# 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 transformers import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TextGenerationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class TextGenerationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING
tf_model_mapping = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def test_small_model_pt(self):
text_generator = pipeline(task="text-generation", model="sshleifer/tiny-ctrl", framework="pt")
# 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 test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
" oscope. FiliFili@@"
)
}
],
)
outputs = text_generator(["This is a test", "This is a second test"])
self.assertEqual(
outputs,
[
[
{
"generated_text": (
"This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
" oscope. FiliFili@@"
)
}
],
[
{
"generated_text": (
"This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"
" oscope. oscope. FiliFili@@"
)
}
],
],
)
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)},
],
)
text_generator.tokenizer.pad_token_id = text_generator.model.config.eos_token_id
text_generator.tokenizer.pad_token = "<pad>"
outputs = text_generator(
["This is a test", "This is a second test"],
do_sample=True,
num_return_sequences=2,
batch_size=2,
return_tensors=True,
)
self.assertEqual(
outputs,
[
[
{"generated_token_ids": ANY(list)},
{"generated_token_ids": ANY(list)},
],
[
{"generated_token_ids": ANY(list)},
{"generated_token_ids": ANY(list)},
],
],
)
@require_tf
def test_small_model_tf(self):
text_generator = pipeline(task="text-generation", model="sshleifer/tiny-ctrl", framework="tf")
# 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 test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
" please,"
)
}
],
)
outputs = text_generator(["This is a test", "This is a second test"], do_sample=False)
self.assertEqual(
outputs,
[
[
{
"generated_text": (
"This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
" please,"
)
}
],
[
{
"generated_text": (
"This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"
" Cannes 閲閲Cannes Cannes Cannes 攵 please,"
)
}
],
],
)
def get_test_pipeline(self, model, tokenizer, processor):
text_generator = TextGenerationPipeline(model=model, tokenizer=tokenizer)
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")
output = text_generator(prompt)
self.assertEqual(
output,
[{"generated_text": "Hello I believe in fe fe fe fe fe fe fe 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)
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 slighly 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("")
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# 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.
if tokenizer.model_max_length < 10000 and "XGLM" not in tokenizer.__class__.__name__:
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)):
text_generator("This is a test" * 500, max_new_tokens=20)
outputs = text_generator("This is a test" * 500, handle_long_generation="hole", max_new_tokens=20)
# Hole strategy cannot work
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_gpu
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", "torch_dtype": torch.bfloat16},
)
self.assertEqual(pipe.model.device, torch.device(0))
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 test test test test test 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", torch_dtype=torch.bfloat16)
self.assertEqual(pipe.model.device, torch.device(0))
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 test test test test test test test test test test"
" test"
)
}
],
)
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
pipe = pipeline(model="hf-internal-testing/tiny-random-bloom", device_map="auto")
self.assertEqual(pipe.model.device, torch.device(0))
self.assertEqual(pipe.model.lm_head.weight.dtype, torch.float32)
out = pipe("This is a test")
self.assertEqual(
out,
[
{
"generated_text": (
"This is a test test test test test test test test test test test test test test test test"
" test"
)
}
],
)
@require_torch
@require_torch_gpu
def test_small_model_fp16(self):
import torch
pipe = pipeline(model="hf-internal-testing/tiny-random-bloom", device=0, torch_dtype=torch.float16)
pipe("This is a test")
@require_torch
@require_accelerate
@require_torch_gpu
def test_pipeline_accelerate_top_p(self):
import torch
pipe = pipeline(model="hf-internal-testing/tiny-random-bloom", device_map="auto", torch_dtype=torch.float16)
pipe("This is a test", do_sample=True, top_p=0.5)