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

Run Details

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

23724 of 36644 relevant lines covered (64.74%)

368.06 hits per line

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

0.0

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

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

MinimumNode::MinimumNode(
    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_Minimum, shape) {}

bool MinimumNode::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);

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

    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(), false);
        auto& tasklet = builder.add_tasklet(code_block, tasklet_code, "_out", {"_in1", "_in2"});

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

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

    return true;
}

std::unique_ptr<data_flow::DataFlowNode> MinimumNode::
    clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {
    return std::unique_ptr<
        data_flow::DataFlowNode>(new MinimumNode(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/minimum_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	MinimumNode::MinimumNode(
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_Minimum, shape) {}	×
21
22	bool MinimumNode::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	) {	×
34	auto& code_block = builder.add_block(body);	×
35	data_flow::AccessNode* input_node_a;	×
36	if (builder.subject().exists(input_name_a)) {	×
37	input_node_a = &builder.add_access(code_block, input_name_a);	×
38	} else {	×
NEW 39	types::Scalar const_type(input_type_a.primitive_type());	×
NEW 40	input_node_a = &builder.add_constant(code_block, input_name_a, const_type);	×
41	}	×
42	data_flow::AccessNode* input_node_b;	×
43	if (builder.subject().exists(input_name_b)) {	×
44	input_node_b = &builder.add_access(code_block, input_name_b);	×
45	} else {	×
NEW 46	types::Scalar const_type(input_type_b.primitive_type());	×
NEW 47	input_node_b = &builder.add_constant(code_block, input_name_b, const_type);	×
48	}	×
49	auto& output_node = builder.add_access(code_block, output_name);	×
50
51	bool is_int = types::is_integer(input_type_a.primitive_type());	×
52
53	if (is_int) {	×
54	// Use tasklets for integer types - distinguish between signed and unsigned
55	auto tasklet_code = TensorNode::get_integer_minmax_tasklet(input_type_a.primitive_type(), false);	×
56	auto& tasklet = builder.add_tasklet(code_block, tasklet_code, "_out", {"_in1", "_in2"});	×
57
NEW 58	builder.add_computational_memlet(code_block, *input_node_a, tasklet, "_in1", subset, input_type_a);	×
NEW 59	builder.add_computational_memlet(code_block, *input_node_b, tasklet, "_in2", subset, input_type_b);	×
60	builder.add_computational_memlet(code_block, tasklet, "_out", output_node, subset, output_type);	×
61	} else {	×
62	// Use intrinsics for floating-point types with correct suffix
63	auto& node = builder.add_library_node<	×
64	cmath::CMathNode>(code_block, this->debug_info(), cmath::CMathFunction::fmin, input_type_a.primitive_type());	×
65
NEW 66	builder.add_computational_memlet(code_block, *input_node_a, node, "_in1", subset, input_type_a, DebugInfo());	×
NEW 67	builder.add_computational_memlet(code_block, *input_node_b, node, "_in2", subset, input_type_b, DebugInfo());	×
68	builder.add_computational_memlet(code_block, node, "_out", output_node, subset, output_type, DebugInfo());	×
69	}	×
70
71	return true;	×
72	}	×
73
74	std::unique_ptr<data_flow::DataFlowNode> MinimumNode::
75	clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {	×
76	return std::unique_ptr<	×
77	data_flow::DataFlowNode>(new MinimumNode(element_id, this->debug_info(), vertex, parent, this->shape_));	×
78	}	×
79
80	} // namespace tensor
81	} // namespace math
82	} // namespace sdfg

daisytuner / docc / 22151769687

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