[Operator] premute for reduction tensors & welford var_mean #64
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StrongSpoon
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Jun 13, 2024
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StrongSpoon
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- implement Welford var_mean and two-pass kernels to take place of the low-perf var_meak_kernel
- implement compress_dim function for multiple reduction dims
- remove the limit of commit python version
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StrongSpoon
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tongxin
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Jun 14, 2024
| @@ -135,8 +132,7 @@ def all_dims(inp, dim=None, keepdim=False): | |||
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| shape = list(inp.shape) | |||
| dim = [d % inp.ndim for d in dim] | |||
| order = [i for i in range(inp.ndim) if i not in dim] + dim | |||
| inp = inp.permute(order).contiguous() | |||
| inp = dim_compress(inp, dim) | |||
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looks like merge_dims or coalesce_dims might be better names?
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