Dynamic Batching

Dynamic batching operates at runtime, not compile time. An inference server collects incoming requests within a configurable time window or until a maximum batch size is reached. This allows the system to adapt to real-time load, grouping requests that arrive asynchronously. For example, a server might wait 5-10 milliseconds to aggregate requests before sending the batch to the NPU. This contrasts with static batching, where the batch size is fixed before deployment.

A core challenge dynamic batching solves is processing requests with different input tensor shapes. Common techniques include:

• Padding: Adding zeros to shorter sequences to match the longest sequence in the batch.
• Bucket Batching: Grouping requests with similar sequence lengths into buckets to minimize wasted computation from excessive padding.
• Ragged Batching (Jagged Tensors): Using specialized data structures and kernels that can natively handle variable dimensions without explicit padding, though this requires specific hardware and compiler support. This is critical for models like transformers, where input text length varies significantly.

Dynamic batching introduces a fundamental engineering trade-off:

• Increased Throughput: By fully utilizing the NPU's parallel compute units, hardware utilization rises dramatically, often leading to 5-10x higher inferences per second compared to sequential processing.
• Increased Tail Latency: Requests wait in a queue for the batching window to close. While average latency may improve, the 99th percentile (p99) latency increases due to this waiting period. The batching window size is a key tuning parameter to balance this trade-off for specific service-level agreements.

An advanced form of dynamic batching used for autoregressive models like large language models. Instead of batching entire requests, it batches at the token generation level.

• New requests can join a running batch as soon as previous requests in the batch finish their generation.
• This eliminates the need for a request to wait for the slowest sequence in a batch to complete, drastically improving latency for long-generation tasks.
• It requires sophisticated KV cache management and scheduler logic within the inference runtime (e.g., NVIDIA's TensorRT-LLM, vLLM).

The primary mechanical goal is to saturate the NPU's compute and memory bandwidth. Underutilized hardware leads to wasted resources and cost inefficiency. Dynamic batching addresses this by:

• Keeping vector/SIMD units busy with parallel operations across batch elements.
• Amortizing the fixed overhead of kernel launches and memory transfers over more work.
• Improving data locality and prefetching efficiency as more data is processed in a contiguous manner. The effectiveness is measured by metrics like SM (Streaming Multiprocessor) utilization and achieved memory bandwidth percentage.

Dynamic batching requires support across the software stack:

• Compiler: Must generate kernels that can accept a batch dimension as a runtime argument.
• Runtime Scheduler: Manages the request queue, batching window, and dispatches batches to the execution engine.
• Memory Allocator: Handles flexible memory allocation for variable-sized batches, often using a memory pool to reduce allocation overhead.
• Kernel Implementations: Kernels for operations like matrix multiplication must be optimized for a range of batch sizes, not just a single static size. This is often achieved through parameterized kernel auto-tuning.

The core architectural pattern involves a request queue, a batching scheduler, and a dynamic-shape-aware execution engine.

Key Components:

• Scheduler: Polls the request queue, applying a batching policy (e.g., max batch size, timeout, priority).
• Kernel Launch: Must launch kernels with the actual batch dimension (not a padded maximum), requiring kernels compiled for dynamic shapes.
• Memory Management: Uses techniques like unified tensors or ragged tensors to handle variable-sized inputs within a batch without wasteful padding.

This pattern is replicated from cloud-scale serving down to edge deployment engines.

Implementing dynamic batching introduces engineering complexities that systems must navigate.

• Tail Latency vs. Throughput: A longer batch timeout increases throughput but can degrade latency for early-arriving requests. Systems must tune this trade-off.
• Kernel Optimization: Kernels optimized for a fixed batch size (via static shape compilation) often outperform dynamic-shape kernels. Systems may use a hybrid approach, selecting from pre-compiled kernels for common batch sizes.
• Memory Fragmentation: Aggressively reusing memory buffers for variable-sized batches can lead to fragmentation. Advanced allocators or memory pooling are required.
• Complex Control Flow: Models with internal conditionals (e.g., tf.cond) are challenging to batch dynamically, often requiring graph transformations or execution fallbacks.

A more advanced form of batching used in large language model inference servers. Unlike static batching (fixed batch size) or basic dynamic batching (collecting requests within a time window), continuous batching (also known as iteration-level batching or incremental batching) allows new requests to join and finished requests to leave a batch at each decoding step.

• Key Mechanism: It treats each token generation step as a micro-batch, dynamically adjusting the active batch size.
• Benefit: Dramatically improves GPU utilization and reduces latency for LLM inference by eliminating head-of-line blocking, where a single slow request holds up the entire batch.

A foundational compiler optimization that enables efficient batching. It merges multiple, adjacent computational operations (kernels) from a neural network graph into a single, compound kernel.

• Direct Enabler for Batching: By fusing element-wise operations (like ReLU, Add) with a preceding matrix multiplication, the fused kernel can process an entire batch through the combined operation in one launch.
• Primary Benefits:
  • Reduces kernel launch overhead, which is critical when launching the same sequence for many batch items.
  • Minimizes intermediate memory reads/writes by keeping data in registers or cache.
  • Increases arithmetic intensity, making better use of NPU/GPU compute units.

A compile-time analysis step that often precedes kernel fusion and informs batching strategies. It involves grouping sets of operators within a computational graph into clusters.

• Purpose: To identify subgraphs that should be executed together, typically on the same hardware unit (e.g., an NPU core).
• Relation to Dynamic Batching: The compiler must understand cluster boundaries to correctly apply batch execution across multiple requests. A cluster defines the unit of work that will be replicated for each item in the batch.
• Challenge: Clustering must balance parallelism, memory constraints, and data dependencies to create efficient, batchable execution units.

A critical compiler pass that allocates memory buffers for all tensors in a computational graph. It is intimately connected to batching efficiency.

• Batch-Aware Allocation: The planner must account for the batch dimension when allocating buffers. For dynamic batching, it often uses a static shape upper bound or employs dynamic memory allocation strategies.
• Key Technique: Memory Reuse: The planner identifies tensors with non-overlapping lifetimes, allowing their memory to be reused. This is crucial for batching, as activations for different batch items or different layers can share memory, reducing the peak memory footprint.
• Goal: To minimize total device memory usage, enabling larger batch sizes and preventing out-of-memory errors.

A dynamic compilation strategy where parts of the computational graph are compiled during program execution, often in response to specific input characteristics.

• Synergy with Dynamic Batching: JIT compilation can specialize kernels for the actual batch size encountered at runtime, unlike Ahead-of-Time (AOT) compilation which may use a fixed size. This can generate more optimal code (e.g., better loop unrolling).
• Use Case: In frameworks like PyTorch, a model's graph may be JIT-compiled on the first inference run. If dynamic batching is used, the JIT compiler can optimize the fused kernels for the observed batch size distribution.
• Trade-off: Introduces one-time compilation latency ("warm-up" time) in exchange for potentially higher throughput.

The compiler analysis that determines the dimensions (shape) of all tensors in a computational graph at compile time. Its precision directly impacts batching optimizations.

• For Static Batching: All dimensions, including batch size, are known and fixed at compile time, enabling aggressive optimizations.
• For Dynamic Batching: The batch dimension is often dynamic (symbolic). The compiler must still infer the shapes of feature dimensions (e.g., image height/width, embedding size). Partial shape inference allows the compiler to generate code that works for a variable batch size N while knowing other dimensions are fixed.
• Importance: Accurate shape inference is necessary for memory planning, kernel selection, and generating efficient data access patterns within batched kernels.