use native mmap

comfy/model_patcher.py

@@ -27,7 +27,10 @@ import uuid
 from typing import Callable, Optional
 
 import torch
-import tensordict
+import os
+import tempfile
+import weakref
+import gc
 
 import comfy.float
 import comfy.hooks
@@ -39,8 +42,77 @@ from comfy.comfy_types import UnetWrapperFunction
 from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP
 from comfy.model_management import get_free_memory
 
-def to_mmap(t: torch.Tensor) -> tensordict.MemoryMappedTensor:
-    return tensordict.MemoryMappedTensor.from_tensor(t)
+
+def to_mmap(t: torch.Tensor, filename: Optional[str] = None) -> torch.Tensor:
+    """
+    Convert a tensor to a memory-mapped CPU tensor using PyTorch's native mmap support.
+    """
+    # Move to CPU if needed
+    if t.is_cuda:
+        cpu_tensor = t.cpu()
+    else:
+        cpu_tensor = t
+    
+    # Create temporary file
+    if filename is None:
+        temp_file = tempfile.mktemp(suffix='.pt', prefix='comfy_mmap_')
+    else:
+        temp_file = filename
+    
+    # Save tensor to file
+    torch.save(cpu_tensor, temp_file)
+    
+    # If we created a CPU copy from CUDA, delete it to free memory
+    if t.is_cuda:
+        del cpu_tensor
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+    
+    # Load with mmap - this doesn't load all data into RAM
+    mmap_tensor = torch.load(temp_file, map_location='cpu', mmap=True, weights_only=False)
+    
+    # Register cleanup callback
+    def _cleanup():
+        try:
+            if os.path.exists(temp_file):
+                os.remove(temp_file)
+                logging.debug(f"Cleaned up mmap file: {temp_file}")
+        except Exception:
+            pass
+    
+    weakref.finalize(mmap_tensor, _cleanup)
+    
+    # Save original 'to' method
+    original_to = mmap_tensor.to
+    
+    # Create custom 'to' method that cleans up file when moving to CUDA
+    def custom_to(*args, **kwargs):
+        # Determine target device
+        target_device = None
+        if len(args) > 0:
+            if isinstance(args[0], torch.device):
+                target_device = args[0]
+            elif isinstance(args[0], str):
+                target_device = torch.device(args[0])
+        if 'device' in kwargs:
+            target_device = kwargs['device']
+            if isinstance(target_device, str):
+                target_device = torch.device(target_device)
+        
+        # Call original 'to' method first to move data
+        result = original_to(*args, **kwargs)
+        
+        # If moved to CUDA, cleanup the mmap file after the move
+        if target_device is not None and target_device.type == 'cuda':
+            _cleanup()
+        
+        return result
+    
+    # Replace the 'to' method
+    mmap_tensor.to = custom_to
+    
+    return mmap_tensor
                 
 def model_to_mmap(model: torch.nn.Module):
     """Convert all parameters and buffers to memory-mapped tensors

tests/execution/test_model_mmap.py

@@ -0,0 +1,280 @@
+import pytest
+import torch
+import torch.nn as nn
+import psutil
+import os
+import gc
+import tempfile
+from comfy.model_patcher import model_to_mmap, to_mmap
+
+
+class LargeModel(nn.Module):
+    """A simple model with large parameters for testing memory mapping"""
+    
+    def __init__(self, size_gb=10):
+        super().__init__()
+        # Calculate number of float32 elements needed for target size
+        # 1 GB = 1024^3 bytes, float32 = 4 bytes
+        bytes_per_gb = 1024 * 1024 * 1024
+        elements_per_gb = bytes_per_gb // 4  # float32 is 4 bytes
+        total_elements = int(size_gb * elements_per_gb)
+        
+        # Create a large linear layer
+        # Split into multiple layers to avoid single tensor size limits
+        self.layers = nn.ModuleList()
+        elements_per_layer = 500 * 1024 * 1024  # 500M elements per layer (~2GB)
+        num_layers = (total_elements + elements_per_layer - 1) // elements_per_layer
+        
+        for i in range(num_layers):
+            if i == num_layers - 1:
+                # Last layer gets the remaining elements
+                remaining = total_elements - (i * elements_per_layer)
+                in_features = int(remaining ** 0.5)
+                out_features = (remaining + in_features - 1) // in_features
+            else:
+                in_features = int(elements_per_layer ** 0.5)
+                out_features = (elements_per_layer + in_features - 1) // in_features
+            
+            # Create layer without bias to control size precisely
+            self.layers.append(nn.Linear(in_features, out_features, bias=False))
+    
+    def forward(self, x):
+        for layer in self.layers:
+            x = layer(x)
+        return x
+
+
+def get_process_memory_gb():
+    """Get current process memory usage in GB"""
+    process = psutil.Process(os.getpid())
+    mem_info = process.memory_info()
+    return mem_info.rss / (1024 ** 3)  # Convert to GB
+
+
+def get_model_size_gb(model):
+    """Calculate model size in GB"""
+    total_size = 0
+    for param in model.parameters():
+        total_size += param.nelement() * param.element_size()
+    for buffer in model.buffers():
+        total_size += buffer.nelement() * buffer.element_size()
+    return total_size / (1024 ** 3)
+
+
+def test_model_to_mmap_memory_efficiency():
+    """Test that model_to_mmap reduces memory usage for a 10GB model to less than 1GB
+    
+    The typical use case is:
+    1. Load a large model on CUDA
+    2. Convert to mmap to offload from GPU to disk-backed memory
+    3. This frees GPU memory and reduces CPU RAM usage
+    """
+    
+    # Check if CUDA is available
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA is not available, skipping test")
+    
+    # Force garbage collection before starting
+    gc.collect()
+    torch.cuda.empty_cache()
+    
+    # Record initial memory
+    initial_cpu_memory = get_process_memory_gb()
+    initial_gpu_memory = torch.cuda.memory_allocated() / (1024 ** 3)
+    print(f"\nInitial CPU memory: {initial_cpu_memory:.2f} GB")
+    print(f"Initial GPU memory: {initial_gpu_memory:.2f} GB")
+    
+    # Create a 10GB model
+    print("Creating 10GB model...")
+    model = LargeModel(size_gb=10)
+    
+    # Verify model size
+    model_size = get_model_size_gb(model)
+    print(f"Model size: {model_size:.2f} GB")
+    assert model_size >= 9.5, f"Model size {model_size:.2f} GB is less than expected 10 GB"
+    
+    # Move model to CUDA
+    print("Moving model to CUDA...")
+    model = model.cuda()
+    torch.cuda.synchronize()
+    
+    # Memory after moving to CUDA
+    cpu_after_cuda = get_process_memory_gb()
+    gpu_after_cuda = torch.cuda.memory_allocated() / (1024 ** 3)
+    print(f"CPU memory after moving to CUDA: {cpu_after_cuda:.2f} GB")
+    print(f"GPU memory after moving to CUDA: {gpu_after_cuda:.2f} GB")
+    
+    # Convert to mmap (this should move model from GPU to disk-backed memory)
+    # Note: model_to_mmap modifies the model in-place via _apply()
+    # so model and model_mmap will be the same object
+    print("Converting model to mmap...")
+    model_mmap = model_to_mmap(model)
+    
+    # Verify that model and model_mmap are the same object (in-place modification)
+    assert model is model_mmap, "model_to_mmap should modify the model in-place"
+    
+    # Force garbage collection and clear CUDA cache
+    # The original CUDA tensors should be automatically freed when replaced
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.synchronize()
+    
+    # Memory after mmap conversion
+    cpu_after_mmap = get_process_memory_gb()
+    gpu_after_mmap = torch.cuda.memory_allocated() / (1024 ** 3)
+    print(f"CPU memory after mmap: {cpu_after_mmap:.2f} GB")
+    print(f"GPU memory after mmap: {gpu_after_mmap:.2f} GB")
+    
+    # Calculate memory changes from CUDA state (the baseline we're converting from)
+    cpu_increase = cpu_after_mmap - cpu_after_cuda
+    gpu_decrease = gpu_after_cuda - gpu_after_mmap  # Should be positive (freed)
+    print(f"\nCPU memory increase from CUDA: {cpu_increase:.2f} GB")
+    print(f"GPU memory freed: {gpu_decrease:.2f} GB")
+    
+    # Verify that CPU memory usage increase is less than 1GB
+    # The mmap should use disk-backed storage, keeping CPU RAM usage low
+    # We use 1.5 GB threshold to account for overhead
+    assert cpu_increase < 1.5, (
+        f"CPU memory increase after mmap ({cpu_increase:.2f} GB) should be less than 1.5 GB. "
+        f"CUDA state: {cpu_after_cuda:.2f} GB, After mmap: {cpu_after_mmap:.2f} GB"
+    )
+    
+    # Verify that GPU memory has been freed
+    # We expect at least 9 GB to be freed (original 10GB model with some tolerance)
+    assert gpu_decrease > 9.0, (
+        f"GPU memory should be freed after mmap. "
+        f"Freed: {gpu_decrease:.2f} GB (from {gpu_after_cuda:.2f} to {gpu_after_mmap:.2f} GB), expected > 9 GB"
+    )
+    
+    # Verify the model is still functional (basic sanity check)
+    assert model_mmap is not None
+    assert len(list(model_mmap.parameters())) > 0
+    
+    print(f"\n✓ Test passed!")
+    print(f"  CPU memory increase: {cpu_increase:.2f} GB < 1.5 GB")
+    print(f"  GPU memory freed: {gpu_decrease:.2f} GB > 9.0 GB")
+    print(f"  Model successfully offloaded from GPU to disk-backed memory")
+    
+    # Cleanup (model and model_mmap are the same object)
+    del model, model_mmap
+    gc.collect()
+    torch.cuda.empty_cache()
+
+
+def test_to_mmap_cuda_cycle():
+    """Test CUDA -> mmap -> CUDA cycle
+    
+    This test verifies:
+    1. CUDA tensor can be converted to mmap tensor
+    2. CPU memory increase is minimal when using mmap (< 0.1 GB)
+    3. GPU memory is freed when converting to mmap
+    4. mmap tensor can be moved back to CUDA
+    5. Data remains consistent throughout the cycle
+    6. mmap file is automatically cleaned up when moved to CUDA
+    """
+    
+    # Check if CUDA is available
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA is not available, skipping test")
+    
+    # Force garbage collection
+    gc.collect()
+    torch.cuda.empty_cache()
+    
+    print("\nTest: CUDA -> mmap -> CUDA cycle")
+    
+    # Record initial CPU memory
+    initial_cpu_memory = get_process_memory_gb()
+    print(f"Initial CPU memory: {initial_cpu_memory:.2f} GB")
+    
+    # Step 1: Create a CUDA tensor
+    print("\n1. Creating CUDA tensor...")
+    original_data = torch.randn(5000, 5000).cuda()
+    original_sum = original_data.sum().item()
+    print(f"   Shape: {original_data.shape}")
+    print(f"   Device: {original_data.device}")
+    print(f"   Sum: {original_sum:.2f}")
+    
+    # Record GPU and CPU memory after CUDA allocation
+    cpu_after_cuda = get_process_memory_gb()
+    gpu_before_mmap = torch.cuda.memory_allocated() / (1024 ** 3)
+    print(f"   GPU memory: {gpu_before_mmap:.2f} GB")
+    print(f"   CPU memory: {cpu_after_cuda:.2f} GB")
+    
+    # Step 2: Convert to mmap tensor
+    print("\n2. Converting to mmap tensor...")
+    mmap_tensor = to_mmap(original_data)
+    del original_data
+    gc.collect()
+    torch.cuda.empty_cache()
+    
+    print(f"   Device: {mmap_tensor.device}")
+    print(f"   Sum: {mmap_tensor.sum().item():.2f}")
+    
+    # Verify GPU memory is freed
+    gpu_after_mmap = torch.cuda.memory_allocated() / (1024 ** 3)
+    cpu_after_mmap = get_process_memory_gb()
+    print(f"   GPU memory freed: {gpu_before_mmap - gpu_after_mmap:.2f} GB")
+    print(f"   CPU memory: {cpu_after_mmap:.2f} GB")
+    
+    # Verify GPU memory is freed
+    assert gpu_after_mmap < 0.1, f"GPU memory should be freed, but {gpu_after_mmap:.2f} GB still allocated"
+    
+    # Verify CPU memory increase is minimal (should be close to 0 due to mmap)
+    cpu_increase = cpu_after_mmap - cpu_after_cuda
+    print(f"   CPU memory increase: {cpu_increase:.2f} GB")
+    assert cpu_increase < 0.1, f"CPU memory should increase minimally, but increased by {cpu_increase:.2f} GB"
+    
+    # Get the temp file path (we'll check if it gets cleaned up)
+    # The file should exist at this point
+    temp_files_before = len([f for f in os.listdir(tempfile.gettempdir()) if f.startswith('comfy_mmap_')])
+    print(f"   Temp mmap files exist: {temp_files_before}")
+    
+    # Step 3: Move back to CUDA
+    print("\n3. Moving back to CUDA...")
+    cuda_tensor = mmap_tensor.to('cuda')
+    torch.cuda.synchronize()
+    
+    print(f"   Device: {cuda_tensor.device}")
+    final_sum = cuda_tensor.sum().item()
+    print(f"   Sum: {final_sum:.2f}")
+    
+    # Verify GPU memory is used again
+    gpu_after_cuda = torch.cuda.memory_allocated() / (1024 ** 3)
+    print(f"   GPU memory: {gpu_after_cuda:.2f} GB")
+    
+    # Step 4: Verify data consistency
+    print("\n4. Verifying data consistency...")
+    sum_diff = abs(original_sum - final_sum)
+    print(f"   Original sum: {original_sum:.2f}")
+    print(f"   Final sum: {final_sum:.2f}")
+    print(f"   Difference: {sum_diff:.6f}")
+    assert sum_diff < 0.01, f"Data should be consistent, but difference is {sum_diff:.6f}"
+    
+    # Step 5: Verify file cleanup
+    print("\n5. Verifying file cleanup...")
+    gc.collect()
+    import time
+    time.sleep(0.1)  # Give OS time to clean up
+    temp_files_after = len([f for f in os.listdir(tempfile.gettempdir()) if f.startswith('comfy_mmap_')])
+    print(f"   Temp mmap files after: {temp_files_after}")
+    # File should be cleaned up when moved to CUDA
+    assert temp_files_after <= temp_files_before, "mmap file should be cleaned up after moving to CUDA"
+    
+    print("\n✓ Test passed!")
+    print("  CUDA -> mmap -> CUDA cycle works correctly")
+    print(f"  CPU memory increase: {cpu_increase:.2f} GB < 0.1 GB (mmap efficiency)")
+    print("  Data consistency maintained")
+    print("  File cleanup successful")
+    
+    # Cleanup
+    del mmap_tensor, cuda_tensor
+    gc.collect()
+    torch.cuda.empty_cache()
+
+
+if __name__ == "__main__":
+    # Run the tests directly
+    test_model_to_mmap_memory_efficiency()
+    test_to_mmap_cuda_cycle()
+