CUDA Device-Side Assert Triggered
A CUDA kernel hit a device-side assertion. Almost always an out-of-bounds index into an embedding, loss, or gather/scatter op. Because CUDA is asynchronous, the reported stack trace points at an unrelated later line, so the real cause is hidden unless you force synchronous execution.
A CUDA kernel hit a device-side assertion. Almost always an out-of-bounds index into an embedding, loss, or gather/scatter op.
What this failure is
CUDA Device-Side Assert Triggered is a Training Stability failure seen during ML training runs. A CUDA kernel hit a device-side assertion. Almost always an out-of-bounds index into an embedding, loss, or gather/scatter op. Because CUDA is asynchronous, the reported stack trace points at an unrelated later line, so the real cause is hidden unless you force synchronous execution. Common tags: Device Side Assert, Cuda, Index Out Of Bounds, Embedding.
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Why it happens (the mechanism)
A label or index is outside the valid range for an embedding/loss layer (e.g. label == num_classes, or pad id >= vocab_size). CUDA executes asynchronously, so the trace points at the next synchronizing call, not the faulting kernel. Target tensor contains -1 or a sentinel id the loss does not ignore. Vocabulary/num_classes drifted from the data after a config change. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- Training crashes with 'CUDA error: device-side assert triggered'
- The reported line is unrelated to the real fault
- Subsequent CUDA calls all fail until the process restarts
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| RuntimeError: CUDA error: device-side assert triggered | A label or index is outside the valid range for an embedding/loss layer (e.g. label == num_classes, or pad id >= vocab_size) |
| Assertion `srcIndex < srcSelectDimSize` failed | CUDA executes asynchronously, so the trace points at the next synchronizing call, not the faulting kernel |
| nll_loss / index_select / embedding op in the trace | Target tensor contains -1 or a sentinel id the loss does not ignore |
| Error disappears on CPU but the loss is wrong (label out of range) | Vocabulary/num_classes drifted from the data after a config change |
Which systems are affected
- Classification heads with label index >= num_classes
- Embedding lookups with token id >= vocab_size
- Models after a tokenizer/vocab change
- Any gather/scatter with a stale index tensor
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: RuntimeError: CUDA error: device-side assert triggered
- ✓Verified signal present: Assertion `srcIndex < srcSelectDimSize` failed
- ✓Verified signal present: nll_loss / index_select / embedding op in the trace
- ✓Verified signal present: Error disappears on CPU but the loss is wrong (label out of range)
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
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Install the free VS Code extensionCUDA errors in context
CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.
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Root cause
- A label or index is outside the valid range for an embedding/loss layer (e.g. label == num_classes, or pad id >= vocab_size)
- CUDA executes asynchronously, so the trace points at the next synchronizing call, not the faulting kernel
- Target tensor contains -1 or a sentinel id the loss does not ignore
- Vocabulary/num_classes drifted from the data after a config change
The fix and how to prevent it
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