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17316805645

Committed 29 Aug 2025 06:46AM UTC coverage: 60.01% (+0.2%) from 59.781%

Build # 17316805645

Build Type

Pull #210

github

Committed by

web-flow

Commit Message

Merge cd9cb386d into 18d34db1e

Pull Request Pull Request #210: New debug info

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351 of 562 new or added lines in 37 files covered. (62.46%)

15 existing lines in 8 files now uncovered.

9574 of 15954 relevant lines covered (60.01%)

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0.0

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

#include "sdfg/data_flow/library_nodes/math/ml/batch_normalization.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 {

BatchNormalizationNode::BatchNormalizationNode(
    size_t element_id,
    const DebugInfoRegion &debug_info,
    const graph::Vertex vertex,
    data_flow::DataFlowGraph &parent,
    int axis,
    const std::string &epsilon
)
    : MathNode(
          element_id,
          debug_info,
          vertex,
          parent,
          LibraryNodeType_BatchNormalization,
          {"Y"},
          {"X", "Scale", "B", "input_mean", "input_var"},
          data_flow::ImplementationType_NONE
      ),
      axis_(axis), epsilon_(epsilon) {}

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

bool BatchNormalizationNode::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));

    // Locate edges
    const data_flow::Memlet *iedge_input = nullptr;
    const data_flow::Memlet *iedge_scale = nullptr;
    const data_flow::Memlet *iedge_bias = nullptr;
    const data_flow::Memlet *iedge_mean = nullptr;
    const data_flow::Memlet *iedge_var = nullptr;
    const data_flow::Memlet *oedge_output = nullptr;
    for (const auto &edge : dataflow.in_edges(*this)) {
        if (edge.dst_conn() == "X") {
            iedge_input = &edge;
        } else if (edge.dst_conn() == "Scale") {
            iedge_scale = &edge;
        } else if (edge.dst_conn() == "B") {
            iedge_bias = &edge;
        } else if (edge.dst_conn() == "input_mean") {
            iedge_mean = &edge;
        } else if (edge.dst_conn() == "input_var") {
            iedge_var = &edge;
        }
    }
    for (const auto &edge : dataflow.out_edges(*this)) {
        if (edge.src_conn() == "Y") {
            oedge_output = &edge;
        }
    }
    if (!iedge_input || !iedge_scale || !iedge_bias || !iedge_mean || !iedge_var || !oedge_output) return false;

    std::string input_name = static_cast<const data_flow::AccessNode &>(iedge_input->src()).data();
    std::string scale_name = static_cast<const data_flow::AccessNode &>(iedge_scale->src()).data();
    std::string bias_name = static_cast<const data_flow::AccessNode &>(iedge_bias->src()).data();
    std::string mean_name = static_cast<const data_flow::AccessNode &>(iedge_mean->src()).data();
    std::string var_name = static_cast<const data_flow::AccessNode &>(iedge_var->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, block.debug_info()).first;
    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);
    }

    // Create normalization block
    auto &norm_block = builder.add_block(*last_scope);

    // Create access nodes for normalization
    auto &input_access_norm = builder.add_access(norm_block, input_name);
    auto &scale_access_norm = builder.add_access(norm_block, scale_name);
    auto &bias_access_norm = builder.add_access(norm_block, bias_name);
    auto &mean_access_norm = builder.add_access(norm_block, mean_name);
    auto &var_access_norm = builder.add_access(norm_block, var_name);
    auto &output_access_norm = builder.add_access(norm_block, output_name);

    // Add epsilon to variance and compute standard deviation
    auto &add_epsilon_tasklet =
        builder.add_tasklet(norm_block, data_flow::TaskletCode::add, "_out", {"_in1", epsilon_});
    auto &var_eps_access = builder.add_access(norm_block, builder.find_new_name("_var_eps"));
    builder.add_computational_memlet(
        norm_block, var_access_norm, add_epsilon_tasklet, "_in1", loop_syms, iedge_var->base_type()
    );
    builder
        .add_computational_memlet(norm_block, add_epsilon_tasklet, "_out", var_eps_access, {}, iedge_var->base_type());

    auto &sqrt_tasklet = builder.add_tasklet(norm_block, data_flow::TaskletCode::sqrt, "_out", {"_in"});
    auto &std_dev_access = builder.add_access(norm_block, builder.find_new_name("_std_dev"));
    builder.add_computational_memlet(norm_block, var_eps_access, sqrt_tasklet, "_in", {}, iedge_var->base_type());
    builder.add_computational_memlet(norm_block, sqrt_tasklet, "_out", std_dev_access, {}, iedge_var->base_type());

    // Normalize: (x - mean) / std_dev
    auto &sub_norm_tasklet = builder.add_tasklet(norm_block, data_flow::TaskletCode::sub, "_out", {"_in1", "_in2"});
    auto &centered_access = builder.add_access(norm_block, builder.find_new_name("_centered"));
    builder.add_computational_memlet(
        norm_block, input_access_norm, sub_norm_tasklet, "_in1", loop_syms, iedge_input->base_type()
    );
    builder.add_computational_memlet(
        norm_block, mean_access_norm, sub_norm_tasklet, "_in2", loop_syms, iedge_mean->base_type()
    );
    builder
        .add_computational_memlet(norm_block, sub_norm_tasklet, "_out", centered_access, {}, iedge_input->base_type());

    auto &div_norm_tasklet = builder.add_tasklet(norm_block, data_flow::TaskletCode::div, "_out", {"_in1", "_in2"});
    auto &normalized_access = builder.add_access(norm_block, builder.find_new_name("_normalized"));
    builder
        .add_computational_memlet(norm_block, centered_access, div_norm_tasklet, "_in1", {}, iedge_input->base_type());
    builder
        .add_computational_memlet(norm_block, std_dev_access, div_norm_tasklet, "_in2", loop_syms, iedge_var->base_type());
    builder
        .add_computational_memlet(norm_block, div_norm_tasklet, "_out", normalized_access, {}, iedge_input->base_type());

    // Apply scale and bias: scale * normalized + bias
    auto &mul_scale_tasklet = builder.add_tasklet(norm_block, data_flow::TaskletCode::mul, "_out", {"_in1", "_in2"});
    auto &scaled_access = builder.add_access(norm_block, builder.find_new_name("_scaled"));
    builder
        .add_computational_memlet(norm_block, normalized_access, mul_scale_tasklet, "_in1", {}, iedge_input->base_type());
    builder.add_computational_memlet(
        norm_block, scale_access_norm, mul_scale_tasklet, "_in2", loop_syms, iedge_scale->base_type()
    );
    builder.add_computational_memlet(norm_block, mul_scale_tasklet, "_out", scaled_access, {}, iedge_input->base_type());

    auto &add_bias_tasklet = builder.add_tasklet(norm_block, data_flow::TaskletCode::add, "_out", {"_in1", "_in2"});
    builder.add_computational_memlet(norm_block, scaled_access, add_bias_tasklet, "_in1", {}, iedge_input->base_type());
    builder.add_computational_memlet(
        norm_block, bias_access_norm, add_bias_tasklet, "_in2", loop_syms, iedge_bias->base_type()
    );
    builder.add_computational_memlet(
        norm_block, add_bias_tasklet, "_out", output_access_norm, loop_syms, oedge_output->base_type()
    );

    // Cleanup old block
    builder.remove_memlet(block, *iedge_input);
    builder.remove_memlet(block, *iedge_scale);
    if (iedge_bias) {
        builder.remove_memlet(block, *iedge_bias);
    }
    builder.remove_memlet(block, *iedge_mean);
    builder.remove_memlet(block, *iedge_var);
    builder.remove_memlet(block, *oedge_output);
    builder.remove_node(block, *this);
    builder.remove_child(parent, block);

    return true;
}

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

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

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

    return j;
}

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

    sdfg::serializer::JSONSerializer serializer;
    DebugInfoRegion debug_info = serializer.json_to_debug_info_region(j["debug_info"], builder.debug_info());

    auto axis = j["axis"].get<int>();
    auto epsilon = j["epsilon"].get<std::string>();

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

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

1	#include "sdfg/data_flow/library_nodes/math/ml/batch_normalization.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	BatchNormalizationNode::BatchNormalizationNode(	×
12	size_t element_id,
13	const DebugInfoRegion &debug_info,
14	const graph::Vertex vertex,
15	data_flow::DataFlowGraph &parent,
16	int axis,
17	const std::string &epsilon
18	)
19	: MathNode(	×
NEW 20	element_id,	×
NEW 21	debug_info,	×
NEW 22	vertex,	×
NEW 23	parent,	×
24	LibraryNodeType_BatchNormalization,
NEW 25	{"Y"},	×
NEW 26	{"X", "Scale", "B", "input_mean", "input_var"},	×
27	data_flow::ImplementationType_NONE
28	),
29	axis_(axis), epsilon_(epsilon) {}	×
30
31	void BatchNormalizationNode::validate(const Function &) const { /* TODO */ }	×
32
33	bool BatchNormalizationNode::expand(builder::StructuredSDFGBuilder &builder, analysis::AnalysisManager &analysis_manager) {	×
34	auto &dataflow = this->get_parent();	×
35	auto &block = static_cast<structured_control_flow::Block &>(*dataflow.get_parent());	×
36
37	auto &scope_analysis = analysis_manager.get<analysis::ScopeAnalysis>();	×
38	auto &parent = static_cast<structured_control_flow::Sequence &>(*scope_analysis.parent_scope(&block));	×
39
40	// Locate edges
41	const data_flow::Memlet *iedge_input = nullptr;	×
42	const data_flow::Memlet *iedge_scale = nullptr;	×
43	const data_flow::Memlet *iedge_bias = nullptr;	×
44	const data_flow::Memlet *iedge_mean = nullptr;	×
45	const data_flow::Memlet *iedge_var = nullptr;	×
46	const data_flow::Memlet *oedge_output = nullptr;	×
47	for (const auto &edge : dataflow.in_edges(*this)) {	×
48	if (edge.dst_conn() == "X") {	×
49	iedge_input = &edge;	×
50	} else if (edge.dst_conn() == "Scale") {	×
51	iedge_scale = &edge;	×
52	} else if (edge.dst_conn() == "B") {	×
53	iedge_bias = &edge;	×
54	} else if (edge.dst_conn() == "input_mean") {	×
55	iedge_mean = &edge;	×
56	} else if (edge.dst_conn() == "input_var") {	×
57	iedge_var = &edge;	×
58	}	×
59	}
60	for (const auto &edge : dataflow.out_edges(*this)) {	×
61	if (edge.src_conn() == "Y") {	×
62	oedge_output = &edge;	×
63	}	×
64	}
65	if (!iedge_input \|\| !iedge_scale \|\| !iedge_bias \|\| !iedge_mean \|\| !iedge_var \|\| !oedge_output) return false;	×
66
67	std::string input_name = static_cast<const data_flow::AccessNode &>(iedge_input->src()).data();	×
68	std::string scale_name = static_cast<const data_flow::AccessNode &>(iedge_scale->src()).data();	×
69	std::string bias_name = static_cast<const data_flow::AccessNode &>(iedge_bias->src()).data();	×
70	std::string mean_name = static_cast<const data_flow::AccessNode &>(iedge_mean->src()).data();	×
71	std::string var_name = static_cast<const data_flow::AccessNode &>(iedge_var->src()).data();	×
72	std::string output_name = static_cast<const data_flow::AccessNode &>(oedge_output->dst()).data();	×
73
74	// Create new sequence before
75	auto &new_sequence = builder.add_sequence_before(parent, block, block.debug_info()).first;	×
76	structured_control_flow::Sequence *last_scope = &new_sequence;	×
77
78	// Create maps over output subset dims (parallel dims)
79	data_flow::Subset domain_begin = oedge_output->begin_subset();	×
80	data_flow::Subset domain_end = oedge_output->end_subset();	×
81
82	std::vector<symbolic::Expression> loop_syms;	×
83	structured_control_flow::Map *last_map = nullptr;	×
84	for (size_t d = 0; d < domain_begin.size(); ++d) {	×
85	std::string indvar_str = builder.find_new_name("_i");	×
86	builder.add_container(indvar_str, types::Scalar(types::PrimitiveType::UInt64));	×
87	auto indvar = symbolic::symbol(indvar_str);	×
88	auto init = domain_begin[d];	×
89	auto update = symbolic::add(indvar, symbolic::one());	×
90	auto cond = symbolic::Lt(indvar, symbolic::add(domain_end[d], symbolic::one()));	×
91	last_map = &builder.add_map(	×
92	*last_scope,	×
93	indvar,
94	cond,
95	init,
96	update,
97	structured_control_flow::ScheduleType_Sequential,
98	{},	×
99	block.debug_info()	×
100	);
101	last_scope = &last_map->root();	×
102	loop_syms.push_back(indvar);	×
103	}	×
104
105	// Create normalization block
106	auto &norm_block = builder.add_block(*last_scope);	×
107
108	// Create access nodes for normalization
109	auto &input_access_norm = builder.add_access(norm_block, input_name);	×
110	auto &scale_access_norm = builder.add_access(norm_block, scale_name);	×
111	auto &bias_access_norm = builder.add_access(norm_block, bias_name);	×
112	auto &mean_access_norm = builder.add_access(norm_block, mean_name);	×
113	auto &var_access_norm = builder.add_access(norm_block, var_name);	×
114	auto &output_access_norm = builder.add_access(norm_block, output_name);	×
115
116	// Add epsilon to variance and compute standard deviation
NEW 117	auto &add_epsilon_tasklet =	×
NEW 118	builder.add_tasklet(norm_block, data_flow::TaskletCode::add, "_out", {"_in1", epsilon_});	×
119	auto &var_eps_access = builder.add_access(norm_block, builder.find_new_name("_var_eps"));	×
NEW 120	builder.add_computational_memlet(	×
NEW 121	norm_block, var_access_norm, add_epsilon_tasklet, "_in1", loop_syms, iedge_var->base_type()	×
122	);
NEW 123	builder	×
NEW 124	.add_computational_memlet(norm_block, add_epsilon_tasklet, "_out", var_eps_access, {}, iedge_var->base_type());	×
125
126	auto &sqrt_tasklet = builder.add_tasklet(norm_block, data_flow::TaskletCode::sqrt, "_out", {"_in"});	×
127	auto &std_dev_access = builder.add_access(norm_block, builder.find_new_name("_std_dev"));	×
128	builder.add_computational_memlet(norm_block, var_eps_access, sqrt_tasklet, "_in", {}, iedge_var->base_type());	×
129	builder.add_computational_memlet(norm_block, sqrt_tasklet, "_out", std_dev_access, {}, iedge_var->base_type());	×
130
131	// Normalize: (x - mean) / std_dev
132	auto &sub_norm_tasklet = builder.add_tasklet(norm_block, data_flow::TaskletCode::sub, "_out", {"_in1", "_in2"});	×
133	auto &centered_access = builder.add_access(norm_block, builder.find_new_name("_centered"));	×
NEW 134	builder.add_computational_memlet(	×
NEW 135	norm_block, input_access_norm, sub_norm_tasklet, "_in1", loop_syms, iedge_input->base_type()	×
136	);
NEW 137	builder.add_computational_memlet(	×
NEW 138	norm_block, mean_access_norm, sub_norm_tasklet, "_in2", loop_syms, iedge_mean->base_type()	×
139	);
NEW 140	builder	×
NEW 141	.add_computational_memlet(norm_block, sub_norm_tasklet, "_out", centered_access, {}, iedge_input->base_type());	×
142
143	auto &div_norm_tasklet = builder.add_tasklet(norm_block, data_flow::TaskletCode::div, "_out", {"_in1", "_in2"});	×
144	auto &normalized_access = builder.add_access(norm_block, builder.find_new_name("_normalized"));	×
NEW 145	builder	×
NEW 146	.add_computational_memlet(norm_block, centered_access, div_norm_tasklet, "_in1", {}, iedge_input->base_type());	×
NEW 147	builder	×
NEW 148	.add_computational_memlet(norm_block, std_dev_access, div_norm_tasklet, "_in2", loop_syms, iedge_var->base_type());	×
NEW 149	builder	×
NEW 150	.add_computational_memlet(norm_block, div_norm_tasklet, "_out", normalized_access, {}, iedge_input->base_type());	×
151
152	// Apply scale and bias: scale * normalized + bias
153	auto &mul_scale_tasklet = builder.add_tasklet(norm_block, data_flow::TaskletCode::mul, "_out", {"_in1", "_in2"});	×
154	auto &scaled_access = builder.add_access(norm_block, builder.find_new_name("_scaled"));	×
NEW 155	builder	×
NEW 156	.add_computational_memlet(norm_block, normalized_access, mul_scale_tasklet, "_in1", {}, iedge_input->base_type());	×
NEW 157	builder.add_computational_memlet(	×
NEW 158	norm_block, scale_access_norm, mul_scale_tasklet, "_in2", loop_syms, iedge_scale->base_type()	×
159	);
UNCOV 160	builder.add_computational_memlet(norm_block, mul_scale_tasklet, "_out", scaled_access, {}, iedge_input->base_type());	×
161
162	auto &add_bias_tasklet = builder.add_tasklet(norm_block, data_flow::TaskletCode::add, "_out", {"_in1", "_in2"});	×
163	builder.add_computational_memlet(norm_block, scaled_access, add_bias_tasklet, "_in1", {}, iedge_input->base_type());	×
NEW 164	builder.add_computational_memlet(	×
NEW 165	norm_block, bias_access_norm, add_bias_tasklet, "_in2", loop_syms, iedge_bias->base_type()	×
166	);
NEW 167	builder.add_computational_memlet(	×
NEW 168	norm_block, add_bias_tasklet, "_out", output_access_norm, loop_syms, oedge_output->base_type()	×
169	);
170
171	// Cleanup old block
172	builder.remove_memlet(block, *iedge_input);	×
173	builder.remove_memlet(block, *iedge_scale);	×
174	if (iedge_bias) {	×
175	builder.remove_memlet(block, *iedge_bias);	×
176	}	×
177	builder.remove_memlet(block, *iedge_mean);	×
178	builder.remove_memlet(block, *iedge_var);	×
179	builder.remove_memlet(block, *oedge_output);	×
180	builder.remove_node(block, *this);	×
181	builder.remove_child(parent, block);	×
182
183	return true;	×
184	}	×
185
186	std::unique_ptr<data_flow::DataFlowNode> BatchNormalizationNode::
187	clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph &parent) const {	×
NEW 188	return std::unique_ptr<data_flow::DataFlowNode>(	×
NEW 189	new BatchNormalizationNode(element_id, this->debug_info(), vertex, parent, axis_, epsilon_)	×
190	);
UNCOV 191	}	×
192
193	nlohmann::json BatchNormalizationNodeSerializer::serialize(const data_flow::LibraryNode &library_node) {	×
194	const BatchNormalizationNode &node = static_cast<const BatchNormalizationNode &>(library_node);	×
195	nlohmann::json j;	×
196
197	j["code"] = node.code().value();	×
198	j["axis"] = node.axis();	×
199	j["epsilon"] = node.epsilon();	×
200
201	return j;	×
202	}	×
203
204	data_flow::LibraryNode &BatchNormalizationNodeSerializer::deserialize(	×
205	const nlohmann::json &j, builder::StructuredSDFGBuilder &builder, structured_control_flow::Block &parent
206	) {
207	auto code = j["code"].get<std::string>();	×
208	if (code != LibraryNodeType_BatchNormalization.value()) {	×
209	throw std::runtime_error("Invalid library node code");	×
210	}
211
212	sdfg::serializer::JSONSerializer serializer;	×
NEW 213	DebugInfoRegion debug_info = serializer.json_to_debug_info_region(j["debug_info"], builder.debug_info());	×
214
215	auto axis = j["axis"].get<int>();	×
216	auto epsilon = j["epsilon"].get<std::string>();	×
217
218	return builder.add_library_node<BatchNormalizationNode>(parent, debug_info, axis, epsilon);	×
219	}	×
220
221	} // namespace ml
222	} // namespace math
223	} // namespace sdfg

daisytuner / sdfglib / 17316805645

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