medal-solvers/build_task028_onnx.py

"""Build optimized ONNX model for Task 028.

Rule (265/265 verified):
1. Input is 10x10 with exactly 2 colored dots on a bg of 0
2. Upper dot (closer to row 0) → fills upper zone with its color
3. Lower dot (closer to row 9) → fills lower zone with its color  
4. Each zone pattern: full row at dot position, full row at border (0 or 9),
   columns 0 and 9 filled in between. Interior stays 0.
5. Boundary between zones is midpoint of the two dot rows.

ONNX approach:
- Slice to 10x10
- Detect non-bg channels, find dot row positions via ArgMax on row sums
- Generate masks using row/col grids and comparisons
- Combine with color one-hots
- Pad back to 30x30

Base: 10 nodes, 12610 params, score 12.83
Target: ~40 nodes, ~1500 params → score ~15+ (gain +2.5)
"""
import sys, os
sys.path.insert(0, '/app/repo/medal-solvers')
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

from onnx import TensorProto
import numpy as np
import onnx
from onnx import helper, numpy_helper
import math


def build_task028():
    nodes, inits, vis = [], [], []
    counter = [0]

    def nm():
        counter[0] += 1
        return f"t{counter[0]}"

    def const(name, val, dtype='f'):
        arr = np.array(val, dtype=np.float32 if dtype == 'f' else np.int64)
        inits.append(numpy_helper.from_array(arr, name))

    def vi(name, shape, dt=TensorProto.FLOAT):
        vis.append(helper.make_tensor_value_info(name, dt, shape))

    def nd(op, ins, outs_shapes, **kwargs):
        out_names = []
        for sd in outs_shapes:
            if isinstance(sd, tuple):
                shape, dt = sd
            else:
                shape, dt = sd, TensorProto.FLOAT
            n = nm()
            vi(n, shape, dt)
            out_names.append(n)
        nodes.append(helper.make_node(op, ins, out_names, **kwargs))
        return out_names[0] if len(out_names) == 1 else out_names

    # === CONSTANTS ===
    const('c_half', [0.5])
    const('c_one', [1.0])
    const('c_zero', [0.0])
    const('c_two', [2.0])
    const('c_9f', [9.0])
    
    const('sl_start', [0, 0, 0, 0], 'i')
    const('sl_end', [1, 10, 10, 10], 'i')
    const('sl_axes', [0, 1, 2, 3], 'i')
    const('axes3', [3], 'i')  # cols
    const('axes23', [2, 3], 'i')
    const('axes1', [1], 'i')
    
    # Row grid [1,1,10,1] - row index values
    const('row_grid', np.arange(10, dtype=np.float32).reshape(1, 1, 10, 1))
    # Col grid [1,1,1,10]
    const('col_grid', np.arange(10, dtype=np.float32).reshape(1, 1, 1, 10))
    
    const('shape_1_1_1_1', [1, 1, 1, 1], 'i')
    const('shape_1_10_1_1', [1, 10, 1, 1], 'i')
    
    const('depth_10', [10.0])
    const('oh_vals', [0.0, 1.0])
    
    # Pad to get back to 30x30
    const('pad_10_to_30', [0, 0, 0, 0, 0, 0, 20, 20], 'i')
    const('pad_val_zero', [0.0])

    # Indices
    const('idx_0', [0], 'i')
    const('idx_1', [1], 'i')
    const('idx_bg', [0], 'i')
    
    # Slice to get non-bg channels [1, 9, 10, 10]
    const('sl_ch_start', [0, 1, 0, 0], 'i')
    const('sl_ch_end', [1, 10, 10, 10], 'i')

    # === STEP 1: Slice input to 10x10 ===
    inp10 = nd('Slice', ['input', 'sl_start', 'sl_end', 'sl_axes'], [[1, 10, 10, 10]])

    # === STEP 2: Find the two dots ===
    # Sum non-bg channels to get overall non-zero mask [1,1,10,10]
    nonbg = nd('Slice', [inp10, 'sl_ch_start', 'sl_ch_end', 'sl_axes'], [[1, 9, 10, 10]])
    active = nd('ReduceSum', [nonbg, 'axes1'], [[1, 1, 10, 10]], keepdims=1)
    
    # Sum per row to find which rows have dots [1,1,10,1]
    row_sums = nd('ReduceSum', [active, 'axes3'], [[1, 1, 10, 1]], keepdims=1)
    
    # Find the two dot rows: first dot = ArgMax of row_sums
    # But we have 2 dots. Use: first non-zero row from top = upper dot
    # For upper dot: row_sums * row_grid gives weighted; we want min non-zero row
    # Trick: add large value where row_sum==0, then ArgMin
    const('c_big', [100.0])
    has_dot_b = nd('Greater', [row_sums, 'c_half'], [([1, 1, 10, 1], TensorProto.BOOL)])
    has_dot = nd('Cast', [has_dot_b], [[1, 1, 10, 1]], to=1)
    no_dot = nd('Sub', ['c_one', has_dot], [[1, 1, 10, 1]])
    
    # For upper dot (minimum row with a dot):
    row_masked_upper = nd('Add', [nd('Mul', ['row_grid', has_dot], [[1, 1, 10, 1]]),
                                   nd('Mul', ['c_big', no_dot], [[1, 1, 10, 1]])], [[1, 1, 10, 1]])
    const('axes2', [2], 'i')
    upper_row_idx = nd('ArgMin', [row_masked_upper], [([1, 1, 1, 1], TensorProto.INT64)], axis=2, keepdims=1)
    upper_row_f = nd('Cast', [upper_row_idx], [[1, 1, 1, 1]], to=1)
    
    # For lower dot (maximum row with a dot):
    row_masked_lower = nd('Mul', ['row_grid', has_dot], [[1, 1, 10, 1]])
    lower_row_idx = nd('ArgMax', [row_masked_lower], [([1, 1, 1, 1], TensorProto.INT64)], axis=2, keepdims=1)
    lower_row_f = nd('Cast', [lower_row_idx], [[1, 1, 1, 1]], to=1)
    
    # Midpoint between dots
    sum_rows = nd('Add', [upper_row_f, lower_row_f], [[1, 1, 1, 1]])
    mid_row = nd('Div', [sum_rows, 'c_two'], [[1, 1, 1, 1]])
    
    # === STEP 3: Find dot colors ===
    # Channel sums per row: for upper dot's row, which channel has the dot?
    # Gather the input at upper_row → [1, 10, 1, 10] then sum over cols → [1, 10, 1, 1]
    # Simpler: total channel sums weighted by position
    
    # Upper dot color: extract channels at upper dot row
    # Use: for each channel, check if it has a 1 at the upper dot row
    # channel_at_upper[c] = sum over cols of inp10[0, c, upper_row, :]
    # Since there's exactly one dot per row, the channel with max sum IS the color
    
    # Sum each channel over all spatial positions → [1, 10, 1, 1]
    # This gives total pixel count per channel. But we have 2 dots of possibly different colors.
    # Instead: create mask for upper zone and lower zone, then find color per zone.
    
    # Upper zone mask: rows <= mid_row [1,1,10,1]
    in_upper_b = nd('Less', ['row_grid', nd('Add', [mid_row, 'c_half'], [[1, 1, 1, 1]])],
                    [([1, 1, 10, 1], TensorProto.BOOL)])
    in_upper = nd('Cast', [in_upper_b], [[1, 1, 10, 1]], to=1)  # [1,1,10,1]
    in_lower = nd('Sub', ['c_one', in_upper], [[1, 1, 10, 1]])
    
    # Upper color: which channel has pixels in the upper zone?
    # Mask input by upper zone: inp10 * in_upper [1,10,10,10] * [1,1,10,1] → [1,10,10,10]
    upper_masked = nd('Mul', [inp10, in_upper], [[1, 10, 10, 10]])
    upper_ch_sums = nd('ReduceSum', [upper_masked, 'axes23'], [[1, 10, 1, 1]], keepdims=1)
    # Skip channel 0 (bg)
    const('bg_mask_10', np.array([[[[0, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]], dtype=np.float32).reshape(1, 10, 1, 1))
    upper_ch_masked = nd('Mul', [upper_ch_sums, 'bg_mask_10'], [[1, 10, 1, 1]])
    upper_color_idx = nd('ArgMax', [upper_ch_masked], [([1, 1, 1, 1], TensorProto.INT64)], axis=1, keepdims=1)
    upper_color_1d = nd('Reshape', [upper_color_idx, 'idx_1'], [([1], TensorProto.INT64)])
    
    # Lower color
    lower_masked = nd('Mul', [inp10, in_lower], [[1, 10, 10, 10]])
    lower_ch_sums = nd('ReduceSum', [lower_masked, 'axes23'], [[1, 10, 1, 1]], keepdims=1)
    lower_ch_masked = nd('Mul', [lower_ch_sums, 'bg_mask_10'], [[1, 10, 1, 1]])
    lower_color_idx = nd('ArgMax', [lower_ch_masked], [([1, 1, 1, 1], TensorProto.INT64)], axis=1, keepdims=1)
    lower_color_1d = nd('Reshape', [lower_color_idx, 'idx_1'], [([1], TensorProto.INT64)])
    
    # === STEP 4: Build output masks ===
    # Upper zone pattern [1,1,10,10]:
    # Full row at row 0, full row at upper_dot_row, cols 0&9 in between
    const('c_0f', [0.0])
    
    # Row == 0
    at_row0_b = nd('Less', ['row_grid', 'c_half'], [([1, 1, 10, 1], TensorProto.BOOL)])
    at_row0 = nd('Cast', [at_row0_b], [[1, 1, 10, 1]], to=1)
    
    # Row == upper_dot_row
    diff_upper = nd('Abs', [nd('Sub', ['row_grid', upper_row_f], [[1, 1, 10, 1]])], [[1, 1, 10, 1]])
    at_upper_b = nd('Less', [diff_upper, 'c_half'], [([1, 1, 10, 1], TensorProto.BOOL)])
    at_upper = nd('Cast', [at_upper_b], [[1, 1, 10, 1]], to=1)
    
    # Full rows in upper zone: row 0 OR dot row
    full_upper_rows = nd('Max', [at_row0, at_upper], [[1, 1, 10, 1]])
    
    # Edge cols: col==0 or col==9
    const('c_8_5', [8.5])
    at_col0_b = nd('Less', ['col_grid', 'c_half'], [([1, 1, 1, 10], TensorProto.BOOL)])
    at_col0 = nd('Cast', [at_col0_b], [[1, 1, 1, 10]], to=1)
    at_col9_b = nd('Greater', ['col_grid', 'c_8_5'], [([1, 1, 1, 10], TensorProto.BOOL)])
    at_col9 = nd('Cast', [at_col9_b], [[1, 1, 1, 10]], to=1)
    edge_cols = nd('Max', [at_col0, at_col9], [[1, 1, 1, 10]])
    
    # Upper mask: (full_rows broadcast to 10x10) OR (edge_cols * in_upper_zone)
    # full_rows [1,1,10,1] → broadcast with ones [1,1,1,10] → [1,1,10,10]
    # But in ONNX: Max already broadcasts
    upper_full = full_upper_rows  # [1,1,10,1] will broadcast
    upper_edges = nd('Mul', [edge_cols, in_upper], [[1, 1, 10, 10]])  # [1,1,1,10]*[1,1,10,1]→[1,1,10,10]
    
    upper_mask_raw = nd('Max', [upper_full, upper_edges], [[1, 1, 10, 10]])
    upper_mask = nd('Mul', [upper_mask_raw, in_upper], [[1, 1, 10, 10]])  # clip to upper zone
    
    # Lower zone pattern:
    # Full row at row 9, full row at lower_dot_row, cols 0&9 in between
    at_row9_b = nd('Greater', ['row_grid', 'c_8_5'], [([1, 1, 10, 1], TensorProto.BOOL)])
    at_row9 = nd('Cast', [at_row9_b], [[1, 1, 10, 1]], to=1)
    
    diff_lower = nd('Abs', [nd('Sub', ['row_grid', lower_row_f], [[1, 1, 10, 1]])], [[1, 1, 10, 1]])
    at_lower_b = nd('Less', [diff_lower, 'c_half'], [([1, 1, 10, 1], TensorProto.BOOL)])
    at_lower = nd('Cast', [at_lower_b], [[1, 1, 10, 1]], to=1)
    
    full_lower_rows = nd('Max', [at_row9, at_lower], [[1, 1, 10, 1]])
    
    lower_full = full_lower_rows
    lower_edges = nd('Mul', [edge_cols, in_lower], [[1, 1, 10, 10]])
    
    lower_mask_raw = nd('Max', [lower_full, lower_edges], [[1, 1, 10, 10]])
    lower_mask = nd('Mul', [lower_mask_raw, in_lower], [[1, 1, 10, 10]])
    
    # === STEP 5: Apply colors ===
    # Upper color one-hot [1, 10, 1, 1]
    upper_oh = nd('OneHot', [upper_color_1d, 'depth_10', 'oh_vals'], [[1, 10]], axis=1)
    upper_oh_4d = nd('Reshape', [upper_oh, 'shape_1_10_1_1'], [[1, 10, 1, 1]])
    upper_out = nd('Mul', [upper_oh_4d, upper_mask], [[1, 10, 10, 10]])
    
    # Lower color one-hot [1, 10, 1, 1]
    lower_oh = nd('OneHot', [lower_color_1d, 'depth_10', 'oh_vals'], [[1, 10]], axis=1)
    lower_oh_4d = nd('Reshape', [lower_oh, 'shape_1_10_1_1'], [[1, 10, 1, 1]])
    lower_out = nd('Mul', [lower_oh_4d, lower_mask], [[1, 10, 10, 10]])
    
    # Combine colored channels
    colored_10 = nd('Add', [upper_out, lower_out], [[1, 10, 10, 10]])
    
    # === STEP 6: Add background channel ===
    # ch0 = 1 where no other channel is active (inside 10x10)
    # Slice channels 1-9, take max, subtract from 1
    nonbg_10 = nd('Slice', [colored_10, 'sl_ch_start', 'sl_ch_end', 'sl_axes'], [[1, 9, 10, 10]])
    any_color = nd('ReduceMax', [nonbg_10, 'axes1'], [[1, 1, 10, 10]], keepdims=1)
    bg_ch = nd('Sub', ['c_one', any_color], [[1, 1, 10, 10]])
    
    # Build ch0 one-hot and add
    const('ch0_idx', [0], 'i')
    ch0_oh = nd('OneHot', ['ch0_idx', 'depth_10', 'oh_vals'], [[1, 10]], axis=1)
    ch0_oh_4d = nd('Reshape', [ch0_oh, 'shape_1_10_1_1'], [[1, 10, 1, 1]])
    bg_full = nd('Mul', [ch0_oh_4d, bg_ch], [[1, 10, 10, 10]])
    
    out_10 = nd('Add', [colored_10, bg_full], [[1, 10, 10, 10]])
    
    # === STEP 7: Pad to 30x30 ===
    final = nd('Pad', [out_10, 'pad_10_to_30', 'pad_val_zero'], [[1, 10, 30, 30]])
    
    # Build model
    x = helper.make_tensor_value_info('input', TensorProto.FLOAT, [1, 10, 30, 30])
    y = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10, 30, 30])
    nodes.append(helper.make_node('Identity', [final], ['output']))
    
    graph = helper.make_graph(nodes, 'task028', [x], [y], initializer=inits, value_info=vis)
    model = helper.make_model(graph, ir_version=10, opset_imports=[helper.make_opsetid('', 18)])
    return model


if __name__ == '__main__':
    import onnxruntime as ort
    import json
    from huggingface_hub import hf_hub_download
    import zipfile
    
    print("Building Task 028 ONNX model...")
    model = build_task028()
    
    output_path = '/app/task028.onnx'
    onnx.save(model, output_path)
    fsize = os.path.getsize(output_path)
    print(f"  Nodes: {len(model.graph.node)}")
    print(f"  File size: {fsize:,} bytes")
    
    # Validate
    sess = ort.InferenceSession(output_path)
    
    task_path = hf_hub_download('rogermt/neurogolf-solver', 'own-solver/neurogolf-2026.zip')
    with zipfile.ZipFile(task_path, 'r') as zf:
        data = json.loads(zf.read('task028.json'))
    
    all_examples = data['train'] + data['test'] + data.get('arc-gen', [])
    right_count, wrong_count = 0, 0
    
    for i, ex in enumerate(all_examples):
        inp_grid = ex['input']
        inp = np.zeros((1, 10, 30, 30), dtype=np.float32)
        for r, row in enumerate(inp_grid):
            for c, v in enumerate(row):
                if r < 30 and c < 30:
                    inp[0][v][r][c] = 1.0
        
        result = sess.run(['output'], {'input': inp})
        out = (result[0] > 0.0).astype(float)
        
        exp = np.zeros((1, 10, 30, 30), dtype=np.float32)
        for r, row in enumerate(ex['output']):
            for c, v in enumerate(row):
                if r < 30 and c < 30:
                    exp[0][v][r][c] = 1.0
        
        if np.array_equal(out, exp):
            right_count += 1
        else:
            wrong_count += 1
            if wrong_count <= 3:
                diff_locs = np.where(out != exp)
                print(f"  FAIL {i}: {len(diff_locs[0])} diffs")
    
    print(f"\nResults: {right_count} pass, {wrong_count} fail out of {len(all_examples)}")
    
    if wrong_count == 0:
        # Score estimate
        params = sum(int(np.prod(init.dims)) for init in model.graph.initializer)
        mem_est = sum(int(np.prod([d.dim_value for d in vi.type.tensor_type.shape.dim])) * 4
                      for vi in model.graph.value_info
                      if vi.type.HasField('tensor_type') and vi.type.tensor_type.HasField('shape'))
        score_est = max(1.0, 25.0 - math.log(max(1.0, mem_est + params)))
        print(f"  Params: {params:,}, Memory est: {mem_est:,}")
        print(f"  Score est: {score_est:.3f} (base: 12.831)")
        print(f"  Gain est: +{score_est - 12.831:.3f}")