#!/usr/bin/env python3
"""
Layer 0 comparison test: original checkpoint vs NVFP4 checkpoint + our kernel.

Loads layer 0 expert weights from both checkpoints, runs the same deterministic
MoE forward pass, and compares the results. No vLLM, no Docker, no tensor
parallelism — just raw weights + our GEMM kernel.

Usage:
    python3 layertest.py
"""

import os
import sys
import json
import glob
import torch
from safetensors import safe_open

# ── Constants ──────────────────────────────────────────────────────────

ORIG_MODEL_DIR = "/root/nvidia-meeting/DeepSeek-V4-Pro"
NVFP4_MODEL_DIR = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
LAYER_IDX = 0
DEVICE = "cuda"

# E2M1 FP4 lookup table (shared by both formats)
E2M1_LUT = torch.tensor([
    0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0,
    -0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0,
], dtype=torch.float32)

# ── Checkpoint loading ─────────────────────────────────────────────────

def find_shards(model_dir):
    """Find all safetensors shards and return {key: shard_path} mapping."""
    index_path = os.path.join(model_dir, "model.safetensors.index.json")
    key_to_shard = {}
    
    if os.path.exists(index_path):
        with open(index_path) as f:
            index = json.load(f)
        for key, shard in index["weight_map"].items():
            key_to_shard[key] = os.path.join(model_dir, shard)
    else:
        # Single shard
        for sf in glob.glob(os.path.join(model_dir, "*.safetensors")):
            with safe_open(sf, framework="pt") as f:
                for key in f.keys():
                    key_to_shard[key] = sf
    
    return key_to_shard


def load_layer_tensors(model_dir, layer_idx, prefix_filter=None):
    """Load all tensors for a specific layer from the checkpoint.
    
    Returns dict of {key: tensor} for all keys matching the layer.
    """
    key_to_shard = find_shards(model_dir)
    layer_prefix = f"model.layers.{layer_idx}."
    
    # Group by shard to minimize file opens
    shard_to_keys = {}
    for key, shard in key_to_shard.items():
        if not key.startswith(layer_prefix):
            continue
        if prefix_filter and prefix_filter not in key:
            continue
        shard_to_keys.setdefault(shard, []).append(key)
    
    tensors = {}
    for shard, keys in shard_to_keys.items():
        with safe_open(shard, framework="pt") as f:
            for key in keys:
                tensors[key] = f.get_tensor(key)
    
    return tensors


def print_layer_keys(tensors, label):
    """Print sorted tensor keys with shapes and dtypes."""
    print(f"\n{'='*70}")
    print(f"  {label} — {len(tensors)} tensors")
    print(f"{'='*70}")
    for key in sorted(tensors.keys()):
        t = tensors[key]
        print(f"  {key}: dtype={t.dtype} shape={tuple(t.shape)}")


# ── Dequantization: Original checkpoint (MXFP4) ───────────────────────

def dequantize_mxfp4_weight(packed_uint8, scale_e8m0):
    """Dequantize MXFP4 (E2M1 + E8M0, block_size=32) to BF16.
    
    Original checkpoint format:
      packed_uint8: (out_features, in_features//2) uint8
      scale_e8m0: (out_features, in_features//32) float8_e8m0fnu
    """
    device = packed_uint8.device
    lut = E2M1_LUT.to(device)
    
    lower = lut[(packed_uint8 & 0x0F).long()]
    upper = lut[((packed_uint8 >> 4) & 0x0F).long()]
    
    out_features = packed_uint8.shape[0]
    in_features = packed_uint8.shape[1] * 2
    
    unpacked = torch.empty(out_features, in_features, dtype=torch.float32, device=device)
    unpacked[:, 0::2] = lower
    unpacked[:, 1::2] = upper
    
    # E8M0 → float32: exponent-only format, represents 2^(x - bias)
    scale_f32 = scale_e8m0.float()
    scale_expanded = scale_f32.repeat_interleave(32, dim=1)[:, :in_features]
    
    return (unpacked * scale_expanded).to(torch.bfloat16)


def dequantize_mxfp4_experts(orig_tensors, layer_idx, expert_indices):
    """Dequantize expert weights from original MXFP4 checkpoint.
    
    Returns dict: {expert_id: {gate_proj, up_proj, down_proj}} each as BF16.
    """
    experts = {}
    for e in expert_indices:
        expert = {}
        for proj in ["gate_proj", "up_proj", "down_proj"]:
            weight_key = f"model.layers.{layer_idx}.mlp.experts.{e}.{proj}.weight"
            scale_key = f"model.layers.{layer_idx}.mlp.experts.{e}.{proj}.scale"
            
            if weight_key not in orig_tensors:
                # Expert 211 has no down_proj
                if proj == "down_proj" and e == 211:
                    continue
                raise KeyError(f"Missing {weight_key}")
            
            weight = orig_tensors[weight_key].to(DEVICE)
            scale = orig_tensors[scale_key].to(DEVICE)
            expert[proj] = dequantize_mxfp4_weight(weight, scale)
        
        experts[e] = expert
    return experts


# ── Dequantization: NVFP4 checkpoint ──────────────────────────────────

def dequantize_nvfp4_weight(packed_uint8, scale_e4m3, global_scale):
    """Dequantize NVFP4 (E2M1 + E4M3 block scale + float32 global) to BF16.
    
    NVFP4 checkpoint format:
      packed_uint8: (out_features, in_features//2) uint8
      scale_e4m3: (out_features, in_features//16) float8_e4m3fn
      global_scale: float32 scalar
    """
    device = packed_uint8.device
    lut = E2M1_LUT.to(device)
    
    lower = lut[(packed_uint8 & 0x0F).long()]
    upper = lut[((packed_uint8 >> 4) & 0x0F).long()]
    
    out_features = packed_uint8.shape[0]
    in_features = packed_uint8.shape[1] * 2
    
    unpacked = torch.empty(out_features, in_features, dtype=torch.float32, device=device)
    unpacked[:, 0::2] = lower
    unpacked[:, 1::2] = upper
    
    block_scale = scale_e4m3.float()  # float8_e4m3fn → float32
    block_expanded = block_scale.repeat_interleave(16, dim=1)[:, :in_features]
    
    # Weight dequant = e2m1 * block_scale * global_scale
    return (unpacked * block_expanded * global_scale).to(torch.bfloat16)


def dequantize_nvfp4_experts(nvfp4_tensors, layer_idx, expert_indices):
    """Dequantize expert weights from NVFP4 checkpoint.
    
    Returns dict: {expert_id: {gate_proj, up_proj, down_proj}} each as BF16.
    """
    experts = {}
    for e in expert_indices:
        expert = {}
        for proj in ["gate_proj", "up_proj", "down_proj"]:
            weight_key = f"model.layers.{layer_idx}.mlp.experts.{e}.{proj}.weight"
            scale_key = f"model.layers.{layer_idx}.mlp.experts.{e}.{proj}.weight_scale"
            gs_key = f"model.layers.{layer_idx}.mlp.experts.{e}.{proj}.weight_scale_2"
            
            if weight_key not in nvfp4_tensors:
                if proj == "down_proj" and e == 211:
                    continue
                raise KeyError(f"Missing {weight_key}")
            
            weight = nvfp4_tensors[weight_key].to(DEVICE)
            scale = nvfp4_tensors[scale_key].to(DEVICE)
            global_scale = nvfp4_tensors[gs_key].item()
            expert[proj] = dequantize_nvfp4_weight(weight, scale, global_scale)
        
        experts[e] = expert
    return experts


# ── MoE Forward Pass (BF16 reference) ─────────────────────────────────

def moe_forward_bf16(hidden_states, experts, expert_ids, expert_weights):
    """Run MoE forward pass in pure BF16.
    
    Args:
        hidden_states: (num_tokens, hidden_size) BF16
        experts: dict {expert_id: {gate_proj, up_proj, down_proj}} BF16
        expert_ids: (num_tokens, top_k) int — which expert per token per slot
        expert_weights: (num_tokens, top_k) float32 — routing weights
    
    Returns:
        output: (num_tokens, hidden_size) BF16
    """
    num_tokens, hidden_size = hidden_states.shape
    top_k = expert_ids.shape[1]
    output = torch.zeros(num_tokens, hidden_size, dtype=torch.bfloat16, device=DEVICE)
    
    for t in range(num_tokens):
        for k in range(top_k):
            e = expert_ids[t, k].item()
            w = expert_weights[t, k].item()
            
            if e not in experts:
                continue
            
            x = hidden_states[t]  # (hidden_size,)
            gate = x @ experts[e]["gate_proj"].T  # (intermediate//2,)
            up = x @ experts[e]["up_proj"].T       # (intermediate//2,)
            activated = torch.nn.functional.silu(gate) * up  # (intermediate//2,)
            
            if "down_proj" in experts[e]:
                y = activated @ experts[e]["down_proj"].T  # (hidden_size,)
            else:
                y = activated[:hidden_size]  # shared expert, no down_proj
            
            output[t] += w * y
    
    return output


# ── MoE Forward Pass (NVFP4 kernel) ───────────────────────────────────

def moe_forward_nvfp4(hidden_states, nvfp4_tensors, layer_idx, expert_ids, expert_weights):
    """Run MoE forward pass using our NVFP4 kernel.
    
    Loads weights directly from NVFP4 checkpoint (no vLLM), transforms them
    for CUTLASS, and runs the grouped GEMM.
    """
    from nvfp4_megamoe_kernel import (
        stage_activation,
        nvfp4_mega_moe_full,
        transform_nvfp4_weights_for_mega_moe,
        SymmBuffer,
        get_symm_buffer_for_nvfp4_mega_moe,
    )
    
    num_tokens, hidden_size = hidden_states.shape
    top_k = expert_ids.shape[1]
    
    # Collect the experts we need
    unique_experts = sorted(set(expert_ids.flatten().tolist()))
    num_experts = len(unique_experts)
    expert_map = {e: i for i, e in enumerate(unique_experts)}
    
    # Load NVFP4 weights for these experts
    # Shapes: gate_proj.weight = (3072, 3584) uint8, weight_scale = (3072, 448) float8_e4m3fn
    intermediate_half = 3072  # intermediate_size // 2
    hidden_half = hidden_size // 2
    
    l1_weights = []  # gate + up fused
    l1_scales = []
    l1_global_scales = []
    l2_weights = []  # down
    l2_scales = []
    l2_global_scales = []
    
    for e in unique_experts:
        # L1: gate_proj + up_proj fused
        gate_w_key = f"model.layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight"
        gate_sf_key = f"model.layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight_scale"
        gate_gs_key = f"model.layers.{layer_idx}.mlp.experts.{e}.gate_proj.weight_scale_2"
        up_w_key = f"model.layers.{layer_idx}.mlp.experts.{e}.up_proj.weight"
        up_sf_key = f"model.layers.{layer_idx}.mlp.experts.{e}.up_proj.weight_scale"
        up_gs_key = f"model.layers.{layer_idx}.mlp.experts.{e}.up_proj.weight_scale_2"
        
        gate_w = nvfp4_tensors[gate_w_key].view(torch.int8).to(DEVICE)
        gate_sf = nvfp4_tensors[gate_sf_key].to(DEVICE)
        gate_gs = nvfp4_tensors[gate_gs_key].item()
        up_w = nvfp4_tensors[up_w_key].view(torch.int8).to(DEVICE)
        up_sf = nvfp4_tensors[up_sf_key].to(DEVICE)
        up_gs = nvfp4_tensors[up_gs_key].item()
        
        # Fuse gate + up: stack along dim 0 → (2*3072, 3584)
        l1_w = torch.cat([gate_w, up_w], dim=0)
        l1_sf = torch.cat([gate_sf, up_sf], dim=0)
        l1_gs = torch.tensor([gate_gs, up_gs], dtype=torch.float32, device=DEVICE)
        
        l1_weights.append(l1_w)
        l1_scales.append(l1_sf)
        l1_global_scales.append(l1_gs)
        
        # L2: down_proj
        down_w_key = f"model.layers.{layer_idx}.mlp.experts.{e}.down_proj.weight"
        if down_w_key in nvfp4_tensors:
            down_w = nvfp4_tensors[down_w_key].view(torch.int8).to(DEVICE)
            down_sf_key = f"model.layers.{layer_idx}.mlp.experts.{e}.down_proj.weight_scale"
            down_gs_key = f"model.layers.{layer_idx}.mlp.experts.{e}.down_proj.weight_scale_2"
            down_sf = nvfp4_tensors[down_sf_key].to(DEVICE)
            down_gs = nvfp4_tensors[down_gs_key].item()
        else:
            # Expert 211 has no down_proj — use zeros
            down_w = torch.zeros(hidden_size, intermediate_half, dtype=torch.int8, device=DEVICE)
            down_sf = torch.ones(hidden_size, intermediate_half // 16, dtype=torch.float8_e4m3fn, device=DEVICE)
            down_gs = 1.0
        
        l2_weights.append(down_w)
        l2_scales.append(down_sf)
        l2_global_scales.append(torch.tensor([down_gs], dtype=torch.float32, device=DEVICE))
    
    # Stack into (num_experts, ...) tensors
    l1_w = torch.stack(l1_weights)   # (E, 2*3072, 3584) int8
    l1_sf = torch.stack(l1_scales)   # (E, 2*3072, 448) float8_e4m3fn
    l1_gs = torch.stack(l1_global_scales)  # (E, 2) float32
    l2_w = torch.stack(l2_weights)   # (E, hidden, intermediate_half) int8
    l2_sf = torch.stack(l2_scales)   # (E, hidden, intermediate_half//16) float8_e4m3fn
    l2_gs = torch.stack(l2_global_scales)  # (E, 1) float32
    
    # Transform weights for CUTLASS
    (l1_w, l1_sf, l1_global_sf), (l2_w, l2_sf, l2_global_sf) = \
        transform_nvfp4_weights_for_mega_moe(
            (l1_w, l1_sf),
            (l2_w, l2_sf),
            l1_weight_scale_2=l1_gs,
            l2_weight_scale_2=l2_gs,
        )
    
    # Build slot mapping: each (token, top_k) pair → slot
    num_slots = num_tokens * top_k
    slot_expert = torch.zeros(num_slots, dtype=torch.int32, device=DEVICE)
    slot_token = torch.zeros(num_slots, dtype=torch.int64, device=DEVICE)
    slot_weight = torch.zeros(num_slots, dtype=torch.float32, device=DEVICE)
    
    for t in range(num_tokens):
        for k in range(top_k):
            slot = t * top_k + k
            e = expert_ids[t, k].item()
            slot_expert[slot] = expert_map[e]
            slot_token[slot] = t
            slot_weight[slot] = expert_weights[t, k].item()
    
    # SymmBuffer
    symm_buffer = get_symm_buffer_for_nvfp4_mega_moe(
        group=None,  # no EP
        num_experts=num_experts,
        max_num_tokens=num_tokens,
        top_k=top_k,
        hidden_size=hidden_size,
        intermediate_size=6144,  # 2 * 3072
    )
    
    # Stage activation
    x_fp4, x_sf, input_global_scale = stage_activation(hidden_states)
    symm_buffer.x[:num_tokens].copy_(x_fp4)
    symm_buffer.x_sf[:num_tokens].copy_(x_sf)
    symm_buffer.input_global_scale = input_global_scale
    symm_buffer.topk_idx[:num_tokens].copy_(expert_ids[:, 0:1].expand(-1, top_k))
    symm_buffer.topk_weights[:num_tokens].copy_(expert_weights)
    symm_buffer.experts_start_idx = 0
    
    # Run
    y = torch.zeros(num_tokens, hidden_size, dtype=torch.bfloat16, device=DEVICE)
    nvfp4_mega_moe_full(
        y,
        (l1_w, l1_sf, l1_global_sf),
        (l2_w, l2_sf, l2_global_sf),
        symm_buffer,
    )
    
    return y


# ── Main ───────────────────────────────────────────────────────────────

def main():
    torch.manual_seed(42)
    
    expert_indices = [0, 1, 2]  # Test with 3 experts
    top_k = 2
    num_tokens = 4
    
    # ── Step 1: Load original checkpoint layer 0 ──
    print("\n" + "="*70)
    print("  STEP 1: Loading original MXFP4 checkpoint")
    print("="*70)
    
    orig_tensors = load_layer_tensors(ORIG_MODEL_DIR, LAYER_IDX)
    print_layer_keys(orig_tensors, "Original checkpoint (MXFP4)")
    
    # Dequantize to BF16
    print("\nDequantizing MXFP4 → BF16...")
    orig_experts_bf16 = dequantize_mxfp4_experts(orig_tensors, LAYER_IDX, expert_indices)
    for e in expert_indices:
        for proj, w in orig_experts_bf16[e].items():
            print(f"  Expert {e} {proj}: shape={tuple(w.shape)} amax={w.abs().max():.4f}")
    
    # ── Step 2: Run BF16 reference forward pass ──
    print("\n" + "="*70)
    print("  STEP 2: BF16 reference forward pass")
    print("="*70)
    
    hidden_size = 7168
    hidden_states = torch.randn(num_tokens, hidden_size, dtype=torch.bfloat16, device=DEVICE) * 2.0
    
    # Deterministic routing: each token picks experts 0,1
    expert_ids = torch.tensor([[0, 1]] * num_tokens, dtype=torch.int32, device=DEVICE)
    expert_weights = torch.tensor([[0.6, 0.4]] * num_tokens, dtype=torch.float32, device=DEVICE)
    
    ref_output = moe_forward_bf16(hidden_states, orig_experts_bf16, expert_ids, expert_weights)
    print(f"  Reference output: shape={tuple(ref_output.shape)} amax={ref_output.abs().max():.4f} mean={ref_output.float().mean():.6f}")
    print(f"  First token first 10: {ref_output[0, :10].tolist()}")
    
    del orig_tensors, orig_experts_bf16  # Free memory
    torch.cuda.empty_cache()
    
    # ── Step 3: Load NVFP4 checkpoint layer 0 ──
    print("\n" + "="*70)
    print("  STEP 3: Loading NVFP4 checkpoint")
    print("="*70)
    
    nvfp4_tensors = load_layer_tensors(NVFP4_MODEL_DIR, LAYER_IDX)
    print_layer_keys(nvfp4_tensors, "NVFP4 checkpoint")
    
    # Verify dtype of weight_scale (should be float8_e4m3fn, NOT float8_e8m0fnu)
    for e in expert_indices[:1]:
        for proj in ["gate_proj", "up_proj", "down_proj"]:
            key = f"model.layers.{LAYER_IDX}.mlp.experts.{e}.{proj}.weight_scale"
            if key in nvfp4_tensors:
                dt = nvfp4_tensors[key].dtype
                print(f"  {proj}.weight_scale dtype = {dt}  {'✓ E4M3' if dt == torch.float8_e4m3fn else '✗ WRONG (expected float8_e4m3fn)'}")
    
    # Dequantize NVFP4 → BF16 (for BF16 reference comparison)
    print("\nDequantizing NVFP4 → BF16...")
    nvfp4_experts_bf16 = dequantize_nvfp4_experts(nvfp4_tensors, LAYER_IDX, expert_indices)
    for e in expert_indices:
        for proj, w in nvfp4_experts_bf16[e].items():
            print(f"  Expert {e} {proj}: shape={tuple(w.shape)} amax={w.abs().max():.4f}")
    
    # ── Step 4: Compare dequantized weights ──
    print("\n" + "="*70)
    print("  STEP 4: Weight comparison (original dequant vs NVFP4 dequant)")
    print("="*70)
    
    # Note: the original was MXFP4 (E8M0, block=32) and NVFP4 is (E4M3, block=16)
    # They were quantized independently so weights will differ — this is expected.
    # The comparison is to verify the NVFP4 dequant matches its own re-dequant.
    print("  (MXFP4 and NVFP4 were independently quantized — weight values will differ)")
    print("  (This is expected. The comparison is: NVFP4 dequant vs NVFP4 kernel)")
    
    # ── Step 5: Run NVFP4 BF16 reference (using NVFP4-dequantized weights) ──
    print("\n" + "="*70)
    print("  STEP 5: NVFP4 BF16 reference forward pass")
    print("="*70)
    
    nvfp4_ref_output = moe_forward_bf16(hidden_states, nvfp4_experts_bf16, expert_ids, expert_weights)
    print(f"  NVFP4 BF16 ref: shape={tuple(nvfp4_ref_output.shape)} amax={nvfp4_ref_output.abs().max():.4f} mean={nvfp4_ref_output.float().mean():.6f}")
    print(f"  First token first 10: {nvfp4_ref_output[0, :10].tolist()}")
    
    # Compare against original dequant
    cos_orig_vs_nvfp4bf16 = torch.nn.functional.cosine_similarity(
        ref_output.flatten().unsqueeze(0).float(),
        nvfp4_ref_output.flatten().unsqueeze(0).float(),
    ).item()
    print(f"  Cosine (orig BF16 ref vs NVFP4 BF16 ref): {cos_orig_vs_nvfp4bf16:.6f}")
    
    # ── Step 6: Run our NVFP4 kernel ──
    print("\n" + "="*70)
    print("  STEP 6: NVFP4 kernel forward pass")
    print("="*70)
    
    try:
        kernel_output = moe_forward_nvfp4(hidden_states, nvfp4_tensors, LAYER_IDX, expert_ids, expert_weights)
        print(f"  Kernel output: shape={tuple(kernel_output.shape)} amax={kernel_output.abs().max():.4f} mean={kernel_output.float().mean():.6f}")
        print(f"  First token first 10: {kernel_output[0, :10].tolist()}")
        
        # Compare kernel vs NVFP4 BF16 reference
        cos_kernel_vs_nvfp4bf16 = torch.nn.functional.cosine_similarity(
            kernel_output.flatten().unsqueeze(0).float(),
            nvfp4_ref_output.flatten().unsqueeze(0).float(),
        ).item()
        mse = (kernel_output.float() - nvfp4_ref_output.float()).pow(2).mean().item()
        print(f"  Cosine (kernel vs NVFP4 BF16 ref): {cos_kernel_vs_nvfp4bf16:.6f}")
        print(f"  MSE  (kernel vs NVFP4 BF16 ref): {mse:.6e}")
        
        # Compare kernel vs original BF16 reference
        cos_kernel_vs_orig = torch.nn.functional.cosine_similarity(
            kernel_output.flatten().unsqueeze(0).float(),
            ref_output.flatten().unsqueeze(0).float(),
        ).item()
        print(f"  Cosine (kernel vs orig BF16 ref):  {cos_kernel_vs_orig:.6f}")
        
    except Exception as e:
        print(f"  KERNEL FAILED: {e}")
        import traceback
        traceback.print_exc()
    
    # ── Summary ──
    print("\n" + "="*70)
    print("  SUMMARY")
    print("="*70)
    print(f"  Original BF16 reference:   amax={ref_output.abs().max():.4f} mean={ref_output.float().mean():.6f}")
    print(f"  NVFP4 BF16 reference:      amax={nvfp4_ref_output.abs().max():.4f} mean={nvfp4_ref_output.float().mean():.6f}")
    print(f"  Cosine (orig vs NVFP4 BF16): {cos_orig_vs_nvfp4bf16:.6f}")
    if 'kernel_output' in dir():
        cos_k = torch.nn.functional.cosine_similarity(
            kernel_output.flatten().unsqueeze(0).float(),
            nvfp4_ref_output.flatten().unsqueeze(0).float(),
        ).item()
        print(f"  Cosine (kernel vs NVFP4 BF16): {cos_k:.6f}")
        if cos_k > 0.99:
            print(f"  ✅ Kernel matches BF16 reference — bug is in vLLM integration")
        elif cos_k > 0.9:
            print(f"  ⚠️  Kernel is close but not perfect — minor numerical issue")
        else:
            print(f"  ❌ Kernel is far from BF16 reference — bug is in the kernel or weight pipeline")


if __name__ == "__main__":
    main()