[2/2] Support deterministic inference for temperature > 0 (#10678)

Co-authored-by: Baizhou Zhang <sobereddiezhang@gmail.com>
Co-authored-by: hebiao064 <hebiaobuaa@gmail.com>

python/sglang/srt/layers/sampler.py

 import logging
-from typing import List, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 import torch.distributed as dist
@@ -65,6 +65,7 @@ class Sampler(nn.Module):
         return_logprob: bool,
         top_logprobs_nums: List[int],
         token_ids_logprobs: List[List[int]],
+        positions: torch.Tensor,
     ):
         """Run a sampler & compute logprobs and update logits_output accordingly.
 
@@ -77,6 +78,8 @@ class Sampler(nn.Module):
             batch_next_token_ids: next token IDs. If set, skip sampling and only
                 compute output logprobs It is used for speculative decoding which
                 performs sampling in draft workers.
+            positions: The positions of the tokens in the sequence. Used for deterministic sampling
+                to get the unique seed for each position.
         """
         logits = logits_output.next_token_logits
 
@@ -124,6 +127,8 @@ class Sampler(nn.Module):
                         sampling_info.top_ps,
                         sampling_info.min_ps,
                         sampling_info.need_min_p_sampling,
+                        sampling_info.sampling_seed,
+                        positions,
                     )
                 else:
                     raise ValueError(
@@ -189,6 +194,7 @@ class Sampler(nn.Module):
         Optimized for prefill-only scoring requests that need token probabilities
         but don't require next token generation.
         """
+
         if logits_output.next_token_logits is None:
             logger.warning("No logits available for logprob computation")
             return
@@ -230,8 +236,14 @@ def top_k_top_p_min_p_sampling_from_probs_torch(
     top_ps: torch.Tensor,
     min_ps: torch.Tensor,
     need_min_p_sampling: bool,
+    sampling_seed: Optional[torch.Tensor],
+    positions: torch.Tensor,
 ):
-    """A top-k, top-p and min-p sampling implementation with native pytorch operations."""
+    """
+    A top-k, top-p and min-p sampling implementation with native pytorch operations.
+    When sampling_seed is not None, deterministic inference will be enabled, it will sample
+    with the sampling_seed of each request.
+    """
     probs_sort, probs_idx = probs.sort(dim=-1, descending=True)
     probs_sum = torch.cumsum(probs_sort, dim=-1)
     probs_sort[
@@ -243,7 +255,9 @@ def top_k_top_p_min_p_sampling_from_probs_torch(
     if need_min_p_sampling:
         min_p_thresholds = probs_sort[:, 0] * min_ps
         probs_sort[probs_sort < min_p_thresholds.view(-1, 1)] = 0.0
-
+    if sampling_seed is not None:
+        sampled_index = multinomial_with_seed(probs_sort, sampling_seed, positions)
+    else:
         sampled_index = torch.multinomial(probs_sort, num_samples=1)
     # int32 range is enough to represent the token ids
     probs_idx = probs_idx.to(torch.int32)
@@ -251,6 +265,40 @@ def top_k_top_p_min_p_sampling_from_probs_torch(
     return batch_next_token_ids
 
 
+def multinomial_with_seed(
+    inputs: torch.Tensor, seed: torch.Tensor, positions: torch.Tensor
+) -> torch.Tensor:
+    """
+    Samples n elements from an input tensor `inputs` of shape (n, m) using
+    a unique random seed for each row. This is a deterministic batched alternative to
+    `torch.multinomial`.
+
+    Args:
+        inputs: A float tensor of shape (n, m) representing n categorical
+                distributions with m categories each. The values are treated
+                as weights and do not need to sum to 1.
+        seed:   An integer tensor of shape (n,) containing the random seed
+                for each corresponding row in `inputs`.
+        positions: The positions of the tokens in the sequence. Used for deterministic sampling
+                to get the unique seed for each position.
+
+    Returns:
+        A tensor of shape (n,) where the i-th element is an index sampled
+        from the distribution in `inputs[i]` using `seed[i]`.
+    """
+    n, m = inputs.shape
+    col_indices = torch.arange(m, device=inputs.device).unsqueeze(0)
+    step_seed = seed * 19349663 ^ positions * 73856093
+    seed_expanded = step_seed.unsqueeze(-1)
+    hashed = seed_expanded * 8589934591 ^ col_indices * 479001599
+    uniform_samples = (hashed % (2**24)).float() / (2**24)
+    epsilon = 1e-9
+    gumbel_noise = -torch.log(-torch.log(uniform_samples + epsilon) + epsilon)
+    log_probs = torch.log(inputs + epsilon)
+    perturbed_log_probs = log_probs + gumbel_noise
+    return torch.argmax(perturbed_log_probs, dim=1, keepdim=True)
+
+
 def sampling_from_probs_torch(probs: torch.Tensor):
     """A sampling implementation with native pytorch operations, without
     top-k, top-p, or min-p filtering."""

python/sglang/srt/managers/schedule_batch.py

@@ -67,7 +67,7 @@ from sglang.srt.mem_cache.swa_radix_cache import SWARadixCache
 from sglang.srt.metrics.collector import SchedulerMetricsCollector, TimeStats
 from sglang.srt.model_executor.forward_batch_info import CaptureHiddenMode, ForwardMode
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
-from sglang.srt.sampling.sampling_params import SamplingParams
+from sglang.srt.sampling.sampling_params import DEFAULT_SAMPLING_SEED, SamplingParams
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import flatten_nested_list, support_triton
 

python/sglang/srt/managers/tp_worker.py

@@ -270,9 +270,7 @@ class TpModelWorker:
                         logits_output, model_worker_batch
                     )
             else:
-                next_token_ids = self.model_runner.sample(
-                    logits_output, model_worker_batch
-                )
+                next_token_ids = self.model_runner.sample(logits_output, forward_batch)
 
             return logits_output, next_token_ids, can_run_cuda_graph
         else:

python/sglang/srt/model_executor/model_runner.py

@@ -2049,7 +2049,6 @@ class ModelRunner:
             )
 
         self._preprocess_logits(logits_output, forward_batch.sampling_info)
-
         # Sample the next tokens
         next_token_ids = self.sampler(
             logits_output,
@@ -2057,6 +2056,12 @@ class ModelRunner:
             forward_batch.return_logprob,
             forward_batch.top_logprobs_nums,
             forward_batch.token_ids_logprobs,
+            # For prefill, we only use the position of the last token.
+            (
+                forward_batch.positions
+                if forward_batch.forward_mode.is_decode()
+                else forward_batch.seq_lens - 1
+            ),
         )
         return next_token_ids
 

python/sglang/srt/sampling/sampling_batch_info.py

@@ -60,6 +60,9 @@ class SamplingBatchInfo:
         Dict[int, Tuple[CustomLogitProcessor, torch.Tensor]]
     ] = None
 
+    # Used for deterministic sampling
+    sampling_seed: Optional[torch.Tensor] = None
+
     # Device
     device: str = "cuda"
 
@@ -93,6 +96,15 @@ class SamplingBatchInfo:
         min_ps = torch.tensor(
             [r.sampling_params.min_p for r in reqs], dtype=torch.float, device=device
         )
+        sampling_seed = (
+            torch.tensor(
+                [r.sampling_params.sampling_seed for r in reqs],
+                dtype=torch.int32,
+                device=device,
+            )
+            if enable_deterministic
+            else None
+        )
 
         logit_bias = None
         if any(r.sampling_params.logit_bias is not None for r in reqs):
@@ -158,6 +170,7 @@ class SamplingBatchInfo:
             top_ps=top_ps,
             top_ks=top_ks,
             min_ps=min_ps,
+            sampling_seed=sampling_seed,
             is_all_greedy=all(r.sampling_params.top_k <= 1 for r in reqs),
             need_top_p_sampling=any(r.sampling_params.top_p != 1.0 for r in reqs),
             need_top_k_sampling=any(r.sampling_params.top_k != TOP_K_ALL for r in reqs),
@@ -239,8 +252,10 @@ class SamplingBatchInfo:
             "top_ps",
             "top_ks",
             "min_ps",
+            "sampling_seed",
         ]:
             value = getattr(self, item, None)
+            if value is not None:
                 setattr(self, item, value[keep_indices_device])
 
         if self.logit_bias is not None:
@@ -343,9 +358,11 @@ class SamplingBatchInfo:
             "top_ps",
             "top_ks",
             "min_ps",
+            "sampling_seed",
         ]:
             self_val = getattr(self, item, None)
             other_val = getattr(other, item, None)
+            if self_val is not None and other_val is not None:
                 setattr(self, item, torch.cat([self_val, other_val]))
 
         self.is_all_greedy &= other.is_all_greedy

python/sglang/srt/sampling/sampling_params.py

@@ -15,8 +15,11 @@
 
 from typing import Any, Dict, List, Optional, Union
 
+from sglang.srt.utils import get_bool_env_var
+
 _SAMPLING_EPS = 1e-6
 TOP_K_ALL = 1 << 30
+DEFAULT_SAMPLING_SEED = 42
 
 
 class SamplingParams:
@@ -53,6 +56,7 @@ class SamplingParams:
         custom_params: Optional[Dict[str, Any]] = None,
         stream_interval: Optional[int] = None,
         logit_bias: Optional[Dict[str, float]] = None,
+        sampling_seed: Optional[int] = None,
     ) -> None:
         self.max_new_tokens = max_new_tokens
         self.stop_strs = stop
@@ -80,6 +84,14 @@ class SamplingParams:
         self.custom_params = custom_params
         self.stream_interval = stream_interval
         self.logit_bias = logit_bias
+        # Used for deterministic sampling
+        if (
+            get_bool_env_var("SGLANG_ENABLE_DETERMINISTIC_INFERENCE")
+            and sampling_seed is None
+        ):
+            # If deterministic inference is enabled and sampling_seed is not set, use the default seed
+            sampling_seed = DEFAULT_SAMPLING_SEED
+        self.sampling_seed = sampling_seed
 
         # Process some special cases
         if 0 <= self.temperature < _SAMPLING_EPS:

python/sglang/srt/server_args.py

@@ -988,6 +988,12 @@ class ServerArgs:
                     "batch_invariant_ops is not installed. Please install it from https://github.com/thinking-machines-lab/batch_invariant_ops/."
                 )
 
+            # Check some settings
+            self.sampling_backend = "pytorch"
+            logger.warning(
+                "Sampling backend is set to pytorch for deterministic inference."
+            )
+            # Currently, only FA3 supports radix cache. Support for other backends is in progress
             if self.attention_backend != "fa3":
                 self.disable_radix_cache = True
                 logger.warning(

python/sglang/test/test_deterministic.py

@@ -29,6 +29,7 @@ class BenchArgs:
     port: int = 30000
     batch_size: int = 1
     temperature: float = 0.0
+    sampling_seed: int = None
     max_new_tokens: int = 100
     frequency_penalty: float = 0.0
     presence_penalty: float = 0.0
@@ -45,6 +46,9 @@ class BenchArgs:
         parser.add_argument("--port", type=int, default=BenchArgs.port)
         parser.add_argument("--n-trials", type=int, default=50)
         parser.add_argument("--temperature", type=float, default=BenchArgs.temperature)
+        parser.add_argument(
+            "--sampling-seed", type=int, default=BenchArgs.sampling_seed
+        )
         parser.add_argument(
             "--max-new-tokens", type=int, default=BenchArgs.max_new_tokens
         )
@@ -92,6 +96,7 @@ def send_single(
             "max_new_tokens": args.max_new_tokens,
             "frequency_penalty": args.frequency_penalty,
             "presence_penalty": args.presence_penalty,
+            "sampling_seed": args.sampling_seed,
         },
         "return_logprob": args.return_logprob,
         "stream": args.stream,
@@ -140,6 +145,7 @@ def send_mixed(args, batch_size: int):
             "max_new_tokens": args.max_new_tokens,
             "frequency_penalty": args.frequency_penalty,
             "presence_penalty": args.presence_penalty,
+            "sampling_seed": args.sampling_seed,
         },
         "return_logprob": args.return_logprob,
         "stream": args.stream,
@@ -186,6 +192,7 @@ def send_prefix(args, batch_size: int, prompts: List[str]):
             "max_new_tokens": args.max_new_tokens,
             "frequency_penalty": args.frequency_penalty,
             "presence_penalty": args.presence_penalty,
+            "sampling_seed": args.sampling_seed,
         },
         "return_logprob": args.return_logprob,
         "stream": args.stream,

sgl-router/benches/request_processing.rs

@@ -97,6 +97,7 @@ fn default_completion_request() -> CompletionRequest {
         lora_path: None,
         session_params: None,
         return_hidden_states: false,
+        sampling_seed: None,
         other: serde_json::Map::new(),
     }
 }

sgl-router/src/protocols/spec.rs

@@ -367,6 +367,10 @@ pub struct ChatCompletionRequest {
     /// Return model hidden states
     #[serde(default)]
     pub return_hidden_states: bool,
+
+    /// Random seed for sampling for deterministic outputs
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub sampling_seed: Option<u64>,
 }
 
 impl GenerationRequest for ChatCompletionRequest {
@@ -608,6 +612,10 @@ pub struct CompletionRequest {
     #[serde(default)]
     pub return_hidden_states: bool,
 
+    /// Sampling seed for deterministic outputs
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub sampling_seed: Option<u64>,
+
     /// Additional fields including bootstrap info for PD routing
     #[serde(flatten)]
     pub other: serde_json::Map<String, serde_json::Value>,
@@ -1749,6 +1757,8 @@ pub struct SamplingParams {
     pub stop_token_ids: Option<Vec<i32>>,
     #[serde(skip_serializing_if = "Option::is_none")]
     pub no_stop_trim: Option<bool>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub sampling_seed: Option<u64>,
 }
 
 #[derive(Clone, Debug, Serialize, Deserialize)]

sgl-router/src/routers/http/openai_router.rs

@@ -240,6 +240,7 @@ impl super::super::RouterTrait for OpenAIRouter {
                 "chat_template_kwargs",
                 "return_hidden_states",
                 "repetition_penalty",
+                "sampling_seed",
             ] {
                 obj.remove(key);
             }

sgl-router/tests/test_openai_routing.rs

@@ -68,6 +68,7 @@ fn create_minimal_completion_request() -> CompletionRequest {
         lora_path: None,
         session_params: None,
         return_hidden_states: false,
+        sampling_seed: None,
         other: serde_json::Map::new(),
     }
 }