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Specifications and Common Mistakes

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mindspore.ops.moe_token_permute

mindspore.ops.moe_token_permute(tokens, indices, num_out_tokens=None, padded_mode=False)

Permute the tokens based on the indices. Token with the same index will be grouped together.

Warning

• It is only supported on Atlas A2 Training Series Products.

• When indices is 2-D, the size of the second dim must be less than or equal to 512.

Parameters

• tokens (Tensor) – The input token tensor to be permuted. The dtype is bfloat16, float16 or float32. The shape is $(num\mathrm{\_}tokens,hidden\mathrm{\_}size)$ , where num_tokens and hidden_size are positive integers.

• indices (Tensor) – The tensor specifies indices used to permute the tokens. The dtype is int32 or int64. The shape is $(num\mathrm{\_}tokens,topk)$ or $(num\mathrm{\_}tokens,)$, where num_tokens and topk are positive integers. If the shape is the latter case, topk is implied to be 1.

• num_out_tokens (int, optional) – The effective output token count, when enabling the capacity factor, should equal the number of tokens not dropped. It should be non-negative integer. Default: None, meaning no tokens are dropped.

• padded_mode (bool, optional) – If True, indicating the indices are padded to denote selected tokens per expert. It can only be False currently. Default: False .

Returns

tuple (Tensor), tuple of 2 tensors, containing the permuted tokens and sorted indices.

• permuted_tokens (Tensor) - The permuted tensor of the same dtype as tokens.

• sorted_indices (Tensor) - The indices Tensor of dtype int32, corresponds to permuted tensor.

Raises

• TypeError – If tokens or indices is not a Tensor.

• TypeError – If dtype of indices is not int32 or int64.

• TypeError – If specified num_out_tokens is not an integer.

• TypeError – If specified padded_mode is not a bool.

• ValueError – If second dim of indices is greater than 512 when exists.

• ValueError – If padded_node is set to True.

• ValueError – If tokens is not 2-D or indices is not 1-D or 2-D Tensor.

• RuntimeError – If first dimensions of tokens and indices are not consistent.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, ops
>>> tokens = Tensor([[1, 1, 1],
...                  [7, 7, 7],
...                  [2, 2, 2],
...                  [1, 1, 1],
...                  [2, 2, 2],
...                  [3, 3, 3]], dtype=mindspore.bfloat16)
>>> indices = Tensor([5, 0, 3, 1, 2, 4], dtype=mindspore.int32)
>>> out = ops.moe_token_permute(tokens, indices)
>>> print(out)
(Tensor(shape=[6, 3], dtype=BFloat16, value=
[[7, 7, 7],
 [1, 1, 1],
 [2, 2, 2],
 [2, 2, 2],
 [3, 3, 3],
 [1, 1, 1]]), Tensor(shape=[6], dtype=Int32, value= [5, 0, 3, 1, 2, 4]))