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Fix the lora adapter when lora path is none #4799

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Merged
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Mar 28, 2025
Merged

Fix the lora adapter when lora path is none #4799

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4 changes: 0 additions & 4 deletions python/sglang/srt/lora/lora_manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -133,10 +133,6 @@ def prepare_lora_batch(self, forward_batch: ForwardBatch):
assert len(cur_uids) <= self.max_loras_per_batch
self.memory_pool.prepare_lora_batch(cur_uids, self.loras)

# FIXME: Handle lora uid with None more safely
if cur_uids == set([None]):
return

# set up batch info shared by all lora moruldes
bs = forward_batch.batch_size
seg_lens = (
Expand Down
2 changes: 1 addition & 1 deletion python/sglang/srt/lora/mem_pool.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,7 +163,7 @@ def load_lora_weight_to_buffer(
if uid is None:
for i in range(self.num_layer):
for k in self.A_buffer.keys():
self.A_buffer[k][i][buffer_id] *= 0
self.A_buffer[k][i][buffer_id] = 0
return

assert lora_adapter is not None
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25 changes: 16 additions & 9 deletions test/srt/models/lora/test_lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,6 +95,11 @@ def inference(self, prompts, lora_set, tp_size, torch_dtype, max_new_tokens):
srt_outputs = srt_runner.forward(
prompts, max_new_tokens=max_new_tokens, lora_paths=batch_lora_paths
)
srt_outputs_lora_path_none = srt_runner.forward(
prompts,
max_new_tokens=max_new_tokens,
lora_paths=[None] * len(prompts),
)

with HFRunner(
base_path, torch_dtype=torch_dtype, model_type="generation"
Expand Down Expand Up @@ -168,18 +173,20 @@ def inference(self, prompts, lora_set, tp_size, torch_dtype, max_new_tokens):
print(f"{srt_outputs.output_strs=}")
print(f"{hf_no_lora_outputs.output_strs=}")
print(f"{srt_no_lora_outputs.output_strs=}")
print(f"{srt_outputs_lora_path_none.output_strs=}")
for i in range(len(prompts)):
assert srt_outputs.output_strs[i].strip(" ") == hf_outputs.output_strs[i], (
srt_outputs.output_strs[i].strip(" "),
hf_outputs.output_strs[i],
)
# assert (
# srt_no_lora_outputs.output_strs[i].strip(" ")
# == hf_no_lora_outputs.output_strs[i]
# ), (
# srt_no_lora_outputs.output_strs[i].strip(" "),
# hf_no_lora_outputs.output_strs[i],
# )
assert (
srt_no_lora_outputs.output_strs[i].strip(" ")
== hf_no_lora_outputs.output_strs[i]
), (
srt_no_lora_outputs.output_strs[i].strip(" "),
hf_no_lora_outputs.output_strs[i],
)
assert srt_outputs_lora_path_none == srt_no_lora_outputs

def serving(self, prompts, lora_set, tp_size, torch_dtype, max_new_tokens):
print("=================== testing serving =======================")
Expand Down Expand Up @@ -256,7 +263,7 @@ def base_inference(self, prompts, lora_set, tp_size, torch_dtype, max_new_tokens
srt_no_lora_logprobs = torch.Tensor(
srt_no_lora_outputs.top_input_logprobs[i]
)
srt_logprobs = torch.uensor(srt_outputs.top_input_logprobs[i])
srt_logprobs = torch.Tensor(srt_outputs.top_input_logprobs[i])
print("max_diff", torch.max(abs(srt_no_lora_logprobs - srt_logprobs)))

print(f"{srt_no_lora_outputs.output_strs=}")
Expand All @@ -279,7 +286,7 @@ def test_all(self):
tp_size = 1
max_new_tokens = 32
self.inference(PROMPTS, lora_set, tp_size, torch_dtype, max_new_tokens)
# self.serving(PROMPTS, lora_set, tp_size, torch_dtype, max_new_tokens)
self.serving(PROMPTS, lora_set, tp_size, torch_dtype, max_new_tokens)
# self.base_inference(
# PROMPTS, lora_set, tp_size, torch_dtype, max_new_tokens
# )
Expand Down
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