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`grad_dtype` is a new attribute on Tensor to control gradient dtype: - Access/setting is leaf-only. - grad_dtype is respected when (1) when assigning to .grad, and (2) in the engine after the previous node produces incoming gradients for AccumulateGrad. (See table below for details) - Not setting grad_dtype preserves the current behavior. Accessing it returns `t.dtype` - `grad_dtype` cannot be set when there is already a `.grad` present and the dtypes conflict. | `grad_dtype` setting | Setting `.grad` manually | Incoming gradient from autograd engine | |-----------------------|--------------------------|-----------------------------------------| | **Default (tensor’s dtype)** | `.grad` must match tensor’s dtype | Engine casts incoming grad to tensor’s dtype | | **Set to specific dtype** | `.grad` must match that dtype | Engine casts incoming grad to the specified dtype | | **Set to `None`** | `.grad` may be any dtype | Engine does not cast; accepts incoming grad dtype as-is | Pull Request resolved: https://github.com/pytorch/pytorch/pull/162815 Approved by: https://github.com/albanD |
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| _acc | ||
| _dynamo | ||
| _export | ||
| __init__.pyi.in | ||
| _aoti.pyi | ||
| _autograd.pyi | ||
| _cpu.pyi | ||
| _cudnn.pyi | ||
| _cusparselt.pyi | ||
| _distributed_autograd.pyi | ||
| _distributed_c10d.pyi | ||
| _distributed_rpc_testing.pyi | ||
| _distributed_rpc.pyi | ||
| _functionalization.pyi | ||
| _functions.pyi | ||
| _functorch.pyi | ||
| _instruction_counter.pyi | ||
| _itt.pyi | ||
| _jit_tree_views.pyi | ||
| _lazy_ts_backend.pyi | ||
| _lazy.pyi | ||
| _monitor.pyi | ||
| _nn.pyi.in | ||
| _nvtx.pyi | ||
| _onnx.pyi | ||
| _profiler.pyi | ||
| _VariableFunctions.pyi.in | ||
| _verbose.pyi | ||
| build.bzl | ||
| return_types.pyi.in | ||