Module, Model, and Tensor Serialization/Deserialization
Extremely fast model loads from HTTP/HTTPS, Redis, and S3 endpoints.
GPT-J (20GB) loads at wire-speed (~5GB/s) on a 40GbE network, and
is only bottlenecked by the Linux kernel TCP stack.
CoreWeave and our customers use KNative to deploy models as serverless
functions. How long a model takes to load is a major factor in the latency
of KNative scale-up. tensorizer is a tool to serialize models and their
associated tensors into a single file that can be loaded quickly and
efficiently off an HTTP/HTTPS or S3 endpoint.
By not embedding the model in the container image, we can reduce the
container image size and the time it takes to load the model. This is
especially important for models that are large in size, such as
EleutherAI/gpt-neox-20B
that weighs in at ~40GB.
This decoupling of the model from the container image also allows us to update the model without having to rebuild the container image. This allows us to quickly iterate on the model and deploy new versions without having to wait for the container image to build or for the container image cache to be populated.
tensorizer has S3 support, so we can store the serialized model in S3
object storage, and perform streaming loads from S3. This allows us to
stream the model directly from S3 into the container without having to
download the model to the container's local filesystem. This also
pertains to HTTP/HTTPS endpoints, as S3 is just an HTTP/HTTPS endpoint.
tensorizer also has support for loading models from a local filesystem,
so you can use it to serialize models locally and load them locally. This
is extremely fast, as the same principles that make it fast for HTTP/HTTPS
and S3 endpoints also apply to local filesystems.
tensorizer has preliminary support for Redis, but it is not recommended
for model deployment due to the lack of distributed caching. It is intended
for sharing state between inference pods, or for loading data on a per-request
basis from a Redis cache.
tensorizer's deserialization speed is primarily network-bound.
The following graph presents data collected from the scripts and Kubernetes
manifests in examples/benchmark_buffer_size
comparing the various deserialization modes available in tensorizer release
2.5.0—along with the raw network speed, and the speed of torch.load().
tensorizer can be installed from PyPI with pip:
python -m pip install tensorizerYou can also install tensorizer from source using pip.
To clone the repository and install tensorizer in
editable mode,
run:
git clone https://github.com/coreweave/tensorizer
cd tensorizer
python -m pip install -e .Or, run the following for pip to install tensorizer
directly from GitHub:
python -m pip install git+https://github.com/coreweave/tensorizerSerialization is done with the TensorSerializer class. It takes a
path_uri argument that can be a local filesystem path, an HTTP/HTTPS
endpoint, or an S3 endpoint.
write_module is the main method of the TensorSerializer class. It
takes a torch.nn.Module and serializes the tensors to the path_uri
endpoint.
The below example serializes the EleutherAI/gpt-j-6B model to an S3
endpoint. It assumes that you have already configured your S3
credentials in ~/.s3cfg.
NOTE: Loading and serializing gpt-j-6B will take a lot of CPU RAM,
up to ~20GB. Additionally, when loading gpt-j-6B into a GPU, you
will need about ~16GB of VRAM. If you don't have that much RAM or VRAM,
you can use the smaller gpt-neo-125M model instead.
NOTE2: The below examples require the transformers and accelerate
libraries. You can install them with pip:
python -m pip install transformers accelerateimport torch
from tensorizer import TensorSerializer
from transformers import AutoModelForCausalLM
model_ref = "EleutherAI/gpt-j-6B"
# For less intensive requirements, swap above with the line below:
# model_ref = "EleutherAI/gpt-neo-125M"
model_name = model_ref.split("/")[-1]
# Change this to your S3 bucket.
s3_bucket = "bucket"
s3_uri = f"s3://{s3_bucket}/{model_name}.tensors"
model = AutoModelForCausalLM.from_pretrained(
model_ref,
revision="float16",
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
)
serializer = TensorSerializer(s3_uri)
serializer.write_module(model)
serializer.close()Conversely, deserialization is done with the TensorDeserializer class.
It takes a path_uri argument that can be a local filesystem path, an
HTTP/HTTPS endpoint, or an S3 endpoint.
load_into_module is the main method of the TensorDeserializer class.
It takes a torch.nn.Module and loads the tensors from the path_uri
endpoint into the torch.nn.Module.
The below example loads the EleutherAI/gpt-j-6B model from an S3
endpoint.
import time
import torch
from tensorizer import TensorDeserializer
from tensorizer.utils import no_init_or_tensor, convert_bytes, get_mem_usage
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
model_ref = "EleutherAI/gpt-j-6B"
# To run this at home, swap this with the line below for a smaller example:
# model_ref = "EleutherAI/gpt-neo-125M"
model_name = model_ref.split("/")[-1]
# Change this to your S3 bucket.
s3_bucket = "bucket"
s3_uri = f"s3://{s3_bucket}/{model_name}.tensors"
config = AutoConfig.from_pretrained(model_ref)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# This ensures that the pretrained model weights are not initialized,
# and non-persistent buffers (generated at runtime) are on the correct device.
with torch.device(device), no_init_or_tensor():
model = AutoModelForCausalLM.from_config(config)
print(f"Deserializing to {device}:")
before_mem = get_mem_usage()
# Lazy load the tensors from S3 into the model.
start = time.perf_counter()
deserializer = TensorDeserializer(s3_uri, device=device)
deserializer.load_into_module(model)
end = time.perf_counter()
after_mem = get_mem_usage()
# 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)
deserializer.close()
print(f"Deserialized {total_bytes_str} in {end - start:0.2f}s, {per_second}/s")
print(f"Memory usage before: {before_mem}")
print(f"Memory usage after: {after_mem}")
# Tokenize and generate
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_ref)
eos = tokenizer.eos_token_id
input_ids = tokenizer.encode(
"¡Hola! Encantado de conocerte. hoy voy a", return_tensors="pt"
).to(device)
with torch.no_grad():
output = model.generate(
input_ids, max_new_tokens=50, do_sample=True, pad_token_id=eos
)
print(f"Output: {tokenizer.decode(output[0], skip_special_tokens=True)}")It should produce output similar to the following, with GPT-J-6B:
Deserialized model in 6.25 seconds
Test Output: ¡Hola! Encantado de conocerte. hoy voy a comentar por primera
vez una teoría de trineo, que quizá te parezca
algo desconocido, ya que en este mundo han
llegado a dominar tantos
More practical examples for the usage of tensorizer can be found in
examples/hf_serialization.py,
where df_main() serializes models from
HuggingFace Diffusers
and hf_main() serializes
HuggingFace Transformers models.
tensorizer supports fast tensor weight encryption and decryption during
serialization and deserialization, respectively.
Be aware that metadata (tensor names, dtypes, shapes, etc.) are not encrypted, only the weights themselves.
Note
Refer to docs/encryption.md for details, instructions,
and warnings on using tensorizer encryption correctly and safely.
To use tensorizer encryption, a recent version of libsodium must be
installed. Install libsodium with apt-get install libsodium23
on Ubuntu or Debian, or follow
the instructions in libsodium's documentation
for other platforms.
The following outline demonstrates how to encrypt and decrypt a tensorized model with a randomly-generated encryption key:
from tensorizer import (
EncryptionParams, DecryptionParams, TensorDeserializer, TensorSerializer
)
# Serialize and encrypt a model:
encryption_params = EncryptionParams.random()
serializer = TensorSerializer("model.tensors", encryption=encryption_params)
serializer.write_module(...) # or write_state_dict(), etc.
serializer.close()
# Save the randomly-generated encryption key somewhere
with open("tensor.key", "wb") as key_file:
key_file.write(encryption_params.key)
# Then decrypt it again:
# Load the randomly-generated key from where it was saved
with open("tensor.key", "rb") as key_file:
key: bytes = key_file.read()
decryption_params = DecryptionParams.from_key(key)
deserializer = TensorDeserializer("model.tensors", encryption=decryption_params)
deserializer.load_into_module(...)
deserializer.close()For more detail, refer to docs/encryption.md.
A complete example is also available as
examples/encryption.py.
The EncryptionParams and DecryptionParams class docstrings additionally
contain some usage information for quick reference from an IDE.
An example command line tool to add or remove encryption from existing serialized models is also available as examples/encryption.py.
You can run your own benchmarks on CoreWeave or your own Kubernetes cluster
by using the benchmark.yaml file in the examples/benchmark_buffer_size
directory. Please see the README.
The following models are available on the CoreWeave Cloud for free, and can be
used with the TensorDeserializer class. The S3 support defaults to the
accel-object.ord1.coreweave.com endpoint, and the bucket to use as tensorized.
We name the keys in the S3 bucket after the HuggingFace model identifier, and
append the /fp16 suffix for the half-precision version.
For example, the S3 URI for the EleutherAI/gpt-j-6B model is:
s3://tensorized/EleutherAI/gpt-j-6B/fp16/model.tensors
The below table shows the available models and their S3 URIs.
| Model | Precision | S3 URI |
|---|---|---|
| EleutherAI/gpt-neo-125M | fp32 |
s3://tensorized/EleutherAI/gpt-neo-125M/model.tensors |
| EleutherAI/gpt-neo-125M | fp16 |
s3://tensorized/EleutherAI/gpt-neo-125M/fp16/model.tensors |
| EleutherAI/gpt-neo-1.3B | fp32 |
s3://tensorized/EleutherAI/gpt-neo-1.3B/model.tensors |
| EleutherAI/gpt-neo-1.3B | fp16 |
s3://tensorized/EleutherAI/gpt-neo-1.3B/fp16/model.tensors |
| EleutherAI/gpt-neo-2.7B | fp32 |
s3://tensorized/EleutherAI/gpt-neo-2.7B/model.tensors |
| EleutherAI/gpt-neo-2.7B | fp16 |
s3://tensorized/EleutherAI/gpt-neo-2.7B/fp16/model.tensors |
| EleutherAI/gpt-j-6B | fp32 |
s3://tensorized/EleutherAI/gpt-j-6B/model.tensors |
| EleutherAI/gpt-j-6B | fp16 |
s3://tensorized/EleutherAI/gpt-j-6B/fp16/model.tensors |
| EleutherAI/gpt-neox-20b | fp32 |
s3://tensorized/EleutherAI/gpt-neox-20b/model.tensors |
| EleutherAI/gpt-neox-20b | fp16 |
s3://tensorized/EleutherAI/gpt-neox-20b/fp16/model.tensors |
| EleutherAI/pythia-70m | fp32 |
s3://tensorized/EleutherAI/pythia-70m/model.tensors |
| EleutherAI/pythia-70m | fp16 |
s3://tensorized/EleutherAI/pythia-70m/fp16/model.tensors |
| EleutherAI/pythia-1.4b | fp32 |
s3://tensorized/EleutherAI/pythia-1.4b/model.tensors |
| EleutherAI/pythia-1.4b | fp16 |
s3://tensorized/EleutherAI/pythia-1.4b/fp16/model.tensors |
| EleutherAI/pythia-2.8b | fp32 |
s3://tensorized/EleutherAI/pythia-2.8b/model.tensors |
| EleutherAI/pythia-2.8b | fp16 |
s3://tensorized/EleutherAI/pythia-2.8b/fp16/model.tensors |
| EleutherAI/pythia-6.9b | fp32 |
s3://tensorized/EleutherAI/pythia-6.9b/model.tensors |
| EleutherAI/pythia-6.9b | fp16 |
s3://tensorized/EleutherAI/pythia-6.9b/fp16/model.tensors |
| EleutherAI/pythia-12b | fp32 |
s3://tensorized/EleutherAI/pythia-12b/model.tensors |
| EleutherAI/pythia-12b | fp16 |
s3://tensorized/EleutherAI/pythia-12b/fp16/model.tensors |
| EleutherAI/pythia-70m-deduped | fp32 |
s3://tensorized/EleutherAI/pythia-70m-deduped/model.tensors |
| EleutherAI/pythia-70m-deduped | fp16 |
s3://tensorized/EleutherAI/pythia-70m-deduped/fp16/model.tensors |
| EleutherAI/pythia-1.4b-deduped | fp32 |
s3://tensorized/EleutherAI/pythia-1.4b-deduped/model.tensors |
| EleutherAI/pythia-1.4b-deduped | fp16 |
s3://tensorized/EleutherAI/pythia-1.4b-deduped/fp16/model.tensors |
| EleutherAI/pythia-2.8b-deduped | fp32 |
s3://tensorized/EleutherAI/pythia-2.8b-deduped/model.tensors |
| EleutherAI/pythia-2.8b-deduped | fp16 |
s3://tensorized/EleutherAI/pythia-2.8b-deduped/fp16/model.tensors |
| EleutherAI/pythia-6.9b-deduped | fp32 |
s3://tensorized/EleutherAI/pythia-6.9b-deduped/model.tensors |
| EleutherAI/pythia-6.9b-deduped | fp16 |
s3://tensorized/EleutherAI/pythia-6.9b-deduped/fp16/model.tensors |
| EleutherAI/pythia-12b-deduped | fp32 |
s3://tensorized/EleutherAI/pythia-12b-deduped/model.tensors |
| EleutherAI/pythia-12b-deduped | fp16 |
s3://tensorized/EleutherAI/pythia-12b-deduped/fp16/model.tensors |
| KoboldAI/fairseq-dense-125M | fp32 |
s3://tensorized/KoboldAI/fairseq-dense-125M/model.tensors |
| KoboldAI/fairseq-dense-125M | fp16 |
s3://tensorized/KoboldAI/fairseq-dense-125M/fp16/model.tensors |
| KoboldAI/fairseq-dense-355M | fp32 |
s3://tensorized/KoboldAI/fairseq-dense-355M/model.tensors |
| KoboldAI/fairseq-dense-355M | fp16 |
s3://tensorized/KoboldAI/fairseq-dense-355M/fp16/model.tensors |
| KoboldAI/fairseq-dense-2.7B | fp32 |
s3://tensorized/KoboldAI/fairseq-dense-2.7B/model.tensors |
| KoboldAI/fairseq-dense-2.7B | fp16 |
s3://tensorized/KoboldAI/fairseq-dense-2.7B/fp16/model.tensors |
| KoboldAI/fairseq-dense-6.7B | fp32 |
s3://tensorized/KoboldAI/fairseq-dense-6.7B/model.tensors |
| KoboldAI/fairseq-dense-6.7B | fp16 |
s3://tensorized/KoboldAI/fairseq-dense-6.7B/fp16/model.tensors |
| KoboldAI/fairseq-dense-13B | fp32 |
s3://tensorized/KoboldAI/fairseq-dense-13B/model.tensors |
| KoboldAI/fairseq-dense-13B | fp16 |
s3://tensorized/KoboldAI/fairseq-dense-13B/fp16/model.tensors |
| Salesforce/codegen-350M-mono | fp32 |
s3://tensorized/Salesforce/codegen-350M-mono/model.tensors |
| Salesforce/codegen-350M-mono | fp16 |
s3://tensorized/Salesforce/codegen-350M-mono/fp16/model.tensors |
| Salesforce/codegen-350M-multi | fp32 |
s3://tensorized/Salesforce/codegen-350M-multi/model.tensors |
| Salesforce/codegen-350M-multi | fp16 |
s3://tensorized/Salesforce/codegen-350M-multi/fp16/model.tensors |
| Salesforce/codegen-2B-multi | fp32 |
s3://tensorized/Salesforce/codegen-2B-multi/model.tensors |
| Salesforce/codegen-2B-multi | fp16 |
s3://tensorized/Salesforce/codegen-2B-multi/fp16/model.tensors |
| Salesforce/codegen-6B-mono | fp32 |
s3://tensorized/Salesforce/codegen-6B-mono/model.tensors |
| Salesforce/codegen-6B-mono | fp16 |
s3://tensorized/Salesforce/codegen-6B-mono/fp16/model.tensors |
| Salesforce/codegen-6B-multi | fp32 |
s3://tensorized/Salesforce/codegen-6B-multi/model.tensors |
| Salesforce/codegen-6B-multi | fp16 |
s3://tensorized/Salesforce/codegen-6B-multi/fp16/model.tensors |
| Salesforce/codegen-16B-mono | fp32 |
s3://tensorized/Salesforce/codegen-16B-mono/model.tensors |
| Salesforce/codegen-16B-mono | fp16 |
s3://tensorized/Salesforce/codegen-16B-mono/fp16/model.tensors |
| Salesforce/codegen-16B-multi | fp32 |
s3://tensorized/Salesforce/codegen-16B-multi/model.tensors |
| Salesforce/codegen-16B-multi | fp16 |
s3://tensorized/Salesforce/codegen-16B-multi/fp16/model.tensors |
| Model | Component | Precision | S3 URI |
|---|---|---|---|
| RunwayML/stable-diffusion-v1-5 | VAE |
fp32 |
s3://tensorized/runwayml/stable-diffusion-v1-5/vae.tensors |
| RunwayML/stable-diffusion-v1-5 | UNet |
fp32 |
s3://tensorized/runwayml/stable-diffusion-v1-5/unet.tensors |
| RunwayML/stable-diffusion-v1-5 | TextEnc |
fp32 |
s3://tensorized/runwayml/stable-diffusion-v1-5/text_encoder.tensors |
| RunwayML/stable-diffusion-v1-5 | VAE |
fp16 |
s3://tensorized/runwayml/stable-diffusion-v1-5/fp16/vae.tensors |
| RunwayML/stable-diffusion-v1-5 | UNet |
fp16 |
s3://tensorized/runwayml/stable-diffusion-v1-5/fp16/unet.tensors |
| RunwayML/stable-diffusion-v1-5 | TextEnc |
fp16 |
s3://tensorized/runwayml/stable-diffusion-v1-5/fp16/text_encoder.tensors |
| StabilityAI/stable-diffusion-2-1 | VAE |
fp32 |
s3://tensorized/stabilityai/stable-diffusion-2-1/vae.tensors |
| StabilityAI/stable-diffusion-2-1 | UNet |
fp32 |
s3://tensorized/stabilityai/stable-diffusion-2-1/unet.tensors |
| StabilityAI/stable-diffusion-2-1 | TextEnc |
fp32 |
s3://tensorized/stabilityai/stable-diffusion-2-1/text_encoder.tensors |
| StabilityAI/stable-diffusion-2-1 | VAE |
fp16 |
s3://tensorized/stabilityai/stable-diffusion-2-1/fp16/vae.tensors |
| StabilityAI/stable-diffusion-2-1 | UNet |
fp16 |
s3://tensorized/stabilityai/stable-diffusion-2-1/fp16/unet.tensors |
| StabilityAI/stable-diffusion-2-1 | TextEnc |
fp16 |
s3://tensorized/stabilityai/stable-diffusion-2-1/fp16/text_encoder.tensors |
| StabilityAI/stable-diffusion-xl-base-1.0 | VAE |
fp32 |
s3://tensorized/stabilityai/stable-diffusion-xl-base-1.0/vae.tensors |
| StabilityAI/stable-diffusion-xl-base-1.0 | UNet |
fp32 |
s3://tensorized/stabilityai/stable-diffusion-xl-base-1.0/unet.tensors |
| StabilityAI/stable-diffusion-xl-base-1.0 | TextEnc |
fp32 |
s3://tensorized/stabilityai/stable-diffusion-xl-base-1.0/text_encoder.tensors |
| StabilityAI/stable-diffusion-xl-base-1.0 | TextEnc2 |
fp32 |
s3://tensorized/stabilityai/stable-diffusion-xl-base-1.0/text_encoder_2.tensors |
| StabilityAI/stable-diffusion-xl-base-1.0 | VAE |
fp16 |
s3://tensorized/stabilityai/stable-diffusion-xl-base-1.0/fp16/vae.tensors |
| StabilityAI/stable-diffusion-xl-base-1.0 | UNet |
fp16 |
s3://tensorized/stabilityai/stable-diffusion-xl-base-1.0/fp16/unet.tensors |
| StabilityAI/stable-diffusion-xl-base-1.0 | TextEnc |
fp16 |
s3://tensorized/stabilityai/stable-diffusion-xl-base-1.0/fp16/text_encoder.tensors |
| StabilityAI/stable-diffusion-xl-base-1.0 | TextEnc2 |
fp16 |
s3://tensorized/stabilityai/stable-diffusion-xl-base-1.0/fp16/text_encoder_2.tensors |
tensorizer uses the boto3 library to interact with S3. The easiest way
to use tensorizer with S3 is to configure your S3 credentials in
~/.s3cfg.
If you don't want to use ~/.s3cfg, or wish to use a .s3cfg config file
saved at a nonstandard location (e.g. under /var/run), you can also specify
your S3 credentials using the tensorizer.stream_io.open_stream() function,
and then pass that into the TensorSerializer or TensorDeserializer
constructor.
The stream_io.open_stream() function takes a path_uri argument, which can
be an s3:// URI, and accepts the following keyword arguments:
s3_access_key_id: S3 access key IDs3_secret_access_key: S3 secret access keys3_endpoint: S3 endpoint
Or,
s3_config_path: Alternative filesystem path to a.s3cfgconfig file
For example:
TensorSerializer(
open_stream(s3_uri,
"wb",
s3_access_key_id=ACCESS_KEY,
s3_secret_access_key=SECRET_KEY,
s3_endpoint="object.ord1.coreweave.com"))and...
TensorDeserializer(
open_stream(s3_uri,
"rb",
s3_access_key_id=ACCESS_KEY,
s3_secret_access_key=SECRET_KEY,
s3_endpoint="object.ord1.coreweave.com"))NOTE: For faster object downloads in the CoreWeave Cloud, you can use
the accel-object.ord1.coreweave.com endpoint. This endpoint is optimized
for object downloads, and will be faster than the object.ord1.coreweave.com
endpoint once the object is cached.
NOTE2: The cache above does not get invalidated when the object is updated in S3. If you update an object in S3, you will need to wait for the cache to expire before you can download the updated object. This takes 24 hours since the last download.
For this reason, it is recommended to use a unique S3 key for each version
of a model if you use the accel-object.ord1.coreweave.com endpoint.
tensorizer has a few additional features that make it more useful than
just a serialization/deserialization tool.
The TensorDeserializer class has a num_readers argument that controls
how many threads are allowed to read concurrently from the source file.
This can greatly improve performance, since in many cases the network or the
file is the bottleneck. A few caveats to running with num_readers > 1:
- The specified file must be able to be reopened, so that the
TensorDeserializercan open more streams against the source.- Local files, paths, and HTTP(S) and S3 URIs / open streams are all able to be reopened
- Special files like pipes and sockets, or synthetic file-like objects such as
BytesIOare not currently able to be reopened
- For HTTP(S) and S3 streams and URIs, the host must support the
Rangeheader. Each reader will read a stream from a different Range offset in the source.
The default is num_readers=1, which has no special requirements.
The TensorDeserializer object can be used as-is as a state_dict for
torch.nn.Module.load_state_dict. This is useful for loading the tensors
into a torch.nn.Module that is already initialized, or for inspection.
Keep in mind that load_state_dict is not a fast operation, and will
likely be much slower than load_into_module.
The state_dict can also be used to initialize a HuggingFace Transformers
AutoModel. But HuggingFace Transformers performs three or more copies of
the data, so memory use will explode.
Tensorizer supports models using the bfloat16 data type. However, tensorizer
uses numpy to save the tensors as binary and numpy doesn't support bfloat16.
This means that special conversions need to be applied.
To be saved, the torch tensor is cast to int16 before being converted to
numpy, which doesn't change any of the underlying data. When serialized, the
original bfloat16 datatype string is also saved so that it will be cast back
to bfloat16 during the deserialization process.
The complex32 datatype is supported in a similar way, by casting to int32.
The quantized datatypes (qint8, qint32, etc.) are not currently supported
by tensorizer as they would require supplemental quantization parameters to be
deserialized correctly.
NOTE: The exact choice of intermediate types as int16 and int32 is
considered an implementation detail, and is subject to change,
so they should not be relied upon.
NOTE2: This does not interfere with storing actual int datatypes
used in tensors in tensorized files.
Tensorizer can be used with numpy directly to read and write
numpy.ndarrays.
The serializer's write_tensor function handles supplying both
torch.Tensors and numpy.ndarrays.
The deserializer has a separate function read_numpy_arrays that will return
the data as numpy.ndarrays.
As explained above in bfloat16 support, tensorizer uses
special conversions to write "opaque" datatypes, those not supported by numpy.
Therefore, special considerations need to be taken when loading such data as
numpy.ndarrays.
By default, the TensorDeserializer.read_numpy_arrays function sets its
allow_raw_data parameter to False. This means that if a file contains
opaque datatypes, a ValueError will be raised during deserialization.
If you want to return the raw data regardless, set allow_raw_data to True.
Otherwise, the file may be read with TensorDeserializer.read_tensors
instead, which yields torch.Tensor objects of the correct datatype.
A fifth and sixth variable are also returned by the read_numpy_arrays
generator. The fifth is a bool that indicates whether the returned array
has an opaque datatype and requires special handling (only legal when
allow_raw_data=True). The sixth is a string describing the true, non-numpy
datatype that the raw data should be interpreted as in such cases.
For all other datatypes that require no special handling, these are returned as
False and None, respectively.
The exact numpy datatypes used by the returned opaque numpy.ndarray objects
is not guaranteed, and should not be relied upon.
Older versions of Tensorizer had an argument called plaid_mode that reused
buffers when copying to CUDA devices. This now happens automatically.
plaid_mode and plaid_mode_buffers are left as arguments for backwards
compatibility but are deprecated and have no effect.
tensorizer uses unittest for testing.
The tests have their own set of dependencies, which can be installed with
pip install -r tests/requirements.txt.
Some tests require a GPU, and will be skipped if no GPU is available. To run the tests, run the following in the root of the repository:
python -m pip install -e .
python -m pip install -r tests/requirements.txt
python -m unittest discover tests/ --verboseYou may want to do Serialization in a separate process so that your main process can continue executing and not get bogged down by GIL contention. See docs/subprocess-serialization.md for more details.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent