chore: update ablation and clean configs

configs/glm5-8xb200.yaml

@@ -1,66 +1,39 @@
-# GLM-5 (zai-org/GLM-5) served as a single tensor-parallel instance on
-# 8 x NVIDIA B200 SXM (192GB each, 1.5 TB aggregate HBM).
+# GLM-5 using HuggingFace config.json + hardware preset.
 #
-# GLM-5 is a 744B-total / 40B-active Mixture-of-Experts model (BF16),
-# using DeepSeek Sparse Attention (DSA). The HF card does not publish
-# layer/head shapes, so the values below are reasonable estimates based
-# on the GLM-4.5 lineage; adjust once the official config.json is public.
+# This config demonstrates the simplified format:
+#   model.config_json  — loads architecture from HF config.json
+#   hardware.type      — loads GPU specs from built-in preset
 #
-# Hardware values below represent the *aggregate* of the 8-GPU TP group
-# (one simulated "instance" == one 8xB200 serving replica). This is how
-# the roofline in src/instance/compute.rs wants to see it: gpu_flops and
-# gpu_mem_bw are the effective peaks seen by the TP'd model.
-#
-# Calibrate `flops_per_token_prefill` and `attn_quadratic_coeff` against
-# measured prefill latency before trusting absolute TTFT numbers.
+# Only deployment-specific fields need to be set explicitly.
+# Any field from config_json or the preset can be overridden in YAML.
 
 model:
-  name: glm-5
-  # --- estimates; refine from official config.json when available ---
-  num_layers: 92
-  num_kv_heads: 8              # GQA
-  head_dim: 128
-  dtype_bytes: 2               # BF16
-  block_size_tokens: 16        # trace convention
-  # Active-params-driven roofline: MoE activates ~40B params per token,
-  # so non-attention prefill FLOPs/token ≈ 2 * 40e9 = 8e10.
-  flops_per_token_prefill: 8.0e10
-  # Quadratic attention term ≈ 2 * num_heads * head_dim. GLM-5 uses
-  # DeepSeek Sparse Attention which is sub-quadratic in practice, so
-  # this coefficient is an upper bound — lower it if your measurements
-  # show DSA kicking in for long prompts.
-  attn_quadratic_coeff:    2048.0
-  bytes_per_token_prefill: 0.0
+  # Auto-detect architecture: MoE, MLA, DSA, head dims, etc.
+  config_json: ../models/GLM-5/config.json
+  name: glm-5                    # override HF model_type
+  dtype_bytes: 1                 # BF16 (not in HF config.json)
+  block_size_tokens: 512         # matches bailian-traces blksz_512
 
 hardware:
-  # Aggregate of 8 x B200 in one tensor-parallel group.
-  gpu_flops:        1.80e16    # 8 * 2.25 PFLOPS BF16 dense
-  gpu_mem_bw:       6.40e13    # 8 * 8 TB/s HBM3e
-  # KV-cache budget after weights + activations. GLM-5 @ BF16 is ~1.49TB,
-  # which barely fits in 1.5TB HBM; realistic serving uses FP8 weights
-  # (~744GB), leaving ~500GB for activations + KV cache. Adjust if your
-  # deployment uses a different weight dtype.
-  hbm_bytes:        500.0e9
-  dram_bytes:       1.5e12     # ~1.5 TB usable CPU DRAM / v6d per node
-  pcie_bw:          128.0e9    # PCIe Gen6 x16 ~ 128 GB/s per direction
-  pcie_latency_us:  4.0
-  rdma_bw:          50.0e9     # ConnectX-7 400 Gbps ≈ 50 GB/s
-  rdma_latency_us:  6.0
-  max_batch_slots:  256
-  prefill_chunk_tokens: 2048
+  type: 8xb200                   # 8 x B200 SXM (192GB each)
+  # Override preset values for this specific deployment:
+  hbm_bytes: 500.0e9             # KV budget after FP8 weights + activations
+  dram_bytes: 1.5e12             # ~1.5 TB usable CPU DRAM per node
+  max_batch_slots: 256
 
 cluster:
-  num_instances: 8             # 8 TP replicas -> 64 B200s cluster-wide
+  num_instances: 32
   meta_store:
-    ttl_seconds: 120.0
+    ttl_seconds: 300.0
   router:
-    mode: ttl_aware
+    mode: min_pd
     precise_probe_latency_us: 50.0
     precise_probe_topk: 4
     load_alpha: 1.0
+    prefix_k: 8
 
 sim:
-  trace_path: qwen-bailian-usagetraces-anon/qwen_coder_blksz_16.jsonl
+  trace_path: bailian-traces/glm_coder_blksz_512_040915-040917.jsonl
   max_requests: null
   output_dir: runs/glm5_8xb200
   sample_interval_s: 1.0