[Misc] Add tqdm progress bar during graph capture (#11349)

Signed-off-by: mgoin <michael@neuralmagic.com>

vllm/worker/model_runner.py

@@ -13,6 +13,7 @@ import numpy as np
 import torch
 import torch.distributed
 import torch.nn as nn
+from tqdm import tqdm
 
 import vllm.envs as envs
 from vllm.attention import AttentionMetadata, get_attn_backend
@@ -21,7 +22,8 @@ from vllm.attention.backends.utils import CommonAttentionState
 from vllm.config import CompilationLevel, VllmConfig
 from vllm.core.scheduler import SchedulerOutputs
 from vllm.distributed import get_kv_transfer_group, get_pp_group
-from vllm.distributed.parallel_state import graph_capture
+from vllm.distributed.parallel_state import (get_tensor_model_parallel_rank,
+                                             graph_capture)
 from vllm.forward_context import set_forward_context
 from vllm.inputs import INPUT_REGISTRY, InputRegistry
 from vllm.logger import init_logger
@@ -1413,8 +1415,8 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         logger.info("Capturing cudagraphs for decoding. This may lead to "
                     "unexpected consequences if the model is not static. To "
                     "run the model in eager mode, set 'enforce_eager=True' or "
-                    "use '--enforce-eager' in the CLI.")
-        logger.info("If out-of-memory error occurs during cudagraph capture,"
+                    "use '--enforce-eager' in the CLI. "
+                    "If out-of-memory error occurs during cudagraph capture,"
                     " consider decreasing `gpu_memory_utilization` or "
                     "switching to eager mode. You can also reduce the "
                     "`max_num_seqs` as needed to decrease memory usage.")
@@ -1451,8 +1453,14 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
             # memory usage of CUDA graph.
             for virtual_engine in range(
                     self.parallel_config.pipeline_parallel_size):
-                for batch_size in \
-                    self.vllm_config.compilation_config.capture_sizes:
+                # Only rank 0 should print progress bar during capture
+                capture_sizes = (
+                    tqdm(
+                        self.vllm_config.compilation_config.capture_sizes,
+                        desc="Capturing CUDA graph shapes",
+                    ) if get_tensor_model_parallel_rank() == 0 else
+                    self.vllm_config.compilation_config.capture_sizes)
+                for batch_size in capture_sizes:
                     attn_metadata = (
                         self.attn_state.graph_capture_get_metadata_for_batch(
                             batch_size,