Generate: validate model_kwargs on TF (and catch typos in generate arguments) (#18651)
This commit is contained in:
Joao Gante
@@ -75,11 +75,9 @@ if is_tf_available():
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TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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BertConfig,
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TFAutoModel,
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TFAutoModelForCausalLM,
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TFAutoModelForSequenceClassification,
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TFBertModel,
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TFSharedEmbeddings,
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tf_top_k_top_p_filtering,
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)
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from transformers.generation_tf_utils import (
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TFBeamSampleDecoderOnlyOutput,
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@@ -1824,100 +1822,6 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None, dtype=None):
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@require_tf
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class UtilsFunctionsTest(unittest.TestCase):
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# tests whether the top_k_top_p_filtering function behaves as expected
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def test_top_k_top_p_filtering(self):
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logits = tf.convert_to_tensor(
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[
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[
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8.2220991, # 3rd highest value; idx. 0
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-0.5620044,
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5.23229752,
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4.0386393,
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-6.8798378,
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-0.54785802,
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-3.2012153,
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2.92777176,
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1.88171953,
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7.35341276, # 5th highest value; idx. 9
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8.43207833, # 2nd highest value; idx. 10
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-9.85711836,
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-5.96209236,
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-1.13039161,
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-7.1115294,
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-0.8369633,
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-5.3186408,
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7.06427407,
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0.81369344,
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-0.82023817,
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-5.9179796,
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0.58813443,
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-6.99778438,
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4.71551189,
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-0.18771637,
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7.44020759, # 4th highest value; idx. 25
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9.38450987, # 1st highest value; idx. 26
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2.12662941,
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-9.32562038,
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2.35652522,
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], # cummulative prob of 5 highest values <= 0.6
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[
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0.58425518,
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4.53139238,
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-5.57510464,
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-6.28030699,
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-7.19529503,
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-4.02122551,
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1.39337037,
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-6.06707057,
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1.59480517,
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-9.643119,
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0.03907799,
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0.67231762,
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-8.88206726,
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6.27115922, # 4th highest value; idx. 13
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2.28520723,
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4.82767506,
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4.30421368,
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8.8275313, # 2nd highest value; idx. 17
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5.44029958, # 5th highest value; idx. 18
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-4.4735794,
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7.38579536, # 3rd highest value; idx. 20
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-2.91051663,
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2.61946077,
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-2.5674762,
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-9.48959302,
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-4.02922645,
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-1.35416918,
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9.67702323, # 1st highest value; idx. 27
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-5.89478553,
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1.85370467,
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], # cummulative prob of 5 highest values <= 0.6
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],
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dtype=tf.float32,
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)
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non_inf_expected_idx = tf.convert_to_tensor(
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[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
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dtype=tf.int32,
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) # expected non filtered idx as noted above
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non_inf_expected_output = tf.convert_to_tensor(
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[8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],
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dtype=tf.float32,
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) # expected non filtered values as noted above
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output = tf_top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
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non_inf_output = output[output != -float("inf")]
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non_inf_idx = tf.cast(
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tf.where(tf.not_equal(output, tf.constant(-float("inf"), dtype=tf.float32))),
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dtype=tf.int32,
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)
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tf.debugging.assert_near(non_inf_output, non_inf_expected_output, rtol=1e-12)
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tf.debugging.assert_equal(non_inf_idx, non_inf_expected_idx)
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def test_cached_files_are_used_when_internet_is_down(self):
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# A mock response for an HTTP head request to emulate server down
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response_mock = mock.Mock()
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@@ -2179,46 +2083,6 @@ class UtilsFunctionsTest(unittest.TestCase):
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for p1, p2 in zip(model.weights, new_model.weights):
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self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
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def test_generate_tf_function_export(self):
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test_model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
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max_length = 2
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class DummyModel(tf.Module):
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def __init__(self, model):
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super(DummyModel, self).__init__()
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self.model = model
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@tf.function(
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input_signature=(
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tf.TensorSpec((None, max_length), tf.int32, name="input_ids"),
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tf.TensorSpec((None, max_length), tf.int32, name="attention_mask"),
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),
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jit_compile=True,
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)
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def serving(self, input_ids, attention_mask):
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outputs = self.model.generate(
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input_ids=input_ids,
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attention_mask=attention_mask,
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max_new_tokens=max_length,
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return_dict_in_generate=True,
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)
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return {"sequences": outputs["sequences"]}
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dummy_input_ids = [[2, 0], [102, 103]]
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dummy_attention_masks = [[1, 0], [1, 1]]
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dummy_model = DummyModel(model=test_model)
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with tempfile.TemporaryDirectory() as tmp_dir:
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tf.saved_model.save(dummy_model, tmp_dir, signatures={"serving_default": dummy_model.serving})
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serving_func = tf.saved_model.load(tmp_dir).signatures["serving_default"]
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for batch_size in range(1, len(dummy_input_ids) + 1):
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inputs = {
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"input_ids": tf.constant(dummy_input_ids[:batch_size]),
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"attention_mask": tf.constant(dummy_attention_masks[:batch_size]),
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}
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tf_func_outputs = serving_func(**inputs)["sequences"]
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tf_model_outputs = test_model.generate(**inputs, max_new_tokens=max_length)
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tf.debugging.assert_equal(tf_func_outputs, tf_model_outputs)
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@require_tf
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@is_staging_test
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