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, subtest_name: Literal['test_internode', 'test_intranode', 'test_low_latency', 'test_ep'] = 'test_internode', deep_ep_root: str = '/workspace/DeepEP', legacy_tests_root: str | None = None, elastic_tests_root: str | None = None, python_executable: str = 'python', num_processes: int = 8, num_tokens: int = 4096, hidden: int = 7168, num_topk: int = 8, num_experts: int = 256, num_topk_groups: int | None = None, allow_mnnvl: bool = False, test_ll_compatibility: bool = False, pressure_test_mode: int = 0, pressure_test: bool = False, shrink_test: bool = False, disable_nvlink: bool = False, use_logfmt: bool = False, num_sms: int = 0, num_qps: int = 0, num_allocated_qps: int = 0, num_gpu_timeout_secs: int = 100, num_cpu_timeout_secs: int = 100, sl_idx: int = 0, do_cpu_sync: int = 1, allow_hybrid_mode: int = 1, allow_multiple_reduction: int = 1, prefer_overlap_with_compute: int = 0, deterministic: bool = False, seed: int = 0, skip_check: bool = False, skip_perf_test: bool = False, do_pressure_test: bool = False, reuse_elastic_buffer: bool = False, test_first_only: bool = False, unbalanced_ratio: float = 1.0, precise_unbalanced_ratio: bool = False, masked_ratio: float = 0.0, dump_profile_traces: str = '', ignore_local_traffic: bool = False, **extra_data: Any)[source]

Bases: CmdArgs

Command arguments for the official DeepEP test scripts.

Test Definition

class cloudai.workloads.deepep.deepep.DeepEPTestDefinition(*, name: str, description: str, test_template_name: str, cmd_args: ~cloudai.workloads.deepep.deepep.DeepEPCmdArgs, dse_excluded_args: list[str] = <factory>, extra_env_vars: dict[str, str | ~typing.List[str]] = {}, extra_cmd_args: dict[str, str] = {}, extra_container_mounts: list[str] = [], git_repos: list[~cloudai._core.installables.git_repo.GitRepo] = [], nsys: ~cloudai.models.workload.NsysConfiguration | None = None, predictor: ~cloudai.models.workload.PredictorConfig | None = None, training_report: ~cloudai.models.workload.TrainingReportConfig | None = None, agent: str = 'grid_search', agent_steps: int = 1, agent_metrics: list[str] = ['default'], agent_reward_function: str = 'inverse', agent_config: dict[str, ~typing.Any] | None = None, env_params: dict[str, ~cloudai.configurator.env_params.EnvParamSpec] = <factory>)[source]

Bases: TestDefinition

Test object for official DeepEP v1/v2 test scripts.

property is_domain_randomization_enabled: bool

at least one env_params annotation.

Type:

Whether the config declares domain randomization

is_dse_excluded_arg(path: str) → bool

Return whether a dot-separated cmd_args path should be ignored by DSE.

is_env_sampled(cmd_args_path: str) → bool

Whether a cmd_args field is env-sampled (env draws it per trial, not the agent).

validator validate_env_params  »  all fields

Validate env_params annotations against cmd_args.

env_params is an annotation: each key names a cmd_args field whose value is the candidate set (the single source of truth), and the entry carries only how to sample. So each key must name a real cmd_args field whose value is a candidate list; a scalar is already fixed, so annotating it is a meaningless label and is rejected here. When weights are declared, the list needs >= 2 values and the weights must align 1:1 with it. Sampling, persistence, the per-trial cmd_args overlay, and the cache key all live in CloudAIGymEnv; keeping this shape check in core lets the overlay stay agent- and workload-agnostic rather than re-implemented per workload.