>From the [discussion about running sparse >CNNs](https://discuss.tvm.ai/t/running-a-cnn-using-sparsity-convertor/7267/11), > I have implemented prototypes of a dense NCHW GEMM convolution, and what I >think is a working CSR NCHW GEMM convolution.
I will share the code once it's a bit more mature. I'm doing sparse GEMM convolution first, rather than sparse spatial pack, since there are already sparse GEMM methods in TVM I can leverage, whereas a sparse spatial pack would require some cleverness. However, when building my standalone function as a module, I am getting the following error: ``` from tvm.contrib import sparse # create placeholder tensors ... n = out_c k = kdim_h * kdim_w * in_c sparse_weights = sparse.placeholder((n,k), nonzeros=(1-sparsity)*n*k, name='W') # weights ... # create output using compositions of te.compute conv = gemm_conv_sparse(sparse_weights, ...) # build function s = te.create_schedule(conv.op) dtype = 'float32' func = tvm.build(s, [data, sparse_weights, conv], target=target, name='gemm_conv2d') # ^ returns ValueError: args must be Tensor, Buffer or Var ``` Any ideas on how I'd pass my `sparse.placeholder` to `tvm.build`? --- [Visit Topic](https://discuss.tvm.ai/t/pass-sparse-tensor-to-tvm-build/7739/1) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/a68bc10b57cb3d7036e664665391a73545c8a1383018fe3568079f633ff36114).
