[TOPI][OP] cuda for argwhere

python/tvm/relay/op/_transform.py

@@ -83,21 +83,7 @@ def compute_strided_set(attrs, inputs, output_type):
 _reg.register_pattern("auto_scheduler_layout_transform", OpPattern.INJECTIVE)
 
 # argwhere
-@_reg.register_compute("argwhere")
-def compute_argwhere(attrs, inputs, output_type):
-    """Compute definition of argwhere"""
-    output_shape = []
-    for s in output_type.shape:
-        if hasattr(s, "value"):
-            output_shape.append(s)
-        else:
-            # see Any, replace it with a var
-            output_shape.append(te.var("any_dim", "int32"))
-    new_output_type = tvm.relay.ty.TensorType(output_shape, "int32")
-    return [topi.argwhere(new_output_type, inputs[0])]
-
-
-_reg.register_schedule("argwhere", strategy.schedule_argwhere)
+_reg.register_strategy("argwhere", strategy.argwhere_strategy)
 
 # scatter
 @_reg.register_compute("scatter")

python/tvm/relay/op/strategy/cuda.py

@@ -921,3 +921,15 @@ def correlation_strategy_cuda(attrs, inputs, out_type, target):
         name="correlation.cuda",
     )
     return strategy
+
+
+@argwhere_strategy.register(["cuda", "gpu"])
+def argwhere_strategy_cuda(attrs, inputs, out_type, target):
+    """argwhere cuda strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_argwhere(topi.cuda.argwhere),
+        wrap_topi_schedule(topi.cuda.schedule_argwhere),
+        name="argwhere.cuda",
+    )
+    return strategy

python/tvm/relay/op/strategy/generic.py

@@ -19,7 +19,7 @@
 import logging
 
 import re
-from tvm import topi, _ffi
+from tvm import topi, _ffi, te, ir
 from tvm.topi.utils import get_const_int, get_const_float, get_const_tuple, get_float_tuple
 from tvm.target import generic_func, override_native_generic_func
 from .. import op as _op
@@ -1034,14 +1034,6 @@ def proposal_strategy(attrs, inputs, out_type, target):
     return strategy
 
 
-# argwhere
-@generic_func
-def schedule_argwhere(attrs, outs, target):
-    """schedule argwhere"""
-    with target:
-        return topi.generic.schedule_argwhere(outs)
-
-
 # scatter
 @override_native_generic_func("scatter_strategy")
 def scatter_strategy(attrs, outs, out_type, target):
@@ -1223,3 +1215,32 @@ def correlation_strategy(attrs, inputs, out_type, target):
         name="correlation.generic",
     )
     return strategy
+
+
+# argwhere
+def wrap_compute_argwhere(topi_compute):
+    """wrap argwhere topi compute"""
+
+    def _compute_argwhere(attrs, inputs, out_type):
+        output_shape = []
+        for s in out_type.shape:
+            if hasattr(s, "value"):
+                output_shape.append(s)
+            else:
+                output_shape.append(te.var("any_dim", "int32"))
+        new_output_type = ir.TensorType(output_shape, "int32")
+        return [topi_compute(new_output_type, inputs[0])]
+
+    return _compute_argwhere
+
+
+@override_native_generic_func("argwhere_strategy")
+def argwhere_strategy(attrs, inputs, out_type, target):
+    """argwhere generic strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_argwhere(topi.argwhere),
+        wrap_topi_schedule(topi.generic.schedule_argwhere),
+        name="argwhere.generic",
+    )
+    return strategy

python/tvm/topi/argwhere.py

@@ -16,6 +16,7 @@
 # under the License.
 # pylint: disable=invalid-name, too-many-arguments, too-many-nested-blocks
 """Argwhere operator"""
+import tvm
 from tvm.te import hybrid
 
 
@@ -169,6 +170,7 @@ def hybrid_argwhere_5d(output_shape, condition):
     return a
 
 
+@tvm.target.generic_func
 def argwhere(output_shape, condition):
     """Find the indices of elements of a tensor that are non-zero.
 

python/tvm/topi/cuda/__init__.py

@@ -54,3 +54,4 @@
 from .conv2d_hwnc_tensorcore import *
 from .correlation import *
 from .sparse import *
+from .argwhere import *