feat: add original logprobs to response (#8375)
Co-authored-by: Chayenne <zhaochen20@outlook.com> Co-authored-by: luhongyu.4869 <luhongyu.4869@bytedance.com>
This commit is contained in:
narutolhy
@@ -87,6 +87,7 @@ suites = {
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TestFile("test_mla_fp8.py", 93),
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TestFile("test_no_chunked_prefill.py", 108),
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TestFile("test_no_overlap_scheduler.py", 234),
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TestFile("test_original_logprobs.py", 200),
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TestFile("test_penalty.py", 41),
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TestFile("test_page_size.py", 60),
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TestFile("test_pytorch_sampling_backend.py", 66),
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196
test/srt/test_original_logprobs.py
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test/srt/test_original_logprobs.py
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"""Test original log probability alignment between SGLang and Hugging Face.
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This test suite verifies the correctness of the `origin_logprobs` output (temperature=1)
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and the `logprobs` output (temperature=0.5) in SGLang by comparing it against
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raw logit-based probabilities computed directly from a reference Hugging Face model.
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The test covers the following scenarios:
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- Next-token prediction: Verifies that the log probability of the next token from
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SGLang matches the Hugging Face model.
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- Top-k logprobs: Ensures that the top-k original logprobs returned by SGLang are
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consistent with Hugging Face outputs.
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- Specified token IDs: Confirms that the original logprobs for specific token IDs
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match the values computed from Hugging Face logits.
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"""
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import os
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import random
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import unittest
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import numpy as np
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import torch
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import torch.nn.functional as F
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import sglang as sgl
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from sglang.test.test_utils import DEFAULT_SMALL_MODEL_NAME_FOR_TEST
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# ------------------------- Configurable via env ------------------------- #
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MODEL_ID = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
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PROMPTS = [
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"Hello, my name is",
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"The future of AI is",
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"The president of the United States is",
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"The capital of France is ",
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]
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TOP_LOGPROBS_NUM = 50
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NUM_RANDOM_TOKEN_IDS = 10
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RTOL = 0.20
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ATOL = 0.00
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# ------------------------------------------------
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torch.manual_seed(1234)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(1234)
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torch.backends.cuda.matmul.allow_tf32 = False
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torch.backends.cudnn.allow_tf32 = False
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class TestOriginalLogprob(unittest.TestCase):
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def setUp(self):
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# ----- HF side (float32 weights) -----
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self.tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, padding_side="right")
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self.hf_model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID, torch_dtype=torch.float32, device_map="auto"
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)
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# Shared sampling parameters
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self.sampling_params = {
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"temperature": 0.5, # SGLang uses 0.5, but original logprobs are used 1.0
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"top_p": 1.0,
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"top_k": 10,
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"max_new_tokens": 1,
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}
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# ---------------------------------------------------------------------
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# Helper: compare one SGLang block (token_logprobs / top_logprobs / ids_logprobs)
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# against a reference HF log‑prob vector.
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# ---------------------------------------------------------------------
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def assert_logprobs_block_equal(
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self,
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hf_log_probs: torch.Tensor, # [V]
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token_log_probs: list,
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top_log_probs: list,
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ids_log_probs: list,
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random_token_ids: list,
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tag: str = "",
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):
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vals, idxs, _ = zip(*token_log_probs)
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sgl_vals = torch.tensor(vals, device=self.hf_model.device, dtype=torch.float32)
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sgl_idxs = torch.tensor(idxs, device=self.hf_model.device, dtype=torch.long)
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hf_vals = hf_log_probs[sgl_idxs]
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self.assertTrue(
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torch.allclose(hf_vals, sgl_vals, rtol=RTOL, atol=ATOL),
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msg=f"[{tag}] token‑level mismatch at indices {sgl_idxs.tolist()}",
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)
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hf_topk, _ = torch.topk(hf_log_probs, k=TOP_LOGPROBS_NUM, dim=-1)
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sgl_topk = torch.tensor(
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[float(t[0]) for t in top_log_probs[0] if t and t[0] is not None][
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:TOP_LOGPROBS_NUM
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],
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dtype=torch.float32,
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device=self.hf_model.device,
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)
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k = min(hf_topk.numel(), sgl_topk.numel())
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self.assertTrue(
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torch.allclose(hf_topk[:k], sgl_topk[:k], rtol=RTOL, atol=ATOL),
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msg=f"[{tag}] top‑k mismatch",
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)
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indices = torch.tensor(
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random_token_ids, dtype=torch.long, device=hf_log_probs.device
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)
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hf_token_ids = hf_log_probs[indices]
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sgl_token_ids = torch.tensor(
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[v for v, _, _ in ids_log_probs[0]],
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device=self.hf_model.device,
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dtype=torch.float32,
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)
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self.assertTrue(
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torch.allclose(hf_token_ids, sgl_token_ids, rtol=RTOL, atol=ATOL),
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msg=f"[{tag}] token‑IDs mismatch",
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)
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# Optional: print max abs diff for quick diagnostics
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max_diff = torch.max(torch.abs(hf_vals - sgl_vals)).item()
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print(f"[{tag}] max|diff| token‑level = {max_diff:.4f}")
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def test_logprob_match(self):
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vocab_size = self.tokenizer.vocab_size
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for env_val in ["True", "False"]:
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with self.subTest(return_original_logprob=env_val):
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os.environ["RETURN_ORIGINAL_LOGPROB"] = env_val
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# ----- SGLang side -----
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sgl_engine = sgl.Engine(
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model_path=MODEL_ID,
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skip_tokenizer_init=True,
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trust_remote_code=True,
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mem_fraction_static=0.60,
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)
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for prompt in PROMPTS:
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random_token_ids = sorted(
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random.sample(range(vocab_size), NUM_RANDOM_TOKEN_IDS)
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)
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enc = self.tokenizer(prompt, return_tensors="pt")
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input_ids = enc["input_ids"].to(self.hf_model.device)
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attn_mask = enc["attention_mask"].to(self.hf_model.device)
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with torch.inference_mode():
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hf_out = self.hf_model(
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input_ids=input_ids,
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attention_mask=attn_mask,
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return_dict=True,
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)
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logits = hf_out.logits[:, -1, :] # [1, V]
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hf_log_probs = F.log_softmax(
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logits.float() / self.sampling_params["temperature"], dim=-1
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)[0]
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hf_original_log_probs = F.log_softmax(logits.float(), dim=-1)[0]
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outputs = sgl_engine.generate(
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input_ids=input_ids[0].tolist(),
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sampling_params=self.sampling_params,
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return_logprob=True,
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top_logprobs_num=TOP_LOGPROBS_NUM,
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token_ids_logprob=random_token_ids,
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)
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if isinstance(outputs, list):
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outputs = outputs[0]
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meta = outputs["meta_info"]
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# Check original logprobs only if enabled
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if env_val.lower() == "true":
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self.assert_logprobs_block_equal(
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hf_log_probs=hf_original_log_probs,
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token_log_probs=meta["output_token_logprobs"],
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top_log_probs=meta["output_top_logprobs"],
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ids_log_probs=meta["output_token_ids_logprobs"],
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random_token_ids=random_token_ids,
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tag=f"Original logprobs SGLang vs HF: {prompt} ({env_val})",
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)
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else:
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# Always check regular logprobs
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self.assert_logprobs_block_equal(
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hf_log_probs=hf_log_probs,
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token_log_probs=meta["output_token_logprobs"],
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top_log_probs=meta["output_top_logprobs"],
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ids_log_probs=meta["output_token_ids_logprobs"],
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random_token_ids=random_token_ids,
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tag=f"logprobs SGLang vs HF: {prompt} ({env_val})",
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)
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sgl_engine.shutdown()
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if __name__ == "__main__":
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unittest.main()
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