={"serving_default": dummy_model.serving})
serving_func = tf.saved_model.load(tmp_dir).signatures["serving_default"]
for batch_size in range(1, len(dummy_input_ids) + 1):
inputs = {
"input_ids": tf.constant(dummy_input_ids[:batch_size]),
"attention_mask": tf.constant(dummy_attention_masks[:batch_size]),
}
tf_func_outputs = serving_func(**inputs)["sequences"]
tf_model_outputs = test_model.generate(**inputs, max_new_tokens=max_length)
tf.debugging.assert_equal(tf_func_outputs, tf_model_outputs)
@require_tf
@is_staging_test
......
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