[GitHub] [tvm] jcf94 commented on a change in pull request #8065: [Autoscheduler] Add sparse conv2d(1*1) support for auto_scheduler

[GitBox <gi...@apache.org>]

jcf94 commented on a change in pull request #8065:
URL: https://github.com/apache/tvm/pull/8065#discussion_r634013363



##########
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:
       Let's keep this tutorial simple.
   
   Suggest to move the sparse_block_size setting inside this function, for example directly set the block size here.
   
   So that we can avoid to modify the function parameters and add `sparse_block_size = (8, 1)` outside.

##########
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:
       How about to pack these 4 condition func & apply func to a single entrance. For example:
   ```python
   def sparse_sketch_rules() -> List[PreloadCustomSketchRule]
       ...
   
   ...
   init_search_callbacks=sparse_sketch_rules(),
   ...
   ```




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