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DannyYuyang-quicDannyYuyang-quic
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Qualcomm AI Engine Direct - oss model enablement (EfficientSAM)
 - e2e script for https://github.com/yformer/EfficientSAM - Fastvit breakage fix - Passes order correction - Add support for cum_sum
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19 files changed

+752
-13
lines changed

19 files changed

+752
-13
lines changed

‎backends/qualcomm/_passes/layout_transform.py‎

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@@ -52,6 +52,7 @@ class LayoutTransform(ExportPass):
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exir_ops.edge.aten.ceil.default,
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exir_ops.edge.aten.clamp.default,
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exir_ops.edge.aten.constant_pad_nd.default,
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exir_ops.edge.aten.cumsum.default,
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exir_ops.edge.aten.div.Tensor,
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exir_ops.edge.aten.eq.Tensor,
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exir_ops.edge.aten.full.default,

‎backends/qualcomm/_passes/lift_constant_scalar_operands.py‎

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@@ -46,6 +46,7 @@ class TensorOpInfo:
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aten.pow.Tensor_Scalar: TensorOpInfo(aten.pow.Tensor_Tensor, False),
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# The scalar number arg[1] is missing when using default. Result in a corner case to deal
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aten.leaky_relu.default: TensorOpInfo(aten.prelu.default, True),
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aten.where.ScalarOther: TensorOpInfo(aten.where.self, False),
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}
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‎backends/qualcomm/_passes/recompose_pixel_unshuffle.py‎

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@@ -45,13 +45,11 @@ def call(self, graph_module: torch.fx.GraphModule):
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continue
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view_node = premute_node.args[0]
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if any(
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[
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view_node.op != "call_function",
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view_node.target != self.view_target,
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len(view_node.args[1]) != 6,
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len(premute_node.args[1]) != 6,
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]
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if (
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view_node.op != "call_function"
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or view_node.target != self.view_target
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or len(view_node.args[1]) != 6
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or len(premute_node.args[1]) != 6
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):
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continue
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‎backends/qualcomm/_passes/utils.py‎

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@@ -93,7 +93,7 @@ def get_passes_dependency_for_capture_program():
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ConvertToLinear: [RecomposePixelUnshuffle],
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DecomposeAny: [RemoveRedundancy],
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DecomposeLinalgVectorNorm: [RemoveRedundancy],
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ExpandBroadcastTensorShape: [RemoveRedundancy],
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ExpandBroadcastTensorShape: [ConstantI64toI32, TensorI64toI32],
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FoldQDQ: [AnnotateQuantAttrs, AnnotateDecomposed],
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LayoutTransform: [
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AnnotateQuantAttrs,

‎backends/qualcomm/builders/__init__.py‎

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@@ -19,6 +19,7 @@
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op_clamp,
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op_conv2d,
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op_cos,
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op_cum_sum,
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op_depth_to_space,
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op_dequantize,
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op_div,
@@ -98,6 +99,7 @@
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op_clamp,
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op_conv2d,
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op_cos,
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op_cum_sum,
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op_depth_to_space,
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op_dequantize,
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op_div,

‎backends/qualcomm/builders/op_cos.py‎

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@@ -3,7 +3,6 @@
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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.
6-
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from typing import Dict
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import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper

‎backends/qualcomm/builders/op_cum_sum.py‎

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@@ -0,0 +1,84 @@
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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 cast, Dict
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import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper
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import numpy as np
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import torch
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from executorch.backends.qualcomm.utils.constants import QCOM_AXIS_ORDER, QCOM_DATA
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from .node_visitor import NodeVisitor, register_node_visitor
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from .qnn_constants import OpCumulativeSum, QNN_OP_PACKAGE_NAME_QTI_AISW
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@register_node_visitor
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class CumulativeSum(NodeVisitor):
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target = ["aten.cumsum.default"]
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def __init__(self, *args) -> None:
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super().__init__(*args)
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def get_param(self, node, input_tensor):
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dim = node.args[1]
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if dim < 0:
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dim = dim % len(input_tensor.shape)
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if QCOM_AXIS_ORDER in node.meta:
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dim = node.meta[QCOM_AXIS_ORDER].index(dim)
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return cast(np.uint32, dim)
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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 = 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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dim = self.get_param(node, input_tensor)
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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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cumsum_op = PyQnnWrapper.PyQnnOpWrapper(
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node.name,
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QNN_OP_PACKAGE_NAME_QTI_AISW,
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OpCumulativeSum.op_name,
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)
66+
cumsum_op.AddInputTensors([input_tensor_wrapper])
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cumsum_op.AddOutputTensors([output_tensor_wrapper])
68+
cumsum_op.AddScalarParam(
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OpCumulativeSum.param_axis,
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PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32,
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{QCOM_DATA: dim},
72+
)
73+
cumsum_op.AddScalarParam(
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OpCumulativeSum.param_exclusive,
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PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_BOOL_8,
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{QCOM_DATA: False},
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)
78+
cumsum_op.AddScalarParam(
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OpCumulativeSum.param_reverse,
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PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_BOOL_8,
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{QCOM_DATA: False},
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)
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return cumsum_op

‎backends/qualcomm/builders/op_sin.py‎

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@@ -3,7 +3,6 @@
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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

‎backends/qualcomm/builders/qnn_constants.py‎

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@@ -50,6 +50,14 @@ class OpConvert:
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op_name: str = "Convert"
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@dataclass(init=False, frozen=True)
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class OpCumulativeSum:
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op_name = "CumulativeSum"
56+
param_axis = "axis"
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param_exclusive = "exclusive"
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param_reverse = "reverse"
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@dataclass(init=False, frozen=True)
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class OpDepthToSpace:
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op_name: str = "DepthToSpace"

‎backends/qualcomm/quantizer/annotators.py‎

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@@ -920,6 +920,11 @@ def annotate_conv2d(node: Node, quantization_config: QuantizationConfig) -> None
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)
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@register_annotator([torch.ops.aten.cumsum.default])
924+
def annotate_cumsum(node: Node, quantization_config: QuantizationConfig) -> None:
925+
annotate_single_in_single_out(node, quantization_config)
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@register_annotator([torch.ops.aten.linear.default])
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def annotate_linear(node: Node, quantization_config: QuantizationConfig) -> None:
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act_node = node.args[0]

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