diff --git a/benchmarks/benchmark_lib.sh b/benchmarks/benchmark_lib.sh index 2ba3d8923d..5fd8b94601 100644 --- a/benchmarks/benchmark_lib.sh +++ b/benchmarks/benchmark_lib.sh @@ -1797,7 +1797,11 @@ build_replay_cmd() { # X-Correlation-ID is useful tracing metadata but does not establish that # binding by itself. AIPerf emits nvext.session_control bind/close actions # keyed by the stable conversation correlation ID when this flag is set. - if [[ "${FRAMEWORK:-}" == dynamo-* ]]; then + # Opt-out: recipes set AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING=0 to skip this. + # aiperf's conv-aware routing emits nvext.session_control, a removed POC field + # (dynamo #9920 / v1.3.0-dev) that current dynamo builds reject with a 400 + # (they moved to router/routing_constraints/agent_context). Default stays on. + if [[ "${FRAMEWORK:-}" == dynamo-* && "${AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING:-1}" != "0" ]]; then REPLAY_CMD+=" --use-dynamo-conv-aware-routing" # The upstream 300s affinity TTL is shorter than an overloaded # high-concurrency agentic request. Keep bindings alive across long diff --git a/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/agg-gb300-tp4-agentic.yaml b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/agg-gb300-tp4-agentic.yaml new file mode 100644 index 0000000000..95d4ffc884 --- /dev/null +++ b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/agg-gb300-tp4-agentic.yaml @@ -0,0 +1,144 @@ +name: "svf-vllm-agg-gb300-tp4-agentic" + +# Low-latency aggregate DeepSeek-V4-Pro AgentX recipe on one four-GPU GB300 +# node. The worker serves both prefill and decode with TP4. + +model: + path: "deepseek-v4-pro" + container: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96" + precision: "fp4" + +identity: + model: + repo: "deepseek-ai/DeepSeek-V4-Pro" + container: + image: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96" + frameworks: + dynamo: "1.2.1" + vllm: "0.17.2rc1.dev3675+gc188b96eb" + +dynamo: + hash: "919682da679aa699d5bca9c872f4c1d9a530bbc0" + install: true + +setup_script: vllm-container-deps.sh + +slurm: + time_limit: "8:00:00" + +health_check: + max_attempts: 2160 + interval_seconds: 10 + +resources: + gpu_type: "gb300" + gpus_per_node: 4 + agg_nodes: 1 + agg_workers: 1 + gpus_per_agg: 4 + +infra: + etcd_nats_dedicated_node: false + nats_max_payload_mb: 32 + +frontend: + type: dynamo + enable_multiple_frontends: false + args: + router-mode: "kv" + router-reset-states: true + router-temperature: 0.0 + router-queue-threshold: 65536 + active-decode-blocks-threshold: "None" + active-prefill-tokens-threshold: "None" + active-prefill-tokens-threshold-frac: "None" + tokenizer: "fastokens" + +backend: + type: vllm + connector: null + mooncake_kv_store: + store_config: + metadata_server: "P2PHANDSHAKE" + global_segment_size: "150GB" + local_buffer_size: "4GB" + protocol: "rdma" + device_name: "mlx5_0,mlx5_1,mlx5_2,mlx5_3" + mode: "embedded" + enable_offload: false + aggregated_environment: + DYN_REQUEST_PLANE: "tcp" + ETCD_LEASE_TTL: "120" + VLLM_ENGINE_READY_TIMEOUT_S: "3600" + VLLM_RPC_TIMEOUT: "600000" + TILELANG_CLEANUP_TEMP_FILES: "1" + VLLM_ALLREDUCE_USE_SYMM_MEM: "0" + VLLM_USE_BREAKABLE_CUDAGRAPH: "0" + TORCH_SYMMMEM: "NVSHMEM" + NCCL_CUMEM_ENABLE: "1" + NCCL_MNNVL_ENABLE: "1" + NCCL_NVLS_ENABLE: "1" + VLLM_SERVER_DEV_MODE: "1" + VLLM_USE_V2_MODEL_RUNNER: "1" + VLLM_USE_RUST_FRONTEND: "1" + VLLM_MOONCAKE_LOAD_RECV_THREADS: "4" + VLLM_CONNECTOR_PREFETCH_DEPTH: "8" + VLLM_CONNECTOR_PREFETCH_KV_CAP: "0.65" + DG_JIT_CACHE_DIR: "/tmp/dg-cache-dsv4-agentx-{job_id}" + VLLM_DSV4_MEGA_FP8_COMBINE: "1" + VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: "1800" + VLLM_PREFIX_CACHE_RETENTION_INTERVAL: "32768" + VLLM_SPARSE_INDEXER_MAX_LOGITS_MB: "1024" + UCX_MEMTYPE_CACHE: "n" + UCX_MEMTYPE_REG_WHOLE: "n" + UCX_RCACHE_MAX_UNRELEASED: "1024" + UCX_TLS: "cuda_copy,cuda_ipc,tcp" + NCCL_P2P_LEVEL: NVL + MC_ENABLE_DEST_DEVICE_AFFINITY: "1" + + vllm_config: + aggregated: + kv-transfer-config: '{"kv_connector":"MooncakeStoreConnector","kv_role":"kv_both","kv_connector_extra_config":{"load_async":true,"lookup_async":true,"enable_offload":false}}' + served-model-name: "deepseek-ai/DeepSeek-V4-Pro" + kv-cache-dtype: "fp8" + tensor-parallel-size: 4 + pipeline-parallel-size: 1 + enable-cumem-allocator: true + numa-bind: true + numa-bind-nodes: [0, 0, 1, 1] + attention-config: '{"backend":"FLASHINFER_MLA_SPARSE_DSV4","use_prefill_query_quantization":true,"use_fp4_indexer_cache":true}' + max-model-len: 1048576 + max-num-seqs: 32 + max-num-batched-tokens: 16384 + trust-remote-code: true + no-enable-flashinfer-autotune: true + block-size: 256 + compilation-config: '{"cudagraph_mode":"FULL_DECODE_ONLY","mode":0}' + max-cudagraph-capture-size: 32 + gpu-memory-utilization: 0.94 + stream-interval: 10 + no-disable-hybrid-kv-cache-manager: true + tokenizer-mode: deepseek_v4 + +sbatch_directives: + cpus-per-task: "72" + +srun_options: + container-remap-root: "" + +benchmark: + type: custom + command: bash /infmax-workspace/benchmarks/multi_node/agentic_srt.sh + env: + INFMAX_CONTAINER_WORKSPACE: /infmax-workspace + RESULT_DIR: /logs/agentic + PORT: "8000" + IS_MULTINODE: "false" + TP: "4" + EP_SIZE: "1" + DP_ATTENTION: "false" + AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING: "0" + AIPERF_DYNAMO_SESSION_TIMEOUT_SECONDS: "14400" + AIPERF_DATASET_MMAP_CACHE_DIR: "/aiperf_mmap_cache" + HF_HUB_CACHE: "/hf_hub_cache" + WEKA_LOADER_OVERRIDE: "semianalysis_cc_traces_weka_062126" diff --git a/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/agg-gb300-tp8-agentic.yaml b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/agg-gb300-tp8-agentic.yaml new file mode 100644 index 0000000000..089fbd63bc --- /dev/null +++ b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/agg-gb300-tp8-agentic.yaml @@ -0,0 +1,144 @@ +name: "svf-vllm-agg-gb300-tp8-agentic" + +# Low-latency aggregate DeepSeek-V4-Pro AgentX recipe spanning two four-GPU +# GB300 nodes. The worker serves both prefill and decode with TP8. + +model: + path: "deepseek-v4-pro" + container: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96" + precision: "fp4" + +identity: + model: + repo: "deepseek-ai/DeepSeek-V4-Pro" + container: + image: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96" + frameworks: + dynamo: "1.2.1" + vllm: "0.17.2rc1.dev3675+gc188b96eb" + +dynamo: + hash: "919682da679aa699d5bca9c872f4c1d9a530bbc0" + install: true + +setup_script: vllm-container-deps.sh + +slurm: + time_limit: "8:00:00" + +health_check: + max_attempts: 2160 + interval_seconds: 10 + +resources: + gpu_type: "gb300" + gpus_per_node: 4 + agg_nodes: 2 + agg_workers: 1 + gpus_per_agg: 8 + +infra: + etcd_nats_dedicated_node: false + nats_max_payload_mb: 32 + +frontend: + type: dynamo + enable_multiple_frontends: false + args: + router-mode: "kv" + router-reset-states: true + router-temperature: 0.0 + router-queue-threshold: 65536 + active-decode-blocks-threshold: "None" + active-prefill-tokens-threshold: "None" + active-prefill-tokens-threshold-frac: "None" + tokenizer: "fastokens" + +backend: + type: vllm + connector: null + mooncake_kv_store: + store_config: + metadata_server: "P2PHANDSHAKE" + global_segment_size: "150GB" + local_buffer_size: "4GB" + protocol: "rdma" + device_name: "mlx5_0,mlx5_1,mlx5_2,mlx5_3" + mode: "embedded" + enable_offload: false + aggregated_environment: + DYN_REQUEST_PLANE: "tcp" + ETCD_LEASE_TTL: "120" + VLLM_ENGINE_READY_TIMEOUT_S: "3600" + VLLM_RPC_TIMEOUT: "600000" + TILELANG_CLEANUP_TEMP_FILES: "1" + VLLM_ALLREDUCE_USE_SYMM_MEM: "0" + VLLM_USE_BREAKABLE_CUDAGRAPH: "0" + TORCH_SYMMMEM: "NVSHMEM" + NCCL_CUMEM_ENABLE: "1" + NCCL_MNNVL_ENABLE: "1" + NCCL_NVLS_ENABLE: "1" + VLLM_SERVER_DEV_MODE: "1" + VLLM_USE_V2_MODEL_RUNNER: "1" + VLLM_USE_RUST_FRONTEND: "1" + VLLM_MOONCAKE_LOAD_RECV_THREADS: "4" + VLLM_CONNECTOR_PREFETCH_DEPTH: "8" + VLLM_CONNECTOR_PREFETCH_KV_CAP: "0.65" + DG_JIT_CACHE_DIR: "/tmp/dg-cache-dsv4-agentx-{job_id}" + VLLM_DSV4_MEGA_FP8_COMBINE: "1" + VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: "1800" + VLLM_PREFIX_CACHE_RETENTION_INTERVAL: "32768" + VLLM_SPARSE_INDEXER_MAX_LOGITS_MB: "1024" + UCX_MEMTYPE_CACHE: "n" + UCX_MEMTYPE_REG_WHOLE: "n" + UCX_RCACHE_MAX_UNRELEASED: "1024" + UCX_TLS: "cuda_copy,cuda_ipc,tcp" + NCCL_P2P_LEVEL: NVL + MC_ENABLE_DEST_DEVICE_AFFINITY: "1" + + vllm_config: + aggregated: + kv-transfer-config: '{"kv_connector":"MooncakeStoreConnector","kv_role":"kv_both","kv_connector_extra_config":{"load_async":true,"lookup_async":true,"enable_offload":false}}' + served-model-name: "deepseek-ai/DeepSeek-V4-Pro" + kv-cache-dtype: "fp8" + tensor-parallel-size: 8 + pipeline-parallel-size: 1 + enable-cumem-allocator: true + numa-bind: true + numa-bind-nodes: [0, 0, 1, 1] + attention-config: '{"backend":"FLASHINFER_MLA_SPARSE_DSV4","use_prefill_query_quantization":true,"use_fp4_indexer_cache":true}' + max-model-len: 1048576 + max-num-seqs: 32 + max-num-batched-tokens: 32768 + trust-remote-code: true + no-enable-flashinfer-autotune: true + block-size: 256 + compilation-config: '{"cudagraph_mode":"FULL_DECODE_ONLY","mode":0}' + max-cudagraph-capture-size: 32 + gpu-memory-utilization: 0.95 + stream-interval: 10 + no-disable-hybrid-kv-cache-manager: true + tokenizer-mode: deepseek_v4 + +sbatch_directives: + cpus-per-task: "72" + +srun_options: + container-remap-root: "" + +benchmark: + type: custom + command: bash /infmax-workspace/benchmarks/multi_node/agentic_srt.sh + env: + INFMAX_CONTAINER_WORKSPACE: /infmax-workspace + RESULT_DIR: /logs/agentic + PORT: "8000" + IS_MULTINODE: "false" + TP: "8" + EP_SIZE: "1" + DP_ATTENTION: "false" + AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING: "0" + AIPERF_DYNAMO_SESSION_TIMEOUT_SECONDS: "14400" + AIPERF_DATASET_MMAP_CACHE_DIR: "/aiperf_mmap_cache" + HF_HUB_CACHE: "/hf_hub_cache" + WEKA_LOADER_OVERRIDE: "semianalysis_cc_traces_weka_062126" diff --git a/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb300-1p1d-dep8-dep8-agentic.yaml b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb300-1p1d-dep8-dep8-agentic.yaml new file mode 100644 index 0000000000..ff95dbc812 --- /dev/null +++ b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb300-1p1d-dep8-dep8-agentic.yaml @@ -0,0 +1,194 @@ +name: "svf-vllm-disagg-gb300-1p1d-dep8-dep8-agentic" + +# GB300 AgentX reference from NVIDIA/srt-slurm#229: one DEP8 prefill worker +# feeding one DEP8 decode worker at concurrency 512. + +model: + path: "deepseek-v4-pro" + container: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96" + precision: "fp4" + +identity: + model: + repo: "deepseek-ai/DeepSeek-V4-Pro" + container: + image: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96" + frameworks: + dynamo: "af0dbfe8233fafdfaaa5d12e17518758c99e8da5" + vllm: "0.17.2rc1.dev3675+gc188b96eb" + +dynamo: + hash: "af0dbfe8233fafdfaaa5d12e17518758c99e8da5" + install: true + +setup_script: vllm-container-deps.sh + +slurm: + time_limit: "8:00:00" + +health_check: + max_attempts: 2160 + interval_seconds: 10 + +resources: + gpu_type: "gb300" + gpus_per_node: 4 + het_jobs: false + spread_workers: false + prefill_nodes: 2 + decode_nodes: 2 + prefill_workers: 1 + decode_workers: 1 + gpus_per_prefill: 8 + gpus_per_decode: 8 + +infra: + etcd_nats_dedicated_node: false + nats_max_payload_mb: 32 + +frontend: + type: dynamo + enable_multiple_frontends: false + args: + router-mode: "kv" + router-reset-states: true + router-temperature: 0.0 + router-queue-threshold: 65536 + active-decode-blocks-threshold: "None" + active-prefill-tokens-threshold: "None" + active-prefill-tokens-threshold-frac: "None" + tokenizer: "fastokens" + +backend: + type: vllm + dp_launch_mode: per_node + connector: null + kv_events_config: + prefill: true + mooncake_kv_store: + store_config: + metadata_server: "P2PHANDSHAKE" + global_segment_size: "150GB" + local_buffer_size: "4GB" + protocol: "rdma" + device_name: "mlx5_0,mlx5_1,mlx5_2,mlx5_3" + mode: "embedded" + enable_offload: false + prefill_environment: + DG_JIT_CACHE_DIR: "/tmp/dg-cache-dsv4-agentx-{job_id}" + TORCH_SYMMMEM: "NVSHMEM" + VLLM_ENGINE_READY_TIMEOUT_S: "3600" + VLLM_RPC_TIMEOUT: "600000" + VLLM_LOG_STATS_INTERVAL: "1" + VLLM_V2_WARMUP_MAX_NUM_SEQS: "20" + VLLM_SERVER_DEV_MODE: "1" + VLLM_USE_V2_MODEL_RUNNER: "1" + VLLM_MOONCAKE_LOAD_RECV_THREADS: "20" + VLLM_CONNECTOR_PREFETCH_DEPTH: "8" + VLLM_DSV4_MEGA_FP8_COMBINE: "1" + VLLM_ALLREDUCE_USE_SYMM_MEM: "0" + TILELANG_CLEANUP_TEMP_FILES: "1" + UCX_MEMTYPE_CACHE: "n" + UCX_TLS: "rc,cuda_copy" + NCCL_CUMEM_ENABLE: "1" + NCCL_MNNVL_ENABLE: "1" + NCCL_NVLS_ENABLE: "1" + VLLM_RANDOMIZE_DP_DUMMY_INPUTS: "1" + VLLM_PREFIX_CACHE_RETENTION_INTERVAL: "32768" + VLLM_USE_BREAKABLE_CUDAGRAPH: "0" + MC_ENABLE_DEST_DEVICE_AFFINITY: "1" + decode_environment: + DG_JIT_CACHE_DIR: "/tmp/dg-cache-dsv4-agentx-{job_id}" + TORCH_SYMMMEM: "NVSHMEM" + VLLM_ENGINE_READY_TIMEOUT_S: "3600" + VLLM_RPC_TIMEOUT: "600000" + VLLM_LOG_STATS_INTERVAL: "1" + VLLM_V2_WARMUP_MAX_NUM_SEQS: "20" + VLLM_SERVER_DEV_MODE: "1" + VLLM_USE_V2_MODEL_RUNNER: "1" + VLLM_MOONCAKE_LOAD_RECV_THREADS: "20" + VLLM_DSV4_MEGA_FP8_COMBINE: "1" + VLLM_ALLREDUCE_USE_SYMM_MEM: "0" + TILELANG_CLEANUP_TEMP_FILES: "1" + UCX_MEMTYPE_CACHE: "n" + UCX_TLS: "rc,cuda_copy" + NCCL_CUMEM_ENABLE: "1" + NCCL_MNNVL_ENABLE: "1" + NCCL_NVLS_ENABLE: "1" + VLLM_RANDOMIZE_DP_DUMMY_INPUTS: "1" + VLLM_PREFIX_CACHE_RETENTION_INTERVAL: "32768" + MC_ENABLE_DEST_DEVICE_AFFINITY: "1" + + vllm_config: + prefill: + kv-transfer-config: '{"kv_connector":"MultiConnector","kv_role":"kv_both","kv_connector_extra_config":{"connectors":[{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail","kv_buffer_device":"cuda","kv_connector_extra_config":{"enforce_handshake_compat":false,"enable_cross_layers_blocks":false}},{"kv_connector":"MooncakeStoreConnector","kv_role":"kv_both","kv_connector_extra_config":{"load_async":true,"lookup_async":true,"enable_cross_layers_blocks":false,"enable_offload":false}}]}}' + served-model-name: "deepseek-ai/DeepSeek-V4-Pro" + kv-cache-dtype: "fp8" + tensor-parallel-size: 1 + pipeline-parallel-size: 1 + data-parallel-size: 8 + data-parallel-rpc-port: 13345 + data-parallel-hybrid-lb: true + enable-cumem-allocator: true + enable-expert-parallel: true + enable-ep-weight-filter: true + max-model-len: 1048576 + max-num-seqs: 32 + max-num-batched-tokens: 8192 + trust-remote-code: true + no-enable-flashinfer-autotune: true + block-size: 256 + gpu-memory-utilization: 0.90 + no-disable-hybrid-kv-cache-manager: true + tokenizer-mode: deepseek_v4 + attention-config: '{"backend":"FLASHINFER_MLA_SPARSE_DSV4","use_prefill_query_quantization":true,"use_fp4_indexer_cache":true}' + moe-backend: "deep_gemm_amxf4_mega_moe" + numa-bind: true + numa-bind-nodes: [0, 0, 1, 1] + decode: + kv-transfer-config: '{"kv_connector":"MultiConnector","kv_role":"kv_both","kv_connector_extra_config":{"connectors":[{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail","kv_buffer_device":"cuda","kv_connector_extra_config":{"enforce_handshake_compat":false,"enable_cross_layers_blocks":false}},{"kv_connector":"MooncakeStoreConnector","kv_role":"kv_both","kv_connector_extra_config":{"load_async":true,"lookup_async":true,"enable_cross_layers_blocks":false,"enable_offload":false}}]}}' + served-model-name: "deepseek-ai/DeepSeek-V4-Pro" + kv-cache-dtype: "fp8" + tensor-parallel-size: 1 + pipeline-parallel-size: 1 + data-parallel-size: 8 + data-parallel-rpc-port: 13345 + data-parallel-hybrid-lb: true + enable-cumem-allocator: true + enable-expert-parallel: true + enable-ep-weight-filter: true + max-model-len: 1048576 + max-num-seqs: 128 + max-num-batched-tokens: 256 + trust-remote-code: true + no-enable-flashinfer-autotune: true + block-size: 256 + compilation-config: '{"cudagraph_mode":"FULL_DECODE_ONLY","mode":0}' + max-cudagraph-capture-size: 128 + gpu-memory-utilization: 0.90 + no-disable-hybrid-kv-cache-manager: true + tokenizer-mode: deepseek_v4 + attention-config: '{"backend":"FLASHINFER_MLA_SPARSE_DSV4","use_prefill_query_quantization":true,"use_fp4_indexer_cache":true}' + moe-backend: "deep_gemm_amxf4_mega_moe" + numa-bind: true + numa-bind-nodes: [0, 0, 1, 1] + +sbatch_directives: + cpus-per-task: "72" + +srun_options: + container-remap-root: "" + +benchmark: + type: custom + command: bash /infmax-workspace/benchmarks/multi_node/agentic_srt.sh + env: + INFMAX_CONTAINER_WORKSPACE: /infmax-workspace + RESULT_DIR: /logs/agentic + PORT: "8000" + IS_MULTINODE: "true" + AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING: "0" + AIPERF_DYNAMO_SESSION_TIMEOUT_SECONDS: "14400" + AIPERF_DATASET_MMAP_CACHE_DIR: "/aiperf_mmap_cache" + HF_HUB_CACHE: "/hf_hub_cache" + WEKA_LOADER_OVERRIDE: "semianalysis_cc_traces_weka_062126" diff --git a/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb300-2p1d-dep8-dep8-agentic.yaml b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb300-2p1d-dep8-dep8-agentic.yaml new file mode 100644 index 0000000000..b4ae0a1e4f --- /dev/null +++ b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb300-2p1d-dep8-dep8-agentic.yaml @@ -0,0 +1,192 @@ +name: "svf-vllm-disagg-gb300-2p1d-dep8-dep8-agentic" + +# GB300 AgentX reference from NVIDIA/srt-slurm#229: two DEP8 prefill workers +# feeding one DEP8 decode worker at concurrency 1280. + +model: + path: "deepseek-v4-pro" + container: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96" + precision: "fp4" + +identity: + model: + repo: "deepseek-ai/DeepSeek-V4-Pro" + container: + image: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96" + frameworks: + dynamo: "af0dbfe8233fafdfaaa5d12e17518758c99e8da5" + vllm: "0.17.2rc1.dev3675+gc188b96eb" + +dynamo: + hash: "af0dbfe8233fafdfaaa5d12e17518758c99e8da5" + install: true + +setup_script: vllm-container-deps.sh + +slurm: + time_limit: "8:00:00" + +health_check: + max_attempts: 2160 + interval_seconds: 10 + +resources: + gpu_type: "gb300" + gpus_per_node: 4 + het_jobs: false + spread_workers: false + prefill_nodes: 4 + decode_nodes: 2 + prefill_workers: 2 + decode_workers: 1 + gpus_per_prefill: 8 + gpus_per_decode: 8 + +infra: + etcd_nats_dedicated_node: false + nats_max_payload_mb: 32 + +frontend: + type: dynamo + enable_multiple_frontends: false + args: + router-mode: "kv" + router-reset-states: true + router-temperature: 0.0 + router-queue-threshold: 65536 + active-decode-blocks-threshold: "None" + active-prefill-tokens-threshold: "None" + active-prefill-tokens-threshold-frac: "None" + tokenizer: "fastokens" + +backend: + type: vllm + dp_launch_mode: per_node + connector: null + kv_events_config: + prefill: true + mooncake_kv_store: + store_config: + metadata_server: "P2PHANDSHAKE" + global_segment_size: "150GB" + local_buffer_size: "4GB" + protocol: "rdma" + device_name: "mlx5_0,mlx5_1,mlx5_2,mlx5_3" + mode: "embedded" + enable_offload: false + prefill_environment: + DG_JIT_CACHE_DIR: "/tmp/dg-cache-dsv4-agentx-{job_id}" + VLLM_ENGINE_READY_TIMEOUT_S: "3600" + VLLM_RPC_TIMEOUT: "600000" + VLLM_LOG_STATS_INTERVAL: "1" + VLLM_V2_WARMUP_MAX_NUM_SEQS: "20" + VLLM_SERVER_DEV_MODE: "1" + VLLM_USE_V2_MODEL_RUNNER: "1" + VLLM_MOONCAKE_LOAD_RECV_THREADS: "20" + VLLM_CONNECTOR_PREFETCH_DEPTH: "8" + VLLM_DSV4_MEGA_FP8_COMBINE: "1" + VLLM_ALLREDUCE_USE_SYMM_MEM: "0" + TILELANG_CLEANUP_TEMP_FILES: "1" + UCX_MEMTYPE_CACHE: "n" + UCX_TLS: "rc,cuda_copy" + NCCL_CUMEM_ENABLE: "1" + NCCL_MNNVL_ENABLE: "1" + NCCL_NVLS_ENABLE: "1" + VLLM_RANDOMIZE_DP_DUMMY_INPUTS: "1" + VLLM_PREFIX_CACHE_RETENTION_INTERVAL: "32768" + VLLM_USE_BREAKABLE_CUDAGRAPH: "0" + MC_ENABLE_DEST_DEVICE_AFFINITY: "1" + decode_environment: + DG_JIT_CACHE_DIR: "/tmp/dg-cache-dsv4-agentx-{job_id}" + VLLM_ENGINE_READY_TIMEOUT_S: "3600" + VLLM_RPC_TIMEOUT: "600000" + VLLM_LOG_STATS_INTERVAL: "1" + VLLM_V2_WARMUP_MAX_NUM_SEQS: "20" + VLLM_SERVER_DEV_MODE: "1" + VLLM_USE_V2_MODEL_RUNNER: "1" + VLLM_MOONCAKE_LOAD_RECV_THREADS: "20" + VLLM_DSV4_MEGA_FP8_COMBINE: "1" + VLLM_ALLREDUCE_USE_SYMM_MEM: "0" + TILELANG_CLEANUP_TEMP_FILES: "1" + UCX_MEMTYPE_CACHE: "n" + UCX_TLS: "rc,cuda_copy" + NCCL_CUMEM_ENABLE: "1" + NCCL_MNNVL_ENABLE: "1" + NCCL_NVLS_ENABLE: "1" + VLLM_RANDOMIZE_DP_DUMMY_INPUTS: "1" + VLLM_PREFIX_CACHE_RETENTION_INTERVAL: "32768" + MC_ENABLE_DEST_DEVICE_AFFINITY: "1" + + vllm_config: + prefill: + kv-transfer-config: '{"kv_connector":"MultiConnector","kv_role":"kv_both","kv_connector_extra_config":{"connectors":[{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail","kv_buffer_device":"cuda","kv_connector_extra_config":{"enforce_handshake_compat":false,"enable_cross_layers_blocks":false}},{"kv_connector":"MooncakeStoreConnector","kv_role":"kv_both","kv_connector_extra_config":{"load_async":true,"lookup_async":true,"enable_cross_layers_blocks":false,"enable_offload":false}}]}}' + served-model-name: "deepseek-ai/DeepSeek-V4-Pro" + kv-cache-dtype: "fp8" + tensor-parallel-size: 1 + pipeline-parallel-size: 1 + data-parallel-size: 8 + data-parallel-rpc-port: 13345 + data-parallel-hybrid-lb: true + enable-cumem-allocator: true + enable-expert-parallel: true + enable-ep-weight-filter: true + max-model-len: 1048576 + max-num-seqs: 32 + max-num-batched-tokens: 8192 + trust-remote-code: true + no-enable-flashinfer-autotune: true + block-size: 256 + gpu-memory-utilization: 0.92 + no-disable-hybrid-kv-cache-manager: true + tokenizer-mode: deepseek_v4 + attention-config: '{"backend":"FLASHINFER_MLA_SPARSE_DSV4","use_prefill_query_quantization":true,"use_fp4_indexer_cache":true}' + moe-backend: "deep_gemm_amxf4_mega_moe" + numa-bind: true + numa-bind-nodes: [0, 0, 1, 1] + decode: + kv-transfer-config: '{"kv_connector":"MultiConnector","kv_role":"kv_both","kv_connector_extra_config":{"connectors":[{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail","kv_buffer_device":"cuda","kv_connector_extra_config":{"enforce_handshake_compat":false,"enable_cross_layers_blocks":false}},{"kv_connector":"MooncakeStoreConnector","kv_role":"kv_both","kv_connector_extra_config":{"load_async":true,"lookup_async":true,"enable_cross_layers_blocks":false,"enable_offload":false}}]}}' + served-model-name: "deepseek-ai/DeepSeek-V4-Pro" + kv-cache-dtype: "fp8" + tensor-parallel-size: 1 + pipeline-parallel-size: 1 + data-parallel-size: 8 + data-parallel-rpc-port: 13345 + data-parallel-hybrid-lb: true + enable-cumem-allocator: true + enable-expert-parallel: true + enable-ep-weight-filter: true + max-model-len: 1048576 + max-num-seqs: 256 + max-num-batched-tokens: 512 + trust-remote-code: true + no-enable-flashinfer-autotune: true + block-size: 256 + compilation-config: '{"cudagraph_mode":"FULL_DECODE_ONLY","mode":0}' + max-cudagraph-capture-size: 256 + gpu-memory-utilization: 0.95 + no-disable-hybrid-kv-cache-manager: true + tokenizer-mode: deepseek_v4 + attention-config: '{"backend":"FLASHINFER_MLA_SPARSE_DSV4","use_prefill_query_quantization":true,"use_fp4_indexer_cache":true}' + moe-backend: "deep_gemm_amxf4_mega_moe" + numa-bind: true + numa-bind-nodes: [0, 0, 1, 1] + +sbatch_directives: + cpus-per-task: "72" + +srun_options: + container-remap-root: "" + +benchmark: + type: custom + command: bash /infmax-workspace/benchmarks/multi_node/agentic_srt.sh + env: + INFMAX_CONTAINER_WORKSPACE: /infmax-workspace + RESULT_DIR: /logs/agentic + PORT: "8000" + IS_MULTINODE: "true" + AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING: "0" + AIPERF_DYNAMO_SESSION_TIMEOUT_SECONDS: "14400" + AIPERF_DATASET_MMAP_CACHE_DIR: "/aiperf_mmap_cache" + HF_HUB_CACHE: "/hf_hub_cache" + WEKA_LOADER_OVERRIDE: "semianalysis_cc_traces_weka_062126" diff --git a/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb300-3p1d-dep8-dep16-agentic.yaml b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb300-3p1d-dep8-dep16-agentic.yaml new file mode 100644 index 0000000000..157047e468 --- /dev/null +++ b/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic/disagg-gb300-3p1d-dep8-dep16-agentic.yaml @@ -0,0 +1,192 @@ +name: "svf-vllm-disagg-gb300-3p1d-dep8-dep16-agentic" + +# GB300 AgentX reference from NVIDIA/srt-slurm#229: three DEP8 prefill workers +# feeding one DEP16 decode worker at concurrency 1536. + +model: + path: "deepseek-v4-pro" + container: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96" + precision: "fp4" + +identity: + model: + repo: "deepseek-ai/DeepSeek-V4-Pro" + container: + image: "vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96" + frameworks: + dynamo: "af0dbfe8233fafdfaaa5d12e17518758c99e8da5" + vllm: "0.17.2rc1.dev3675+gc188b96eb" + +dynamo: + hash: "af0dbfe8233fafdfaaa5d12e17518758c99e8da5" + install: true + +setup_script: vllm-container-deps.sh + +slurm: + time_limit: "8:00:00" + +health_check: + max_attempts: 2160 + interval_seconds: 10 + +resources: + gpu_type: "gb300" + gpus_per_node: 4 + het_jobs: false + spread_workers: false + prefill_nodes: 6 + decode_nodes: 4 + prefill_workers: 3 + decode_workers: 1 + gpus_per_prefill: 8 + gpus_per_decode: 16 + +infra: + etcd_nats_dedicated_node: false + nats_max_payload_mb: 32 + +frontend: + type: dynamo + enable_multiple_frontends: false + args: + router-mode: "kv" + router-reset-states: true + router-temperature: 0.0 + router-queue-threshold: 65536 + active-decode-blocks-threshold: "None" + active-prefill-tokens-threshold: "None" + active-prefill-tokens-threshold-frac: "None" + tokenizer: "fastokens" + +backend: + type: vllm + dp_launch_mode: per_node + connector: null + kv_events_config: + prefill: true + mooncake_kv_store: + store_config: + metadata_server: "P2PHANDSHAKE" + global_segment_size: "150GB" + local_buffer_size: "4GB" + protocol: "rdma" + device_name: "mlx5_0,mlx5_1,mlx5_2,mlx5_3" + mode: "embedded" + enable_offload: false + prefill_environment: + DG_JIT_CACHE_DIR: "/tmp/dg-cache-dsv4-agentx-{job_id}" + VLLM_ENGINE_READY_TIMEOUT_S: "3600" + VLLM_RPC_TIMEOUT: "600000" + VLLM_LOG_STATS_INTERVAL: "1" + VLLM_V2_WARMUP_MAX_NUM_SEQS: "20" + VLLM_SERVER_DEV_MODE: "1" + VLLM_USE_V2_MODEL_RUNNER: "1" + VLLM_MOONCAKE_LOAD_RECV_THREADS: "20" + VLLM_CONNECTOR_PREFETCH_DEPTH: "8" + VLLM_DSV4_MEGA_FP8_COMBINE: "1" + VLLM_ALLREDUCE_USE_SYMM_MEM: "0" + TILELANG_CLEANUP_TEMP_FILES: "1" + UCX_MEMTYPE_CACHE: "n" + UCX_TLS: "rc,cuda_copy" + NCCL_CUMEM_ENABLE: "1" + NCCL_MNNVL_ENABLE: "1" + NCCL_NVLS_ENABLE: "1" + VLLM_RANDOMIZE_DP_DUMMY_INPUTS: "1" + VLLM_PREFIX_CACHE_RETENTION_INTERVAL: "32768" + VLLM_USE_BREAKABLE_CUDAGRAPH: "0" + MC_ENABLE_DEST_DEVICE_AFFINITY: "1" + decode_environment: + DG_JIT_CACHE_DIR: "/tmp/dg-cache-dsv4-agentx-{job_id}" + VLLM_ENGINE_READY_TIMEOUT_S: "3600" + VLLM_RPC_TIMEOUT: "600000" + VLLM_LOG_STATS_INTERVAL: "1" + VLLM_V2_WARMUP_MAX_NUM_SEQS: "20" + VLLM_SERVER_DEV_MODE: "1" + VLLM_USE_V2_MODEL_RUNNER: "1" + VLLM_MOONCAKE_LOAD_RECV_THREADS: "20" + VLLM_DSV4_MEGA_FP8_COMBINE: "1" + VLLM_ALLREDUCE_USE_SYMM_MEM: "0" + TILELANG_CLEANUP_TEMP_FILES: "1" + UCX_MEMTYPE_CACHE: "n" + UCX_TLS: "rc,cuda_copy" + NCCL_CUMEM_ENABLE: "1" + NCCL_MNNVL_ENABLE: "1" + NCCL_NVLS_ENABLE: "1" + VLLM_RANDOMIZE_DP_DUMMY_INPUTS: "1" + VLLM_PREFIX_CACHE_RETENTION_INTERVAL: "32768" + MC_ENABLE_DEST_DEVICE_AFFINITY: "1" + + vllm_config: + prefill: + kv-transfer-config: '{"kv_connector":"MultiConnector","kv_role":"kv_both","kv_connector_extra_config":{"connectors":[{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail","kv_buffer_device":"cuda","kv_connector_extra_config":{"enforce_handshake_compat":false,"enable_cross_layers_blocks":false}},{"kv_connector":"MooncakeStoreConnector","kv_role":"kv_both","kv_connector_extra_config":{"load_async":true,"lookup_async":true,"enable_cross_layers_blocks":false,"enable_offload":false}}]}}' + served-model-name: "deepseek-ai/DeepSeek-V4-Pro" + kv-cache-dtype: "fp8" + tensor-parallel-size: 1 + pipeline-parallel-size: 1 + data-parallel-size: 8 + data-parallel-rpc-port: 13345 + data-parallel-hybrid-lb: true + enable-cumem-allocator: true + enable-expert-parallel: true + enable-ep-weight-filter: true + max-model-len: 1048576 + max-num-seqs: 32 + max-num-batched-tokens: 8192 + trust-remote-code: true + no-enable-flashinfer-autotune: true + block-size: 256 + gpu-memory-utilization: 0.90 + no-disable-hybrid-kv-cache-manager: true + tokenizer-mode: deepseek_v4 + attention-config: '{"backend":"FLASHINFER_MLA_SPARSE_DSV4","use_prefill_query_quantization":true,"use_fp4_indexer_cache":true}' + moe-backend: "deep_gemm_amxf4_mega_moe" + numa-bind: true + numa-bind-nodes: [0, 0, 1, 1] + decode: + kv-transfer-config: '{"kv_connector":"MultiConnector","kv_role":"kv_both","kv_connector_extra_config":{"connectors":[{"kv_connector":"NixlConnector","kv_role":"kv_both","kv_load_failure_policy":"fail","kv_buffer_device":"cuda","kv_connector_extra_config":{"enforce_handshake_compat":false,"enable_cross_layers_blocks":false}},{"kv_connector":"MooncakeStoreConnector","kv_role":"kv_both","kv_connector_extra_config":{"load_async":true,"lookup_async":true,"enable_cross_layers_blocks":false,"enable_offload":false}}]}}' + served-model-name: "deepseek-ai/DeepSeek-V4-Pro" + kv-cache-dtype: "fp8" + tensor-parallel-size: 1 + pipeline-parallel-size: 1 + data-parallel-size: 16 + data-parallel-rpc-port: 13345 + data-parallel-hybrid-lb: true + enable-cumem-allocator: true + enable-expert-parallel: true + enable-ep-weight-filter: true + max-model-len: 1048576 + max-num-seqs: 128 + max-num-batched-tokens: 256 + trust-remote-code: true + no-enable-flashinfer-autotune: true + block-size: 256 + compilation-config: '{"cudagraph_mode":"FULL_DECODE_ONLY","mode":0}' + max-cudagraph-capture-size: 128 + gpu-memory-utilization: 0.90 + no-disable-hybrid-kv-cache-manager: true + tokenizer-mode: deepseek_v4 + attention-config: '{"backend":"FLASHINFER_MLA_SPARSE_DSV4","use_prefill_query_quantization":true,"use_fp4_indexer_cache":true}' + moe-backend: "deep_gemm_amxf4_mega_moe" + numa-bind: true + numa-bind-nodes: [0, 0, 1, 1] + +sbatch_directives: + cpus-per-task: "72" + +srun_options: + container-remap-root: "" + +benchmark: + type: custom + command: bash /infmax-workspace/benchmarks/multi_node/agentic_srt.sh + env: + INFMAX_CONTAINER_WORKSPACE: /infmax-workspace + RESULT_DIR: /logs/agentic + PORT: "8000" + IS_MULTINODE: "true" + AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING: "0" + AIPERF_DYNAMO_SESSION_TIMEOUT_SECONDS: "14400" + AIPERF_DATASET_MMAP_CACHE_DIR: "/aiperf_mmap_cache" + HF_HUB_CACHE: "/hf_hub_cache" + WEKA_LOADER_OVERRIDE: "semianalysis_cc_traces_weka_062126" diff --git a/benchmarks/multi_node/srt-slurm-vllm-per-node-health.patch b/benchmarks/multi_node/srt-slurm-vllm-per-node-health.patch new file mode 100644 index 0000000000..d42553fb61 --- /dev/null +++ b/benchmarks/multi_node/srt-slurm-vllm-per-node-health.patch @@ -0,0 +1,63 @@ +diff --git a/src/srtctl/cli/mixins/benchmark_stage.py b/src/srtctl/cli/mixins/benchmark_stage.py +index fe6cbf1..f282fe6 100644 +--- a/src/srtctl/cli/mixins/benchmark_stage.py ++++ b/src/srtctl/cli/mixins/benchmark_stage.py +@@ -46,12 +46,32 @@ def _vllm_data_parallel_size(config: "SrtConfig", mode: str) -> int: + return int(mode_config.get("data-parallel-size") or mode_config.get("data_parallel_size") or 1) + + +-def _get_health_expectations(config: "SrtConfig") -> tuple[int, int, str, int]: ++def _vllm_health_entries( ++ config: "SrtConfig", ++ mode: str, ++ logical_workers: int, ++ backend_processes: list["Process"] | None, ++) -> int: ++ """Return expected Dynamo generate registrations for a vLLM worker mode.""" ++ dp_size = _vllm_data_parallel_size(config, mode) ++ if dp_size > 1 and getattr(config.backend, "dp_launch_mode", "per_gpu") == "per_node": ++ if backend_processes is None: ++ raise ValueError("backend_processes are required for per-node DP health expectations") ++ endpoint_mode = "agg" if mode == "aggregated" else mode ++ return sum(process.endpoint_mode == endpoint_mode for process in backend_processes) ++ ++ return logical_workers * dp_size ++ ++ ++def _get_health_expectations( ++ config: "SrtConfig", backend_processes: list["Process"] | None = None ++) -> tuple[int, int, str, int]: + """Compute expected health counts in the units reported by the frontend. + + Dynamo's /health endpoint reports registered generate instances. For vLLM +- DP workers, that means one entry per DP rank, not one entry per logical +- srt-slurm worker. Other frontends keep using logical worker counts. ++ DP workers, per-GPU launch registers one entry per DP rank, while per-node ++ launch registers one entry per node-local process. Other frontends keep ++ using logical worker counts. + """ + r = config.resources + +@@ -67,10 +87,10 @@ def _get_health_expectations(config: "SrtConfig") -> tuple[int, int, str, int]: + if config.frontend.type == "dynamo" and getattr(config.backend, "type", None) == "vllm": + if r.num_agg > 0: + n_prefill = 0 +- n_decode = logical_decode * _vllm_data_parallel_size(config, "aggregated") ++ n_decode = _vllm_health_entries(config, "aggregated", logical_decode, backend_processes) + else: +- n_prefill = logical_prefill * _vllm_data_parallel_size(config, "prefill") +- n_decode = logical_decode * _vllm_data_parallel_size(config, "decode") ++ n_prefill = _vllm_health_entries(config, "prefill", logical_prefill, backend_processes) ++ n_decode = _vllm_health_entries(config, "decode", logical_decode, backend_processes) + + count_desc = f"{n_prefill}P + {n_decode}D Dynamo generate instances; logical workers: {worker_desc}" + return n_prefill, n_decode, count_desc, n_prefill + n_decode +@@ -131,7 +151,7 @@ class BenchmarkStageMixin: + """Run the benchmark.""" + logger.info("Waiting for workers to be ready...") + +- n_prefill, n_decode, count_desc, num_workers = _get_health_expectations(self.config) ++ n_prefill, n_decode, count_desc, num_workers = _get_health_expectations(self.config, self.backend_processes) + logger.info("Waiting for server health (expecting %d health entries: %s)...", num_workers, count_desc) + + hc = self.config.health_check diff --git a/configs/nvidia-master.yaml b/configs/nvidia-master.yaml index 3aac1b8378..4de9fcb498 100644 --- a/configs/nvidia-master.yaml +++ b/configs/nvidia-master.yaml @@ -7663,6 +7663,135 @@ dsv4-fp4-gb200-dynamo-vllm-agentic-2p1d-dep8-dep8: ep: 8 dp-attn: true +dsv4-fp4-gb300-dynamo-vllm-agentic-agg: + image: vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96 + model: deepseek-ai/DeepSeek-V4-Pro + model-prefix: dsv4 + runner: cluster:gb300-nv + precision: fp4 + framework: dynamo-vllm + router: { name: dynamo-router, version: "1.2.1" } + multinode: true + disagg: false + scenarios: + agentic-coding: + - search-space: + - spec-decoding: none + conc-list: [1, 4, 8] + prefill: + num-worker: 1 + tp: 4 + ep: 1 + dp-attn: false + additional-settings: + - "CONFIG_FILE=recipes/vllm/deepseek-v4/agentic/agg-gb300-tp4-agentic.yaml" + decode: + num-worker: 0 + tp: 4 + ep: 1 + dp-attn: false + - spec-decoding: none + conc-list: [1, 8, 16] + prefill: + num-worker: 1 + tp: 8 + ep: 1 + dp-attn: false + additional-settings: + - "CONFIG_FILE=recipes/vllm/deepseek-v4/agentic/agg-gb300-tp8-agentic.yaml" + decode: + num-worker: 0 + tp: 8 + ep: 1 + dp-attn: false + +dsv4-fp4-gb300-dynamo-vllm-agentic-1p1d-dep8-dep8: + image: vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96 + model: deepseek-ai/DeepSeek-V4-Pro + model-prefix: dsv4 + runner: cluster:gb300-nv + precision: fp4 + framework: dynamo-vllm + router: { name: dynamo-router, version: "af0dbfe8233fafdfaaa5d12e17518758c99e8da5" } + kv-p2p-transfer: nixl + multinode: true + disagg: true + scenarios: + agentic-coding: + - search-space: + - spec-decoding: none + conc-list: [256, 512] + prefill: + num-worker: 1 + tp: 8 + ep: 8 + dp-attn: true + additional-settings: + - "CONFIG_FILE=recipes/vllm/deepseek-v4/agentic/disagg-gb300-1p1d-dep8-dep8-agentic.yaml" + decode: + num-worker: 1 + tp: 8 + ep: 8 + dp-attn: true + +dsv4-fp4-gb300-dynamo-vllm-agentic-2p1d-dep8-dep8: + image: vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96 + model: deepseek-ai/DeepSeek-V4-Pro + model-prefix: dsv4 + runner: cluster:gb300-nv + precision: fp4 + framework: dynamo-vllm + router: { name: dynamo-router, version: "af0dbfe8233fafdfaaa5d12e17518758c99e8da5" } + kv-p2p-transfer: nixl + multinode: true + disagg: true + scenarios: + agentic-coding: + - search-space: + - spec-decoding: none + conc-list: [960, 1280] + prefill: + num-worker: 2 + tp: 8 + ep: 8 + dp-attn: true + additional-settings: + - "CONFIG_FILE=recipes/vllm/deepseek-v4/agentic/disagg-gb300-2p1d-dep8-dep8-agentic.yaml" + decode: + num-worker: 1 + tp: 8 + ep: 8 + dp-attn: true + +dsv4-fp4-gb300-dynamo-vllm-agentic-3p1d-dep8-dep16: + image: vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96 + model: deepseek-ai/DeepSeek-V4-Pro + model-prefix: dsv4 + runner: cluster:gb300-nv + precision: fp4 + framework: dynamo-vllm + router: { name: dynamo-router, version: "af0dbfe8233fafdfaaa5d12e17518758c99e8da5" } + kv-p2p-transfer: nixl + multinode: true + disagg: true + scenarios: + agentic-coding: + - search-space: + - spec-decoding: none + conc-list: [1280, 1536] + prefill: + num-worker: 3 + tp: 8 + ep: 8 + dp-attn: true + additional-settings: + - "CONFIG_FILE=recipes/vllm/deepseek-v4/agentic/disagg-gb300-3p1d-dep8-dep16-agentic.yaml" + decode: + num-worker: 1 + tp: 16 + ep: 16 + dp-attn: true + dsv4-fp4-gb300-dynamo-sglang-agentic-agg: image: lmsysorg/sglang:nightly-dev-cu13-20260711-7de33ce8 model: deepseek-ai/DeepSeek-V4-Pro diff --git a/perf-changelog.yaml b/perf-changelog.yaml index 005967fc36..dbf631c895 100644 --- a/perf-changelog.yaml +++ b/perf-changelog.yaml @@ -4966,3 +4966,14 @@ - "Run 29651235293 showed the 1M-context corpus working set outgrowing the HBM KV pool past conc 8 (TP8) / conc 64 (DP8): gpu_kv_cache_usage pinned at 1.0 and the radix hit rate collapsed from a ~0.97 theoretical ceiling to 0.04-0.06, so every post-knee turn re-prefilled its full history and throughput fell together with interactivity" - "HiCache spills evicted prefixes to host DRAM and restores them at C2C bandwidth instead of recomputing; sizing follows the qwen3.5-fp8-b300-sglang-agentic-hicache recipe (GLM-5.2 is plain GQA: one host pool per rank, GB-based --hicache-size)" pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2280 + +- config-keys: + - dsv4-fp4-gb300-dynamo-vllm-agentic-agg + - dsv4-fp4-gb300-dynamo-vllm-agentic-1p1d-dep8-dep8 + - dsv4-fp4-gb300-dynamo-vllm-agentic-2p1d-dep8-dep8 + - dsv4-fp4-gb300-dynamo-vllm-agentic-3p1d-dep8-dep16 + description: + - "Add GB300 Dynamo-vLLM AgentX aggregate TP4/TP8 low-latency sweeps at conc [1,4,8,16]." + - "Add NVIDIA/srt-slurm#229-derived GB300 P/D topologies: 1P/1D DEP8/DEP8 at c512, 2P/1D DEP8/DEP8 at c1280, and 3P/1D DEP8/DEP16 at c1536." + - "Use vllm/vllm-openai:nightly-dev-arm64-cu13.0.1-c188b96 for all new recipes." + pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2269 diff --git a/runners/launch_gb300-nv.sh b/runners/launch_gb300-nv.sh index d450cdb86d..8391ee3052 100644 --- a/runners/launch_gb300-nv.sh +++ b/runners/launch_gb300-nv.sh @@ -150,9 +150,8 @@ if [[ "$IS_AGENTIC" == "1" && $FRAMEWORK == "dynamo-sglang" && $MODEL_PREFIX == cp -rT "$GITHUB_WORKSPACE/benchmarks/multi_node/srt-slurm-recipes/sglang/deepseek-v4/agentic" \ recipes/sglang/deepseek-v4/agentic elif [[ "$IS_AGENTIC" == "1" ]]; then - # Agentic multi-node uses cquil11/srt-slurm-nv@cam/no-preflight-flag, - # a thin branch off NVIDIA/srt-slurm@127597c that adds one CLI flag - # (`srtctl apply --no-preflight`) — needed because: + # Agentic multi-node pins NVIDIA/srt-slurm v1.0.27, whose + # `srtctl apply --no-preflight` flag is needed because: # # - We want MODEL_PATH=/scratch/models/DeepSeek-V4-Pro (node-local # NVMe, fast) instead of the NFS path under /data/home/sa-shared. @@ -167,18 +166,46 @@ elif [[ "$IS_AGENTIC" == "1" ]]; then # vLLM still fails loudly at runtime if the path is genuinely # missing on the compute node. # - # All other upstream schema features we need are inherited from - # NVIDIA HEAD: + # Other required schema features are also present in v1.0.27: # - BenchmarkType.CUSTOM + benchmark.command + benchmark.env # (hook that hands off to benchmarks/multi_node/agentic_srt.sh) - # - DynamoConfig.wheel (so vllm recipes can pin the ai-dynamo wheel) + # - DynamoConfig.hash (so vLLM recipes can pin the ai-dynamo source) # - sbatch_directives / srun_options (top-level recipe fields) - git clone https://github.com/cquil11/srt-slurm-nv.git "$SRT_REPO_DIR" + SRT_SLURM_AGENTIC_SHA="f6eb42aee4664207dcf2ec601e3bd57bd527efd6" + git clone --branch v1.0.27 --depth 1 https://github.com/NVIDIA/srt-slurm.git "$SRT_REPO_DIR" cd "$SRT_REPO_DIR" - # 854b3fd = --no-preflight flag - # 6e34b8b = benchmark_stage propagates srun_options (needed for - # container-remap-root to reach the agentic_srt.sh srun) - git checkout 6e34b8b83229634d732e41a4e2d6595f46ef60b5 + if [[ "$(git rev-parse HEAD)" != "$SRT_SLURM_AGENTIC_SHA" ]]; then + echo "Error: NVIDIA/srt-slurm v1.0.27 did not resolve to $SRT_SLURM_AGENTIC_SHA" >&2 + exit 1 + fi + + # Backport NVIDIA/srt-slurm#90. GB300 P/D workers use one vLLM process + # per physical node, with that process managing all node-local DP ranks. + SRT_SLURM_PER_NODE_DP_SHA="1a0f9e3633318ab1ee9428d2129161b583786b18" + git fetch --depth 2 origin refs/pull/90/head + if [[ "$(git rev-parse FETCH_HEAD)" != "$SRT_SLURM_PER_NODE_DP_SHA" ]]; then + echo "Error: NVIDIA/srt-slurm PR #90 commit did not resolve to $SRT_SLURM_PER_NODE_DP_SHA" >&2 + exit 1 + fi + git cherry-pick --no-commit "$SRT_SLURM_PER_NODE_DP_SHA" + + # Multi-node TP8 needs distinct internal ZMQ ports for its node-local + # vLLM ranks rather than the inherited process-level VLLM_PORT. + SRT_SLURM_MULTINODE_VLLM_PORT_SHA="de1a4f0257dae5bf871881dc4696e35389c37483" + git fetch --depth 2 origin "$SRT_SLURM_MULTINODE_VLLM_PORT_SHA" + if [[ "$(git rev-parse FETCH_HEAD)" != "$SRT_SLURM_MULTINODE_VLLM_PORT_SHA" ]]; then + echo "Error: NVIDIA/srt-slurm multi-node VLLM_PORT fix did not resolve to $SRT_SLURM_MULTINODE_VLLM_PORT_SHA" >&2 + exit 1 + fi + git cherry-pick --no-commit "$SRT_SLURM_MULTINODE_VLLM_PORT_SHA" + + # Per-node DP launches one Dynamo generate endpoint per node-local process, + # not one per DP rank. Backport the health-count fix from + # ivanium/srt-slurm@ca0880138fa606130ae4acbb8d0afddfb84c69fa. + SRT_SLURM_PER_NODE_HEALTH_PATCH="$GITHUB_WORKSPACE/benchmarks/multi_node/srt-slurm-vllm-per-node-health.patch" + git apply --check "$SRT_SLURM_PER_NODE_HEALTH_PATCH" + git apply "$SRT_SLURM_PER_NODE_HEALTH_PATCH" + mkdir -p recipes/vllm/deepseek-v4/agentic cp -rT "$GITHUB_WORKSPACE/benchmarks/multi_node/srt-slurm-recipes/vllm/deepseek-v4/agentic" \ recipes/vllm/deepseek-v4/agentic diff --git a/utils/matrix_logic/generate_sweep_configs.py b/utils/matrix_logic/generate_sweep_configs.py index e14c50c66b..5272fe78e6 100644 --- a/utils/matrix_logic/generate_sweep_configs.py +++ b/utils/matrix_logic/generate_sweep_configs.py @@ -24,7 +24,9 @@ MIN_EVAL_CONC = 16 # Bound how many multinode agentic conc points share one server allocation. -MAX_MULTINODE_AGENTIC_CONCURRENCIES_PER_ALLOCATION = 4 +# One task/Slurm allocation per concurrency gives each benchmark point a +# fresh server deployment, matching single-node agentic sweep isolation. +MAX_MULTINODE_AGENTIC_CONCURRENCIES_PER_ALLOCATION = 1 BYTES_PER_MIB = 1024 * 1024 BYTES_PER_GB = 1_000_000_000 # 3 TB decimal DRAM cap, expressed in MiB, before utilization scaling. diff --git a/utils/matrix_logic/test_generate_sweep_configs.py b/utils/matrix_logic/test_generate_sweep_configs.py index 9a1c67d86e..3ab912e82c 100644 --- a/utils/matrix_logic/test_generate_sweep_configs.py +++ b/utils/matrix_logic/test_generate_sweep_configs.py @@ -2253,8 +2253,8 @@ def test_agentic_node_dram_rejects_tp_above_runner_gpus(self, sample_runner_conf with pytest.raises(ValueError, match="exceeds gpus-per-node"): generate_test_config_sweep(args, config, runner_config) - def test_multinode_agentic_groups_concurrencies_per_search_entry(self): - """One server allocation should run the selected concurrency batch.""" + def test_multinode_agentic_uses_one_allocation_per_concurrency(self): + """Each concurrency should get its own server allocation.""" config = { "dsv4-agentic-2p1d": { "image": "vllm/vllm-openai:v0.23.0", @@ -2292,17 +2292,27 @@ def test_multinode_agentic_groups_concurrencies_per_search_entry(self): result = generate_test_config_sweep(args, config) - assert len(result) == 2 - assert result[0]["conc"] == [16, 32, 64, 128] - assert result[0]["exp-name"] == "dsv4_p2x4_d1x4_conc16x32x64x128" + assert len(result) == 5 + assert [entry["conc"] for entry in result] == [ + [16], + [32], + [64], + [128], + [256], + ] + assert [entry["exp-name"] for entry in result] == [ + "dsv4_p2x4_d1x4_conc16", + "dsv4_p2x4_d1x4_conc32", + "dsv4_p2x4_d1x4_conc64", + "dsv4_p2x4_d1x4_conc128", + "dsv4_p2x4_d1x4_conc256", + ] assert result[0]["prefill"]["pp"] == 2 assert result[0]["prefill"]["dcp-size"] == 2 assert result[0]["prefill"]["pcp-size"] == 2 assert result[0]["decode"]["pp"] == 2 assert result[0]["decode"]["dcp-size"] == 2 assert result[0]["decode"]["pcp-size"] == 1 - assert result[1]["conc"] == [256] - assert result[1]["exp-name"] == "dsv4_p2x4_d1x4_conc256" assert all(entry["router"] == {"name": "dynamo-router", "version": "1.3.0"} for entry in result) assert all(entry["kv-p2p-transfer"] == "nixl" for entry in result) @@ -2493,9 +2503,11 @@ def test_node_type_filters_apply_to_agentic_configs( assert "prefill" not in single_result[0] assert single_result[0]["runner"] == "cluster:b300-nv" assert single_result[0]["pp"] == 2 - assert len(multi_result) == 1 - assert "prefill" in multi_result[0] + assert len(multi_result) == 2 + assert [entry["conc"] for entry in multi_result] == [[16], [32]] + assert all("prefill" in entry for entry in multi_result) assert multi_result[0]["runner"] == "cluster:gb200-nv" + assert multi_result[1]["runner"] == multi_result[0]["runner"] assert ( multi_result[0]["prefill"]["pp"], multi_result[0]["prefill"]["dcp-size"],