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[Fix] Improve Lora tests and reduce CI runtime (#4925)

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This commit is contained in:
Baizhou Zhang
2025-03-30 19:40:14 -07:00
committed by GitHub
parent 4814ecaff9
commit 42873eac09
4 changed files with 257 additions and 539 deletions
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217
test/srt/models/lora/test_lora_backend.py
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@@ -17,217 +17,38 @@ import os
import unittest
from typing import List
import torch
from utils import BACKENDS, TORCH_DTYPES, LoRAAdaptor, LoRAModelCase
from utils import (
ALL_OTHER_LORA_MODELS,
BACKENDS,
CI_LORA_MODELS,
DEFAULT_PROMPTS,
TORCH_DTYPES,
LoRAModelCase,
run_batch_lora_test,
)
from sglang.test.runners import HFRunner, SRTRunner
from sglang.test.test_utils import CustomTestCase, 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=2,
),
]
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:
""",
]
from sglang.test.test_utils import CustomTestCase, is_in_ci
class TestLoRABackend(CustomTestCase):
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,
disable_custom_all_reduce=False,
) 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,
disable_custom_all_reduce=False,
) 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
DEFAULT_PROMPTS
if not model_case.skip_long_prompt
else [p for p in PROMPTS if len(p) < 1000]
else [p for p in DEFAULT_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,
)
run_batch_lora_test(
prompts,
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)