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Qualcomm AI Engine Direct - Op enablement round, floor, atan (#12298)
### Summary • Enable round, floor, atan • test cases ### Test plan python backends/qualcomm/tests/test_qnn_delegate.py -k TestQNNQuantizedOperators.test_qnn_backend_round -s $DEVICE_SERIAL -m SM8650 -b build-android/ python backends/qualcomm/tests/test_qnn_delegate.py -k TestQNNFloatingPointOperators.test_qnn_backend_round -s $DEVICE_SERIAL -m SM8650 -b build-android/ python backends/qualcomm/tests/test_qnn_delegate.py -k TestQNNQuantizedOperators.test_qnn_backend_floor -s $DEVICE_SERIAL -m SM8650 -b build-android/ python backends/qualcomm/tests/test_qnn_delegate.py -k TestQNNFloatingPointOperators.test_qnn_backend_floor -s $DEVICE_SERIAL -m SM8650 -b build-android/ python backends/qualcomm/tests/test_qnn_delegate.py -k TestQNNQuantizedOperators.test_qnn_backend_atan -s $DEVICE_SERIAL -m SM8650 -b build-android/ python backends/qualcomm/tests/test_qnn_delegate.py -k TestQNNFloatingPointOperators.test_qnn_backend_atan -s $DEVICE_SERIAL -m SM8650 -b build-android/ Author: @thchenqti Co-authored-by: thchenqti <[email protected]>
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‎backends/qualcomm/_passes/layout_transform.py‎

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@@ -63,6 +63,7 @@ class LayoutTransform(ExportPass):
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exir_ops.edge.aten.abs.default,
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exir_ops.edge.aten.add.Tensor,
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exir_ops.edge.aten.amax.default,
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exir_ops.edge.aten.atan.default,
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exir_ops.edge.aten.bitwise_or.Tensor,
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exir_ops.edge.aten.bmm.default,
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exir_ops.edge.aten.bitwise_and.Tensor,
@@ -75,6 +76,7 @@ class LayoutTransform(ExportPass):
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exir_ops.edge.aten.elu.default,
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exir_ops.edge.aten.eq.Tensor,
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exir_ops.edge.aten.exp.default,
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exir_ops.edge.aten.floor.default,
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exir_ops.edge.aten.full.default,
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exir_ops.edge.aten.full_like.default,
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exir_ops.edge.aten.ge.Tensor,
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exir_ops.edge.aten.pow.Tensor_Scalar,
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exir_ops.edge.aten.prelu.default,
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exir_ops.edge.aten.repeat.default,
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exir_ops.edge.aten.round.default,
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exir_ops.edge.aten.relu.default,
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exir_ops.edge.aten.sigmoid.default,
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exir_ops.edge.aten.split_with_sizes.default,

‎backends/qualcomm/builders/README.md‎

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@@ -360,7 +360,7 @@ The operator now should be functional for Qualcomm backends. For operator to wor
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## Operator Support Status
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Please help update following table if you are contributing new operators:
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| Operators | HTP - 77/116 Enabled |
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| Operators | HTP - 80/116 Enabled |
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|-----------|---------|
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| Argmax | &cross; |
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| Argmin | &check; |
@@ -382,14 +382,14 @@ Please help update following table if you are contributing new operators:
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| ElementWiseAdd | &check; |
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| ElementWiseAnd | &check; |
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| ElementWiseAsin | &cross; |
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| ElementWiseAtan | &cross; |
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| ElementWiseAtan | &check; |
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| ElementWiseBinary | &cross; |
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| ElementWiseCeil | &check; |
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| ElementWiseCos | &check; |
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| ElementWiseDivide | &check; |
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| ElementWiseEqual | &check; |
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| ElementWiseExp | &check; |
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| ElementWiseFloor | &cross; |
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| ElementWiseFloor | &check; |
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| ElementWiseFloorDiv | &cross; |
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| ElementWiseGreater | &check; |
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| ElementWiseGreaterEqual | &check; |
@@ -405,7 +405,7 @@ Please help update following table if you are contributing new operators:
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| ElementWiseNotEqual | &check; |
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| ElementWiseOr | &check; |
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| ElementWisePower | &check; |
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| ElementWiseRound | &cross; |
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| ElementWiseRound | &check; |
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| ElementWiseRsqrt | &check; |
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| ElementWiseSelect | &check; |
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| ElementWiseSign | &cross; |

‎backends/qualcomm/builders/__init__.py‎

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@@ -13,6 +13,7 @@
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op_and,
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op_arange,
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op_argmin,
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op_atan,
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op_avg_pool2d,
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op_batch_norm,
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op_bmm,
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op_eq,
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op_exp,
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op_expand,
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op_floor,
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op_full,
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op_full_like,
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op_gather,
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op_reshape,
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op_resize,
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op_rms_norm,
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op_round,
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op_rsqrt,
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op_scalar_tensor,
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op_select_copy,
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op_and,
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op_arange,
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op_argmin,
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op_atan,
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op_avg_pool2d,
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op_batch_norm,
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op_bmm,
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op_eq,
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op_exp,
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op_expand,
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op_floor,
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op_full,
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op_full_like,
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op_gather,
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op_reshape,
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op_resize,
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op_rms_norm,
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op_round,
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op_rsqrt,
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op_scalar_tensor,
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op_select_copy,

‎backends/qualcomm/builders/op_atan.py‎

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@@ -0,0 +1,55 @@
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# Copyright (c) Qualcomm Innovation Center, Inc.
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# All rights reserved
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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from typing import Dict
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import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper
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import torch
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from .node_visitor import NodeVisitor
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from .node_visitor_manager import register_node_visitor
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from .qnn_constants import OpElementWiseAtan, QNN_OP_PACKAGE_NAME_QTI_AISW
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@register_node_visitor
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class Atan(NodeVisitor):
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target = ["aten.atan.default"]
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def __init__(self, *args) -> None:
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super().__init__(*args)
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def define_node(
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self,
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node: torch.fx.Node,
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nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper],
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) -> PyQnnWrapper.PyQnnOpWrapper:
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input_node = self.get_node(node.args[0])
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input_tensor = self.get_tensor(input_node, node)
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input_tensor_wrapper = self.define_tensor(
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input_node,
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node,
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input_tensor,
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PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
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nodes_to_wrappers,
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)
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output_tensor = self.get_tensor(node, node)
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output_tensor_wrapper = self.define_tensor(
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node,
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node,
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output_tensor,
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PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
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nodes_to_wrappers,
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)
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atan_op = PyQnnWrapper.PyQnnOpWrapper(
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node.name,
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QNN_OP_PACKAGE_NAME_QTI_AISW,
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OpElementWiseAtan.op_name,
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)
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atan_op.AddInputTensors([input_tensor_wrapper])
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atan_op.AddOutputTensors([output_tensor_wrapper])
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return atan_op

‎backends/qualcomm/builders/op_floor.py‎

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# Copyright (c) Qualcomm Innovation Center, Inc.
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# All rights reserved
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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from typing import Dict
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import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper
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import torch
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from .node_visitor import NodeVisitor
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from .node_visitor_manager import register_node_visitor
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from .qnn_constants import OpElementWiseFloor, QNN_OP_PACKAGE_NAME_QTI_AISW
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@register_node_visitor
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class Floor(NodeVisitor):
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target = ["aten.floor.default"]
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def __init__(self, *args) -> None:
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super().__init__(*args)
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def define_node(
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self,
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node: torch.fx.Node,
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nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper],
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) -> PyQnnWrapper.PyQnnOpWrapper:
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input_node = self.get_node(node.args[0])
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input_tensor = self.get_tensor(input_node, node)
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floor_inp_tensor_wrapper = self.define_tensor(
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input_node,
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node,
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input_tensor,
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PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
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nodes_to_wrappers,
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)
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floor_input_tensors = [floor_inp_tensor_wrapper]
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39+
output_tensor = self.get_tensor(node, node)
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output_tensor_wrapper = self.define_tensor(
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node,
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node,
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output_tensor,
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PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
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nodes_to_wrappers,
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)
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floor_output_tensors = [output_tensor_wrapper]
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floor_op = PyQnnWrapper.PyQnnOpWrapper(
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node.name,
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QNN_OP_PACKAGE_NAME_QTI_AISW,
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OpElementWiseFloor.op_name,
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)
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floor_op.AddInputTensors(floor_input_tensors)
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floor_op.AddOutputTensors(floor_output_tensors)
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return floor_op

‎backends/qualcomm/builders/op_round.py‎

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import warnings
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from typing import Dict
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import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper
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import torch
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from .node_visitor import NodeVisitor
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from .node_visitor_manager import register_node_visitor
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from .qnn_constants import OpElementWiseRound, QNN_OP_PACKAGE_NAME_QTI_AISW
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@register_node_visitor
14+
class Round(NodeVisitor):
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target = ["aten.round.default"]
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def __init__(self, *args) -> None:
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super().__init__(*args)
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def define_node(
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self,
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node: torch.fx.Node,
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nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper],
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) -> PyQnnWrapper.PyQnnOpWrapper:
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input_node = self.get_node(node.args[0])
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input_tensor = self.get_tensor(input_node, node)
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input_tensor_wrapper = self.define_tensor(
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input_node,
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node,
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input_tensor,
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PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
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nodes_to_wrappers,
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)
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if len(node.args) > 1:
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warnings.warn(
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"[QNN Delegate Op Builder]: QNN dose not support decimals",
38+
stacklevel=1,
39+
)
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return None
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output_tensor = self.get_tensor(node, node)
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output_tensor_wrapper = self.define_tensor(
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node,
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node,
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output_tensor,
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PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
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nodes_to_wrappers,
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)
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round_op = PyQnnWrapper.PyQnnOpWrapper(
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node.name,
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QNN_OP_PACKAGE_NAME_QTI_AISW,
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OpElementWiseRound.op_name,
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)
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round_op.AddInputTensors([input_tensor_wrapper])
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round_op.AddOutputTensors([output_tensor_wrapper])
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return round_op

‎backends/qualcomm/builders/qnn_constants.py‎

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op_name: str = "ElementWiseAnd"
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@dataclass(init=False, frozen=True)
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class OpElementWiseAtan:
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op_name: str = "ElementWiseAtan"
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@dataclass(init=False, frozen=True)
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class OpElementWiseCeil:
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op_name = "ElementWiseCeil"
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op_name: str = "ElementWiseEqual"
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132137

138+
@dataclass(init=False, frozen=True)
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class OpElementWiseFloor:
140+
op_name: str = "ElementWiseFloor"
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@dataclass(init=False, frozen=True)
134144
class OpElementWiseGreater:
135145
op_name: str = "ElementWiseGreater"
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op_name: str = "ElementWisePower"
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205215

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@dataclass(init=False, frozen=True)
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class OpElementWiseRound:
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op_name: str = "ElementWiseRound"
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@dataclass(init=False, frozen=True)
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class OpElementWiseRsqrt:
208223
op_name: str = "ElementWiseRsqrt"

‎backends/qualcomm/quantizer/annotators.py‎

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)
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@register_annotator([torch.ops.aten.atan.default])
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def annotate_atan(node: Node, quantization_config: QuantizationConfig) -> None:
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annotate_single_in_single_out(node, quantization_config)
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@register_annotator([torch.ops.aten.topk.default])
167172
def annotate_topk(node: Node, quantization_config: QuantizationConfig) -> None:
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if _is_annotated([node]):
@@ -404,6 +409,11 @@ def annotate_clamp(node: Node, quantization_config: QuantizationConfig) -> None:
404409
annotate_single_in_single_out(node, quantization_config)
405410

406411

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@register_annotator([torch.ops.aten.floor.default])
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def annotate_floor(node: Node, quantization_config: QuantizationConfig) -> None:
414+
annotate_single_in_single_out(node, quantization_config)
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407417
@register_annotator([torch.ops.aten.relu.default, torch.ops.aten.relu_.default])
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def annotate_relu(node: Node, quantization_config: QuantizationConfig) -> None:
409419
annotate_single_in_single_out(node, quantization_config)
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414424
annotate_single_in_single_out(node, quantization_config)
415425

416426

427+
@register_annotator([torch.ops.aten.round.default])
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def annotate_round(node: Node, quantization_config: QuantizationConfig) -> None:
429+
annotate_single_in_single_out(node, quantization_config)
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431+
417432
@register_annotator([torch.ops.aten.cos.default])
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def annotate_cos(node: Node, quantization_config: QuantizationConfig) -> None:
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annotate_single_in_single_out(node, quantization_config)

‎backends/qualcomm/tests/models.py‎

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return squeeze_out, conv_out
147147

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class Atan(torch.nn.Module):
150+
def __init__(self):
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super().__init__()
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def forward(self, x):
154+
return torch.atan(x)
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149157
class AvgPoolModule(torch.nn.Module):
150158
def __init__(self, kernel_size, stride, padding, ceil_mode):
151159
super().__init__()
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741749
return torch.special.expm1(x)
742750

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752+
class Floor(torch.nn.Module):
753+
def __init__(self):
754+
super().__init__()
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def forward(self, x):
757+
return torch.floor(x)
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744760
class Fold(torch.nn.Module):
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def __init__(self):
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super().__init__()
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14491465
return torch.roll(x, shifts=self.shifts, dims=self.dims)
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14511467

1468+
class Round(torch.nn.Module):
1469+
def __init__(self):
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super().__init__()
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def forward(self, x):
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return torch.round(x)
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class Rsqrt(torch.nn.Module):
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def __init__(self):
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super().__init__()

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