Metadata-Version: 2.4
Name: batchbench
Version: 0.5.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Rust
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Dist: pyyaml>=6.0
Requires-Dist: requests>=2.28.0 ; extra == 'harness'
Requires-Dist: paramiko>=3.0.0 ; extra == 'harness'
Requires-Dist: prime ; extra == 'harness'
Provides-Extra: harness
Summary: Offline and online benchmarking utilities for large language model workloads
Author: BatchBench Contributors
License: Apache-2.0
Requires-Python: >=3.9
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM

# BatchBench

BatchBench ships a Rust benchmarking core with a thin Python wrapper.

You can install it with:

```bash
uv pip install batchbench
```

The Python package exposes Rust functionality for request generation and benchmark execution.

## Python API

```python
import batchbench

config = {
    "endpoint": "https://example.com/v1/chat/completions",
    "user_count": 1,
    "mode": batchbench.finite_mode(requests_per_user=1),
    "request_body": batchbench.request_entry(
        {
            "model": "gpt-4o-mini",
            "messages": [{"role": "user", "content": "ping"}],
        },
        line_idx=0,
        input_tokens=1,
    ),
    "requests": [
        batchbench.request_entry(
            {
                "model": "gpt-4o-mini",
                "messages": [{"role": "user", "content": "ping"}],
                "max_tokens": 4,
            },
            line_idx=0,
            input_tokens=1,
        )
    ],
    "dry_run": True,
}

report = batchbench.run_benchmark(config)
print(report)
```

Request generation:

```python
requests = batchbench.generate_requests(
    {
        "count": 16,
        "prefix_overlap": 0.2,
        "target_tokens": 128,
        "tokenizer_model": "Qwen/Qwen3-VL-235B-A22B-Instruct-FP8",
        "dist_mode": "fixed",
    },
    model="Qwen/Qwen3-VL-235B-A22B-Instruct-FP8",
)
```

## Python CLI

The package installs `batchbench`, which forwards directly to the Rust CLI implementation.
Use the same flags as the Rust binary:

```bash
batchbench \
  --model gpt-4o-mini \
  --users 8 \
  --requests-per-user 2 \
  --input-tokens 256 \
  --output-tokens 64 \
  --output-vary 0
```

Use `--sglang` to apply output token constraints via `min_new_tokens`/`max_new_tokens`
instead of `min_tokens`/`max_tokens`.

Press `Ctrl+C` during a run to cancel active requests and print a partial summary.

## Agent-loop benchmark

`batchbench-agent` is a separate entrypoint for stateful agent workloads. It starts
`--agents` independent loops concurrently. Within each agent, model requests remain
sequential: the returned assistant message and a synthetic tool/environment response
are appended to `messages`, then the complete growing conversation is sent again.
This makes every request after the first one reuse that agent's previous prompt as a
prefix and exercises server-side KV caching.

Fixed-length example:

```bash
batchbench-agent \
  --model Qwen/Qwen3-8B \
  --host http://localhost:8000 \
  --agents 16 \
  --input-tokens 256 \
  --output-tokens 64 \
  --environment-tokens 128 \
  --tool-invocations 8 \
  --tool-call-latency-ms 250
```

Log-normal example:

```bash
batchbench-agent \
  --model Qwen/Qwen3-8B \
  --host http://localhost:8000 \
  --agents 16 \
  --input-lognorm-median 256 \
  --input-lognorm-sigma 0.5 \
  --input-lognorm-max 2048 \
  --output-lognorm-median 64 \
  --output-lognorm-sigma 0.4 \
  --output-lognorm-max 512 \
  --environment-lognorm-median 128 \
  --environment-lognorm-sigma 0.6 \
  --environment-lognorm-max 1024 \
  --tool-invocations-lognorm-median 8 \
  --tool-invocations-lognorm-sigma 0.3 \
  --tool-invocations-lognorm-max 32 \
  --tool-call-latency-lognorm-median-ms 250 \
  --tool-call-latency-lognorm-sigma 0.5 \
  --tool-call-latency-lognorm-max-ms 2000 \
  --seed 42
```

Each log-normal family accepts either `*-lognorm-median` or `*-lognorm-mu`,
requires `*-lognorm-sigma`, and optionally accepts `*-lognorm-max`. Samples are
independent between agents and turns; `--seed` makes the sampled workload
reproducible. `--tokenizer-model` can be supplied when the endpoint's model name is
not also a Hugging Face tokenizer identifier. It accepts a Hugging Face model ID,
a local `tokenizer.json` file, or a local directory containing `tokenizer.json`.

Every request includes a `user` field and an `X-SMG-Routing-Key` header containing
the same UUID, which remains stable for the life of that agent and differs between
agents. Use `--disable-user-tagging` to omit both, or `--user-prefix <prefix>` to
use deterministic `<prefix>-<agent_id>` values instead of UUIDs.

For deterministic data-parallel routing, pass both
`--dp-rank-perfect-routing` and `--dp-rank-perfect-routing-num <ranks>`.
Each request then includes `X-SMG-Target-Worker: <agent_id % ranks>`, distributing
consecutive agents evenly across the configured ranks. This routing is independent
of user tagging.

Tool-call latency defaults to zero. Set a fixed delay with
`--tool-call-latency-ms`, or sample milliseconds independently for every invocation
with `--tool-call-latency-lognorm-median-ms` (or
`--tool-call-latency-lognorm-mu`), `--tool-call-latency-lognorm-sigma`, and the
optional `--tool-call-latency-lognorm-max-ms`. After a model response succeeds, the
agent sleeps for the sampled duration before making the environment result
available and submitting its next model request.

One tool invocation means one unconstrained model request followed by one synthetic
environment response. BatchBench treats generated assistant output as opaque state:
it preserves content and reasoning output but ignores any model-generated tool calls.
It then adds its own valid synthetic `environment` tool call and appends the sampled
environment response. This keeps the trajectory protocol-valid without assuming
anything about the generated output.

The final report includes:

- total input tokens sent, from successful responses' `usage.prompt_tokens`;
- total output tokens generated, from `usage.completion_tokens`;
- estimated cached input tokens under perfect prefix caching;
- total simulated tool-call latency across all agents;
- request-latency p50, p90, and p99 across successful requests;
- end-to-end p50, p90, and p99 across completed agents, measured from the start
  of each agent loop through its final synthetic tool delay.

Failed agents are excluded from the end-to-end latency distribution because their
lifetimes end early. Their request failures are still included in the failure report.

For each successful request after an agent's first, the cache estimate adds that
same agent's preceding prompt-token count (capped by the current prompt count).
Retries and failed requests are excluded because they do not provide reliable usage
data. Use `--results-csv <path>` to persist the same totals, or `--dry-run` to inspect
the independently sampled workload without sending requests.

## Rust CLI

The existing Rust CLI is unchanged:

```bash
cargo build --release --manifest-path rust/Cargo.toml --bin batchbench
./rust/target/release/batchbench --help
```

The agent-loop binary can likewise be run directly:

```bash
cargo build --release --manifest-path rust/Cargo.toml --bin batchbench-agent
./rust/target/release/batchbench-agent --help
```

## Releases and PyPI

- CI (`.github/workflows/ci.yaml`) checks Rust build/test, builds a wheel, and runs smoke tests.
- Release Please (`.github/workflows/release-please.yaml`) opens/updates release PRs and, on merge, creates `v*` tags/releases.
- Python release workflow (`.github/workflows/python-release.yaml`) builds and publishes prebuilt platform wheels to PyPI on `v*` tag pushes.

