20564758202

Committed 29 Dec 2025 12:13AM UTC coverage: 40.366% (+1.4%) from 38.976%

Build # 20564758202

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push

github

Committed by

web-flow

Commit Message

Merge pull request #409 from daisytuner/lib-nodes-refactor

restructures library nodes

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14298 of 45900 branches covered (31.15%)

Branch coverage included in aggregate %.

259 of 388 new or added lines in 19 files covered. (66.75%)

28 existing lines in 2 files now uncovered.

12247 of 19861 relevant lines covered (61.66%)

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56.03

/src/data_flow/library_nodes/math/tensor/elementwise_ops/hard_sigmoid_node.cpp

#include "sdfg/data_flow/library_nodes/math/tensor/elementwise_ops/hard_sigmoid_node.h"

#include "sdfg/analysis/analysis.h"
#include "sdfg/builder/structured_sdfg_builder.h"

#include "sdfg/analysis/scope_analysis.h"

#include "sdfg/data_flow/library_nodes/math/cmath/cmath_node.h"

namespace sdfg {
namespace math {
namespace tensor {

HardSigmoidNode::HardSigmoidNode(
    size_t element_id,
    const DebugInfo& debug_info,
    const graph::Vertex vertex,
    data_flow::DataFlowGraph& parent,
    const std::vector<symbolic::Expression>& shape
)
    : ElementWiseUnaryNode(element_id, debug_info, vertex, parent, LibraryNodeType_HardSigmoid, shape) {
    this->inputs_.push_back("alpha");
    this->inputs_.push_back("beta");
}

bool HardSigmoidNode::expand_operation(
    builder::StructuredSDFGBuilder& builder,
    analysis::AnalysisManager& analysis_manager,
    structured_control_flow::Sequence& body,
    const std::string& input_name,
    const std::string& output_name,
    const types::IType& input_type,
    const types::IType& output_type,
    const data_flow::Subset& subset
) {
    // Add code
    auto& code_block = builder.add_block(body);
    auto& input_node = builder.add_access(code_block, input_name);
    auto& output_node_fma = builder.add_access(code_block, output_name);
    auto& output_node_min = builder.add_access(code_block, output_name);
    auto& output_node_max = builder.add_access(code_block, output_name);

    // alpha * x + beta
    {
        auto& tasklet =
            builder.add_tasklet(code_block, data_flow::TaskletCode::fp_fma, "_out", {"_in1", "_in2", "_in3"});
        builder.add_computational_memlet(code_block, input_node, tasklet, "_in1", subset, input_type);
        builder.add_computational_memlet(code_block, tasklet, "_out", output_node_fma, subset, output_type);
    }
    // min(1, x)
    {
        auto& one_node = builder.add_constant(code_block, "1.0f", types::Scalar(output_type.primitive_type()));
        auto& tasklet =
            builder.add_library_node<math::cmath::CMathNode>(code_block, code_block.debug_info(), "fmin", 2);
        builder.add_computational_memlet(code_block, output_node_fma, tasklet, "_in1", subset, output_type);
        builder.add_computational_memlet(code_block, one_node, tasklet, "_in2", subset, output_type);
        builder.add_computational_memlet(code_block, tasklet, "_out", output_node_min, subset, output_type);
    }
    // max(0, x)
    {
        auto& zero_node = builder.add_constant(code_block, "0.0f", types::Scalar(output_type.primitive_type()));
        auto& tasklet =
            builder.add_library_node<math::cmath::CMathNode>(code_block, code_block.debug_info(), "fmax", 2);
        builder.add_computational_memlet(code_block, output_node_min, tasklet, "_in1", subset, output_type);
        builder.add_computational_memlet(code_block, zero_node, tasklet, "_in2", subset, output_type);
        builder.add_computational_memlet(code_block, tasklet, "_out", output_node_max, subset, output_type);
    }

    return true;
}

std::unique_ptr<data_flow::DataFlowNode> HardSigmoidNode::
    clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {
    return std::unique_ptr<
        data_flow::DataFlowNode>(new HardSigmoidNode(element_id, this->debug_info(), vertex, parent, this->shape_));
}

} // namespace tensor
} // namespace math
} // namespace sdfg

1	#include "sdfg/data_flow/library_nodes/math/tensor/elementwise_ops/hard_sigmoid_node.h"
2
3	#include "sdfg/analysis/analysis.h"
4	#include "sdfg/builder/structured_sdfg_builder.h"
5
6	#include "sdfg/analysis/scope_analysis.h"
7
8	#include "sdfg/data_flow/library_nodes/math/cmath/cmath_node.h"
9
10	namespace sdfg {
11	namespace math {
12	namespace tensor {
13
14	HardSigmoidNode::HardSigmoidNode(	4✔
15	size_t element_id,
16	const DebugInfo& debug_info,
17	const graph::Vertex vertex,
18	data_flow::DataFlowGraph& parent,
19	const std::vector<symbolic::Expression>& shape
20	)
21	: ElementWiseUnaryNode(element_id, debug_info, vertex, parent, LibraryNodeType_HardSigmoid, shape) {	4✔
22	this->inputs_.push_back("alpha");	4!
23	this->inputs_.push_back("beta");	4!
24	}	4✔
25
26	bool HardSigmoidNode::expand_operation(	4✔
27	builder::StructuredSDFGBuilder& builder,
28	analysis::AnalysisManager& analysis_manager,
29	structured_control_flow::Sequence& body,
30	const std::string& input_name,
31	const std::string& output_name,
32	const types::IType& input_type,
33	const types::IType& output_type,
34	const data_flow::Subset& subset
35	) {
36	// Add code
37	auto& code_block = builder.add_block(body);	4!
38	auto& input_node = builder.add_access(code_block, input_name);	4!
39	auto& output_node_fma = builder.add_access(code_block, output_name);	4!
40	auto& output_node_min = builder.add_access(code_block, output_name);	4!
41	auto& output_node_max = builder.add_access(code_block, output_name);	4!
42
43	// alpha * x + beta
44	{
45	auto& tasklet =	4✔
46	builder.add_tasklet(code_block, data_flow::TaskletCode::fp_fma, "_out", {"_in1", "_in2", "_in3"});	4!
47	builder.add_computational_memlet(code_block, input_node, tasklet, "_in1", subset, input_type);	4!
48	builder.add_computational_memlet(code_block, tasklet, "_out", output_node_fma, subset, output_type);	4!
49	}
50	// min(1, x)
51	{
52	auto& one_node = builder.add_constant(code_block, "1.0f", types::Scalar(output_type.primitive_type()));	4!
53	auto& tasklet =	4✔
54	builder.add_library_node<math::cmath::CMathNode>(code_block, code_block.debug_info(), "fmin", 2);	4✔
55	builder.add_computational_memlet(code_block, output_node_fma, tasklet, "_in1", subset, output_type);	4!
56	builder.add_computational_memlet(code_block, one_node, tasklet, "_in2", subset, output_type);	4!
57	builder.add_computational_memlet(code_block, tasklet, "_out", output_node_min, subset, output_type);	4!
58	}
59	// max(0, x)
60	{
61	auto& zero_node = builder.add_constant(code_block, "0.0f", types::Scalar(output_type.primitive_type()));	4!
62	auto& tasklet =	4✔
63	builder.add_library_node<math::cmath::CMathNode>(code_block, code_block.debug_info(), "fmax", 2);	4✔
64	builder.add_computational_memlet(code_block, output_node_min, tasklet, "_in1", subset, output_type);	4!
65	builder.add_computational_memlet(code_block, zero_node, tasklet, "_in2", subset, output_type);	4!
66	builder.add_computational_memlet(code_block, tasklet, "_out", output_node_max, subset, output_type);	4!
67	}
68
69	return true;	4✔
70	}	×
71
72	std::unique_ptr<data_flow::DataFlowNode> HardSigmoidNode::
73	clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {	×
NEW 74	return std::unique_ptr<	×
NEW 75	data_flow::DataFlowNode>(new HardSigmoidNode(element_id, this->debug_info(), vertex, parent, this->shape_));	×
76	}	×
77
78	} // namespace tensor
79	} // namespace math
80	} // namespace sdfg

daisytuner / sdfglib / 20564758202

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