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add operator
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backends/xnnpack/operators/__init__.py

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op_hardtanh,
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op_leaky_relu,
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op_linear,
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op_log,
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op_matrix_multiplication,
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op_max_dim,
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op_max_pool2d,
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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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 torch
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from executorch.backends.xnnpack.operators.node_visitor import (
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NodeVisitor,
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register_node_visitor,
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)
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from executorch.backends.xnnpack.serialization.xnnpack_graph_schema import (
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XNNGraph,
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XNNLog,
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XNode,
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)
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from executorch.backends.xnnpack.utils.utils import get_input_node
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@register_node_visitor
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class LogVisitor(NodeVisitor):
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target = "aten.log.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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xnn_graph: XNNGraph,
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vals_to_ids: Dict[torch.fx.Node, int],
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debug_handle: int,
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) -> None:
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self.define_nodes_tensor_inputs_outputs(node, xnn_graph, vals_to_ids)
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# input
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input_id = vals_to_ids[get_input_node(node, 0)]
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# output
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output_id = vals_to_ids[node]
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ser_node = XNode(
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xnode_union=XNNLog(
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input_id=input_id,
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output_id=output_id,
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flags=0,
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),
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debug_handle=debug_handle,
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)
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xnn_graph.xnodes.append(ser_node)

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