update sparsegpt and wandab

.gitignore

@@ -9,7 +9,6 @@ logs/
 runs/
 outputs/
 results/
-experiments/
 
 
 # Backup files
@@ -161,7 +160,6 @@ dmypy.json
 /runs/
 /outputs/
 /results/
-/experiments/
 
 # Temporary files
 *.tmp

configs/prune_llm/README.md

@@ -15,7 +15,7 @@ Configurations for generating results in the SCAR LLM pruning paper.
 
 Run all experiments:
 ```bash
-bash drafts/LLM_prune/paper/slurm/run_all_paper.sh
+bash slurm_jobs/prune_llm/run_all_paper.sh
 ```
 
 Run single model:

configs/prune_llm/llama2_7b_full.yaml

@@ -77,6 +77,13 @@ llm:
 
   evaluate_perplexity: true
   evaluation_num_samples: 100
+  # Use NVIDIA Minitron official few-shot settings for downstream tasks.
+  use_nvidia_fewshot: true
+  # Match OATS Table 19 / common pruning-paper protocol for WikiText-2 perplexity:
+  # concatenate full test set and evaluate in contiguous 2048-token blocks (no padding).
+  perplexity_protocol: "oats"
+  wikitext_subset: "wikitext-2-raw-v1"
+  perplexity_seq_len: 2048
 
   evaluation_metrics:
     - "perplexity"
@@ -137,6 +144,20 @@ supernode:
   core_fraction: 0.01
   follower_fraction: 0.10
   halo_fraction: 0.10
+  # Connectivity definition (SCAR-Conn): fraction of a channel's down_proj write-mass
+  # that lands on the top-K hidden dimensions most written-to by supernodes.
+  connectivity_topk: 256
+  # Optional post-processing for Conn (defaults keep current behavior)
+  connectivity_rank_normalize: false
+  connectivity_power: 1.0
+  # Analysis-only: also estimate redundancy-to-core for a small random sample of non-halo channels
+  # (used for paper mechanism plots; does NOT affect pruning decisions).
+  non_halo_sample_size: 256
+  non_halo_sample_seed: 0
+  # Protection mapping (rank-power): Protect = alpha + (1-alpha)*(1 - rank^gamma)
+  protection_normalization: "rank_power"
+  protection_rank_power: 8.0
+  protection_floor: 0.2
   protect_core: true
   protect_core_metrics:
     - "scar_loss_proxy"                 # SCAR-LP
@@ -232,7 +253,7 @@ pruning:
   dependency_aware: true
 
   sparsity_levels: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
-  selection_modes: ["low", "high"]
+  selection_modes: ["low"]
 
   algorithms:
     - "rayleigh_quotient"

configs/prune_llm/llama2_7b_unified.yaml

@@ -139,7 +139,7 @@ cascade_analysis:
 pruning:
   enabled: true
   ratios: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
-  selection_modes: ["low", "high"]
+  selection_modes: ["low"]
   distribution: "uniform"
   min_per_layer: 0.0
   max_per_layer: 0.95

configs/prune_llm/llama3_8b_full.yaml

@@ -87,6 +87,14 @@ llm:
 
   evaluate_perplexity: true
   evaluation_num_samples: 100
+  # Use NVIDIA Minitron official few-shot settings for downstream tasks
+  # (MMLU 5-shot, HellaSwag 10-shot, ARC 25-shot, WinoGrande 5-shot, etc.).
+  use_nvidia_fewshot: true
+  # Match OATS Table 19 / common pruning-paper protocol for WikiText-2 perplexity:
+  # concatenate full test set and evaluate in contiguous 2048-token blocks (no padding).
+  perplexity_protocol: "oats"
+  wikitext_subset: "wikitext-2-raw-v1"
+  perplexity_seq_len: 2048
 
   evaluation_metrics:
     # Language modeling
@@ -99,6 +107,7 @@ llm:
     - "accuracy_hellaswag"
     - "accuracy_arc_easy"
     - "accuracy_arc_challenge"
+    - "accuracy_openbookqa"
 
     # Common Sense
     - "accuracy_winogrande"
@@ -174,6 +183,21 @@ supernode:
   core_fraction: 0.01
   follower_fraction: 0.10
   halo_fraction: 0.10
+  # Connectivity definition (SCAR-Conn): fraction of a channel's down_proj write-mass
+  # that lands on the top-K hidden dimensions most written-to by supernodes.
+  # (Avoids the ~1/hidden_dim collapse of L1-normalized dot-product overlap for dense matrices.)
+  connectivity_topk: 256
+  # Optional post-processing for Conn (defaults keep current behavior)
+  connectivity_rank_normalize: false
+  connectivity_power: 1.0
+  # Analysis-only: also estimate redundancy-to-core for a small random sample of non-halo channels
+  # (used for paper mechanism plots; does NOT affect pruning decisions).
+  non_halo_sample_size: 256
+  non_halo_sample_seed: 0
+  # Protection mapping (rank-power): Protect = alpha + (1-alpha)*(1 - rank^gamma)
+  protection_normalization: "rank_power"
+  protection_rank_power: 8.0
+  protection_floor: 0.2
   protect_core: true
   # Apply hard supernode protection only for the listed pruning metrics.
   # If omitted, legacy behavior is to protect for *all* pruning metrics.
@@ -286,7 +310,9 @@ pruning:
   dependency_aware: true
 
   sparsity_levels: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
-  selection_modes: ["low", "high"]
+  # We only report (and run) the standard pruning direction: prune *low*-scoring channels.
+  # The "high" mode (prune highest scores) is a pathological control and is excluded from paper runs.
+  selection_modes: ["low"]
 
   # ALL algorithms including SOTA baselines
   algorithms:

configs/prune_llm/llama3_8b_random_only.yaml

@@ -0,0 +1,87 @@
+# ============================================================================
+# LLAMA-3.1-8B RANDOM (CHANNEL) BASELINE
+# ============================================================================
+#
+# Purpose:
+# - Fill the missing "Random (channel)" baseline row in paper tables.
+# - Run ONLY one pruning strategy (random) at 50% sparsity.
+# - Keep evaluation protocol consistent with the main paper run (few-shot settings, ppl protocol).
+#
+# This is intentionally lightweight: we skip SCAR analyses/plots and only do:
+# - Baseline eval
+# - Random structured channel pruning @ 50%
+# - Post-prune eval
+# ============================================================================
+
+experiment:
+  name: "llama3_8b_paper_results_random"
+  type: "llm_alignment"
+  output_dir: "./results/paper/llama3_8b_random"
+  seed: 42
+  device: "cuda"
+  save_activations: false
+  num_networks: 1
+
+model:
+  name: "hf_causal_lm"
+  model_id: "meta-llama/Llama-3.1-8B"
+  dtype: "bfloat16"
+  device_map: "auto"
+  trust_remote_code: true
+
+dataset:
+  name: "wikitext"
+  batch_size: 1
+  num_workers: 0
+
+llm:
+  evaluate_perplexity: true
+  evaluation_num_samples: 100
+  use_nvidia_fewshot: true
+  perplexity_protocol: "oats"
+  wikitext_subset: "wikitext-2-raw-v1"
+  perplexity_seq_len: 2048
+
+  evaluation_metrics:
+    - "perplexity"
+    - "accuracy_openbookqa"
+    - "accuracy_mmlu"
+    - "accuracy_hellaswag"
+    - "accuracy_piqa"
+    - "accuracy_boolq"
+    - "accuracy_winogrande"
+    - "accuracy_arc_easy"
+    - "accuracy_arc_challenge"
+
+# Disable heavy analyses for this baseline-only run
+analysis:
+  generate_plots: false
+  save_scores: false
+
+do_scar_metrics: false
+do_directed_redundancy: false
+do_connectivity_pruning: false
+do_halo_analysis: false
+do_generalized_importance: false
+
+pruning:
+  enabled: true
+  target: "ffn"
+  structured: true
+  dependency_aware: true
+  distribution: "uniform"
+  min_per_layer: 0.0
+  max_per_layer: 0.95
+
+  # Single point needed for table_full_benchmarks_50
+  sparsity_levels: [0.5]
+
+  # Random structured pruning: selection done by mode="random"
+  selection_modes: ["random"]
+
+  # Only one strategy for this run; scores are generated in-code (deterministic).
+  algorithms:
+    - "random"
+
+  single_strategy: "random"
+

configs/prune_llm/llama3_8b_unified.yaml

@@ -10,7 +10,7 @@
 # - All experiment-specific settings in `extra:` section
 # - Same pruning/evaluation/visualization structure
 #
-# Usage: python scripts/run_experiment.py --config configs/unified/llama3_8b_unified.yaml
+# Usage: python scripts/run_experiment.py --config configs/prune_llm/llama3_8b_unified.yaml
 # Estimated runtime: ~6-8 hours on 1x A100
 # =============================================================================
 
@@ -156,7 +156,7 @@ cascade_analysis:
 pruning:
   enabled: true
   ratios: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
-  selection_modes: ["low", "high"]
+  selection_modes: ["low"]
   distribution: "uniform"
   min_per_layer: 0.0
   max_per_layer: 0.95

configs/prune_llm/mistral_7b_full.yaml

@@ -76,6 +76,13 @@ llm:
 
   evaluate_perplexity: true
   evaluation_num_samples: 100
+  # Use NVIDIA Minitron official few-shot settings for downstream tasks.
+  use_nvidia_fewshot: true
+  # Match OATS Table 19 / common pruning-paper protocol for WikiText-2 perplexity:
+  # concatenate full test set and evaluate in contiguous 2048-token blocks (no padding).
+  perplexity_protocol: "oats"
+  wikitext_subset: "wikitext-2-raw-v1"
+  perplexity_seq_len: 2048
 
   evaluation_metrics:
     - "perplexity"
@@ -136,6 +143,20 @@ supernode:
   core_fraction: 0.01
   follower_fraction: 0.10
   halo_fraction: 0.10
+  # Connectivity definition (SCAR-Conn): fraction of a channel's down_proj write-mass
+  # that lands on the top-K hidden dimensions most written-to by supernodes.
+  connectivity_topk: 256
+  # Optional post-processing for Conn (defaults keep current behavior)
+  connectivity_rank_normalize: false
+  connectivity_power: 1.0
+  # Analysis-only: also estimate redundancy-to-core for a small random sample of non-halo channels
+  # (used for paper mechanism plots; does NOT affect pruning decisions).
+  non_halo_sample_size: 256
+  non_halo_sample_seed: 0
+  # Protection mapping (rank-power): Protect = alpha + (1-alpha)*(1 - rank^gamma)
+  protection_normalization: "rank_power"
+  protection_rank_power: 8.0
+  protection_floor: 0.2
   protect_core: true
   protect_core_metrics:
     - "scar_loss_proxy"                 # SCAR-LP
@@ -231,7 +252,7 @@ pruning:
   dependency_aware: true
 
   sparsity_levels: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
-  selection_modes: ["low", "high"]
+  selection_modes: ["low"]
 
   algorithms:
     - "rayleigh_quotient"

configs/prune_llm/mistral_7b_unified.yaml

@@ -138,7 +138,7 @@ cascade_analysis:
 pruning:
   enabled: true
   ratios: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
-  selection_modes: ["low", "high"]
+  selection_modes: ["low"]
   distribution: "uniform"
   min_per_layer: 0.0
   max_per_layer: 0.95

configs/prune_llm/qwen2_7b_full.yaml

@@ -77,6 +77,13 @@ llm:
 
   evaluate_perplexity: true
   evaluation_num_samples: 100
+  # Use NVIDIA Minitron official few-shot settings for downstream tasks.
+  use_nvidia_fewshot: true
+  # Match OATS Table 19 / common pruning-paper protocol for WikiText-2 perplexity:
+  # concatenate full test set and evaluate in contiguous 2048-token blocks (no padding).
+  perplexity_protocol: "oats"
+  wikitext_subset: "wikitext-2-raw-v1"
+  perplexity_seq_len: 2048
 
   evaluation_metrics:
     - "perplexity"
@@ -137,6 +144,20 @@ supernode:
   core_fraction: 0.01
   follower_fraction: 0.10
   halo_fraction: 0.10
+  # Connectivity definition (SCAR-Conn): fraction of a channel's down_proj write-mass
+  # that lands on the top-K hidden dimensions most written-to by supernodes.
+  connectivity_topk: 256
+  # Optional post-processing for Conn (defaults keep current behavior)
+  connectivity_rank_normalize: false
+  connectivity_power: 1.0
+  # Analysis-only: also estimate redundancy-to-core for a small random sample of non-halo channels
+  # (used for paper mechanism plots; does NOT affect pruning decisions).
+  non_halo_sample_size: 256
+  non_halo_sample_seed: 0
+  # Protection mapping (rank-power): Protect = alpha + (1-alpha)*(1 - rank^gamma)
+  protection_normalization: "rank_power"
+  protection_rank_power: 8.0
+  protection_floor: 0.2
   protect_core: true
   protect_core_metrics:
     - "scar_loss_proxy"                 # SCAR-LP
@@ -232,7 +253,7 @@ pruning:
   dependency_aware: true
 
   sparsity_levels: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
-  selection_modes: ["low", "high"]
+  selection_modes: ["low"]
 
   algorithms:
     - "rayleigh_quotient"

configs/prune_llm/qwen2_7b_unified.yaml

@@ -139,7 +139,7 @@ cascade_analysis:
 pruning:
   enabled: true
   ratios: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
-  selection_modes: ["low", "high"]
+  selection_modes: ["low"]
   distribution: "uniform"
   min_per_layer: 0.0
   max_per_layer: 0.95

configs/vision_prune/mobilenetv2_cifar10_unified.yaml

@@ -74,7 +74,7 @@ metrics:
 
   rayleigh_quotient:
     enabled: true
-    relative: true
+    relative: false  # Standard Rayleigh quotient (no trace-normalization)
     shrinkage: true
 
   redundancy:
@@ -143,7 +143,7 @@ cascade_analysis:
 # More sensitive to pruning - interesting to see which metrics matter
 pruning:
   enabled: true
-  distribution: "uniform"            # uniform, global_threshold, adaptive_sensitivity
+  distribution: "global_threshold"   # uniform, global_threshold, adaptive_sensitivity
   dependency_aware: true             # MobileNet has inverted residuals
   min_per_layer: 0.0
   max_per_layer: 0.95
@@ -156,6 +156,7 @@ pruning:
     # =========================================================================
     - "random"              # Random baseline
     - "magnitude"           # Standard magnitude pruning (prune low)
+    - "activation_mean"     # Mean |activation| baseline
     - "taylor"              # Gradient-based importance
     - "network_slimming"    # Network Slimming (BN gamma) baseline
     - "geometric_median"    # FPGM-style geometric median baseline
@@ -167,6 +168,8 @@ pruning:
     - "rq_low"              # Prune low Rayleigh Quotient
     - "redundancy_low"      # Prune low redundancy (MI)
     - "synergy_low"         # Prune low synergy
+    - "redundancy_high"     # Control: prune high redundancy
+    - "synergy_high"        # Control: prune high synergy
 
     # =========================================================================
     # COMPOSITE COMBINATIONS
@@ -183,6 +186,7 @@ pruning:
     # CLUSTER-AWARE
     # =========================================================================
     - "cluster_aware"               # Original: protect critical, target redundant
+    - "cluster_aware_annealed"      # Annealed mixing / constraints schedule
     - "cluster_aware_protect_redundant"  # Inverted: protect redundant
 
   scoring_methods:
@@ -203,7 +207,7 @@ pruning:
     epochs: 5
     learning_rate: 0.0001
     weight_decay: 0.00001
-    max_batches: 100
+    max_batches: 200
 
 # -----------------------------------------------------------------------------
 # EVALUATION (Enhanced for Vision)