20561540736

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

Build # 20561540736

Build Type

push

github

Committed by

web-flow

Commit Message

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

restructures library nodes

Run Details

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%)

89.04 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

45.0

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

#include "sdfg/data_flow/library_nodes/math/tensor/elementwise_ops/elu_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 {

EluNode::EluNode(
    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_Elu, shape) {
    this->inputs_.push_back("alpha");
}

bool EluNode::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_exp = builder.add_access(code_block, output_name);
    auto& output_node_sub = builder.add_access(code_block, output_name);
    auto& output_node_mul = builder.add_access(code_block, output_name);

    types::Scalar desc(types::PrimitiveType::Float);

    // 1. exp(x)
    {
        auto& tasklet =
            builder.add_library_node<math::cmath::CMathNode>(code_block, code_block.debug_info(), "expf", 1);
        builder.add_computational_memlet(code_block, input_node, tasklet, "_in1", subset, input_type);
        builder.add_computational_memlet(code_block, tasklet, "_out", output_node_exp, subset, output_type);
    }
    // 2. x - 1.0f
    {
        auto& one_node = builder.add_constant(code_block, "1.0f", types::Scalar(output_type.primitive_type()));
        auto& tasklet = builder.add_tasklet(code_block, data_flow::TaskletCode::fp_sub, "_out", {"_in1", "_in2"});
        builder.add_computational_memlet(code_block, output_node_exp, tasklet, "_in1", subset, output_type);
        builder.add_computational_memlet(code_block, one_node, tasklet, "_in2", {}, desc);
        builder.add_computational_memlet(code_block, tasklet, "_out", output_node_sub, subset, output_type);
    }
    // 3. alpha * x
    {
        auto& tasklet = builder.add_tasklet(code_block, data_flow::TaskletCode::fp_mul, "_out", {"_in1", "_in2"});
        builder.add_computational_memlet(code_block, output_node_sub, tasklet, "_in1", subset, output_type);
        builder.add_computational_memlet(code_block, tasklet, "_out", output_node_mul, subset, output_type);

        // Find alpha node
        auto& graph = this->get_parent();
        const data_flow::Memlet* alpha_memlet = nullptr;
        for (auto& in_edge : graph.in_edges(*this)) {
            if (in_edge.dst_conn() == "alpha") {
                alpha_memlet = &in_edge;
                break;
            }
        }

        data_flow::AccessNode* alpha_node = nullptr;
        if (alpha_memlet) {
            auto& src = dynamic_cast<const data_flow::AccessNode&>(alpha_memlet->src());
            if (auto const_node = dynamic_cast<const data_flow::ConstantNode*>(&src)) {
                alpha_node = &builder.add_constant(code_block, const_node->data(), const_node->type());
            } else {
                alpha_node = &builder.add_access(code_block, src.data());
            }
            builder.add_computational_memlet(
                code_block, *alpha_node, tasklet, "_in2", alpha_memlet->subset(), alpha_memlet->base_type()
            );
        } else {
            alpha_node = &builder.add_constant(code_block, "1.0f", desc);
            builder.add_computational_memlet(code_block, *alpha_node, tasklet, "_in2", {}, desc);
        }
    }

    return true;
}

std::unique_ptr<data_flow::DataFlowNode> EluNode::
    clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {
    return std::unique_ptr<
        data_flow::DataFlowNode>(new EluNode(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/elu_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	EluNode::EluNode(	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_Elu, shape) {	4✔
22	this->inputs_.push_back("alpha");	4!
23	}	4✔
24
25	bool EluNode::expand_operation(	4✔
26	builder::StructuredSDFGBuilder& builder,
27	analysis::AnalysisManager& analysis_manager,
28	structured_control_flow::Sequence& body,
29	const std::string& input_name,
30	const std::string& output_name,
31	const types::IType& input_type,
32	const types::IType& output_type,
33	const data_flow::Subset& subset
34	) {
35	// Add code
36	auto& code_block = builder.add_block(body);	4!
37	auto& input_node = builder.add_access(code_block, input_name);	4!
38	auto& output_node_exp = builder.add_access(code_block, output_name);	4!
39	auto& output_node_sub = builder.add_access(code_block, output_name);	4!
40	auto& output_node_mul = builder.add_access(code_block, output_name);	4!
41
42	types::Scalar desc(types::PrimitiveType::Float);	4✔
43
44	// 1. exp(x)
45	{
46	auto& tasklet =	4✔
47	builder.add_library_node<math::cmath::CMathNode>(code_block, code_block.debug_info(), "expf", 1);	4!
48	builder.add_computational_memlet(code_block, input_node, tasklet, "_in1", subset, input_type);	4!
49	builder.add_computational_memlet(code_block, tasklet, "_out", output_node_exp, subset, output_type);	4!
50	}
51	// 2. x - 1.0f
52	{
53	auto& one_node = builder.add_constant(code_block, "1.0f", types::Scalar(output_type.primitive_type()));	4!
54	auto& tasklet = builder.add_tasklet(code_block, data_flow::TaskletCode::fp_sub, "_out", {"_in1", "_in2"});	4!
55	builder.add_computational_memlet(code_block, output_node_exp, tasklet, "_in1", subset, output_type);	4!
56	builder.add_computational_memlet(code_block, one_node, tasklet, "_in2", {}, desc);	4!
57	builder.add_computational_memlet(code_block, tasklet, "_out", output_node_sub, subset, output_type);	4!
58	}
59	// 3. alpha * x
60	{
61	auto& tasklet = builder.add_tasklet(code_block, data_flow::TaskletCode::fp_mul, "_out", {"_in1", "_in2"});	4!
62	builder.add_computational_memlet(code_block, output_node_sub, tasklet, "_in1", subset, output_type);	4!
63	builder.add_computational_memlet(code_block, tasklet, "_out", output_node_mul, subset, output_type);	4!
64
65	// Find alpha node
66	auto& graph = this->get_parent();	4!
67	const data_flow::Memlet* alpha_memlet = nullptr;	4✔
68	for (auto& in_edge : graph.in_edges(*this)) {	8!
69	if (in_edge.dst_conn() == "alpha") {	4!
70	alpha_memlet = &in_edge;	×
71	break;	×
72	}
73	}
74
75	data_flow::AccessNode* alpha_node = nullptr;	4✔
76	if (alpha_memlet) {	4!
NEW 77	auto& src = dynamic_cast<const data_flow::AccessNode&>(alpha_memlet->src());	×
NEW 78	if (auto const_node = dynamic_cast<const data_flow::ConstantNode*>(&src)) {	×
NEW 79	alpha_node = &builder.add_constant(code_block, const_node->data(), const_node->type());	×
NEW 80	} else {	×
NEW 81	alpha_node = &builder.add_access(code_block, src.data());	×
82	}
NEW 83	builder.add_computational_memlet(	×
NEW 84	code_block, *alpha_node, tasklet, "_in2", alpha_memlet->subset(), alpha_memlet->base_type()	×
85	);
86	} else {	×
87	alpha_node = &builder.add_constant(code_block, "1.0f", desc);	4!
88	builder.add_computational_memlet(code_block, *alpha_node, tasklet, "_in2", {}, desc);	4!
89	}
90	}
91
92	return true;
93	}	4✔
94
95	std::unique_ptr<data_flow::DataFlowNode> EluNode::
96	clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {	×
NEW 97	return std::unique_ptr<	×
NEW 98	data_flow::DataFlowNode>(new EluNode(element_id, this->debug_info(), vertex, parent, this->shape_));	×
99	}	×
100
101	} // namespace tensor
102	} // namespace math
103	} // namespace sdfg

daisytuner / sdfglib / 20561540736

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous