Ssameni/puzzletron bypass 3 integration

examples/puzzletron/Nemotron-3-Nano-30B-A3B-Base-BF16.md

@@ -0,0 +1,95 @@
+# Bypass Distillation Tutorial: Nemotron-3-Nano-30B-A3B (KV-heads-only)
+
+A minimal end-to-end demonstration that **bypass distillation improves quality** at the same compression budget. The setup is a **toy pruning task on a real production model** — we compress only KV heads (12 → 9, a modest 25% reduction) so a single comparison surfaces the bypass benefit cleanly without needing extensive downstream evaluation. The model itself ([Nemotron-3-Nano-30B-A3B-Base-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16)) is a real 30B-A3B MoE-Mamba hybrid, not a tiny stand-in.
+
+## What this tutorial does
+
+The teacher has 6 attention layers (each with `num_key_value_heads=2`) interleaved between Mamba and MoE-FFN blocks — **12 KV heads total** across the whole model. We compress to **9 KV heads (75% of teacher)** in two ways and compare:
+
+1. **Without bypass** — replacement library uses the `PruneKVHeads` initialization from activation scoring (selected KV heads copied from the teacher; no further training).
+2. **With bypass** — the bypass step runs ~50M tokens of per-block knowledge distillation, training a 1-KV-head variant per attention layer against the teacher.
+
+Both runs use the same MIP solver and the same constraint (`target_num_kv_heads: 9`), so MIP picks per attention layer from `{teacher 2-head, 1-head}`. FFN/MoE/Mamba blocks are copied verbatim from the teacher in both runs — only attention weights change.
+
+**Metrics:** `lm_loss` and `token_accuracy_top_1` measured against the same held-out dataset by the realize-model step (printed automatically to `puzzle_dir/log.txt`).
+
+## Hardware & install
+
+- 8×H100 80GB (the teacher needs ≥60 GiB for activation scoring on a 4096 context).
+- Container: `nvcr.io/nvidia/nemo:26.04` or later.
+- `pip install -e ".[dev]"` from the modelopt repo root.
+- Mamba kernels (required by Nemotron-3-Nano's hybrid backbone):
+
+  ```bash
+  pip install mamba-ssm[causal-conv1d] --no-build-isolation
+  ```
+
+- HF auth set up so the model is downloadable: `huggingface-cli login`.
+
+## Step A — pipeline without bypass
+
+Edit `examples/puzzletron/configs/nemotron-3-nano-30b-a3b/nemotron-3-nano-30b-a3b.yaml` to point `puzzle_dir` and `dataset_path` at writable locations, then:
+
+```bash
+torchrun --nproc_per_node=8 examples/puzzletron/main.py \
+    --config examples/puzzletron/configs/nemotron-3-nano-30b-a3b/nemotron-3-nano-30b-a3b.yaml
+```
+
+This runs the no-bypass puzzletron pipeline (convert → score pruning activations → prune → build replacement library → score replacements → MIP → realize). The progress counter includes start/complete messages and prints `N/8`. With `bypass:` added in Step B the pipeline grows to 9 steps. Wall-clock: roughly **1h on 8×H100** for this KV-heads-only task (KV-head importance scoring is one forward pass via `IndependentKvHeadContributionHook`, much cheaper than iterative FFN-channel scoring).
+
+When the realize-model step finishes, the log lines at `${puzzle_dir}/log.txt` contain:
+
+```text
+validate_model_with_kl_div(model_name='teacher', ...)
+Average losses = {'lm_loss': ..., 'token_accuracy_top_1': ..., 'token_accuracy_top_5': ..., 'token_accuracy_top_10': ...}
+...
+validate_model_with_kl_div(model_name='solution_0', ...)
+Average losses = {..., 'token_accuracy_top_1': ..., ...}
+```
+
+Record the teacher's `token_accuracy_top_1` and `solution_0`'s `token_accuracy_top_1`. **Move or rename `${puzzle_dir}/single_sequence_replacement_solutions--validation/` and `${puzzle_dir}/mip/` aside** before Step B if you want to keep the no-bypass artifacts — Step B reuses the same `puzzle_dir` and the library/scoring/MIP outputs will be overwritten.
+
+## Step B — pipeline with bypass
+
+Use the bypass-enabled config, which overrides the base config's empty `- bypass:` entry with `bypass: defaults`:
+
+```yaml
+defaults:
+  - nemotron-3-nano-30b-a3b
+  - override bypass: defaults
+  - _self_
+```
+
+Run the bypass config:
+
+```bash
+torchrun --nproc_per_node=8 examples/puzzletron/main.py \
+    --config examples/puzzletron/configs/nemotron-3-nano-30b-a3b/nemotron-3-nano-30b-a3b-with-bypass.yaml
+```
+
+Skip-if-done caching reuses Step A's converted teacher checkpoint, activation scores, and pruned checkpoints. Only Step 5 (bypass distillation, ~50M tokens) and the downstream library/scoring/MIP rerun.
+
+Bypass writes its outputs under `${puzzle_dir}/bypass/bypass_runs/<bypass_experiment_id>/` and creates a symlink `${puzzle_dir}/ckpts/<bypass_experiment_id>` that the replacement library builder picks up automatically.
+
+Capture `solution_0`'s `token_accuracy_top_1` from the new realize-model log section.
+
+## Results
+
+Reducing total KV heads from 12 → 9 (75% of teacher) at fixed FFN/MoE/Mamba on Nemotron-3-Nano-30B-A3B-Base-BF16:
+
+| Run | `target_num_kv_heads` | `lm_loss` | `token_accuracy_top_1` |
+|------------------------------|----------------------:|----------:|-----------------------:|
+| Teacher                      | 12                    | 0.5950    | 0.8468                 |
+| Pruned, **no bypass** (`PruneKVHeads` init) | 9        | 0.6347    | 0.8373                 |
+| Pruned, **with bypass** (50M-token BLD) | 9          | **0.6055**| **0.8441**             |
+
+**Bypass closes ~74% of the regression gap** at this compression budget:
+
+- `lm_loss` gap to teacher: `0.0397` without bypass → `0.0105` with bypass — bypass recovers **74%**.
+- `token_accuracy_top_1` gap to teacher: `0.0095` without bypass → `0.0027` with bypass — bypass recovers **72%**.
+
+For 50M tokens of per-block KD, that's a substantial lift on a real 30B-A3B teacher.
+
+## Going further: full accuracy recovery
+
+Bypass distillation is Stage 1 of the PUZZLE pipeline — local, per-block KD that tightens the replacement library. For larger compression targets (or more aggressive KV pruning) you'll want Stage 2: **global knowledge distillation** on the realized student. See [`examples/pruning/puzzletron/`](../pruning/puzzletron/) for the Megatron-Bridge recipe and concrete MMLU recovery numbers.

examples/puzzletron/README.md

@@ -11,7 +11,7 @@ To use the Puzzle algorithm effectively, we need to specify the target number of
 
 In this example, we compress the [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) model reducing GPU memory usage from 113 GiB to 96 GiB (15% reduction) with less than 1% regression in the token_accuracy_top_10 metric. Other supported models should be compressed in a similar way. For GptOss there is one [additional step to be performed](GPTOSS.md).
 
-> **Note:** Other models are also supported. See the [configs](./configs/) directory for additional model configurations (e.g., Llama-3.2-3B-Instruct on 1x H100, Qwen2.5-7B-Instruct on 1x H100, Qwen3-8B on 1x H100, Nemotron-Nano-12B-v2 on 1x H100, Mistral-Small-24B-Instruct-2501 on 4x H100). For information on adding support for new models, see the [AnyModel Guide](../../modelopt/torch/puzzletron/anymodel/README.md).
+> **Note:** Other models are also supported. See the [configs](./configs/) directory for additional model configurations (e.g., Llama-3.2-3B-Instruct on 1x H100, Qwen2.5-7B-Instruct on 1x H100, Qwen3-8B on 1x H100, Nemotron-Nano-12B-v2 on 1x H100, Mistral-Small-24B-Instruct-2501 on 4x H100, Nemotron-3-Nano-30B-A3B-Base-BF16 on 8x H100 — see the [bypass distillation tutorial](Nemotron-3-Nano-30B-A3B-Base-BF16.md)). For information on adding support for new models, see the [AnyModel Guide](../../modelopt/torch/puzzletron/anymodel/README.md).
 
 ## Environment
 

examples/puzzletron/configs/llama-3_1-8B_pruneffn_memory/bypass/defaults.yaml

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+# @package bypass
+# Bypass Distillation Configuration
+# This config defines parameters for blockwise local distillation (BLD),
+# which trains alternative transformer block configurations using per-block
+# knowledge distillation from a teacher model.
+
+# Runtime Configuration
+dtype: "bf16"  # Model precision: bf16 for efficiency, fp32 for stability
+seed: 42       # Random seed for reproducibility
+
+# Experiment Tracking
+experiment_id:      # Unique identifier for this experiment. Will be dynamically set
+experiment_dir:     # Directory for this experiment. Will be dynamically set
+iter_num: 1         # Current iteration number
+step_num: 1         # Current step number within iteration
+token_count: 0      # Token count tracker (auto-updated during training)
+
+# Data Configuration
+data:
+  data_column: "messages"
+  block_size: 512                   # Sequence length (tokens per sample)
+  bos_rate: 0.5
+  fim_rate: 0
+  fim_spm_rate: 0
+  source_datasets_to_discard: []
+  load_from_disk: true              # Load preprocessed data from disk or from stream
+  keep_in_memory: false
+  val_dataset_name: valid
+  max_eval_samples: 4
+  eval_samples_per_process:         # Samples per GPU during distributed eval (auto if null)
+  shuffle_train_data_seed: ${random_int:0,9999}     # Seed for shuffling train data
+
+# Training Configuration
+training:
+  learning_rate: 1e-4               # Initial learning rate (1e-4 = 0.0001)
+  training_tokens: 1e+4             # Total training tokens (10K tokens - sanity check)
+  micro_batch_size: 2
+  val_micro_batch_size: 1
+  warmup_ratio: 0.05
+  warmup_steps: ${warmup_steps:${.training_tokens},${..data.block_size},${.micro_batch_size},${.grad_accumulation_steps},${.warmup_ratio}}  # Auto-calculated warmup steps
+  min_lr_factor: 1e-5
+  grad_accumulation_steps: 1
+  skip_first_batches: 0           # Use for debugging or to skip few batches which cause crashes or optimization issues.
+  weight_decay: 0.1
+  decay_lr: true
+  beta1: 0.9
+  beta2: 0.95
+  use_grad_scaling: false
+  grad_clip: 1.0
+  grad_clip_type: norm
+  clipping_count: 0
+  log_interval: 5
+  eval_interval: 5
+
+# Model Loading Configuration
+resume_checkpoint_path:         # Path to resume training from checkpoint
+find_last_ckpt_for_resume: true   # Auto-resume by finding last checkpoint (bool)
+parameter_count:
+init_checkpoint_path:           # Path to initialize weights from
+
+model:
+  student_weights_dtype: "bf16"   # Student model weight precision
+
+  model_overrides:
+    delete_old_checkpoints: true     # Clean up old checkpoints to save disk space
+    save_interval_seconds: 12900     # Save checkpoint every ~3.5 hours
+    save_interval: 1e+9              # Save checkpoint every 1B steps (effectively disabled)
+    save_checkpoint_when_done: true  # Save final checkpoint when training completes
+
+# Architecture modifications for student model
+  model_config_overrides:
+    ffn:
+      - intermediate_size:
+        no_op:                  # Disable FFN entirely (true/false)
+    attention:
+      - num_key_value_heads:       # Number of kv-heads (for GQA)
+        no_op:                  # Disable attention entirely (true/false)
+
+# Model Factory Configuration - Controls student model creation and initialization
+model_factory:
+  factory: bypass_factory_fn                 # Unified factory supporting all layer types
+  block_loss_func: normalized_mse_loss      # Loss function for comparing teacher/student blocks. vectorwise_normalized_mse_loss / batched_normalized_mse_loss / normalized_mse_loss
+  gqa_init_mode: AverageKV                  # How to initialize K/V heads in GQA. All options here: GQAInitMode
+  mlp_init_mode: Truncate                   # MLP initialization. All options here: MlpInitMode
+  mlp_init_config:                          # Configuration for MLP initialization (if needed)
+    activations_log_dir:                    # Directory with activation statistics (required for PruneByActivationsLog)
+  linear_init_mode: FromTeacher             # How to initialize linear layers: FromTeacher, Random, etc.
+  submodule_for_loss_calculation:           # Specific submodule for loss calc.
+  keys_to_learn:                            # Subblock(s) to train: entire_block, subblock_attention, subblock_ffn, subblock_mamba, or a list of those.
+
+# Validation Configuration
+disable_initial_validate: false
+validate_teacher_model: true
+validate_student_model: true
+disable_validation: false          # Enable validation to exercise all code paths
+best_val_loss: 1e+9                # Track best validation loss achieved
+
+# Performance Optimization
+compile: false                     # Use PyTorch compilation
+disable_fa2: false                 # Disable Flash Attention 2 (false = use FA2 if available)
+teacher_model_load_on_cpu: false
+
+# Checkpoint Management
+save_checkpoint_before_training: false   # Save initial checkpoint before training
+disable_checkpoint_save: false          # Disable all checkpoint saving
+save_best_ckpt: true                    # Save checkpoint when validation improves
+kill_after_first_save: false           # Exit after first checkpoint save (for testing)
+realize_best_or_latest: "best"
+
+wandb_log: false
+wandb:
+  project:
+  entity:
+
+# Multiple bypass configurations to train sequentially.
+# Each entry overrides model.model_config_overrides and optionally model_factory.keys_to_learn.
+# If empty or absent, a single run uses the settings above.
+configs:
+  - model_config_overrides:
+      ffn:
+        - intermediate_size: 3072
+      attention:
+        - num_key_value_heads: 8
+    keys_to_learn: subblock_ffn
+  - model_config_overrides:
+      ffn:
+        - intermediate_size: 5888
+      attention:
+        - num_key_value_heads: 8
+    keys_to_learn: subblock_ffn

examples/puzzletron/configs/nemotron-3-nano-30b-a3b/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16.yaml

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+defaults:
+  - pruning: kv_heads_pruning
+  - scoring: ../validate_solutions_defaults
+  - realize_model: ../validate_solutions_defaults
+  - bypass:
+  - override hydra/hydra_logging: disabled
+  - _self_
+
+puzzle_dir: ???
+descriptor: nemotron_h
+teacher_dir: ${puzzle_dir}/ckpts/teacher/
+replacement_library_path: ${puzzle_dir}/replacement_library.json
+dataset_path: ??? # path to Nemotron-Post-Training-Dataset-v2
+
+skip_realize_model: false
+
+# KV-heads-only pruning: lock off FFN/MoE-side variants. The replacement library
+# exposes {teacher 2-head, 1-head} per attention layer; FFN and Mamba
+# blocks are copied verbatim from the teacher.
+build_replacement_library:
+  add_ffn_no_ops: false
+  add_attention_no_ops: false
+
+calc_subblock_stats:
+  batch_sizes: [64, 96, 128]
+  prefill_seq_len: 4096
+  generation_seq_len: 4096
+  num_active_tokens_override: # Optional override for sequence lengths
+  prefill_queue_size: 0
+  allocate_prefill_query: false
+  benchmark_iterations: # Set to a number (e.g., 1000) to enable runtime benchmarking
+  merge_with_existing_stats: false
+  subblock_stats_filename: "subblock_stats.json"
+  moe_stats_filename: "moe_stats.json"
+  runtime_stats:
+    backend: trt_torch
+
+scoring:
+  descriptor: ${descriptor}
+  solutions_to_validate:
+  skip_existing_solutions: true
+
+  replacement_library_path: ${replacement_library_path}
+  solutions_path: ${to_path:${puzzle_dir}/single_sequence_replacement_solutions.json}
+  teacher_dir: ${to_path:${teacher_dir}}
+  output_dir: ${puzzle_dir}/single_sequence_replacement_solutions--validation
+
+  eval_samples: 128
+  micro_batch_size: 1
+  seed: 42
+  shuffle_seed: 444
+  dataset_path: ${dataset_path}
+
+mip:
+  single_block_replacement_validation_dir: ${to_path:${scoring.output_dir}}
+  subblock_stats_path: ${to_path:${puzzle_dir}/${calc_subblock_stats.subblock_stats_filename}}
+  output_path: ${to_path:${puzzle_dir}/mip/puzzle_solutions}
+  gathered_metrics_path:
+  puzzle_profile:
+
+  objective: metrics.cosine_embedding_loss_hidden_states
+  bigger_is_better: false
+
+  subblock_stats_args:
+    - batch_size: 96
+      weights_dtype: torch.bfloat16
+      activations_dtype: torch.bfloat16
+      kv_cache_dtype: torch.bfloat16
+
+  report_additional_costs:
+    - stats.memory_mib
+    - stats.num_params
+    - stats.num_kv_heads
+    - stats.has_attention
+    - stats.has_ffn
+    - stats.kv_cache_memory_mib
+    - stats.attention_memory_mib
+    - stats.ffn_memory_mib
+    - stats.ffn_num_params
+    - stats.attention_num_params
+
+  human_constraints:
+    target_num_kv_heads: 9     # toy KV-heads-only target; see nemotron-3-nano-30b-a3b.yaml
+
+  mip_constraints:
+  metric_overrides:
+  max_seconds_per_solution: 60
+
+realize_model:
+  descriptor: ${descriptor}
+  teacher_dir: ${to_path:${teacher_dir}}
+  tokenizer_name: ${to_path:${teacher_dir}}
+  replacement_library_path: ${replacement_library_path}
+  save_models: true
+  solutions_path: # Filled dynamically
+
+  # Validate params
+  skip_validation: false
+  eval_samples: 128
+  micro_batch_size: 1
+  seed: 42
+  shuffle_seed: 444
+  dataset_path: ${dataset_path}
+
+nccl_timeout_minutes: ${timedelta_minutes:10}
+
+# This section redirects Hydra outputs
+hydra:
+  run:
+    dir: ${puzzle_dir}/hydra_logs/${now:%Y-%m-%d}/${now:%H-%M-%S}