eval/prune_weights.py

"""
BATCHED WEIGHT PRUNING (GPU-optimized)
======================================
Phase 1: Batch eval all candidates in parallel
Phase 2: Apply all successes at once, binary search if conflicts
"""

import torch
import time
import argparse
from safetensors.torch import save_file
from iron_eval import BatchedFitnessEvaluator, create_population, load_model

torch.manual_seed(0)


def format_time(seconds):
    if seconds < 60:
        return f"{seconds:.1f}s"
    elif seconds < 3600:
        return f"{seconds/60:.1f}m"
    else:
        return f"{seconds/3600:.1f}h"


def format_eta(elapsed, done, total):
    if done == 0:
        return "calculating..."
    rate = done / elapsed
    remaining = (total - done) / rate
    return format_time(remaining)


def apply_reductions(model, reductions):
    """Apply a list of (name, flat_idx, shape, old_val) reductions."""
    for name, flat_idx, shape, old_val in reductions:
        new_val = old_val - 1 if old_val > 0 else old_val + 1
        flat = model[name].flatten()
        if flat[flat_idx].item() == old_val:
            flat[flat_idx] = new_val
            model[name] = flat.view(shape)


def revert_reductions(model, reductions):
    """Revert a list of reductions."""
    for name, flat_idx, shape, old_val in reductions:
        flat = model[name].flatten()
        new_val = old_val - 1 if old_val > 0 else old_val + 1
        if flat[flat_idx].item() == new_val:
            flat[flat_idx] = old_val
            model[name] = flat.view(shape)


def check_fitness(model, evaluator, device):
    """Check model fitness."""
    torch.manual_seed(0)
    pop = create_population(model, 1, device)
    return evaluator.evaluate(pop, debug=False)[0].item()


def sequential_conflict_resolution(model, evaluator, device, candidates, base_magnitude):
    """
    Sequential fallback - tests and applies reductions one at a time.
    Slower but guarantees no interaction bugs.
    """
    accepted = []
    for i, (name, flat_idx, shape, old_val) in enumerate(candidates):
        apply_reductions(model, [(name, flat_idx, shape, old_val)])
        fitness = check_fitness(model, evaluator, device)
        if fitness >= 0.9999:
            accepted.append((name, flat_idx, shape, old_val))
            if (i + 1) % 50 == 0:
                current_mag = sum(t.abs().sum().item() for t in model.values())
                reduction_pct = 100 * (1 - current_mag / base_magnitude)
                print(f"          Sequential: {len(accepted)}/{i+1} accepted | mag={current_mag:.0f} (-{reduction_pct:.2f}%)")
        else:
            revert_reductions(model, [(name, flat_idx, shape, old_val)])
    return accepted


def batched_conflict_resolution(model, evaluator, device, candidates, base_magnitude):
    """
    Batched binary search - evaluates multiple branches in parallel.
    Uses BFS instead of DFS to maximize batching opportunities.
    Verifies cumulative effect after each batch to prevent interaction bugs.
    """
    if len(candidates) == 0:
        return []

    # First try all at once
    print(f"        Trying {len(candidates)} reductions at once...")
    apply_reductions(model, candidates)
    fitness = check_fitness(model, evaluator, device)

    if fitness >= 0.9999:
        current_mag = sum(t.abs().sum().item() for t in model.values())
        reduction_pct = 100 * (1 - current_mag / base_magnitude)
        print(f"        ALL {len(candidates)} OK | fitness={fitness:.6f} | "
              f"mag={current_mag:.0f} (-{reduction_pct:.2f}%)")
        return candidates

    # Conflict - revert and use batched BFS
    revert_reductions(model, candidates)
    print(f"        CONFLICT (fitness={fitness:.6f}), starting batched resolution...")

    accepted = []
    # Queue of (candidate_list, depth) to process
    pending = [(candidates, 0)]

    while pending:
        # Collect all pending groups for batch evaluation
        to_eval = []
        for group, depth in pending:
            if len(group) == 0:
                continue
            elif len(group) == 1:
                to_eval.append((group, depth, 'single'))
            else:
                to_eval.append((group, depth, 'group'))

        pending = []

        if not to_eval:
            break

        # Build batch: create model variants for each group
        batch_size = len(to_eval)
        print(f"          Batch evaluating {batch_size} groups...")

        # Create population for batch eval
        pop = {}
        for name, tensor in model.items():
            pop[name] = tensor.unsqueeze(0).expand(batch_size, *tensor.shape).clone().to(device)

        # Apply each group's reductions to its population slot
        for idx, (group, depth, gtype) in enumerate(to_eval):
            for name, flat_idx, shape, old_val in group:
                new_val = old_val - 1 if old_val > 0 else old_val + 1
                flat_view = pop[name][idx].flatten()
                # Check if not already modified in base model
                base_val = model[name].flatten()[flat_idx].item()
                if base_val == old_val:
                    flat_view[flat_idx] = new_val

        # Batch evaluate
        torch.manual_seed(0)
        fitnesses = evaluator.evaluate(pop, debug=False)

        # Process results - collect accepted groups first, then verify
        batch_accepted = []
        ok_count = 0
        conflict_count = 0
        fail_count = 0

        for idx, (group, depth, gtype) in enumerate(to_eval):
            fit = fitnesses[idx].item()
            indent = "          " + "  " * depth

            if fit >= 0.9999:
                batch_accepted.append((group, depth, indent))
                ok_count += len(group)
            else:
                if len(group) == 1:
                    name, flat_idx, shape, old_val = group[0]
                    print(f"{indent}[1/1] FAIL {name}[{flat_idx}] | fitness={fit:.6f}")
                    fail_count += 1
                else:
                    mid = len(group) // 2
                    left = group[:mid]
                    right = group[mid:]
                    print(f"{indent}CONFLICT ({len(group)}) fitness={fit:.6f} -> split {len(left)}+{len(right)}")
                    pending.append((left, depth + 1))
                    pending.append((right, depth + 1))
                    conflict_count += 1

        # Apply all batch-accepted reductions
        all_batch_reductions = []
        for group, depth, indent in batch_accepted:
            apply_reductions(model, group)
            all_batch_reductions.extend(group)

        # Verify cumulative effect
        if all_batch_reductions:
            verify_fitness = check_fitness(model, evaluator, device)
            if verify_fitness >= 0.9999:
                # All good - commit these reductions
                for group, depth, indent in batch_accepted:
                    current_mag = sum(t.abs().sum().item() for t in model.values())
                    reduction_pct = 100 * (1 - current_mag / base_magnitude)
                    if len(group) == 1:
                        name, flat_idx, shape, old_val = group[0]
                        print(f"{indent}[1/1] OK {name}[{flat_idx}] | mag={current_mag:.0f} (-{reduction_pct:.2f}%)")
                    else:
                        print(f"{indent}ALL {len(group)} OK | mag={current_mag:.0f} (-{reduction_pct:.2f}%)")
                accepted.extend(all_batch_reductions)
                print(f"          Batch result: {ok_count} accepted, {conflict_count} split, {fail_count} failed")
            else:
                # Interaction bug detected - revert and use sequential fallback
                print(f"          INTERACTION BUG detected (batch fitness={verify_fitness:.6f})")
                print(f"          Reverting {len(all_batch_reductions)} reductions, falling back to sequential...")
                revert_reductions(model, all_batch_reductions)

                # Process each group sequentially
                seq_accepted = sequential_conflict_resolution(
                    model, evaluator, device, all_batch_reductions, base_magnitude
                )
                accepted.extend(seq_accepted)
                print(f"          Sequential fallback: {len(seq_accepted)}/{len(all_batch_reductions)} accepted")
        else:
            print(f"          Batch result: {ok_count} accepted, {conflict_count} split, {fail_count} failed")

    return accepted


def prune_weights(
    passes: int = 10,
    batch_size: int = 5000,
    device: str = 'cuda',
    checkpoint_path: str = "D:/8bit-threshold-computer/pruned.safetensors"
):
    print("=" * 80)
    print(" BATCHED WEIGHT PRUNING (GPU-optimized)")
    print("=" * 80)
    print(f" Device: {device}")
    print(f" Batch size: {batch_size}")
    print(f" Max passes: {passes}")
    print("=" * 80)

    # Load model
    print("\n[1/4] LOADING MODEL...")
    load_start = time.perf_counter()
    model = load_model()
    load_time = time.perf_counter() - load_start

    n_params = sum(t.numel() for t in model.values())
    n_tensors = len(model)
    base_magnitude = sum(t.abs().sum().item() for t in model.values())
    base_max = max(t.abs().max().item() for t in model.values())
    nonzero_params = sum((t != 0).sum().item() for t in model.values())

    print(f"       Loaded in {load_time:.2f}s")
    print(f"       Tensors: {n_tensors}")
    print(f"       Parameters: {n_params}")
    print(f"       Non-zero parameters: {nonzero_params}")
    print(f"       Total magnitude: {base_magnitude:.0f}")
    print(f"       Max weight: {base_max:.0f}")

    # Initialize evaluator
    print("\n[2/4] INITIALIZING EVALUATOR...")
    eval_start = time.perf_counter()
    evaluator = BatchedFitnessEvaluator(device=device)
    eval_time = time.perf_counter() - eval_start
    print(f"       Initialized in {eval_time:.2f}s")

    # Verify initial fitness
    print("\n[3/4] VERIFYING BASE MODEL...")
    initial_fitness = check_fitness(model, evaluator, device)
    print(f"       Fitness: {initial_fitness:.6f}")

    if initial_fitness < 0.9999:
        print(f"       ERROR: Base model fitness {initial_fitness:.6f} < 0.9999")
        return None

    print(f"       STATUS: PASS")

    # Build parameter list
    print("\n[4/4] BUILDING PARAMETER INDEX...")
    param_list = []
    for name, tensor in model.items():
        flat = tensor.flatten()
        for i in range(len(flat)):
            param_list.append((name, i, tensor.shape))
    print(f"       Indexed {len(param_list)} parameters")

    # Main pruning loop
    print("\n" + "=" * 80)
    print(" PRUNING STARTED")
    print("=" * 80)

    total_reductions = 0
    pruning_start = time.perf_counter()

    for pass_num in range(passes):
        torch.manual_seed(0)
        pass_start = time.perf_counter()

        print(f"\n{'='*80}")
        print(f" PASS {pass_num + 1}/{passes}")
        print(f"{'='*80}")

        # Count candidates
        candidates = []
        for name, idx, shape in param_list:
            flat = model[name].flatten()
            val = flat[idx].item()
            if val != 0:
                candidates.append((name, idx, shape, val))

        n_candidates = len(candidates)
        print(f"\n    Candidates: {n_candidates} non-zero weights")

        if n_candidates == 0:
            print(f"    No candidates remaining. Stopping.")
            break

        # Phase 1: Batch evaluation
        print(f"\n    PHASE 1: Batch evaluation (testing each reduction independently)")
        print(f"    " + "-" * 60)
        phase1_start = time.perf_counter()
        successful_candidates = []
        n_batches = (n_candidates + batch_size - 1) // batch_size

        for batch_idx, batch_start_idx in enumerate(range(0, n_candidates, batch_size)):
            batch = candidates[batch_start_idx:batch_start_idx + batch_size]
            batch_len = len(batch)
            batch_start_time = time.perf_counter()

            # Build population
            pop = {}
            for name, tensor in model.items():
                pop[name] = tensor.unsqueeze(0).expand(batch_len, *tensor.shape).clone().to(device)

            # Apply reductions
            for pop_idx, (name, flat_idx, shape, old_val) in enumerate(batch):
                new_val = old_val - 1 if old_val > 0 else old_val + 1
                flat_view = pop[name][pop_idx].flatten()
                flat_view[flat_idx] = new_val

            # Evaluate
            torch.manual_seed(0)
            if device == 'cuda':
                torch.cuda.synchronize()
            fitness = evaluator.evaluate(pop, debug=False)
            if device == 'cuda':
                torch.cuda.synchronize()

            # Collect successes
            batch_successes = 0
            for pop_idx, (name, flat_idx, shape, old_val) in enumerate(batch):
                if fitness[pop_idx].item() >= 0.9999:
                    successful_candidates.append((name, flat_idx, shape, old_val))
                    batch_successes += 1

            batch_time = time.perf_counter() - batch_start_time
            elapsed = time.perf_counter() - phase1_start
            done = batch_start_idx + batch_len
            eta = format_eta(elapsed, done, n_candidates)
            throughput = batch_len / batch_time

            print(f"      Batch {batch_idx + 1}/{n_batches}: "
                  f"{batch_successes}/{batch_len} passed ({100*batch_successes/batch_len:.1f}%) | "
                  f"Total OK: {len(successful_candidates)} | "
                  f"Progress: {done}/{n_candidates} ({100*done/n_candidates:.1f}%) | "
                  f"Speed: {throughput:.0f}/s | "
                  f"ETA: {eta}")

        phase1_time = time.perf_counter() - phase1_start
        print(f"\n    Phase 1 complete: {len(successful_candidates)}/{n_candidates} candidates "
              f"({100*len(successful_candidates)/n_candidates:.1f}%) in {format_time(phase1_time)}")

        # Phase 2: Apply with conflict resolution
        if len(successful_candidates) == 0:
            print(f"\n    No reductions possible. Stopping.")
            break

        print(f"\n    PHASE 2: Apply reductions with conflict resolution")
        print(f"    " + "-" * 60)
        phase2_start = time.perf_counter()

        accepted = batched_conflict_resolution(model, evaluator, device, successful_candidates, base_magnitude)
        pass_reductions = len(accepted)

        phase2_time = time.perf_counter() - phase2_start
        print(f"\n    Phase 2 complete: {pass_reductions} reductions applied in {format_time(phase2_time)}")

        # Pass summary
        total_reductions += pass_reductions
        current_magnitude = sum(t.abs().sum().item() for t in model.values())
        current_nonzero = sum((t != 0).sum().item() for t in model.values())
        pass_time = time.perf_counter() - pass_start
        reduction_pct = 100 * (1 - current_magnitude / base_magnitude)

        print(f"\n    PASS {pass_num + 1} SUMMARY:")
        print(f"      Reductions this pass: {pass_reductions}")
        print(f"      Total reductions: {total_reductions}")
        print(f"      Current magnitude: {current_magnitude:.0f} (-{reduction_pct:.2f}%)")
        print(f"      Current non-zero: {current_nonzero}")
        print(f"      Pass time: {format_time(pass_time)}")

        # Verify after pass
        print(f"\n    Verifying model integrity...")
        fitness = check_fitness(model, evaluator, device)
        print(f"      Fitness: {fitness:.6f} {'PASS' if fitness >= 0.9999 else 'FAIL'}")

        # Save checkpoint after each pass
        checkpoint_name = checkpoint_path.replace('.safetensors', f'_pass{pass_num + 1}.safetensors')
        print(f"\n    Saving checkpoint: {checkpoint_name}")
        save_file(model, checkpoint_name)
        print(f"      Saved. Magnitude: {current_magnitude:.0f} (-{reduction_pct:.2f}%)")

        # Also save as "latest" for easy access
        latest_path = checkpoint_path.replace('.safetensors', '_latest.safetensors')
        save_file(model, latest_path)
        print(f"      Also saved as: {latest_path}")

        if pass_reductions == 0:
            print(f"\n    No reductions achieved. Stopping early.")
            break

    # Final summary
    pruning_time = time.perf_counter() - pruning_start
    final_magnitude = sum(t.abs().sum().item() for t in model.values())
    final_max = max(t.abs().max().item() for t in model.values())
    final_nonzero = sum((t != 0).sum().item() for t in model.values())
    reduction_pct = 100 * (1 - final_magnitude / base_magnitude)

    print("\n" + "=" * 80)
    print(" PRUNING COMPLETE")
    print("=" * 80)
    print(f"\n RESULTS:")
    print(f"   Original magnitude:  {base_magnitude:.0f}")
    print(f"   Final magnitude:     {final_magnitude:.0f}")
    print(f"   Reduction:           {reduction_pct:.2f}%")
    print(f"   Total reductions:    {total_reductions}")
    print(f"   Original non-zero:   {nonzero_params}")
    print(f"   Final non-zero:      {final_nonzero}")
    print(f"   Zeros created:       {nonzero_params - final_nonzero}")
    print(f"   Max weight:          {final_max:.0f}")
    print(f"   Total time:          {format_time(pruning_time)}")

    # Save
    print(f"\n SAVING to {checkpoint_path}...")
    save_file(model, checkpoint_path)
    print(f"   Saved.")

    # Final verification
    print(f"\n FINAL VERIFICATION...")
    from safetensors import safe_open
    f = safe_open(checkpoint_path, framework='numpy')
    verify_model = {name: torch.tensor(f.get_tensor(name)).float() for name in f.keys()}
    verify_fitness = check_fitness(verify_model, evaluator, device)
    print(f"   Fitness: {verify_fitness:.6f}")

    if verify_fitness >= 0.9999:
        print(f"   STATUS: PASS")
    else:
        print(f"   STATUS: FAIL - Model corrupted!")

    print("\n" + "=" * 80)
    return model


MAX_BATCH_SIZE = 80000

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Batched Weight Pruning')
    parser.add_argument('--passes', type=int, default=10,
                        help='Maximum pruning passes (default: 10)')
    parser.add_argument('--batch_size', type=int, default=80000,
                        help=f'Batch size for parallel evaluation (default: 80000, max: {MAX_BATCH_SIZE})')
    parser.add_argument('--device', type=str, default='cuda',
                        help='Device: cuda or cpu (default: cuda)')
    parser.add_argument('--output', type=str,
                        default='D:/8bit-threshold-computer/pruned.safetensors',
                        help='Output path')
    args = parser.parse_args()

    if args.batch_size > MAX_BATCH_SIZE:
        print(f"WARNING: batch_size {args.batch_size} exceeds maximum {MAX_BATCH_SIZE}. Clamping.")
        args.batch_size = MAX_BATCH_SIZE

    print(f"\nStarting at {time.strftime('%Y-%m-%d %H:%M:%S')}\n")

    prune_weights(
        passes=args.passes,
        batch_size=args.batch_size,
        device=args.device,
        checkpoint_path=args.output
    )

    print(f"\nFinished at {time.strftime('%Y-%m-%d %H:%M:%S')}")