yuchaoli commented on a change in pull request #8065:
URL: https://github.com/apache/tvm/pull/8065#discussion_r634060352
##########
File path: tutorials/auto_scheduler/tune_network_x86.py
##########
@@ -138,7 +140,9 @@ def get_network(name, batch_size, layout="NHWC",
dtype="float32", use_sparse=Fal
if use_sparse:
from tvm.topi.sparse.utils import convert_model_dense_to_sparse
- mod, params = convert_model_dense_to_sparse(mod, params,
random_params=True)
+ mod, params = convert_model_dense_to_sparse(
+ mod, params, bs_r=sparse_block_size[0], bs_c=sparse_block_size[1],
random_params=True
Review comment:
Done.
##########
File path: tutorials/auto_scheduler/tune_network_x86.py
##########
@@ -205,7 +215,33 @@ def run_tuning():
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
- tuner.tune(tune_option)
+ if use_sparse:
+ from tvm.topi.sparse.utils import (
+ sparse_conv2d_meet_condition_func,
+ sparse_conv2d_apply_func,
+ sparse_dense_meet_condition_func,
+ sparse_dense_apply_func,
+ )
+
+ search_policy = [
+ auto_scheduler.SketchPolicy(
+ task,
+ program_cost_model=auto_scheduler.XGBModel(),
+ init_search_callbacks=[
+ auto_scheduler.PreloadCustomSketchRule(
+ sparse_conv2d_meet_condition_func,
sparse_conv2d_apply_func, "SparseConv2D"
+ ),
+ auto_scheduler.PreloadCustomSketchRule(
+ sparse_dense_meet_condition_func,
sparse_dense_apply_func, "SparseDense"
+ ),
+ ],
+ )
+ for task in tasks
+ ]
Review comment:
Done.
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