Task Orchestration Plan Configuration

Overview

The Task Orchestration Plan Configuration (Plan for short) declares the complete runtime scheme for a kernel generation task and is loaded by Task at runtime to drive agent collaboration and execution.

The plan mainly includes:

• agent_model_config: assign LLM presets to each agent (presets from core/llm/llm_config.yaml).
• workflow_config_path: points to the workflow YAML that defines execution flow (see Workflow System Design Document).
• docs_dir: reference documentation directories for agents (see Doc-Driven Integration Guide).
• log_dir: root directory for task logs.
• profile_settings: performance testing parameters (e.g., run_times, warmup_times).
• verify_timeout: verification timeout in seconds.

Configuration File Structure

The plan uses YAML format and mainly contains the following items:

# Agent model configuration
agent_model_config:
  designer: model_name
  coder: model_name
  conductor: model_name
  api_generator: model_name

# Log configuration
log_dir: "~/aikg_logs"

# Workflow configuration
workflow_config_path: "config/workflow_file.yaml"

# Documentation directory configuration
docs_dir:
  designer: "path/to/designer/docs"
  coder: "path/to/coder/docs"

# Performance analysis configuration
profile_settings:
  run_times: 50
  warmup_times: 5

# Verification configuration
verify_timeout: 300  # Verification timeout in seconds

Main Configuration Items

1. Agent Model Configuration (agent_model_config)

agent_model_config assigns the LLM preset used by each agent. Values must come from core/llm/llm_config.yaml (e.g., deepseek_r1_default). Common keys: designer, coder, conductor, api_generator (extensible). Invalid names will be rejected during validation.

# Example agent model configuration
agent_model_config:
  designer: deepseek_r1_default
  coder: deepseek_r1_default

2. Log Configuration (log_dir)

Specifies the storage directory for task execution logs:

• Format: String path
• Support: Absolute and relative paths (supports ~ for user home directory)
• Default: "~/aikg_logs"
• Final path shape: A subdirectory like Task_{random_id} will be created under this directory at runtime.

3. Workflow Configuration (workflow_config_path)

• Points to the workflow YAML for this task, configuring AIKG's task execution flow. Example: "config/default_workflow.yaml".
• See Workflow System Design Document.

4. Documentation Directory Configuration (docs_dir)

See the Doc-Driven Integration Guide.

# Example docs configuration
docs_dir:
  designer: "resources/docs/triton_ascend_docs"    # Designer docs (e.g., DSL syntax)
  coder: "resources/docs/triton_ascend_docs"       # Coder docs (e.g., DSL syntax)

5. Performance Analysis Configuration (profile_settings)

Configures execution parameters for performance testing:

profile_settings:
  run_times: 50      # Number of performance test runs
  warmup_times: 5    # Number of warmup runs

6. Verification Configuration (verify_timeout)

Sets the timeout for code verification:

• Unit: Seconds
• Default: 300 seconds (5 minutes)
• Purpose: Prevents verification process from waiting indefinitely

Preset Plans

Triton Configuration (default_triton_cuda_config.yaml / default_triton_ascend_config.yaml)

Use Case: Triton kernel development

Features:

• Uses predefined design language
• Targets Triton code generation
• Supports Ascend NPU / CUDA GPU backend

Configuration Examples:

• config/default_triton_cuda_config.yaml (for CUDA backend)
• config/default_triton_ascend_config.yaml (for Ascend backend)

SWFT Configuration (default_swft_config.yaml)

Use Case: SWFT kernel development on Huawei Ascend NPUs

Features:

• Uses predefined design language
• Targets SWFT code generation
• Supports Ascend NPU backend

Usage

from ai_kernel_generator.config.config_validator import load_config

# Option 1: Load preset by DSL (e.g., triton)
config = load_config(dsl="triton_ascend", backend="ascend")  # or "triton_cuda" for CUDA backend

# Option 2: Load by explicit plan path (local vLLM coder-only)
config = load_config(config_path="python/ai_kernel_generator/config/vllm_triton_ascend_coderonly_config.yaml")  # Ascend
# config = load_config(config_path="python/ai_kernel_generator/config/vllm_triton_cuda_coderonly_config.yaml")    # CUDA

task = Task(op_name="relu", task_desc="...", dsl="triton_ascend", config=config)  # or "triton_cuda" for CUDA

2. Custom Configuration Override

# Customize based on default configuration
config = load_config(dsl="triton_ascend", backend="ascend")  # or "triton_cuda" for CUDA backend

# Override specific configuration items
config.update({
    "log_dir": "/custom/log/path",
    "agent_model_config": {
        "designer": "custom_model",
        "coder": "custom_model"
    }
})

3. Creating a New Plan File

# custom_orchestration_plan.yaml
agent_model_config:
  designer: model_name
  coder: model_name
  conductor: model_name

log_dir: "~/custom_logs"

workflow_config_path: "config/custom_workflow.yaml"

docs_dir:
  designer: "resources/docs/custom_design_docs"
  coder: "resources/docs/custom_dsl_docs"

profile_settings:
  run_times: 100
  warmup_times: 10

verify_timeout: 600

Configuration Extension Guide

1. Adding New Agent Configurations

agent_model_config:
  designer: model_name
  coder: model_name
  conductor: model_name
  optimizer: model_name    # New optimizer agent

2. Extending Documentation Directory Configuration

docs_dir:
  designer: "path/to/design/docs"
  coder: "path/to/code/docs"
  optimizer: "path/to/optimization/docs"  # New optimization docs