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.

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0.0

/src/data_flow/library_nodes/math/tensor/broadcast_node.cpp

#include "sdfg/data_flow/library_nodes/math/tensor/broadcast_node.h"
#include "sdfg/analysis/scope_analysis.h"
#include "sdfg/builder/structured_sdfg_builder.h"
#include "sdfg/structured_control_flow/for.h"

namespace sdfg {
namespace math {
namespace tensor {

BroadcastNode::BroadcastNode(
    size_t element_id,
    const DebugInfo& debug_info,
    const graph::Vertex vertex,
    data_flow::DataFlowGraph& parent,
    const std::vector<symbolic::Expression>& input_shape,
    const std::vector<symbolic::Expression>& output_shape
)
    : TensorNode(
          element_id,
          debug_info,
          vertex,
          parent,
          LibraryNodeType_Broadcast,
          {"Y"},
          {"X"},
          data_flow::ImplementationType_NONE
      ),
      input_shape_(input_shape), output_shape_(output_shape) {}

symbolic::SymbolSet BroadcastNode::symbols() const {
    symbolic::SymbolSet syms;
    for (const auto& dim : input_shape_) {
        for (auto& atom : symbolic::atoms(dim)) {
            syms.insert(atom);
        }
    }
    for (const auto& dim : output_shape_) {
        for (auto& atom : symbolic::atoms(dim)) {
            syms.insert(atom);
        }
    }
    return syms;
}

void BroadcastNode::replace(const symbolic::Expression old_expression, const symbolic::Expression new_expression) {
    for (auto& dim : input_shape_) {
        dim = symbolic::subs(dim, old_expression, new_expression);
    }
    for (auto& dim : output_shape_) {
        dim = symbolic::subs(dim, old_expression, new_expression);
    }
}

bool BroadcastNode::expand(builder::StructuredSDFGBuilder& builder, analysis::AnalysisManager& analysis_manager) {
    auto& dataflow = this->get_parent();
    auto& block = static_cast<structured_control_flow::Block&>(*dataflow.get_parent());

    if (dataflow.in_degree(*this) != 1 || dataflow.out_degree(*this) != 1) {
        return false;
    }

    auto& scope_analysis = analysis_manager.get<analysis::ScopeAnalysis>();
    auto& parent = static_cast<structured_control_flow::Sequence&>(*scope_analysis.parent_scope(&block));

    auto& in_edge = *dataflow.in_edges(*this).begin();
    auto& out_edge = *dataflow.out_edges(*this).begin();
    auto& in_node = static_cast<data_flow::AccessNode&>(in_edge.src());
    auto& out_node = static_cast<data_flow::AccessNode&>(out_edge.dst());

    symbolic::SymbolVec loop_vars;
    structured_control_flow::Sequence* inner_scope = nullptr;

    for (size_t i = 0; i < output_shape_.size(); ++i) {
        std::string var_name = builder.find_new_name("_i" + std::to_string(i));
        builder.add_container(var_name, types::Scalar(types::PrimitiveType::Int64));

        auto sym_var = symbolic::symbol(var_name);
        auto condition = symbolic::Lt(sym_var, output_shape_[i]);
        auto init = symbolic::zero();
        auto update = symbolic::add(sym_var, symbolic::one());

        if (i == 0) {
            auto& loop = builder.add_map_before(
                parent,
                block,
                sym_var,
                condition,
                init,
                update,
                structured_control_flow::ScheduleType_Sequential::create(),
                {},
                this->debug_info()
            );
            inner_scope = &loop.root();
        } else {
            auto& loop = builder.add_map(
                *inner_scope,
                sym_var,
                condition,
                init,
                update,
                structured_control_flow::ScheduleType_Sequential::create(),
                {},
                this->debug_info()
            );
            inner_scope = &loop.root();
        }
        loop_vars.push_back(sym_var);
    }

    auto& tasklet_block = builder.add_block(*inner_scope, {}, this->debug_info());

    auto& in_acc = builder.add_access(tasklet_block, in_node.data());
    auto& out_acc = builder.add_access(tasklet_block, out_node.data());

    auto& tasklet =
        builder.add_tasklet(tasklet_block, data_flow::TaskletCode::assign, "_out", {"_in"}, this->debug_info());

    symbolic::Expression input_linear_index = symbolic::zero();
    for (size_t i = 0; i < input_shape_.size(); ++i) {
        if (!symbolic::eq(input_shape_[i], symbolic::one())) {
            input_linear_index = symbolic::add(symbolic::mul(input_linear_index, input_shape_[i]), loop_vars[i]);
        }
    }

    symbolic::Expression output_linear_index = symbolic::zero();
    for (size_t i = 0; i < output_shape_.size(); ++i) {
        output_linear_index = symbolic::add(symbolic::mul(output_linear_index, output_shape_[i]), loop_vars[i]);
    }

    data_flow::Subset input_subset;
    if (in_edge.base_type().type_id() != types::TypeID::Scalar) {
        input_subset = {input_linear_index};
    }
    data_flow::Subset output_subset;
    if (out_edge.base_type().type_id() != types::TypeID::Scalar) {
        output_subset = {output_linear_index};
    }
    builder.add_computational_memlet(
        tasklet_block, in_acc, tasklet, "_in", input_subset, in_edge.base_type(), this->debug_info()
    );
    builder.add_computational_memlet(
        tasklet_block, tasklet, "_out", out_acc, output_subset, out_edge.base_type(), this->debug_info()
    );

    builder.remove_memlet(block, in_edge);
    builder.remove_memlet(block, out_edge);
    builder.remove_node(block, in_node);
    builder.remove_node(block, out_node);
    builder.remove_node(block, *this);

    int index = parent.index(block);
    builder.remove_child(parent, index);

    return true;
}

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

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

1	#include "sdfg/data_flow/library_nodes/math/tensor/broadcast_node.h"
2	#include "sdfg/analysis/scope_analysis.h"
3	#include "sdfg/builder/structured_sdfg_builder.h"
4	#include "sdfg/structured_control_flow/for.h"
5
6	namespace sdfg {
7	namespace math {
8	namespace tensor {
9
10	BroadcastNode::BroadcastNode(	×
11	size_t element_id,
12	const DebugInfo& debug_info,
13	const graph::Vertex vertex,
14	data_flow::DataFlowGraph& parent,
15	const std::vector<symbolic::Expression>& input_shape,
16	const std::vector<symbolic::Expression>& output_shape
17	)
NEW 18	: TensorNode(	×
19	element_id,	×
20	debug_info,	×
21	vertex,	×
22	parent,	×
23	LibraryNodeType_Broadcast,
24	{"Y"},	×
25	{"X"},	×
UNCOV 26	data_flow::ImplementationType_NONE	×
27	),
28	input_shape_(input_shape), output_shape_(output_shape) {}	×
29
30	symbolic::SymbolSet BroadcastNode::symbols() const {	×
31	symbolic::SymbolSet syms;	×
32	for (const auto& dim : input_shape_) {	×
33	for (auto& atom : symbolic::atoms(dim)) {	×
34	syms.insert(atom);	×
35	}
36	}
37	for (const auto& dim : output_shape_) {	×
38	for (auto& atom : symbolic::atoms(dim)) {	×
39	syms.insert(atom);	×
40	}
41	}
42	return syms;	×
43	}	×
44
45	void BroadcastNode::replace(const symbolic::Expression old_expression, const symbolic::Expression new_expression) {	×
46	for (auto& dim : input_shape_) {	×
47	dim = symbolic::subs(dim, old_expression, new_expression);	×
48	}
49	for (auto& dim : output_shape_) {	×
50	dim = symbolic::subs(dim, old_expression, new_expression);	×
51	}
52	}	×
53
54	bool BroadcastNode::expand(builder::StructuredSDFGBuilder& builder, analysis::AnalysisManager& analysis_manager) {	×
UNCOV 55	auto& dataflow = this->get_parent();	×
56	auto& block = static_cast<structured_control_flow::Block&>(*dataflow.get_parent());	×
57
58	if (dataflow.in_degree(this) != 1 \|\| dataflow.out_degree(this) != 1) {	×
59	return false;	×
60	}
61
62	auto& scope_analysis = analysis_manager.get<analysis::ScopeAnalysis>();	×
63	auto& parent = static_cast<structured_control_flow::Sequence&>(*scope_analysis.parent_scope(&block));	×
64
65	auto& in_edge = dataflow.in_edges(this).begin();	×
66	auto& out_edge = dataflow.out_edges(this).begin();	×
67	auto& in_node = static_cast<data_flow::AccessNode&>(in_edge.src());	×
68	auto& out_node = static_cast<data_flow::AccessNode&>(out_edge.dst());	×
69
70	symbolic::SymbolVec loop_vars;	×
71	structured_control_flow::Sequence* inner_scope = nullptr;	×
72
73	for (size_t i = 0; i < output_shape_.size(); ++i) {	×
74	std::string var_name = builder.find_new_name("_i" + std::to_string(i));	×
75	builder.add_container(var_name, types::Scalar(types::PrimitiveType::Int64));	×
76
77	auto sym_var = symbolic::symbol(var_name);	×
78	auto condition = symbolic::Lt(sym_var, output_shape_[i]);	×
79	auto init = symbolic::zero();	×
80	auto update = symbolic::add(sym_var, symbolic::one());	×
81
82	if (i == 0) {	×
83	auto& loop = builder.add_map_before(	×
84	parent,	×
85	block,	×
86	sym_var,	×
87	condition,	×
88	init,	×
89	update,	×
90	structured_control_flow::ScheduleType_Sequential::create(),	×
91	{},	×
92	this->debug_info()	×
93	);
94	inner_scope = &loop.root();	×
95	} else {	×
96	auto& loop = builder.add_map(	×
97	*inner_scope,	×
98	sym_var,	×
99	condition,	×
100	init,	×
101	update,	×
102	structured_control_flow::ScheduleType_Sequential::create(),	×
103	{},	×
104	this->debug_info()	×
105	);
106	inner_scope = &loop.root();	×
107	}
108	loop_vars.push_back(sym_var);	×
109	}	×
110
111	auto& tasklet_block = builder.add_block(*inner_scope, {}, this->debug_info());	×
112
113	auto& in_acc = builder.add_access(tasklet_block, in_node.data());	×
114	auto& out_acc = builder.add_access(tasklet_block, out_node.data());	×
115
116	auto& tasklet =	×
117	builder.add_tasklet(tasklet_block, data_flow::TaskletCode::assign, "_out", {"_in"}, this->debug_info());	×
118
119	symbolic::Expression input_linear_index = symbolic::zero();	×
120	for (size_t i = 0; i < input_shape_.size(); ++i) {	×
121	if (!symbolic::eq(input_shape_[i], symbolic::one())) {	×
122	input_linear_index = symbolic::add(symbolic::mul(input_linear_index, input_shape_[i]), loop_vars[i]);	×
123	}	×
124	}	×
125
126	symbolic::Expression output_linear_index = symbolic::zero();	×
127	for (size_t i = 0; i < output_shape_.size(); ++i) {	×
128	output_linear_index = symbolic::add(symbolic::mul(output_linear_index, output_shape_[i]), loop_vars[i]);	×
129	}	×
130
131	data_flow::Subset input_subset;	×
132	if (in_edge.base_type().type_id() != types::TypeID::Scalar) {	×
133	input_subset = {input_linear_index};	×
134	}	×
135	data_flow::Subset output_subset;	×
136	if (out_edge.base_type().type_id() != types::TypeID::Scalar) {	×
137	output_subset = {output_linear_index};	×
138	}	×
139	builder.add_computational_memlet(	×
140	tasklet_block, in_acc, tasklet, "_in", input_subset, in_edge.base_type(), this->debug_info()	×
141	);
142	builder.add_computational_memlet(	×
143	tasklet_block, tasklet, "_out", out_acc, output_subset, out_edge.base_type(), this->debug_info()	×
144	);
145
146	builder.remove_memlet(block, in_edge);	×
147	builder.remove_memlet(block, out_edge);	×
148	builder.remove_node(block, in_node);	×
149	builder.remove_node(block, out_node);	×
150	builder.remove_node(block, *this);	×
151
152	int index = parent.index(block);	×
153	builder.remove_child(parent, index);	×
154
155	return true;	×
156	}	×
157
158	std::unique_ptr<data_flow::DataFlowNode> BroadcastNode::
159	clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {	×
160	return std::unique_ptr<data_flow::DataFlowNode>(	×
161	new BroadcastNode(element_id, this->debug_info(), vertex, parent, input_shape_, output_shape_)	×
162	);
163	}	×
164
165	} // namespace tensor
166	} // namespace math
167	} // namespace sdfg

daisytuner / sdfglib / 20621841097

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