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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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 copy 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 unittest
from unittest.mock import patch
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_torch,
require_torch_accelerator,
torch_device,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch
class TextGenerationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def test_small_model_pt(self):
text_generator = pipeline(
task="text-generation",
model="hf-internal-testing/tiny-random-LlamaForCausalLM",
max_new_tokens=10,
)
# Using `do_sample=False` to force deterministic output
outputs = text_generator("This is a test", do_sample=False)
self.assertEqual(outputs, [{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}])
outputs = text_generator(["This is a test", "This is a second test"], do_sample=False)
self.assertEqual(
outputs,
[
[{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}],
[{"generated_text": "This is a second testкт MéxicoWSAnimImportдели Düsseld bootstrap learn user"}],
],
)
outputs = text_generator("This is a test", do_sample=True, num_return_sequences=2, return_tensors=True)
self.assertEqual(
outputs,
[
{"generated_token_ids": ANY(list)},
{"generated_token_ids": ANY(list)},
],
)
@require_torch
def test_small_chat_model_pt(self):
text_generator = pipeline(
task="text-generation",
model="hf-internal-testing/tiny-gpt2-with-chatml-template",
)
# Using `do_sample=False` to force deterministic output
chat1 = [
{"role": "system", "content": "This is a system message."},
{"role": "user", "content": "This is a test"},
]
chat2 = [
{"role": "system", "content": "This is a system message."},
{"role": "user", "content": "This is a second test"},
]
outputs = text_generator(chat1, do_sample=False, max_new_tokens=10)
expected_chat1 = chat1 + [
{
"role": "assistant",
"content": " factors factors factors factors factors factors factors factors factors factors",
}
]
self.assertEqual(
outputs,
[
{"generated_text": expected_chat1},
],
)
outputs = text_generator([chat1, chat2], do_sample=False, max_new_tokens=10)
expected_chat2 = chat2 + [
{
"role": "assistant",
"content": " stairs stairs stairs stairs stairs stairs stairs stairs stairs stairs",
}
]
self.assertEqual(
outputs,
[
[{"generated_text": expected_chat1}],
[{"generated_text": expected_chat2}],
],
)
@require_torch
def test_small_chat_model_continue_final_message(self):
# Here we check that passing a chat that ends in an assistant message is handled correctly
# by continuing the final message rather than starting a new one
text_generator = pipeline(
task="text-generation",
model="hf-internal-testing/tiny-gpt2-with-chatml-template",
)
# Using `do_sample=False` to force deterministic output
chat1 = [
{"role": "system", "content": "This is a system message."},
{"role": "user", "content": "This is a test"},
{"role": "assistant", "content": "This is"},
]
outputs = text_generator(chat1, do_sample=False, max_new_tokens=10)
# Assert that we continued the last message and there isn't a sneaky <|im_end|>
self.assertEqual(
outputs,
[
{
"generated_text": [
{"role": "system", "content": "This is a system message."},
{"role": "user", "content": "This is a test"},
{
"role": "assistant",
"content": "This is stairs stairs stairs stairs stairs stairs stairs stairs stairs stairs",
},
]
}
],
)
@require_torch
def test_small_chat_model_continue_final_message_override(self):
# Here we check that passing a chat that ends in an assistant message is handled correctly
# by continuing the final message rather than starting a new one
text_generator = pipeline(
task="text-generation",
model="hf-internal-testing/tiny-gpt2-with-chatml-template",
)
# Using `do_sample=False` to force deterministic output
chat1 = [
{"role": "system", "content": "This is a system message."},
{"role": "user", "content": "This is a test"},
]
outputs = text_generator(chat1, do_sample=False, max_new_tokens=10, continue_final_message=True)
# Assert that we continued the last message and there isn't a sneaky <|im_end|>
self.assertEqual(
outputs,
[
{
"generated_text": [
{"role": "system", "content": "This is a system message."},
{
"role": "user",
"content": "This is a test stairs stairs stairs stairs stairs stairs stairs stairs stairs stairs",
},
]
}
],
)
@require_torch
def test_small_chat_model_with_dataset_pt(self):
from torch.utils.data import Dataset
from transformers.pipelines.pt_utils import KeyDataset
class MyDataset(Dataset):
data = [
[
{"role": "system", "content": "This is a system message."},
{"role": "user", "content": "This is a test"},
],
]
def __len__(self):
return 1
def __getitem__(self, i):
return {"text": self.data[i]}
text_generator = pipeline(
task="text-generation",
model="hf-internal-testing/tiny-gpt2-with-chatml-template",
)
dataset = MyDataset()
key_dataset = KeyDataset(dataset, "text")
for outputs in text_generator(key_dataset, do_sample=False, max_new_tokens=10):
expected_chat = dataset.data[0] + [
{
"role": "assistant",
"content": " factors factors factors factors factors factors factors factors factors factors",
}
]
self.assertEqual(
outputs,
[
{"generated_text": expected_chat},
],
)
@require_torch
def test_small_chat_model_with_iterator_pt(self):
from transformers.pipelines.pt_utils import PipelineIterator
text_generator = pipeline(
task="text-generation",
model="hf-internal-testing/tiny-gpt2-with-chatml-template",
)
# Using `do_sample=False` to force deterministic output
chat1 = [
{"role": "system", "content": "This is a system message."},
{"role": "user", "content": "This is a test"},
]
chat2 = [
{"role": "system", "content": "This is a system message."},
{"role": "user", "content": "This is a second test"},
]
expected_chat1 = chat1 + [
{
"role": "assistant",
"content": " factors factors factors factors factors factors factors factors factors factors",
}
]
expected_chat2 = chat2 + [
{
"role": "assistant",
"content": " stairs stairs stairs stairs stairs stairs stairs stairs stairs stairs",
}
]
def data():
yield from [chat1, chat2]
outputs = text_generator(data(), do_sample=False, max_new_tokens=10)
assert isinstance(outputs, PipelineIterator)
outputs = list(outputs)
self.assertEqual(
outputs,
[
[{"generated_text": expected_chat1}],
[{"generated_text": expected_chat2}],
],
)
def get_test_pipeline(
self,
model,
tokenizer=None,
image_processor=None,
feature_extractor=None,
processor=None,
dtype="float32",
):
text_generator = TextGenerationPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
image_processor=image_processor,
processor=processor,
dtype=dtype,
max_new_tokens=5,
)
return text_generator, ["This is a test", "Another test"]
def test_stop_sequence_stopping_criteria(self):
prompt = """Hello I believe in"""
text_generator = pipeline(
"text-generation", model="hf-internal-testing/tiny-random-gpt2", max_new_tokens=5, do_sample=False
)
output = text_generator(prompt)
self.assertEqual(
output,
[{"generated_text": "Hello I believe in fe fe fe fe fe"}],
)
output = text_generator(prompt, stop_sequence=" fe")
self.assertEqual(output, [{"generated_text": "Hello I believe in fe"}])
def run_pipeline_test(self, text_generator, _):
model = text_generator.model
tokenizer = text_generator.tokenizer
outputs = text_generator("This is a test")
self.assertEqual(outputs, [{"generated_text": ANY(str)}])
self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"))
outputs = text_generator("This is a test", return_full_text=False)
self.assertEqual(outputs, [{"generated_text": ANY(str)}])
self.assertNotIn("This is a test", outputs[0]["generated_text"])
text_generator = pipeline(
task="text-generation", model=model, tokenizer=tokenizer, return_full_text=False, max_new_tokens=5
)
outputs = text_generator("This is a test")
self.assertEqual(outputs, [{"generated_text": ANY(str)}])
self.assertNotIn("This is a test", outputs[0]["generated_text"])
outputs = text_generator("This is a test", return_full_text=True)
self.assertEqual(outputs, [{"generated_text": ANY(str)}])
self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"))
outputs = text_generator(["This is great !", "Something else"], num_return_sequences=2, do_sample=True)
self.assertEqual(
outputs,
[
[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
],
)
if text_generator.tokenizer.pad_token is not None:
outputs = text_generator(
["This is great !", "Something else"], num_return_sequences=2, batch_size=2, do_sample=True
)
self.assertEqual(
outputs,
[
[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
[{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
],
)
with self.assertRaises(ValueError):
outputs = text_generator("test", return_full_text=True, return_text=True)
with self.assertRaises(ValueError):
outputs = text_generator("test", return_full_text=True, return_tensors=True)
with self.assertRaises(ValueError):
outputs = text_generator("test", return_text=True, return_tensors=True)
# Empty prompt is slightly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
outputs = text_generator("")
self.assertEqual(outputs, [{"generated_text": ANY(str)}])
else:
with self.assertRaises((ValueError, AssertionError)):
outputs = text_generator("", add_special_tokens=False)
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS = [
"RwkvForCausalLM",
"XGLMForCausalLM",
"GPTNeoXForCausalLM",
"GPTNeoXJapaneseForCausalLM",
"FuyuForCausalLM",
"LlamaForCausalLM",
]
if (
tokenizer.model_max_length < 10000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
if str(text_generator.device) == "cpu":
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)):
text_generator("This is a test" * 500, max_new_tokens=5)
outputs = text_generator("This is a test" * 500, handle_long_generation="hole", max_new_tokens=5)
# Hole strategy cannot work
if str(text_generator.device) == "cpu":
with self.assertRaises(ValueError):
text_generator(
"This is a test" * 500,
handle_long_generation="hole",
max_new_tokens=tokenizer.model_max_length + 10,
)
@require_torch
@require_accelerate
@require_torch_accelerator
def test_small_model_pt_bloom_accelerate(self):
import torch
# Classic `model_kwargs`
pipe = pipeline(
model="hf-internal-testing/tiny-random-bloom",
model_kwargs={"device_map": "auto", "dtype": torch.bfloat16},
max_new_tokens=5,
do_sample=False,
)
self.assertEqual(pipe.model.lm_head.weight.dtype, torch.bfloat16)
out = pipe("This is a test")
self.assertEqual(
out,
[{"generated_text": ("This is a test test test test test test")}],
)
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
pipe = pipeline(
model="hf-internal-testing/tiny-random-bloom",
device_map="auto",
dtype=torch.bfloat16,
max_new_tokens=5,
do_sample=False,
)
self.assertEqual(pipe.model.lm_head.weight.dtype, torch.bfloat16)
out = pipe("This is a test")
self.assertEqual(
out,
[{"generated_text": ("This is a test test test test test test")}],
)
# dtype will be automatically set to torch.bfloat16 if not provided - check: https://github.com/huggingface/transformers/pull/38882
pipe = pipeline(
model="hf-internal-testing/tiny-random-bloom", device_map="auto", max_new_tokens=5, do_sample=False
)
self.assertEqual(pipe.model.lm_head.weight.dtype, torch.bfloat16)
out = pipe("This is a test")
self.assertEqual(
out,
[{"generated_text": ("This is a test test test test test test")}],
)
@require_torch
@require_torch_accelerator
def test_small_model_fp16(self):
import torch
pipe = pipeline(
model="hf-internal-testing/tiny-random-bloom",
device=torch_device,
dtype=torch.float16,
max_new_tokens=3,
)
pipe("This is a test")
@require_torch
@require_accelerate
@require_torch_accelerator
def test_pipeline_accelerate_top_p(self):
import torch
pipe = pipeline(
model="hf-internal-testing/tiny-random-bloom",
device_map=torch_device,
dtype=torch.float16,
max_new_tokens=3,
)
pipe("This is a test", do_sample=True, top_p=0.5)
def test_pipeline_length_setting_warning(self):
prompt = """Hello world"""
text_generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-gpt2", max_new_tokens=5)
logger = logging.get_logger("transformers.generation.utils")
logger_msg = "Both `max_new_tokens`" # The beginning of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(logger) as cl:
_ = text_generator(prompt, max_length=10, max_new_tokens=1)
self.assertIn(logger_msg, cl.out)
# The user only sets one -> no warning
with CaptureLogger(logger) as cl:
_ = text_generator(prompt, max_new_tokens=1)
self.assertNotIn(logger_msg, cl.out)
with CaptureLogger(logger) as cl:
_ = text_generator(prompt, max_length=10, max_new_tokens=None)
self.assertNotIn(logger_msg, cl.out)
def test_return_dict_in_generate(self):
text_generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-gpt2", max_new_tokens=2)
out = text_generator(
["This is great !", "Something else"], return_dict_in_generate=True, output_logits=True, output_scores=True
)
self.assertEqual(
out,
[
[
{
"generated_text": ANY(str),
"logits": ANY(list),
"scores": ANY(list),
},
],
[
{
"generated_text": ANY(str),
"logits": ANY(list),
"scores": ANY(list),
},
],
],
)
@require_torch
def test_pipeline_assisted_generation(self):
"""Tests that we can run assisted generation in the pipeline"""
model = "hf-internal-testing/tiny-random-MistralForCausalLM"
pipe = pipeline("text-generation", model=model, assistant_model=model, max_new_tokens=2)
# We can run the pipeline
prompt = "Hello world"
_ = pipe(prompt)
# It is running assisted generation under the hood (e.g. flags incompatible with assisted gen will crash)
with self.assertRaises(ValueError):
_ = pipe(prompt, generate_kwargs={"num_beams": 2})
@require_torch
def test_pipeline_skip_special_tokens(self):
"""Tests that we can use `skip_special_tokens=False` to get the special tokens in the output"""
model_id = "google/gemma-3-270m-it"
chat = [{"role": "user", "content": "What's your name?"}]
generator = pipeline("text-generation", model=model_id)
# normal pipeline use
output = generator(chat, max_new_tokens=20, do_sample=False)
self.assertNotIn("<end_of_turn>", str(output[0]["generated_text"]))
# forcing special tokens to be included in the output
output = generator(chat, max_new_tokens=1000, do_sample=False, skip_special_tokens=False)
self.assertIn("<end_of_turn>", str(output[0]["generated_text"]))
@require_torch
def test_forward_tokenizer_kwargs(self):
chat = [
{"role": "system", "content": "This is a system message."},
{"role": "user", "content": "This is a test"},
]
model = "hf-internal-testing/tiny-gpt2-with-chatml-template"
text_generator = pipeline("text-generation", model, max_new_tokens=5)
tokenizer = text_generator.tokenizer
with patch.object(tokenizer, "apply_chat_template", wraps=tokenizer.apply_chat_template) as mock:
text_generator(chat, tokenizer_encode_kwargs={"enable_thinking": True})
self.assertGreater(mock.call_count, 0)
kw_call_args = mock.call_args[1]
self.assertIn("enable_thinking", kw_call_args)
self.assertEqual(kw_call_args["enable_thinking"], True)