Fix the deadlock in multi-node tp (#1122)

benchmark/gsm8k/bench_sglang.py

@@ -64,7 +64,9 @@ def main(args):
     @sgl.function
     def few_shot_gsm8k(s, question):
         s += few_shot_examples + question
-        s += sgl.gen("answer", max_tokens=512, stop=["Question", "Assistant:"])
+        s += sgl.gen(
+            "answer", max_tokens=512, stop=["Question", "Assistant:", "<|separator|>"]
+        )
 
     #####################################
     ########## SGL Program End ##########

python/sglang/srt/layers/logits_processor.py

@@ -67,10 +67,12 @@ class LogitsMetadata:
 
 
 class LogitsProcessor(nn.Module):
-    def __init__(self, config):
+    def __init__(self, config, skip_all_gather: bool = False):
         super().__init__()
         self.config = config
-        self.tp_size = get_tensor_model_parallel_world_size()
+        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, logits_metadata: LogitsMetadata
@@ -159,7 +161,7 @@ class LogitsProcessor(nn.Module):
             last_hidden = hidden_states[last_index]
 
         last_logits = torch.matmul(last_hidden, weight.T)
-        if self.tp_size > 1:
+        if self.do_tensor_parallel_all_gather:
             last_logits = tensor_model_parallel_all_gather(last_logits)
         last_logits = last_logits[:, : self.config.vocab_size].float()
 
@@ -204,7 +206,7 @@ class LogitsProcessor(nn.Module):
                 )
             else:
                 all_logits = torch.matmul(hidden_states, weight.T)
-                if self.tp_size > 1:
+                if self.do_tensor_parallel_all_gather:
                     all_logits = tensor_model_parallel_all_gather(all_logits)
                 all_logits = all_logits[:, : self.config.vocab_size].float()
 

python/sglang/srt/managers/schedule_batch.py

@@ -21,7 +21,9 @@ from dataclasses import dataclass
 from typing import List, Optional, Union
 
 import torch
+import torch.distributed as dist
 from flashinfer.sampling import top_k_top_p_sampling_from_probs
+from vllm.distributed import get_tensor_model_parallel_group
 
 import sglang.srt.sampling.penaltylib as penaltylib
 from sglang.global_config import global_config
@@ -724,7 +726,7 @@ class ScheduleBatch:
                 )
             self.logit_bias = torch.concat([self.logit_bias, other.logit_bias])
 
-    def sample(self, logits: torch.Tensor):
+    def sample(self, logits: torch.Tensor, is_multi_node_tp=False):
         # TODO(lsyin): move this into a part of layer and run with CUDA Graph
         # Post process logits
         logits = logits.contiguous()
@@ -779,6 +781,16 @@ class ScheduleBatch:
 
         self.penalizer_orchestrator.cumulate_output_tokens(batch_next_token_ids)
 
+        if is_multi_node_tp:
+            # If the tensor parallelism spans across multiple nodes, there is some indeterminism
+            # that can cause the TP workers to generate different tokens, so we need to
+            # sync here
+            torch.distributed.all_reduce(
+                batch_next_token_ids,
+                op=dist.ReduceOp.MIN,
+                group=get_tensor_model_parallel_group().device_group,
+            )
+
         return batch_next_token_ids
 
 

python/sglang/srt/managers/tp_worker.py

@@ -85,10 +85,6 @@ class ModelTpServer:
         self.schedule_policy = server_args.schedule_policy
         self.disable_regex_jump_forward = server_args.disable_regex_jump_forward
 
-        # Chunked prefill
-        self.chunked_prefill_size = server_args.chunked_prefill_size
-        self.current_inflight_req = None
-
         # Init model and tokenizer
         self.model_config = ModelConfig(
             server_args.model_path,
@@ -175,6 +171,10 @@ class ModelTpServer:
         self.num_generated_tokens = 0
         self.last_stats_tic = time.time()
 
+        # Chunked prefill
+        self.chunked_prefill_size = server_args.chunked_prefill_size
+        self.current_inflight_req = None
+
         # Init the FSM cache for constrained generation
         if not server_args.skip_tokenizer_init:
             self.regex_fsm_cache = FSMCache(
@@ -444,7 +444,9 @@ class ModelTpServer:
             # Forward and sample the next tokens
             if batch.extend_num_tokens != 0:
                 output = self.model_runner.forward(batch, ForwardMode.EXTEND)
-                next_token_ids = batch.sample(output.next_token_logits)
+                next_token_ids = batch.sample(
+                    output.next_token_logits, self.model_runner.is_multi_node_tp
+                )
 
                 # Move logprobs to cpu
                 if output.next_token_logprobs is not None:
@@ -603,7 +605,9 @@ class ModelTpServer:
 
         # Forward and sample the next tokens
         output = self.model_runner.forward(batch, ForwardMode.DECODE)
-        next_token_ids = batch.sample(output.next_token_logits)
+        next_token_ids = batch.sample(
+            output.next_token_logits, self.model_runner.is_multi_node_tp
+        )
 
         # Move logprobs to cpu
         if output.next_token_logprobs is not None:

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -142,7 +142,7 @@ class CudaGraphRunner:
             set_torch_compile_config()
 
     def can_run(self, batch_size):
-        return batch_size < self.max_bs
+        return batch_size <= self.max_bs
 
     def capture(self, batch_size_list):
         self.batch_size_list = batch_size_list
@@ -239,12 +239,23 @@ class CudaGraphRunner:
             return forward(input_ids, input_metadata.positions, input_metadata)
 
         for _ in range(2):
+            torch.cuda.synchronize()
+            self.model_runner.tp_group.barrier()
+
             run_once()
 
             torch.cuda.synchronize()
+            self.model_runner.tp_group.barrier()
+
+        torch.cuda.synchronize()
+        self.model_runner.tp_group.barrier()
+
         with torch.cuda.graph(graph, pool=self.graph_memory_pool, stream=stream):
             out = run_once()
+
         torch.cuda.synchronize()
+        self.model_runner.tp_group.barrier()
+
         self.graph_memory_pool = graph.pool()
         return graph, None, out, flashinfer_decode_wrapper
 
@@ -278,7 +289,9 @@ class CudaGraphRunner:
         )
 
         # Replay
+        torch.cuda.synchronize()
         self.graphs[bs].replay()
+        torch.cuda.synchronize()
         output = self.output_buffers[bs]
 
         # Unpad

python/sglang/srt/model_executor/model_runner.py

@@ -38,6 +38,7 @@ from vllm.distributed import (
     init_distributed_environment,
     initialize_model_parallel,
 )
+from vllm.distributed.parallel_state import in_the_same_node_as
 from vllm.model_executor.model_loader import get_model
 from vllm.model_executor.models import ModelRegistry
 
@@ -112,10 +113,13 @@ class ModelRunner:
             distributed_init_method=nccl_init_method,
         )
         initialize_model_parallel(tensor_model_parallel_size=self.tp_size)
-        self.tp_group = get_tp_group()
         total_gpu_memory = get_available_gpu_memory(
             self.gpu_id, distributed=self.tp_size > 1
         )
+        self.tp_group = get_tp_group()
+        self.is_multi_node_tp = not all(
+            in_the_same_node_as(self.tp_group.cpu_group, source_rank=0)
+        )
 
         if self.tp_size > 1:
             total_local_gpu_memory = get_available_gpu_memory(self.gpu_id)

python/sglang/srt/models/grok.py

@@ -295,8 +295,9 @@ class Grok1ModelForCausalLM(nn.Module):
         self.config = config
         self.quant_config = quant_config
         self.model = Grok1Model(config, quant_config=quant_config)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
-        self.logits_processor = LogitsProcessor(config)
+        # self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.lm_head = ReplicatedLinear(config.hidden_size, config.vocab_size)
+        self.logits_processor = LogitsProcessor(config, skip_all_gather=True)
 
         # Monkey patch _prepare_weights to load pre-sharded weights
         setattr(DefaultModelLoader, "_prepare_weights", _prepare_presharded_weights)