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[V0 deprecation]clean up is_v1_supported_oracle (#28116)
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
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wangxiyuan
GitHub
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@ -1,55 +1,17 @@
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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 os
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import pytest
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import vllm.envs as envs
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from vllm import LLM
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from vllm.engine.arg_utils import AsyncEngineArgs
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MODEL = "meta-llama/Llama-3.2-1B-Instruct"
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def test_reject_bad_config(monkeypatch):
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with monkeypatch.context() as m:
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m.setenv("VLLM_USE_V1", "0")
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def test_unsupported_configs(monkeypatch):
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with monkeypatch.context() as m:
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m.setenv("VLLM_USE_V1", "1")
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with pytest.raises(NotImplementedError):
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AsyncEngineArgs(
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model=MODEL,
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speculative_config={
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"model": MODEL,
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},
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).create_engine_config()
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def test_enable_by_default_fallback(monkeypatch):
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with monkeypatch.context() as m:
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if os.getenv("VLLM_USE_V1", None):
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m.delenv("VLLM_USE_V1")
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# Should default to V1 for supported config.
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_ = AsyncEngineArgs(
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def test_unsupported_configs():
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with pytest.raises(NotImplementedError):
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AsyncEngineArgs(
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model=MODEL,
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enforce_eager=True,
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speculative_config={
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"model": MODEL,
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},
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).create_engine_config()
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assert envs.VLLM_USE_V1
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m.delenv("VLLM_USE_V1")
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def test_v1_llm_by_default(monkeypatch):
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with monkeypatch.context() as m:
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if os.getenv("VLLM_USE_V1", None):
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m.delenv("VLLM_USE_V1")
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# Should default to V1 for supported config.
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llm = LLM(MODEL, enforce_eager=True, enable_lora=True)
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print(llm.generate("Hello my name is"))
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assert hasattr(llm.llm_engine, "engine_core")
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m.delenv("VLLM_USE_V1")
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@ -1290,15 +1290,7 @@ class EngineArgs:
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"""
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Create the VllmConfig.
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NOTE: for autoselection of V0 vs V1 engine, we need to
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create the ModelConfig first, since ModelConfig's attrs
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(e.g. the model arch) are needed to make the decision.
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This function set VLLM_USE_V1=X if VLLM_USE_V1 is
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unspecified by the user.
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If VLLM_USE_V1 is specified by the user but the VllmConfig
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is incompatible, we raise an error.
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NOTE: If VllmConfig is incompatible, we raise an error.
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"""
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current_platform.pre_register_and_update()
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@ -1324,22 +1316,7 @@ class EngineArgs:
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self.model = model_config.model
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self.tokenizer = model_config.tokenizer
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# * If VLLM_USE_V1 is unset, we enable V1 for "supported features"
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# and fall back to V0 for experimental or unsupported features.
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# * If VLLM_USE_V1=1, we enable V1 for supported + experimental
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# features and raise error for unsupported features.
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# * If VLLM_USE_V1=0, we disable V1.
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use_v1 = False
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try_v1 = envs.VLLM_USE_V1 or not envs.is_set("VLLM_USE_V1")
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if try_v1 and self._is_v1_supported_oracle(model_config):
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use_v1 = True
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# If user explicitly set VLLM_USE_V1, sanity check we respect it.
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if envs.is_set("VLLM_USE_V1"):
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assert use_v1 == envs.VLLM_USE_V1
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# Otherwise, set the VLLM_USE_V1 variable globally.
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else:
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envs.set_vllm_use_v1(use_v1)
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self._check_feature_supported(model_config)
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# Set default arguments for V1 Engine.
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self._set_default_args(usage_context, model_config)
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@ -1708,17 +1685,10 @@ class EngineArgs:
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return config
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def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
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"""Oracle for whether to use V0 or V1 Engine by default."""
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#############################################################
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# Unsupported Feature Flags on V1.
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def _check_feature_supported(self, model_config: ModelConfig):
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"""Raise an error if the feature is not supported."""
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if self.logits_processor_pattern != EngineArgs.logits_processor_pattern:
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_raise_or_fallback(
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feature_name="--logits-processor-pattern", recommend_to_remove=False
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)
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return False
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_raise_unsupported_error(feature_name="--logits-processor-pattern")
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# No Concurrent Partial Prefills so far.
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if (
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@ -1726,12 +1696,9 @@ class EngineArgs:
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or self.max_long_partial_prefills
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!= SchedulerConfig.max_long_partial_prefills
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):
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_raise_or_fallback(
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feature_name="Concurrent Partial Prefill", recommend_to_remove=False
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)
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return False
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_raise_unsupported_error(feature_name="Concurrent Partial Prefill")
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# V1 supports N-gram, Medusa, and Eagle speculative decoding.
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# N-gram, Medusa, and Eagle are supported for speculative decoding.
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if self.speculative_config is not None:
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# speculative_config could still be a dict at this point
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if isinstance(self.speculative_config, dict):
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@ -1746,35 +1713,6 @@ class EngineArgs:
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"such as ngram, medusa, eagle, or mtp."
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)
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V1_BACKENDS = [
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"FLASH_ATTN",
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"PALLAS",
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"TRITON_ATTN",
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"TRITON_MLA",
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"CUTLASS_MLA",
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"FLASHMLA",
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"FLASH_ATTN_MLA",
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"FLASHINFER",
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"FLASHINFER_MLA",
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"ROCM_AITER_MLA",
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"TORCH_SDPA",
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"FLEX_ATTENTION",
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"TREE_ATTN",
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"XFORMERS",
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"ROCM_ATTN",
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"ROCM_AITER_UNIFIED_ATTN",
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]
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if (
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envs.is_set("VLLM_ATTENTION_BACKEND")
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and envs.VLLM_ATTENTION_BACKEND not in V1_BACKENDS
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):
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name = f"VLLM_ATTENTION_BACKEND={envs.VLLM_ATTENTION_BACKEND}"
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_raise_or_fallback(feature_name=name, recommend_to_remove=True)
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return False
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#############################################################
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# Experimental Features - allow users to opt in.
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if self.pipeline_parallel_size > 1:
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supports_pp = getattr(
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self.distributed_executor_backend, "supports_pp", False
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@ -1790,18 +1728,10 @@ class EngineArgs:
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"executor or multiprocessing executor or external "
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"launcher"
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)
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_raise_or_fallback(feature_name=name, recommend_to_remove=False)
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return False
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_raise_unsupported_error(feature_name=name)
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if current_platform.is_cpu() and model_config.get_sliding_window() is not None:
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_raise_or_fallback(
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feature_name="sliding window (CPU backend)", recommend_to_remove=False
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)
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return False
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#############################################################
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return True
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_raise_unsupported_error(feature_name="sliding window (CPU backend)")
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def _set_default_args(
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self, usage_context: UsageContext, model_config: ModelConfig
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@ -2000,17 +1930,12 @@ class AsyncEngineArgs(EngineArgs):
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return parser
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def _raise_or_fallback(feature_name: str, recommend_to_remove: bool):
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if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1:
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raise NotImplementedError(
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f"VLLM_USE_V1=1 is not supported with {feature_name}."
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)
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msg = f"{feature_name} is not supported by the V1 Engine. "
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msg += "Falling back to V0. "
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if recommend_to_remove:
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msg += f"We recommend to remove {feature_name} from your config "
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msg += "in favor of the V1 Engine."
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logger.warning(msg)
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def _raise_unsupported_error(feature_name: str):
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msg = (
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f"{feature_name} is not supported. We recommend to "
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f"remove {feature_name} from your config."
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)
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raise NotImplementedError(msg)
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def human_readable_int(value):
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