mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
9ac586b3c8
* Rework pipeline tests * Try to fix Flax tests * Try to put it before * Use a new decorator instead * Remove ignore marker since it doesn't work * Filter pipeline tests * Woopsie * Use the fitlered list * Clean up and fake modif * Remove init * Revert fake modif
296 lines
12 KiB
Python
296 lines
12 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TextGenerationPipeline, pipeline
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from transformers.testing_utils import (
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require_accelerate,
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require_tf,
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require_torch,
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require_torch_gpu,
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require_torch_or_tf,
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)
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from .test_pipelines_common import ANY, PipelineTestCaseMeta
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@require_torch_or_tf
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class TextGenerationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING
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tf_model_mapping = TF_MODEL_FOR_CAUSAL_LM_MAPPING
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@require_torch
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def test_small_model_pt(self):
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text_generator = pipeline(task="text-generation", model="sshleifer/tiny-ctrl", framework="pt")
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# Using `do_sample=False` to force deterministic output
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outputs = text_generator("This is a test", do_sample=False)
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self.assertEqual(
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outputs,
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[
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{
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"generated_text": (
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"This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
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" oscope. FiliFili@@"
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)
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}
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],
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)
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outputs = text_generator(["This is a test", "This is a second test"])
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self.assertEqual(
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outputs,
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[
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[
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{
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"generated_text": (
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"This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
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" oscope. FiliFili@@"
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)
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}
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],
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[
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{
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"generated_text": (
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"This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"
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" oscope. oscope. FiliFili@@"
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)
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}
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],
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],
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)
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outputs = text_generator("This is a test", do_sample=True, num_return_sequences=2, return_tensors=True)
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self.assertEqual(
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outputs,
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[
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{"generated_token_ids": ANY(list)},
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{"generated_token_ids": ANY(list)},
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],
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)
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text_generator.tokenizer.pad_token_id = text_generator.model.config.eos_token_id
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text_generator.tokenizer.pad_token = "<pad>"
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outputs = text_generator(
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["This is a test", "This is a second test"],
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do_sample=True,
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num_return_sequences=2,
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batch_size=2,
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return_tensors=True,
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)
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self.assertEqual(
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outputs,
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[
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[
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{"generated_token_ids": ANY(list)},
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{"generated_token_ids": ANY(list)},
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],
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[
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{"generated_token_ids": ANY(list)},
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{"generated_token_ids": ANY(list)},
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],
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],
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)
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@require_tf
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def test_small_model_tf(self):
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text_generator = pipeline(task="text-generation", model="sshleifer/tiny-ctrl", framework="tf")
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# Using `do_sample=False` to force deterministic output
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outputs = text_generator("This is a test", do_sample=False)
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self.assertEqual(
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outputs,
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[
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{
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"generated_text": (
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"This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
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" please,"
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)
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}
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],
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)
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outputs = text_generator(["This is a test", "This is a second test"], do_sample=False)
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self.assertEqual(
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outputs,
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[
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[
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{
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"generated_text": (
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"This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
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" please,"
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)
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}
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],
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[
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{
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"generated_text": (
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"This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"
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" Cannes 閲閲Cannes Cannes Cannes 攵 please,"
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)
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}
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],
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],
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)
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def get_test_pipeline(self, model, tokenizer, feature_extractor):
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text_generator = TextGenerationPipeline(model=model, tokenizer=tokenizer)
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return text_generator, ["This is a test", "Another test"]
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def test_stop_sequence_stopping_criteria(self):
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prompt = """Hello I believe in"""
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text_generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-gpt2")
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output = text_generator(prompt)
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self.assertEqual(
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output,
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[{"generated_text": "Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"}],
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)
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output = text_generator(prompt, stop_sequence=" fe")
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self.assertEqual(output, [{"generated_text": "Hello I believe in fe"}])
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def run_pipeline_test(self, text_generator, _):
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model = text_generator.model
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tokenizer = text_generator.tokenizer
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outputs = text_generator("This is a test")
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self.assertEqual(outputs, [{"generated_text": ANY(str)}])
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self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"))
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outputs = text_generator("This is a test", return_full_text=False)
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self.assertEqual(outputs, [{"generated_text": ANY(str)}])
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self.assertNotIn("This is a test", outputs[0]["generated_text"])
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text_generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer, return_full_text=False)
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outputs = text_generator("This is a test")
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self.assertEqual(outputs, [{"generated_text": ANY(str)}])
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self.assertNotIn("This is a test", outputs[0]["generated_text"])
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outputs = text_generator("This is a test", return_full_text=True)
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self.assertEqual(outputs, [{"generated_text": ANY(str)}])
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self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"))
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outputs = text_generator(["This is great !", "Something else"], num_return_sequences=2, do_sample=True)
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self.assertEqual(
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outputs,
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[
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[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
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[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
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],
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)
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if text_generator.tokenizer.pad_token is not None:
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outputs = text_generator(
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["This is great !", "Something else"], num_return_sequences=2, batch_size=2, do_sample=True
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)
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self.assertEqual(
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outputs,
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[
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[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
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[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
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],
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)
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# Empty prompt is slighly special
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# it requires BOS token to exist.
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# Special case for Pegasus which will always append EOS so will
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# work even without BOS.
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if text_generator.tokenizer.bos_token_id is not None or "Pegasus" in tokenizer.__class__.__name__:
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outputs = text_generator("")
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self.assertEqual(outputs, [{"generated_text": ANY(str)}])
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else:
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with self.assertRaises((ValueError, AssertionError)):
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outputs = text_generator("")
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if text_generator.framework == "tf":
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# TF generation does not support max_new_tokens, and it's impossible
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# to control long generation with only max_length without
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# fancy calculation, dismissing tests for now.
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return
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# We don't care about infinite range models.
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# They already work.
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# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
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if tokenizer.model_max_length < 10000 and "XGLM" not in tokenizer.__class__.__name__:
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# Handling of large generations
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with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)):
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text_generator("This is a test" * 500, max_new_tokens=20)
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outputs = text_generator("This is a test" * 500, handle_long_generation="hole", max_new_tokens=20)
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# Hole strategy cannot work
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with self.assertRaises(ValueError):
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text_generator(
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"This is a test" * 500,
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handle_long_generation="hole",
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max_new_tokens=tokenizer.model_max_length + 10,
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)
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@require_torch
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@require_accelerate
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@require_torch_gpu
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def test_small_model_pt_bloom_accelerate(self):
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import torch
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# Classic `model_kwargs`
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pipe = pipeline(
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model="hf-internal-testing/tiny-random-bloom",
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model_kwargs={"device_map": "auto", "torch_dtype": torch.bfloat16},
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)
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self.assertEqual(pipe.model.device, torch.device(0))
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self.assertEqual(pipe.model.lm_head.weight.dtype, torch.bfloat16)
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out = pipe("This is a test")
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self.assertEqual(
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out,
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[
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{
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"generated_text": (
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"This is a test test test test test test test test test test test test test test test test"
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" test"
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)
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}
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],
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)
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# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
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pipe = pipeline(model="hf-internal-testing/tiny-random-bloom", device_map="auto", torch_dtype=torch.bfloat16)
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self.assertEqual(pipe.model.device, torch.device(0))
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self.assertEqual(pipe.model.lm_head.weight.dtype, torch.bfloat16)
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out = pipe("This is a test")
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self.assertEqual(
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out,
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[
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{
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"generated_text": (
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"This is a test test test test test test test test test test test test test test test test"
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" test"
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)
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}
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],
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)
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# torch_dtype not necessary
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pipe = pipeline(model="hf-internal-testing/tiny-random-bloom", device_map="auto")
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self.assertEqual(pipe.model.device, torch.device(0))
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self.assertEqual(pipe.model.lm_head.weight.dtype, torch.bfloat16)
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out = pipe("This is a test")
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self.assertEqual(
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out,
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[
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{
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"generated_text": (
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"This is a test test test test test test test test test test test test test test test test"
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" test"
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)
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}
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],
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)
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