Track simulator fidelity experiment artifacts
This commit is contained in:
@@ -0,0 +1,333 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3MoeForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"decoder_sparse_step": 1,
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 12288,
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 94,
|
||||
"mlp_only_layers": [],
|
||||
"model_type": "qwen3_moe",
|
||||
"moe_intermediate_size": 1536,
|
||||
"norm_topk_prob": true,
|
||||
"num_attention_heads": 64,
|
||||
"num_experts": 128,
|
||||
"num_experts_per_tok": 8,
|
||||
"num_hidden_layers": 94,
|
||||
"num_key_value_heads": 4,
|
||||
"output_router_logits": false,
|
||||
"rms_norm_eps": 0.000001,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
||||
"router_aux_loss_coef": 0.001,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.51.0",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936,
|
||||
"quantization_config": {
|
||||
"activation_scheme": "dynamic",
|
||||
"modules_to_not_convert": [
|
||||
"lm_head",
|
||||
"model.layers.0.input_layernorm",
|
||||
"model.layers.0.mlp.gate",
|
||||
"model.layers.0.post_attention_layernorm",
|
||||
"model.layers.1.input_layernorm",
|
||||
"model.layers.1.mlp.gate",
|
||||
"model.layers.1.post_attention_layernorm",
|
||||
"model.layers.2.input_layernorm",
|
||||
"model.layers.2.mlp.gate",
|
||||
"model.layers.2.post_attention_layernorm",
|
||||
"model.layers.3.input_layernorm",
|
||||
"model.layers.3.mlp.gate",
|
||||
"model.layers.3.post_attention_layernorm",
|
||||
"model.layers.4.input_layernorm",
|
||||
"model.layers.4.mlp.gate",
|
||||
"model.layers.4.post_attention_layernorm",
|
||||
"model.layers.5.input_layernorm",
|
||||
"model.layers.5.mlp.gate",
|
||||
"model.layers.5.post_attention_layernorm",
|
||||
"model.layers.6.input_layernorm",
|
||||
"model.layers.6.mlp.gate",
|
||||
"model.layers.6.post_attention_layernorm",
|
||||
"model.layers.7.input_layernorm",
|
||||
"model.layers.7.mlp.gate",
|
||||
"model.layers.7.post_attention_layernorm",
|
||||
"model.layers.8.input_layernorm",
|
||||
"model.layers.8.mlp.gate",
|
||||
"model.layers.8.post_attention_layernorm",
|
||||
"model.layers.9.input_layernorm",
|
||||
"model.layers.9.mlp.gate",
|
||||
"model.layers.9.post_attention_layernorm",
|
||||
"model.layers.10.input_layernorm",
|
||||
"model.layers.10.mlp.gate",
|
||||
"model.layers.10.post_attention_layernorm",
|
||||
"model.layers.11.input_layernorm",
|
||||
"model.layers.11.mlp.gate",
|
||||
"model.layers.11.post_attention_layernorm",
|
||||
"model.layers.12.input_layernorm",
|
||||
"model.layers.12.mlp.gate",
|
||||
"model.layers.12.post_attention_layernorm",
|
||||
"model.layers.13.input_layernorm",
|
||||
"model.layers.13.mlp.gate",
|
||||
"model.layers.13.post_attention_layernorm",
|
||||
"model.layers.14.input_layernorm",
|
||||
"model.layers.14.mlp.gate",
|
||||
"model.layers.14.post_attention_layernorm",
|
||||
"model.layers.15.input_layernorm",
|
||||
"model.layers.15.mlp.gate",
|
||||
"model.layers.15.post_attention_layernorm",
|
||||
"model.layers.16.input_layernorm",
|
||||
"model.layers.16.mlp.gate",
|
||||
"model.layers.16.post_attention_layernorm",
|
||||
"model.layers.17.input_layernorm",
|
||||
"model.layers.17.mlp.gate",
|
||||
"model.layers.17.post_attention_layernorm",
|
||||
"model.layers.18.input_layernorm",
|
||||
"model.layers.18.mlp.gate",
|
||||
"model.layers.18.post_attention_layernorm",
|
||||
"model.layers.19.input_layernorm",
|
||||
"model.layers.19.mlp.gate",
|
||||
"model.layers.19.post_attention_layernorm",
|
||||
"model.layers.20.input_layernorm",
|
||||
"model.layers.20.mlp.gate",
|
||||
"model.layers.20.post_attention_layernorm",
|
||||
"model.layers.21.input_layernorm",
|
||||
"model.layers.21.mlp.gate",
|
||||
"model.layers.21.post_attention_layernorm",
|
||||
"model.layers.22.input_layernorm",
|
||||
"model.layers.22.mlp.gate",
|
||||
"model.layers.22.post_attention_layernorm",
|
||||
"model.layers.23.input_layernorm",
|
||||
"model.layers.23.mlp.gate",
|
||||
"model.layers.23.post_attention_layernorm",
|
||||
"model.layers.24.input_layernorm",
|
||||
"model.layers.24.mlp.gate",
|
||||
"model.layers.24.post_attention_layernorm",
|
||||
"model.layers.25.input_layernorm",
|
||||
"model.layers.25.mlp.gate",
|
||||
"model.layers.25.post_attention_layernorm",
|
||||
"model.layers.26.input_layernorm",
|
||||
"model.layers.26.mlp.gate",
|
||||
"model.layers.26.post_attention_layernorm",
|
||||
"model.layers.27.input_layernorm",
|
||||
"model.layers.27.mlp.gate",
|
||||
"model.layers.27.post_attention_layernorm",
|
||||
"model.layers.28.input_layernorm",
|
||||
"model.layers.28.mlp.gate",
|
||||
"model.layers.28.post_attention_layernorm",
|
||||
"model.layers.29.input_layernorm",
|
||||
"model.layers.29.mlp.gate",
|
||||
"model.layers.29.post_attention_layernorm",
|
||||
"model.layers.30.input_layernorm",
|
||||
"model.layers.30.mlp.gate",
|
||||
"model.layers.30.post_attention_layernorm",
|
||||
"model.layers.31.input_layernorm",
|
||||
"model.layers.31.mlp.gate",
|
||||
"model.layers.31.post_attention_layernorm",
|
||||
"model.layers.32.input_layernorm",
|
||||
"model.layers.32.mlp.gate",
|
||||
"model.layers.32.post_attention_layernorm",
|
||||
"model.layers.33.input_layernorm",
|
||||
"model.layers.33.mlp.gate",
|
||||
"model.layers.33.post_attention_layernorm",
|
||||
"model.layers.34.input_layernorm",
|
||||
"model.layers.34.mlp.gate",
|
||||
"model.layers.34.post_attention_layernorm",
|
||||
"model.layers.35.input_layernorm",
|
||||
"model.layers.35.mlp.gate",
|
||||
"model.layers.35.post_attention_layernorm",
|
||||
"model.layers.36.input_layernorm",
|
||||
"model.layers.36.mlp.gate",
|
||||
"model.layers.36.post_attention_layernorm",
|
||||
"model.layers.37.input_layernorm",
|
||||
"model.layers.37.mlp.gate",
|
||||
"model.layers.37.post_attention_layernorm",
|
||||
"model.layers.38.input_layernorm",
|
||||
"model.layers.38.mlp.gate",
|
||||
"model.layers.38.post_attention_layernorm",
|
||||
"model.layers.39.input_layernorm",
|
||||
"model.layers.39.mlp.gate",
|
||||
"model.layers.39.post_attention_layernorm",
|
||||
"model.layers.40.input_layernorm",
|
||||
"model.layers.40.mlp.gate",
|
||||
"model.layers.40.post_attention_layernorm",
|
||||
"model.layers.41.input_layernorm",
|
||||
"model.layers.41.mlp.gate",
|
||||
"model.layers.41.post_attention_layernorm",
|
||||
"model.layers.42.input_layernorm",
|
||||
"model.layers.42.mlp.gate",
|
||||
"model.layers.42.post_attention_layernorm",
|
||||
"model.layers.43.input_layernorm",
|
||||
"model.layers.43.mlp.gate",
|
||||
"model.layers.43.post_attention_layernorm",
|
||||
"model.layers.44.input_layernorm",
|
||||
"model.layers.44.mlp.gate",
|
||||
"model.layers.44.post_attention_layernorm",
|
||||
"model.layers.45.input_layernorm",
|
||||
"model.layers.45.mlp.gate",
|
||||
"model.layers.45.post_attention_layernorm",
|
||||
"model.layers.46.input_layernorm",
|
||||
"model.layers.46.mlp.gate",
|
||||
"model.layers.46.post_attention_layernorm",
|
||||
"model.layers.47.input_layernorm",
|
||||
"model.layers.47.mlp.gate",
|
||||
"model.layers.47.post_attention_layernorm",
|
||||
"model.layers.48.input_layernorm",
|
||||
"model.layers.48.mlp.gate",
|
||||
"model.layers.48.post_attention_layernorm",
|
||||
"model.layers.49.input_layernorm",
|
||||
"model.layers.49.mlp.gate",
|
||||
"model.layers.49.post_attention_layernorm",
|
||||
"model.layers.50.input_layernorm",
|
||||
"model.layers.50.mlp.gate",
|
||||
"model.layers.50.post_attention_layernorm",
|
||||
"model.layers.51.input_layernorm",
|
||||
"model.layers.51.mlp.gate",
|
||||
"model.layers.51.post_attention_layernorm",
|
||||
"model.layers.52.input_layernorm",
|
||||
"model.layers.52.mlp.gate",
|
||||
"model.layers.52.post_attention_layernorm",
|
||||
"model.layers.53.input_layernorm",
|
||||
"model.layers.53.mlp.gate",
|
||||
"model.layers.53.post_attention_layernorm",
|
||||
"model.layers.54.input_layernorm",
|
||||
"model.layers.54.mlp.gate",
|
||||
"model.layers.54.post_attention_layernorm",
|
||||
"model.layers.55.input_layernorm",
|
||||
"model.layers.55.mlp.gate",
|
||||
"model.layers.55.post_attention_layernorm",
|
||||
"model.layers.56.input_layernorm",
|
||||
"model.layers.56.mlp.gate",
|
||||
"model.layers.56.post_attention_layernorm",
|
||||
"model.layers.57.input_layernorm",
|
||||
"model.layers.57.mlp.gate",
|
||||
"model.layers.57.post_attention_layernorm",
|
||||
"model.layers.58.input_layernorm",
|
||||
"model.layers.58.mlp.gate",
|
||||
"model.layers.58.post_attention_layernorm",
|
||||
"model.layers.59.input_layernorm",
|
||||
"model.layers.59.mlp.gate",
|
||||
"model.layers.59.post_attention_layernorm",
|
||||
"model.layers.60.input_layernorm",
|
||||
"model.layers.60.mlp.gate",
|
||||
"model.layers.60.post_attention_layernorm",
|
||||
"model.layers.61.input_layernorm",
|
||||
"model.layers.61.mlp.gate",
|
||||
"model.layers.61.post_attention_layernorm",
|
||||
"model.layers.62.input_layernorm",
|
||||
"model.layers.62.mlp.gate",
|
||||
"model.layers.62.post_attention_layernorm",
|
||||
"model.layers.63.input_layernorm",
|
||||
"model.layers.63.mlp.gate",
|
||||
"model.layers.63.post_attention_layernorm",
|
||||
"model.layers.64.input_layernorm",
|
||||
"model.layers.64.mlp.gate",
|
||||
"model.layers.64.post_attention_layernorm",
|
||||
"model.layers.65.input_layernorm",
|
||||
"model.layers.65.mlp.gate",
|
||||
"model.layers.65.post_attention_layernorm",
|
||||
"model.layers.66.input_layernorm",
|
||||
"model.layers.66.mlp.gate",
|
||||
"model.layers.66.post_attention_layernorm",
|
||||
"model.layers.67.input_layernorm",
|
||||
"model.layers.67.mlp.gate",
|
||||
"model.layers.67.post_attention_layernorm",
|
||||
"model.layers.68.input_layernorm",
|
||||
"model.layers.68.mlp.gate",
|
||||
"model.layers.68.post_attention_layernorm",
|
||||
"model.layers.69.input_layernorm",
|
||||
"model.layers.69.mlp.gate",
|
||||
"model.layers.69.post_attention_layernorm",
|
||||
"model.layers.70.input_layernorm",
|
||||
"model.layers.70.mlp.gate",
|
||||
"model.layers.70.post_attention_layernorm",
|
||||
"model.layers.71.input_layernorm",
|
||||
"model.layers.71.mlp.gate",
|
||||
"model.layers.71.post_attention_layernorm",
|
||||
"model.layers.72.input_layernorm",
|
||||
"model.layers.72.mlp.gate",
|
||||
"model.layers.72.post_attention_layernorm",
|
||||
"model.layers.73.input_layernorm",
|
||||
"model.layers.73.mlp.gate",
|
||||
"model.layers.73.post_attention_layernorm",
|
||||
"model.layers.74.input_layernorm",
|
||||
"model.layers.74.mlp.gate",
|
||||
"model.layers.74.post_attention_layernorm",
|
||||
"model.layers.75.input_layernorm",
|
||||
"model.layers.75.mlp.gate",
|
||||
"model.layers.75.post_attention_layernorm",
|
||||
"model.layers.76.input_layernorm",
|
||||
"model.layers.76.mlp.gate",
|
||||
"model.layers.76.post_attention_layernorm",
|
||||
"model.layers.77.input_layernorm",
|
||||
"model.layers.77.mlp.gate",
|
||||
"model.layers.77.post_attention_layernorm",
|
||||
"model.layers.78.input_layernorm",
|
||||
"model.layers.78.mlp.gate",
|
||||
"model.layers.78.post_attention_layernorm",
|
||||
"model.layers.79.input_layernorm",
|
||||
"model.layers.79.mlp.gate",
|
||||
"model.layers.79.post_attention_layernorm",
|
||||
"model.layers.80.input_layernorm",
|
||||
"model.layers.80.mlp.gate",
|
||||
"model.layers.80.post_attention_layernorm",
|
||||
"model.layers.81.input_layernorm",
|
||||
"model.layers.81.mlp.gate",
|
||||
"model.layers.81.post_attention_layernorm",
|
||||
"model.layers.82.input_layernorm",
|
||||
"model.layers.82.mlp.gate",
|
||||
"model.layers.82.post_attention_layernorm",
|
||||
"model.layers.83.input_layernorm",
|
||||
"model.layers.83.mlp.gate",
|
||||
"model.layers.83.post_attention_layernorm",
|
||||
"model.layers.84.input_layernorm",
|
||||
"model.layers.84.mlp.gate",
|
||||
"model.layers.84.post_attention_layernorm",
|
||||
"model.layers.85.input_layernorm",
|
||||
"model.layers.85.mlp.gate",
|
||||
"model.layers.85.post_attention_layernorm",
|
||||
"model.layers.86.input_layernorm",
|
||||
"model.layers.86.mlp.gate",
|
||||
"model.layers.86.post_attention_layernorm",
|
||||
"model.layers.87.input_layernorm",
|
||||
"model.layers.87.mlp.gate",
|
||||
"model.layers.87.post_attention_layernorm",
|
||||
"model.layers.88.input_layernorm",
|
||||
"model.layers.88.mlp.gate",
|
||||
"model.layers.88.post_attention_layernorm",
|
||||
"model.layers.89.input_layernorm",
|
||||
"model.layers.89.mlp.gate",
|
||||
"model.layers.89.post_attention_layernorm",
|
||||
"model.layers.90.input_layernorm",
|
||||
"model.layers.90.mlp.gate",
|
||||
"model.layers.90.post_attention_layernorm",
|
||||
"model.layers.91.input_layernorm",
|
||||
"model.layers.91.mlp.gate",
|
||||
"model.layers.91.post_attention_layernorm",
|
||||
"model.layers.92.input_layernorm",
|
||||
"model.layers.92.mlp.gate",
|
||||
"model.layers.92.post_attention_layernorm",
|
||||
"model.layers.93.input_layernorm",
|
||||
"model.layers.93.mlp.gate",
|
||||
"model.layers.93.post_attention_layernorm"
|
||||
],
|
||||
"fmt": "e4m3",
|
||||
"quant_method": "fp8",
|
||||
"weight_block_size": [
|
||||
128,
|
||||
128
|
||||
],
|
||||
"is_checkpoint_fp8_serialized": true
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,6 @@
|
||||
time_stats.moe_gating_linear.min,time_stats.moe_gating_linear.max,time_stats.moe_gating_linear.mean,time_stats.moe_gating_linear.median,time_stats.moe_gating_linear.std,time_stats.moe_gating_routing_topk.min,time_stats.moe_gating_routing_topk.max,time_stats.moe_gating_routing_topk.mean,time_stats.moe_gating_routing_topk.median,time_stats.moe_gating_routing_topk.std,time_stats.moe_shuffling.min,time_stats.moe_shuffling.max,time_stats.moe_shuffling.mean,time_stats.moe_shuffling.median,time_stats.moe_shuffling.std,time_stats.moe_grouped_gemm.min,time_stats.moe_grouped_gemm.max,time_stats.moe_grouped_gemm.mean,time_stats.moe_grouped_gemm.median,time_stats.moe_grouped_gemm.std,num_tokens,num_experts,num_experts_per_device,expert_parallel_size,routing_runtime_path,routing_assignment_policy,routing_weight_policy,routing_uses_router_logits,gating_runtime_context,gating_runtime_context_impl,router_topk,hidden_dim,expert_hidden_dim,use_gated,num_tensor_parallel_workers,total_routed_tokens,model_expansion_ratio,tokens_per_expert_avg,tokens_to_experts_ratio,expert_utilization,min_load_ratio,load_imbalance_cv,max_load_ratio,load_entropy,load_gini_coefficient,load_distribution,seed,moe_grouped_gemm_backend,measurement_type,profiling_precision,model_arch,quant_signature
|
||||
0.03094400092959404,0.052000001072883606,0.03423200035467744,0.032816000282764435,0.004586225105504425,0.05225599929690361,0.08137600123882294,0.06316960025578737,0.0586559996008873,0.009046516570964667,0.025087999179959297,0.05766399949789047,0.030939200054854156,0.028768000192940235,0.006844666670168347,0.23388800024986267,0.31091201305389404,0.24544477462768555,0.23836800456047058,0.018666831776499748,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.609375,0.0,1.0231690964840563,4.0,6.122626857503489,0.5433349609375,uniform,0,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.030368000268936157,0.05008000135421753,0.03488799966871738,0.03270399942994118,0.004964435488742052,0.05142400041222572,0.07401599735021591,0.05652640014886856,0.054847998544573784,0.005118024227402908,0.02534399926662445,0.0424639992415905,0.02885119989514351,0.028447999618947506,0.004027125677475938,0.24774399399757385,0.26895999908447266,0.25432640314102173,0.25200000405311584,0.00582256680354476,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.6171875,0.0,0.9682458365518543,4.0,6.171569533299451,0.521240234375,uniform,1,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.0307839997112751,0.04447999969124794,0.03474240032956004,0.03252799995243549,0.00435773849976965,0.051231998950242996,0.07468800246715546,0.057651200145483014,0.0561280008405447,0.006050027441177822,0.02579200081527233,0.033504001796245575,0.02867360021919012,0.02796800062060356,0.00238178652420847,0.2533760070800781,0.2958720028400421,0.2585648000240326,0.25679999589920044,0.009045161306858063,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.640625,0.0,0.9842509842514764,4.0,6.193092091810443,0.5164794921875,uniform,2,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.030592000111937523,0.04108799993991852,0.03223840007558465,0.031279999762773514,0.002451635813407811,0.05177599936723709,0.077504001557827,0.05718399975448847,0.0533440001308918,0.00711033940595454,0.02457600086927414,0.051552001386880875,0.03192960014566779,0.028431999497115612,0.007824328074264588,0.2531839907169342,0.28329598903656006,0.26060959696769714,0.2584640085697174,0.006803486030548811,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.671875,0.0,0.9354143466934853,5.0,6.268369216857352,0.48486328125,uniform,3,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.030527999624609947,0.03500799834728241,0.03184479987248778,0.03139200061559677,0.001123070738289876,0.05104000121355057,0.07103999704122543,0.05895199999213219,0.05676800012588501,0.0068221931474717916,0.024383999407291412,0.04211200028657913,0.02812959998846054,0.026959999464452267,0.003887262202895282,0.2314240038394928,0.2559039890766144,0.23696160316467285,0.23375999927520752,0.006377317477017641,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.59375,0.0,1.1319231422671772,6.0,6.039891775809659,0.5689697265625,uniform,4,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
|
@@ -0,0 +1,27 @@
|
||||
block_shape: null
|
||||
device: h20
|
||||
disable_ray: true
|
||||
disable_replicated: false
|
||||
enable_load_imbalance: true
|
||||
expert_parallel_sizes:
|
||||
- 1
|
||||
extra_num_tokens: null
|
||||
gating_runtime_context: prefill_hot
|
||||
load_distributions:
|
||||
- uniform
|
||||
max_tokens: 16
|
||||
models:
|
||||
- Qwen3-235B-A22B-FP8
|
||||
num_gpus: 1
|
||||
num_samples_per_distribution: 5
|
||||
num_tensor_parallel_workers:
|
||||
- 4
|
||||
num_tokens_list:
|
||||
- 16
|
||||
output_dir: /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/profiles-moe-factorial-r5/aligned
|
||||
per_channel_quant: false
|
||||
precision: null
|
||||
profile_method: cuda_event
|
||||
routing_runtime_path: standard_fused_topk
|
||||
skip_confirmation: true
|
||||
use_fp8: null
|
||||
@@ -0,0 +1,6 @@
|
||||
time_stats.moe_gating_linear.min,time_stats.moe_gating_linear.max,time_stats.moe_gating_linear.mean,time_stats.moe_gating_linear.median,time_stats.moe_gating_linear.std,time_stats.moe_gating_routing_topk.min,time_stats.moe_gating_routing_topk.max,time_stats.moe_gating_routing_topk.mean,time_stats.moe_gating_routing_topk.median,time_stats.moe_gating_routing_topk.std,time_stats.moe_shuffling.min,time_stats.moe_shuffling.max,time_stats.moe_shuffling.mean,time_stats.moe_shuffling.median,time_stats.moe_shuffling.std,time_stats.moe_grouped_gemm.min,time_stats.moe_grouped_gemm.max,time_stats.moe_grouped_gemm.mean,time_stats.moe_grouped_gemm.median,time_stats.moe_grouped_gemm.std,num_tokens,num_experts,num_experts_per_device,expert_parallel_size,routing_runtime_path,routing_assignment_policy,routing_weight_policy,routing_uses_router_logits,gating_runtime_context,gating_runtime_context_impl,router_topk,hidden_dim,expert_hidden_dim,use_gated,num_tensor_parallel_workers,total_routed_tokens,model_expansion_ratio,tokens_per_expert_avg,tokens_to_experts_ratio,expert_utilization,min_load_ratio,load_imbalance_cv,max_load_ratio,load_entropy,load_gini_coefficient,load_distribution,seed,moe_grouped_gemm_backend,measurement_type,profiling_precision,model_arch,quant_signature
|
||||
0.03232000023126602,0.07411199808120728,0.040055999718606475,0.03792000003159046,0.010027335937042725,0.05955199897289276,0.09151999652385712,0.06805919948965311,0.06542399898171425,0.009156033646696494,0.02735999971628189,0.04198399931192398,0.031628800183534624,0.03081599995493889,0.0038650835976484594,0.29440000653266907,0.37036800384521484,0.3045775890350342,0.3012160062789917,0.01622786745429039,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.609375,0.0,1.0231690964840563,4.0,6.122626857503489,0.5433349609375,uniform,0,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.03190400078892708,0.0639680027961731,0.036531200259923936,0.03387199901044369,0.007307958553439168,0.05407999828457832,0.08684799820184708,0.0631104001775384,0.059248000383377075,0.008437822166776614,0.02537599951028824,0.045343998819589615,0.029841599892824887,0.028095999732613564,0.004578510902843549,0.29868799448013306,0.3216319978237152,0.30579519271850586,0.30294400453567505,0.006667278707027435,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.6171875,0.0,0.9682458365518543,4.0,6.171569533299451,0.521240234375,uniform,1,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.031199999153614044,0.06745599955320358,0.03718719966709614,0.03391999937593937,0.008084069620200455,0.05353600159287453,0.06828799843788147,0.059427200257778166,0.05902400054037571,0.0036973349098869714,0.026688000187277794,0.03788800165057182,0.030459199845790864,0.02908799983561039,0.0032231049972104124,0.30588799715042114,0.3216319978237152,0.3109855651855469,0.31036800146102905,0.004134960938245058,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.640625,0.0,0.9842509842514764,4.0,6.193092091810443,0.5164794921875,uniform,2,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.031488001346588135,0.04790399968624115,0.03488959986716509,0.033615998923778534,0.003995903359891917,0.05503999814391136,0.08563199639320374,0.06150399968028068,0.057312000542879105,0.007950341155323642,0.026335999369621277,0.04598399996757507,0.030313600040972232,0.02792000025510788,0.005356499011224523,0.31516799330711365,0.32972800731658936,0.3209056258201599,0.3196159899234772,0.004338567610830069,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.671875,0.0,0.9354143466934853,5.0,6.268369216857352,0.48486328125,uniform,3,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.030751999467611313,0.044895999133586884,0.03544640000909567,0.03302400000393391,0.00457756723742277,0.05331199988722801,0.07606399804353714,0.05961279980838299,0.05753600038588047,0.006503363955077464,0.02630399912595749,0.04368000105023384,0.03112160013988614,0.028815999627113342,0.005258638278682459,0.2905920147895813,0.3538239896297455,0.3015664219856262,0.29631999135017395,0.014866933226585388,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.59375,0.0,1.1319231422671772,6.0,6.039891775809659,0.5689697265625,uniform,4,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
|
@@ -0,0 +1,27 @@
|
||||
block_shape: null
|
||||
device: h20
|
||||
disable_ray: true
|
||||
disable_replicated: false
|
||||
enable_load_imbalance: true
|
||||
expert_parallel_sizes:
|
||||
- 1
|
||||
extra_num_tokens: null
|
||||
gating_runtime_context: prefill_hot
|
||||
load_distributions:
|
||||
- uniform
|
||||
max_tokens: 16
|
||||
models:
|
||||
- Qwen3-235B-A22B-FP8
|
||||
num_gpus: 1
|
||||
num_samples_per_distribution: 5
|
||||
num_tensor_parallel_workers:
|
||||
- 4
|
||||
num_tokens_list:
|
||||
- 16
|
||||
output_dir: /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/profiles-moe-factorial-r5/compute_type
|
||||
per_channel_quant: false
|
||||
precision: null
|
||||
profile_method: cuda_event
|
||||
routing_runtime_path: standard_fused_topk
|
||||
skip_confirmation: true
|
||||
use_fp8: null
|
||||
@@ -0,0 +1,6 @@
|
||||
time_stats.moe_gating_linear.min,time_stats.moe_gating_linear.max,time_stats.moe_gating_linear.mean,time_stats.moe_gating_linear.median,time_stats.moe_gating_linear.std,time_stats.moe_gating_routing_topk.min,time_stats.moe_gating_routing_topk.max,time_stats.moe_gating_routing_topk.mean,time_stats.moe_gating_routing_topk.median,time_stats.moe_gating_routing_topk.std,time_stats.moe_shuffling.min,time_stats.moe_shuffling.max,time_stats.moe_shuffling.mean,time_stats.moe_shuffling.median,time_stats.moe_shuffling.std,time_stats.moe_grouped_gemm.min,time_stats.moe_grouped_gemm.max,time_stats.moe_grouped_gemm.mean,time_stats.moe_grouped_gemm.median,time_stats.moe_grouped_gemm.std,num_tokens,num_experts,num_experts_per_device,expert_parallel_size,routing_runtime_path,routing_assignment_policy,routing_weight_policy,routing_uses_router_logits,gating_runtime_context,gating_runtime_context_impl,router_topk,hidden_dim,expert_hidden_dim,use_gated,num_tensor_parallel_workers,total_routed_tokens,model_expansion_ratio,tokens_per_expert_avg,tokens_to_experts_ratio,expert_utilization,min_load_ratio,load_imbalance_cv,max_load_ratio,load_entropy,load_gini_coefficient,load_distribution,seed,moe_grouped_gemm_backend,measurement_type,profiling_precision,model_arch,quant_signature
|
||||
0.029888000339269638,0.05558399856090546,0.036182400118559596,0.03598400019109249,0.00590375348379892,0.05135999992489815,0.07407999783754349,0.05939359981566668,0.05599999986588955,0.007274243962538696,0.024831999093294144,0.060575999319553375,0.03204159988090396,0.029280000366270542,0.008986441399241034,0.23401600122451782,0.2914240062236786,0.24253761768341064,0.23852799832820892,0.012656064704060555,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.609375,0.0,1.0231690964840563,4.0,6.122626857503489,0.5433349609375,uniform,0,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.0306560005992651,0.04927999898791313,0.0345616003498435,0.032096000388264656,0.005272357401818311,0.0514880008995533,0.08454400300979614,0.05950720049440861,0.055904000997543335,0.008599599958802731,0.024639999493956566,0.04726399853825569,0.029726399946957825,0.028032000176608562,0.005891950556036845,0.24633599817752838,0.276095986366272,0.2544096112251282,0.25091201066970825,0.006771203130483627,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.6171875,0.0,0.9682458365518543,4.0,6.171569533299451,0.521240234375,uniform,1,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.030208000913262367,0.062144000083208084,0.03510080017149449,0.03203200176358223,0.007286512102806094,0.050592001527547836,0.07017599791288376,0.05696159955114126,0.054735999554395676,0.006055532934843585,0.0244159996509552,0.04364800080657005,0.02875520009547472,0.02700799982994795,0.00429941663275523,0.252703994512558,0.27452799677848816,0.25873440504074097,0.2567040026187897,0.006180537864565849,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.640625,0.0,0.9842509842514764,4.0,6.193092091810443,0.5164794921875,uniform,2,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.028863999992609024,0.04185599833726883,0.03244960019364953,0.03129600081592798,0.00309197601199972,0.05049600079655647,0.07241600006818771,0.05591519977897406,0.05273599922657013,0.006825017255220366,0.024000000208616257,0.04150399938225746,0.027609600126743315,0.02619200013577938,0.0041488046270812895,0.25491198897361755,0.27008000016212463,0.2593088150024414,0.25811201333999634,0.003932233899831772,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.671875,0.0,0.9354143466934853,5.0,6.268369216857352,0.48486328125,uniform,3,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.030400000512599945,0.039135999977588654,0.03235360030084848,0.031888000667095184,0.002007792126883597,0.050944000482559204,0.06815999746322632,0.054641599953174594,0.053279999643564224,0.003969876178544029,0.024064000695943832,0.03145600110292435,0.027134399861097336,0.026559999212622643,0.0021875101737656014,0.23164799809455872,0.2622720003128052,0.2388928234577179,0.23686400055885315,0.007063580676913261,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.59375,0.0,1.1319231422671772,6.0,6.039891775809659,0.5689697265625,uniform,4,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
|
@@ -0,0 +1,27 @@
|
||||
block_shape: null
|
||||
device: h20
|
||||
disable_ray: true
|
||||
disable_replicated: false
|
||||
enable_load_imbalance: true
|
||||
expert_parallel_sizes:
|
||||
- 1
|
||||
extra_num_tokens: null
|
||||
gating_runtime_context: prefill_hot
|
||||
load_distributions:
|
||||
- uniform
|
||||
max_tokens: 16
|
||||
models:
|
||||
- Qwen3-235B-A22B-FP8
|
||||
num_gpus: 1
|
||||
num_samples_per_distribution: 5
|
||||
num_tensor_parallel_workers:
|
||||
- 4
|
||||
num_tokens_list:
|
||||
- 16
|
||||
output_dir: /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/profiles-moe-factorial-r5/config_key
|
||||
per_channel_quant: false
|
||||
precision: null
|
||||
profile_method: cuda_event
|
||||
routing_runtime_path: standard_fused_topk
|
||||
skip_confirmation: true
|
||||
use_fp8: null
|
||||
@@ -0,0 +1,6 @@
|
||||
time_stats.moe_gating_linear.min,time_stats.moe_gating_linear.max,time_stats.moe_gating_linear.mean,time_stats.moe_gating_linear.median,time_stats.moe_gating_linear.std,time_stats.moe_gating_routing_topk.min,time_stats.moe_gating_routing_topk.max,time_stats.moe_gating_routing_topk.mean,time_stats.moe_gating_routing_topk.median,time_stats.moe_gating_routing_topk.std,time_stats.moe_shuffling.min,time_stats.moe_shuffling.max,time_stats.moe_shuffling.mean,time_stats.moe_shuffling.median,time_stats.moe_shuffling.std,time_stats.moe_grouped_gemm.min,time_stats.moe_grouped_gemm.max,time_stats.moe_grouped_gemm.mean,time_stats.moe_grouped_gemm.median,time_stats.moe_grouped_gemm.std,num_tokens,num_experts,num_experts_per_device,expert_parallel_size,routing_runtime_path,routing_assignment_policy,routing_weight_policy,routing_uses_router_logits,gating_runtime_context,gating_runtime_context_impl,router_topk,hidden_dim,expert_hidden_dim,use_gated,num_tensor_parallel_workers,total_routed_tokens,model_expansion_ratio,tokens_per_expert_avg,tokens_to_experts_ratio,expert_utilization,min_load_ratio,load_imbalance_cv,max_load_ratio,load_entropy,load_gini_coefficient,load_distribution,seed,moe_grouped_gemm_backend,measurement_type,profiling_precision,model_arch,quant_signature
|
||||
0.03094400092959404,0.05427199974656105,0.03751839986070991,0.03566399961709976,0.0063355673062837495,0.052960000932216644,0.10150399804115295,0.06679840013384819,0.06265599839389324,0.012010412593353396,0.025087999179959297,0.0607680007815361,0.03164320001378655,0.028655999340116978,0.008879480144565048,0.29548799991607666,0.3161599934101105,0.30511200428009033,0.30371201038360596,0.006655826233327389,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.609375,0.0,1.0231690964840563,4.0,6.122626857503489,0.5433349609375,uniform,0,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.030368000268936157,0.045184001326560974,0.03435200024396181,0.03299199976027012,0.003866832846889978,0.053247999399900436,0.10054399818181992,0.061161600053310394,0.05702400021255016,0.010951609505981603,0.025248000398278236,0.039872001856565475,0.029195200372487306,0.027520000003278255,0.003716525371772022,0.29631999135017395,0.3163839876651764,0.30516156554222107,0.30246400833129883,0.0062532913871109486,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.6171875,0.0,0.9682458365518543,4.0,6.171569533299451,0.521240234375,uniform,1,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.030688000842928886,0.04044799879193306,0.03336800048127771,0.032368000596761703,0.002819613112867307,0.053568001836538315,0.06889600306749344,0.05800320040434599,0.055616000667214394,0.00500911399891226,0.025631999596953392,0.040991999208927155,0.028961599990725517,0.027951999567449093,0.003321893930324548,0.30828800797462463,0.3516480028629303,0.3159376084804535,0.3127039968967438,0.009800842963159084,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.640625,0.0,0.9842509842514764,4.0,6.193092091810443,0.5164794921875,uniform,2,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.031007999554276466,0.044319998472929,0.03418559962883592,0.03271999955177307,0.0038270996330861703,0.0541439987719059,0.07507199794054031,0.06345439981669188,0.06393599882721901,0.006346258200489106,0.025407999753952026,0.03494400158524513,0.028180800192058087,0.027375999838113785,0.0024874374625372497,0.3179520070552826,0.331712007522583,0.3228943943977356,0.32150399684906006,0.004389750771224499,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.671875,0.0,0.9354143466934853,5.0,6.268369216857352,0.48486328125,uniform,3,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
0.030912000685930252,0.04745600000023842,0.03517600009217858,0.03243200108408928,0.005035846835878437,0.053279999643564224,0.09200000017881393,0.06018720027059317,0.05721599981188774,0.008845081624923161,0.025151999667286873,0.04851200059056282,0.0323488000780344,0.02820800058543682,0.0077821073314592575,0.2917119860649109,0.3189440071582794,0.301118403673172,0.29817599058151245,0.008174914866685867,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.59375,0.0,1.1319231422671772,6.0,6.039891775809659,0.5689697265625,uniform,4,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
|
@@ -0,0 +1,27 @@
|
||||
block_shape: null
|
||||
device: h20
|
||||
disable_ray: true
|
||||
disable_replicated: false
|
||||
enable_load_imbalance: true
|
||||
expert_parallel_sizes:
|
||||
- 1
|
||||
extra_num_tokens: null
|
||||
gating_runtime_context: prefill_hot
|
||||
load_distributions:
|
||||
- uniform
|
||||
max_tokens: 16
|
||||
models:
|
||||
- Qwen3-235B-A22B-FP8
|
||||
num_gpus: 1
|
||||
num_samples_per_distribution: 5
|
||||
num_tensor_parallel_workers:
|
||||
- 4
|
||||
num_tokens_list:
|
||||
- 16
|
||||
output_dir: /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/profiles-moe-factorial-r5/original
|
||||
per_channel_quant: false
|
||||
precision: null
|
||||
profile_method: cuda_event
|
||||
routing_runtime_path: standard_fused_topk
|
||||
skip_confirmation: true
|
||||
use_fp8: null
|
||||
@@ -0,0 +1,5 @@
|
||||
time_stats.attn_input_reshape.min,time_stats.attn_input_reshape.max,time_stats.attn_input_reshape.mean,time_stats.attn_input_reshape.median,time_stats.attn_input_reshape.std,time_stats.attn_kv_cache_save.min,time_stats.attn_kv_cache_save.max,time_stats.attn_kv_cache_save.mean,time_stats.attn_kv_cache_save.median,time_stats.attn_kv_cache_save.std,time_stats.attn_prefill.min,time_stats.attn_prefill.max,time_stats.attn_prefill.mean,time_stats.attn_prefill.median,time_stats.attn_prefill.std,time_stats.attn_decode.min,time_stats.attn_decode.max,time_stats.attn_decode.mean,time_stats.attn_decode.median,time_stats.attn_decode.std,time_stats.attn_output_reshape.min,time_stats.attn_output_reshape.max,time_stats.attn_output_reshape.mean,time_stats.attn_output_reshape.median,time_stats.attn_output_reshape.std,n_embd,n_q_head,n_kv_head,block_size,num_tensor_parallel_workers,max_model_len,batch_size,prefill_chunk_size,kv_cache_size,is_prefill,attention_backend,is_mixed_batch,mode,seq_lens,total_tokens,max_seq_len,min_seq_len,avg_seq_len,equal_seq_len,seq_len_variance,seq_len_std,seq_len_cv,is_chunked_prefill_sample,chunk_start_token,chunk_end_token,total_prefill_tokens,profiling_precision,model_arch,quant_signature,measurement_type
|
||||
0.010463999584317207,0.04681599885225296,0.018502399697899817,0.01104000024497509,0.014205381674858167,0.021247999742627144,0.033055998384952545,0.025439999625086786,0.024639999493956566,0.004202660707027564,0.05206400156021118,0.09040000289678574,0.0665344014763832,0.06451199948787689,0.013110543934264527,0.007327999919652939,0.011680000461637974,0.008480000123381615,0.0077760000713169575,0.0016149067494504018,0.009472000412642956,0.011103999800980091,0.010118400119245053,0.009600000455975533,0.0007061491557578713,4096,64,4,16,4,40960,1,128,0,True,FLASHINFER,False,even,[128],128,128,128,128.0,128,0.0,0.0,0.0,False,0,128,128,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128,CUDA_EVENT
|
||||
0.010751999914646149,0.012896000407636166,0.011795200034976005,0.011359999887645245,0.0009191526000700493,0.020191999152302742,0.02755199931561947,0.02319999970495701,0.021247999742627144,0.002944486774412461,0.049215998500585556,0.06700800359249115,0.05813760012388229,0.058240000158548355,0.006895964777123649,0.007519999984651804,0.009727999567985535,0.008140799775719642,0.007807999849319458,0.0008127242879711131,0.009472000412642956,0.025248000398278236,0.01303040012717247,0.010015999898314476,0.0061233119538994085,4096,64,4,16,4,40960,1,32,0,True,FLASHINFER,False,even,[32],32,32,32,32.0,32,0.0,0.0,0.0,False,0,32,32,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128,CUDA_EVENT
|
||||
0.010239999741315842,0.011008000001311302,0.010515199974179268,0.010432000271975994,0.0002635681936991548,0.02112000063061714,0.03843199834227562,0.025894399732351303,0.023231999948620796,0.006378760808481684,0.05417599901556969,0.06438399851322174,0.05709439888596535,0.055615998804569244,0.003697870830170538,0.007135999854654074,0.00774399982765317,0.007500800024718046,0.007552000228315592,0.0002009640969704077,0.009279999881982803,0.012736000120639801,0.01031040009111166,0.009568000212311745,0.0012940667684018582,4096,64,4,16,4,40960,1,64,0,True,FLASHINFER,False,even,[64],64,64,64,64.0,64,0.0,0.0,0.0,False,0,64,64,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128,CUDA_EVENT
|
||||
0.010367999784648418,0.02332800067961216,0.013459200039505959,0.010847999714314938,0.004984552697426283,0.02054399996995926,0.0261439997702837,0.02255360037088394,0.022112000733613968,0.001928344367854761,0.04851200059056282,0.07968000322580338,0.058873600512743,0.056352000683546066,0.010813603870069636,0.00723200011998415,0.008352000266313553,0.007686400134116411,0.007648000027984381,0.00039524862182609345,0.008960000239312649,0.011103999800980091,0.009881599992513656,0.00940799992531538,0.0009090251198168286,4096,64,4,16,4,40960,1,96,0,True,FLASHINFER,False,even,[96],96,96,96,96.0,96,0.0,0.0,0.0,False,0,96,96,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128,CUDA_EVENT
|
||||
|
@@ -0,0 +1,5 @@
|
||||
time_stats.attn_input_reshape.min,time_stats.attn_input_reshape.max,time_stats.attn_input_reshape.mean,time_stats.attn_input_reshape.median,time_stats.attn_input_reshape.std,time_stats.attn_kv_cache_save.min,time_stats.attn_kv_cache_save.max,time_stats.attn_kv_cache_save.mean,time_stats.attn_kv_cache_save.median,time_stats.attn_kv_cache_save.std,time_stats.attn_prefill.min,time_stats.attn_prefill.max,time_stats.attn_prefill.mean,time_stats.attn_prefill.median,time_stats.attn_prefill.std,time_stats.attn_decode.min,time_stats.attn_decode.max,time_stats.attn_decode.mean,time_stats.attn_decode.median,time_stats.attn_decode.std,time_stats.attn_output_reshape.min,time_stats.attn_output_reshape.max,time_stats.attn_output_reshape.mean,time_stats.attn_output_reshape.median,time_stats.attn_output_reshape.std,n_embd,n_q_head,n_kv_head,block_size,num_tensor_parallel_workers,max_model_len,batch_size,prefill_chunk_size,kv_cache_size,is_prefill,attention_backend,is_mixed_batch,mode,seq_lens,total_tokens,max_seq_len,min_seq_len,avg_seq_len,equal_seq_len,seq_len_variance,seq_len_std,seq_len_cv,is_chunked_prefill_sample,chunk_start_token,chunk_end_token,total_prefill_tokens,profiling_precision,model_arch,quant_signature,measurement_type,is_true_mixed_batch
|
||||
0.010463999584317207,0.04681599885225296,0.018502399697899817,0.01104000024497509,0.014205381674858167,0.021247999742627144,0.033055998384952545,0.025439999625086786,0.024639999493956566,0.004202660707027564,0.05206400156021118,0.09040000289678574,0.0665344014763832,0.06451199948787689,0.013110543934264527,0.007327999919652939,0.011680000461637974,0.008480000123381615,0.0077760000713169575,0.0016149067494504018,0.009472000412642956,0.011103999800980091,0.010118400119245053,0.009600000455975533,0.0007061491557578713,4096,64,4,16,4,40960,1,128,0,True,FLASHINFER,False,even,[128],128,128,128,128.0,128,0.0,0.0,0.0,False,0,128,128,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128,CUDA_EVENT,False
|
||||
0.010751999914646149,0.012896000407636166,0.011795200034976005,0.011359999887645245,0.0009191526000700493,0.020191999152302742,0.02755199931561947,0.02319999970495701,0.021247999742627144,0.002944486774412461,0.049215998500585556,0.06700800359249115,0.05813760012388229,0.058240000158548355,0.006895964777123649,0.007519999984651804,0.009727999567985535,0.008140799775719642,0.007807999849319458,0.0008127242879711131,0.009472000412642956,0.025248000398278236,0.01303040012717247,0.010015999898314476,0.0061233119538994085,4096,64,4,16,4,40960,1,32,0,True,FLASHINFER,False,even,[32],32,32,32,32.0,32,0.0,0.0,0.0,False,0,32,32,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128,CUDA_EVENT,False
|
||||
0.010239999741315842,0.011008000001311302,0.010515199974179268,0.010432000271975994,0.0002635681936991548,0.02112000063061714,0.03843199834227562,0.025894399732351303,0.023231999948620796,0.006378760808481684,0.05417599901556969,0.06438399851322174,0.05709439888596535,0.055615998804569244,0.003697870830170538,0.007135999854654074,0.00774399982765317,0.007500800024718046,0.007552000228315592,0.0002009640969704077,0.009279999881982803,0.012736000120639801,0.01031040009111166,0.009568000212311745,0.0012940667684018582,4096,64,4,16,4,40960,1,64,0,True,FLASHINFER,False,even,[64],64,64,64,64.0,64,0.0,0.0,0.0,False,0,64,64,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128,CUDA_EVENT,False
|
||||
0.010367999784648418,0.02332800067961216,0.013459200039505959,0.010847999714314938,0.004984552697426283,0.02054399996995926,0.0261439997702837,0.02255360037088394,0.022112000733613968,0.001928344367854761,0.04851200059056282,0.07968000322580338,0.058873600512743,0.056352000683546066,0.010813603870069636,0.00723200011998415,0.008352000266313553,0.007686400134116411,0.007648000027984381,0.00039524862182609345,0.008960000239312649,0.011103999800980091,0.009881599992513656,0.00940799992531538,0.0009090251198168286,4096,64,4,16,4,40960,1,96,0,True,FLASHINFER,False,even,[96],96,96,96,96.0,96,0.0,0.0,0.0,False,0,96,96,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128,CUDA_EVENT,False
|
||||
|
@@ -0,0 +1,3 @@
|
||||
time_stats.attn_pre_proj.min,time_stats.attn_pre_proj.max,time_stats.attn_pre_proj.mean,time_stats.attn_pre_proj.median,time_stats.attn_pre_proj.std,time_stats.attn_rope.min,time_stats.attn_rope.max,time_stats.attn_rope.mean,time_stats.attn_rope.median,time_stats.attn_rope.std,time_stats.attn_post_proj.min,time_stats.attn_post_proj.max,time_stats.attn_post_proj.mean,time_stats.attn_post_proj.median,time_stats.attn_post_proj.std,time_stats.emb.min,time_stats.emb.max,time_stats.emb.mean,time_stats.emb.median,time_stats.emb.std,time_stats.input_layernorm.min,time_stats.input_layernorm.max,time_stats.input_layernorm.mean,time_stats.input_layernorm.median,time_stats.input_layernorm.std,time_stats.post_attention_layernorm.min,time_stats.post_attention_layernorm.max,time_stats.post_attention_layernorm.mean,time_stats.post_attention_layernorm.median,time_stats.post_attention_layernorm.std,n_head,n_kv_head,n_embd,n_expanded_embd,vocab_size,use_gated_mlp,use_qk_norm,attn_output_gate,num_tokens,num_tensor_parallel_workers,padded_n_embd,padded_n_expanded_embd,model_arch,is_step2_mini,share_expert_dim,share_q_dim,measurement_type,profiling_precision,quant_signature
|
||||
0.19225600361824036,0.26678401231765747,0.22318400144577027,0.22147200256586075,0.020290217906394733,0.024639999493956566,0.04320000112056732,0.028563199937343596,0.026335999369621277,0.006067654243700026,0.09071999788284302,0.12992000579833984,0.11053120009601117,0.11033599823713303,0.011489030217015594,,,,,,,,,,,,,,,,64,4,4096,1536,151936,True,True,False,16,4,4096,1536,generic,False,,,CUDA_EVENT,BF16,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
,,,,,,,,,,,,,,,0.05990400165319443,0.1345279961824417,0.08581680012866855,0.0899839997291565,0.020920650895404905,0.020160000771284103,0.0360959991812706,0.02369120018556714,0.021824000403285027,0.00439515404502141,0.01833599992096424,0.02425600029528141,0.020108799915760756,0.019407999701797962,0.001409577392533308,64,4,4096,1536,151936,True,True,False,16,1,4096,1536,generic,False,,,CUDA_EVENT,BF16,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
|
@@ -0,0 +1,2 @@
|
||||
time_stats.moe_gating_linear.min,time_stats.moe_gating_linear.max,time_stats.moe_gating_linear.mean,time_stats.moe_gating_linear.median,time_stats.moe_gating_linear.std,time_stats.moe_gating_routing_topk.min,time_stats.moe_gating_routing_topk.max,time_stats.moe_gating_routing_topk.mean,time_stats.moe_gating_routing_topk.median,time_stats.moe_gating_routing_topk.std,time_stats.moe_shuffling.min,time_stats.moe_shuffling.max,time_stats.moe_shuffling.mean,time_stats.moe_shuffling.median,time_stats.moe_shuffling.std,time_stats.moe_grouped_gemm.min,time_stats.moe_grouped_gemm.max,time_stats.moe_grouped_gemm.mean,time_stats.moe_grouped_gemm.median,time_stats.moe_grouped_gemm.std,num_tokens,num_experts,num_experts_per_device,expert_parallel_size,routing_runtime_path,routing_assignment_policy,routing_weight_policy,routing_uses_router_logits,gating_runtime_context,gating_runtime_context_impl,router_topk,hidden_dim,expert_hidden_dim,use_gated,num_tensor_parallel_workers,total_routed_tokens,model_expansion_ratio,tokens_per_expert_avg,tokens_to_experts_ratio,expert_utilization,min_load_ratio,load_imbalance_cv,max_load_ratio,load_entropy,load_gini_coefficient,load_distribution,seed,moe_grouped_gemm_backend,measurement_type,profiling_precision,model_arch,quant_signature
|
||||
0.03097599931061268,0.049056001007556915,0.03467839974910021,0.03254400007426739,0.005093522706269737,0.05193600058555603,0.08419200032949448,0.06054240055382252,0.05641600117087364,0.009051489911083033,0.025919999927282333,0.04064000025391579,0.030527999717742206,0.030608000233769417,0.0040231266205605675,0.29603201150894165,0.3494400084018707,0.3075023889541626,0.30137598514556885,0.014090820215642452,16,128,128,1,standard_fused_topk,logit_topk,softmax_renorm,True,prefill_hot,ffn_like_prefix_20x,8,4096,1536,True,4,128,0.375,1.0,1.0,0.609375,0.0,1.0231690964840563,4.0,6.122626857503489,0.5433349609375,uniform,0,vllm_fused,CUDA_EVENT,BF16,generic,method=fp8|act=dynamic|serialized=True|block=128x128
|
||||
|
@@ -0,0 +1,65 @@
|
||||
attention_backend: FLASHINFER
|
||||
batch_size_list:
|
||||
- 1
|
||||
block_shape: null
|
||||
block_size: 16
|
||||
decode_kv_cache_size_list: null
|
||||
device: h20
|
||||
disable_ray: true
|
||||
disable_replicated: false
|
||||
enable_chunked_prefill_grid_search: false
|
||||
enable_mixed_prefill: false
|
||||
enable_true_mixed: false
|
||||
fixed_chunked_prefill_size: 128
|
||||
max_batch_size: 1
|
||||
max_mixed_batch_size: 8
|
||||
max_model_len: 40960
|
||||
max_pipeline_parallel_size: 1
|
||||
max_seq_len: 128
|
||||
min_batch_size: 1
|
||||
mixed_batch_size_list: null
|
||||
mixed_batch_size_max: 32
|
||||
mixed_batch_size_min: 2
|
||||
mixed_kv_cache_size_list:
|
||||
- 0
|
||||
mixed_mode: both
|
||||
mixed_num_samples: 3
|
||||
mixed_profile_strategy: default
|
||||
mixed_shapes_per_point: 2
|
||||
mixed_total_tokens_list: null
|
||||
mixed_total_tokens_max: 1055
|
||||
mixed_total_tokens_min: 1025
|
||||
models:
|
||||
- Qwen3-235B-A22B-FP8
|
||||
num_gpus: 1
|
||||
num_tensor_parallel_workers:
|
||||
- 4
|
||||
output_dir: /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/profiles
|
||||
precision: null
|
||||
profile_method: cuda_event
|
||||
profile_only_decode: false
|
||||
profile_only_prefill: true
|
||||
skip_confirmation: true
|
||||
true_mixed_decode_batch_sizes:
|
||||
- 1
|
||||
- 2
|
||||
- 4
|
||||
- 8
|
||||
true_mixed_decode_kv_cache_sizes:
|
||||
- 128
|
||||
- 256
|
||||
- 512
|
||||
- 1024
|
||||
- 2048
|
||||
true_mixed_prefill_batch_sizes:
|
||||
- 1
|
||||
- 2
|
||||
- 4
|
||||
true_mixed_prefill_chunk_sizes:
|
||||
- 64
|
||||
- 128
|
||||
- 256
|
||||
- 512
|
||||
- 1024
|
||||
true_mixed_prefill_kv_cache_size: 0
|
||||
use_fp8: null
|
||||
@@ -0,0 +1,23 @@
|
||||
attn_tp: null
|
||||
block_shape: null
|
||||
device: h20
|
||||
disable_ray: true
|
||||
disable_replicated: false
|
||||
extra_num_tokens: null
|
||||
ffn_tp: null
|
||||
include_target_embedded_mtp: false
|
||||
is_moe: true
|
||||
max_tokens: 16
|
||||
models:
|
||||
- Qwen3-235B-A22B-FP8
|
||||
num_gpus: 1
|
||||
num_tensor_parallel_workers:
|
||||
- 4
|
||||
num_tokens_list:
|
||||
- 16
|
||||
output_dir: /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/profiles
|
||||
precision: null
|
||||
profile_method: cuda_event
|
||||
ray_enable_datasets_serializers: false
|
||||
skip_confirmation: true
|
||||
use_fp8: null
|
||||
@@ -0,0 +1,27 @@
|
||||
block_shape: null
|
||||
device: h20
|
||||
disable_ray: true
|
||||
disable_replicated: false
|
||||
enable_load_imbalance: true
|
||||
expert_parallel_sizes:
|
||||
- 1
|
||||
extra_num_tokens: null
|
||||
gating_runtime_context: prefill_hot
|
||||
load_distributions:
|
||||
- uniform
|
||||
max_tokens: 16
|
||||
models:
|
||||
- Qwen3-235B-A22B-FP8
|
||||
num_gpus: 1
|
||||
num_samples_per_distribution: 1
|
||||
num_tensor_parallel_workers:
|
||||
- 4
|
||||
num_tokens_list:
|
||||
- 16
|
||||
output_dir: /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/profiles
|
||||
per_channel_quant: false
|
||||
precision: null
|
||||
profile_method: cuda_event
|
||||
routing_runtime_path: standard_fused_topk
|
||||
skip_confirmation: true
|
||||
use_fp8: null
|
||||
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"flashinfer_metadata_version": "0.3.1.post1",
|
||||
"flashinfer_path": "/tmp/wjh-frontier-vllm0102-smoke/.venv/lib/python3.12/site-packages/flashinfer/__init__.py",
|
||||
"frontier_metadata_version": "0.1.0",
|
||||
"frontier_path": "/home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/Frontier-d9cfeb6/frontier",
|
||||
"python": "3.12.3",
|
||||
"torch": "2.8.0+cu128",
|
||||
"torch_cuda": "12.8",
|
||||
"vllm_import_version": "0.10.2",
|
||||
"vllm_metadata_version": "0.10.2",
|
||||
"vllm_path": "/tmp/wjh-frontier-vllm0102-smoke/.venv/lib/python3.12/site-packages/vllm/__init__.py"
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"flashinfer-python": "0.3.1.post1",
|
||||
"frontier-simulator": "0.1.0",
|
||||
"python": "3.12.3",
|
||||
"tokenizers": "0.21.4",
|
||||
"torch": "2.8.0",
|
||||
"transformers": "4.55.2",
|
||||
"vllm": "0.10.2"
|
||||
}
|
||||
@@ -0,0 +1,2 @@
|
||||
5184cb3f458685616364a09db28eee1603af26d58894073428a67ab677d655f5 /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/provenance/environment.post_transformers_pin.json
|
||||
2d17f91ca22ed56dbffc4549972814be911901f321963f8f07a2b940df191340 /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/provenance/requirements.freeze.txt
|
||||
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"flashinfer_metadata_version": "0.3.1.post1",
|
||||
"flashinfer_path": "/tmp/wjh-frontier-vllm0102-smoke/.venv/lib/python3.12/site-packages/flashinfer/__init__.py",
|
||||
"frontier_metadata_version": "0.1.0",
|
||||
"frontier_path": "/home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/Frontier-d9cfeb6/frontier",
|
||||
"python": "3.12.3",
|
||||
"torch": "2.8.0+cu128",
|
||||
"torch_cuda": "12.8",
|
||||
"vllm_import_version": "0.10.2",
|
||||
"vllm_metadata_version": "0.10.2",
|
||||
"vllm_path": "/tmp/wjh-frontier-vllm0102-smoke/.venv/lib/python3.12/site-packages/vllm/__init__.py"
|
||||
}
|
||||
@@ -0,0 +1,2 @@
|
||||
8564395327d10aff62c48e0e4fb74bdd050d17faed3d8d3948ff2e96e767d03a /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/provenance/environment.json
|
||||
a931219d3038697371775d2e5d797321adefc4ca48da2ab5999e1206e17a3881 /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/provenance/requirements.freeze.txt
|
||||
@@ -0,0 +1,332 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3MoeForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"decoder_sparse_step": 1,
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 12288,
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 94,
|
||||
"mlp_only_layers": [],
|
||||
"model_type": "qwen3_moe",
|
||||
"moe_intermediate_size": 1536,
|
||||
"norm_topk_prob": true,
|
||||
"num_attention_heads": 64,
|
||||
"num_experts": 128,
|
||||
"num_experts_per_tok": 8,
|
||||
"num_hidden_layers": 94,
|
||||
"num_key_value_heads": 4,
|
||||
"output_router_logits": false,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
||||
"router_aux_loss_coef": 0.001,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.51.0",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936,
|
||||
"quantization_config": {
|
||||
"activation_scheme": "dynamic",
|
||||
"modules_to_not_convert": [
|
||||
"lm_head",
|
||||
"model.layers.0.input_layernorm",
|
||||
"model.layers.0.mlp.gate",
|
||||
"model.layers.0.post_attention_layernorm",
|
||||
"model.layers.1.input_layernorm",
|
||||
"model.layers.1.mlp.gate",
|
||||
"model.layers.1.post_attention_layernorm",
|
||||
"model.layers.2.input_layernorm",
|
||||
"model.layers.2.mlp.gate",
|
||||
"model.layers.2.post_attention_layernorm",
|
||||
"model.layers.3.input_layernorm",
|
||||
"model.layers.3.mlp.gate",
|
||||
"model.layers.3.post_attention_layernorm",
|
||||
"model.layers.4.input_layernorm",
|
||||
"model.layers.4.mlp.gate",
|
||||
"model.layers.4.post_attention_layernorm",
|
||||
"model.layers.5.input_layernorm",
|
||||
"model.layers.5.mlp.gate",
|
||||
"model.layers.5.post_attention_layernorm",
|
||||
"model.layers.6.input_layernorm",
|
||||
"model.layers.6.mlp.gate",
|
||||
"model.layers.6.post_attention_layernorm",
|
||||
"model.layers.7.input_layernorm",
|
||||
"model.layers.7.mlp.gate",
|
||||
"model.layers.7.post_attention_layernorm",
|
||||
"model.layers.8.input_layernorm",
|
||||
"model.layers.8.mlp.gate",
|
||||
"model.layers.8.post_attention_layernorm",
|
||||
"model.layers.9.input_layernorm",
|
||||
"model.layers.9.mlp.gate",
|
||||
"model.layers.9.post_attention_layernorm",
|
||||
"model.layers.10.input_layernorm",
|
||||
"model.layers.10.mlp.gate",
|
||||
"model.layers.10.post_attention_layernorm",
|
||||
"model.layers.11.input_layernorm",
|
||||
"model.layers.11.mlp.gate",
|
||||
"model.layers.11.post_attention_layernorm",
|
||||
"model.layers.12.input_layernorm",
|
||||
"model.layers.12.mlp.gate",
|
||||
"model.layers.12.post_attention_layernorm",
|
||||
"model.layers.13.input_layernorm",
|
||||
"model.layers.13.mlp.gate",
|
||||
"model.layers.13.post_attention_layernorm",
|
||||
"model.layers.14.input_layernorm",
|
||||
"model.layers.14.mlp.gate",
|
||||
"model.layers.14.post_attention_layernorm",
|
||||
"model.layers.15.input_layernorm",
|
||||
"model.layers.15.mlp.gate",
|
||||
"model.layers.15.post_attention_layernorm",
|
||||
"model.layers.16.input_layernorm",
|
||||
"model.layers.16.mlp.gate",
|
||||
"model.layers.16.post_attention_layernorm",
|
||||
"model.layers.17.input_layernorm",
|
||||
"model.layers.17.mlp.gate",
|
||||
"model.layers.17.post_attention_layernorm",
|
||||
"model.layers.18.input_layernorm",
|
||||
"model.layers.18.mlp.gate",
|
||||
"model.layers.18.post_attention_layernorm",
|
||||
"model.layers.19.input_layernorm",
|
||||
"model.layers.19.mlp.gate",
|
||||
"model.layers.19.post_attention_layernorm",
|
||||
"model.layers.20.input_layernorm",
|
||||
"model.layers.20.mlp.gate",
|
||||
"model.layers.20.post_attention_layernorm",
|
||||
"model.layers.21.input_layernorm",
|
||||
"model.layers.21.mlp.gate",
|
||||
"model.layers.21.post_attention_layernorm",
|
||||
"model.layers.22.input_layernorm",
|
||||
"model.layers.22.mlp.gate",
|
||||
"model.layers.22.post_attention_layernorm",
|
||||
"model.layers.23.input_layernorm",
|
||||
"model.layers.23.mlp.gate",
|
||||
"model.layers.23.post_attention_layernorm",
|
||||
"model.layers.24.input_layernorm",
|
||||
"model.layers.24.mlp.gate",
|
||||
"model.layers.24.post_attention_layernorm",
|
||||
"model.layers.25.input_layernorm",
|
||||
"model.layers.25.mlp.gate",
|
||||
"model.layers.25.post_attention_layernorm",
|
||||
"model.layers.26.input_layernorm",
|
||||
"model.layers.26.mlp.gate",
|
||||
"model.layers.26.post_attention_layernorm",
|
||||
"model.layers.27.input_layernorm",
|
||||
"model.layers.27.mlp.gate",
|
||||
"model.layers.27.post_attention_layernorm",
|
||||
"model.layers.28.input_layernorm",
|
||||
"model.layers.28.mlp.gate",
|
||||
"model.layers.28.post_attention_layernorm",
|
||||
"model.layers.29.input_layernorm",
|
||||
"model.layers.29.mlp.gate",
|
||||
"model.layers.29.post_attention_layernorm",
|
||||
"model.layers.30.input_layernorm",
|
||||
"model.layers.30.mlp.gate",
|
||||
"model.layers.30.post_attention_layernorm",
|
||||
"model.layers.31.input_layernorm",
|
||||
"model.layers.31.mlp.gate",
|
||||
"model.layers.31.post_attention_layernorm",
|
||||
"model.layers.32.input_layernorm",
|
||||
"model.layers.32.mlp.gate",
|
||||
"model.layers.32.post_attention_layernorm",
|
||||
"model.layers.33.input_layernorm",
|
||||
"model.layers.33.mlp.gate",
|
||||
"model.layers.33.post_attention_layernorm",
|
||||
"model.layers.34.input_layernorm",
|
||||
"model.layers.34.mlp.gate",
|
||||
"model.layers.34.post_attention_layernorm",
|
||||
"model.layers.35.input_layernorm",
|
||||
"model.layers.35.mlp.gate",
|
||||
"model.layers.35.post_attention_layernorm",
|
||||
"model.layers.36.input_layernorm",
|
||||
"model.layers.36.mlp.gate",
|
||||
"model.layers.36.post_attention_layernorm",
|
||||
"model.layers.37.input_layernorm",
|
||||
"model.layers.37.mlp.gate",
|
||||
"model.layers.37.post_attention_layernorm",
|
||||
"model.layers.38.input_layernorm",
|
||||
"model.layers.38.mlp.gate",
|
||||
"model.layers.38.post_attention_layernorm",
|
||||
"model.layers.39.input_layernorm",
|
||||
"model.layers.39.mlp.gate",
|
||||
"model.layers.39.post_attention_layernorm",
|
||||
"model.layers.40.input_layernorm",
|
||||
"model.layers.40.mlp.gate",
|
||||
"model.layers.40.post_attention_layernorm",
|
||||
"model.layers.41.input_layernorm",
|
||||
"model.layers.41.mlp.gate",
|
||||
"model.layers.41.post_attention_layernorm",
|
||||
"model.layers.42.input_layernorm",
|
||||
"model.layers.42.mlp.gate",
|
||||
"model.layers.42.post_attention_layernorm",
|
||||
"model.layers.43.input_layernorm",
|
||||
"model.layers.43.mlp.gate",
|
||||
"model.layers.43.post_attention_layernorm",
|
||||
"model.layers.44.input_layernorm",
|
||||
"model.layers.44.mlp.gate",
|
||||
"model.layers.44.post_attention_layernorm",
|
||||
"model.layers.45.input_layernorm",
|
||||
"model.layers.45.mlp.gate",
|
||||
"model.layers.45.post_attention_layernorm",
|
||||
"model.layers.46.input_layernorm",
|
||||
"model.layers.46.mlp.gate",
|
||||
"model.layers.46.post_attention_layernorm",
|
||||
"model.layers.47.input_layernorm",
|
||||
"model.layers.47.mlp.gate",
|
||||
"model.layers.47.post_attention_layernorm",
|
||||
"model.layers.48.input_layernorm",
|
||||
"model.layers.48.mlp.gate",
|
||||
"model.layers.48.post_attention_layernorm",
|
||||
"model.layers.49.input_layernorm",
|
||||
"model.layers.49.mlp.gate",
|
||||
"model.layers.49.post_attention_layernorm",
|
||||
"model.layers.50.input_layernorm",
|
||||
"model.layers.50.mlp.gate",
|
||||
"model.layers.50.post_attention_layernorm",
|
||||
"model.layers.51.input_layernorm",
|
||||
"model.layers.51.mlp.gate",
|
||||
"model.layers.51.post_attention_layernorm",
|
||||
"model.layers.52.input_layernorm",
|
||||
"model.layers.52.mlp.gate",
|
||||
"model.layers.52.post_attention_layernorm",
|
||||
"model.layers.53.input_layernorm",
|
||||
"model.layers.53.mlp.gate",
|
||||
"model.layers.53.post_attention_layernorm",
|
||||
"model.layers.54.input_layernorm",
|
||||
"model.layers.54.mlp.gate",
|
||||
"model.layers.54.post_attention_layernorm",
|
||||
"model.layers.55.input_layernorm",
|
||||
"model.layers.55.mlp.gate",
|
||||
"model.layers.55.post_attention_layernorm",
|
||||
"model.layers.56.input_layernorm",
|
||||
"model.layers.56.mlp.gate",
|
||||
"model.layers.56.post_attention_layernorm",
|
||||
"model.layers.57.input_layernorm",
|
||||
"model.layers.57.mlp.gate",
|
||||
"model.layers.57.post_attention_layernorm",
|
||||
"model.layers.58.input_layernorm",
|
||||
"model.layers.58.mlp.gate",
|
||||
"model.layers.58.post_attention_layernorm",
|
||||
"model.layers.59.input_layernorm",
|
||||
"model.layers.59.mlp.gate",
|
||||
"model.layers.59.post_attention_layernorm",
|
||||
"model.layers.60.input_layernorm",
|
||||
"model.layers.60.mlp.gate",
|
||||
"model.layers.60.post_attention_layernorm",
|
||||
"model.layers.61.input_layernorm",
|
||||
"model.layers.61.mlp.gate",
|
||||
"model.layers.61.post_attention_layernorm",
|
||||
"model.layers.62.input_layernorm",
|
||||
"model.layers.62.mlp.gate",
|
||||
"model.layers.62.post_attention_layernorm",
|
||||
"model.layers.63.input_layernorm",
|
||||
"model.layers.63.mlp.gate",
|
||||
"model.layers.63.post_attention_layernorm",
|
||||
"model.layers.64.input_layernorm",
|
||||
"model.layers.64.mlp.gate",
|
||||
"model.layers.64.post_attention_layernorm",
|
||||
"model.layers.65.input_layernorm",
|
||||
"model.layers.65.mlp.gate",
|
||||
"model.layers.65.post_attention_layernorm",
|
||||
"model.layers.66.input_layernorm",
|
||||
"model.layers.66.mlp.gate",
|
||||
"model.layers.66.post_attention_layernorm",
|
||||
"model.layers.67.input_layernorm",
|
||||
"model.layers.67.mlp.gate",
|
||||
"model.layers.67.post_attention_layernorm",
|
||||
"model.layers.68.input_layernorm",
|
||||
"model.layers.68.mlp.gate",
|
||||
"model.layers.68.post_attention_layernorm",
|
||||
"model.layers.69.input_layernorm",
|
||||
"model.layers.69.mlp.gate",
|
||||
"model.layers.69.post_attention_layernorm",
|
||||
"model.layers.70.input_layernorm",
|
||||
"model.layers.70.mlp.gate",
|
||||
"model.layers.70.post_attention_layernorm",
|
||||
"model.layers.71.input_layernorm",
|
||||
"model.layers.71.mlp.gate",
|
||||
"model.layers.71.post_attention_layernorm",
|
||||
"model.layers.72.input_layernorm",
|
||||
"model.layers.72.mlp.gate",
|
||||
"model.layers.72.post_attention_layernorm",
|
||||
"model.layers.73.input_layernorm",
|
||||
"model.layers.73.mlp.gate",
|
||||
"model.layers.73.post_attention_layernorm",
|
||||
"model.layers.74.input_layernorm",
|
||||
"model.layers.74.mlp.gate",
|
||||
"model.layers.74.post_attention_layernorm",
|
||||
"model.layers.75.input_layernorm",
|
||||
"model.layers.75.mlp.gate",
|
||||
"model.layers.75.post_attention_layernorm",
|
||||
"model.layers.76.input_layernorm",
|
||||
"model.layers.76.mlp.gate",
|
||||
"model.layers.76.post_attention_layernorm",
|
||||
"model.layers.77.input_layernorm",
|
||||
"model.layers.77.mlp.gate",
|
||||
"model.layers.77.post_attention_layernorm",
|
||||
"model.layers.78.input_layernorm",
|
||||
"model.layers.78.mlp.gate",
|
||||
"model.layers.78.post_attention_layernorm",
|
||||
"model.layers.79.input_layernorm",
|
||||
"model.layers.79.mlp.gate",
|
||||
"model.layers.79.post_attention_layernorm",
|
||||
"model.layers.80.input_layernorm",
|
||||
"model.layers.80.mlp.gate",
|
||||
"model.layers.80.post_attention_layernorm",
|
||||
"model.layers.81.input_layernorm",
|
||||
"model.layers.81.mlp.gate",
|
||||
"model.layers.81.post_attention_layernorm",
|
||||
"model.layers.82.input_layernorm",
|
||||
"model.layers.82.mlp.gate",
|
||||
"model.layers.82.post_attention_layernorm",
|
||||
"model.layers.83.input_layernorm",
|
||||
"model.layers.83.mlp.gate",
|
||||
"model.layers.83.post_attention_layernorm",
|
||||
"model.layers.84.input_layernorm",
|
||||
"model.layers.84.mlp.gate",
|
||||
"model.layers.84.post_attention_layernorm",
|
||||
"model.layers.85.input_layernorm",
|
||||
"model.layers.85.mlp.gate",
|
||||
"model.layers.85.post_attention_layernorm",
|
||||
"model.layers.86.input_layernorm",
|
||||
"model.layers.86.mlp.gate",
|
||||
"model.layers.86.post_attention_layernorm",
|
||||
"model.layers.87.input_layernorm",
|
||||
"model.layers.87.mlp.gate",
|
||||
"model.layers.87.post_attention_layernorm",
|
||||
"model.layers.88.input_layernorm",
|
||||
"model.layers.88.mlp.gate",
|
||||
"model.layers.88.post_attention_layernorm",
|
||||
"model.layers.89.input_layernorm",
|
||||
"model.layers.89.mlp.gate",
|
||||
"model.layers.89.post_attention_layernorm",
|
||||
"model.layers.90.input_layernorm",
|
||||
"model.layers.90.mlp.gate",
|
||||
"model.layers.90.post_attention_layernorm",
|
||||
"model.layers.91.input_layernorm",
|
||||
"model.layers.91.mlp.gate",
|
||||
"model.layers.91.post_attention_layernorm",
|
||||
"model.layers.92.input_layernorm",
|
||||
"model.layers.92.mlp.gate",
|
||||
"model.layers.92.post_attention_layernorm",
|
||||
"model.layers.93.input_layernorm",
|
||||
"model.layers.93.mlp.gate",
|
||||
"model.layers.93.post_attention_layernorm"
|
||||
],
|
||||
"fmt": "e4m3",
|
||||
"quant_method": "fp8",
|
||||
"weight_block_size": [
|
||||
128,
|
||||
128
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,2 @@
|
||||
702c46d431bb984db9035a1225186bbfdb52c0d19c82104df4a37cd005e0369e /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/provenance/model_config.original.json
|
||||
1d7389f77563bb26bdb8cad077f9ae81f94dbe0b7f2e51cc72f7f4ff0f550acb /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/Frontier-d9cfeb6/data/config/models/Qwen3-235B-A22B-FP8.json
|
||||
@@ -0,0 +1,2 @@
|
||||
{'name': 'Qwen3-235B-A22B-FP8', 'num_layers': 94, 'num_q_heads': 64, 'num_kv_heads': 4, 'embedding_dim': 4096, 'mlp_hidden_dim': 1536, 'max_position_embeddings': 40960, 'use_gated_mlp': True, 'use_bias': False, 'use_qkv_bias': False, 'activation': 'silu', 'norm': 'rms_norm', 'post_attn_norm': True, 'vocab_size': 151936, 'is_neox_style': True, 'rope_theta': 1000000.0, 'rope_scaling': None, 'partial_rotary_factor': 1.0, 'no_tensor_parallel': False, 'is_moe': True, 'num_experts': 128, 'num_experts_per_tok': 8, 'moe_layers_enum': None, 'use_qk_norm': True, 'attn_output_gate': False, 'rms_norm_eps': 1e-06, 'dtype': 'BF16', 'model_type': 'qwen3_moe', 'fused_add_norm_capability': True, 'model_arch': 'generic', 'share_expert_dim': None, 'share_q_dim': None, 'head_dim': 128, 'quantization_config': {'quant_method': 'fp8', 'activation_scheme': 'dynamic', 'is_checkpoint_fp8_serialized': True, 'weight_block_size': (128, 128), 'ignored_layers': []}, 'tie_word_embeddings': False}
|
||||
{'tp_size': 4, 'attn_enabled': True, 'ffn_enabled': True, 'attn_sharded_enabled': True, 'ffn_sharded_enabled': True, 'replicated_enabled': True, 'disable_replicated': False, 'enabled_ops': ['input_layernorm', 'post_attention_layernorm', 'add', 'emb', 'attn_pre_proj', 'attn_rope', 'attn_post_proj'], 'disabled_ops': [], 'replicated_ops': ['input_layernorm', 'post_attention_layernorm', 'add', 'emb'], 'padded_n_embd': 4096, 'padded_n_expanded_embd': 1536, 'skip_reasons': []}
|
||||
@@ -0,0 +1 @@
|
||||
4459cca6fe7f01c44477460ccca85ec1f2a0eacdc764170c68fa02a102f4104e /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/provenance/platform.txt
|
||||
@@ -0,0 +1,99 @@
|
||||
captured_utc=2026-07-15T09:34:36Z
|
||||
ds-07429c65-1-6c5fd97778-9vhkr
|
||||
Linux ds-07429c65-1-6c5fd97778-9vhkr 5.10.134-013.8.2.kangaroo.al8.x86_64 #1 SMP Thu Mar 12 10:20:37 UTC 2026 x86_64 x86_64 x86_64 GNU/Linux
|
||||
PRETTY_NAME="Ubuntu 24.04.2 LTS"
|
||||
NAME="Ubuntu"
|
||||
VERSION_ID="24.04"
|
||||
VERSION="24.04.2 LTS (Noble Numbat)"
|
||||
VERSION_CODENAME=noble
|
||||
ID=ubuntu
|
||||
ID_LIKE=debian
|
||||
HOME_URL="https://www.ubuntu.com/"
|
||||
SUPPORT_URL="https://help.ubuntu.com/"
|
||||
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
|
||||
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
|
||||
UBUNTU_CODENAME=noble
|
||||
LOGO=ubuntu-logo
|
||||
Architecture: x86_64
|
||||
CPU op-mode(s): 32-bit, 64-bit
|
||||
Address sizes: 52 bits physical, 57 bits virtual
|
||||
Byte Order: Little Endian
|
||||
CPU(s): 160
|
||||
On-line CPU(s) list: 0-159
|
||||
Vendor ID: GenuineIntel
|
||||
Model name: Intel(R) Xeon(R) Processor
|
||||
CPU family: 6
|
||||
Model: 143
|
||||
Thread(s) per core: 1
|
||||
Core(s) per socket: 80
|
||||
Socket(s): 2
|
||||
Stepping: 8
|
||||
BogoMIPS: 5200.00
|
||||
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd avx512vbmi umip pku waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities
|
||||
Hypervisor vendor: KVM
|
||||
Virtualization type: full
|
||||
L1d cache: 3.8 MiB (80 instances)
|
||||
L1i cache: 2.5 MiB (80 instances)
|
||||
L2 cache: 160 MiB (80 instances)
|
||||
L3 cache: 195 MiB (2 instances)
|
||||
NUMA node(s): 2
|
||||
NUMA node0 CPU(s): 0-79
|
||||
NUMA node1 CPU(s): 80-159
|
||||
Vulnerability Itlb multihit: Not affected
|
||||
Vulnerability L1tf: Not affected
|
||||
Vulnerability Mds: Not affected
|
||||
Vulnerability Meltdown: Not affected
|
||||
Vulnerability Mmio stale data: Not affected
|
||||
Vulnerability Retbleed: Not affected
|
||||
Vulnerability Spec rstack overflow: Not affected
|
||||
Vulnerability Spec store bypass: Vulnerable
|
||||
Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
|
||||
Vulnerability Spectre v2: Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Vulnerable
|
||||
Vulnerability Srbds: Not affected
|
||||
Vulnerability Tsx async abort: Not affected
|
||||
0, NVIDIA H20, GPU-ad3e049a-5bf0-44b7-e7f1-9af297b172af, 580.95.05, 97871 MiB, 9.0
|
||||
1, NVIDIA H20, GPU-8c088079-d0f5-ba23-8650-5e6b1436691f, 580.95.05, 97871 MiB, 9.0
|
||||
2, NVIDIA H20, GPU-a9f6fe67-324b-8bb2-19b3-c7f1b9cce96a, 580.95.05, 97871 MiB, 9.0
|
||||
3, NVIDIA H20, GPU-6bcd68b7-ffa7-26b5-df6a-b7eb3f65c901, 580.95.05, 97871 MiB, 9.0
|
||||
4, NVIDIA H20, GPU-b409f9c9-05b6-55ef-3f3b-12eaa7c6ebfe, 580.95.05, 97871 MiB, 9.0
|
||||
5, NVIDIA H20, GPU-56932433-efce-8215-6418-98166d8ab798, 580.95.05, 97871 MiB, 9.0
|
||||
6, NVIDIA H20, GPU-ddcd1b77-e38d-75f5-ac24-788c76e56c89, 580.95.05, 97871 MiB, 9.0
|
||||
7, NVIDIA H20, GPU-9b4a31bd-5e78-b5a7-55aa-d786ae5a3f21, 580.95.05, 97871 MiB, 9.0
|
||||
[4mGPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 CPU Affinity NUMA Affinity GPU NUMA ID[0m
|
||||
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 PIX PHB SYS SYS 0-79 0 N/A
|
||||
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 PXB PHB SYS SYS 0-79 0 N/A
|
||||
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 PHB PIX SYS SYS 0-79 0 N/A
|
||||
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 PHB PXB SYS SYS 0-79 0 N/A
|
||||
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS PIX PHB 80-159 1 N/A
|
||||
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS PXB PHB 80-159 1 N/A
|
||||
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS PHB PIX 80-159 1 N/A
|
||||
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS PHB PXB 80-159 1 N/A
|
||||
NIC0 PIX PXB PHB PHB SYS SYS SYS SYS X PHB SYS SYS
|
||||
NIC1 PHB PHB PIX PXB SYS SYS SYS SYS PHB X SYS SYS
|
||||
NIC2 SYS SYS SYS SYS PIX PXB PHB PHB SYS SYS X PHB
|
||||
NIC3 SYS SYS SYS SYS PHB PHB PIX PXB SYS SYS PHB X
|
||||
|
||||
Legend:
|
||||
|
||||
X = Self
|
||||
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
|
||||
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
|
||||
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
|
||||
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
|
||||
PIX = Connection traversing at most a single PCIe bridge
|
||||
NV# = Connection traversing a bonded set of # NVLinks
|
||||
|
||||
NIC Legend:
|
||||
|
||||
NIC0: mlx5_0
|
||||
NIC1: mlx5_1
|
||||
NIC2: mlx5_2
|
||||
NIC3: mlx5_3
|
||||
|
||||
nvcc: NVIDIA (R) Cuda compiler driver
|
||||
Copyright (c) 2005-2025 NVIDIA Corporation
|
||||
Built on Tue_May_27_02:21:03_PDT_2025
|
||||
Cuda compilation tools, release 12.9, V12.9.86
|
||||
Build cuda_12.9.r12.9/compiler.36037853_0
|
||||
gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
|
||||
ldd (Ubuntu GLIBC 2.39-0ubuntu8.4) 2.39
|
||||
@@ -0,0 +1 @@
|
||||
2d17f91ca22ed56dbffc4549972814be911901f321963f8f07a2b940df191340 /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/provenance/requirements.freeze.txt
|
||||
@@ -0,0 +1,163 @@
|
||||
aiohappyeyeballs==2.7.1
|
||||
aiohttp==3.14.1
|
||||
aiosignal==1.4.0
|
||||
annotated-doc==0.0.4
|
||||
annotated-types==0.7.0
|
||||
anyio==4.14.2
|
||||
astor==0.8.1
|
||||
attrs==26.1.0
|
||||
blake3==1.0.9
|
||||
cachetools==7.1.4
|
||||
cbor2==6.1.3
|
||||
certifi==2026.6.17
|
||||
cffi==2.1.0
|
||||
charset-normalizer==3.4.9
|
||||
click==8.4.2
|
||||
cloudpickle==3.1.2
|
||||
compressed-tensors==0.11.0
|
||||
cuda-pathfinder==1.5.6
|
||||
cupy-cuda12x==14.1.1
|
||||
ddsketch==3.0.1
|
||||
depyf==0.19.0
|
||||
detect-installer==0.1.0
|
||||
dill==0.4.1
|
||||
diskcache==5.6.3
|
||||
distro==1.9.0
|
||||
dnspython==2.8.0
|
||||
einops==0.8.2
|
||||
email-validator==2.3.0
|
||||
fastapi==0.139.0
|
||||
fastapi-cli==0.0.29
|
||||
fastapi-cloud-cli==0.22.2
|
||||
fastar==0.11.0
|
||||
fasteners==0.20
|
||||
filelock==3.29.7
|
||||
flashinfer-python==0.3.1.post1
|
||||
-e file:///home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/Frontier-d9cfeb6
|
||||
frozendict==2.4.7
|
||||
frozenlist==1.8.0
|
||||
fsspec==2026.6.0
|
||||
gguf==0.19.0
|
||||
h11==0.16.0
|
||||
hf-xet==1.5.1
|
||||
httpcore==1.0.9
|
||||
httptools==0.8.0
|
||||
httpx==0.28.1
|
||||
huggingface-hub==0.36.2
|
||||
idna==3.18
|
||||
iniconfig==2.3.0
|
||||
interegular==0.3.3
|
||||
jinja2==3.1.6
|
||||
jiter==0.16.0
|
||||
joblib==1.5.3
|
||||
jsonschema==4.26.0
|
||||
jsonschema-specifications==2025.9.1
|
||||
lark==1.2.2
|
||||
llguidance==0.7.30
|
||||
llvmlite==0.44.0
|
||||
lm-format-enforcer==0.11.3
|
||||
markdown-it-py==4.2.0
|
||||
markupsafe==3.0.3
|
||||
mdurl==0.1.2
|
||||
mistral-common==1.11.5
|
||||
mpmath==1.3.0
|
||||
msgpack==1.2.1
|
||||
msgspec==0.21.1
|
||||
multidict==6.7.1
|
||||
narwhals==2.24.0
|
||||
networkx==3.6.1
|
||||
ninja==1.13.0
|
||||
numba==0.61.2
|
||||
numpy==2.2.6
|
||||
nvidia-cublas-cu12==12.8.4.1
|
||||
nvidia-cuda-cupti-cu12==12.8.90
|
||||
nvidia-cuda-nvrtc-cu12==12.8.93
|
||||
nvidia-cuda-runtime-cu12==12.8.90
|
||||
nvidia-cudnn-cu12==9.10.2.21
|
||||
nvidia-cudnn-frontend==1.26.0
|
||||
nvidia-cufft-cu12==11.3.3.83
|
||||
nvidia-cufile-cu12==1.13.1.3
|
||||
nvidia-curand-cu12==10.3.9.90
|
||||
nvidia-cusolver-cu12==11.7.3.90
|
||||
nvidia-cusparse-cu12==12.5.8.93
|
||||
nvidia-cusparselt-cu12==0.7.1
|
||||
nvidia-ml-py==13.610.43
|
||||
nvidia-nccl-cu12==2.27.3
|
||||
nvidia-nvjitlink-cu12==12.8.93
|
||||
nvidia-nvtx-cu12==12.8.90
|
||||
openai==2.45.0
|
||||
openai-harmony==0.0.8
|
||||
opencv-python-headless==5.0.0.93
|
||||
outlines-core==0.2.11
|
||||
packaging==26.2
|
||||
pandas==3.0.3
|
||||
partial-json-parser==0.2.1.1.post7
|
||||
pillow==12.3.0
|
||||
plotly==6.9.0
|
||||
pluggy==1.6.0
|
||||
prometheus-client==0.25.0
|
||||
prometheus-fastapi-instrumentator==8.0.2
|
||||
propcache==0.5.2
|
||||
protobuf==7.35.1
|
||||
psutil==7.2.2
|
||||
py-cpuinfo==9.0.0
|
||||
pybase64==1.4.3
|
||||
pycountry==26.2.16
|
||||
pycparser==3.0
|
||||
pydantic==2.13.4
|
||||
pydantic-core==2.46.4
|
||||
pydantic-extra-types==2.11.1
|
||||
pydantic-settings==2.14.2
|
||||
pygments==2.20.0
|
||||
pynvml==13.0.1
|
||||
pytest==9.1.1
|
||||
python-dateutil==2.9.0.post0
|
||||
python-dotenv==1.2.2
|
||||
python-json-logger==4.1.0
|
||||
python-multipart==0.0.32
|
||||
pyyaml==6.0.3
|
||||
pyzmq==27.1.0
|
||||
ray==2.56.0
|
||||
referencing==0.37.0
|
||||
regex==2026.7.10
|
||||
requests==2.34.2
|
||||
rich==15.0.0
|
||||
rich-toolkit==0.20.3
|
||||
rignore==0.7.6
|
||||
rpds-py==2026.6.3
|
||||
safetensors==0.8.0
|
||||
scikit-learn==1.9.0
|
||||
scipy==1.18.0
|
||||
sentencepiece==0.2.2
|
||||
sentry-sdk==2.65.0
|
||||
setproctitle==1.3.7
|
||||
setuptools==79.0.1
|
||||
shellingham==1.5.4
|
||||
six==1.17.0
|
||||
sniffio==1.3.1
|
||||
soundfile==0.14.0
|
||||
soxr==1.1.0
|
||||
starlette==1.3.1
|
||||
sympy==1.14.0
|
||||
tabulate==0.10.0
|
||||
threadpoolctl==3.6.0
|
||||
tiktoken==0.13.0
|
||||
tokenizers==0.21.4
|
||||
torch==2.8.0
|
||||
torchaudio==2.8.0
|
||||
torchvision==0.23.0
|
||||
tqdm==4.68.4
|
||||
transformers==4.55.2
|
||||
triton==3.4.0
|
||||
typer==0.26.8
|
||||
typing-extensions==4.16.0
|
||||
typing-inspection==0.4.2
|
||||
urllib3==2.7.0
|
||||
uvicorn==0.51.0
|
||||
uvloop==0.22.1
|
||||
vllm==0.10.2
|
||||
watchfiles==1.2.0
|
||||
websockets==16.1
|
||||
xformers==0.0.32.post1
|
||||
xgrammar==0.1.23
|
||||
yarl==1.24.2
|
||||
@@ -0,0 +1,163 @@
|
||||
aiohappyeyeballs==2.7.1
|
||||
aiohttp==3.14.1
|
||||
aiosignal==1.4.0
|
||||
annotated-doc==0.0.4
|
||||
annotated-types==0.7.0
|
||||
anyio==4.14.2
|
||||
astor==0.8.1
|
||||
attrs==26.1.0
|
||||
blake3==1.0.9
|
||||
cachetools==7.1.4
|
||||
cbor2==6.1.3
|
||||
certifi==2026.6.17
|
||||
cffi==2.1.0
|
||||
charset-normalizer==3.4.9
|
||||
click==8.4.2
|
||||
cloudpickle==3.1.2
|
||||
compressed-tensors==0.11.0
|
||||
cuda-pathfinder==1.5.6
|
||||
cupy-cuda12x==14.1.1
|
||||
ddsketch==3.0.1
|
||||
depyf==0.19.0
|
||||
detect-installer==0.1.0
|
||||
dill==0.4.1
|
||||
diskcache==5.6.3
|
||||
distro==1.9.0
|
||||
dnspython==2.8.0
|
||||
einops==0.8.2
|
||||
email-validator==2.3.0
|
||||
fastapi==0.139.0
|
||||
fastapi-cli==0.0.29
|
||||
fastapi-cloud-cli==0.22.2
|
||||
fastar==0.11.0
|
||||
fasteners==0.20
|
||||
filelock==3.29.7
|
||||
flashinfer-python==0.3.1.post1
|
||||
-e file:///home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/Frontier-d9cfeb6
|
||||
frozendict==2.4.7
|
||||
frozenlist==1.8.0
|
||||
fsspec==2026.6.0
|
||||
gguf==0.19.0
|
||||
h11==0.16.0
|
||||
hf-xet==1.5.1
|
||||
httpcore==1.0.9
|
||||
httptools==0.8.0
|
||||
httpx==0.28.1
|
||||
huggingface-hub==1.23.0
|
||||
idna==3.18
|
||||
iniconfig==2.3.0
|
||||
interegular==0.3.3
|
||||
jinja2==3.1.6
|
||||
jiter==0.16.0
|
||||
joblib==1.5.3
|
||||
jsonschema==4.26.0
|
||||
jsonschema-specifications==2025.9.1
|
||||
lark==1.2.2
|
||||
llguidance==0.7.30
|
||||
llvmlite==0.44.0
|
||||
lm-format-enforcer==0.11.3
|
||||
markdown-it-py==4.2.0
|
||||
markupsafe==3.0.3
|
||||
mdurl==0.1.2
|
||||
mistral-common==1.11.5
|
||||
mpmath==1.3.0
|
||||
msgpack==1.2.1
|
||||
msgspec==0.21.1
|
||||
multidict==6.7.1
|
||||
narwhals==2.24.0
|
||||
networkx==3.6.1
|
||||
ninja==1.13.0
|
||||
numba==0.61.2
|
||||
numpy==2.2.6
|
||||
nvidia-cublas-cu12==12.8.4.1
|
||||
nvidia-cuda-cupti-cu12==12.8.90
|
||||
nvidia-cuda-nvrtc-cu12==12.8.93
|
||||
nvidia-cuda-runtime-cu12==12.8.90
|
||||
nvidia-cudnn-cu12==9.10.2.21
|
||||
nvidia-cudnn-frontend==1.26.0
|
||||
nvidia-cufft-cu12==11.3.3.83
|
||||
nvidia-cufile-cu12==1.13.1.3
|
||||
nvidia-curand-cu12==10.3.9.90
|
||||
nvidia-cusolver-cu12==11.7.3.90
|
||||
nvidia-cusparse-cu12==12.5.8.93
|
||||
nvidia-cusparselt-cu12==0.7.1
|
||||
nvidia-ml-py==13.610.43
|
||||
nvidia-nccl-cu12==2.27.3
|
||||
nvidia-nvjitlink-cu12==12.8.93
|
||||
nvidia-nvtx-cu12==12.8.90
|
||||
openai==2.45.0
|
||||
openai-harmony==0.0.8
|
||||
opencv-python-headless==5.0.0.93
|
||||
outlines-core==0.2.11
|
||||
packaging==26.2
|
||||
pandas==3.0.3
|
||||
partial-json-parser==0.2.1.1.post7
|
||||
pillow==12.3.0
|
||||
plotly==6.9.0
|
||||
pluggy==1.6.0
|
||||
prometheus-client==0.25.0
|
||||
prometheus-fastapi-instrumentator==8.0.2
|
||||
propcache==0.5.2
|
||||
protobuf==7.35.1
|
||||
psutil==7.2.2
|
||||
py-cpuinfo==9.0.0
|
||||
pybase64==1.4.3
|
||||
pycountry==26.2.16
|
||||
pycparser==3.0
|
||||
pydantic==2.13.4
|
||||
pydantic-core==2.46.4
|
||||
pydantic-extra-types==2.11.1
|
||||
pydantic-settings==2.14.2
|
||||
pygments==2.20.0
|
||||
pynvml==13.0.1
|
||||
pytest==9.1.1
|
||||
python-dateutil==2.9.0.post0
|
||||
python-dotenv==1.2.2
|
||||
python-json-logger==4.1.0
|
||||
python-multipart==0.0.32
|
||||
pyyaml==6.0.3
|
||||
pyzmq==27.1.0
|
||||
ray==2.56.0
|
||||
referencing==0.37.0
|
||||
regex==2026.7.10
|
||||
requests==2.34.2
|
||||
rich==15.0.0
|
||||
rich-toolkit==0.20.3
|
||||
rignore==0.7.6
|
||||
rpds-py==2026.6.3
|
||||
safetensors==0.8.0
|
||||
scikit-learn==1.9.0
|
||||
scipy==1.18.0
|
||||
sentencepiece==0.2.2
|
||||
sentry-sdk==2.65.0
|
||||
setproctitle==1.3.7
|
||||
setuptools==79.0.1
|
||||
shellingham==1.5.4
|
||||
six==1.17.0
|
||||
sniffio==1.3.1
|
||||
soundfile==0.14.0
|
||||
soxr==1.1.0
|
||||
starlette==1.3.1
|
||||
sympy==1.14.0
|
||||
tabulate==0.10.0
|
||||
threadpoolctl==3.6.0
|
||||
tiktoken==0.13.0
|
||||
tokenizers==0.22.2
|
||||
torch==2.8.0
|
||||
torchaudio==2.8.0
|
||||
torchvision==0.23.0
|
||||
tqdm==4.68.4
|
||||
transformers==5.13.1
|
||||
triton==3.4.0
|
||||
typer==0.26.8
|
||||
typing-extensions==4.16.0
|
||||
typing-inspection==0.4.2
|
||||
urllib3==2.7.0
|
||||
uvicorn==0.51.0
|
||||
uvloop==0.22.1
|
||||
vllm==0.10.2
|
||||
watchfiles==1.2.0
|
||||
websockets==16.1
|
||||
xformers==0.0.32.post1
|
||||
xgrammar==0.1.23
|
||||
yarl==1.24.2
|
||||
@@ -0,0 +1,209 @@
|
||||
# Community Qwen235B / Frontier smoke report
|
||||
|
||||
## Bottom line
|
||||
|
||||
The community-vLLM TP4 feasibility smoke passed, but the unmodified Frontier
|
||||
profiles are not valid inputs for a formal simulator-versus-runtime ranking
|
||||
test yet. The smoke exposed an execution-semantic mismatch inside the MoE
|
||||
profiler: serving selects vLLM's tuned H20 block-FP8 Triton config, while the
|
||||
standalone Frontier profiler omits the FP8 dtype key and silently falls back to
|
||||
a default config. At the tested TP4/EP1/16-token point, fixing this lookup
|
||||
reduces measured grouped-GEMM time by about 19%, with the direction consistent
|
||||
across five paired routing seeds.
|
||||
|
||||
This is precisely why merely installing the same vLLM release on both sides is
|
||||
not a sufficient alignment contract.
|
||||
|
||||
## Change
|
||||
|
||||
- Built an isolated Python 3.12 environment around community vLLM 0.10.2 and
|
||||
Frontier commit `d9cfeb6d8791fbf2f295dd9744c56a666171776e`.
|
||||
- Pinned Transformers 4.55.2 and tokenizers 0.21.4 after reproducing an
|
||||
incompatibility with the unconstrained Transformers 5.13.1 resolver result.
|
||||
- Added Frontier metadata for the local block-FP8 checkpoint without changing
|
||||
the original Hugging Face config.
|
||||
- Collected representative FP8 linear, FlashInfer prefill-attention, FP8 MoE,
|
||||
and TP4 NCCL all-reduce measurements.
|
||||
- Loaded the full Qwen3-235B-A22B-FP8 checkpoint on four H20s through community
|
||||
vLLM and completed one real request.
|
||||
- Ran a 2x2 MoE diagnostic that independently toggles the runtime FP8 tuning
|
||||
key and BF16 compute type.
|
||||
|
||||
## Expected effect
|
||||
|
||||
The smoke was intended to determine whether community vLLM can provide a
|
||||
shared, reproducible profiler/serving substrate before paying for the complete
|
||||
response surface. It was not intended to establish Frontier ranking accuracy.
|
||||
|
||||
Success required all representative operator and collective paths to execute,
|
||||
the TP4 server to become ready, one request to return successfully, and all GPU
|
||||
processes to be released. A discovered profiler/runtime semantic mismatch was
|
||||
treated as a gate on the later simulator comparison rather than calibrated
|
||||
away with end-to-end serving data.
|
||||
|
||||
## Frozen environment
|
||||
|
||||
- Host: `dash0`; 8 NVIDIA H20 GPUs.
|
||||
- Model: `/home/admin/cpfs/wjh/models/Qwen/Qwen3-235B-A22B-FP8`.
|
||||
- Original model-config SHA256:
|
||||
`702c46d431bb984db9035a1225186bbfdb52c0d19c82104df4a37cd005e0369e`.
|
||||
- Python 3.12.3, torch 2.8.0+cu128, vLLM 0.10.2,
|
||||
flashinfer-python 0.3.1.post1, Transformers 4.55.2, tokenizers 0.21.4.
|
||||
- Ubuntu 24.04.2, kernel 5.10.134, CUDA runtime 12.8, CUDA toolkit 12.9,
|
||||
NVIDIA driver 580.95.05, GCC 13.3.0, glibc 2.39.
|
||||
- Two 80-core Intel Xeon sockets (160 online CPUs), 8 H20 GPUs with
|
||||
97,871 MiB each and all-to-all NV18 links; GPU0--3 share NUMA node 0.
|
||||
- Serving: TP4, FlashInfer, eager execution, custom all-reduce disabled,
|
||||
FP8 weights, BF16 KV cache, MNS=64, MBT=8192, max model length 40,960,
|
||||
prefix caching and speculative decoding disabled.
|
||||
|
||||
The first environment resolved Transformers 5.13.1 because vLLM 0.10.2 only
|
||||
declares `transformers>=4.55.2`. That environment failed before model loading
|
||||
because vLLM accesses `all_special_tokens_extended`, an API absent from the
|
||||
new tokenizer implementation. Pinning the declared minimum restored the API;
|
||||
the original and corrected freezes are both retained.
|
||||
|
||||
## Verification
|
||||
|
||||
### End-to-end smoke
|
||||
|
||||
| Stage | Result |
|
||||
|---|---|
|
||||
| FP8 linear, TP4, 16 tokens | Passed; QKV projection mean 0.2232 ms and output projection mean 0.1105 ms |
|
||||
| FlashInfer prefill attention, TP4 | Passed for sequence lengths 32/64/96/128; attention mean 0.0571--0.0665 ms |
|
||||
| FP8 MoE, TP4/EP1, 16 tokens | Passed; unmodified grouped-GEMM mean 0.3075 ms in the initial point |
|
||||
| TP4 NCCL all-reduce, 128 KiB/rank | Passed; mean 0.03694 ms, p50 0.03138 ms, p95 0.05843 ms |
|
||||
| Community-vLLM TP4 model load | Passed; 55.1328 GiB of weights/rank, 627 seconds |
|
||||
| KV-cache sizing | 18.72 GiB/rank, 417,616 tokens; reported 10.20x concurrency at length 40,960 |
|
||||
| API readiness and one-token completion | Passed; `/v1/models` and `/v1/completions` returned HTTP 200 |
|
||||
| Cleanup | Passed; all eight GPUs returned to zero compute processes and zero MiB used |
|
||||
|
||||
The successful serving-only run started at 09:09:52 UTC and completed at
|
||||
09:22:29 UTC (12 minutes 37 seconds). The one-request response contains one
|
||||
completion choice and one completion token. The copied artifacts match the
|
||||
remote SHA256 manifests.
|
||||
|
||||
### FP8 path audit
|
||||
|
||||
The Frontier confirmation UI prints `FP8 Quantization: N/A` and labels the
|
||||
operations BF16 because it displays the unset CLI override rather than the
|
||||
model-config-derived operation precision. This is misleading, not evidence of
|
||||
a BF16-weight profile:
|
||||
|
||||
- the quantization manager configured `attn_pre_proj`, `attn_post_proj`, and
|
||||
`moe_grouped_gemm` as FP8 from the model config;
|
||||
- the linear implementation invokes `apply_w8a8_block_fp8_linear` with a
|
||||
128x128 weight block;
|
||||
- the MoE implementation quantizes weights and activations and invokes vLLM's
|
||||
fused kernel with `use_fp8_w8a8=True`;
|
||||
- `FRONTIER_FP8_GEMM_SURROGATE` was not enabled;
|
||||
- each CSV carries
|
||||
`method=fp8|act=dynamic|serialized=True|block=128x128` separately from its
|
||||
BF16 output/compute dtype metadata.
|
||||
|
||||
### MoE runtime-alignment factorial
|
||||
|
||||
Point: Qwen3-235B-A22B-FP8, TP4, EP1, 16 input tokens, uniform routing seeds
|
||||
0--4. Values are the mean of each row's CUDA-event samples, then averaged
|
||||
across the five paired routing seeds.
|
||||
|
||||
| Variant | Grouped GEMM mean (ms) | Paired delta vs original | Routing-seed 95% CI |
|
||||
|---|---:|---:|---:|
|
||||
| Original: default config + FP16 compute type | 0.3100 | 0.00% | -- |
|
||||
| FP8 config key only + FP16 compute type | 0.2508 | -19.12% | [-21.26%, -16.98%] |
|
||||
| Default config + BF16 compute type only | 0.3088 | -0.40% | [-1.31%, 0.51%] |
|
||||
| FP8 config key + BF16 compute type | 0.2512 | -18.99% | [-21.14%, -16.85%] |
|
||||
|
||||
These intervals describe variation across the five routing seeds, not
|
||||
independent process/server-run uncertainty. Even with that limitation, the
|
||||
factorial localizes the dominant error at this point to kernel tuning-config
|
||||
selection rather than FP16-versus-BF16 compute type.
|
||||
|
||||
The concrete lookup difference is:
|
||||
|
||||
- Frontier calls `get_config_dtype_str(base_dtype)`. For BF16 this returns
|
||||
`None`, so it searches for a config without an FP8 dtype component and uses
|
||||
the default when that file is absent.
|
||||
- vLLM serving calls the same helper with `use_fp8_w8a8=True`, obtains
|
||||
`fp8_w8a8`, and loads
|
||||
`E=128,N=384,device_name=NVIDIA_H20,dtype=fp8_w8a8,block_shape=[128,128].json`.
|
||||
|
||||
Frontier also hard-codes `tl.float16` as the FP8 kernel compute type, while the
|
||||
serving path uses BF16 for this model. That difference was not material at this
|
||||
single point, but it should still be corrected to make the counterfactual
|
||||
execution semantics identical.
|
||||
|
||||
## Validity audit
|
||||
|
||||
### Headline claims and benchmark surface
|
||||
|
||||
| Claim | Verdict | Scope |
|
||||
|---|---|---|
|
||||
| Community vLLM 0.10.2 can load and serve this model on TP4 H20 | PASS | One frozen TP4 configuration and one real request |
|
||||
| Unmodified Frontier chooses a different MoE tuning config, causing a material operator-time error at the tested point | PASS | TP4/EP1, 16 tokens, five paired routing seeds |
|
||||
| Frontier is or is not sufficient as a Qwen235B config ranker | NEEDS EVIDENCE | No aligned full profiles, frozen simulator surface, or real config surface yet |
|
||||
|
||||
The mechanism diagnostic uses the unmodified Frontier wrapper as baseline,
|
||||
grouped-GEMM CUDA-event latency as its direct metric, and the same model,
|
||||
hardware, vLLM binary, token count, TP/EP point, and routing seeds in all four
|
||||
cells. Absolute times, paired relative differences, across-seed standard
|
||||
deviations, and a paired routing-seed interval are retained. The data range is
|
||||
deliberately narrow and cannot support an end-to-end or cross-config claim.
|
||||
|
||||
| Benchmark issue | Verdict | Severity | Evidence / required action |
|
||||
|---|---|---|---|
|
||||
| Microbenchmark presented as end-to-end performance | PASS | -- | The 19% number is reported only as mechanism evidence; ranking remains blocked. |
|
||||
| Simplified profiler matches the real execution path | FAIL | Blocking | Tuning-key and compute-type semantics differ. Apply the alignment patch and test exact config dictionaries before simulation. |
|
||||
| Statistical significance / repeat protocol | NEEDS EVIDENCE | Major | Five paired routing seeds are not independent process repeats. Repeat in reverse/randomized variant order and at separated times. |
|
||||
| Selective parameter range | NEEDS EVIDENCE | Major | Only TP4/EP1/16 tokens is measured. Cover the full decision-relevant token and TP range, including boundary values. |
|
||||
| Calibration set equals evaluation set | PASS | -- | No serving result calibrated the profile; the protocol freezes simulator outputs before ground truth. |
|
||||
| Proper diagnostic baseline | PASS | -- | Each ablation is compared with the exact unmodified Frontier commit on the same point. |
|
||||
| Platform and absolute values missing | PASS | -- | OS, kernel, CPU, GPU topology, driver/toolkit/runtime versions, commands, absolute metrics, logs, and hashes are retained. |
|
||||
| Full ranking baseline/SOTA comparison | N/A | -- | This smoke makes no ranking or tuner-superiority claim; it becomes required in the formal evaluation. |
|
||||
|
||||
Overall audit decision: the feasibility smoke and localized mismatch result are
|
||||
reportable, but any Frontier-sufficiency or config-ranking claim is **Block**
|
||||
until the required reruns and end-to-end comparison are complete.
|
||||
|
||||
## Result
|
||||
|
||||
The shared community stack is feasible: profiles can be collected and the
|
||||
235B checkpoint can serve on TP4 within memory. The stronger result, however,
|
||||
is a falsification of the current alignment assumption. Same model, hardware,
|
||||
vLLM package, quantization signature, and nominal backend did not imply the
|
||||
same kernel configuration. A small wrapper-level omission produced a roughly
|
||||
19% operator-time error before any scheduling approximation was involved.
|
||||
|
||||
Therefore no Frontier config ranking should be generated from the original
|
||||
MoE CSV and presented as a fair simulator-versus-real comparison. The correct
|
||||
next boundary is: first make the profiler reproduce the serving kernel
|
||||
selection, then freeze profile-only simulator outputs, and only then collect
|
||||
the real response surface.
|
||||
|
||||
## Remaining risk and next gate
|
||||
|
||||
- The 19% result is one TP4/EP1/16-token point. It must be checked across the
|
||||
decision-relevant token counts and TP4/TP8 before claiming its effect on
|
||||
config ordering.
|
||||
- Frontier's shuffling helper still performs a separate BF16/full-expert-width
|
||||
config lookup and reports a missing `E=128,N=1536` H20 file. At 16 tokens its
|
||||
default `BLOCK_SIZE_M` matches the tuned path, but this must be validated over
|
||||
the full token grid.
|
||||
- The derived Frontier model config adds
|
||||
`is_checkpoint_fp8_serialized=true`, which Frontier requires to parse the
|
||||
checkpoint metadata. This adaptation must remain explicit and hash-tracked.
|
||||
- The representative attention and collective smokes do not provide the
|
||||
profile coverage needed by the prefill candidate grid. Decode-dominant EP8
|
||||
remains blocked on all-to-all measurement/consumption.
|
||||
- A TP4 cold start spends about 10.5 minutes reading weights. Using that time as
|
||||
a lower-bound proxy, eight TP4/TP8 cells already cost roughly 8.4
|
||||
H20-GPU-hours in weight loading alone; a real one-pass response surface will
|
||||
exceed 10 H20-GPU-hours after initialization and load probes. It requires a
|
||||
separate launch approval after simulator outputs are frozen.
|
||||
|
||||
Recommended next step: upstream the two semantic fixes with regression tests
|
||||
that compare the exact vLLM config dictionary selected by profiler and serving
|
||||
for each `(M, TP, EP, quantization)` point; align the shuffling block-size
|
||||
lookup; regenerate TP4/TP8 profile closure; then run and hash the Frontier
|
||||
prefill predictions. Do not start the eight-cell real serving sweep before
|
||||
that gate passes.
|
||||
@@ -0,0 +1 @@
|
||||
{"object":"list","data":[{"id":"qwen3-235b-community-smoke","object":"model","created":1784107344,"owned_by":"vllm","root":"/home/admin/cpfs/wjh/models/Qwen/Qwen3-235B-A22B-FP8","parent":null,"max_model_len":40960,"permission":[{"id":"modelperm-1715f628151642daa743a9bc47bffc88","object":"model_permission","created":1784107344,"allow_create_engine":false,"allow_sampling":true,"allow_logprobs":true,"allow_search_indices":false,"allow_view":true,"allow_fine_tuning":false,"organization":"*","group":null,"is_blocking":false}]}]}
|
||||
@@ -0,0 +1,6 @@
|
||||
b186a5da9809c3f2a3ac8e008837719af2f0dd65c583e022d44183b3bd7587d0 /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/results/moe_factorial_r5_summary.json
|
||||
ef9e8136bc6fc6d97a675cab95cb5b0425ac727d6d1e77598beadbf3d9cf4331 /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/results/moe_factorial_r5_cells.csv
|
||||
8d9a6aad4ee9fbb3ed054ef1798850042077df40f16c4cf3e490abe7d416bf6e /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/profiles-moe-factorial-r5/aligned/compute/h20/Qwen3-235B-A22B-FP8/moe.csv
|
||||
bc2bf6fdb6987dc73d07c17034037f539ab2ba79901ee3ff6d33094c6beb2ffd /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/profiles-moe-factorial-r5/compute_type/compute/h20/Qwen3-235B-A22B-FP8/moe.csv
|
||||
9769cd09cb35060ce6e3ed6610700485edf0ab00eb74c97199b200a0f90bf3bc /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/profiles-moe-factorial-r5/config_key/compute/h20/Qwen3-235B-A22B-FP8/moe.csv
|
||||
0607dcb87a952ded5679f74ce151ca1624aa8f0484a40924920f0fd2ccb85634 /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/profiles-moe-factorial-r5/original/compute/h20/Qwen3-235B-A22B-FP8/moe.csv
|
||||
@@ -0,0 +1,21 @@
|
||||
variant,seed,grouped_gemm_mean_ms,paired_delta_vs_original_pct
|
||||
original,0,0.3051120042800903,0.0
|
||||
original,1,0.305161565542221,0.0
|
||||
original,2,0.3159376084804535,0.0
|
||||
original,3,0.3228943943977356,0.0
|
||||
original,4,0.301118403673172,0.0
|
||||
config_key,0,0.2425376176834106,-20.508660989699024
|
||||
config_key,1,0.2544096112251282,-16.631175104543427
|
||||
config_key,2,0.2587344050407409,-18.105854416901945
|
||||
config_key,3,0.2593088150024414,-19.692376361594743
|
||||
config_key,4,0.2388928234577179,-20.664821364752093
|
||||
compute_type,0,0.3045775890350342,-0.17515379190571334
|
||||
compute_type,1,0.3057951927185058,0.20763662526077642
|
||||
compute_type,2,0.3109855651855469,-1.567411780675354
|
||||
compute_type,3,0.3209056258201599,-0.6159192020924187
|
||||
compute_type,4,0.3015664219856262,0.14878476605517665
|
||||
aligned,0,0.2454447746276855,-19.55584467847773
|
||||
aligned,1,0.2543264031410217,-16.658441999690808
|
||||
aligned,2,0.2585648000240326,-18.159537489811207
|
||||
aligned,3,0.2606095969676971,-19.289525773964723
|
||||
aligned,4,0.2369616031646728,-21.306170504986376
|
||||
|
@@ -0,0 +1,55 @@
|
||||
{
|
||||
"ci_scope": "paired t interval across routing seeds; not independent server-run uncertainty",
|
||||
"metric": "time_stats.moe_grouped_gemm.mean",
|
||||
"point": {
|
||||
"ep": 1,
|
||||
"model": "Qwen3-235B-A22B-FP8",
|
||||
"routing_seeds": [
|
||||
0,
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
4
|
||||
],
|
||||
"tokens": 16,
|
||||
"tp": 4
|
||||
},
|
||||
"variants": {
|
||||
"aligned": {
|
||||
"mean_ms": 0.25118143558502193,
|
||||
"paired_delta_pct_95ci": [
|
||||
-21.135576186756534,
|
||||
-16.8522319920158
|
||||
],
|
||||
"paired_delta_pct_mean": -18.993904089386167,
|
||||
"sd_across_seeds_ms": 0.009854035105320383
|
||||
},
|
||||
"compute_type": {
|
||||
"mean_ms": 0.3087660789489746,
|
||||
"paired_delta_pct_95ci": [
|
||||
-1.3065750560434517,
|
||||
0.5057497027004384
|
||||
],
|
||||
"paired_delta_pct_mean": -0.4004126766715066,
|
||||
"sd_across_seeds_ms": 0.007591103189515662
|
||||
},
|
||||
"config_key": {
|
||||
"mean_ms": 0.2507766544818878,
|
||||
"paired_delta_pct_95ci": [
|
||||
-21.259089864169376,
|
||||
-16.98206543082712
|
||||
],
|
||||
"paired_delta_pct_mean": -19.120577647498248,
|
||||
"sd_across_seeds_ms": 0.009466111912783425
|
||||
},
|
||||
"original": {
|
||||
"mean_ms": 0.3100447952747345,
|
||||
"paired_delta_pct_95ci": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"paired_delta_pct_mean": 0.0,
|
||||
"sd_across_seeds_ms": 0.009051191520196651
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1 @@
|
||||
{"id":"cmpl-78d1852cd51e445dab3372e1e2025d75","object":"text_completion","created":1784107344,"model":"qwen3-235b-community-smoke","choices":[{"index":0,"text":" ","logprobs":null,"finish_reason":"length","stop_reason":null,"token_ids":null,"prompt_logprobs":null,"prompt_token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":1,"total_tokens":2,"completion_tokens":1,"prompt_tokens_details":null},"kv_transfer_params":null}
|
||||
@@ -0,0 +1,3 @@
|
||||
a44ff3d6a0998324b52df358baf778b375f98be3e03c24b07f903f9c51525beb /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/results/allreduce_tp4.jsonl
|
||||
b6edf2c29065cdd303ec642bac85ceafcec30494dcce7ddf3d401c36de5162fe /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/results/models.json
|
||||
921505259d86a9037f2241cf5d611942cdf2985352c7080d1af0d39750fab211 /home/admin/cpfs/wjh/frontier-community-qwen235-smoke-20260715/results/one_request.json
|
||||
@@ -0,0 +1,17 @@
|
||||
case,cell_id,tp,dp,ep,mns,mbt,gpu_count,score_req_s_per_gpu,capacity_lower_bound_req_s_per_gpu,capacity_upper_bound_req_s_per_gpu,capacity_bracket_width_req_s_per_gpu,best_request_rate_req_s,best_sampling_u,best_pass_rate,probe_count,best_source,completed_with_probe_failure,fully_valid,result_path,result_sha256
|
||||
qwen235b_prefill_only,tp4_mns64_mbt8192,4,1,1,64,8192,4,0.1175,0.1175,0.13333333333333333,0.015833333333333338,0.47,0.021484375,0.9609929078014184,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-prefill-c2-tp-mns-mbt-dash1-d8899c5-20260702T025518Z/store/interaction-qwen235b-prefill-c2-tp-mns-mbt-dash1-d8899c5-20260702T025518Z/trials/trial-0001/result.json,cc9d8e3611c9719b5d8f8a14d376c24f08b348162cc49022889d9435816da13b
|
||||
qwen235b_prefill_only,tp4_mns64_mbt16384,4,1,1,64,16384,4,0.10666666666666667,0.10666666666666667,0.1175,0.01083333333333332,0.4266666666666667,0.01953125,0.9609375,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-prefill-c2-tp-mns-mbt-dash1-d8899c5-20260702T025518Z/store/interaction-qwen235b-prefill-c2-tp-mns-mbt-dash1-d8899c5-20260702T025518Z/trials/trial-0002/result.json,768360741f64ab39b3e7af497757c4fd92c66d5a0eff0125cc3d13f2814a0186
|
||||
qwen235b_prefill_only,tp4_mns128_mbt8192,4,1,1,128,8192,4,0.1175,0.1175,0.13333333333333333,0.015833333333333338,0.47,0.021484375,0.9609929078014184,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-prefill-c2-tp-mns-mbt-dash1-d8899c5-20260702T025518Z/store/interaction-qwen235b-prefill-c2-tp-mns-mbt-dash1-d8899c5-20260702T025518Z/trials/trial-0003/result.json,6cb56fb8ad0f9dc6e68b30528ed8993207c3951ef152ce8705a04c7fcf45a02a
|
||||
qwen235b_prefill_only,tp4_mns128_mbt16384,4,1,1,128,16384,4,0.10666666666666667,0.10666666666666667,0.1175,0.01083333333333332,0.4266666666666667,0.01953125,0.9609375,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-prefill-c2-remainder-dash1-d8899c5-20260702T163624Z/store/interaction-qwen235b-prefill-c2-remainder-dash1-d8899c5-20260702T163624Z/trials/trial-0001/result.json,3c485648244243d529df8d7f16b0fb3d782cef1beec695f67cdb1fd5ea02b440
|
||||
qwen235b_prefill_only,tp8_mns64_mbt8192,8,1,1,64,8192,8,0.17270833333333332,0.17270833333333332,0.17791666666666667,0.005208333333333343,1.3816666666666666,0.0546875,0.9638118214716526,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-prefill-c2-remainder-dash1-d8899c5-20260702T163624Z/store/interaction-qwen235b-prefill-c2-remainder-dash1-d8899c5-20260702T163624Z/trials/trial-0002/result.json,95c38d14ddc88f7f08730b7486ebb11538da6cec9ede11f5d16862b683290850
|
||||
qwen235b_prefill_only,tp8_mns64_mbt16384,8,1,1,64,16384,8,0.17270833333333332,0.17270833333333332,0.17791666666666667,0.005208333333333343,1.3816666666666666,0.0546875,0.9565741857659831,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-prefill-c2-remainder-dash1-d8899c5-20260702T163624Z/store/interaction-qwen235b-prefill-c2-remainder-dash1-d8899c5-20260702T163624Z/trials/trial-0003/result.json,97b96526c568a572f10de602075c59a0a14555114f65d7c8e928b51fcf1cb787
|
||||
qwen235b_prefill_only,tp8_mns128_mbt8192,8,1,1,128,8192,8,0.17270833333333332,0.17270833333333332,0.17791666666666667,0.005208333333333343,1.3816666666666666,0.0546875,0.9601930036188179,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-prefill-c2-remainder-dash1-d8899c5-20260702T163624Z/store/interaction-qwen235b-prefill-c2-remainder-dash1-d8899c5-20260702T163624Z/trials/trial-0004/result.json,f2695efe64c9c728f381e0d39ee42e7553b771928547f7a74a2cb2dafdc892f4
|
||||
qwen235b_prefill_only,tp8_mns128_mbt16384,8,1,1,128,16384,8,0.17270833333333332,0.17270833333333332,0.17791666666666667,0.005208333333333343,1.3816666666666666,0.0546875,0.9589867310012062,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-prefill-c2-remainder-dash1-d8899c5-20260702T163624Z/store/interaction-qwen235b-prefill-c2-remainder-dash1-d8899c5-20260702T163624Z/trials/trial-0005/result.json,1bc4d53f5ecbf4fbbd7c749a29ecf0f62e8382b84d899cc366d5bb485d78e261
|
||||
qwen235b_decode_only,tp4_dp2_ep8_mns64_mbt256,4,2,8,64,256,8,0.05354166666666667,0.05354166666666667,0.058958333333333335,0.005416666666666667,0.42833333333333334,0.01953125,1.0,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/store/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/trials/trial-0001/result.json,f5a1c9ff3e8f98c37f8d3623e0376f34f67990f2e71e3b012beb8ac8ed447367
|
||||
qwen235b_decode_only,tp4_dp2_ep8_mns64_mbt384,4,2,8,64,384,8,0.05354166666666667,0.05354166666666667,0.058958333333333335,0.005416666666666667,0.42833333333333334,0.01953125,0.9922178988326849,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/store/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/trials/trial-0002/result.json,adaafb060dc3ba642da34af7b69ee09ee0d04d857b8d4b5121a4b124373e1c57
|
||||
qwen235b_decode_only,tp4_dp2_ep8_mns128_mbt256,4,2,8,128,256,8,0.058958333333333335,0.058958333333333335,0.066875,0.007916666666666669,0.4716666666666667,0.021484375,0.9929328621908127,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/store/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/trials/trial-0003/result.json,3555b3ecdef6337050c4e132e2b6f36a3cb98ea002a96f3efe118d301ad9d258
|
||||
qwen235b_decode_only,tp4_dp2_ep8_mns128_mbt384,4,2,8,128,384,8,0.058958333333333335,0.058958333333333335,0.066875,0.007916666666666669,0.4716666666666667,0.021484375,0.9929328621908127,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/store/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/trials/trial-0004/result.json,3a010b17bc2cb785a57ef80a08646972109ad2dae28eeb38c153b35a1e3b6be2
|
||||
qwen235b_decode_only,tp2_dp4_ep8_mns64_mbt256,2,4,8,64,256,8,0.058958333333333335,0.058958333333333335,0.066875,0.007916666666666669,0.4716666666666667,0.021484375,0.9752650176678446,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/store/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/trials/trial-0005/result.json,79649819c5599c8b81a115281a8e73aab5bc1a8188a1f611ace2329f9ae2f418
|
||||
qwen235b_decode_only,tp2_dp4_ep8_mns64_mbt384,2,4,8,64,384,8,0.05354166666666667,0.05354166666666667,0.058958333333333335,0.005416666666666667,0.42833333333333334,0.01953125,0.9961089494163424,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/store/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/trials/trial-0006/result.json,6fdfeccfdbeb92bad8669251b26546966f46b42d667ae70eacdc544b44ad231d
|
||||
qwen235b_decode_only,tp2_dp4_ep8_mns128_mbt256,2,4,8,128,256,8,0.058958333333333335,0.058958333333333335,0.066875,0.007916666666666669,0.4716666666666667,0.021484375,0.9787985865724381,6,primary_search,False,True,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/store/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/trials/trial-0007/result.json,8bc1c359e338de1d62bc87c43b1fa270b15023d223450552bffe5d1b778458f3
|
||||
qwen235b_decode_only,tp2_dp4_ep8_mns128_mbt384,2,4,8,128,384,8,0.058958333333333335,0.058958333333333335,0.066875,0.007916666666666669,0.4716666666666667,0.021484375,0.9823321554770318,6,partial_probe_before_failure,True,False,recovered-stores/aituner-interaction-runs-dash1-20260710/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/store/interaction-qwen235b-decode-c3-topo-mns-mbt-fixed-dash1-d8899c5-20260703T022514Z/trials/trial-0008/result.json,c371c2dd38db875db827ab85d4f399d0219cad513dde3c3b4db8cc782ec4debb
|
||||
|
@@ -0,0 +1,75 @@
|
||||
{
|
||||
"cases": [
|
||||
{
|
||||
"best_score_req_s_per_gpu": 0.17270833333333332,
|
||||
"case": "qwen235b_prefill_only",
|
||||
"cell_count": 8,
|
||||
"distinct_score_count": 3,
|
||||
"distinct_scores_req_s_per_gpu": [
|
||||
0.17270833333333332,
|
||||
0.1175,
|
||||
0.10666666666666667
|
||||
],
|
||||
"fully_valid_cell_count": 8,
|
||||
"informative_pair_count": 20,
|
||||
"informative_pair_fraction": 0.7142857142857143,
|
||||
"invalid_cells": [],
|
||||
"pair_count": 28,
|
||||
"possibly_optimal_set_from_search_brackets": [
|
||||
"tp8_mns128_mbt16384",
|
||||
"tp8_mns128_mbt8192",
|
||||
"tp8_mns64_mbt16384",
|
||||
"tp8_mns64_mbt8192"
|
||||
],
|
||||
"possibly_optimal_set_size": 4,
|
||||
"random_top_set_hit_rate": 0.5,
|
||||
"tied_pair_count": 8,
|
||||
"top_set": [
|
||||
"tp8_mns128_mbt16384",
|
||||
"tp8_mns128_mbt8192",
|
||||
"tp8_mns64_mbt16384",
|
||||
"tp8_mns64_mbt8192"
|
||||
],
|
||||
"top_set_size": 4
|
||||
},
|
||||
{
|
||||
"best_score_req_s_per_gpu": 0.058958333333333335,
|
||||
"case": "qwen235b_decode_only",
|
||||
"cell_count": 8,
|
||||
"distinct_score_count": 2,
|
||||
"distinct_scores_req_s_per_gpu": [
|
||||
0.058958333333333335,
|
||||
0.05354166666666667
|
||||
],
|
||||
"fully_valid_cell_count": 7,
|
||||
"informative_pair_count": 15,
|
||||
"informative_pair_fraction": 0.5357142857142857,
|
||||
"invalid_cells": [
|
||||
"tp2_dp4_ep8_mns128_mbt384"
|
||||
],
|
||||
"pair_count": 28,
|
||||
"possibly_optimal_set_from_search_brackets": [
|
||||
"tp2_dp4_ep8_mns128_mbt256",
|
||||
"tp2_dp4_ep8_mns128_mbt384",
|
||||
"tp2_dp4_ep8_mns64_mbt256",
|
||||
"tp2_dp4_ep8_mns64_mbt384",
|
||||
"tp4_dp2_ep8_mns128_mbt256",
|
||||
"tp4_dp2_ep8_mns128_mbt384",
|
||||
"tp4_dp2_ep8_mns64_mbt256",
|
||||
"tp4_dp2_ep8_mns64_mbt384"
|
||||
],
|
||||
"possibly_optimal_set_size": 8,
|
||||
"random_top_set_hit_rate": 0.625,
|
||||
"tied_pair_count": 13,
|
||||
"top_set": [
|
||||
"tp2_dp4_ep8_mns128_mbt256",
|
||||
"tp2_dp4_ep8_mns128_mbt384",
|
||||
"tp2_dp4_ep8_mns64_mbt256",
|
||||
"tp4_dp2_ep8_mns128_mbt256",
|
||||
"tp4_dp2_ep8_mns128_mbt384"
|
||||
],
|
||||
"top_set_size": 5
|
||||
}
|
||||
],
|
||||
"schema": "frontier-multicase-ground-truth-v0"
|
||||
}
|
||||
@@ -0,0 +1,47 @@
|
||||
# Qwen235B ground-truth audit
|
||||
|
||||
Objective: maximum SLO-feasible offered request throughput per GPU.
|
||||
This report contains real-machine data only; it makes no Frontier match claim.
|
||||
|
||||
| case | valid cells | score levels | top-set size | random top-set hit | informative pairs |
|
||||
|---|---:|---:|---:|---:|---:|
|
||||
| qwen235b_prefill_only | 8/8 | 3 | 4/8 | 50.0% | 20/28 (71.4%) |
|
||||
| qwen235b_decode_only | 7/8 | 2 | 5/8 | 62.5% | 15/28 (53.6%) |
|
||||
|
||||
## Cells
|
||||
|
||||
### qwen235b_prefill_only
|
||||
|
||||
| cell | capacity bracket (req/s/GPU) | valid | probes | source |
|
||||
|---|---:|---:|---:|---|
|
||||
| tp4_mns128_mbt16384 | [0.106666667, 0.117500000) | yes | 6 | primary_search |
|
||||
| tp4_mns128_mbt8192 | [0.117500000, 0.133333333) | yes | 6 | primary_search |
|
||||
| tp4_mns64_mbt16384 | [0.106666667, 0.117500000) | yes | 6 | primary_search |
|
||||
| tp4_mns64_mbt8192 | [0.117500000, 0.133333333) | yes | 6 | primary_search |
|
||||
| tp8_mns128_mbt16384 | [0.172708333, 0.177916667) | yes | 6 | primary_search |
|
||||
| tp8_mns128_mbt8192 | [0.172708333, 0.177916667) | yes | 6 | primary_search |
|
||||
| tp8_mns64_mbt16384 | [0.172708333, 0.177916667) | yes | 6 | primary_search |
|
||||
| tp8_mns64_mbt8192 | [0.172708333, 0.177916667) | yes | 6 | primary_search |
|
||||
|
||||
Top set: `tp8_mns128_mbt16384, tp8_mns128_mbt8192, tp8_mns64_mbt16384, tp8_mns64_mbt8192`.
|
||||
Possibly optimal under binary-search brackets: `tp8_mns128_mbt16384, tp8_mns128_mbt8192, tp8_mns64_mbt16384, tp8_mns64_mbt8192`.
|
||||
|
||||
### qwen235b_decode_only
|
||||
|
||||
| cell | capacity bracket (req/s/GPU) | valid | probes | source |
|
||||
|---|---:|---:|---:|---|
|
||||
| tp2_dp4_ep8_mns128_mbt256 | [0.058958333, 0.066875000) | yes | 6 | primary_search |
|
||||
| tp2_dp4_ep8_mns128_mbt384 | [0.058958333, 0.066875000) | no | 6 | partial_probe_before_failure |
|
||||
| tp2_dp4_ep8_mns64_mbt256 | [0.058958333, 0.066875000) | yes | 6 | primary_search |
|
||||
| tp2_dp4_ep8_mns64_mbt384 | [0.053541667, 0.058958333) | yes | 6 | primary_search |
|
||||
| tp4_dp2_ep8_mns128_mbt256 | [0.058958333, 0.066875000) | yes | 6 | primary_search |
|
||||
| tp4_dp2_ep8_mns128_mbt384 | [0.058958333, 0.066875000) | yes | 6 | primary_search |
|
||||
| tp4_dp2_ep8_mns64_mbt256 | [0.053541667, 0.058958333) | yes | 6 | primary_search |
|
||||
| tp4_dp2_ep8_mns64_mbt384 | [0.053541667, 0.058958333) | yes | 6 | primary_search |
|
||||
|
||||
Top set: `tp2_dp4_ep8_mns128_mbt256, tp2_dp4_ep8_mns128_mbt384, tp2_dp4_ep8_mns64_mbt256, tp4_dp2_ep8_mns128_mbt256, tp4_dp2_ep8_mns128_mbt384`.
|
||||
Possibly optimal under binary-search brackets: `tp2_dp4_ep8_mns128_mbt256, tp2_dp4_ep8_mns128_mbt384, tp2_dp4_ep8_mns64_mbt256, tp2_dp4_ep8_mns64_mbt384, tp4_dp2_ep8_mns128_mbt256, tp4_dp2_ep8_mns128_mbt384, tp4_dp2_ep8_mns64_mbt256, tp4_dp2_ep8_mns64_mbt384`.
|
||||
|
||||
## Interpretation guardrail
|
||||
|
||||
A Frontier top-set hit is insufficient by itself because the surfaces contain large ties. The later comparison must report selected-config regret and tie-aware pairwise ranking, and must keep invalid real cells visible.
|
||||
@@ -0,0 +1,44 @@
|
||||
{
|
||||
"calibration": {
|
||||
"fit_fixture": "coder_200_ts2",
|
||||
"fitted_a_tp": {
|
||||
"1": 0.7234810457606639,
|
||||
"2": 0.4680889959260082,
|
||||
"4": 0.3521372005220769
|
||||
},
|
||||
"holdout_fixture": "coder_200_ts3",
|
||||
"loss": "[log(G_raw_rerun(tp,scale2;a)/F_raw(tp,scale2))]^2 per TP",
|
||||
"refit_on_holdout": false
|
||||
},
|
||||
"rows": [
|
||||
{
|
||||
"agreement": 37,
|
||||
"false_feasible": 0,
|
||||
"false_infeasible": 55,
|
||||
"kendall_tau_b": 0.0,
|
||||
"mode": "uncalibrated/SLO-gated",
|
||||
"optimistic_real_regret": 0.25634517766497456,
|
||||
"pairwise_exact_sign_accuracy": 0.3787878787878788,
|
||||
"selected_cells": [
|
||||
"tp4_mns32",
|
||||
"tp4_mns64"
|
||||
],
|
||||
"worst_case_real_regret": 0.25634517766497456
|
||||
},
|
||||
{
|
||||
"agreement": 64,
|
||||
"false_feasible": 21,
|
||||
"false_infeasible": 7,
|
||||
"kendall_tau_b": 0.9668009539030813,
|
||||
"mode": "frozen-calibrated/SLO-gated",
|
||||
"optimistic_real_regret": 0.0,
|
||||
"pairwise_exact_sign_accuracy": 0.9393939393939394,
|
||||
"selected_cells": [
|
||||
"tp2_mns32",
|
||||
"tp2_mns64"
|
||||
],
|
||||
"worst_case_real_regret": 0.0076142131979695165
|
||||
}
|
||||
],
|
||||
"schema": "frontier-qwen30-calibration-audit-v0"
|
||||
}
|
||||
@@ -0,0 +1,10 @@
|
||||
# Qwen30B Frontier baseline audit
|
||||
|
||||
| mode | selected cells | worst real regret | Kendall tau-b | pair sign accuracy | feasibility (agree/FP/FN) |
|
||||
|---|---|---:|---:|---:|---:|
|
||||
| uncalibrated/SLO-gated | tp4_mns32, tp4_mns64 | 25.63% | 0.0000 | 37.88% | 37/0/55 |
|
||||
| frozen-calibrated/SLO-gated | tp2_mns32, tp2_mns64 | 0.76% | 0.9668 | 93.94% | 64/21/7 |
|
||||
|
||||
The calibrated mode applies a distinct end-to-end execution-time scale per TP: TP1=0.723481, TP2=0.468089, TP4=0.352137.
|
||||
|
||||
Those scales were fitted against real total throughput on `coder_200_ts2` and checked without refitting on `coder_200_ts3`. This validates within-workload transfer of the calibration, not zero-shot Frontier prediction across TP.
|
||||
Reference in New Issue
Block a user