254 lines
12 KiB
Python
254 lines
12 KiB
Python
# Copyright 2024 HuggingFace 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 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 json
|
|
import shutil
|
|
import tempfile
|
|
import unittest
|
|
|
|
import jinja2
|
|
import numpy as np
|
|
|
|
from transformers import CsmProcessor
|
|
from transformers.testing_utils import require_torch
|
|
from transformers.utils import is_torch_available
|
|
|
|
from ...test_processing_common import ProcessorTesterMixin
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
|
|
@require_torch
|
|
class CsmProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
|
processor_class = CsmProcessor
|
|
audio_input_name = "input_values"
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls.checkpoint = "hf-internal-testing/namespace-sesame-repo_name_csm-1b"
|
|
processor = CsmProcessor.from_pretrained(cls.checkpoint)
|
|
cls.audio_token = processor.audio_token
|
|
cls.audio_token_id = processor.audio_token_id
|
|
cls.pad_token_id = processor.tokenizer.pad_token_id
|
|
cls.bos_token_id = processor.tokenizer.bos_token_id
|
|
cls.tmpdirname = tempfile.mkdtemp()
|
|
processor.save_pretrained(cls.tmpdirname)
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
shutil.rmtree(cls.tmpdirname, ignore_errors=True)
|
|
|
|
def prepare_processor_dict(self):
|
|
return {"chat_template": "\n{%- for message in messages %}\n {#-- Validate role is a stringified integer --#}\n {%- if not message['role'] is string or not message['role'].isdigit() %}\n {{- raise_exception(\"The role must be an integer or a stringified integer (e.g. '0') designating the speaker id\") }}\n {%- endif %}\n\n {#-- Validate content is a list --#}\n {%- set content = message['content'] %}\n {%- if content is not iterable or content is string %}\n {{- raise_exception(\"The content must be a list\") }}\n {%- endif %}\n\n {#-- Collect content types --#}\n {%- set content_types = content | map(attribute='type') | list %}\n {%- set is_last = loop.last %}\n\n {#-- Last message validation --#}\n {%- if is_last %}\n {%- if 'text' not in content_types %}\n {{- raise_exception(\"The last message must include one item of type 'text'\") }}\n {%- elif (content_types | select('equalto', 'text') | list | length > 1) or (content_types | select('equalto', 'audio') | list | length > 1) %}\n {{- raise_exception(\"At most two items are allowed in the last message: one 'text' and one 'audio'\") }}\n {%- endif %}\n\n {#-- All other messages validation --#}\n {%- else %}\n {%- if content_types | select('equalto', 'text') | list | length != 1\n or content_types | select('equalto', 'audio') | list | length != 1 %}\n {{- raise_exception(\"Each message (except the last) must contain exactly one 'text' and one 'audio' item\") }}\n {%- elif content_types | reject('in', ['text', 'audio']) | list | length > 0 %}\n {{- raise_exception(\"Only 'text' and 'audio' types are allowed in content\") }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n\n{%- for message in messages %}\n {{- bos_token }}\n {{- '[' + message['role'] + ']' }}\n {{- message['content'][0]['text'] }}\n {{- eos_token }}\n {%- if message['content']|length > 1 %}\n {{- '<|AUDIO|><|audio_eos|>' }}\n {%- endif %}\n{%- endfor %}\n"} # fmt: skip
|
|
|
|
def test_chat_template_is_saved(self):
|
|
processor_loaded = self.processor_class.from_pretrained(self.tmpdirname)
|
|
processor_dict_loaded = json.loads(processor_loaded.to_json_string())
|
|
# chat templates aren't serialized to json in processors
|
|
self.assertFalse("chat_template" in processor_dict_loaded)
|
|
|
|
# they have to be saved as separate file and loaded back from that file
|
|
# so we check if the same template is loaded
|
|
processor_dict = self.prepare_processor_dict()
|
|
self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None))
|
|
|
|
@require_torch
|
|
def _test_apply_chat_template(
|
|
self,
|
|
modality: str,
|
|
batch_size: int,
|
|
return_tensors: str,
|
|
input_name: str,
|
|
processor_name: str,
|
|
input_data: list[str],
|
|
):
|
|
if return_tensors != "pt":
|
|
self.skipTest("CSM only supports PyTorch tensors")
|
|
processor = self.get_processor()
|
|
if processor.chat_template is None:
|
|
self.skipTest("Processor has no chat template")
|
|
|
|
if processor_name not in self.processor_class.attributes:
|
|
self.skipTest(f"{processor_name} attribute not present in {self.processor_class}")
|
|
|
|
# some models have only Fast image processor
|
|
if getattr(processor, processor_name).__class__.__name__.endswith("Fast"):
|
|
return_tensors = "pt"
|
|
|
|
batch_messages = [
|
|
[
|
|
{
|
|
"role": "0",
|
|
"content": [{"type": "text", "text": "Describe this."}],
|
|
},
|
|
]
|
|
] * batch_size
|
|
|
|
# Test that jinja can be applied
|
|
formatted_prompt = processor.apply_chat_template(batch_messages, add_generation_prompt=True, tokenize=False)
|
|
self.assertEqual(len(formatted_prompt), batch_size)
|
|
|
|
# Test that tokenizing with template and directly with `self.tokenizer` gives same output
|
|
formatted_prompt_tokenized = processor.apply_chat_template(
|
|
batch_messages, add_generation_prompt=True, tokenize=True, return_tensors=return_tensors
|
|
)
|
|
add_special_tokens = True
|
|
if processor.tokenizer.bos_token is not None and formatted_prompt[0].startswith(processor.tokenizer.bos_token):
|
|
add_special_tokens = False
|
|
tok_output = processor.tokenizer(
|
|
formatted_prompt, return_tensors=return_tensors, add_special_tokens=add_special_tokens
|
|
)
|
|
expected_output = tok_output.input_ids
|
|
self.assertListEqual(expected_output.tolist(), formatted_prompt_tokenized.tolist())
|
|
|
|
# Test that kwargs passed to processor's `__call__` are actually used
|
|
tokenized_prompt_100 = processor.apply_chat_template(
|
|
batch_messages,
|
|
add_generation_prompt=True,
|
|
tokenize=True,
|
|
padding="max_length",
|
|
truncation=True,
|
|
return_tensors=return_tensors,
|
|
max_length=100,
|
|
)
|
|
self.assertEqual(len(tokenized_prompt_100[0]), 100)
|
|
|
|
# Test that `return_dict=True` returns text related inputs in the dict
|
|
out_dict_text = processor.apply_chat_template(
|
|
batch_messages,
|
|
add_generation_prompt=True,
|
|
tokenize=True,
|
|
return_dict=True,
|
|
return_tensors=return_tensors,
|
|
)
|
|
self.assertTrue(all(key in out_dict_text for key in ["input_ids", "attention_mask"]))
|
|
self.assertEqual(len(out_dict_text["input_ids"]), batch_size)
|
|
self.assertEqual(len(out_dict_text["attention_mask"]), batch_size)
|
|
|
|
# Test that with modality URLs and `return_dict=True`, we get modality inputs in the dict
|
|
for idx, url in enumerate(input_data[:batch_size]):
|
|
batch_messages[idx][0]["content"] = [batch_messages[idx][0]["content"][0], {"type": modality, "url": url}]
|
|
|
|
out_dict = processor.apply_chat_template(
|
|
batch_messages,
|
|
add_generation_prompt=True,
|
|
tokenize=True,
|
|
return_dict=True,
|
|
return_tensors=return_tensors,
|
|
num_frames=2, # by default no more than 2 frames, otherwise too slow
|
|
)
|
|
input_name = getattr(self, input_name)
|
|
print(f"================ input_name={input_name} =================")
|
|
print(f"out_dict={out_dict.keys()}")
|
|
self.assertTrue(input_name in out_dict)
|
|
self.assertEqual(len(out_dict["input_ids"]), batch_size)
|
|
self.assertEqual(len(out_dict["attention_mask"]), batch_size)
|
|
self.assertEqual(len(out_dict[input_name]), batch_size)
|
|
|
|
return_tensor_to_type = {"pt": torch.Tensor, "np": np.ndarray, None: list}
|
|
for k in out_dict:
|
|
self.assertIsInstance(out_dict[k], return_tensor_to_type[return_tensors])
|
|
|
|
# Test continue from final message
|
|
assistant_message = {
|
|
"role": "1",
|
|
"content": [{"type": "text", "text": "It is the sound of"}],
|
|
}
|
|
for idx, url in enumerate(input_data[:batch_size]):
|
|
batch_messages[idx] = batch_messages[idx] + [assistant_message]
|
|
continue_prompt = processor.apply_chat_template(batch_messages, continue_final_message=True, tokenize=False)
|
|
for prompt in continue_prompt:
|
|
self.assertTrue(prompt.endswith("It is the sound of")) # no `eos` token at the end
|
|
|
|
def test_apply_chat_template(self):
|
|
# Message contains content which a mix of lists with images and image urls and string
|
|
messages = [
|
|
{
|
|
"role": "0",
|
|
"content": [
|
|
{"type": "text", "text": "This is a test sentence 0."},
|
|
{"type": "audio"},
|
|
],
|
|
},
|
|
{
|
|
"role": "1",
|
|
"content": [
|
|
{"type": "text", "text": "This is a test sentence 1."},
|
|
{"type": "audio"},
|
|
],
|
|
},
|
|
{
|
|
"role": "0",
|
|
"content": [
|
|
{"type": "text", "text": "This is a prompt."},
|
|
],
|
|
},
|
|
]
|
|
processor = CsmProcessor.from_pretrained(self.tmpdirname)
|
|
rendered = processor.apply_chat_template(messages, tokenize=False)
|
|
|
|
expected_rendered = (
|
|
"<|begin_of_text|>[0]This is a test sentence 0.<|end_of_text|>"
|
|
"<|AUDIO|><|audio_eos|>"
|
|
"<|begin_of_text|>[1]This is a test sentence 1.<|end_of_text|>"
|
|
"<|AUDIO|><|audio_eos|>"
|
|
"<|begin_of_text|>[0]This is a prompt.<|end_of_text|>"
|
|
)
|
|
self.assertEqual(rendered, expected_rendered)
|
|
|
|
messages = [
|
|
{
|
|
"role": "0",
|
|
"content": [
|
|
{"type": "text", "text": "This is a test sentence."},
|
|
],
|
|
},
|
|
{
|
|
"role": "1",
|
|
"content": [
|
|
{"type": "text", "text": "This is a test sentence."},
|
|
],
|
|
},
|
|
]
|
|
|
|
# this should raise an error because the CSM processor requires audio content in the messages expect the last one
|
|
with self.assertRaises(jinja2.exceptions.TemplateError):
|
|
input_ids = processor.apply_chat_template(messages, tokenize=False)
|
|
|
|
# now let's very that it expands audio tokens correctly
|
|
messages = [
|
|
{
|
|
"role": "0",
|
|
"content": [
|
|
{"type": "text", "text": "This is a test sentence."},
|
|
{"type": "audio", "audio": np.zeros(4096)},
|
|
],
|
|
},
|
|
]
|
|
|
|
input_ids = processor.apply_chat_template(messages, tokenize=True)
|
|
|
|
# 4096 audio input values should give 3 audio tokens
|
|
expected_ids = torch.tensor(
|
|
[[128000, 58, 15, 60, 2028, 374, 264, 1296, 11914, 13, 128001, 128002, 128002, 128002, 128003]]
|
|
)
|
|
torch.testing.assert_close(input_ids, expected_ids)
|
|
|
|
@require_torch
|
|
@unittest.skip("CSM doesn't need assistant masks as an audio generation model")
|
|
def test_apply_chat_template_assistant_mask(self):
|
|
pass
|