guberti commented on code in PR #13752:
URL: https://github.com/apache/tvm/pull/13752#discussion_r1113408170
##########
python/tvm/relay/qnn/strategy/arm_cpu.py:
##########
@@ -21,9 +21,55 @@
regular/depthwise conv2d is supported, but qnn_dense will be added
eventually."""
from tvm import topi, TVMError
-from .generic import qnn_conv2d_strategy
+from tvm.topi.utils import get_const_tuple
from ... import op as _op
from ...op.strategy.generic import is_depthwise_conv2d
+from .generic import (
+ qnn_conv2d_strategy,
+ qnn_dense_strategy,
+ qnn_dequantize_strategy,
+ qnn_quantize_strategy,
+ wrap_compute_dequantize,
+ wrap_compute_quantize,
+ wrap_topi_qnn_dense,
+ wrap_topi_schedule,
+)
+
+
+@qnn_quantize_strategy.register("arm_cpu")
+def qnn_quantize_strategy_arm_cpu(_attrs, _inputs, _out_type, _target):
+ """qnn.quantize strategy for arm_cpu"""
+ strategy = _op.OpStrategy()
+ strategy.add_implementation(
+ wrap_compute_quantize(topi.hexagon.qnn_quantize),
+ wrap_topi_schedule(topi.hexagon.schedule_qnn_quantize),
+ name="qnn_quantize.arm_cpu",
+ )
+ return strategy
+
+
+@qnn_dequantize_strategy.register("arm_cpu")
+def qnn_dequantize_strategy_arm_cpu(_attrs, _inputs, _out_type, _target):
+ """qnn.dequantize strategy for arm_cpu"""
+ strategy = _op.OpStrategy()
+ strategy.add_implementation(
+ wrap_compute_dequantize(topi.hexagon.qnn_dequantize),
+ wrap_topi_schedule(topi.hexagon.schedule_qnn_dequantize),
+ name="qnn_dequantize.arm_cpu",
+ )
+ return strategy
+
+
+@qnn_dense_strategy.register("arm_cpu")
+def qnn_dense_strategy_arm_cpu(_attrs, _inputs, _out_type, _target):
+ """qnn.dense strategy for arm_cpu"""
+ strategy = _op.OpStrategy()
+ strategy.add_implementation(
+ wrap_topi_qnn_dense(topi.hexagon.qnn_dense),
+ wrap_topi_schedule(topi.hexagon.schedule_qnn_dense),
Review Comment:
It's fine for the time being. I know @mkatanbaf is working on a Cortex-M
schedule for `dense`, but these operations do not take very much time on
convolutional models.
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