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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | import multiprocessing as mp
import random
import time
import unittest
import torch
from transformers import AutoConfig, AutoTokenizer
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.runners import DEFAULT_PROMPTS, HFRunner, SRTRunner
from sglang.test.test_utils import CustomTestCase, get_similarities, is_in_ci
# Encoder embedding model tests (CUDA only)
# 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.
# ==============================================================================
# python -m unittest test_encoder_embedding_models.TestEncoderEmbeddingModels.test_prefill_logits
register_cuda_ci(est_time=270, suite="stage-b-test-small-1-gpu")
MODELS = [("BAAI/bge-small-en", 1, 1e-5), ("BAAI/bge-m3", 1, 1e-5)]
ATTENTION_BACKEND = ["torch_native", "triton", "flashinfer"]
BATCH_SIZE = [1, 2]
TORCH_DTYPES = [torch.float32, torch.float16]
sgl_to_st_ratio = []
class TestEncoderEmbeddingModels(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", 512) - 20
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,
attention_backend,
batch_size,
) -> None:
truncated_prompts = self._truncate_prompts(prompts, model_path)
truncated_prompts = truncated_prompts * batch_size
with HFRunner(
model_path,
torch_dtype=torch_dtype,
model_type="embedding",
) as hf_runner:
# warm up
hf_outputs = hf_runner.forward(truncated_prompts)
st_start_time = time.perf_counter()
hf_outputs = hf_runner.forward(truncated_prompts)
st_end_time = time.perf_counter()
with SRTRunner(
model_path,
tp_size=tp_size,
torch_dtype=torch_dtype,
model_type="embedding",
attention_backend=attention_backend,
chunked_prefill_size=-1,
disable_radix_cache=True,
) as srt_runner:
# warm up
srt_outputs = srt_runner.forward(truncated_prompts)
sgl_start_time = time.perf_counter()
srt_outputs = srt_runner.forward(truncated_prompts)
sgl_end_time = time.perf_counter()
transformer_time = st_end_time - st_start_time
sgl_time = sgl_end_time - sgl_start_time
sgl_to_st_ratio.append(sgl_time / transformer_time)
for i in range(len(truncated_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))
# If something is wrong, uncomment this to observe similarity.
# print("similarity diff", abs(similarity - 1))
if len(truncated_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 attention_backend in ATTENTION_BACKEND:
for batch_size in BATCH_SIZE:
for torch_dtype in TORCH_DTYPES:
# NOTE: FlashInfer currently has limitations with head_dim = 32 or
# other dimensions.
# The FlashInfer head_dim limitation itself is tracked here:
# https://github.com/flashinfer-ai/flashinfer/issues/1048
#
# Flashinfer does not support torch.float32 for dtype_q, so skip it
if attention_backend == "flashinfer":
if (
model == "BAAI/bge-small-en"
or torch_dtype == torch.float32
):
continue
self.assert_close_prefill_logits(
DEFAULT_PROMPTS,
model,
tp_size,
torch_dtype,
prefill_tolerance,
attention_backend,
batch_size,
)
for i in range(len(BATCH_SIZE)):
print(
"bacth size: ",
BATCH_SIZE[i] * 5,
"sgl_time/st_time",
round(sgl_to_st_ratio[i], 3),
)
if __name__ == "__main__":
unittest.main()
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