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a7f91224eb
* add `Tensor.isclose()` * support `equal_nan` so as to match PyTorch's behavior * update unit tests * remove some tests temporarily * re-enable one test * re-enable other test * try to fix failing tests during CI * save one line of code --------- Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
52 lines
1.3 KiB
Markdown
52 lines
1.3 KiB
Markdown
## Reduce
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::: tinygrad.Tensor.sum
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::: tinygrad.Tensor.prod
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::: tinygrad.Tensor.max
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::: tinygrad.Tensor.min
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::: tinygrad.Tensor.any
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::: tinygrad.Tensor.all
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::: tinygrad.Tensor.isclose
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::: tinygrad.Tensor.mean
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::: tinygrad.Tensor.var
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::: tinygrad.Tensor.std
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::: tinygrad.Tensor.std_mean
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::: tinygrad.Tensor.softmax
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::: tinygrad.Tensor.log_softmax
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::: tinygrad.Tensor.logsumexp
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::: tinygrad.Tensor.logcumsumexp
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::: tinygrad.Tensor.argmax
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::: tinygrad.Tensor.argmin
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## Processing
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::: tinygrad.Tensor.avg_pool2d
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::: tinygrad.Tensor.max_pool2d
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::: tinygrad.Tensor.conv2d
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::: tinygrad.Tensor.conv_transpose2d
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::: tinygrad.Tensor.dot
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::: tinygrad.Tensor.matmul
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::: tinygrad.Tensor.einsum
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::: tinygrad.Tensor.cumsum
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::: tinygrad.Tensor.cummax
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::: tinygrad.Tensor.triu
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::: tinygrad.Tensor.tril
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::: tinygrad.Tensor.interpolate
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::: tinygrad.Tensor.scatter
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::: tinygrad.Tensor.scatter_reduce
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## Neural Network (functional)
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::: tinygrad.Tensor.linear
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::: tinygrad.Tensor.sequential
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::: tinygrad.Tensor.layernorm
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::: tinygrad.Tensor.batchnorm
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::: tinygrad.Tensor.dropout
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::: tinygrad.Tensor.one_hot
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::: tinygrad.Tensor.scaled_dot_product_attention
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::: tinygrad.Tensor.binary_crossentropy
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::: tinygrad.Tensor.binary_crossentropy_logits
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::: tinygrad.Tensor.sparse_categorical_crossentropy
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::: tinygrad.Tensor.cross_entropy
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::: tinygrad.Tensor.nll_loss
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