Mlir Backend: Tensor Dialect Support#45
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It is really cool ! Congrats ! When you say that you wait for an xDSL release, is it because you know that the feature will be added ? |
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Thanks! yeah I contributed tensor.pad but its probably easiest to just wait for the next release to add it to xtc. |
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Motivation
Support for ops in the tensor dialect allows for tracking of producer-consumer relationships and broadcasting, which allow for operator fusion and element-wise operations respectively.
Description
The mlir backend now has an option
use_tensor_dialectthat causes ops to be generated in the tensor dialect. The tensor dialect gets lowered into memref by a new bufferization pass after the transform pass is applied (can be printed withprint_bufferization_ir=True).Discussion
How the Tensor Dialect Affects the IR:
matmul and conv2d:
The bufferization results in the exact same lowered mlir as the memref dialect ops.
relu:
Collapsing the shape to 1 dim requires the tensor to be expanded (unlike the memref), resulting in an extra memory allocation after bufferization. So the relu for the tensor dialect is non-collapsing, which is also required for fusion to work properly.
pad and unpad:
The tensor implementation uses a
linalg.genericwhich is needed for fusion. It has dynamic dims which requires mlir: updated extra-tools version #70 an update to the extra tools for the c backend to work properly.