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61ba51e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | # Copyright 2023-2024 SGLang Team
# 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 multiprocessing as mp
import random
import unittest
from typing import Optional
import torch
from transformers import AutoConfig, AutoTokenizer
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
from sglang.test.runners import DEFAULT_PROMPTS, HFRunner, SRTRunner
from sglang.test.test_utils import (
CustomTestCase,
get_similarities,
is_in_amd_ci,
is_in_ci,
)
# Embedding model tests
register_amd_ci(
est_time=73,
suite="stage-b-test-small-1-gpu-amd",
disabled="see https://github.com/sgl-project/sglang/issues/11127",
)
register_cuda_ci(est_time=73, suite="stage-b-test-small-1-gpu")
MODEL_TO_CONFIG = {
"Alibaba-NLP/gte-Qwen2-1.5B-instruct": (1, 1e-5),
"intfloat/e5-mistral-7b-instruct": (1, 1e-5),
"marco/mcdse-2b-v1": (1, 1e-5),
"Qwen/Qwen3-Embedding-8B": (1, 1e-5),
# Temporarily disable before this model is fixed
# "jason9693/Qwen2.5-1.5B-apeach": (1, 1e-5),
}
MODELS = [(key, *MODEL_TO_CONFIG[key]) for key in MODEL_TO_CONFIG]
TORCH_DTYPES = [torch.float16]
class TestEmbeddingModels(CustomTestCase):
@classmethod
def setUpClass(cls):
mp.set_start_method("spawn", force=True)
def _truncate_prompts(self, prompts, model_path):
config = AutoConfig.from_pretrained(model_path)
max_length = getattr(config, "max_position_embeddings", 2048)
tokenizer = AutoTokenizer.from_pretrained(model_path)
truncated_prompts = []
for prompt in prompts:
tokens = tokenizer(prompt, return_tensors="pt", truncation=False)
if len(tokens.input_ids[0]) > max_length:
truncated_text = tokenizer.decode(
tokens.input_ids[0][: max_length - 1], skip_special_tokens=True
)
truncated_prompts.append(truncated_text)
else:
truncated_prompts.append(prompt)
return truncated_prompts
def assert_close_prefill_logits(
self,
prompts,
model_path,
tp_size,
torch_dtype,
prefill_tolerance,
matryoshka_dim: Optional[int] = None,
) -> None:
truncated_prompts = self._truncate_prompts(prompts, model_path)
with HFRunner(
model_path,
torch_dtype=torch_dtype,
model_type="embedding",
matryoshka_dim=matryoshka_dim,
) as hf_runner:
hf_outputs = hf_runner.forward(truncated_prompts)
attention_backend = "triton" if is_in_amd_ci() else None
with SRTRunner(
model_path,
tp_size=tp_size,
torch_dtype=torch_dtype,
model_type="embedding",
attention_backend=attention_backend,
json_model_override_args=(
{"matryoshka_dimensions": [matryoshka_dim]} if matryoshka_dim else None
),
) as srt_runner:
srt_outputs = srt_runner.forward(
truncated_prompts, dimensions=matryoshka_dim
)
for i in range(len(prompts)):
hf_logits = torch.Tensor(hf_outputs.embed_logits[i])
srt_logits = torch.Tensor(srt_outputs.embed_logits[i])
similarity = torch.tensor(get_similarities(hf_logits, srt_logits))
print("similarity diff", abs(similarity - 1))
if len(prompts[i]) <= 1000:
assert torch.all(
abs(similarity - 1) < prefill_tolerance
), "embeddings are not all close"
def test_prefill_logits(self):
models_to_test = MODELS
if is_in_ci():
models_to_test = [random.choice(MODELS)]
for model, tp_size, prefill_tolerance in models_to_test:
for torch_dtype in TORCH_DTYPES:
self.assert_close_prefill_logits(
DEFAULT_PROMPTS, model, tp_size, torch_dtype, prefill_tolerance
)
def test_matryoshka_embedding(self):
models_to_test = [
(
"Alibaba-NLP/gte-Qwen2-1.5B-instruct",
*MODEL_TO_CONFIG["Alibaba-NLP/gte-Qwen2-1.5B-instruct"],
)
]
for model, tp_size, prefill_tolerance in models_to_test:
for torch_dtype in TORCH_DTYPES:
self.assert_close_prefill_logits(
DEFAULT_PROMPTS,
model,
tp_size,
torch_dtype,
prefill_tolerance,
matryoshka_dim=128,
)
if __name__ == "__main__":
unittest.main()
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