/docs/design/sdk/batch.md

Batched Stream SDK

Wrapper over the Stream SDK for batching many requests

Goal

LMCacheStream drives one request. LMCacheBatchedStream groups many of them and runs each phase — prefill, modify, decode — across the whole batch at once (one thread per stream), then aggregates the per-stream StreamPerfMetrics into a single Metrics report.

import lmcache.sdk.stream as lmc_stream
import lmcache.sdk.batch as lmc_batch

batch = lmc_batch.LMCacheBatchedStream()
for toks in prompts:
    batch.add(lmc_stream.create_request(ctx, post_completion, toks))

batch.prefill({"temperature": 1.0}) # max_tokens forced to 1
batch.modify(drop_tokens) # edit every stream's KV concurrently
results = batch.decode({"max_tokens": 256})  # returns Metrics
results.emit() # print the Metric in a table on terminal
data = results.to_dict() # to get the Metric as a dictionary

See example in token-dropping example.

Pipeline

The three high-level methods mirror the stream phases and each return a Metrics:

• prefill(sampling_params, fmt="terminal", width=80, stream_ids=None): forces max_tokens=1, runs every stream, reports prefill metrics.
• modify(fn, fmt="terminal", width=80, stream_ids=None): applies fn to every stream's KV (modify_kv). Only reports duration (s).
• decode(sampling_params, fmt="terminal", width=80, stream_ids=None): runs every stream and reports decode metrics.

Lower-level pieces:

• add(stream) / get_stream(stream_id): register / fetch a stream (keyed by stream.stream_id()).
• run_streams(sampling_params, stream_ids=None) returns duration (s). Batches stream.generate(sampling_params) across thread pool and stores each result in perf_metrics. Used by prefill/decode.
• modify_stream(fn, stream_ids=None) returns duration (s). Batches stream.modify_kv(fn) across thread pool.
• get_perf_metrics(duration, fmt, width, mode, stream_ids=None) returns the aggregated Metrics. mode can be "prefill" or "decode".

stream_ids=None means "all streams in the batch".