Simplify pytorch sampling kernel and logit processor (#2491)

python/sglang/srt/layers/logits_processor.py

@@ -100,82 +100,9 @@ class LogitsProcessor(nn.Module):
         self.do_tensor_parallel_all_gather = (
             not skip_all_gather and get_tensor_model_parallel_world_size() > 1
         )
-
-    def _get_normalized_prompt_logprobs(
-        self,
-        input_token_logprobs: torch.Tensor,
-        logits_metadata: LogitsMetadata,
-    ):
-        logprobs_cumsum = torch.cumsum(input_token_logprobs, dim=0, dtype=torch.float32)
-        pruned_lens = torch.tensor(
-            logits_metadata.extend_logprob_pruned_lens_cpu, device="cuda"
-        )
-
-        start = torch.zeros_like(pruned_lens)
-        start[1:] = torch.cumsum(pruned_lens[:-1], dim=0)
-        end = torch.clamp(
-            start + pruned_lens - 2, min=0, max=logprobs_cumsum.shape[0] - 1
+        self.final_logit_softcapping = getattr(
+            self.config, "final_logit_softcapping", None
         )
-        sum_logp = (
-            logprobs_cumsum[end] - logprobs_cumsum[start] + input_token_logprobs[start]
-        )
-        normalized_prompt_logprobs = sum_logp / (pruned_lens - 1).clamp(min=1)
-        return normalized_prompt_logprobs
-
-    @staticmethod
-    def get_top_logprobs(all_logprobs: torch.Tensor, logits_metadata: LogitsMetadata):
-        max_k = max(logits_metadata.top_logprobs_nums)
-        ret = all_logprobs.topk(max_k, dim=1)
-        values = ret.values.tolist()
-        indices = ret.indices.tolist()
-
-        if logits_metadata.forward_mode.is_decode():
-            output_top_logprobs_val = []
-            output_top_logprobs_idx = []
-            for i, k in enumerate(logits_metadata.top_logprobs_nums):
-                output_top_logprobs_val.append(values[i][:k])
-                output_top_logprobs_idx.append(indices[i][:k])
-            return None, None, output_top_logprobs_val, output_top_logprobs_idx
-        else:
-            input_top_logprobs_val, input_top_logprobs_idx = [], []
-            output_top_logprobs_val, output_top_logprobs_idx = [], []
-
-            pt = 0
-            for k, pruned_len in zip(
-                logits_metadata.top_logprobs_nums,
-                logits_metadata.extend_logprob_pruned_lens_cpu,
-            ):
-                if pruned_len <= 0:
-                    input_top_logprobs_val.append([])
-                    input_top_logprobs_idx.append([])
-                    output_top_logprobs_val.append([])
-                    output_top_logprobs_idx.append([])
-                    continue
-
-                input_top_logprobs_val.append(
-                    [values[pt + j][:k] for j in range(pruned_len - 1)]
-                )
-                input_top_logprobs_idx.append(
-                    [indices[pt + j][:k] for j in range(pruned_len - 1)]
-                )
-                output_top_logprobs_val.append(
-                    list(
-                        values[pt + pruned_len - 1][:k],
-                    )
-                )
-                output_top_logprobs_idx.append(
-                    list(
-                        indices[pt + pruned_len - 1][:k],
-                    )
-                )
-                pt += pruned_len
-
-            return (
-                input_top_logprobs_val,
-                input_top_logprobs_idx,
-                output_top_logprobs_val,
-                output_top_logprobs_idx,
-            )
 
     def forward(
         self,
@@ -201,10 +128,10 @@ class LogitsProcessor(nn.Module):
             last_logits = tensor_model_parallel_all_gather(last_logits)
         last_logits = last_logits[:, : self.config.vocab_size].float()
 
-        if hasattr(self.config, "final_logit_softcapping"):
-            last_logits.div_(self.config.final_logit_softcapping)
+        if self.final_logit_softcapping:
+            last_logits.div_(self.final_logit_softcapping)
             torch.tanh(last_logits, out=last_logits)
-            last_logits.mul_(self.config.final_logit_softcapping)
+            last_logits.mul_(self.final_logit_softcapping)
 
         # Return only last_logits if logprob is not requested
         if not logits_metadata.return_logprob:
@@ -212,7 +139,9 @@ class LogitsProcessor(nn.Module):
                 next_token_logits=last_logits,
             )
         else:
-            last_logprobs = torch.nn.functional.log_softmax(last_logits, dim=-1)
+            last_logprobs = self.compute_temp_top_p_normalized_logprobs(
+                last_logits, logits_metadata
+            )
 
             if logits_metadata.forward_mode.is_decode():
                 if logits_metadata.return_top_logprob:
@@ -248,14 +177,17 @@ class LogitsProcessor(nn.Module):
                 # extra logits that this padding may have produced.
                 all_logits = all_logits[:, : self.config.vocab_size].float()
 
-                if hasattr(self.config, "final_logit_softcapping"):
-                    all_logits.div_(self.config.final_logit_softcapping)
+                if self.final_logit_softcapping:
+                    all_logits.div_(self.final_logit_softcapping)
                     torch.tanh(all_logits, out=all_logits)
-                    all_logits.mul_(self.config.final_logit_softcapping)
+                    all_logits.mul_(self.final_logit_softcapping)
 
                 all_logprobs = all_logits
                 del all_logits, hidden_states
-                all_logprobs[:] = torch.nn.functional.log_softmax(all_logprobs, dim=-1)
+
+                all_logprobs = self.compute_temp_top_p_normalized_logprobs(
+                    all_logprobs, logits_metadata
+                )
 
                 # Get the logprob of top-k tokens
                 if logits_metadata.return_top_logprob:
@@ -309,11 +241,93 @@ class LogitsProcessor(nn.Module):
             # GGUF models
             logits = lm_head.linear_method.apply(lm_head, hidden_states, embedding_bias)
 
-        # Optional scaling factor, backported from vLLM 0.4
+        # Optional scaling factor
         if self.logit_scale is not None:
             logits.mul_(self.logit_scale)  # In-place multiply
         return logits
 
+    @staticmethod
+    def _get_normalized_prompt_logprobs(
+        input_token_logprobs: torch.Tensor,
+        logits_metadata: LogitsMetadata,
+    ):
+        logprobs_cumsum = torch.cumsum(input_token_logprobs, dim=0, dtype=torch.float32)
+        pruned_lens = torch.tensor(
+            logits_metadata.extend_logprob_pruned_lens_cpu, device="cuda"
+        )
+
+        start = torch.zeros_like(pruned_lens)
+        start[1:] = torch.cumsum(pruned_lens[:-1], dim=0)
+        end = torch.clamp(
+            start + pruned_lens - 2, min=0, max=logprobs_cumsum.shape[0] - 1
+        )
+        sum_logp = (
+            logprobs_cumsum[end] - logprobs_cumsum[start] + input_token_logprobs[start]
+        )
+        normalized_prompt_logprobs = sum_logp / (pruned_lens - 1).clamp(min=1)
+        return normalized_prompt_logprobs
+
+    @staticmethod
+    def get_top_logprobs(all_logprobs: torch.Tensor, logits_metadata: LogitsMetadata):
+        max_k = max(logits_metadata.top_logprobs_nums)
+        ret = all_logprobs.topk(max_k, dim=1)
+        values = ret.values.tolist()
+        indices = ret.indices.tolist()
+
+        if logits_metadata.forward_mode.is_decode():
+            output_top_logprobs_val = []
+            output_top_logprobs_idx = []
+            for i, k in enumerate(logits_metadata.top_logprobs_nums):
+                output_top_logprobs_val.append(values[i][:k])
+                output_top_logprobs_idx.append(indices[i][:k])
+            return None, None, output_top_logprobs_val, output_top_logprobs_idx
+        else:
+            input_top_logprobs_val, input_top_logprobs_idx = [], []
+            output_top_logprobs_val, output_top_logprobs_idx = [], []
+
+            pt = 0
+            for k, pruned_len in zip(
+                logits_metadata.top_logprobs_nums,
+                logits_metadata.extend_logprob_pruned_lens_cpu,
+            ):
+                if pruned_len <= 0:
+                    input_top_logprobs_val.append([])
+                    input_top_logprobs_idx.append([])
+                    output_top_logprobs_val.append([])
+                    output_top_logprobs_idx.append([])
+                    continue
+
+                input_top_logprobs_val.append(
+                    [values[pt + j][:k] for j in range(pruned_len - 1)]
+                )
+                input_top_logprobs_idx.append(
+                    [indices[pt + j][:k] for j in range(pruned_len - 1)]
+                )
+                output_top_logprobs_val.append(
+                    list(
+                        values[pt + pruned_len - 1][:k],
+                    )
+                )
+                output_top_logprobs_idx.append(
+                    list(
+                        indices[pt + pruned_len - 1][:k],
+                    )
+                )
+                pt += pruned_len
+
+            return (
+                input_top_logprobs_val,
+                input_top_logprobs_idx,
+                output_top_logprobs_val,
+                output_top_logprobs_idx,
+            )
+
+    @staticmethod
+    def compute_temp_top_p_normalized_logprobs(
+        last_logits: torch.Tensor, logits_metadata: LogitsMetadata
+    ) -> torch.Tensor:
+        return torch.nn.functional.log_softmax(last_logits, dim=-1)
+
 
 def test():
     all_logprobs = torch.tensor(

python/sglang/srt/layers/sampler.py

@@ -51,7 +51,6 @@ class Sampler(nn.Module):
             # Post process logits
             logits.div_(sampling_info.temperatures)
             probs = torch.softmax(logits, dim=-1)
-            logits = None
             del logits
 
             if global_server_args_dict["sampling_backend"] == "flashinfer":
@@ -84,6 +83,7 @@ class Sampler(nn.Module):
                     sampling_info.top_ks,
                     sampling_info.top_ps,
                     sampling_info.min_ps,
+                    sampling_info.need_min_p_sampling,
                 )
             else:
                 raise ValueError(
@@ -98,20 +98,42 @@ def top_k_top_p_min_p_sampling_from_probs_torch(
     top_ks: torch.Tensor,
     top_ps: torch.Tensor,
     min_ps: torch.Tensor,
+    need_min_p_sampling: bool,
 ):
     """A top-k, top-p and min-p sampling implementation with native pytorch operations."""
     probs_sort, probs_idx = probs.sort(dim=-1, descending=True)
     probs_sum = torch.cumsum(probs_sort, dim=-1)
-    min_p_thresholds = probs_sort[:, 0] * min_ps
-    probs_sort[(probs_sum - probs_sort) > top_ps.view(-1, 1)] = 0.0
     probs_sort[
         torch.arange(0, probs.shape[-1], device=probs.device).view(1, -1)
         >= top_ks.view(-1, 1)
     ] = 0.0
-    probs_sort[probs_sort < min_p_thresholds.view(-1, 1)] = 0.0
-    probs_sort.div_(probs_sort.max(dim=-1, keepdim=True)[0])
+    probs_sort[(probs_sum - probs_sort) > top_ps.view(-1, 1)] = 0.0
+
+    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
+
     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)
     batch_next_token_ids = torch.gather(probs_idx, dim=1, index=sampled_index).view(-1)
     return batch_next_token_ids
+
+
+def top_p_normalize_probs(
+    probs: torch.Tensor,
+    top_ps: torch.Tensor,
+):
+    if global_server_args_dict["sampling_backend"] == "flashinfer":
+        return top_p_renorm_prob(probs, top_ps)
+    elif global_server_args_dict["sampling_backend"] == "pytorch":
+        # See also top_k_top_p_min_p_sampling_from_probs_torch
+        probs_sort, probs_idx = probs.sort(dim=-1, descending=True)
+        probs_sum = torch.cumsum(probs_sort, dim=-1)
+        probs_sort[(probs_sum - probs_sort) > top_ps.view(-1, 1)] = 0.0
+        probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+        return torch.zeros_like(probs_sort).scatter_(-1, probs_idx, probs_sort)
+    else:
+        raise ValueError(
+            f"Invalid sampling backend: {global_server_args_dict['sampling_backend']}"
+        )

python/sglang/srt/managers/schedule_batch.py

@@ -1086,9 +1086,9 @@ class ScheduleBatch:
             self.top_logprobs_nums = [0] * len(self.reqs) + other.top_logprobs_nums
         self.reqs.extend(other.reqs)
 
-        self.return_logprob = self.return_logprob or other.return_logprob
-        self.has_stream = self.has_stream or other.has_stream
-        self.has_grammar = self.has_grammar or other.has_grammar
+        self.return_logprob |= other.return_logprob
+        self.has_stream |= other.has_stream
+        self.has_grammar |= other.has_grammar
 
     def get_model_worker_batch(self):
         if self.forward_mode.is_decode() or self.forward_mode.is_idle():
@@ -1115,7 +1115,6 @@ class ScheduleBatch:
             seq_lens=self.seq_lens,
             out_cache_loc=self.out_cache_loc,
             seq_lens_sum=self.seq_lens_sum,
-            req_to_token_pool_records=self.req_to_token_pool.get_write_records(),
             return_logprob=self.return_logprob,
             top_logprobs_nums=self.top_logprobs_nums,
             global_num_tokens=self.global_num_tokens,
@@ -1170,9 +1169,6 @@ class ModelWorkerBatch:
     # The sum of all sequence lengths
     seq_lens_sum: int
 
-    # The memory pool operation records
-    req_to_token_pool_records: Optional[List[Tuple[Tuple, torch.Tensor]]]
-
     # For logprob
     return_logprob: bool
     top_logprobs_nums: Optional[List[int]]

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -387,8 +387,14 @@ class CudaGraphRunner:
 
         # Extract logprobs
         if forward_batch.return_logprob:
-            next_token_logprobs = torch.nn.functional.log_softmax(
-                next_token_logits, dim=-1
+            logits_metadata = LogitsMetadata(
+                forward_mode=ForwardMode.DECODE,
+                top_logprobs_nums=forward_batch.top_logprobs_nums,
+            )
+            next_token_logprobs = (
+                LogitsProcessor.compute_temp_top_p_normalized_logprobs(
+                    next_token_logits, logits_metadata
+                )
             )
             logits_output = LogitsProcessorOutput(
                 next_token_logits=next_token_logits,
@@ -396,10 +402,6 @@ class CudaGraphRunner:
             )
             return_top_logprob = any(x > 0 for x in forward_batch.top_logprobs_nums)
             if return_top_logprob:
-                logits_metadata = LogitsMetadata(
-                    forward_mode=ForwardMode.DECODE,
-                    top_logprobs_nums=forward_batch.top_logprobs_nums,
-                )
                 (
                     logits_output.output_top_logprobs_val,
                     logits_output.output_top_logprobs_idx,

python/sglang/srt/server_args.py

@@ -698,11 +698,6 @@ class ServerArgs:
             action="store_true",
             help="Disable Multi-head Latent Attention (MLA) for DeepSeek-V2.",
         )
-        parser.add_argument(
-            "--disable-nan-detection",
-            action="store_true",
-            help="Disable the NaN detection for better performance.",
-        )
         parser.add_argument(
             "--disable-overlap-schedule",
             action="store_true",