17368505925

Committed 01 Sep 2025 05:29AM UTC coverage: 58.982% (-0.8%) from 59.781%

Build # 17368505925

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Commit Message

Merge pull request #216 from daisytuner/transitions-bug

Updates API for Sequence transitions to prevent leakage of assignments

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0.0

/src/data_flow/library_nodes/math/ml/reduce_mean.cpp

#include "sdfg/data_flow/library_nodes/math/ml/reduce_mean.h"

#include "sdfg/analysis/analysis.h"
#include "sdfg/analysis/scope_analysis.h"
#include "sdfg/builder/structured_sdfg_builder.h"

namespace sdfg {
namespace math {
namespace ml {

ReduceMeanNode::ReduceMeanNode(
    size_t element_id,
    const DebugInfo &debug_info,
    const graph::Vertex vertex,
    data_flow::DataFlowGraph &parent,
    int axis
)
    : MathNode(
          element_id,
          debug_info,
          vertex,
          parent,
          LibraryNodeType_ReduceMean,
          {"output"},
          {"input"},
          data_flow::ImplementationType_NONE
      ),
      axis_(axis) {}

void ReduceMeanNode::validate(const Function &) const { /* TODO */ }

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

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

    // Locate edges
    const data_flow::Memlet *iedge_input = nullptr;
    const data_flow::Memlet *oedge_output = nullptr;
    for (const auto &edge : dataflow.in_edges(*this)) {
        if (edge.dst_conn() == "input") {
            iedge_input = &edge;
        }
    }
    for (const auto &edge : dataflow.out_edges(*this)) {
        if (edge.src_conn() == "output") {
            oedge_output = &edge;
        }
    }
    if (!iedge_input || !oedge_output) return false;

    std::string input_name = static_cast<const data_flow::AccessNode &>(iedge_input->src()).data();
    std::string output_name = static_cast<const data_flow::AccessNode &>(oedge_output->dst()).data();

    // Create new sequence before
    auto &new_sequence = builder.add_sequence_before(parent, block, transition.assignments(), block.debug_info());
    structured_control_flow::Sequence *last_scope = &new_sequence;

    // Create maps over output subset dims (parallel dims)
    data_flow::Subset domain_begin = oedge_output->begin_subset();
    data_flow::Subset domain_end = oedge_output->end_subset();

    std::vector<symbolic::Expression> loop_syms;
    structured_control_flow::Map *last_map = nullptr;
    for (size_t d = 0; d < domain_begin.size(); ++d) {
        std::string indvar_str = builder.find_new_name("_i");
        builder.add_container(indvar_str, types::Scalar(types::PrimitiveType::UInt64));
        auto indvar = symbolic::symbol(indvar_str);
        auto init = domain_begin[d];
        auto update = symbolic::add(indvar, symbolic::one());
        auto cond = symbolic::Lt(indvar, symbolic::add(domain_end[d], symbolic::one()));
        last_map = &builder.add_map(
            *last_scope,
            indvar,
            cond,
            init,
            update,
            structured_control_flow::ScheduleType_Sequential,
            {},
            block.debug_info()
        );
        last_scope = &last_map->root();
        loop_syms.push_back(indvar);
    }

    // Initialize temp variable to zero
    std::string temp_name = builder.find_new_name("_tmp");
    types::Scalar temp_type(types::PrimitiveType::Float);
    builder.add_container(temp_name, temp_type);

    auto &init_block = builder.add_block(*last_scope);
    auto &init_tasklet = builder.add_tasklet(init_block, data_flow::TaskletCode::assign, "_out", {"0.0f"});
    auto &tmp_access_init = builder.add_access(init_block, temp_name);
    builder.add_computational_memlet(init_block, init_tasklet, "_out", tmp_access_init, {}, temp_type);

    // add reduction for loop
    symbolic::Expression red_begin;
    symbolic::Expression red_end;
    if (axis_ >= 0) {
        red_begin = iedge_input->begin_subset()[axis_];
        red_end = iedge_input->end_subset()[axis_];
    } else {
        red_begin = iedge_input->begin_subset().back();
        red_end = iedge_input->end_subset().back();
    }
    std::string red_name = builder.find_new_name("_i");
    builder.add_container(red_name, types::Scalar(types::PrimitiveType::UInt64));
    auto red_indvar = symbolic::symbol(red_name);
    auto red_init = red_begin;
    auto red_update = symbolic::add(red_indvar, symbolic::one());
    auto red_cond = symbolic::Lt(red_indvar, symbolic::add(red_end, symbolic::one()));
    auto red_map = &builder.add_for(*last_scope, red_indvar, red_cond, red_init, red_update, {}, block.debug_info());

    // Create innermost block
    auto &code_block = builder.add_block(red_map->root());

    // Create access nodes for input and output
    auto &input_access = builder.add_access(code_block, input_name);
    auto &tmp2_access = builder.add_access(code_block, temp_name);
    auto &tmp_access_out = builder.add_access(code_block, temp_name);
    auto &tmp_access_in = builder.add_access(code_block, temp_name);

    // Create index expressions for input and output
    std::vector<symbolic::Expression> input_subset = loop_syms;

    // Replace the reduction axis index with the reduction variable for input
    if (axis_ >= 0 && axis_ < static_cast<int>(input_subset.size())) {
        input_subset.insert(input_subset.begin() + axis_, red_indvar);
    } else if (axis_ < 0) {
        input_subset.push_back(red_indvar);
    }

    // Compute division
    auto num_elements = symbolic::add(red_end, symbolic::one());
    auto num_elements_int = SymEngine::rcp_static_cast<const SymEngine::Integer>(num_elements)->as_int();
    auto &div_tasklet = builder.add_tasklet(
        code_block, data_flow::TaskletCode::div, "_out", {"_in1", std::to_string(num_elements_int) + ".0f"}
    );
    builder
        .add_computational_memlet(code_block, input_access, div_tasklet, "_in", input_subset, iedge_input->base_type());
    builder.add_computational_memlet(code_block, div_tasklet, "_out", tmp2_access, {}, temp_type);

    // Add to temp (reduction)
    auto &add_tasklet = builder.add_tasklet(code_block, data_flow::TaskletCode::add, "_out", {"_in1", "_in2"});
    builder.add_computational_memlet(code_block, tmp_access_in, add_tasklet, "_in1", {}, temp_type);
    builder.add_computational_memlet(code_block, tmp2_access, add_tasklet, "_in2", {}, temp_type);
    builder.add_computational_memlet(code_block, add_tasklet, "_out", tmp_access_out, {}, temp_type);

    // Create writeback - assign the accumulated sum to output
    auto &writeback_block = builder.add_block(*last_scope);
    auto &tmp_access_wb = builder.add_access(writeback_block, temp_name);
    auto &output_access_wb = builder.add_access(writeback_block, output_name);
    auto &writeback_tasklet = builder.add_tasklet(writeback_block, data_flow::TaskletCode::assign, "_out", {"_in"});
    builder.add_computational_memlet(writeback_block, tmp_access_wb, writeback_tasklet, "_in", {}, temp_type);
    builder.add_computational_memlet(
        writeback_block, writeback_tasklet, "_out", output_access_wb, loop_syms, oedge_output->base_type()
    );

    // Cleanup old block
    builder.remove_memlet(block, *iedge_input);
    builder.remove_memlet(block, *oedge_output);
    builder.remove_node(block, *this);
    builder.remove_child(parent, index + 1);

    return true;
}

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

nlohmann::json ReduceMeanNodeSerializer::serialize(const data_flow::LibraryNode &library_node) {
    const ReduceMeanNode &node = static_cast<const ReduceMeanNode &>(library_node);
    nlohmann::json j;

    j["code"] = node.code().value();
    j["axis"] = node.axis();

    return j;
}

data_flow::LibraryNode &ReduceMeanNodeSerializer::deserialize(
    const nlohmann::json &j, builder::StructuredSDFGBuilder &builder, structured_control_flow::Block &parent
) {
    auto code = j["code"].get<std::string>();
    if (code != LibraryNodeType_ReduceMean.value()) {
        throw std::runtime_error("Invalid library node code");
    }

    sdfg::serializer::JSONSerializer serializer;
    DebugInfo debug_info = serializer.json_to_debug_info(j["debug_info"]);

    auto axis = j["axis"].get<int>();

    return builder.add_library_node<ReduceMeanNode>(parent, debug_info, axis);
}

} // namespace ml
} // namespace math
} // namespace sdfg

1	#include "sdfg/data_flow/library_nodes/math/ml/reduce_mean.h"
2
3	#include "sdfg/analysis/analysis.h"
4	#include "sdfg/analysis/scope_analysis.h"
5	#include "sdfg/builder/structured_sdfg_builder.h"
6
7	namespace sdfg {
8	namespace math {
9	namespace ml {
10
11	ReduceMeanNode::ReduceMeanNode(	×
12	size_t element_id,
13	const DebugInfo &debug_info,
14	const graph::Vertex vertex,
15	data_flow::DataFlowGraph &parent,
16	int axis
17	)
18	: MathNode(	×
NEW 19	element_id,	×
NEW 20	debug_info,	×
NEW 21	vertex,	×
NEW 22	parent,	×
23	LibraryNodeType_ReduceMean,
NEW 24	{"output"},	×
NEW 25	{"input"},	×
26	data_flow::ImplementationType_NONE
27	),
28	axis_(axis) {}	×
29
30	void ReduceMeanNode::validate(const Function &) const { /* TODO */ }	×
31
32	bool ReduceMeanNode::expand(builder::StructuredSDFGBuilder &builder, analysis::AnalysisManager &analysis_manager) {	×
33	auto &dataflow = this->get_parent();	×
34	auto &block = static_cast<structured_control_flow::Block &>(*dataflow.get_parent());	×
35
36	auto &scope_analysis = analysis_manager.get<analysis::ScopeAnalysis>();	×
37	auto &parent = static_cast<structured_control_flow::Sequence &>(*scope_analysis.parent_scope(&block));	×
NEW 38	int index = parent.index(block);	×
NEW 39	auto &transition = parent.at(index).second;	×
40
41	// Locate edges
42	const data_flow::Memlet *iedge_input = nullptr;	×
43	const data_flow::Memlet *oedge_output = nullptr;	×
44	for (const auto &edge : dataflow.in_edges(*this)) {	×
45	if (edge.dst_conn() == "input") {	×
46	iedge_input = &edge;	×
47	}	×
48	}
49	for (const auto &edge : dataflow.out_edges(*this)) {	×
50	if (edge.src_conn() == "output") {	×
51	oedge_output = &edge;	×
52	}	×
53	}
54	if (!iedge_input \|\| !oedge_output) return false;	×
55
56	std::string input_name = static_cast<const data_flow::AccessNode &>(iedge_input->src()).data();	×
57	std::string output_name = static_cast<const data_flow::AccessNode &>(oedge_output->dst()).data();	×
58
59	// Create new sequence before
NEW 60	auto &new_sequence = builder.add_sequence_before(parent, block, transition.assignments(), block.debug_info());	×
61	structured_control_flow::Sequence *last_scope = &new_sequence;	×
62
63	// Create maps over output subset dims (parallel dims)
64	data_flow::Subset domain_begin = oedge_output->begin_subset();	×
65	data_flow::Subset domain_end = oedge_output->end_subset();	×
66
67	std::vector<symbolic::Expression> loop_syms;	×
68	structured_control_flow::Map *last_map = nullptr;	×
69	for (size_t d = 0; d < domain_begin.size(); ++d) {	×
70	std::string indvar_str = builder.find_new_name("_i");	×
71	builder.add_container(indvar_str, types::Scalar(types::PrimitiveType::UInt64));	×
72	auto indvar = symbolic::symbol(indvar_str);	×
73	auto init = domain_begin[d];	×
74	auto update = symbolic::add(indvar, symbolic::one());	×
75	auto cond = symbolic::Lt(indvar, symbolic::add(domain_end[d], symbolic::one()));	×
76	last_map = &builder.add_map(	×
77	*last_scope,	×
78	indvar,
79	cond,
80	init,
81	update,
82	structured_control_flow::ScheduleType_Sequential,
83	{},	×
84	block.debug_info()	×
85	);
86	last_scope = &last_map->root();	×
87	loop_syms.push_back(indvar);	×
88	}	×
89
90	// Initialize temp variable to zero
91	std::string temp_name = builder.find_new_name("_tmp");	×
92	types::Scalar temp_type(types::PrimitiveType::Float);	×
93	builder.add_container(temp_name, temp_type);	×
94
95	auto &init_block = builder.add_block(*last_scope);	×
96	auto &init_tasklet = builder.add_tasklet(init_block, data_flow::TaskletCode::assign, "_out", {"0.0f"});	×
97	auto &tmp_access_init = builder.add_access(init_block, temp_name);	×
98	builder.add_computational_memlet(init_block, init_tasklet, "_out", tmp_access_init, {}, temp_type);	×
99
100	// add reduction for loop
101	symbolic::Expression red_begin;	×
102	symbolic::Expression red_end;	×
103	if (axis_ >= 0) {	×
104	red_begin = iedge_input->begin_subset()[axis_];	×
105	red_end = iedge_input->end_subset()[axis_];	×
106	} else {	×
107	red_begin = iedge_input->begin_subset().back();	×
108	red_end = iedge_input->end_subset().back();	×
109	}
110	std::string red_name = builder.find_new_name("_i");	×
111	builder.add_container(red_name, types::Scalar(types::PrimitiveType::UInt64));	×
112	auto red_indvar = symbolic::symbol(red_name);	×
113	auto red_init = red_begin;	×
114	auto red_update = symbolic::add(red_indvar, symbolic::one());	×
115	auto red_cond = symbolic::Lt(red_indvar, symbolic::add(red_end, symbolic::one()));	×
NEW 116	auto red_map = &builder.add_for(*last_scope, red_indvar, red_cond, red_init, red_update, {}, block.debug_info());	×
117
118	// Create innermost block
119	auto &code_block = builder.add_block(red_map->root());	×
120
121	// Create access nodes for input and output
122	auto &input_access = builder.add_access(code_block, input_name);	×
123	auto &tmp2_access = builder.add_access(code_block, temp_name);	×
124	auto &tmp_access_out = builder.add_access(code_block, temp_name);	×
125	auto &tmp_access_in = builder.add_access(code_block, temp_name);	×
126
127	// Create index expressions for input and output
128	std::vector<symbolic::Expression> input_subset = loop_syms;	×
129
130	// Replace the reduction axis index with the reduction variable for input
131	if (axis_ >= 0 && axis_ < static_cast<int>(input_subset.size())) {	×
132	input_subset.insert(input_subset.begin() + axis_, red_indvar);	×
133	} else if (axis_ < 0) {	×
134	input_subset.push_back(red_indvar);	×
135	}	×
136
137	// Compute division
138	auto num_elements = symbolic::add(red_end, symbolic::one());	×
139	auto num_elements_int = SymEngine::rcp_static_cast<const SymEngine::Integer>(num_elements)->as_int();	×
NEW 140	auto &div_tasklet = builder.add_tasklet(	×
NEW 141	code_block, data_flow::TaskletCode::div, "_out", {"_in1", std::to_string(num_elements_int) + ".0f"}	×
142	);
NEW 143	builder	×
NEW 144	.add_computational_memlet(code_block, input_access, div_tasklet, "_in", input_subset, iedge_input->base_type());	×
UNCOV 145	builder.add_computational_memlet(code_block, div_tasklet, "_out", tmp2_access, {}, temp_type);	×
146
147	// Add to temp (reduction)
148	auto &add_tasklet = builder.add_tasklet(code_block, data_flow::TaskletCode::add, "_out", {"_in1", "_in2"});	×
149	builder.add_computational_memlet(code_block, tmp_access_in, add_tasklet, "_in1", {}, temp_type);	×
150	builder.add_computational_memlet(code_block, tmp2_access, add_tasklet, "_in2", {}, temp_type);	×
151	builder.add_computational_memlet(code_block, add_tasklet, "_out", tmp_access_out, {}, temp_type);	×
152
153	// Create writeback - assign the accumulated sum to output
154	auto &writeback_block = builder.add_block(*last_scope);	×
155	auto &tmp_access_wb = builder.add_access(writeback_block, temp_name);	×
156	auto &output_access_wb = builder.add_access(writeback_block, output_name);	×
157	auto &writeback_tasklet = builder.add_tasklet(writeback_block, data_flow::TaskletCode::assign, "_out", {"_in"});	×
158	builder.add_computational_memlet(writeback_block, tmp_access_wb, writeback_tasklet, "_in", {}, temp_type);	×
NEW 159	builder.add_computational_memlet(	×
NEW 160	writeback_block, writeback_tasklet, "_out", output_access_wb, loop_syms, oedge_output->base_type()	×
161	);
162
163	// Cleanup old block
164	builder.remove_memlet(block, *iedge_input);	×
165	builder.remove_memlet(block, *oedge_output);	×
166	builder.remove_node(block, *this);	×
NEW 167	builder.remove_child(parent, index + 1);	×
168
169	return true;	×
170	}	×
171
172	std::unique_ptr<data_flow::DataFlowNode> ReduceMeanNode::
173	clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph &parent) const {	×
NEW 174	return std::unique_ptr<	×
NEW 175	data_flow::DataFlowNode>(new ReduceMeanNode(element_id, this->debug_info(), vertex, parent, axis_));	×
UNCOV 176	}	×
177
178	nlohmann::json ReduceMeanNodeSerializer::serialize(const data_flow::LibraryNode &library_node) {	×
179	const ReduceMeanNode &node = static_cast<const ReduceMeanNode &>(library_node);	×
180	nlohmann::json j;	×
181
182	j["code"] = node.code().value();	×
183	j["axis"] = node.axis();	×
184
185	return j;	×
186	}	×
187
188	data_flow::LibraryNode &ReduceMeanNodeSerializer::deserialize(	×
189	const nlohmann::json &j, builder::StructuredSDFGBuilder &builder, structured_control_flow::Block &parent
190	) {
191	auto code = j["code"].get<std::string>();	×
192	if (code != LibraryNodeType_ReduceMean.value()) {	×
193	throw std::runtime_error("Invalid library node code");	×
194	}
195
196	sdfg::serializer::JSONSerializer serializer;	×
197	DebugInfo debug_info = serializer.json_to_debug_info(j["debug_info"]);	×
198
199	auto axis = j["axis"].get<int>();	×
200
201	return builder.add_library_node<ReduceMeanNode>(parent, debug_info, axis);	×
202	}	×
203
204	} // namespace ml
205	} // namespace math
206	} // namespace sdfg

daisytuner / sdfglib / 17368505925

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