conftest.py 13.6 KB
Newer Older
1
2
import contextlib
import gc
3
import os
Woosuk Kwon's avatar
Woosuk Kwon committed
4
5
6
7
from typing import List, Optional, Tuple

import pytest
import torch
8
9
10
from PIL import Image
from transformers import (AutoModelForCausalLM, AutoProcessor,
                          LlavaForConditionalGeneration)
Woosuk Kwon's avatar
Woosuk Kwon committed
11
12

from vllm import LLM, SamplingParams
13
from vllm.config import TokenizerPoolConfig, VisionLanguageConfig
14
15
from vllm.model_executor.parallel_utils.parallel_state import (
    destroy_model_parallel)
16
from vllm.sequence import MultiModalData
17
from vllm.transformers_utils.tokenizer import get_tokenizer
Woosuk Kwon's avatar
Woosuk Kwon committed
18

19
20
21
_TEST_DIR = os.path.dirname(__file__)
_TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
_LONG_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "summary.txt")]
22

23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
# Multi modal related
_PIXEL_VALUES_FILES = [
    os.path.join(_TEST_DIR, "images", filename) for filename in
    ["stop_sign_pixel_values.pt", "cherry_blossom_pixel_values.pt"]
]
_IMAGE_FEATURES_FILES = [
    os.path.join(_TEST_DIR, "images", filename) for filename in
    ["stop_sign_image_features.pt", "cherry_blossom_image_features.pt"]
]
_IMAGE_FILES = [
    os.path.join(_TEST_DIR, "images", filename)
    for filename in ["stop_sign.jpg", "cherry_blossom.jpg"]
]
_IMAGE_PROMPTS = [
    "<image>\nUSER: What's the content of the image?\nASSISTANT:",
    "<image>\nUSER: What is the season?\nASSISTANT:"
]
assert len(_PIXEL_VALUES_FILES) == len(_IMAGE_FEATURES_FILES) == len(
    _IMAGE_FILES) == len(_IMAGE_PROMPTS)

43

44
def _read_prompts(filename: str) -> List[str]:
45
    with open(filename, "r") as f:
46
47
        prompts = f.readlines()
        return prompts
Woosuk Kwon's avatar
Woosuk Kwon committed
48
49


50
51
52
53
54
55
56
57
def cleanup():
    destroy_model_parallel()
    with contextlib.suppress(AssertionError):
        torch.distributed.destroy_process_group()
    gc.collect()
    torch.cuda.empty_cache()


58
59
60
61
62
63
64
65
66
@pytest.fixture()
def should_do_global_cleanup_after_test() -> bool:
    """Allow subdirectories to skip global cleanup by overriding this fixture.
    This can provide a ~10x speedup for non-GPU unit tests since they don't need
    to initialize torch.
    """
    return True


67
@pytest.fixture(autouse=True)
68
def cleanup_fixture(should_do_global_cleanup_after_test: bool):
69
    yield
70
71
    if should_do_global_cleanup_after_test:
        cleanup()
72
73


74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
@pytest.fixture(scope="session")
def hf_image_prompts() -> List[str]:
    return _IMAGE_PROMPTS


@pytest.fixture(scope="session")
def hf_images() -> List[Image.Image]:
    return [Image.open(filename) for filename in _IMAGE_FILES]


@pytest.fixture()
def vllm_images(request) -> "torch.Tensor":
    vision_language_config = request.getfixturevalue("model_and_config")[1]
    all_images = []
    if vision_language_config.image_input_type == (
            VisionLanguageConfig.ImageInputType.IMAGE_FEATURES):
        filenames = _IMAGE_FEATURES_FILES
    else:
        filenames = _PIXEL_VALUES_FILES
    for filename in filenames:
        all_images.append(torch.load(filename))
    return torch.concat(all_images, dim=0)


@pytest.fixture()
def vllm_image_prompts(request) -> List[str]:
    vision_language_config = request.getfixturevalue("model_and_config")[1]
    return [
        "<image>" * (vision_language_config.image_feature_size - 1) + p
        for p in _IMAGE_PROMPTS
    ]


Woosuk Kwon's avatar
Woosuk Kwon committed
107
108
@pytest.fixture
def example_prompts() -> List[str]:
109
110
    prompts = []
    for filename in _TEST_PROMPTS:
111
        prompts += _read_prompts(filename)
112
113
114
115
116
117
118
    return prompts


@pytest.fixture
def example_long_prompts() -> List[str]:
    prompts = []
    for filename in _LONG_PROMPTS:
119
        prompts += _read_prompts(filename)
120
    return prompts
Woosuk Kwon's avatar
Woosuk Kwon committed
121
122
123
124
125
126
127
128


_STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.half,
    "bfloat16": torch.bfloat16,
    "float": torch.float,
}

129
130
131
132
_VISION_LANGUAGE_MODELS = {
    "llava-hf/llava-1.5-7b-hf": LlavaForConditionalGeneration,
}

Woosuk Kwon's avatar
Woosuk Kwon committed
133
134
135
136
137
138
139
140
141
142
143

class HfRunner:

    def __init__(
        self,
        model_name: str,
        tokenizer_name: Optional[str] = None,
        dtype: str = "half",
    ) -> None:
        assert dtype in _STR_DTYPE_TO_TORCH_DTYPE
        torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
        self.model_name = model_name
        if model_name not in _VISION_LANGUAGE_MODELS:
            self.model = AutoModelForCausalLM.from_pretrained(
                model_name,
                torch_dtype=torch_dtype,
                trust_remote_code=True,
            ).cuda()
            self.processor = None
        else:
            self.model = _VISION_LANGUAGE_MODELS[model_name].from_pretrained(
                model_name,
                torch_dtype=torch_dtype,
                trust_remote_code=True,
            ).cuda()
            self.processor = AutoProcessor.from_pretrained(
                model_name,
                torch_dtype=torch_dtype,
            )
Woosuk Kwon's avatar
Woosuk Kwon committed
162
163
164
165
166
167
168
        if tokenizer_name is None:
            tokenizer_name = model_name
        self.tokenizer = get_tokenizer(tokenizer_name, trust_remote_code=True)

    def generate(
        self,
        prompts: List[str],
169
        images: Optional[List[Image.Image]] = None,
Woosuk Kwon's avatar
Woosuk Kwon committed
170
171
172
        **kwargs,
    ) -> List[Tuple[List[int], str]]:
        outputs: List[Tuple[List[int], str]] = []
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
        if images:
            assert len(prompts) == len(images)
        for i, prompt in enumerate(prompts):
            if self.model_name not in _VISION_LANGUAGE_MODELS:
                input_ids = self.tokenizer(prompt,
                                           return_tensors="pt").input_ids
                inputs = {"input_ids": input_ids.cuda()}
            else:
                image = images[i] if images else None
                inputs = self.processor(text=prompt,
                                        images=image,
                                        return_tensors="pt")
                inputs = {
                    key: value.cuda() if value is not None else None
                    for key, value in inputs.items()
                }
Woosuk Kwon's avatar
Woosuk Kwon committed
189
            output_ids = self.model.generate(
190
                **inputs,
Woosuk Kwon's avatar
Woosuk Kwon committed
191
192
193
194
195
196
197
                use_cache=True,
                **kwargs,
            )
            output_str = self.tokenizer.batch_decode(
                output_ids,
                skip_special_tokens=True,
                clean_up_tokenization_spaces=False,
198
199
            )
            output_ids = output_ids.cpu().tolist()
Woosuk Kwon's avatar
Woosuk Kwon committed
200
201
202
203
204
205
206
            outputs.append((output_ids, output_str))
        return outputs

    def generate_greedy(
        self,
        prompts: List[str],
        max_tokens: int,
207
        images: Optional["torch.Tensor"] = None,
Woosuk Kwon's avatar
Woosuk Kwon committed
208
    ) -> List[Tuple[List[int], str]]:
209
210
        outputs = self.generate(prompts,
                                do_sample=False,
211
212
                                max_new_tokens=max_tokens,
                                images=images)
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
        for i in range(len(outputs)):
            output_ids, output_str = outputs[i]
            outputs[i] = (output_ids[0], output_str[0])
        return outputs

    def generate_beam_search(
        self,
        prompts: List[str],
        beam_width: int,
        max_tokens: int,
    ) -> List[Tuple[List[int], str]]:
        outputs = self.generate(prompts,
                                do_sample=False,
                                max_new_tokens=max_tokens,
                                num_beams=beam_width,
                                num_return_sequences=beam_width)
        for i in range(len(outputs)):
            output_ids, output_str = outputs[i]
            for j in range(len(output_ids)):
                output_ids[j] = [
                    x for x in output_ids[j]
                    if x != self.tokenizer.pad_token_id
                ]
            outputs[i] = (output_ids, output_str)
        return outputs
Woosuk Kwon's avatar
Woosuk Kwon committed
238

239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
    def generate_greedy_logprobs(
        self,
        prompts: List[str],
        max_tokens: int,
    ) -> List[List[torch.Tensor]]:
        all_logprobs = []
        for prompt in prompts:
            input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
            output = self.model.generate(
                input_ids.cuda(),
                use_cache=True,
                do_sample=False,
                max_new_tokens=max_tokens,
                output_hidden_states=True,
                return_dict_in_generate=True,
            )
            seq_logprobs = []
            for hidden_states in output.hidden_states:
                last_hidden_states = hidden_states[-1][0]
                logits = torch.matmul(
                    last_hidden_states,
                    self.model.get_output_embeddings().weight.t(),
                )
                if self.model.get_output_embeddings().bias is not None:
                    logits += self.model.get_output_embeddings(
                    ).bias.unsqueeze(0)
                logprobs = torch.nn.functional.log_softmax(logits,
                                                           dim=-1,
                                                           dtype=torch.float32)
                seq_logprobs.append(logprobs)
            all_logprobs.append(seq_logprobs)
        return all_logprobs

272
273
274
275
    def __del__(self):
        del self.model
        cleanup()

Woosuk Kwon's avatar
Woosuk Kwon committed
276
277
278
279
280
281
282
283
284
285
286
287

@pytest.fixture
def hf_runner():
    return HfRunner


class VllmRunner:

    def __init__(
        self,
        model_name: str,
        tokenizer_name: Optional[str] = None,
288
289
290
        # Use smaller max model length, otherwise bigger model cannot run due
        # to kv cache size limit.
        max_model_len=1024,
Woosuk Kwon's avatar
Woosuk Kwon committed
291
        dtype: str = "half",
292
        disable_log_stats: bool = True,
293
        tensor_parallel_size: int = 1,
294
295
        block_size: int = 16,
        enable_chunked_prefill: bool = False,
296
        **kwargs,
Woosuk Kwon's avatar
Woosuk Kwon committed
297
298
299
300
301
302
303
    ) -> None:
        self.model = LLM(
            model=model_name,
            tokenizer=tokenizer_name,
            trust_remote_code=True,
            dtype=dtype,
            swap_space=0,
304
            disable_log_stats=disable_log_stats,
305
            tensor_parallel_size=tensor_parallel_size,
306
            max_model_len=max_model_len,
307
308
            block_size=block_size,
            enable_chunked_prefill=enable_chunked_prefill,
309
            **kwargs,
Woosuk Kwon's avatar
Woosuk Kwon committed
310
311
312
313
314
315
        )

    def generate(
        self,
        prompts: List[str],
        sampling_params: SamplingParams,
316
        images: Optional["torch.Tensor"] = None,
Woosuk Kwon's avatar
Woosuk Kwon committed
317
    ) -> List[Tuple[List[int], str]]:
318
319
320
321
322
323
324
325
        if images is not None:
            assert len(prompts) == images.shape[0]
        req_outputs = self.model.generate(
            prompts,
            sampling_params=sampling_params,
            multi_modal_data=MultiModalData(type=MultiModalData.Type.IMAGE,
                                            data=images)
            if images is not None else None)
Woosuk Kwon's avatar
Woosuk Kwon committed
326
327
328
329
        outputs = []
        for req_output in req_outputs:
            prompt_str = req_output.prompt
            prompt_ids = req_output.prompt_token_ids
330
331
332
333
334
335
336
337
            req_sample_output_ids = []
            req_sample_output_strs = []
            for sample in req_output.outputs:
                output_str = sample.text
                output_ids = sample.token_ids
                req_sample_output_ids.append(prompt_ids + output_ids)
                req_sample_output_strs.append(prompt_str + output_str)
            outputs.append((req_sample_output_ids, req_sample_output_strs))
Woosuk Kwon's avatar
Woosuk Kwon committed
338
339
        return outputs

340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
    def generate_w_logprobs(
        self,
        prompts: List[str],
        sampling_params: SamplingParams,
    ) -> List[Tuple[List[int], str]]:
        assert sampling_params.logprobs is not None

        req_outputs = self.model.generate(prompts,
                                          sampling_params=sampling_params)
        outputs = []
        for req_output in req_outputs:
            for sample in req_output.outputs:
                output_str = sample.text
                output_ids = sample.token_ids
                output_logprobs = sample.logprobs
            outputs.append((output_ids, output_str, output_logprobs))
        return outputs

Woosuk Kwon's avatar
Woosuk Kwon committed
358
359
360
361
    def generate_greedy(
        self,
        prompts: List[str],
        max_tokens: int,
362
        images: Optional[torch.Tensor] = None,
Woosuk Kwon's avatar
Woosuk Kwon committed
363
364
    ) -> List[Tuple[List[int], str]]:
        greedy_params = SamplingParams(temperature=0.0, max_tokens=max_tokens)
365
        outputs = self.generate(prompts, greedy_params, images=images)
366
367
        return [(output_ids[0], output_str[0])
                for output_ids, output_str in outputs]
368

369
370
371
372
373
374
375
376
377
378
379
380
381
382
    def generate_greedy_logprobs(
        self,
        prompts: List[str],
        max_tokens: int,
        num_logprobs: int,
    ) -> List[Tuple[List[int], str]]:
        greedy_logprobs_params = SamplingParams(temperature=0.0,
                                                max_tokens=max_tokens,
                                                logprobs=num_logprobs)
        outputs = self.generate_w_logprobs(prompts, greedy_logprobs_params)

        return [(output_ids, output_str, output_logprobs)
                for output_ids, output_str, output_logprobs in outputs]

383
384
385
386
387
388
389
390
391
392
393
394
    def generate_beam_search(
        self,
        prompts: List[str],
        beam_width: int,
        max_tokens: int,
    ) -> List[Tuple[List[int], str]]:
        beam_search_params = SamplingParams(n=beam_width,
                                            use_beam_search=True,
                                            temperature=0.0,
                                            max_tokens=max_tokens)
        outputs = self.generate(prompts, beam_search_params)
        return outputs
Woosuk Kwon's avatar
Woosuk Kwon committed
395

396
397
398
399
    def __del__(self):
        del self.model
        cleanup()

Woosuk Kwon's avatar
Woosuk Kwon committed
400
401
402
403

@pytest.fixture
def vllm_runner():
    return VllmRunner
404
405
406
407
408
409
410
411
412
413


def get_tokenizer_pool_config(tokenizer_group_type):
    if tokenizer_group_type is None:
        return None
    if tokenizer_group_type == "ray":
        return TokenizerPoolConfig(pool_size=1,
                                   pool_type="ray",
                                   extra_config={})
    raise ValueError(f"Unknown tokenizer_group_type: {tokenizer_group_type}")