Comments (1)
I think this is simply a problem with the current memory management strategy that creates buffers that are too small. If you increase the size of the buffer, the test should pass. We are already working on a fix for the memory managment, should be out for the next release.
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Related Issues (20)
- Conflicts bewteen Contributing.md and scripts
- [LibTorch] tensor slice error for aggregated tensors
- ONNX import issue: `index out of bounds` in `pad_config()` due to `node.input` only having len of `2` HOT 5
- CLI output doesn't have enough precision for learning rate
- ONNX import issue: `Ident cannot be a number; use Literal instead` in `TensorType::new()` HOT 6
- FaceONNX models import issues tracker
- Wgpu backend of image-classification-web example cannot work HOT 13
- Fail to build examples/mnist-inference-web HOT 1
- Unable to train models with generic inner modules HOT 3
- Tensor::Interpolate function like torch.nn.functional.interpolate HOT 4
- Burn fusion optimisation HOT 1
- basic-workflow training.md has mistyped code HOT 1
- ONNX import issue: `ceil_mode is not supported` in `avg_pool1d_config()` HOT 3
- Img links of burn/examples /onnx-inference are missing. HOT 1
- Rewrite pooling operations from CPA to CubeCL in Burn-JIT
- Rewrititng select from CPA to CubeCL in Burn-JIT
- Rewrititng gather from CPA to CubeCL in Burn-JIT
- Rewrititng scatter from CPA to CubeCL in Burn-JIT
- ONNX import issue: `Pad: only float values are currently supported for constant value, submit an issue on github` in `pad_config()`
- xtask default args are not used HOT 6
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