Distributed Deployment

Distributed Deployment in LLMIR

LLMIR provides comprehensive support for distributed LLM inference, enabling efficient execution of large models across multiple devices and nodes. This capability is essential for deploying models that exceed the memory capacity of a single device or require higher throughput.

Parallelism Strategies ¶

LLMIR supports multiple parallelism strategies for distributed inference:

Tensor Parallelism ¶

Tensor parallelism splits individual tensors across multiple devices, allowing for parallel computation of large matrix operations:

// Tensor-parallel linear layer
%output = llm.sharded_linear(%input, %weight, %bias) {
  shard_dim = 1 : i32,
  num_shards = 8 : i32,
  shard_id = 2 : i32
} : (tensor<16x1024xf16>, tensor<1024x1024xf16>, tensor<1024xf16>) -> tensor<16x1024xf16>

// Communication primitives for tensor parallelism
%gathered = llm.all_gather(%local_output) {
  dim = 1 : i32,
  group_size = 8 : i32
} : (tensor<16x128xf16>) -> tensor<16x1024xf16>

%reduced = llm.all_reduce(%partial_sum) {
  reduction = "sum",
  group_size = 8 : i32
} : (tensor<16x1024xf16>) -> tensor<16x1024xf16>

Pipeline Parallelism ¶

Pipeline parallelism splits the model across layers, with each device processing different stages of the model:

// Pipeline stage definition
%output = llm.pipeline_stage(%input) {
  stage_id = 2 : i32,
  num_stages = 4 : i32,
  schedule = "1f1b"  // 1F1B scheduling strategy
} : (tensor<16x1024xf16>) -> tensor<16x1024xf16>

// Communication between pipeline stages
%next_input = llm.pipeline_send(%output) {
  dest_stage = 3 : i32
} : (tensor<16x1024xf16>) -> ()

%input = llm.pipeline_recv() {
  source_stage = 1 : i32
} : () -> tensor<16x1024xf16>

Sequence Parallelism ¶

For long sequences, LLMIR supports sequence parallelism to distribute processing across devices:

// Sequence-parallel attention
%partial_attn = llm.sequence_parallel_attention(%query, %key, %value) {
  seq_start = 0 : i32,
  seq_length = 1024 : i32,
  total_length = 8192 : i32
} : (tensor<16x1024x16x64xf16>, tensor<16x8192x16x64xf16>, tensor<16x8192x16x64xf16>) 
    -> tensor<16x1024x16x64xf16>

Distributed Memory Management ¶

LLMIR implements distributed memory management to efficiently handle large models:

Sharded KV Cache ¶

Support for sharded KV caches enables processing very long sequences with attention across multiple devices:

// Sharded KV cache definition
!sharded_kv_t = !llm.sharded_kv_cache<f16, 12, 16, 64, 16, 32768, shards=4>

// Operations on sharded KV cache
%output = llm.distributed_attention(%query, %sharded_kv) {
  num_heads = 16 : i32,
  head_dim = 64 : i32,
  scale = 0.125 : f32
} : (tensor<2x1x16x64xf16>, !sharded_kv_t) -> tensor<2x1x16x64xf16>

Remote Memory Access ¶

LLMIR includes operations for accessing memory across devices:

// Remote memory operations
%remote_data = llm.remote_load(%address) {
  device = 2 : i32
} : (!llm.device_ptr) -> tensor<16x1024xf16>

llm.remote_store(%data, %address) {
  device = 3 : i32
} : (tensor<16x1024xf16>, !llm.device_ptr) -> ()

Compilation for Distributed Execution ¶

LLMIR provides end-to-end compilation support for distributed execution:

Automated Partitioning ¶

The compiler includes passes to automatically partition models for distributed execution:

ModelPartitioningPass: Divide the model based on device constraints
CommunicationInsertionPass: Add necessary communication operations
MemoryPlanningPass: Optimize memory usage across devices

Cost Model ¶

LLMIR uses a cost model to optimize distribution strategies:

// Cost model annotation
llm.cost_annotation(%op) {
  compute_flops = 1024.0 : f32,
  memory_bytes = 8192 : i64,
  communication_bytes = 4096 : i64
} : (!llm.op) -> ()

Runtime Support ¶

The LLMIR runtime provides essential services for distributed execution:

Device Coordination ¶

// Device coordination API (Planned)
class DistributedRuntime {
public:
  // Initialize distributed environment
  static void init(int worldSize, int rank);
  
  // Synchronize devices
  static void barrier();
  
  // Group management
  static DeviceGroup createGroup(const std::vector<int>& ranks);
  
  // Communication primitives
  static void allReduce(void* data, size_t count, DataType dtype, 
                        ReduceOp op, DeviceGroup group);
  static void allGather(void* sendBuf, void* recvBuf, size_t count,
                       DataType dtype, DeviceGroup group);
  // ... other communication operations
};

Scheduling and Load Balancing ¶

The runtime will include efficient scheduling mechanisms:

Dynamic load balancing across devices
Micro-batch scheduling for pipeline parallelism
Automatic adjustment based on device capabilities

Future Directions ¶

As part of LLMIR’s advanced features (Phase 3), distributed deployment support will be enhanced with:

Hybrid Parallelism: Combining multiple parallelism strategies
Fault Tolerance: Recovery mechanisms for device failures
Elastic Scaling: Dynamically adjusting to available resources
Memory Optimization: Techniques like activation recomputation and offloading

This feature is planned for Phase 3 of the LLMIR project development.

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