Comments (5)
Maratyszcza
commented on May 10, 2024
Arbitrary pooling size & stride is already supported.
Convolutional layers with stride will be supported when im2col+sgemm lands. Using FFT for strided convolution would decrease the effective kernel size to kernel.size / kernel.stride, which in practical architectures is about 2. When kernel size is this small, FFT doesn't offer much algorithmic speedup.
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joker512
commented on May 10, 2024
Thanks!
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Maratyszcza
commented on May 10, 2024
Implicit GEMM algorithm and strided convolution is now supported for inference
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lironmo
commented on May 10, 2024
any plans to support this feature for training?
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Maratyszcza
commented on May 10, 2024
@lironmo: no, training in general is low-priority, and no plans to expose strided convolutions in trainingg
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