jcf94 commented on a change in pull request #7313:
URL: https://github.com/apache/tvm/pull/7313#discussion_r566526531
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File path: python/tvm/auto_scheduler/measure.py
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@@ -719,6 +722,87 @@ def local_builder_build(inputs, timeout, n_parallel,
build_func="default", verbo
return results
+def _process_sparse_input(args):
+ sparse_prefix = sparse_data = sparse_indices = sparse_indptr = None
+
+ def _process_inputs(input_tensors, M, N, prefix_init):
+ nonlocal sparse_prefix
+ nonlocal sparse_data
+ nonlocal sparse_indices
+ nonlocal sparse_indptr
+
+ assert len(input_tensors) == 4
+ unsure_tensors = list(input_tensors)
+ # Get the Dense data
+ dense_data = None
+ for tensor in unsure_tensors:
+ if len(tensor.shape) == 2:
+ assert dense_data is None
+ dense_data = tensor
+ assert M == dense_data.shape[0]
+ K = dense_data.shape[1]
+ unsure_tensors.remove(dense_data)
+
+ # Get the Sparse data
+ sparse_data = None
+ for tensor in unsure_tensors:
+ if len(tensor.shape) == 3:
+ assert sparse_data is None
+ sparse_data = tensor
+ block_size, BS_R, BS_C = sparse_data.shape
+ unsure_tensors.remove(sparse_data)
+
+ # Get the Sparse indptr & indices
+ sparse_indices = None
+ for tensor in unsure_tensors:
+ assert len(tensor.shape) == 1
+ if tensor.shape[0] == block_size:
+ assert sparse_indices is None
+ sparse_indices = tensor
+ unsure_tensors.remove(sparse_indices)
+ assert len(unsure_tensors) == 1
+ sparse_indptr = unsure_tensors[0]
+
+ # Generate the sparse_prefix
+ density = 1.0
+ for i in sparse_data.shape:
+ density *= i
+ density /= (K * N)
+ density = density.value
+ sparse_prefix = "%s_%d_%d_%d_%d_%d_%.2f_" % (
Review comment:
Though in my test a schedule seems to have similar performance with
different random sparse data, I think that may still be a potential problem.
Unfortunately, I have not figured out any better solution.
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