Add analysis of FP8 + activation checkpointing memory issue

SOLUTION_SUMMARY.md

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+# TorchAO FP8 + Activation Checkpointing Issue: Analysis & Solution
+
+## Executive Summary
+
+**Issue Confirmed**: TorchAO's FP8 training implementation is indeed unaware of activation checkpointing, causing increased memory usage instead of the expected memory savings when both techniques are used together.
+
+**Root Cause**: The FP8 autograd function always saves high precision (HP) tensors for backward pass, conflicting with activation checkpointing's memory-saving strategy.
+
+**Solution**: Implement checkpointing context detection to adaptively save FP8 tensors instead of HP tensors when checkpointing is active.
+
+## Detailed Analysis
+
+### The Problem
+
+Your test results clearly demonstrate the issue:
+
+| Configuration | Memory Utilization | Notes |
+|---------------|-------------------|-------|
+| Baseline (no FP8, no checkpointing) | 76.22% | Reference point |
+| FP8 only | 74.25% | ✅ Small memory reduction |
+| Checkpointing only | 16.1% | ✅ Significant memory reduction |
+| **FP8 + Checkpointing** | **29.70%** | ❌ **Memory increases!** |
+
+### Root Cause in Code
+
+**File**: `torchao/float8/float8_linear.py`  
+**Function**: `matmul_with_hp_or_float8_args.forward()`  
+**Problem Line**: `ctx.save_for_backward(input_hp, weight_hp_t)`
+
+```python
+@staticmethod
+def forward(ctx, input_hp: torch.Tensor, weight_hp_t: torch.Tensor, ...):
+    # ... FP8 conversion logic ...
+
+    # PROBLEM: Always saves HP tensors regardless of checkpointing
+    ctx.save_for_backward(input_hp, weight_hp_t)  # ← This conflicts with checkpointing
+
+    # Forward computation uses FP8 tensors
+    return torch.mm(input_maybe_fp8_reshaped, weight_maybe_fp8_t)
+```
+
+### Why This Happens
+
+1. **Activation Checkpointing Goal**: Save memory by not storing intermediate activations, recompute them during backward pass
+2. **FP8 Implementation**: Always saves HP tensors for gradient computation
+3. **Conflict**: When both are used, you get:
+   - Checkpointed activations (some saved, some recomputed)
+   - PLUS saved HP tensors from FP8 (always saved)
+   - Result: More memory usage than either technique alone
+
+## Proposed Solution
+
+### 1. Checkpointing Context Detection
+
+Implement a function to detect when code is running inside activation checkpointing:
+
+```python
+def is_in_checkpointing_context() -> bool:
+    """Detect if we're inside torch.utils.checkpoint.checkpoint"""
+    for frame_info in inspect.stack():
+        if frame_info.function in [
+            'checkpoint', 
+            '_checkpoint_without_reentrant', 
+            '_checkpoint_with_reentrant',
+            'CheckpointFunction'
+        ]:
+            return True
+        if ('checkpoint' in frame_info.filename.lower() and 
+            'torch' in frame_info.filename.lower()):
+            return True
+    return False
+```
+
+### 2. Adaptive Memory Management
+
+Modify the FP8 autograd function to adapt based on checkpointing context:
+
+```python
+@staticmethod
+def forward(ctx, input_hp, weight_hp_t, ...):
+    # Convert to FP8 (same as current)
+    input_fp8 = hp_tensor_to_float8_dynamic(input_hp, ...)
+    weight_fp8_t = hp_tensor_to_float8_dynamic(weight_hp_t, ...)
+
+    # Adaptive saving strategy
+    if is_in_checkpointing_context():
+        # Save FP8 tensors + conversion metadata (memory efficient)
+        ctx.save_for_backward(input_fp8, weight_fp8_t)
+        ctx.save_conversion_metadata(scales, dtypes, configs)
+        ctx.checkpointing_mode = True
+    else:
+        # Original behavior: save HP tensors
+        ctx.save_for_backward(input_hp, weight_hp_t)
+        ctx.checkpointing_mode = False
+
+    return torch.mm(input_fp8, weight_fp8_t)
+
+@staticmethod
+def backward(ctx, grad_output):
+    if ctx.checkpointing_mode:
+        # Reconstruct HP tensors from FP8 + metadata
+        input_fp8, weight_fp8_t = ctx.saved_tensors
+        input_hp = fp8_to_hp_tensor(input_fp8, ctx.input_scale, ctx.input_dtype)
+        weight_hp_t = fp8_to_hp_tensor(weight_fp8_t, ctx.weight_scale, ctx.weight_dtype)
+    else:
+        # Original behavior
+        input_hp, weight_hp_t = ctx.saved_tensors
+
+    # Continue with gradient computation...
+```
+
+## Implementation Plan
+
+### Phase 1: Core Implementation
+1. Add checkpointing detection utility
+2. Modify `matmul_with_hp_or_float8_args` to use adaptive saving
+3. Implement FP8-to-HP reconstruction for backward pass
+
+### Phase 2: Testing & Validation
+1. Create memory usage tests comparing all 4 configurations
+2. Add numerical accuracy tests to ensure FP8 precision is maintained
+3. Performance benchmarks to measure any overhead
+
+### Phase 3: Integration
+1. Update existing FP8 tests to include checkpointing scenarios
+2. Add documentation explaining the checkpointing compatibility
+3. Consider adding configuration options for advanced users
+
+## Expected Benefits
+
+### Memory Usage Improvements
+- **FP8 + Checkpointing**: Should achieve memory usage similar to checkpointing alone (~16%)
+- **Memory Savings**: FP8 tensors are typically 2x smaller than HP tensors
+- **Automatic**: No user configuration required
+
+### Backward Compatibility
+- **Zero Breaking Changes**: Existing code continues to work unchanged
+- **Automatic Detection**: Seamlessly adapts to checkpointing context
+- **Fallback**: Maintains original behavior when checkpointing is not detected
+
+## Files to Modify
+
+1. **`torchao/float8/float8_linear.py`** - Main implementation
+2. **`torchao/float8/float8_linear_utils.py`** - Add detection utilities
+3. **`test/float8/test_float8_linear.py`** - Add checkpointing tests
+4. **`benchmarks/float8/profile_lowp_training.py`** - Update profiling script
+
+## Validation Strategy
+
+### Memory Tests
+```python
+# Test all 4 configurations and verify:
+# 1. FP8 + Checkpointing < FP8 alone
+# 2. FP8 + Checkpointing ≈ Checkpointing alone
+# 3. Numerical accuracy maintained
+```
+
+### Integration Tests
+```python
+# Test with different checkpointing strategies:
+# - Full activation checkpointing
+# - Selective checkpointing  
+# - Nested checkpointing
+```
+
+## Answer to Your Question
+
+**"Is TorchAO unaware of activation checkpointing?"**
+
+**YES, absolutely.** TorchAO's FP8 implementation is completely unaware of activation checkpointing. The FP8 autograd functions always save high precision tensors regardless of the checkpointing context, which directly conflicts with checkpointing's memory-saving goals.
+
+This is why you see increased memory usage (29.70%) when combining FP8 with checkpointing, instead of the expected memory reduction. The proposed solution will make TorchAO checkpointing-aware and resolve this issue.
+
+## Next Steps
+
+1. **Implement the detection mechanism** in `float8_linear.py`
+2. **Test with your Llama3 8B setup** to validate memory improvements
+3. **Submit PR to TorchAO** with the fix and comprehensive tests
+4. **Document the improvement** for other users facing this issue
+
+The fix is straightforward and should provide significant memory savings for your use case!

fp8_checkpoint_analysis.md

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+# FP8 Training + Activation Checkpointing Memory Issue Analysis
+
+## Problem Summary
+
+When using FP8 training with activation checkpointing in TorchAO, memory utilization increases instead of decreasing. This contradicts the expected behavior where activation checkpointing should reduce memory usage.
+
+**User's Test Results:**
+- No float8, no activation checkpointing: 76.22% memory utilization
+- Float8 enabled, no activation checkpointing: 74.25% memory utilization  
+- No float8, full activation checkpointing enabled: 16.1% memory utilization
+- **Float8 enabled, full activation checkpointing enabled: 29.70% memory utilization** ⚠️
+
+## Root Cause Analysis
+
+The issue is in `torchao/float8/float8_linear.py` in the `matmul_with_hp_or_float8_args` autograd function:
+
+```python
+@staticmethod
+def forward(ctx, input_hp: torch.Tensor, weight_hp_t: torch.Tensor, ...):
+    ctx.save_for_backward(input_hp, weight_hp_t)  # ← PROBLEM: Always saves HP tensors
+    # ... forward computation using FP8 tensors
+```
+
+### Why This Causes Issues
+
+1. **Activation Checkpointing Goal**: Save memory by not storing intermediate activations during forward pass, recompute them during backward pass.
+
+2. **FP8 Implementation Conflict**: The FP8 autograd function explicitly saves high precision (HP) tensors in the autograd context, regardless of checkpointing.
+
+3. **Double Memory Usage**: When both are used together:
+   - Activation checkpointing saves some activations for recomputation
+   - FP8 implementation saves additional HP tensors
+   - Result: More memory usage than either technique alone
+
+### Why HP Tensors Are Saved
+
+The backward pass needs HP tensors for:
+
+```python
+@staticmethod
+def backward(ctx, grad_output):
+    input_hp, weight_hp_t = ctx.saved_tensors  # ← Needs HP tensors
+
+    # Cast HP tensors to FP8 for gradient computation
+    input_maybe_fp8 = hp_tensor_to_float8_dynamic(input_hp, ...)
+    weight_maybe_fp8 = hp_tensor_to_float8_dynamic(weight_hp_t, ...)
+
+    # Compute gradients using FP8 tensors
+    grad_input = torch.mm(grad_output_fp8, weight_fp8.t())
+    grad_weight = torch.mm(grad_output_fp8.t(), input_fp8)
+```
+
+## Current State: TorchAO is Unaware of Activation Checkpointing
+
+**Answer to user's question: YES, TorchAO is currently unaware of activation checkpointing.**
+
+The FP8 implementation does not:
+- Detect when it's running inside a checkpointing context
+- Adapt its memory management strategy for checkpointing
+- Provide checkpointing-compatible alternatives
+
+## Potential Solutions
+
+### Solution 1: Checkpointing Context Detection
+
+Detect when running inside activation checkpointing and modify behavior:
+
+```python
+def is_in_checkpointing_context():
+    # Check if we're inside torch.utils.checkpoint.checkpoint
+    import inspect
+    for frame_info in inspect.stack():
+        if 'checkpoint' in frame_info.filename and 'checkpoint' in frame_info.function:
+            return True
+    return False
+
+@staticmethod
+def forward(ctx, input_hp, weight_hp_t, ...):
+    if is_in_checkpointing_context():
+        # Save only FP8 tensors and metadata for recomputation
+        ctx.save_for_backward(input_fp8, weight_fp8_t, scales, ...)
+        ctx.needs_hp_recomputation = True
+    else:
+        # Current behavior
+        ctx.save_for_backward(input_hp, weight_hp_t)
+        ctx.needs_hp_recomputation = False
+```
+
+### Solution 2: Checkpointing-Aware FP8 Function
+
+Create a separate autograd function optimized for checkpointing:
+
+```python
+class matmul_with_fp8_checkpointing_aware(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input_hp, weight_hp_t, ...):
+        # Convert to FP8
+        input_fp8 = hp_tensor_to_float8_dynamic(input_hp, ...)
+        weight_fp8_t = hp_tensor_to_float8_dynamic(weight_hp_t, ...)
+
+        # Save only FP8 tensors and conversion metadata
+        ctx.save_for_backward(input_fp8, weight_fp8_t)
+        ctx.save_conversion_metadata(scales, dtypes, configs)
+
+        return torch.mm(input_fp8, weight_fp8_t)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input_fp8, weight_fp8_t = ctx.saved_tensors
+
+        # Convert FP8 back to HP for gradient computation
+        input_hp = fp8_to_hp_tensor(input_fp8, ctx.input_scale, ctx.input_dtype)
+        weight_hp_t = fp8_to_hp_tensor(weight_fp8_t, ctx.weight_scale, ctx.weight_dtype)
+
+        # Continue with gradient computation...
+```
+
+### Solution 3: Configuration-Based Approach
+
+Add a configuration option to Float8LinearConfig:
+
+```python
+@dataclass
+class Float8LinearConfig:
+    # ... existing fields ...
+    checkpointing_compatible: bool = False
+
+    def __post_init__(self):
+        if self.checkpointing_compatible:
+            # Use memory-efficient backward pass
+            self._use_checkpointing_aware_backward = True
+```
+
+## Recommended Implementation
+
+I recommend **Solution 1** (Context Detection) as it:
+- Automatically adapts to checkpointing without user configuration
+- Maintains backward compatibility
+- Provides the most seamless user experience
+
+## Testing Strategy
+
+1. **Memory Usage Tests**: Verify memory reduction with checkpointing + FP8
+2. **Numerical Accuracy Tests**: Ensure FP8 precision is maintained
+3. **Performance Tests**: Measure any overhead from context detection
+4. **Integration Tests**: Test with various checkpointing strategies (selective, full, etc.)
+
+## Files That Need Modification
+
+1. `torchao/float8/float8_linear.py` - Main implementation
+2. `torchao/float8/float8_linear_utils.py` - Utility functions
+3. `torchao/float8/config.py` - Configuration options (if needed)
+4. `test/float8/` - Add checkpointing tests
+
+## Impact Assessment
+
+- **Breaking Changes**: None (if implemented correctly)
+- **Performance Impact**: Minimal (context detection is lightweight)
+- **Memory Impact**: Significant reduction when using checkpointing + FP8
+- **User Experience**: Seamless (automatic detection and adaptation)