nhap.py

import torch
import numpy as np
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from datasets import load_dataset
import gc
import os
from datetime import datetime

# Check for GPU availability
if not torch.cuda.is_available():
    raise RuntimeError("This script requires a CUDA-enabled GPU.")
device = "cuda"

# ============================================
# CONFIGURATION: Choose your mode and model
# ============================================
MODE = "extract"  # Options: "extract" or "load"
LOAD_PATH = "outputs/qwen-moe-a2.7b_20251009_084529"  # Path to load (only used if MODE="load")

# NEW: Compression settings for attention matrices
ATTENTION_DTYPE = np.float16  # Use float16 instead of float32 (50% size reduction)
COMPRESSION_LEVEL = 9  # Maximum compression (1-9, higher = better compression but slower)
SAVE_SPARSE = True  # Convert attention matrices to sparse format if beneficial

AVAILABLE_MODELS = {
    "mixtral-8x7b": "mistralai/Mixtral-8x7B-Instruct-v0.1",
    "mixtral-8x22b": "mistralai/Mixtral-8x22B-Instruct-v0.1",
    "deepseek-v2": "deepseek-ai/DeepSeek-V2",
    "deepseek-v2.5": "deepseek-ai/DeepSeek-V2.5",
    "qwen-moe-a2.7b": "Qwen/Qwen1.5-MoE-A2.7B",
    "qwen-moe-a2.7b-chat": "Qwen/Qwen1.5-MoE-A2.7B-Chat",
    "dbrx": "databricks/dbrx-instruct",
    "arctic": "Snowflake/snowflake-arctic-instruct",
    "switch-base-8": "google/switch-base-8",
    "switch-base-16": "google/switch-base-16",
    "switch-base-32": "google/switch-base-32",
    "switch-base-64": "google/switch-base-64",
    "switch-base-128": "google/switch-base-128",
    "dolly-v2-7b": "databricks/dolly-v2-7b",
    "olmoe-7b": "allenai/OLMoE-1B-7B-0924"
}

# Select model (change this to use different models)
selected_model = "olmoe-7b"  # Change this!
model_id = AVAILABLE_MODELS[selected_model]

sequence_length = 5000

# ============================================
# LOAD MODE: Load and print saved data
# ============================================
if MODE == "load":
    print("=" * 70)
    print(f"LOAD MODE: Loading saved data (Multi-Head Version)")
    print("=" * 70)
    print(f"Load path: {LOAD_PATH}\n")
    
    # Check if directory exists
    if not os.path.exists(LOAD_PATH):
        raise FileNotFoundError(f"Directory not found: {LOAD_PATH}")
    
    # Load metadata
    metadata_file = os.path.join(LOAD_PATH, 'metadata.txt')
    if os.path.exists(metadata_file):
        print("--- Metadata ---")
        with open(metadata_file, 'r') as f:
            print(f.read())
    
    # Load tokenized input
    input_file = os.path.join(LOAD_PATH, 'tokenized_input.npz')
    if os.path.exists(input_file):
        print("\n--- Loading Tokenized Input ---")
        input_data = np.load(input_file)
        print(f"✅ Loaded from: {input_file}")
        print(f"File size: {os.path.getsize(input_file) / (1024**2):.2f} MB")
        
        input_ids = input_data['input_ids']
        print(f"\nTokenized Input:")
        print(f"  Shape: {input_ids.shape}")
        print(f"  Dtype: {input_ids.dtype}")
        print(f"  First 20 token IDs: {input_ids[:20]}")
    else:
        print(f"⚠️ Input file not found: {input_file}")
    
    # Load multi-head attention matrices
    attention_file = os.path.join(LOAD_PATH, 'attention_matrices_multihead.npz')
    if os.path.exists(attention_file):
        print("\n--- Loading Multi-Head Attention Matrices ---")
        attn_data = np.load(attention_file, allow_pickle=True)
        print(f"✅ Loaded from: {attention_file}")
        print(f"File size: {os.path.getsize(attention_file) / (1024**2):.2f} MB")
        print(f"Available layers: {len(attn_data.files)}")
        
        # Print info for first layer
        layer_0 = attn_data['layer_0']
        print(f"\nExample - Layer 0:")
        print(f"  Shape: {layer_0.shape} [num_heads, seq_len, seq_len]")
        print(f"  Dtype: {layer_0.dtype}")
        print(f"  Number of heads: {layer_0.shape[0]}")
        print(f"  Min value: {layer_0.min():.6f}")
        print(f"  Max value: {layer_0.max():.6f}")
        print(f"  Mean value: {layer_0.mean():.6f}")
        
        print(f"\n  Head 0 attention sample (first 5x5 tokens):")
        print(layer_0[0, :5, :5])
        
        # Memory usage
        total_size_mb = sum([attn_data[key].nbytes for key in attn_data.files]) / (1024**2)
        print(f"\nTotal uncompressed size in memory: {total_size_mb:.2f} MB")
        
        # Compression ratio
        compressed_size_mb = os.path.getsize(attention_file) / (1024**2)
        compression_ratio = total_size_mb / compressed_size_mb if compressed_size_mb > 0 else 0
        print(f"Compression ratio: {compression_ratio:.2f}x")
    else:
        print(f"⚠️ Multi-head attention file not found: {attention_file}")
    
    # Load routing matrices
    routing_file = os.path.join(LOAD_PATH, 'routing_matrices.npz')
    if os.path.exists(routing_file):
        print("\n--- Loading Routing Matrices ---")
        routing_data = np.load(routing_file)
        print(f"✅ Loaded from: {routing_file}")
        print(f"File size: {os.path.getsize(routing_file) / (1024**2):.2f} MB")
        print(f"Available layers: {len(routing_data.files)}")
        
        # Print info for first layer
        layer_0 = routing_data['layer_0']
        print(f"\nExample - Layer 0:")
        print(f"  Shape: {layer_0.shape}")
        print(f"  Dtype: {layer_0.dtype}")
    else:
        print(f"⚠️ Routing file not found: {routing_file}")
    
    print("\n" + "=" * 70)
    print("HOW TO USE THE LOADED DATA")
    print("=" * 70)
    print("# Access tokenized input:")
    print("input_ids = input_data['input_ids']")
    print("\n# Access specific layers (all heads):")
    print("layer_5_all_heads = attn_data['layer_5']  # Shape: (num_heads, seq_len, seq_len)")
    print("\n# Access specific head in a layer:")
    print("layer_5_head_3 = attn_data['layer_5'][3]  # Shape: (seq_len, seq_len)")
    print("\n# Iterate through all layers:")
    print("for layer_name in attn_data.files:")
    print("    layer_matrix = attn_data[layer_name]")
    print("    num_heads = layer_matrix.shape[0]")
    print("    for head_idx in range(num_heads):")
    print("        head_attention = layer_matrix[head_idx]")
    print("        # Your analysis here...")
    print("=" * 70)
    
    exit(0)  # Exit after loading

# ============================================
# EXTRACT MODE: Continue with normal extraction
# ============================================

# Create output directory based on model name and timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_dir = f"outputs/{selected_model}_{timestamp}"
os.makedirs(output_dir, exist_ok=True)

print("=" * 70)
print(f"MoE Model Analysis Tool (Multi-Head Attention)")
print("=" * 70)
print(f"Selected Model: {selected_model}")
print(f"Model ID: {model_id}")
print(f"Output Directory: {output_dir}")
print(f"Sequence Length: {sequence_length}")
print(f"Attention dtype: {ATTENTION_DTYPE}")
print(f"Compression level: {COMPRESSION_LEVEL}")
print(f"Sparse matrix optimization: {SAVE_SPARSE}")
print("=" * 70)

print("\n--- 1. Setting up model and tokenizer ---")
# Use 4-bit quantization with optimized settings
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4"
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# Load model
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    quantization_config=quantization_config, 
    device_map="auto", 
    attn_implementation="eager",
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True,
)
print(f"✅ Model loaded on device: {model.device}")

torch.cuda.empty_cache()
gc.collect()

print("\n--- 2. Preparing input data from wikitext ---")
wiki_dataset = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")
long_text = " ".join([example['text'] for example in wiki_dataset if example['text'].strip()])

inputs = tokenizer(
    long_text,
    return_tensors="pt",
    max_length=sequence_length,
    truncation=True
).to(device)
input_ids = inputs.input_ids
print(f"✅ Input tensor shape: {input_ids.shape}")

# Save tokenized input
print("\n--- 2.5. Saving tokenized input ---")
input_ids_np = input_ids.cpu().numpy().squeeze()
input_file = os.path.join(output_dir, 'tokenized_input.npz')
print(f"Saving tokenized input to '{input_file}'...")
np.savez_compressed(
    input_file,
    input_ids=input_ids_np,
    input_text=long_text[:sequence_length * 10]  # Save a portion of the original text for reference
)
file_size_mb = os.path.getsize(input_file) / (1024**2)
print(f"✅ Tokenized input saved! File size: {file_size_mb:.2f} MB")

print("\n--- 3. Performing forward pass to extract matrices ---")
print(f"GPU memory before forward pass: {torch.cuda.memory_allocated() / 1e9:.2f} GB")

# Check if this is a Switch Transformer (encoder-decoder model)
is_switch_transformer = "switch" in selected_model.lower()

with torch.no_grad():
    with torch.inference_mode():
        if is_switch_transformer:
            decoder_input_ids = input_ids[:, :min(512, input_ids.shape[1])]
            outputs = model(
                input_ids,
                decoder_input_ids=decoder_input_ids,
                output_attentions=True,
                output_router_logits=True,
                use_cache=False
            )
        else:
            outputs = model(
                input_ids,
                output_attentions=True,
                output_router_logits=True,
                use_cache=False
            )
print("✅ Forward pass completed.")
print(f"GPU memory after forward pass: {torch.cuda.memory_allocated() / 1e9:.2f} GB")

print("\n--- 4. Extracting and saving MULTI-HEAD attention matrices ---")

# --- Save Multi-Head Attention Matrices as NPZ (highly compressed) ---
attention_matrices = {}
total_uncompressed_size = 0
total_heads = 0

# Switch Transformers have both encoder and decoder attentions
if is_switch_transformer:
    # Encoder attentions
    if hasattr(outputs, 'encoder_attentions') and outputs.encoder_attentions is not None:
        num_encoder_layers = len(outputs.encoder_attentions)
        print(f"Number of encoder attention layers: {num_encoder_layers}")
        
        for layer_idx, layer_attention in enumerate(outputs.encoder_attentions):
            # Don't squeeze batch dim, keep all heads
            # Shape: (batch=1, num_heads, seq_len, seq_len)
            layer_attention = layer_attention.squeeze(0)  # Remove batch dimension only
            # Now shape: (num_heads, seq_len, seq_len)
            
            num_heads = layer_attention.shape[0]
            total_heads += num_heads
            
            # Convert to numpy with optimized dtype
            layer_attention_np = layer_attention.cpu().to(torch.float16).numpy()
            
            # Calculate uncompressed size
            uncompressed_size = layer_attention_np.nbytes / (1024**2)
            total_uncompressed_size += uncompressed_size
            
            attention_matrices[f"encoder_layer_{layer_idx}"] = layer_attention_np
            print(f"  ✅ Encoder Layer {layer_idx}: shape {layer_attention_np.shape} ({num_heads} heads), uncompressed: {uncompressed_size:.2f} MB")
            
            del layer_attention_np, layer_attention
            if layer_idx % 4 == 0:
                torch.cuda.empty_cache()
                gc.collect()
    
    # Decoder attentions
    if hasattr(outputs, 'decoder_attentions') and outputs.decoder_attentions is not None:
        num_decoder_layers = len(outputs.decoder_attentions)
        print(f"Number of decoder attention layers: {num_decoder_layers}")
        
        for layer_idx, layer_attention in enumerate(outputs.decoder_attentions):
            layer_attention = layer_attention.squeeze(0)
            num_heads = layer_attention.shape[0]
            total_heads += num_heads
            
            layer_attention_np = layer_attention.cpu().to(torch.float16).numpy()
            uncompressed_size = layer_attention_np.nbytes / (1024**2)
            total_uncompressed_size += uncompressed_size
            
            attention_matrices[f"decoder_layer_{layer_idx}"] = layer_attention_np
            print(f"  ✅ Decoder Layer {layer_idx}: shape {layer_attention_np.shape} ({num_heads} heads), uncompressed: {uncompressed_size:.2f} MB")
            
            del layer_attention_np, layer_attention
            if layer_idx % 4 == 0:
                torch.cuda.empty_cache()
                gc.collect()
    
    num_layers = len(attention_matrices)
else:
    # Standard decoder-only models
    num_layers = len(outputs.attentions)
    print(f"Number of attention layers: {num_layers}")

    for layer_idx, layer_attention in enumerate(outputs.attentions):
        # Keep all heads: shape (batch=1, num_heads, seq_len, seq_len)
        layer_attention = layer_attention.squeeze(0)  # Remove batch dimension only
        # Now shape: (num_heads, seq_len, seq_len)
        
        num_heads = layer_attention.shape[0]
        total_heads += num_heads
        
        # Convert to optimized dtype
        layer_attention_np = layer_attention.cpu().to(torch.float16).numpy()
        
        # Calculate uncompressed size
        uncompressed_size = layer_attention_np.nbytes / (1024**2)
        total_uncompressed_size += uncompressed_size
        
        attention_matrices[f"layer_{layer_idx}"] = layer_attention_np
        print(f"  ✅ Layer {layer_idx}: shape {layer_attention_np.shape} ({num_heads} heads), uncompressed: {uncompressed_size:.2f} MB")
        
        del layer_attention_np, layer_attention
        if layer_idx % 4 == 0:
            torch.cuda.empty_cache()
            gc.collect()

# Save all multi-head attention matrices in one highly compressed file
attention_file = os.path.join(output_dir, 'attention_matrices_multihead.npz')
print(f"\n🔄 Saving multi-head attention matrices with maximum compression...")
print(f"   Total uncompressed size: {total_uncompressed_size:.2f} MB")
print(f"   Total heads across all layers: {total_heads}")

# Use savez_compressed with explicit compression
np.savez_compressed(attention_file, **attention_matrices)

compressed_size_mb = os.path.getsize(attention_file) / (1024**2)
compression_ratio = total_uncompressed_size / compressed_size_mb if compressed_size_mb > 0 else 0

print(f"✅ Multi-head attention matrices saved!")
print(f"   Compressed file size: {compressed_size_mb:.2f} MB")
print(f"   Compression ratio: {compression_ratio:.2f}x")
print(f"   Space saved: {total_uncompressed_size - compressed_size_mb:.2f} MB ({(1 - compressed_size_mb/total_uncompressed_size)*100:.1f}%)")

# Store metadata info before clearing attention_matrices
attention_layer_names = list(attention_matrices.keys())
first_attention_layer = attention_matrices[attention_layer_names[0]] if attention_layer_names else None

# Clear from memory
del attention_matrices, outputs.attentions
torch.cuda.empty_cache()
gc.collect()

# --- Save Routing Matrices as NPZ (compressed) ---
routing_matrices = {}
num_moe_layers = 0

# Check if model has router_logits (MoE-specific)
if hasattr(outputs, 'router_logits') and outputs.router_logits is not None:
    # For Switch Transformers, router_logits is a tuple: (encoder_router_logits, decoder_router_logits)
    if is_switch_transformer and isinstance(outputs.router_logits, tuple):
        # Encoder routing
        if outputs.router_logits[0] is not None:
            encoder_router_logits = outputs.router_logits[0]
            print(f"\nNumber of encoder MoE routing layers: {len(encoder_router_logits)}")
            for layer_idx, router_logits in enumerate(encoder_router_logits):
                router_logits_np = router_logits.squeeze(0).cpu().half().numpy()
                routing_matrices[f"encoder_layer_{layer_idx}"] = router_logits_np
                print(f"  ✅ Encoder Layer {layer_idx}: shape {router_logits_np.shape}")
                del router_logits_np
                if layer_idx % 4 == 0:
                    gc.collect()
        
        # Decoder routing
        if outputs.router_logits[1] is not None:
            decoder_router_logits = outputs.router_logits[1]
            print(f"\nNumber of decoder MoE routing layers: {len(decoder_router_logits)}")
            for layer_idx, router_logits in enumerate(decoder_router_logits):
                router_logits_np = router_logits.squeeze(0).cpu().half().numpy()
                routing_matrices[f"decoder_layer_{layer_idx}"] = router_logits_np
                print(f"  ✅ Decoder Layer {layer_idx}: shape {router_logits_np.shape}")
                del router_logits_np
                if layer_idx % 4 == 0:
                    gc.collect()
        
        num_moe_layers = len(routing_matrices)
    else:
        # Standard MoE models (Mixtral, Qwen, etc.)
        num_moe_layers = len(outputs.router_logits)
        print(f"\nNumber of MoE routing layers: {num_moe_layers}")

        for layer_idx, router_logits in enumerate(outputs.router_logits):
            router_logits_np = router_logits.squeeze(0).cpu().half().numpy()
            routing_matrices[f"layer_{layer_idx}"] = router_logits_np
            print(f"  ✅ Layer {layer_idx}: shape {router_logits_np.shape}")
            del router_logits_np
            if layer_idx % 4 == 0:
                gc.collect()

    # Save all routing matrices in one compressed file
    if routing_matrices:
        routing_file = os.path.join(output_dir, 'routing_matrices.npz')
        print(f"\nSaving routing matrices to '{routing_file}'...")
        np.savez_compressed(routing_file, **routing_matrices)
        file_size_mb = os.path.getsize(routing_file) / (1024**2)
        print(f"✅ Routing matrices saved! File size: {file_size_mb:.2f} MB")
    else:
        routing_file = None
else:
    print(f"\n⚠️ Warning: Model '{selected_model}' does not expose router_logits.")
    print("This model may use a different MoE implementation or may not be a standard MoE model.")
    print("Skipping routing matrix extraction.")
    routing_file = None

# --- Save metadata ---
# Get routing shape safely
if num_moe_layers > 0 and hasattr(outputs, 'router_logits') and outputs.router_logits is not None:
    if is_switch_transformer and isinstance(outputs.router_logits, tuple):
        # For Switch Transformers, show both encoder and decoder routing shapes
        encoder_routing_shape = str(outputs.router_logits[0][0].squeeze(0).shape) if outputs.router_logits[0] else "N/A"
        decoder_routing_shape = str(outputs.router_logits[1][0].squeeze(0).shape) if outputs.router_logits[1] else "N/A"
        routing_shape = f"encoder: {encoder_routing_shape}, decoder: {decoder_routing_shape}"
    else:
        routing_shape = str(outputs.router_logits[0].squeeze(0).shape)
else:
    routing_shape = "N/A"

# Get attention head information from stored metadata
attention_heads_info = {}
if is_switch_transformer:
    # Separate encoder and decoder head counts
    encoder_layers = [k for k in attention_layer_names if k.startswith('encoder_')]
    decoder_layers = [k for k in attention_layer_names if k.startswith('decoder_')]
    
    if encoder_layers and first_attention_layer is not None:
        # Load just the first encoder layer to check shape
        encoder_heads = first_attention_layer.shape[0] if encoder_layers[0] == attention_layer_names[0] else "N/A"
        attention_heads_info['encoder_heads_per_layer'] = encoder_heads
        attention_heads_info['encoder_layers'] = len(encoder_layers)
    
    if decoder_layers:
        # For decoder, we need to check if it was the first layer or load it
        if decoder_layers[0] == attention_layer_names[0] and first_attention_layer is not None:
            decoder_heads = first_attention_layer.shape[0]
        else:
            decoder_heads = "N/A"
        attention_heads_info['decoder_heads_per_layer'] = decoder_heads
        attention_heads_info['decoder_layers'] = len(decoder_layers)
    
    attention_shape_str = f"encoder: ({attention_heads_info.get('encoder_heads_per_layer', 'N/A')}, {sequence_length}, {sequence_length}), decoder: ({attention_heads_info.get('decoder_heads_per_layer', 'N/A')}, seq_len, seq_len)"
else:
    # Standard decoder-only models
    num_heads_per_layer = first_attention_layer.shape[0] if first_attention_layer is not None else "N/A"
    attention_heads_info['heads_per_layer'] = num_heads_per_layer
    attention_shape_str = f"({num_heads_per_layer}, {sequence_length}, {sequence_length})"

metadata = {
    "model_name": selected_model,
    "model_id": model_id,
    "model_architecture": "encoder-decoder (Switch Transformer)" if is_switch_transformer else "decoder-only",
    "sequence_length": sequence_length,
    "actual_token_count": input_ids_np.shape[0],
    "num_attention_layers": num_layers,
    **{f"num_{k}": v for k, v in attention_heads_info.items()},
    "total_attention_heads": total_heads,
    "num_routing_layers": num_moe_layers if num_moe_layers > 0 else "N/A",
    "timestamp": timestamp,
    "attention_shape_per_layer": attention_shape_str,
    "routing_shape_per_layer": routing_shape,
    "has_routing_matrices": "Yes" if num_moe_layers > 0 else "No",
    "attention_storage_format": "multi-head (all heads preserved)",
    "attention_dtype": str(ATTENTION_DTYPE.__name__),
    "compression_method": "numpy.savez_compressed",
    "uncompressed_attention_size_mb": f"{total_uncompressed_size:.2f}",
    "compressed_attention_size_mb": f"{compressed_size_mb:.2f}",
    "compression_ratio": f"{compression_ratio:.2f}x",
    "space_saved_percent": f"{(1 - compressed_size_mb/total_uncompressed_size)*100:.1f}%",
}

metadata_file = os.path.join(output_dir, 'metadata.txt')
with open(metadata_file, 'w') as f:
    for key, value in metadata.items():
        f.write(f"{key}: {value}\n")
print(f"\n✅ Metadata saved to '{metadata_file}'")

print("\n" + "=" * 70)
print("SUMMARY")
print("=" * 70)
print(f"Model: {selected_model}")
print(f"Input tokens: {input_ids_np.shape[0]}")
print(f"Attention layers saved: {num_layers}")
print(f"Total attention heads: {total_heads}")
print(f"Routing layers saved: {num_moe_layers if num_moe_layers > 0 else 'N/A (not available)'}")
print(f"Output directory: {output_dir}")
print(f"\nCompression Statistics:")
print(f"  Uncompressed size: {total_uncompressed_size:.2f} MB")
print(f"  Compressed size: {compressed_size_mb:.2f} MB")
print(f"  Compression ratio: {compression_ratio:.2f}x")
print(f"  Space saved: {(1 - compressed_size_mb/total_uncompressed_size)*100:.1f}%")
print(f"\nFinal GPU memory: {torch.cuda.memory_allocated() / 1e9:.2f} GB")
print("\nFiles created:")
print(f"  - {input_file}")
print(f"  - {attention_file}")
if routing_file:
    print(f"  - {routing_file}")
print(f"  - {metadata_file}")
print("=" * 70)