23249128422

Committed 18 Mar 2026 02:12PM UTC coverage: 63.938% (+0.3%) from 63.617%

Build # 23249128422

Build Type

Pull #584

github

Committed by

web-flow

Commit Message

Merge 0fcde60dc into 64d54d7de

Pull Request Pull Request #584: adds diamond tiling test

Coverage Stats

18 of 20 new or added lines in 1 file covered. (90.0%)

1180 existing lines in 28 files now uncovered.

26122 of 40855 relevant lines covered (63.94%)

407.69 hits per line

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

81.82

/sdfg/src/data_flow/library_nodes/math/tensor/elementwise_ops/maximum_node.cpp

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

#include "sdfg/analysis/analysis.h"
#include "sdfg/builder/structured_sdfg_builder.h"
#include "sdfg/data_flow/library_nodes/math/cmath/cmath_node.h"
#include "sdfg/data_flow/library_nodes/math/tensor/tensor_node.h"
#include "sdfg/types/type.h"

namespace sdfg {
namespace math {
namespace tensor {

MaximumNode::MaximumNode(
    size_t element_id,
    const DebugInfo& debug_info,
    const graph::Vertex vertex,
    data_flow::DataFlowGraph& parent,
    const std::vector<symbolic::Expression>& shape
)
    : ElementWiseBinaryNode(element_id, debug_info, vertex, parent, LibraryNodeType_Maximum, shape) {}

bool MaximumNode::expand_operation(
    builder::StructuredSDFGBuilder& builder,
    analysis::AnalysisManager& analysis_manager,
    structured_control_flow::Sequence& body,
    const std::string& input_name_a,
    const std::string& input_name_b,
    const std::string& output_name,
    const types::Tensor& input_type_a,
    const types::Tensor& input_type_b,
    const types::Tensor& output_type,
    const data_flow::Subset& subset
) {
    auto& code_block = builder.add_block(body);
    auto& output_node = builder.add_access(code_block, output_name);

    bool is_int = types::is_integer(input_type_a.primitive_type());

    data_flow::CodeNode* code_node;
    if (is_int) {
        // Use tasklets for integer types - distinguish between signed and unsigned
        auto tasklet_code = TensorNode::get_integer_minmax_tasklet(input_type_a.primitive_type(), true);
        code_node = &builder.add_tasklet(code_block, tasklet_code, "_out", {"_in1", "_in2"});

    } else {
        // Use intrinsics for floating-point types with correct suffix
        code_node = &builder.add_library_node<
            cmath::CMathNode>(code_block, this->debug_info(), cmath::CMathFunction::fmax, input_type_a.primitive_type());
    }

    create_input_memlet(builder, "_in1", input_name_a, input_type_a, subset, code_block, *code_node);
    create_input_memlet(builder, "_in2", input_name_b, input_type_b, subset, code_block, *code_node);
    builder.add_computational_memlet(code_block, *code_node, "_out", output_node, subset, output_type);

    return true;
}

std::unique_ptr<data_flow::DataFlowNode> MaximumNode::
    clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {
    return std::unique_ptr<
        data_flow::DataFlowNode>(new MaximumNode(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/maximum_node.h"
2
3	#include "sdfg/analysis/analysis.h"
4	#include "sdfg/builder/structured_sdfg_builder.h"
5	#include "sdfg/data_flow/library_nodes/math/cmath/cmath_node.h"
6	#include "sdfg/data_flow/library_nodes/math/tensor/tensor_node.h"
7	#include "sdfg/types/type.h"
8
9	namespace sdfg {
10	namespace math {
11	namespace tensor {
12
13	MaximumNode::MaximumNode(
14	size_t element_id,
15	const DebugInfo& debug_info,
16	const graph::Vertex vertex,
17	data_flow::DataFlowGraph& parent,
18	const std::vector<symbolic::Expression>& shape
19	)
20	: ElementWiseBinaryNode(element_id, debug_info, vertex, parent, LibraryNodeType_Maximum, shape) {}	7✔
21
22	bool MaximumNode::expand_operation(
23	builder::StructuredSDFGBuilder& builder,
24	analysis::AnalysisManager& analysis_manager,
25	structured_control_flow::Sequence& body,
26	const std::string& input_name_a,
27	const std::string& input_name_b,
28	const std::string& output_name,
29	const types::Tensor& input_type_a,
30	const types::Tensor& input_type_b,
31	const types::Tensor& output_type,
32	const data_flow::Subset& subset
33	) {	7✔
34	auto& code_block = builder.add_block(body);	7✔
35	auto& output_node = builder.add_access(code_block, output_name);	7✔
36
37	bool is_int = types::is_integer(input_type_a.primitive_type());	7✔
38
39	data_flow::CodeNode* code_node;	7✔
40	if (is_int) {	7✔
41	// Use tasklets for integer types - distinguish between signed and unsigned
42	auto tasklet_code = TensorNode::get_integer_minmax_tasklet(input_type_a.primitive_type(), true);	3✔
43	code_node = &builder.add_tasklet(code_block, tasklet_code, "_out", {"_in1", "_in2"});	3✔
44
45	} else {	4✔
46	// Use intrinsics for floating-point types with correct suffix
47	code_node = &builder.add_library_node<	4✔
48	cmath::CMathNode>(code_block, this->debug_info(), cmath::CMathFunction::fmax, input_type_a.primitive_type());	4✔
49	}	4✔
50
51	create_input_memlet(builder, "_in1", input_name_a, input_type_a, subset, code_block, *code_node);	7✔
52	create_input_memlet(builder, "_in2", input_name_b, input_type_b, subset, code_block, *code_node);	7✔
53	builder.add_computational_memlet(code_block, *code_node, "_out", output_node, subset, output_type);	7✔
54
55	return true;	7✔
56	}	7✔
57
58	std::unique_ptr<data_flow::DataFlowNode> MaximumNode::
UNCOV 59	clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {	×
UNCOV 60	return std::unique_ptr<	×
UNCOV 61	data_flow::DataFlowNode>(new MaximumNode(element_id, this->debug_info(), vertex, parent, this->shape_));	×
UNCOV 62	}	×
63
64	} // namespace tensor
65	} // namespace math
66	} // namespace sdfg

daisytuner / docc / 23249128422

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