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import torch.nn as nn
from typing import Any, Dict, List, Tuple
def inspect_model_devices(model, prefix="", max_depth=3, current_depth=0):
"""
Recursively inspect all properties of a PyTorch Lightning model to find
which tensors are on CPU vs CUDA devices and check density/sparsity.
Args:
model: The model/object to inspect
prefix: String prefix for nested attributes
max_depth: Maximum recursion depth to prevent infinite loops
current_depth: Current recursion depth
Returns:
Dict with categorized results
"""
results = {
'cuda_tensors': [],
'cpu_tensors': [],
'mixed_tensors': [], # For modules with tensors on different devices
'sparse_tensors': [], # Sparse tensors
'non_contiguous_tensors': [], # Non-contiguous (non-dense) tensors
'problematic_tensors': [], # CPU or sparse or non-contiguous
'non_tensor_attrs': [],
'errors': []
}
if current_depth >= max_depth:
return results
# Get all attributes of the object
for attr_name in dir(model):
# Skip private attributes and methods
if attr_name.startswith('_'):
continue
try:
attr_value = getattr(model, attr_name)
full_name = f"{prefix}.{attr_name}" if prefix else attr_name
# Check if it's a tensor
if isinstance(attr_value, torch.Tensor):
# Check density and sparsity
is_sparse = attr_value.is_sparse or attr_value.is_sparse_csr
is_contiguous = attr_value.is_contiguous()
is_cuda = attr_value.device.type == 'cuda'
device_info = {
'name': full_name,
'shape': tuple(attr_value.shape),
'dtype': str(attr_value.dtype),
'device': str(attr_value.device),
'requires_grad': attr_value.requires_grad,
'is_sparse': is_sparse,
'is_contiguous': is_contiguous,
'stride': tuple(attr_value.stride()) if not is_sparse else 'N/A (sparse)',
'storage_offset': attr_value.storage_offset() if not is_sparse else 'N/A (sparse)',
'numel': attr_value.numel()
}
# Categorize by issues
has_issues = []
if not is_cuda:
has_issues.append('CPU')
if is_sparse:
has_issues.append('SPARSE')
results['sparse_tensors'].append(device_info)
if not is_contiguous:
has_issues.append('NON_CONTIGUOUS')
results['non_contiguous_tensors'].append(device_info)
if has_issues:
device_info['issues'] = has_issues
results['problematic_tensors'].append(device_info)
# Still categorize by device for backward compatibility
if is_cuda:
results['cuda_tensors'].append(device_info)
else:
results['cpu_tensors'].append(device_info)
# Check if it's a Parameter
elif isinstance(attr_value, nn.Parameter):
is_sparse = attr_value.is_sparse or attr_value.is_sparse_csr
is_contiguous = attr_value.is_contiguous()
is_cuda = attr_value.device.type == 'cuda'
device_info = {
'name': full_name,
'shape': tuple(attr_value.shape),
'dtype': str(attr_value.dtype),
'device': str(attr_value.device),
'requires_grad': attr_value.requires_grad,
'type': 'Parameter',
'is_sparse': is_sparse,
'is_contiguous': is_contiguous,
'stride': tuple(attr_value.stride()) if not is_sparse else 'N/A (sparse)',
'storage_offset': attr_value.storage_offset() if not is_sparse else 'N/A (sparse)',
'numel': attr_value.numel()
}
# Categorize by issues
has_issues = []
if not is_cuda:
has_issues.append('CPU')
if is_sparse:
has_issues.append('SPARSE')
results['sparse_tensors'].append(device_info)
if not is_contiguous:
has_issues.append('NON_CONTIGUOUS')
results['non_contiguous_tensors'].append(device_info)
if has_issues:
device_info['issues'] = has_issues
results['problematic_tensors'].append(device_info)
if is_cuda:
results['cuda_tensors'].append(device_info)
else:
results['cpu_tensors'].append(device_info)
# Check if it's a Module (like torchmetrics)
elif isinstance(attr_value, nn.Module):
# Get device info for the module
module_devices = set()
for param in attr_value.parameters():
module_devices.add(param.device.type)
for buffer in attr_value.buffers():
module_devices.add(buffer.device.type)
if len(module_devices) > 1:
results['mixed_tensors'].append({
'name': full_name,
'type': type(attr_value).__name__,
'devices': list(module_devices)
})
elif len(module_devices) == 1:
device_type = list(module_devices)[0]
module_info = {
'name': full_name,
'type': type(attr_value).__name__,
'device': device_type
}
if device_type == 'cuda':
results['cuda_tensors'].append(module_info)
else:
results['cpu_tensors'].append(module_info)
# Recursively inspect the module
if current_depth < max_depth - 1:
sub_results = inspect_model_devices(attr_value, full_name, max_depth, current_depth + 1)
for key in results:
results[key].extend(sub_results[key])
# Check for other types that might contain tensors
elif hasattr(attr_value, '__dict__') and not callable(attr_value):
if current_depth < max_depth - 1:
sub_results = inspect_model_devices(attr_value, full_name, max_depth, current_depth + 1)
for key in results:
results[key].extend(sub_results[key])
else:
# Non-tensor attribute
if not callable(attr_value):
results['non_tensor_attrs'].append({
'name': full_name,
'type': type(attr_value).__name__,
'value': str(attr_value)[:100] # Truncate long values
})
except Exception as e:
results['errors'].append({
'name': full_name,
'error': str(e)
})
continue
return results
def print_device_report(model, detailed=False):
"""
Print a formatted report of device allocation for all model components.
Args:
model: PyTorch Lightning model to inspect
detailed: If True, show detailed information for each tensor
"""
print("="*80)
print("COMPREHENSIVE DEVICE & TENSOR DENSITY REPORT")
print("="*80)
results = inspect_model_devices(model)
# Show problematic tensors first (most important)
if results['problematic_tensors']:
print(f"\n🚨 PROBLEMATIC TENSORS ({len(results['problematic_tensors'])}) - LIKELY CAUSING ISSUES!")
print("-" * 70)
for item in results['problematic_tensors']:
issues_str = " | ".join(item['issues'])
print(f" ❌ {item['name']}: {issues_str}")
if detailed:
print(f" Shape: {item['shape']} | Device: {item['device']}")
print(f" Contiguous: {item['is_contiguous']} | Sparse: {item['is_sparse']}")
if item['stride'] != 'N/A (sparse)':
print(f" Stride: {item['stride']} | Storage offset: {item['storage_offset']}")
print()
print(f"\n📍 CUDA TENSORS/MODULES ({len(results['cuda_tensors'])})")
print("-" * 40)
for item in results['cuda_tensors']:
status_indicators = []
if not item.get('is_contiguous', True):
status_indicators.append('NON-CONTIGUOUS')
if item.get('is_sparse', False):
status_indicators.append('SPARSE')
status_str = f" [{', '.join(status_indicators)}]" if status_indicators else ""
if detailed:
if 'shape' in item: # It's a tensor
print(f" {item['name']}: {item['shape']} | {item['dtype']} | {item['device']}{status_str}")
if 'stride' in item and item['stride'] != 'N/A (sparse)':
print(f" ↳ Contiguous: {item.get('is_contiguous', 'N/A')} | Stride: {item['stride']}")
else: # It's a module
print(f" {item['name']}: {item['type']} | {item['device']}{status_str}")
else:
print(f" ✅ {item['name']}{status_str}")
print(f"\n💻 CPU TENSORS/MODULES ({len(results['cpu_tensors'])})")
print("-" * 40)
for item in results['cpu_tensors']:
status_indicators = []
if not item.get('is_contiguous', True):
status_indicators.append('NON-CONTIGUOUS')
if item.get('is_sparse', False):
status_indicators.append('SPARSE')
status_str = f" [{', '.join(status_indicators)}]" if status_indicators else ""
if detailed:
if 'shape' in item: # It's a tensor
print(f" {item['name']}: {item['shape']} | {item['dtype']} | {item['device']}{status_str}")
if 'stride' in item and item['stride'] != 'N/A (sparse)':
print(f" ↳ Contiguous: {item.get('is_contiguous', 'N/A')} | Stride: {item['stride']}")
else: # It's a module
print(f" {item['name']}: {item['type']} | {item['device']}{status_str}")
else:
print(f" ❌ {item['name']}{status_str}")
if results['sparse_tensors']:
print(f"\n🕳️ SPARSE TENSORS ({len(results['sparse_tensors'])})")
print("-" * 40)
for item in results['sparse_tensors']:
print(f" {item['name']}: {item['shape']} | {item['device']} | SPARSE")
if results['non_contiguous_tensors']:
print(f"\n📐 NON-CONTIGUOUS TENSORS ({len(results['non_contiguous_tensors'])})")
print("-" * 40)
for item in results['non_contiguous_tensors']:
print(f" {item['name']}: {item['shape']} | Stride: {item['stride']}")
if detailed:
print(f" ↳ Storage offset: {item['storage_offset']}")
if results['mixed_tensors']:
print(f"\n⚠️ MIXED DEVICE MODULES ({len(results['mixed_tensors'])})")
print("-" * 40)
for item in results['mixed_tensors']:
print(f" {item['name']}: {item['type']} | Devices: {item['devices']}")
if results['errors']:
print(f"\n❗ ERRORS ({len(results['errors'])})")
print("-" * 40)
for item in results['errors']:
print(f" {item['name']}: {item['error']}")
print(f"\n📊 DETAILED SUMMARY")
print("-" * 40)
print(f" CUDA components: {len(results['cuda_tensors'])}")
print(f" CPU components: {len(results['cpu_tensors'])}")
print(f" Sparse tensors: {len(results['sparse_tensors'])}")
print(f" Non-contiguous: {len(results['non_contiguous_tensors'])}")
print(f" Mixed components: {len(results['mixed_tensors'])}")
print(f" Errors: {len(results['errors'])}")
print(f" 🚨 TOTAL PROBLEMATIC: {len(results['problematic_tensors'])}")
if results['problematic_tensors']:
print(f"\n⚠️ CRITICAL: Found {len(results['problematic_tensors'])} problematic tensors!")
print(" These are likely causing the 'Tensors must be CUDA and dense' error.")
print(" Focus on fixing the tensors marked with 🚨 above.") |