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[Bugfix] fix confusing OOM errors during v1 init (#28051)
Signed-off-by: Shivam <shivamprasad91@gmail.com> Signed-off-by: shivampr <shivampr.dev@gmail.com> Co-authored-by: Chen Zhang <zhangch99@outlook.com>
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shivampr
GitHub
parent
166ac3c94d
commit
8580919ac3
54
tests/v1/engine/test_init_error_messaging.py
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54
tests/v1/engine/test_init_error_messaging.py
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@ -0,0 +1,54 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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from vllm.v1.core.kv_cache_utils import check_enough_kv_cache_memory
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from vllm.v1.kv_cache_interface import FullAttentionSpec
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def test_kv_cache_oom_no_memory():
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from unittest.mock import MagicMock
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config = MagicMock()
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config.model_config.max_model_len = 2048
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spec = {
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"layer_0": FullAttentionSpec(
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block_size=16,
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num_kv_heads=8,
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head_size=128,
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dtype="float16",
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)
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}
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with pytest.raises(ValueError):
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check_enough_kv_cache_memory(config, spec, 0)
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def test_kv_cache_oom_insufficient_memory(monkeypatch):
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from unittest.mock import MagicMock
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config = MagicMock()
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config.model_config.max_model_len = 2048
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config.cache_config.block_size = 16
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config.parallel_config.tensor_parallel_size = 1
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config.parallel_config.pipeline_parallel_size = 1
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config.parallel_config.decode_context_parallel_size = 1
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monkeypatch.setattr(
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"vllm.v1.core.kv_cache_utils.max_memory_usage_bytes",
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lambda c, s: 100 * 1024**3, # 100 GiB
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)
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spec = {
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"layer_0": FullAttentionSpec(
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block_size=16,
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num_kv_heads=8,
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head_size=128,
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dtype="float16",
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)
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}
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with pytest.raises(ValueError):
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check_enough_kv_cache_memory(config, spec, 1024**3) # 1 GiB
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@ -687,7 +687,9 @@ def check_enough_kv_cache_memory(
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raise ValueError(
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"No available memory for the cache blocks. "
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"Try increasing `gpu_memory_utilization` when "
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"initializing the engine."
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"initializing the engine. "
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"See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ "
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"for more details."
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)
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max_model_len = vllm_config.model_config.max_model_len
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@ -711,8 +713,10 @@ def check_enough_kv_cache_memory(
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f"cache is needed, which is larger than the available KV cache "
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f"memory ({available_memory / GiB_bytes:.2f} GiB). "
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f"{estimated_msg} "
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f"Try increasing `gpu_memory_utilization` or decreasing "
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f"`max_model_len` when initializing the engine."
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f"Try increasing `gpu_memory_utilization` or decreasing `max_model_len` "
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f"when initializing the engine. "
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f"See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ "
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f"for more details."
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)
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@ -3571,74 +3571,89 @@ class GPUModelRunner(
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if self.parallel_config.enable_eplb:
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self.eplb_state = EplbState(self.parallel_config, self.device)
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eplb_models = 0
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with DeviceMemoryProfiler() as m:
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time_before_load = time.perf_counter()
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model_loader = get_model_loader(self.load_config)
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self.model = model_loader.load_model(
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vllm_config=self.vllm_config, model_config=self.model_config
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)
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if self.lora_config:
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self.model = self.load_lora_model(
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self.model, self.vllm_config, self.device
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try:
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with DeviceMemoryProfiler() as m:
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time_before_load = time.perf_counter()
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model_loader = get_model_loader(self.load_config)
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self.model = model_loader.load_model(
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vllm_config=self.vllm_config, model_config=self.model_config
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)
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if hasattr(self, "drafter"):
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logger.info_once("Loading drafter model...")
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self.drafter.load_model(self.model)
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if (
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hasattr(self.drafter, "model")
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and is_mixture_of_experts(self.drafter.model)
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and self.parallel_config.enable_eplb
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):
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spec_config = self.vllm_config.speculative_config
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assert spec_config is not None
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assert spec_config.draft_model_config is not None
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logger.info_once(
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"EPLB is enabled for drafter model %s.",
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spec_config.draft_model_config.model,
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if self.lora_config:
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self.model = self.load_lora_model(
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self.model, self.vllm_config, self.device
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)
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if hasattr(self, "drafter"):
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logger.info_once("Loading drafter model...")
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self.drafter.load_model(self.model)
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if (
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hasattr(self.drafter, "model")
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and is_mixture_of_experts(self.drafter.model)
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and self.parallel_config.enable_eplb
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):
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spec_config = self.vllm_config.speculative_config
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assert spec_config is not None
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assert spec_config.draft_model_config is not None
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logger.info_once(
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"EPLB is enabled for drafter model %s.",
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spec_config.draft_model_config.model,
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)
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global_expert_load = (
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global_expert_loads[eplb_models]
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if global_expert_loads
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else None
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)
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old_global_expert_indices = (
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old_global_expert_indices_per_model[eplb_models]
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if old_global_expert_indices_per_model
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else None
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)
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if self.eplb_state is None:
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self.eplb_state = EplbState(self.parallel_config, self.device)
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self.eplb_state.add_model(
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self.drafter.model,
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spec_config.draft_model_config,
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global_expert_load,
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old_global_expert_indices,
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rank_mapping,
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)
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eplb_models += 1
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global_expert_load = (
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global_expert_loads[eplb_models]
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if global_expert_loads
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else None
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)
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old_global_expert_indices = (
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old_global_expert_indices_per_model[eplb_models]
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if old_global_expert_indices_per_model
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else None
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)
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if self.eplb_state is None:
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self.eplb_state = EplbState(
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self.parallel_config, self.device
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)
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self.eplb_state.add_model(
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self.drafter.model,
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spec_config.draft_model_config,
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global_expert_load,
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old_global_expert_indices,
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rank_mapping,
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)
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eplb_models += 1
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if self.use_aux_hidden_state_outputs:
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if not supports_eagle3(self.get_model()):
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raise RuntimeError(
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"Model does not support EAGLE3 interface but "
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"aux_hidden_state_outputs was requested"
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)
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if self.use_aux_hidden_state_outputs:
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if not supports_eagle3(self.get_model()):
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raise RuntimeError(
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"Model does not support EAGLE3 interface but "
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"aux_hidden_state_outputs was requested"
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)
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# Try to get auxiliary layers from speculative config,
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# otherwise use model's default layers
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aux_layers = self._get_eagle3_aux_layers_from_config()
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if aux_layers:
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logger.info(
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"Using auxiliary layers from speculative config: %s",
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aux_layers,
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)
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else:
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aux_layers = self.model.get_eagle3_aux_hidden_state_layers()
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# Try to get auxiliary layers from speculative config,
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# otherwise use model's default layers
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aux_layers = self._get_eagle3_aux_layers_from_config()
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if aux_layers:
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logger.info(
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"Using auxiliary layers from speculative config: %s",
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aux_layers,
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)
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else:
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aux_layers = self.model.get_eagle3_aux_hidden_state_layers()
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self.model.set_aux_hidden_state_layers(aux_layers)
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time_after_load = time.perf_counter()
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self.model_memory_usage = m.consumed_memory
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self.model.set_aux_hidden_state_layers(aux_layers)
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time_after_load = time.perf_counter()
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self.model_memory_usage = m.consumed_memory
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except torch.cuda.OutOfMemoryError as e:
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msg = (
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"Failed to load model - not enough GPU memory. "
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"Try lowering --gpu-memory-utilization to free memory for weights, "
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"increasing --tensor-parallel-size, or using --quantization. "
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"See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ "
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"for more tips."
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)
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combined_msg = f"{msg} (original error: {e})"
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logger.error(combined_msg)
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raise e
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logger.info_once(
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"Model loading took %.4f GiB memory and %.6f seconds",
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self.model_memory_usage / GiB_bytes,
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