rope support 4D input tensor #18454
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rope support 4D input tensor #18454
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tianleiwu
reviewed
Nov 15, 2023
tianleiwu
reviewed
Nov 15, 2023
tianleiwu
reviewed
Nov 15, 2023
tianleiwu
reviewed
Nov 17, 2023
kunal-vaishnavi
approved these changes
Nov 17, 2023
kleiti
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to kleiti/onnxruntime
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this pull request
Mar 22, 2024
### Description <!-- Describe your changes. --> change RotaryEmbeddings op implementation, add support for 4D input tensor that is with shape of [batch, num_heads, seq_len, head_size]. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Current RotaryEmbedding op only support 3d input tensor with shape [batch, seq_len, hidden_size] For llamav2 model, when using FusionRotaryEmbeddings to only fuse RotaryEmbeddings op, there will be a transpose operation for query and key, and then the input tensor of RotaryEmbeddings becomes 4D [batch, num_heads, seq_len, head_size]. This scenario can't be supported by current RotaryEmbeddings implementation. So it needs to support 4D input tensor.
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Description
change RotaryEmbeddings op implementation, add support for 4D input tensor that is with shape of [batch, num_heads, seq_len, head_size].
Motivation and Context
Current RotaryEmbedding op only support 3d input tensor with shape [batch, seq_len, hidden_size]
For llamav2 model, when using FusionRotaryEmbeddings to only fuse RotaryEmbeddings op, there will be a transpose operation for query and key, and then the input tensor of RotaryEmbeddings becomes 4D [batch, num_heads, seq_len, head_size].
This scenario can't be supported by current RotaryEmbeddings implementation. So it needs to support 4D input tensor.