DeepEP Benchmark

DeepEP Benchmark workload (test_template_name is DeepEP) allows users to execute DeepEP (Deep Expert Parallelism) MoE (Mixture of Experts) benchmarks within the CloudAI framework.

DeepEP is a benchmark for measuring the performance of MoE models with distributed expert parallelism. It supports:

• Two operation modes: Standard and Low-Latency

• Multiple data types: bfloat16 and FP8

• Flexible network configurations: With or without NVLink

• Configurable model parameters: Experts, tokens, hidden size, top-k

• Performance profiling: Kineto profiler support

Test TOML example (Standard Mode):

            

            
name = "deepep_standard"
description = "DeepEP MoE Benchmark - Standard Mode"
test_template_name = "DeepEP"

[cmd_args]
docker_image_url = "<docker container url here>"
mode = "standard"
tokens = 1024
num_experts = 256
num_topk = 8
hidden_size = 7168
data_type = "bfloat16"
num_warmups = 20
num_iterations = 50
        

Test TOML example (Low-Latency Mode):

            

            
name = "deepep_low_latency"
description = "DeepEP MoE Benchmark - Low Latency Mode"
test_template_name = "DeepEP"

[cmd_args]
docker_image_url = "<docker container url here>"
mode = "low_latency"
tokens = 128
num_experts = 256
num_topk = 1
hidden_size = 7168
data_type = "bfloat16"
allow_nvlink_for_low_latency = false
allow_mnnvl = false
        

Test Scenario example:

            

            
name = "deepep-benchmark"

[[Tests]]
id = "Tests.1"
test_name = "deepep_standard"
num_nodes = 2
time_limit = "00:30:00"
        

Test-in-Scenario example:

            

            
name = "deepep-benchmark"

[[Tests]]
id = "Tests.1"
num_nodes = 2
time_limit = "00:30:00"

name = "deepep_standard"
description = "DeepEP MoE Benchmark"
test_template_name = "DeepEP"

  [Tests.cmd_args]
  docker_image_url = "<docker container url here>"
  mode = "standard"
  tokens = 1024
  num_experts = 256
  num_topk = 8
        

Command Arguments

class cloudai.workloads.deepep.deepep.DeepEPCmdArgs(*, docker_image_url: str, mode: Literal['standard', 'low_latency'] = 'standard', tokens: int = 1024, num_experts: int = 256, num_topk: int = 8, hidden_size: int = 7168, data_type: Literal['bfloat16', 'fp8'] = 'bfloat16', allow_nvlink_for_low_latency: bool = False, allow_mnnvl: bool = False, round_scale: bool = False, use_ue8m0: bool = False, num_warmups: int = 20, num_iterations: int = 50, shuffle_columns: bool = False, use_kineto_profiler: bool = False, num_sms: int = 24, num_qps_per_rank: int = 12, config_file_path: str = '/tmp/config.yaml', results_dir: str = '/workspace/dp-benchmark/results', **extra_data: Any)[source]

Bases: CmdArgs

DeepEP benchmark command arguments.

Test Definition

class cloudai.workloads.deepep.deepep.DeepEPTestDefinition(*, name: str, description: str, test_template_name: str, cmd_args: DeepEPCmdArgs, extra_env_vars: dict[str, str | List[str]] = {}, extra_cmd_args: dict[str, str] = {}, extra_container_mounts: list[str] = [], git_repos: list[GitRepo] = [], nsys: NsysConfiguration | None = None, predictor: PredictorConfig | None = None, agent: str = 'grid_search', agent_steps: int = 1, agent_metrics: list[str] = ['default'], agent_reward_function: str = 'inverse', agent_config: dict[str, Any] | None = None)[source]

Bases: TestDefinition

Test object for DeepEP MoE benchmark.

property cmd_args_dict: dict

Return command arguments as dict, excluding CloudAI-specific fields.