Multi forward MCH eviction fix #2836
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Differential Revision: D71491003
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Summary: Pull Request resolved: pytorch#2836 ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Differential Revision: D71491003
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Differential Revision: D71491003
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Differential Revision: D71491003
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Differential Revision: D71491003
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Summary: Pull Request resolved: pytorch#2836 ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Differential Revision: D71491003
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Differential Revision: D71491003
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Reviewed By: dstaay-fb Differential Revision: D71491003
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Reviewed By: dstaay-fb Differential Revision: D71491003
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Summary: Pull Request resolved: pytorch#2836 ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Reviewed By: dstaay-fb Differential Revision: D71491003
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Reviewed By: dstaay-fb Differential Revision: D71491003
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Summary: Pull Request resolved: pytorch#2836 ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Reviewed By: dstaay-fb Differential Revision: D71491003
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Summary: Pull Request resolved: pytorch#2836 ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Reviewed By: dstaay-fb Differential Revision: D71491003
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Reviewed By: dstaay-fb Differential Revision: D71491003
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Reviewed By: dstaay-fb Differential Revision: D71491003
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Reviewed By: dstaay-fb Differential Revision: D71491003
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Summary: Pull Request resolved: pytorch#2836 ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Reviewed By: dstaay-fb Differential Revision: D71491003
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Summary: ## Issue: Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error. ## Solution: Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation. Reviewed By: dstaay-fb Differential Revision: D71491003
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Summary:
Issue:
Direct tensor modification during training with multiple forward passes breaks PyTorch's autograd graph, causing "one of the variables needed for gradient computation has been modified by an inplace operation" runtime error.
Solution:
Use in-place updates with .data accessor to safely reinitialize evicted embeddings without invalidating gradient computation.
Differential Revision: D71491003