Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[CI] Return output logprobs in unit test #1361

Merged
merged 2 commits into from
Sep 9, 2024
+73 −21
Merged

[CI] Return output logprobs in unit test #1361

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
563f6e1
return output logprobs in unit test
Ying1123 Sep 9, 2024
65aa509
Merge branch 'main' into ying-output-logits
Ying1123 Sep 9, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
57 changes: 42 additions & 15 deletions python/sglang/test/runners.py
View file
Original file line number Diff line number Diff line change
@@ -50,6 +50,12 @@ def get_dtype_str(torch_dtype):
raise NotImplementedError()


def get_top_logprobs(logits, k):
logprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
logprobs, top_indices = torch.topk(logprobs, k=k, dim=-1)
return logprobs


@dataclass
class ModelOutput:
output_strs: List[str] = None
@@ -108,7 +114,8 @@ def start_model_process(self, in_queue, out_queue, model_path, torch_dtype):
if prompts is not None:
if self.is_generation:
output_strs = []
prefill_logprobs = []
top_input_logprobs = []
top_output_logprobs = []
for p in prompts:
if isinstance(p, str):
input_ids = self.tokenizer.encode(
@@ -117,32 +124,43 @@ def start_model_process(self, in_queue, out_queue, model_path, torch_dtype):
else:
input_ids = torch.tensor([p], device="cuda")

output_ids = self.model.generate(
input_ids, do_sample=False, max_new_tokens=max_new_tokens
outputs = self.model.generate(
input_ids,
do_sample=False,
temperature=None,
top_p=None,
max_new_tokens=max_new_tokens,
return_dict_in_generate=True,
output_scores=True,
)
output_strs.append(
self.tokenizer.decode(output_ids[0][len(input_ids[0]) :])
self.tokenizer.decode(outputs[0][0][len(input_ids[0]) :])
)
# outputs.scores: (num_token, 1, vocab_size)
top_output_logprobs.append(
[
get_top_logprobs(logits[0], NUM_TOP_LOGPROBS).tolist()
for logits in outputs.scores
]
)
del outputs

logits = self.model.forward(input_ids).logits[0]
logprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
logprobs, top_indices = torch.topk(
logprobs, k=NUM_TOP_LOGPROBS, dim=-1
input_logits = self.model.forward(input_ids).logits[0]
top_input_logprobs.append(
get_top_logprobs(input_logits, NUM_TOP_LOGPROBS).tolist()
)
# print("index", top_indices)
prefill_logprobs.append(logprobs.tolist())
del logits
del logprobs
del input_logits

out_queue.put(
ModelOutput(
output_strs=output_strs, top_input_logprobs=prefill_logprobs
output_strs=output_strs,
top_input_logprobs=top_input_logprobs,
top_output_logprobs=top_output_logprobs,
)
)

else:
logits = self.model.encode(prompts).tolist()

out_queue.put(ModelOutput(embed_logits=logits))

def forward(
@@ -194,6 +212,7 @@ def forward(
# the return value contains logprobs from prefill
output_strs = []
top_input_logprobs = []
top_output_logprobs = []
sampling_params = {"max_new_tokens": max_new_tokens, "temperature": 0}
for prompt in prompts:
response = self.runtime.generate(
@@ -219,9 +238,17 @@ def forward(
]
]
)
top_output_logprobs.append(
[
[tup[0] for tup in x[:NUM_TOP_LOGPROBS]]
for x in response["meta_info"]["output_top_logprobs"]
]
)

return ModelOutput(
output_strs=output_strs, top_input_logprobs=top_input_logprobs
output_strs=output_strs,
top_input_logprobs=top_input_logprobs,
top_output_logprobs=top_output_logprobs,
)
else:
response = self.runtime.encode(prompts)
37 changes: 31 additions & 6 deletions test/srt/models/test_generation_models.py
View file
Original file line number Diff line number Diff line change
@@ -21,9 +21,9 @@
from sglang.test.runners import DEFAULT_PROMPTS, HFRunner, SRTRunner

MODELS = [
("meta-llama/Meta-Llama-3.1-8B-Instruct", 1, 1.1, 3e-2, 1),
("google/gemma-2-2b", 1, 3, 3e-2, 1),
("Alibaba-NLP/gte-Qwen2-1.5B-instruct", 1, None, 6e-2, 1),
("meta-llama/Meta-Llama-3.1-8B-Instruct", 1, 1.1, 3e-2, 4e-2, 1),
("google/gemma-2-2b", 1, 3, 3e-2, 5e-2, 1),
("Alibaba-NLP/gte-Qwen2-1.5B-instruct", 1, None, 6e-2, 4e-2, 1),
]
TORCH_DTYPES = [torch.float16]

@@ -70,6 +70,7 @@ def assert_close_prefill_logits_and_output_strs(
torch_dtype,
max_new_tokens,
prefill_tolerance,
output_tolerance,
rouge_threshold,
long_context_tolerance,
) -> None:
@@ -89,15 +90,37 @@ def assert_close_prefill_logits_and_output_strs(
srt_outputs = srt_runner.forward(prompts, max_new_tokens=max_new_tokens)

for i in range(len(prompts)):
# input logprobs comparison
hf_logprobs = torch.Tensor(hf_outputs.top_input_logprobs[i])
srt_logprobs = torch.Tensor(srt_outputs.top_input_logprobs[i])

print("max_diff", torch.max(abs(hf_logprobs - srt_logprobs)))
if hf_logprobs.shape[0] <= 100:
input_len = hf_logprobs.shape[0]
print(
"prefill logprobs max_diff", torch.max(abs(hf_logprobs - srt_logprobs))
)
if input_len <= 100:
assert torch.all(
abs(hf_logprobs - srt_logprobs) < prefill_tolerance
), f"prefill logprobs are not all close with model_path={model_path} prompts={prompts} prefill_tolerance={prefill_tolerance}"

# output logprobs comparison
hf_logprobs = torch.Tensor(hf_outputs.top_output_logprobs[i])
srt_logprobs = torch.Tensor(srt_outputs.top_output_logprobs[i])
# print(
# "output logprobs diff",
# [
# float(torch.max(abs(hf_logprobs[j] - srt_logprobs[j])))
# for j in range(max_new_tokens)
# ],
# )
print(
"output logprobs max_diff", torch.max(abs(hf_logprobs - srt_logprobs))
)
if input_len <= 100:
assert torch.all(
abs(hf_logprobs - srt_logprobs) < output_tolerance
), f"output logprobs are not all close with model_path={model_path} prompts={prompts}... output_tolerance={output_tolerance}"

# output strings comparison
print(f"hf_outputs.output_strs={hf_outputs.output_strs}")
print(f"srt_outputs.output_strs={srt_outputs.output_strs}")
rouge_l_scores = calculate_rouge_l(
@@ -114,6 +137,7 @@ def test_prefill_logits_and_output_strs(self):
tp_size,
long_context_tolerance,
prefill_tolerance,
output_tolerance,
rouge_threshold,
) in MODELS:
for torch_dtype in TORCH_DTYPES:
@@ -125,6 +149,7 @@ def test_prefill_logits_and_output_strs(self):
torch_dtype,
max_new_tokens,
prefill_tolerance=prefill_tolerance,
output_tolerance=output_tolerance,
rouge_threshold=rouge_threshold,
long_context_tolerance=long_context_tolerance,
)