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[Fix] Fix bugs and refactor codes in lora for better scalability. (#3652)

Browse Source 
Co-authored-by: ShenAo1111 <1377693092@qq.com>
Co-authored-by: zhaochenyang20 <zhaochen20@outlook.com>
This commit is contained in:
aoshen524
2025-02-20 14:51:57 -05:00
committed by GitHub
parent ac05310098
commit e79f7420be
11 changed files with 459 additions and 200 deletions
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18
python/sglang/srt/lora/lora.py
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@@ -18,6 +18,7 @@
# LoRA layers class inheritance adapted from:
# https://github.com/vllm-project/vllm/blob/4abf6336ec65c270343eb895e7b18786e9274176/vllm/lora/layers.py
import logging
import re
from typing import Dict, List
@@ -30,6 +31,8 @@ from sglang.srt.lora.backend import BaseLoRABackend
from sglang.srt.lora.lora_config import LoRAConfig
from sglang.srt.model_loader.loader import DefaultModelLoader
logger = logging.getLogger(__name__)
class LoRALayer(nn.Module):
def __init__(self, config: LoRAConfig, base_hf_config: AutoConfig):
@@ -173,6 +176,18 @@ class LoRAAdapter(nn.Module):
if "gate_proj" in weight_name:
up_name = weight_name.replace("gate_proj", "up_proj")
gate_up_name = weight_name.replace("gate_proj", "gate_up_proj")
if up_name not in weights:
logger.warning(
f"Gate projection {weight_name} does not have a corresponding up projection {up_name}. "
f"Initializing up projection to zero."
)
weights[up_name] = torch.zeros_like(weights[weight_name])
# FIXME: Add gate-only support for flashinfer in future implementations
assert self.lora_backend.name == "triton", (
f"LoRA weight initialization currently only supported for 'triton' backend. "
f"Received backend: {self.lora_backend.name}. Please verify your backend configuration "
f"or consider implementing custom initialization logic for other backends."
)
if "lora_A" in weight_name:
weights[gate_up_name] = torch.cat(
(weights[weight_name], weights[up_name]), 0
@@ -182,4 +197,5 @@ class LoRAAdapter(nn.Module):
[weights[weight_name], weights[up_name]], dim=0
)
weights.pop(weight_name)
weights.pop(up_name)
if up_name in weights:
weights.pop(up_name)

5
python/sglang/srt/lora/lora_config.py
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@@ -26,6 +26,11 @@ class LoRAConfig:
self.path = path
self.hf_config = self.get_lora_config()
self.target_modules = self.hf_config["target_modules"]
# TODO: Support more modules
if any(module in self.target_modules for module in ["embed_tokens", "lm_head"]):
raise ValueError("Not supported yet")
self.r = self.hf_config["r"]
self.lora_alpha = self.hf_config["lora_alpha"]

4
python/sglang/srt/lora/lora_manager.py
View File

@@ -76,9 +76,7 @@ class LoRAManager:
self.hf_target_names: Set[str] = set()
for name, path in self.lora_paths.items():
self.configs[name] = LoRAConfig(path)
self.hf_target_names = set(self.hf_target_names) | set(
self.configs[name].target_modules
)
self.hf_target_names.update(self.configs[name].target_modules)
# Target lora weight names for lora_a and lora_b modules repectively.
# e.g., {("qkv_proj", "q_proj"), ("qkv_proj", "kv_proj")}

25
python/sglang/test/runners.py
View File

@@ -189,9 +189,17 @@ class HFRunner:
return_dict_in_generate=True,
output_scores=(not self.output_str_only),
)
output_strs.append(
self.tokenizer.decode(outputs[0][0][len(input_ids[0]) :])
text = self.tokenizer.decode(
outputs[0][0][len(input_ids[0]) :], skip_special_tokens=True
)
# Check if the text is empty or only whitespace.
if not text.strip():
raise ValueError(
"Received an empty text response. Please verify your input or model configuration."
)
output_strs.append(text)
if not self.output_str_only:
# outputs.scores: (num_token, 1, vocab_size)
top_output_logprobs.append(
@@ -275,6 +283,7 @@ class SRTRunner:
lora_backend: str = "triton",
disable_cuda_graph: bool = False,
disable_radix_cache: bool = False,
mem_fraction_static: float = 0.65,
):
self.model_type = model_type
self.is_generation = model_type == "generation"
@@ -283,7 +292,7 @@ class SRTRunner:
tp_size=tp_size,
dtype=get_dtype_str(torch_dtype),
port=port,
mem_fraction_static=0.65,
mem_fraction_static=mem_fraction_static,
trust_remote_code=False,
is_embedding=not self.is_generation,
lora_paths=lora_paths,
@@ -315,7 +324,15 @@ class SRTRunner:
logprob_start_len=0,
top_logprobs_num=NUM_TOP_LOGPROBS,
)
output_strs.append(response["text"])
text = response["text"]
# Check if the text is empty or only whitespace.
if not text.strip():
raise ValueError(
"Received an empty text response. Please verify your input or model configuration."
)
output_strs.append(text)
top_input_logprobs.append(
[
[tup[0] for tup in x[:NUM_TOP_LOGPROBS]]

0
test/srt/models/test_lora.py → test/srt/models/lora/test_lora.py
View File

253
test/srt/models/lora/test_lora_backend.py Normal file
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@@ -0,0 +1,253 @@
# 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 os
import unittest
from typing import List
import torch
from utils import *
from sglang.test.runners import HFRunner, SRTRunner
from sglang.test.test_utils import calculate_rouge_l, is_in_ci
CI_LORA_MODELS = [
LoRAModelCase(
base="meta-llama/Llama-3.1-8B-Instruct",
adaptors=[
LoRAAdaptor(
name="algoprog/fact-generation-llama-3.1-8b-instruct-lora",
),
],
max_loras_per_batch=1,
),
LoRAModelCase(
base="meta-llama/Llama-3.1-8B-Instruct",
adaptors=[
LoRAAdaptor(
name="Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
prefill_tolerance=1e-1,
),
],
max_loras_per_batch=1,
),
]
ALL_OTHER_LORA_MODELS = [
LoRAModelCase(
base="meta-llama/Llama-2-7b-hf",
adaptors=[LoRAAdaptor(name="winddude/wizardLM-LlaMA-LoRA-7B")],
max_loras_per_batch=1,
),
]
PROMPTS = [
"AI is a field of computer science focused on",
"""
### Instruction:
Tell me about llamas and alpacas
### Response:
Llamas are large, long-necked animals with a woolly coat. They have two toes on each foot instead of three like other camelids (camels, dromedaries). Llamas live in the Andean mountains of South America where they graze on grasses and shrubs. Alpaca is another name for domesticated llama. The word "alpaca" comes from an Incan language meaning "golden fleece." Alpacas look very similar to llamas but are smaller than their wild relatives. Both species were used by ancient people as pack animals and for meat. Today both llamas and alpacas are raised primarily for their fiber which can be spun into yarn or knitted into clothing.
### Question 2:
What do you know about llamas?
### Answer:
""",
]
class TestLoRABackend(unittest.TestCase):
def run_backend(
self,
prompt: str,
model_case: LoRAModelCase,
torch_dtype: torch.dtype,
max_new_tokens: int,
backend: str,
):
"""
Run backend tests for a single prompt and model case.
"""
base_path = model_case.base
adaptor = model_case.adaptors[0]
print(
f"\n========== Testing backend '{backend}' for base '{base_path}' --- "
f"Prompt '{prompt[:50]}...' using adaptor '{adaptor.name}' ---"
)
with SRTRunner(
base_path,
torch_dtype=torch_dtype,
model_type="generation",
tp_size=model_case.tp_size,
lora_paths=[adaptor.name for adaptor in model_case.adaptors],
max_loras_per_batch=model_case.max_loras_per_batch,
lora_backend=backend,
disable_cuda_graph=True,
disable_radix_cache=True,
mem_fraction_static=0.88,
) as srt_runner:
srt_outputs = srt_runner.forward(
[prompt], max_new_tokens=max_new_tokens, lora_paths=[adaptor.name]
)
with HFRunner(
base_path, torch_dtype=torch_dtype, model_type="generation"
) as hf_runner:
hf_outputs = hf_runner.forward(
[prompt], max_new_tokens=max_new_tokens, lora_paths=[adaptor.name]
)
with SRTRunner(
base_path,
torch_dtype=torch_dtype,
model_type="generation",
tp_size=model_case.tp_size,
mem_fraction_static=0.88,
) as srt_runner:
srt_no_lora_outputs = srt_runner.forward(
[prompt], max_new_tokens=max_new_tokens
)
with HFRunner(
base_path,
torch_dtype=torch_dtype,
model_type="generation",
) as hf_runner:
hf_no_lora_outputs = hf_runner.forward(
[prompt], max_new_tokens=max_new_tokens
)
# Use individual adapter tolerances if set, otherwise use model defaults
prefill_tol = (
adaptor.prefill_tolerance
if adaptor.prefill_tolerance is not None
else model_case.prefill_tolerance
)
decode_tol = (
adaptor.decode_tolerance
if adaptor.decode_tolerance is not None
else model_case.decode_tolerance
)
rouge_tol = (
adaptor.rouge_l_tolerance
if adaptor.rouge_l_tolerance is not None
else model_case.rouge_l_tolerance
)
# Compare prefill stage logprobs (HF vs SRTRunner with LoRA)
hf_prefill = torch.tensor(hf_outputs.top_input_logprobs[0])
srt_prefill = torch.tensor(srt_outputs.top_input_logprobs[0])
max_prefill_diff = torch.max(torch.abs(hf_prefill - srt_prefill))
print("Max prefill diff (HF vs SRT):", max_prefill_diff)
# Compare decode stage logprobs
hf_decode = torch.tensor(hf_outputs.top_output_logprobs[0])
srt_decode = torch.tensor(srt_outputs.top_output_logprobs[0])
max_decode_diff = torch.max(torch.abs(hf_decode - srt_decode))
print("Max decode diff (HF vs SRT):", max_decode_diff)
srt_output_str = srt_outputs.output_strs[0].strip()
hf_output_str = hf_outputs.output_strs[0].strip()
rouge_score = calculate_rouge_l([srt_output_str], [hf_output_str])[0]
print("ROUGE-L score:", rouge_score)
print("SRT output:", srt_output_str)
print("HF output:", hf_output_str)
# Additional: compare prefill outputs between base model (no LoRA) and LoRA model for reference
hf_no_lora_prefill = torch.tensor(hf_no_lora_outputs.top_input_logprobs[0])
srt_no_lora_prefill = torch.tensor(srt_no_lora_outputs.top_input_logprobs[0])
print(
"Max diff (SRT base vs SRT LoRA prefill):",
torch.max(torch.abs(srt_no_lora_prefill - srt_prefill)),
)
print(
"Max diff (HF base vs HF LoRA prefill):",
torch.max(torch.abs(hf_no_lora_prefill - hf_prefill)),
)
if hf_prefill.shape[0] <= 100:
assert torch.all(torch.abs(hf_prefill - srt_prefill) < prefill_tol), (
f"Prefill logprobs mismatch for base '{base_path}', adaptor '{adaptor.name}', "
f"backend '{backend}', prompt: '{prompt[:50]}...'"
)
if hf_decode.shape[0] <= 100:
assert torch.all(torch.abs(hf_decode - srt_decode) < decode_tol), (
f"Decode logprobs mismatch for base '{base_path}', adaptor '{adaptor.name}', "
f"backend '{backend}', prompt: '{prompt[:50]}...'"
)
if rouge_score < rouge_tol:
raise AssertionError(
f"ROUGE-L score {rouge_score} below tolerance {rouge_tol} "
f"for base '{base_path}', adaptor '{adaptor.name}', backend '{backend}', prompt: '{prompt[:50]}...'"
)
def run_backend_batch(
self,
prompts: List[str],
model_case: LoRAModelCase,
torch_dtype: torch.dtype,
max_new_tokens: int,
backend: str,
):
# TODO: Implement batch processing version of run_backend
raise NotImplementedError(
"Batch processing version of run_backend is not implemented yet."
)
def _run_backend_on_model_cases(self, model_cases: List[LoRAModelCase]):
for model_case in model_cases:
# If skip_long_prompt is True, filter out prompts longer than 1000 characters
prompts = (
PROMPTS
if not model_case.skip_long_prompt
else [p for p in PROMPTS if len(p) < 1000]
)
for torch_dtype in TORCH_DTYPES:
for backend in BACKENDS:
for prompt in prompts:
self.run_backend(
prompt,
model_case,
torch_dtype,
max_new_tokens=32,
backend=backend,
)
def test_ci_lora_models(self):
self._run_backend_on_model_cases(CI_LORA_MODELS)
def test_all_lora_models(self):
if is_in_ci():
return
# Retain ONLY_RUN check here
filtered_models = []
for model_case in ALL_OTHER_LORA_MODELS:
if "ONLY_RUN" in os.environ and os.environ["ONLY_RUN"] != model_case.base:
continue
filtered_models.append(model_case)
self._run_backend_on_model_cases(filtered_models)
if __name__ == "__main__":
try:
mp.set_start_method("spawn")
except RuntimeError:
pass
unittest.main(warnings="ignore")

109
test/srt/models/lora/test_multi_lora_backend.py Normal file
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@@ -0,0 +1,109 @@
# 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 os
import unittest
from typing import List
import torch
from utils import *
from sglang.test.runners import HFRunner, SRTRunner
from sglang.test.test_utils import calculate_rouge_l, is_in_ci
MULTI_LORA_MODELS = [
LoRAModelCase(
base="meta-llama/Llama-3.1-8B-Instruct",
adaptors=[
LoRAAdaptor(
name="algoprog/fact-generation-llama-3.1-8b-instruct-lora",
),
LoRAAdaptor(
name="some-org/another-lora-adaptor",
),
],
max_loras_per_batch=2,
),
]
# All prompts are used at once in a batch.
PROMPTS = [
"AI is a field of computer science focused on",
"""
### Instruction:
Tell me about llamas and alpacas
### Response:
Llamas are large, long-necked animals with a woolly coat. They have two toes on each foot instead of three like other camelids.
### Question:
What do you know about llamas?
### Answer:
""",
]
class TestMultiLoRABackend(unittest.TestCase):
def run_backend_batch(
self,
prompts: List[str],
model_case: LoRAModelCase,
torch_dtype: torch.dtype,
max_new_tokens: int,
backend: str,
):
"""
The multi-LoRA backend test functionality is not supported yet.
This function uses all prompts at once and prints a message indicating that support is pending.
"""
adaptor_names = [adaptor.name for adaptor in model_case.adaptors]
print(
f"\n========== Testing multi-LoRA backend '{backend}' for base '{model_case.base}' --- "
f"Using prompts {[p[:50] for p in prompts]} with adaptors: {adaptor_names} ---"
)
print(
"run_backend_batch: Multi-LoRA backend test functionality is pending support."
)
def _run_backend_on_model_cases(self, model_cases: List[LoRAModelCase]):
for model_case in model_cases:
# If skip_long_prompt is True, filter out prompts longer than 1000 characters.
batch_prompts = (
PROMPTS
if not model_case.skip_long_prompt
else [p for p in PROMPTS if len(p) < 1000]
)
for torch_dtype in TORCH_DTYPES:
for backend in BACKENDS:
self.run_backend_batch(
batch_prompts,
model_case,
torch_dtype,
max_new_tokens=32,
backend=backend,
)
def test_multi_lora_models(self):
# Optionally skip tests in CI environments.
if is_in_ci():
return
self._run_backend_on_model_cases(MULTI_LORA_MODELS)
if __name__ == "__main__":
try:
mp.set_start_method("spawn")
except RuntimeError:
pass
unittest.main(warnings="ignore")

49
test/srt/models/lora/utils.py Normal file
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@@ -0,0 +1,49 @@
# 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 dataclasses
from typing import List
import torch
@dataclasses.dataclass
class LoRAAdaptor:
name: str
prefill_tolerance: float = None
decode_tolerance: float = None
rouge_l_tolerance: float = None
@dataclasses.dataclass
class LoRAModelCase:
base: str
adaptors: List[LoRAAdaptor]
tp_size: int = 1
prefill_tolerance: float = 5e-2
decode_tolerance: float = 5e-2
rouge_l_tolerance: float = 1.0
max_loras_per_batch: int = 1
skip_long_prompt: bool = False
def __post_init__(self):
if len(self.adaptors) > self.max_loras_per_batch:
raise ValueError(
f"For base '{self.base}', number of adaptors ({len(self.adaptors)}) "
f"must be <= max_loras_per_batch ({self.max_loras_per_batch})"
)
TORCH_DTYPES = [torch.float16]
BACKENDS = ["triton"]

189
test/srt/models/test_lora_backend.py
View File

@@ -1,189 +0,0 @@
# 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 unittest
import torch
from sglang.test.runners import HFRunner, SRTRunner
from sglang.test.test_utils import calculate_rouge_l
LORA_SETS = [
{"base": "meta-llama/Llama-2-7b-hf", "loras": ["winddude/wizardLM-LlaMA-LoRA-7B"]},
{
"base": "meta-llama/Llama-3.1-8B-Instruct",
"loras": ["reissbaker/llama-3.1-8b-abliterated-lora"],
"decode_tolerance": 8e-2,
},
]
TORCH_DTYPES = [torch.float16]
PROMPTS = [
"AI is a field of computer science focused on",
"""
### Instruction:
Tell me about llamas and alpacas
### Response:
Llamas are large, long-necked animals with a woolly coat. They have two toes on each foot instead of three like other camelids (camels, dromedaries). Llamas live in the Andean mountains of South America where they graze on grasses and shrubs. Alpaca is another name for domesticated llama. The word "alpaca" comes from an Incan language meaning "golden fleece." Alpacas look very similar to llamas but are smaller than their wild relatives. Both species were used by ancient people as pack animals and for meat. Today both llamas and alpacas are raised primarily for their fiber which can be spun into yarn or knitted into clothing.
### Question 2:
What do you know about llamas?
### Answer:
""",
]
BACKENDS = ["triton", "flashinfer"]
prefill_tolerance: float = 5e-2
decode_tolerance: float = 5e-2
rouge_l_tolerance: float = 1
class TestLoRABackend(unittest.TestCase):
def run_backend(
self, prompts, lora_set, tp_size, torch_dtype, max_new_tokens, backend
):
print(f"=================== testing {backend} backend =======================")
base_path = lora_set["base"]
all_lora_paths = lora_set["loras"]
batch_lora_paths = []
i = 0
for _ in range(len(prompts)):
batch_lora_paths.append(all_lora_paths[i])
i = (i + 1) % len(all_lora_paths)
print(f"batch lora paths={batch_lora_paths}")
with SRTRunner(
base_path,
torch_dtype=torch_dtype,
model_type="generation",
tp_size=tp_size,
lora_paths=all_lora_paths,
max_loras_per_batch=3,
lora_backend=backend,
disable_cuda_graph=True,
disable_radix_cache=True,
) as srt_runner:
srt_outputs = srt_runner.forward(
prompts, max_new_tokens=max_new_tokens, lora_paths=batch_lora_paths
)
with HFRunner(
base_path, torch_dtype=torch_dtype, model_type="generation"
) as hf_runner:
hf_outputs = hf_runner.forward(
prompts, max_new_tokens=max_new_tokens, lora_paths=batch_lora_paths
)
with SRTRunner(
base_path,
tp_size=tp_size,
torch_dtype=torch_dtype,
model_type="generation",
) as srt_runner:
srt_no_lora_outputs = srt_runner.forward(
prompts, max_new_tokens=max_new_tokens
)
with HFRunner(
base_path,
torch_dtype=torch_dtype,
model_type="generation",
) as hf_runner:
hf_no_lora_outputs = hf_runner.forward(
prompts, max_new_tokens=max_new_tokens
)
for i in range(len(prompts)):
print(f"Prompt {i} with lora path {batch_lora_paths[i]}:")
# compare input logprobs
hf_logprobs = torch.Tensor(hf_outputs.top_input_logprobs[i])
srt_logprobs = torch.Tensor(srt_outputs.top_input_logprobs[i])
hf_no_lora_logprobs = torch.Tensor(hf_no_lora_outputs.top_input_logprobs[i])
srt_no_lora_logprobs = torch.Tensor(
srt_no_lora_outputs.top_input_logprobs[i]
)
print(
"max input diff between hf_lora and srt_lora",
torch.max(abs(hf_logprobs - srt_logprobs)),
)
print(
"max input diff between srt_base and srt_lora",
torch.max(abs(srt_no_lora_logprobs - srt_logprobs)),
)
print(
"max input diff between srt_base and hf_base",
torch.max(abs(srt_no_lora_logprobs - hf_no_lora_logprobs)),
)
print(
"max input diff between hf_lora and hf_base",
torch.max(abs(hf_logprobs - hf_no_lora_logprobs)),
)
if hf_logprobs.shape[0] <= 100:
tol = lora_set.get("prefill_tolerance", prefill_tolerance)
assert torch.all(abs(hf_logprobs - srt_logprobs) < tol), (
f"prefill logprobs are not all close with model_path={base_path},"
f"lora_path={batch_lora_paths[i]}, backend={backend}, prompt={prompts[i]}"
f"prefill_tolerance={prefill_tolerance}."
f"{hf_logprobs=}, {srt_logprobs=}"
)
# compare output logprobs
hf_logprobs = torch.Tensor(hf_outputs.top_output_logprobs[i])
srt_logprobs = torch.Tensor(srt_outputs.top_output_logprobs[i])
print(
"max output diff between hf_lora and srt_lora",
torch.max(abs(hf_logprobs - srt_logprobs)),
"\n",
)
if hf_logprobs.shape[0] <= 100:
tol = lora_set.get("decode_tolerance", decode_tolerance)
assert torch.all(abs(hf_logprobs - srt_logprobs) < tol), (
f"decode logprobs are not all close with model_path={base_path},"
f"lora_path={batch_lora_paths[i]}, backend={backend}, prompt={prompts[i]}"
f"decode_tolerance={decode_tolerance}."
f"{hf_logprobs=}, {srt_logprobs=}"
)
# compare output strings
srt_output_str = srt_outputs.output_strs[i].strip(" ")
hf_output_str = hf_outputs.output_strs[i].strip(" ")
print(f"srt_output_str={srt_output_str}")
print(f"hf_output_str={hf_output_str}")
rouge_l_scores = calculate_rouge_l([srt_output_str], [hf_output_str])
print(f"{rouge_l_scores=}")
assert (
rouge_l_scores[0] >= rouge_l_tolerance
), f"ROUGE-L scores of prompt {i} outputs are greater than rouge_l_tolerance={rouge_l_tolerance}"
def test_all(self):
for lora_set in LORA_SETS:
print(f"Testing lora set {lora_set}: ")
for torch_dtype in TORCH_DTYPES:
tp_size = 1
max_new_tokens = 32
for backend in BACKENDS:
self.run_backend(
PROMPTS, lora_set, tp_size, torch_dtype, max_new_tokens, backend
)
if __name__ == "__main__":
try:
mp.set_start_method("spawn")
except RuntimeError:
pass
unittest.main(warnings="ignore")

2
test/srt/models/test_qwen_models.py
View File

@@ -38,7 +38,7 @@ class TestQwen2(unittest.TestCase):
)
metrics = run_eval(args)
print(f"{metrics=}")
self.assertGreater(metrics["accuracy"], 0.81)
self.assertGreater(metrics["accuracy"], 0.79)
class TestQwen2FP8(unittest.TestCase):

5
test/srt/run_suite.py
View File

@@ -5,10 +5,11 @@ from sglang.test.test_utils import run_unittest_files
suites = {
"per-commit": [
"models/lora/test_lora.py",
"models/lora/test_lora_backend.py",
"models/lora/test_multi_lora_backend.py",
"models/test_embedding_models.py",
"models/test_generation_models.py",
"models/test_lora.py",
"models/test_lora_backend.py",
"models/test_qwen_models.py",
"models/test_reward_models.py",
"sampling/penaltylib",