Metadata-Version: 2.4
Name: tokasaurus
Version: 0.0.3
Summary: The little (LLM) engine that could!
License: 
                                         Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright 2025 Stanford University
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: transformers==4.53.0
Requires-Dist: pydra-config>=0.0.13
Requires-Dist: accelerate
Requires-Dist: art
Requires-Dist: statsd
Requires-Dist: fastapi
Requires-Dist: ninja
Requires-Dist: tabulate
Requires-Dist: uvicorn
Requires-Dist: typer
Requires-Dist: openai
Requires-Dist: loguru
Requires-Dist: python-multipart
Requires-Dist: torch==2.6.0
Requires-Dist: flashinfer-python==0.2.0.post2
Requires-Dist: tqdm
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: datasets; extra == "dev"
Requires-Dist: pyright; extra == "dev"
Requires-Dist: math-verify[antlr4_13_2]; extra == "dev"
Requires-Dist: matplotlib; extra == "dev"
Dynamic: license-file

# Tokasaurus: The Little (LLM) Engine That Could!

Check out our blog post [here](https://scalingintelligence.stanford.edu/blogs/tokasaurus/)!

## Table of Contents

- [What is This?](#what-is-this)
- [Installation](#installation)
- [Quickstart](#quickstart)
- [Walkthrough of CLI Flags](#walkthrough-of-cli-flags)
- [System Design](#system-design)


## What is This?
Tokasaurus is an LLM inference engine designed for high-throughput workloads. Features include:

- OpenAI chat, completions, and batch APIs.
- Data, pipeline, and tensor parallelism (with support for [AsyncTP](https://discuss.pytorch.org/t/distributed-w-torchtitan-introducing-async-tensor-parallelism-in-pytorch/209487)).
- Support for Llama3 and Qwen2 architectures.
- [Paged KV caching](https://arxiv.org/abs/2309.06180) with [prefix caching](https://arxiv.org/abs/2312.07104).
- Efficient attention over shared prefixes with [Hydragen](https://arxiv.org/abs/2402.05099), with automatic detection of shared prefixes across groups of sequences.
- End-to-end torch compile with dynamic shapes.
- CUDA graphs.
- Very low CPU overhead (important for small models/fast GPUs).
- A scheduler that can simulate the number of available KV cache blocks thousands of steps in the future, allowing us to aggressively onboard new sequences and keep our batch size as large as possible.
- No OOMs or recompiles in production: on engine startup, we launch a series of warmup inputs that trigger all torch recompiles ahead-of-time (torch will recompile whenever a tensor has an input dimension is 0 or 1) and make check for OOMs using the largest configured batch size.

NOTE: as a new project, expect some potentially rough edges :).

## Installation

Tokasaurus has been tested on Python >= 3.10. To install from PyPI, run:

```bash

pip install tokasaurus

```

Alternatively, clone the repo and run:

```bash

pip install -e .

```

## Quickstart

Once installed, you can launch the engine with:

```bash

# launch engine for Llama 1B (by default on port 10210).
toka model=meta-llama/Llama-3.2-1B-Instruct

# make a request to the engine (this command just wraps the OpenAI client)
toka-ping prompt='tell me a joke' max_tokens=256 chat=True

# launch a 70B model with pipeline parallelism across 8 gpus
toka model=meta-llama/Llama-3.1-70B-Instruct kv_cache_num_tokens='(512 * 1024)' pp_size=8
```

To ping the engine once it's been launched, you can use the OpenAI client:

```python

from openai import OpenAI
client = OpenAI(
    api_key='fake-key',
    base_url="http://0.0.0.0:10210/v1"
)
response = client.completions.create(
  model="default",
  prompt="On a dark desert highway, cool wind in my hair, warm smell of colitas, rising up through the air, up ahead in the distance, I saw a shimmering light, my head grew heavy and my sight grew dim, I had to stop for the night",
  temperature=0,
  n=2,
  max_tokens=100,
)

```

### LM Eval Harness

Since the engine supports the OpenAI API, you can plug it into the EleutherAI LM Eval harness using their local completions feature. First spin up an engine (see above) and then run evals on it with:

```bash

lm_eval --model local-completions --tasks gsm8k --model_args model=MODEL,base_url=http://0.0.0.0:PORT/v1/completions,num_concurrent=256,max_retries=3,tokenized_requests=False

```

## Walkthrough of CLI Flags

The tokasaurus CLI uses [Pydra](https://github.com/jordan-benjamin/pydra), which uses a `key=value` format to set config flags. It also allows for boolean shorthands (e.g. `key=T` is equivalent to `key=True`) and allows for Python expression evaluation between parentheses (e.g. `key='(2 * 1024)'` is equivalent to `key=2048`).

### The Basics

The only required parameter to launch an engine is the `model` field, which can point to a repo on HF or a local directory where a model is stored in HF format (just like when calling `from_pretrained` on a HF model). By default, the tokenizer will also be loaded using using the `model` flag. This can be overridden by setting the `tokenizer` flag yourself:

```bash
toka model=meta-llama/Llama-3.2-1B-Instruct

# e.g. if you want to load a fine-tuned model you saved to disk
toka model=my_local_dir tokenizer=meta-llama/Llama-3.2-1B-Instruct
```

### Leveraging Multiple GPUs

By default, the engine will only use a single GPU to serve the model. You can change this with the `dp_size`, `pp_size`, and `tp_size` flags to control data, pipeline, and tensor parallelism, respectively. These flags are composable: for example, `dp_size=2` and `pp_size=4` will use 8 GPUs in total by creating two data-parallel replicas that each contain 4 GPUs in a pipeline:

### Managing GPU Memory with KV Cache Limits and Concurrency Controls

The total amount of GPU memory used by the engine is the sum of GPU memory used to store the model weights, the activations, and the KV cache. While the model's GPU memory is fixed for a given model, we can control the size of the KV cache and the amount of activation memory we use.

The KV cache size is controlled with `kv_cache_size_num_tokens`, and we can cap activation memory with the flags `max_tokens_per_forward` and `max_seqs_per_forward`. With `max_tokens_per_forward`, you directly control the number of tokens being sent through the model in a single forward pass, which can include tokens from sequences running either prefill or decode. With `max_seqs_per_forward`, we control the total number of sequences that can be running (i.e. that are in prefill or in decode) at a given time. Importantly, this limits the number of tokens per forward pass that can ever be sent through the language modeling head of the model, which can have a disproportionately large impact on activation memory. Prefill tokens don't run through the LM head (since we don't need to decode anything from them), so they take less activation memory.

How should you tune these flags? Well, one of the most important factors for achieving high throughput is making the batch size as large as possible. A common bottleneck that limits the batch size in practice is the size of the KV cache - once your KV cache is full, you can't run any more sequences concurrently. Therefore, we want to make the KV cache as large as possible. However, in order to benefit from a large KV cache that can fit many sequences, we also must increase `max_seqs_per_forward` and `max_tokens_per_forward`. However, increasing these concurrency control flags increases the amount of used activation memory... decreasing the size of the largest KV cache we can fit.

In practice, what this means is that you should increase your KV cache size and concurrency control flags jointly, making sure that you're not excessively raising one without the other.

Note: when using multiple GPUs, these flags apply to each data-parallel replica separately (and apply collectively to all of the GPUs within a data parallel replica). For example, if you run with` dp_size=2 pp_size=4 kv_cache_size_num_tokens='(1024 * 1024)' max_seqs_per_forward=1024 max_tokens_per_forward=2048`:
- In total, your server will have a KV cache size of 2 million tokens (1 million for each of the data parallel replicas).
- Each replica can have 1024 sequences running at once and 2048 tokens scheduled per forwards pass.
- Note that none of these numbers are multiplies by the pipeline parallel size.

### Torch Compile

Torch compiling your model can make it faster and reduce the amount of used activation memory, allowing you to increase the KV cache size further. You can turn it on with `torch_compile=T`. The reason it's off by default is because it increases server startup time (often by a minute or two, but this can be worse the first time you run the engine on a new machine with compilation enabled). As a rough rule of thumb, turn compilation off for debugging things where fast startup is handy, but keep it on for all long-running jobs.

### Hydragen

[Hydragen](https://arxiv.org/abs/2402.05099) (AKA cascade attention, bifurcated attention) is a method for more efficiently computing attention over a batch of sequences that share a common prefix. You can turn on Hydragen with `use_hydragen=T` and tokasaurus will automatically detect shared prefixes across groups of sequences actively running. You can control the thresholds where groups will be formed with `hydragen_min_group_size` and `hydragen_min_prefix_len`, which define the minimum number of sequences in a shared prefix group, and the minimum token length of a shared prefix measured in tokens, respectively. Note that turning on Hydragen can have a slight numerical impact on your generations since we combine attention results in bfloat16.

### Misc

Here are some other server flags we didn't cover above, with their corresponding defaults:

```bash
port=10210 # The port the server listens on. Note that all data parallel replicas are accessed through the same server port.
page_size=16 # The page size for the paged KV cache.
stop_string_num_token_lookback=5 # How many tokens to look back in the sequence for when checking whether a stop string has been generated. You may need to increase this if you have very long stop strings.
stats_report_seconds=5.0 # How often server stats are printed to the console.
uvicorn_log_level="info" # The logging level for the uvicorn web server handling requests. Set this value to "warning" to disable logs being printed every time a request is finished (which can sometimes be annoying/verbose).
```

## System Design

Tokasaurus has three major components:

1. A web server that interfaces between client requests and the actual engine (see `tokasaurus/server/`).
2. A manager that handles most of the CPU-side complexity (e.g. scheduling, paged kv cache management, hydragen grouping, etc.) (see `tokasaurus/manager/`).
3. A relatively barebones model worker that runs forward passes (see `tokasaurus/model/`).

The server and manager are each their own process, with the model worker corresponding to one or more processes depending on the parallelization flags. These components communicate with each other asynchronously using queues. Importantly, the manager works to ensure that there are multiple items in the model input queue, so that the model can always be running forwards passes (i.e. the GPU can always be active) and never stall waiting for the manager to send it more work.

When data parallelism is used, each replica has its own manager process and set of model worker processes. However, all data parallel replicas share the same server process which handles load balancing.

The entry point for starting up the server and kicking off all the processes is `tokasaurus/entry.py`.


## Citation

If you use Tokasaurus in your research, please cite:

```bibtex

@misc{juravsky2025tokasaurus,
  author       = {Jordan Juravsky and Ayush Chakravarthy and Ryan Ehrlich and Sabri Eyuboglu and Bradley Brown and Joseph Shetaye and Christopher R{\'e} and Azalia Mirhoseini},
  title        = {Tokasaurus: An LLM Inference Engine for High-Throughput Workloads},
  year         = {2025},
  howpublished = {\url{https://scalingintelligence.stanford.edu/blogs/tokasaurus/}}
}

```
