22151769687

Committed 18 Feb 2026 06:09PM UTC coverage: 64.742%. First build

Build # 22151769687

Build Type

push

github

Committed by

web-flow

Commit Message

Merge pull request #526 from daisytuner/native-ndarray

Python - Native Tensor Support: Update operations to use tensor type

Coverage Stats

2783 of 4104 new or added lines in 42 files covered. (67.81%)

23724 of 36644 relevant lines covered (64.74%)

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66.67

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

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

PowNode::PowNode(
    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_Pow, shape) {}

bool PowNode::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);
    data_flow::AccessNode* input_node_a;
    if (builder.subject().exists(input_name_a)) {
        input_node_a = &builder.add_access(code_block, input_name_a);
    } else {
        types::Scalar const_type(input_type_a.primitive_type());
        input_node_a = &builder.add_constant(code_block, input_name_a, const_type);
    }
    data_flow::AccessNode* input_node_b;
    if (builder.subject().exists(input_name_b)) {
        input_node_b = &builder.add_access(code_block, input_name_b);
    } else {
        types::Scalar const_type(input_type_b.primitive_type());
        input_node_b = &builder.add_constant(code_block, input_name_b, const_type);
    }
    auto& output_node = builder.add_access(code_block, output_name);

    auto& tasklet = builder.add_library_node<math::cmath::CMathNode>(
        code_block, code_block.debug_info(), cmath::CMathFunction::pow, input_type_a.primitive_type()
    );

    builder.add_computational_memlet(code_block, *input_node_a, tasklet, "_in1", subset, input_type_a);
    builder.add_computational_memlet(code_block, *input_node_b, tasklet, "_in2", subset, input_type_b);
    builder.add_computational_memlet(code_block, tasklet, "_out", output_node, subset, output_type);

    return true;
}

std::unique_ptr<data_flow::DataFlowNode> PowNode::
    clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {
    return std::unique_ptr<
        data_flow::DataFlowNode>(new PowNode(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/pow_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	PowNode::PowNode(
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	: ElementWiseBinaryNode(element_id, debug_info, vertex, parent, LibraryNodeType_Pow, shape) {}	4✔
22
23	bool PowNode::expand_operation(
24	builder::StructuredSDFGBuilder& builder,
25	analysis::AnalysisManager& analysis_manager,
26	structured_control_flow::Sequence& body,
27	const std::string& input_name_a,
28	const std::string& input_name_b,
29	const std::string& output_name,
30	const types::Tensor& input_type_a,
31	const types::Tensor& input_type_b,
32	const types::Tensor& output_type,
33	const data_flow::Subset& subset
34	) {	4✔
35	auto& code_block = builder.add_block(body);	4✔
36	data_flow::AccessNode* input_node_a;	4✔
37	if (builder.subject().exists(input_name_a)) {	4✔
38	input_node_a = &builder.add_access(code_block, input_name_a);	4✔
39	} else {	4✔
NEW 40	types::Scalar const_type(input_type_a.primitive_type());	×
NEW 41	input_node_a = &builder.add_constant(code_block, input_name_a, const_type);	×
42	}	×
43	data_flow::AccessNode* input_node_b;	4✔
44	if (builder.subject().exists(input_name_b)) {	4✔
45	input_node_b = &builder.add_access(code_block, input_name_b);	4✔
46	} else {	4✔
NEW 47	types::Scalar const_type(input_type_b.primitive_type());	×
NEW 48	input_node_b = &builder.add_constant(code_block, input_name_b, const_type);	×
49	}	×
50	auto& output_node = builder.add_access(code_block, output_name);	4✔
51
52	auto& tasklet = builder.add_library_node<math::cmath::CMathNode>(	4✔
53	code_block, code_block.debug_info(), cmath::CMathFunction::pow, input_type_a.primitive_type()	4✔
54	);	4✔
55
56	builder.add_computational_memlet(code_block, *input_node_a, tasklet, "_in1", subset, input_type_a);	4✔
57	builder.add_computational_memlet(code_block, *input_node_b, tasklet, "_in2", subset, input_type_b);	4✔
58	builder.add_computational_memlet(code_block, tasklet, "_out", output_node, subset, output_type);	4✔
59
60	return true;	4✔
61	}	4✔
62
63	std::unique_ptr<data_flow::DataFlowNode> PowNode::
64	clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {	×
65	return std::unique_ptr<	×
66	data_flow::DataFlowNode>(new PowNode(element_id, this->debug_info(), vertex, parent, this->shape_));	×
67	}	×
68
69	} // namespace tensor
70	} // namespace math
71	} // namespace sdfg

daisytuner / docc / 22151769687

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