[Frontend] [Core] perf: Automatically detect vLLM-tensorized model, update tensorizer to version 2.9.0

.buildkite/test-pipeline.yaml

@@ -60,11 +60,13 @@ steps:
   mirror_hardwares: [amd]
   commands:
     # install aws cli for llava_example.py
-    - pip install awscli
+    # install tensorizer for tensorize_vllm_model.py
+    - pip install awscli tensorizer
     - python3 offline_inference.py
     - python3 offline_inference_with_prefix.py
     - python3 llm_engine_example.py
     - python3 llava_example.py
+    - python3 tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
 
 - label: Kernels Test %N
   command: pytest -v -s kernels --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT

examples/tensorize_vllm_model.py

@@ -1,23 +1,20 @@
 import argparse
 import dataclasses
+import json
 import os
-import time
 import uuid
 from functools import partial
-from typing import Type
 
-import torch
-import torch.nn as nn
-from tensorizer import (DecryptionParams, EncryptionParams, TensorDeserializer,
-                        TensorSerializer, stream_io)
-from tensorizer.utils import convert_bytes, get_mem_usage, no_init_or_tensor
-from transformers import AutoConfig, PretrainedConfig
+from tensorizer import stream_io
 
-from vllm.distributed import initialize_model_parallel
+from vllm import LLM
+from vllm.distributed import (init_distributed_environment,
+                              initialize_model_parallel)
 from vllm.engine.arg_utils import EngineArgs
 from vllm.engine.llm_engine import LLMEngine
-from vllm.model_executor.model_loader.tensorizer import TensorizerArgs
-from vllm.model_executor.models import ModelRegistry
+from vllm.model_executor.model_loader.tensorizer import (TensorizerArgs,
+                                                         TensorizerConfig,
+                                                         serialize_vllm_model)
 
 # yapf conflicts with isort for this docstring
 # yapf: disable
@@ -27,25 +24,25 @@
 to the GPU extremely quickly over an HTTP/HTTPS endpoint, an S3 endpoint,
 or locally. Tensor encryption and decryption is also supported, although 
 libsodium must be installed to use it. Install vllm with tensorizer support 
-using `pip install vllm[tensorizer]`.
+using `pip install vllm[tensorizer]`. To learn more about tensorizer, visit
+https://github.com/coreweave/tensorizer
 
 To serialize a model, install vLLM from source, then run something 
 like this from the root level of this repository:
 
 python -m examples.tensorize_vllm_model \
-   --model EleutherAI/gpt-j-6B \
-   --dtype float16 \
+   --model facebook/opt-125m \
    serialize \
-   --serialized-directory s3://my-bucket/ \
-   --suffix vllm
+   --serialized-directory s3://my-bucket \
+   --suffix v1
 
 Which downloads the model from HuggingFace, loads it into vLLM, serializes it,
 and saves it to your S3 bucket. A local directory can also be used. This
 assumes your S3 credentials are specified as environment variables
-in the form of `S3_ACCESS_KEY_ID`, `S3_SECRET_ACCESS_KEY`, and `S3_ENDPOINT`.
-To provide S3 credentials directly, you can provide `--s3-access-key-id` and 
-`--s3-secret-access-key`, as well as `--s3-endpoint` as CLI args to this 
-script.
+in the form of `S3_ACCESS_KEY_ID`, `S3_SECRET_ACCESS_KEY`, and 
+`S3_ENDPOINT_URL`. To provide S3 credentials directly, you can provide 
+`--s3-access-key-id` and `--s3-secret-access-key`, as well as `--s3-endpoint` 
+as CLI args to this script.
 
 You can also encrypt the model weights with a randomly-generated key by 
 providing a `--keyfile` argument.
@@ -57,7 +54,7 @@
    --model EleutherAI/gpt-j-6B \
    --dtype float16 \
    deserialize \
-   --path-to-tensors s3://my-bucket/vllm/EleutherAI/gpt-j-6B/vllm/model.tensors
+   --path-to-tensors s3://my-bucket/vllm/EleutherAI/gpt-j-6B/v1/model.tensors
 
 Which downloads the model tensors from your S3 bucket and deserializes them.
 
@@ -71,26 +68,30 @@
 
 `python -m examples.tensorize_vllm_model deserialize --help`.
 
-Once a model is serialized, it can be used to load the model when running the
-OpenAI inference client at `vllm/entrypoints/openai/api_server.py` by providing
-the `--tensorizer-uri` CLI argument that is functionally the same as the
-`--path-to-tensors` argument in this script, along with `--vllm-tensorized`, to
-signify that the model to be deserialized is a vLLM model, rather than a 
-HuggingFace `PreTrainedModel`, which can also be deserialized using tensorizer
-in the same inference server, albeit without the speed optimizations. To
-deserialize an encrypted file, the `--encryption-keyfile` argument can be used
-to provide the path to the keyfile used to encrypt the model weights. For
-information on all the arguments that can be used to configure tensorizer's
-deserialization, check out the tensorizer options argument group in the
-`vllm/entrypoints/openai/api_server.py` script with `--help`.
-
-Tensorizer can also be invoked with the `LLM` class directly to load models:
+Once a model is serialized, tensorizer can be invoked with the `LLM` class 
+directly to load models:
 
     llm = LLM(model="facebook/opt-125m",
               load_format="tensorizer",
-              tensorizer_uri=path_to_opt_tensors,
-              num_readers=3,
-              vllm_tensorized=True)
+              model_loader_extra_config=TensorizerConfig(
+                    tensorizer_uri = path_to_tensors,
+                    num_readers=3,
+                    )
+              )
+
+A serialized model can be used during model loading for the vLLM OpenAI
+inference server. `model_loader_extra_config` is exposed as the CLI arg
+`--model-loader-extra-config`, and accepts a JSON string literal of the
+TensorizerConfig arguments desired.
+
+In order to see all of the available arguments usable to configure 
+loading with tensorizer that are given to `TensorizerConfig`, run:
+
+`python -m examples.tensorize_vllm_model deserialize --help`
+
+under the `tensorizer options` section. These can also be used for
+deserialization in this example script, although `--tensorizer-uri` and
+`--path-to-tensors` are functionally the same in this case.
 """
 
 
@@ -158,95 +159,35 @@ def parse_args():
         help=("Path to a binary key to use to decrypt the model weights,"
               " if the model was serialized with encryption"))
 
-    return parser.parse_args()
-
-
-def make_model_contiguous(model):
-    # Ensure tensors are saved in memory contiguously
-    for param in model.parameters():
-        param.data = param.data.contiguous()
-
-
-def _get_vllm_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
-    architectures = getattr(config, "architectures", [])
-    for arch in architectures:
-        model_cls = ModelRegistry.load_model_cls(arch)
-        if model_cls is not None:
-            return model_cls
-    raise ValueError(
-        f"Model architectures {architectures} are not supported for now. "
-        f"Supported architectures: {ModelRegistry.get_supported_archs()}")
-
-
-def serialize():
-
-    eng_args_dict = {f.name: getattr(args, f.name) for f in
-                     dataclasses.fields(EngineArgs)}
-    engine_args = EngineArgs.from_cli_args(argparse.Namespace(**eng_args_dict))
-    engine = LLMEngine.from_engine_args(engine_args)
+    TensorizerArgs.add_cli_args(deserialize_parser)
 
-    model = (engine.model_executor.driver_worker.
-             model_runner.model)
-
-    encryption_params = EncryptionParams.random() if keyfile else None
-    if keyfile:
-        with _write_stream(keyfile) as stream:
-            stream.write(encryption_params.key)
-
-    with _write_stream(model_path) as stream:
-        serializer = TensorSerializer(stream, encryption=encryption_params)
-        serializer.write_module(model)
-        serializer.close()
+    return parser.parse_args()
 
-    print("Serialization complete. Model tensors saved to", model_path)
-    if keyfile:
-        print("Key saved to", keyfile)
 
 
 def deserialize():
-    config = AutoConfig.from_pretrained(model_ref)
-
-    with no_init_or_tensor():
-        model_class = _get_vllm_model_architecture(config)
-        model = model_class(config)
-
-    before_mem = get_mem_usage()
-    start = time.time()
-
-    if keyfile:
-        with _read_stream(keyfile) as stream:
-            key = stream.read()
-            decryption_params = DecryptionParams.from_key(key)
-            tensorizer_args.deserializer_params['encryption'] = \
-                decryption_params
-
-    with (_read_stream(model_path)) as stream, TensorDeserializer(
-            stream, **tensorizer_args.deserializer_params) as deserializer:
-        deserializer.load_into_module(model)
-        end = time.time()
-
-    # Brag about how fast we are.
-    total_bytes_str = convert_bytes(deserializer.total_tensor_bytes)
-    duration = end - start
-    per_second = convert_bytes(deserializer.total_tensor_bytes / duration)
-    after_mem = get_mem_usage()
-    print(
-        f"Deserialized {total_bytes_str} in {end - start:0.2f}s, {per_second}/s"
+    llm = LLM(model=args.model,
+              load_format="tensorizer",
+              model_loader_extra_config=tensorizer_config
     )
-    print(f"Memory usage before: {before_mem}")
-    print(f"Memory usage after: {after_mem}")
+    return llm
 
-    return model
 
 
 args = parse_args()
 
-s3_access_key_id = (args.s3_access_key_id or os.environ.get("S3_ACCESS_KEY_ID")
-                    or None)
-s3_secret_access_key = (args.s3_secret_access_key
-                        or os.environ.get("S3_SECRET_ACCESS_KEY") or None)
+s3_access_key_id = (getattr(args, 's3_access_key_id', None)
+                    or os.environ.get("S3_ACCESS_KEY_ID", None))
+s3_secret_access_key = (getattr(args, 's3_secret_access_key', None)
+                        or os.environ.get("S3_SECRET_ACCESS_KEY", None))
+s3_endpoint = (getattr(args, 's3_endpoint', None)
+               or os.environ.get("S3_ENDPOINT_URL", None))
 
-s3_endpoint = (args.s3_endpoint or os.environ.get("S3_ENDPOINT_URL") or None)
+credentials = {
+    "s3_access_key_id": s3_access_key_id,
+    "s3_secret_access_key": s3_secret_access_key,
+    "s3_endpoint": s3_endpoint
+}
 
 _read_stream, _write_stream = (partial(
     stream_io.open_stream,
@@ -263,20 +204,41 @@ def deserialize():
 os.environ["MASTER_ADDR"] = "127.0.0.1"
 os.environ["MASTER_PORT"] = "8080"
 
-torch.distributed.init_process_group(world_size=1, rank=0)
+init_distributed_environment(world_size=1, rank=0, local_rank=0)
 initialize_model_parallel()
 
 keyfile = args.keyfile if args.keyfile else None
 
+
+if args.model_loader_extra_config:
+    config = json.loads(args.model_loader_extra_config)
+    tensorizer_args = TensorizerConfig(**config)._construct_tensorizer_args()
+    tensorizer_args.tensorizer_uri = args.path_to_tensors
+else:
+    tensorizer_args = None
+
 if args.command == "serialize":
+    eng_args_dict = {f.name: getattr(args, f.name) for f in
+                     dataclasses.fields(EngineArgs)}
+
+    engine_args = EngineArgs.from_cli_args(argparse.Namespace(**eng_args_dict))
+    engine = LLMEngine.from_engine_args(engine_args)
+
     input_dir = args.serialized_directory.rstrip('/')
     suffix = args.suffix if args.suffix else uuid.uuid4().hex
     base_path = f"{input_dir}/vllm/{model_ref}/{suffix}"
     model_path = f"{base_path}/model.tensors"
-    serialize()
+    tensorizer_config = TensorizerConfig(
+        tensorizer_uri=model_path,
+        **credentials)
+    serialize_vllm_model(engine, tensorizer_config, keyfile)
 elif args.command == "deserialize":
-    tensorizer_args = TensorizerArgs.from_cli_args(args)
-    model_path = args.path_to_tensors
+    if not tensorizer_args:
+        tensorizer_config = TensorizerConfig(
+            tensorizer_uri=args.path_to_tensors,
+            encryption_keyfile = keyfile,
+            **credentials
+        )
     deserialize()
 else:
     raise ValueError("Either serialize or deserialize must be specified.")

requirements-dev.txt

@@ -14,7 +14,7 @@ types-setuptools
 
 # testing
 pytest
-tensorizer==2.9.0
+tensorizer>=2.9.0
 pytest-forked
 pytest-asyncio
 pytest-rerunfailures

setup.py

@@ -426,7 +426,7 @@ def _read_requirements(filename: str) -> List[str]:
     install_requires=get_requirements(),
     ext_modules=ext_modules,
     extras_require={
-        "tensorizer": ["tensorizer==2.9.0"],
+        "tensorizer": ["tensorizer>=2.9.0"],
     },
     cmdclass={"build_ext": cmake_build_ext} if not _is_neuron() else {},
     package_data=package_data,