20621841097

Committed 31 Dec 2025 03:18PM UTC coverage: 39.655% (-0.06%) from 39.712%

Build # 20621841097

Build Type

Pull #421

github

Committed by

web-flow

Commit Message

Merge 7662c1b88 into 3b72c335e

Pull Request Pull Request #421: Extend tensor library nodes with primitive type support and refactor CMathNode to use enums

Run Details

14996 of 49220 branches covered (30.47%)

Branch coverage included in aggregate %.

247 of 608 new or added lines in 52 files covered. (40.63%)

38 existing lines in 5 files now uncovered.

12874 of 21062 relevant lines covered (61.12%)

89.39 hits per line

Source File
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41.05

/src/data_flow/library_nodes/math/tensor/reduce_ops/max_node.cpp

#include "sdfg/data_flow/library_nodes/math/tensor/reduce_ops/max_node.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 {

MaxNode::MaxNode(
    size_t element_id,
    const DebugInfo& debug_info,
    const graph::Vertex vertex,
    data_flow::DataFlowGraph& parent,
    const std::vector<symbolic::Expression>& shape,
    const std::vector<int64_t>& axes,
    bool keepdims
)
    : ReduceNode(element_id, debug_info, vertex, parent, LibraryNodeType_Max, shape, axes, keepdims) {}

bool MaxNode::expand_reduction(
    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& input_subset,
    const data_flow::Subset& output_subset
) {
    auto& block = builder.add_block(body, {}, this->debug_info());

    auto& in_access = builder.add_access(block, input_name, this->debug_info());
    auto& out_read_access = builder.add_access(block, output_name, this->debug_info());
    auto& out_write_access = builder.add_access(block, output_name, this->debug_info());

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

    if (is_int) {
        // For integers, use tasklet - distinguish between signed and unsigned
        auto tasklet_code = TensorNode::get_integer_minmax_tasklet(input_type.primitive_type(), true);
        auto& tasklet = builder.add_tasklet(block, tasklet_code, "_out", {"_in1", "_in2"});

        builder
            .add_computational_memlet(block, in_access, tasklet, "_in1", input_subset, input_type, this->debug_info());
        builder.add_computational_memlet(
            block, out_read_access, tasklet, "_in2", output_subset, output_type, this->debug_info()
        );
        builder.add_computational_memlet(
            block, tasklet, "_out", out_write_access, output_subset, output_type, this->debug_info()
        );
    } else {
        // For floating-point, use the correct fmax intrinsic
        auto& libnode = builder.add_library_node<
            math::cmath::CMathNode>(block, this->debug_info(), cmath::CMathFunction::fmax, input_type.primitive_type());

        builder
            .add_computational_memlet(block, in_access, libnode, "_in1", input_subset, input_type, this->debug_info());
        builder.add_computational_memlet(
            block, out_read_access, libnode, "_in2", output_subset, output_type, this->debug_info()
        );
        builder.add_computational_memlet(
            block, libnode, "_out", out_write_access, output_subset, output_type, this->debug_info()
        );
    }

    return true;
}

std::string MaxNode::identity() const { return "-INFINITY"; }

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

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

1	#include "sdfg/data_flow/library_nodes/math/tensor/reduce_ops/max_node.h"
2
3	#include "sdfg/builder/structured_sdfg_builder.h"
4	#include "sdfg/data_flow/library_nodes/math/cmath/cmath_node.h"
5	#include "sdfg/data_flow/library_nodes/math/tensor/tensor_node.h"
6	#include "sdfg/types/type.h"
7
8	namespace sdfg {
9	namespace math {
10	namespace tensor {
11
12	MaxNode::MaxNode(	1✔
13	size_t element_id,
14	const DebugInfo& debug_info,
15	const graph::Vertex vertex,
16	data_flow::DataFlowGraph& parent,
17	const std::vector<symbolic::Expression>& shape,
18	const std::vector<int64_t>& axes,
19	bool keepdims
20	)
21	: ReduceNode(element_id, debug_info, vertex, parent, LibraryNodeType_Max, shape, axes, keepdims) {}	1✔
22
23	bool MaxNode::expand_reduction(	1✔
24	builder::StructuredSDFGBuilder& builder,
25	analysis::AnalysisManager& analysis_manager,
26	structured_control_flow::Sequence& body,
27	const std::string& input_name,
28	const std::string& output_name,
29	const types::IType& input_type,
30	const types::IType& output_type,
31	const data_flow::Subset& input_subset,
32	const data_flow::Subset& output_subset
33	) {
34	auto& block = builder.add_block(body, {}, this->debug_info());	1!
35
36	auto& in_access = builder.add_access(block, input_name, this->debug_info());	1✔
37	auto& out_read_access = builder.add_access(block, output_name, this->debug_info());	1✔
38	auto& out_write_access = builder.add_access(block, output_name, this->debug_info());	1✔
39
40	bool is_int = types::is_integer(input_type.primitive_type());	1✔
41
42	if (is_int) {	1!
43	// For integers, use tasklet - distinguish between signed and unsigned
44	auto tasklet_code = TensorNode::get_integer_minmax_tasklet(input_type.primitive_type(), true);	1✔
45	auto& tasklet = builder.add_tasklet(block, tasklet_code, "_out", {"_in1", "_in2"});	1!
46
47	builder	2✔
48	.add_computational_memlet(block, in_access, tasklet, "_in1", input_subset, input_type, this->debug_info());	1!
49	builder.add_computational_memlet(	2!
50	block, out_read_access, tasklet, "_in2", output_subset, output_type, this->debug_info()	1!
51	);
52	builder.add_computational_memlet(	2!
53	block, tasklet, "_out", out_write_access, output_subset, output_type, this->debug_info()	1!
54	);
55	} else {	1✔
56	// For floating-point, use the correct fmax intrinsic
NEW 57	auto& libnode = builder.add_library_node<	×
NEW 58	math::cmath::CMathNode>(block, this->debug_info(), cmath::CMathFunction::fmax, input_type.primitive_type());	×
59
NEW 60	builder	×
NEW 61	.add_computational_memlet(block, in_access, libnode, "_in1", input_subset, input_type, this->debug_info());	×
NEW 62	builder.add_computational_memlet(	×
NEW 63	block, out_read_access, libnode, "_in2", output_subset, output_type, this->debug_info()	×
64	);
NEW 65	builder.add_computational_memlet(	×
NEW 66	block, libnode, "_out", out_write_access, output_subset, output_type, this->debug_info()	×
67	);
68	}
69
70	return true;	1✔
71	}	×
72
73	std::string MaxNode::identity() const { return "-INFINITY"; }	1!
74
75	std::unique_ptr<data_flow::DataFlowNode> MaxNode::
76	clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {	×
77	return std::unique_ptr<data_flow::DataFlowNode>(	×
78	new MaxNode(element_id, this->debug_info(), vertex, parent, this->shape_, this->axes_, this->keepdims_)	×
79	);
80	}	×
81
82	} // namespace tensor
83	} // namespace math
84	} // namespace sdfg

daisytuner / sdfglib / 20621841097

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