[Frontend] [Core] Integrate Tensorizer in to S3 loading machinery, allow passing arbitrary arguments during save/load (#19619)
Signed-off-by: Sanger Steel <sangersteel@gmail.com> Co-authored-by: Eta <esyra@coreweave.com>
This commit is contained in:
Sanger Steel
@@ -1,36 +1,51 @@
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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 asyncio
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import gc
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import json
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import os
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import pathlib
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import subprocess
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import sys
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from typing import Any
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import pytest
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import torch
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from vllm import SamplingParams
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import vllm.model_executor.model_loader.tensorizer
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from vllm import LLM, SamplingParams
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from vllm.engine.arg_utils import EngineArgs
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# yapf conflicts with isort for this docstring
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# yapf: disable
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from vllm.model_executor.model_loader.tensorizer import (TensorizerConfig,
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TensorSerializer,
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is_vllm_tensorized,
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open_stream,
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tensorize_vllm_model)
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from vllm.model_executor.model_loader.tensorizer_loader import (
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BLACKLISTED_TENSORIZER_ARGS)
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# yapf: enable
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from vllm.utils import PlaceholderModule
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from ..utils import VLLM_PATH
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from ..utils import VLLM_PATH, RemoteOpenAIServer
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from .conftest import DummyExecutor, assert_from_collective_rpc
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try:
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import tensorizer
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from tensorizer import EncryptionParams
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except ImportError:
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tensorizer = PlaceholderModule("tensorizer") # type: ignore[assignment]
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EncryptionParams = tensorizer.placeholder_attr("EncryptionParams")
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class TensorizerCaughtError(Exception):
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pass
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EXAMPLES_PATH = VLLM_PATH / "examples"
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pytest_plugins = "pytest_asyncio",
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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@@ -40,9 +55,37 @@ prompts = [
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# Create a sampling params object.
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95, seed=0)
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model_ref = "facebook/opt-125m"
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tensorize_model_for_testing_script = os.path.join(
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os.path.dirname(__file__), "tensorize_vllm_model_for_testing.py")
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def patch_init_and_catch_error(self, obj, method_name,
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expected_error: type[Exception]):
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original = getattr(obj, method_name, None)
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if original is None:
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raise ValueError("Method '{}' not found.".format(method_name))
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def wrapper(*args, **kwargs):
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try:
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return original(*args, **kwargs)
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except expected_error as err:
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raise TensorizerCaughtError from err
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setattr(obj, method_name, wrapper)
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self.load_model()
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def assert_specific_tensorizer_error_is_raised(
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executor,
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obj: Any,
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method_name: str,
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expected_error: type[Exception],
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):
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with pytest.raises(TensorizerCaughtError):
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executor.collective_rpc(patch_init_and_catch_error,
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args=(
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obj,
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method_name,
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expected_error,
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))
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def is_curl_installed():
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@@ -81,11 +124,10 @@ def test_can_deserialize_s3(vllm_runner):
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@pytest.mark.skipif(not is_curl_installed(), reason="cURL is not installed")
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def test_deserialized_encrypted_vllm_model_has_same_outputs(
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vllm_runner, tmp_path):
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model_ref, vllm_runner, tmp_path, model_path):
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args = EngineArgs(model=model_ref)
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with vllm_runner(model_ref) as vllm_model:
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model_path = tmp_path / (model_ref + ".tensors")
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key_path = tmp_path / (model_ref + ".key")
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key_path = tmp_path / model_ref / "model.key"
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write_keyfile(key_path)
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outputs = vllm_model.generate(prompts, sampling_params)
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@@ -111,9 +153,9 @@ def test_deserialized_encrypted_vllm_model_has_same_outputs(
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def test_deserialized_hf_model_has_same_outputs(hf_runner, vllm_runner,
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tmp_path):
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tmp_path, model_ref,
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model_path):
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with hf_runner(model_ref) as hf_model:
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model_path = tmp_path / (model_ref + ".tensors")
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max_tokens = 50
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outputs = hf_model.generate_greedy(prompts, max_tokens=max_tokens)
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with open_stream(model_path, "wb+") as stream:
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@@ -123,7 +165,7 @@ def test_deserialized_hf_model_has_same_outputs(hf_runner, vllm_runner,
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with vllm_runner(model_ref,
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load_format="tensorizer",
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model_loader_extra_config=TensorizerConfig(
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tensorizer_uri=model_path,
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tensorizer_uri=str(model_path),
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num_readers=1,
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)) as loaded_hf_model:
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deserialized_outputs = loaded_hf_model.generate_greedy(
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@@ -132,7 +174,7 @@ def test_deserialized_hf_model_has_same_outputs(hf_runner, vllm_runner,
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assert outputs == deserialized_outputs
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def test_load_without_tensorizer_load_format(vllm_runner, capfd):
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def test_load_without_tensorizer_load_format(vllm_runner, capfd, model_ref):
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model = None
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try:
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model = vllm_runner(
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@@ -150,7 +192,8 @@ def test_load_without_tensorizer_load_format(vllm_runner, capfd):
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torch.cuda.empty_cache()
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def test_raise_value_error_on_invalid_load_format(vllm_runner, capfd):
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def test_raise_value_error_on_invalid_load_format(vllm_runner, capfd,
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model_ref):
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model = None
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try:
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model = vllm_runner(
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@@ -208,7 +251,7 @@ def test_deserialized_encrypted_vllm_model_with_tp_has_same_outputs(
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outputs = base_model.generate(prompts, sampling_params)
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# load model with two shards and serialize with encryption
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model_path = str(tmp_path / (model_ref + "-%02d.tensors"))
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model_path = str(tmp_path / model_ref / "model-%02d.tensors")
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key_path = tmp_path / (model_ref + ".key")
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tensorizer_config = TensorizerConfig(
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@@ -242,13 +285,12 @@ def test_deserialized_encrypted_vllm_model_with_tp_has_same_outputs(
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@pytest.mark.flaky(reruns=3)
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def test_vllm_tensorized_model_has_same_outputs(vllm_runner, tmp_path):
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def test_vllm_tensorized_model_has_same_outputs(model_ref, vllm_runner,
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tmp_path, model_path):
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gc.collect()
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torch.cuda.empty_cache()
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model_ref = "facebook/opt-125m"
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model_path = tmp_path / (model_ref + ".tensors")
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config = TensorizerConfig(tensorizer_uri=str(model_path))
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args = EngineArgs(model=model_ref, device="cuda")
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args = EngineArgs(model=model_ref)
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with vllm_runner(model_ref) as vllm_model:
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outputs = vllm_model.generate(prompts, sampling_params)
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@@ -264,3 +306,243 @@ def test_vllm_tensorized_model_has_same_outputs(vllm_runner, tmp_path):
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# noqa: E501
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assert outputs == deserialized_outputs
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def test_load_with_just_model_tensors(just_serialize_model_tensors, model_ref):
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# For backwards compatibility, ensure Tensorizer can be still be loaded
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# for inference by passing the model reference name, not a local/S3 dir,
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# and the location of the model tensors
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model_dir = just_serialize_model_tensors
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extra_config = {"tensorizer_uri": f"{model_dir}/model.tensors"}
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## Start OpenAI API server
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args = [
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"--load-format",
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"tensorizer",
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"--model-loader-extra-config",
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json.dumps(extra_config),
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]
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with RemoteOpenAIServer(model_ref, args):
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# This test only concerns itself with being able to load the model
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# and successfully initialize the server
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pass
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def test_assert_serialization_kwargs_passed_to_tensor_serializer(tmp_path):
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serialization_params = {
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"limit_cpu_concurrency": 2,
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}
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model_ref = "facebook/opt-125m"
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model_path = tmp_path / (model_ref + ".tensors")
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config = TensorizerConfig(tensorizer_uri=str(model_path),
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serialization_kwargs=serialization_params)
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llm = LLM(model=model_ref, )
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def serialization_test(self, *args, **kwargs):
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# This is performed in the ephemeral worker process, so monkey-patching
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# will actually work, and cleanup is guaranteed so don't
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# need to reset things
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original_dict = serialization_params
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to_compare = {}
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original = tensorizer.serialization.TensorSerializer.__init__
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def tensorizer_serializer_wrapper(self, *args, **kwargs):
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nonlocal to_compare
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to_compare = kwargs.copy()
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return original(self, *args, **kwargs)
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tensorizer.serialization.TensorSerializer.__init__ = (
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tensorizer_serializer_wrapper)
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tensorizer_config = TensorizerConfig(**kwargs["tensorizer_config"])
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self.save_tensorized_model(tensorizer_config=tensorizer_config, )
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return to_compare | original_dict == to_compare
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kwargs = {"tensorizer_config": config.to_serializable()}
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assert assert_from_collective_rpc(llm, serialization_test, kwargs)
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def test_assert_deserialization_kwargs_passed_to_tensor_deserializer(
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tmp_path, capfd):
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deserialization_kwargs = {
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"num_readers": "bar", # illegal value
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}
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serialization_params = {
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"limit_cpu_concurrency": 2,
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}
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model_ref = "facebook/opt-125m"
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model_path = tmp_path / (model_ref + ".tensors")
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config = TensorizerConfig(tensorizer_uri=str(model_path),
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serialization_kwargs=serialization_params)
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args = EngineArgs(model=model_ref)
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tensorize_vllm_model(args, config)
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loader_tc = TensorizerConfig(
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tensorizer_uri=str(model_path),
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deserialization_kwargs=deserialization_kwargs,
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)
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engine_args = EngineArgs(
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model="facebook/opt-125m",
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load_format="tensorizer",
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model_loader_extra_config=loader_tc.to_serializable(),
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)
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vllm_config = engine_args.create_engine_config()
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executor = DummyExecutor(vllm_config)
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assert_specific_tensorizer_error_is_raised(
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executor,
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tensorizer.serialization.TensorDeserializer,
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"__init__",
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TypeError,
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)
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def test_assert_stream_kwargs_passed_to_tensor_deserializer(tmp_path, capfd):
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deserialization_kwargs = {
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"num_readers": 1,
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}
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serialization_params = {
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"limit_cpu_concurrency": 2,
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}
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model_ref = "facebook/opt-125m"
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model_path = tmp_path / (model_ref + ".tensors")
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config = TensorizerConfig(tensorizer_uri=str(model_path),
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serialization_kwargs=serialization_params)
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args = EngineArgs(model=model_ref)
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tensorize_vllm_model(args, config)
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stream_kwargs = {"mode": "foo"}
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loader_tc = TensorizerConfig(
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tensorizer_uri=str(model_path),
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deserialization_kwargs=deserialization_kwargs,
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stream_kwargs=stream_kwargs,
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)
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engine_args = EngineArgs(
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model="facebook/opt-125m",
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load_format="tensorizer",
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model_loader_extra_config=loader_tc.to_serializable(),
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)
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vllm_config = engine_args.create_engine_config()
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executor = DummyExecutor(vllm_config)
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assert_specific_tensorizer_error_is_raised(
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executor,
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vllm.model_executor.model_loader.tensorizer,
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"open_stream",
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ValueError,
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)
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@pytest.mark.asyncio
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async def test_serialize_and_serve_entrypoints(tmp_path):
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model_ref = "facebook/opt-125m"
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suffix = "test"
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try:
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result = subprocess.run([
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sys.executable,
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f"{VLLM_PATH}/examples/others/tensorize_vllm_model.py", "--model",
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model_ref, "serialize", "--serialized-directory",
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str(tmp_path), "--suffix", suffix, "--serialization-kwargs",
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'{"limit_cpu_concurrency": 4}'
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],
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check=True,
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capture_output=True,
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text=True)
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except subprocess.CalledProcessError as e:
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print("Tensorizing failed.")
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print("STDOUT:\n", e.stdout)
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print("STDERR:\n", e.stderr)
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raise
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assert "Successfully serialized" in result.stdout
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# Next, try to serve with vllm serve
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model_uri = tmp_path / "vllm" / model_ref / suffix / "model.tensors"
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model_loader_extra_config = {
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"tensorizer_uri": str(model_uri),
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"stream_kwargs": {
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"force_http": False,
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},
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"deserialization_kwargs": {
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"verify_hash": True,
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"num_readers": 8,
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}
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}
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cmd = [
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"-m", "vllm.entrypoints.cli.main", "serve", "--host", "localhost",
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"--load-format", "tensorizer", model_ref,
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"--model-loader-extra-config",
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json.dumps(model_loader_extra_config, indent=2)
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]
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proc = await asyncio.create_subprocess_exec(
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sys.executable,
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*cmd,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.STDOUT,
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)
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assert proc.stdout is not None
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fut = proc.stdout.readuntil(b"Application startup complete.")
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try:
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await asyncio.wait_for(fut, 180)
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except asyncio.TimeoutError:
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pytest.fail("Server did not start successfully")
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finally:
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proc.terminate()
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await proc.communicate()
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@pytest.mark.parametrize("illegal_value", BLACKLISTED_TENSORIZER_ARGS)
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def test_blacklisted_parameter_for_loading(tmp_path, vllm_runner, capfd,
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illegal_value):
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serialization_params = {
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"limit_cpu_concurrency": 2,
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}
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model_ref = "facebook/opt-125m"
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model_path = tmp_path / (model_ref + ".tensors")
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config = TensorizerConfig(tensorizer_uri=str(model_path),
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serialization_kwargs=serialization_params)
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args = EngineArgs(model=model_ref)
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tensorize_vllm_model(args, config)
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loader_tc = {"tensorizer_uri": str(model_path), illegal_value: "foo"}
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try:
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vllm_runner(
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model_ref,
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load_format="tensorizer",
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model_loader_extra_config=loader_tc,
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)
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except RuntimeError:
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out, err = capfd.readouterr()
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combined_output = out + err
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assert (f"ValueError: {illegal_value} is not an allowed "
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f"Tensorizer argument.") in combined_output
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