"""Test: Validate gate/up subtile detection (Step 2).

The fused kernel writes:
- Gate subtiles (0,1): SiLU applied, stored to C tensor at positions 0,1
- Up subtiles (2,3): raw values, stored to C tensor at positions 0,1 (overwriting gate)
  (because TMA store uses gate_subtile_idx for up subtiles)

For now, the output is still (M, 2*intermediate). We compare the
gate half of the output against SiLU(gate_ref) and the up half against up_ref.
"""
import torch
import sys
sys.path.insert(0, '/root/dsv4-nvfp4-workspace/kernel')

from cutedsl.bridge import (
    quantize_weight_to_nvfp4,
    quantize_activation_nvfp4,
    make_b_k_major,
    assemble_scales_2d_side,
    assemble_scales_3d_side,
    run_nvfp4_grouped_gemm,
    run_fused_swiglu_grouped_gemm,
    warmup_compilation,
)


def test_gate_up_subtile():
    device = "cuda"
    num_experts = 4
    hidden = 512
    intermediate = 256
    num_tokens = 32

    torch.manual_seed(42)
    x = torch.randn(num_tokens, hidden, dtype=torch.bfloat16, device=device)
    l1_w = torch.randn(num_experts, 2 * intermediate, hidden, dtype=torch.bfloat16, device=device)
    
    l1_fp4_list, l1_sf_list, l1_gs_list = [], [], []
    for e in range(num_experts):
        w_fp4, w_sf, w_gs = quantize_weight_to_nvfp4(l1_w[e].T)
        l1_fp4_list.append(w_fp4)
        l1_sf_list.append(w_sf)
        l1_gs_list.append(w_gs)

    l1_mat_b = make_b_k_major(torch.stack(l1_fp4_list))
    l1_scale_b = assemble_scales_3d_side(l1_sf_list)
    l1_gs = torch.tensor(l1_gs_list, dtype=torch.float32, device=device)

    gs_val = x.abs().max().item() / (6.0 * 448.0)
    x_fp4, x_sf = quantize_activation_nvfp4(x, gs_val)
    tokens_per_expert = [num_tokens // num_experts] * num_experts
    scale_a = assemble_scales_2d_side([x_sf[i*tpe:(i+1)*tpe] for i, tpe in enumerate(tokens_per_expert)])
    expert_offsets = torch.tensor(
        [sum(tokens_per_expert[:e+1]) for e in range(num_experts)],
        dtype=torch.int32, device=device,
    )
    global_scale_a = torch.full((num_experts,), gs_val, dtype=torch.float32, device=device)

    warmup_compilation(num_experts, hidden // 2, (2 * intermediate) // 2, device)

    # Standard L1 GEMM
    out_bf16 = run_nvfp4_grouped_gemm(
        mat_a=x_fp4, mat_b=l1_mat_b,
        scale_a=scale_a, scale_b=l1_scale_b,
        expert_offsets=expert_offsets,
        global_scale_a=global_scale_a, global_scale_b=l1_gs,
    )
    gate_ref = out_bf16[:, :intermediate]
    up_ref = out_bf16[:, intermediate:]
    silu_gate_ref = torch.nn.functional.silu(gate_ref)

    # Fused kernel
    print("Running fused kernel...")
    out_fused = run_fused_swiglu_grouped_gemm(
        mat_a=x_fp4, mat_b=l1_mat_b,
        scale_a=scale_a, scale_b=l1_scale_b,
        expert_offsets=expert_offsets,
        global_scale_a=global_scale_a, global_scale_b=l1_gs,
    )

    print(f"Fused output: shape={out_fused.shape}, amax={out_fused.abs().amax().item():.4f}")
    
    # The output has both gate (with SiLU) and up (raw) subtiles
    # Gate is in the first half, up in the second half
    fused_gate = out_fused[:, :intermediate]
    fused_up = out_fused[:, intermediate:]

    # Compare gate: fused should have SiLU applied
    gate_diff = (fused_gate - silu_gate_ref).float()
    gate_rel_err = gate_diff.norm() / silu_gate_ref.float().norm()
    gate_max_err = gate_diff.abs().max()

    # Compare up: fused should have raw values (no SiLU)
    up_diff = (fused_up - up_ref).float()
    up_rel_err = up_diff.norm() / up_ref.float().norm()
    up_max_err = up_diff.abs().max()

    print(f"\n=== Gate Comparison (SiLU applied) ===")
    print(f"Rel error: {gate_rel_err.item():.6f}")
    print(f"Max abs error: {gate_max_err.item():.6f}")
    print(f"Gate PASS" if gate_rel_err.item() < 0.05 else "Gate FAIL")

    print(f"\n=== Up Comparison (raw values) ===")
    print(f"Rel error: {up_rel_err.item():.6f}")
    print(f"Max abs error: {up_max_err.item():.6f}")
    print(f"Up PASS" if up_rel_err.item() < 0.05 else "Up FAIL")


if __name__ == "__main__":
    test_gate_up_subtile()