22023884668

Committed 14 Feb 2026 08:36PM UTC coverage: 64.903% (-1.4%) from 66.315%

Build # 22023884668

Build Type

Pull #525

github

Committed by

web-flow

Commit Message

Merge 1d47f8bf2 into 9d01cacd5

Pull Request Pull Request #525: Step 3 (Native Tensor Support): Refactor Python Frontend

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/sdfg/src/data_flow/library_nodes/math/tensor/reduce_ops/softmax_node.cpp

#include "sdfg/data_flow/library_nodes/math/tensor/reduce_ops/softmax_node.h"
#include "sdfg/analysis/scope_analysis.h"
#include "sdfg/builder/structured_sdfg_builder.h"
#include "sdfg/data_flow/library_nodes/math/tensor/broadcast_node.h"
#include "sdfg/data_flow/library_nodes/math/tensor/elementwise_ops/div_node.h"
#include "sdfg/data_flow/library_nodes/math/tensor/elementwise_ops/exp_node.h"
#include "sdfg/data_flow/library_nodes/math/tensor/elementwise_ops/sub_node.h"
#include "sdfg/data_flow/library_nodes/math/tensor/reduce_ops/max_node.h"
#include "sdfg/data_flow/library_nodes/math/tensor/reduce_ops/sum_node.h"
#include "sdfg/data_flow/library_nodes/stdlib/malloc.h"
#include "sdfg/structured_control_flow/block.h"
#include "sdfg/structured_control_flow/for.h"
#include "sdfg/types/pointer.h"
#include "sdfg/types/scalar.h"
#include "sdfg/types/utils.h"

namespace sdfg {
namespace math {
namespace tensor {

SoftmaxNode::SoftmaxNode(
    size_t element_id,
    const DebugInfo& debug_info,
    const graph::Vertex vertex,
    data_flow::DataFlowGraph& parent,
    const std::vector<symbolic::Expression>& shape,
    const std::vector<int64_t>& axes,
    bool keepdims
)
    : ReduceNode(element_id, debug_info, vertex, parent, LibraryNodeType_Softmax, shape, axes, keepdims) {
    if (keepdims) {
        throw InvalidSDFGException("Unsupported attribute on library node: softmax");
    }
}

void SoftmaxNode::validate(const Function& function) const {}

bool SoftmaxNode::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));
    int index = parent.index(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());

    // Calculate reduced shape (for Max and Sum)
    std::vector<symbolic::Expression> reduced_shape;
    std::vector<int64_t> sorted_axes = axes_;
    // Normalize negative axes
    for (auto& axis : sorted_axes) {
        if (axis < 0) {
            axis = static_cast<int64_t>(shape_.size()) + axis;
        }
        // Validate axis is in bounds
        if (axis < 0 || axis >= static_cast<int64_t>(shape_.size())) {
            throw InvalidSDFGException(
                "Library Node: Axis value out of bounds. Axis: " + std::to_string(axis) +
                " Shape size: " + std::to_string(shape_.size())
            );
        }
    }
    std::sort(sorted_axes.begin(), sorted_axes.end());

    for (size_t i = 0; i < shape_.size(); ++i) {
        bool is_axis = false;
        for (auto axis : sorted_axes) {
            if (axis == (int64_t) i) {
                is_axis = true;
                break;
            }
        }

        if (is_axis) {
            reduced_shape.push_back(symbolic::one());
        } else {
            reduced_shape.push_back(shape_[i]);
        }
    }

    types::Scalar element_type(this->primitive_type(dataflow));
    types::Pointer pointer_type(element_type);

    // Temporary buffers
    std::string tmp_max_name = builder.find_new_name("_softmax_max");
    builder.add_container(tmp_max_name, pointer_type);

    std::string tmp_max_bcast_name = builder.find_new_name("_softmax_max_bcast");
    builder.add_container(tmp_max_bcast_name, pointer_type);

    std::string tmp_sub_name = builder.find_new_name("_softmax_sub");
    builder.add_container(tmp_sub_name, pointer_type);

    std::string tmp_exp_name = builder.find_new_name("_softmax_exp");
    builder.add_container(tmp_exp_name, pointer_type);

    std::string tmp_sum_name = builder.find_new_name("_softmax_sum");
    builder.add_container(tmp_sum_name, pointer_type);

    std::string tmp_sum_bcast_name = builder.find_new_name("_softmax_sum_bcast");
    builder.add_container(tmp_sum_bcast_name, pointer_type);

    // Mallocs
    {
        symbolic::Expression bytes_elem = types::get_type_size(element_type, false);

        symbolic::Expression bytes_full = bytes_elem;
        for (auto& dim : this->shape_) {
            bytes_full = symbolic::mul(dim, bytes_full);
        }

        symbolic::Expression bytes_reduced = bytes_elem;
        for (auto& dim : reduced_shape) {
            bytes_reduced = symbolic::mul(dim, bytes_reduced);
        }

        auto& alloc_block = builder.add_block_before(parent, block, {}, this->debug_info());

        auto malloc_helper = [&](const std::string& name, const symbolic::Expression& size) {
            auto& access = builder.add_access(alloc_block, name);
            auto& malloc_node = builder.add_library_node<stdlib::MallocNode>(alloc_block, this->debug_info(), size);
            builder
                .add_computational_memlet(alloc_block, malloc_node, "_ret", access, {}, pointer_type, this->debug_info());
        };

        malloc_helper(tmp_max_name, bytes_reduced);
        malloc_helper(tmp_max_bcast_name, bytes_full);
        malloc_helper(tmp_sub_name, bytes_full);
        malloc_helper(tmp_exp_name, bytes_full);
        malloc_helper(tmp_sum_name, bytes_reduced);
        malloc_helper(tmp_sum_bcast_name, bytes_full);
    }

    // 1. Max(X) -> TmpMax
    {
        auto& max_block = builder.add_block_before(parent, block, {}, this->debug_info());
        auto& max_node =
            builder.add_library_node<MaxNode>(max_block, this->debug_info(), this->shape_, this->axes_, true);

        auto& in_access = builder.add_access(max_block, in_node.data());
        auto& out_access = builder.add_access(max_block, tmp_max_name);

        builder
            .add_computational_memlet(max_block, in_access, max_node, "X", {}, in_edge.base_type(), this->debug_info());
        builder
            .add_computational_memlet(max_block, max_node, "Y", out_access, {}, out_edge.base_type(), this->debug_info());
    }

    // 1.5 Broadcast Max -> TmpMaxBcast
    {
        auto& bcast_block = builder.add_block_before(parent, block, {}, this->debug_info());
        auto& bcast_node =
            builder.add_library_node<BroadcastNode>(bcast_block, this->debug_info(), reduced_shape, this->shape_);

        auto& in_access = builder.add_access(bcast_block, tmp_max_name);
        auto& out_access = builder.add_access(bcast_block, tmp_max_bcast_name);

        builder.add_computational_memlet(
            bcast_block, in_access, bcast_node, "X", {}, out_edge.base_type(), this->debug_info()
        );
        builder.add_computational_memlet(
            bcast_block, bcast_node, "Y", out_access, {}, out_edge.base_type(), this->debug_info()
        );
    }

    // 2. Sub(X, TmpMaxBcast) -> TmpSub
    {
        auto& sub_block = builder.add_block_before(parent, block, {}, this->debug_info());
        auto& sub_node = builder.add_library_node<SubNode>(sub_block, this->debug_info(), this->shape_);

        auto& in1_access = builder.add_access(sub_block, in_node.data());
        auto& in2_access = builder.add_access(sub_block, tmp_max_bcast_name);
        auto& out_access = builder.add_access(sub_block, tmp_sub_name);

        builder
            .add_computational_memlet(sub_block, in1_access, sub_node, "A", {}, in_edge.base_type(), this->debug_info());
        builder
            .add_computational_memlet(sub_block, in2_access, sub_node, "B", {}, out_edge.base_type(), this->debug_info());
        builder
            .add_computational_memlet(sub_block, sub_node, "C", out_access, {}, out_edge.base_type(), this->debug_info());
    }

    // 3. Exp(TmpSub) -> TmpExp
    {
        auto& exp_block = builder.add_block_before(parent, block, {}, this->debug_info());
        auto& exp_node = builder.add_library_node<ExpNode>(exp_block, this->debug_info(), this->shape_);

        auto& in_access = builder.add_access(exp_block, tmp_sub_name);
        auto& out_access = builder.add_access(exp_block, tmp_exp_name);

        builder
            .add_computational_memlet(exp_block, in_access, exp_node, "X", {}, out_edge.base_type(), this->debug_info());
        builder
            .add_computational_memlet(exp_block, exp_node, "Y", out_access, {}, out_edge.base_type(), this->debug_info());
    }

    // 4. Sum(TmpExp) -> TmpSum
    {
        auto& sum_block = builder.add_block_before(parent, block, {}, this->debug_info());
        auto& sum_node =
            builder.add_library_node<SumNode>(sum_block, this->debug_info(), this->shape_, this->axes_, true);

        auto& in_access = builder.add_access(sum_block, tmp_exp_name);
        auto& out_access = builder.add_access(sum_block, tmp_sum_name);

        builder
            .add_computational_memlet(sum_block, in_access, sum_node, "X", {}, out_edge.base_type(), this->debug_info());
        builder
            .add_computational_memlet(sum_block, sum_node, "Y", out_access, {}, out_edge.base_type(), this->debug_info());
    }

    // 4.5 Broadcast Sum -> TmpSumBcast
    {
        auto& bcast_block = builder.add_block_before(parent, block, {}, this->debug_info());
        auto& bcast_node =
            builder.add_library_node<BroadcastNode>(bcast_block, this->debug_info(), reduced_shape, this->shape_);

        auto& in_access = builder.add_access(bcast_block, tmp_sum_name);
        auto& out_access = builder.add_access(bcast_block, tmp_sum_bcast_name);

        builder.add_computational_memlet(
            bcast_block, in_access, bcast_node, "X", {}, out_edge.base_type(), this->debug_info()
        );
        builder.add_computational_memlet(
            bcast_block, bcast_node, "Y", out_access, {}, out_edge.base_type(), this->debug_info()
        );
    }

    // 5. Div(TmpExp, TmpSumBcast) -> Output
    {
        auto& div_block = builder.add_block_before(parent, block, {}, this->debug_info());
        auto& div_node = builder.add_library_node<DivNode>(div_block, this->debug_info(), this->shape_);

        auto& in1_access = builder.add_access(div_block, tmp_exp_name);
        auto& in2_access = builder.add_access(div_block, tmp_sum_bcast_name);
        auto& out_access = builder.add_access(div_block, out_node.data());

        builder
            .add_computational_memlet(div_block, in1_access, div_node, "A", {}, out_edge.base_type(), this->debug_info());
        builder
            .add_computational_memlet(div_block, in2_access, div_node, "B", {}, out_edge.base_type(), this->debug_info());
        builder
            .add_computational_memlet(div_block, div_node, "C", out_access, {}, out_edge.base_type(), this->debug_info());
    }

    // Cleanup
    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 last_index = parent.index(block);
    builder.remove_child(parent, last_index);

    return true;
}

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

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

1	#include "sdfg/data_flow/library_nodes/math/tensor/reduce_ops/softmax_node.h"
2	#include "sdfg/analysis/scope_analysis.h"
3	#include "sdfg/builder/structured_sdfg_builder.h"
4	#include "sdfg/data_flow/library_nodes/math/tensor/broadcast_node.h"
5	#include "sdfg/data_flow/library_nodes/math/tensor/elementwise_ops/div_node.h"
6	#include "sdfg/data_flow/library_nodes/math/tensor/elementwise_ops/exp_node.h"
7	#include "sdfg/data_flow/library_nodes/math/tensor/elementwise_ops/sub_node.h"
8	#include "sdfg/data_flow/library_nodes/math/tensor/reduce_ops/max_node.h"
9	#include "sdfg/data_flow/library_nodes/math/tensor/reduce_ops/sum_node.h"
10	#include "sdfg/data_flow/library_nodes/stdlib/malloc.h"
11	#include "sdfg/structured_control_flow/block.h"
12	#include "sdfg/structured_control_flow/for.h"
13	#include "sdfg/types/pointer.h"
14	#include "sdfg/types/scalar.h"
15	#include "sdfg/types/utils.h"
16
17	namespace sdfg {
18	namespace math {
19	namespace tensor {
20
21	SoftmaxNode::SoftmaxNode(
22	size_t element_id,
23	const DebugInfo& debug_info,
24	const graph::Vertex vertex,
25	data_flow::DataFlowGraph& parent,
26	const std::vector<symbolic::Expression>& shape,
27	const std::vector<int64_t>& axes,
28	bool keepdims
29	)
30	: ReduceNode(element_id, debug_info, vertex, parent, LibraryNodeType_Softmax, shape, axes, keepdims) {	×
31	if (keepdims) {	×
32	throw InvalidSDFGException("Unsupported attribute on library node: softmax");	×
33	}	×
34	}	×
35
NEW 36	void SoftmaxNode::validate(const Function& function) const {}	×
37
38	bool SoftmaxNode::expand(builder::StructuredSDFGBuilder& builder, analysis::AnalysisManager& analysis_manager) {	×
39	auto& dataflow = this->get_parent();	×
40	auto& block = static_cast<structured_control_flow::Block&>(*dataflow.get_parent());	×
41
42	if (dataflow.in_degree(this) != 1 \|\| dataflow.out_degree(this) != 1) {	×
43	return false;	×
44	}	×
45
46	auto& scope_analysis = analysis_manager.get<analysis::ScopeAnalysis>();	×
47	auto& parent = static_cast<structured_control_flow::Sequence&>(*scope_analysis.parent_scope(&block));	×
48	int index = parent.index(block);	×
49
50	auto& in_edge = dataflow.in_edges(this).begin();	×
51	auto& out_edge = dataflow.out_edges(this).begin();	×
52	auto& in_node = static_cast<data_flow::AccessNode&>(in_edge.src());	×
53	auto& out_node = static_cast<data_flow::AccessNode&>(out_edge.dst());	×
54
55	// Calculate reduced shape (for Max and Sum)
56	std::vector<symbolic::Expression> reduced_shape;	×
57	std::vector<int64_t> sorted_axes = axes_;	×
58	// Normalize negative axes
NEW 59	for (auto& axis : sorted_axes) {	×
NEW 60	if (axis < 0) {	×
NEW 61	axis = static_cast<int64_t>(shape_.size()) + axis;	×
NEW 62	}	×
63	// Validate axis is in bounds
NEW 64	if (axis < 0 \|\| axis >= static_cast<int64_t>(shape_.size())) {	×
NEW 65	throw InvalidSDFGException(	×
NEW 66	"Library Node: Axis value out of bounds. Axis: " + std::to_string(axis) +	×
NEW 67	" Shape size: " + std::to_string(shape_.size())	×
NEW 68	);	×
NEW 69	}	×
NEW 70	}	×
UNCOV 71	std::sort(sorted_axes.begin(), sorted_axes.end());	×
72
73	for (size_t i = 0; i < shape_.size(); ++i) {	×
74	bool is_axis = false;	×
75	for (auto axis : sorted_axes) {	×
76	if (axis == (int64_t) i) {	×
77	is_axis = true;	×
78	break;	×
79	}	×
80	}	×
81
82	if (is_axis) {	×
83	reduced_shape.push_back(symbolic::one());	×
84	} else {	×
85	reduced_shape.push_back(shape_[i]);	×
86	}	×
87	}	×
88
89	types::Scalar element_type(this->primitive_type(dataflow));	×
90	types::Pointer pointer_type(element_type);	×
91
92	// Temporary buffers
93	std::string tmp_max_name = builder.find_new_name("_softmax_max");	×
94	builder.add_container(tmp_max_name, pointer_type);	×
95
96	std::string tmp_max_bcast_name = builder.find_new_name("_softmax_max_bcast");	×
97	builder.add_container(tmp_max_bcast_name, pointer_type);	×
98
99	std::string tmp_sub_name = builder.find_new_name("_softmax_sub");	×
100	builder.add_container(tmp_sub_name, pointer_type);	×
101
102	std::string tmp_exp_name = builder.find_new_name("_softmax_exp");	×
103	builder.add_container(tmp_exp_name, pointer_type);	×
104
105	std::string tmp_sum_name = builder.find_new_name("_softmax_sum");	×
106	builder.add_container(tmp_sum_name, pointer_type);	×
107
108	std::string tmp_sum_bcast_name = builder.find_new_name("_softmax_sum_bcast");	×
109	builder.add_container(tmp_sum_bcast_name, pointer_type);	×
110
111	// Mallocs
NEW 112	{	×
113	symbolic::Expression bytes_elem = types::get_type_size(element_type, false);	×
114
115	symbolic::Expression bytes_full = bytes_elem;	×
116	for (auto& dim : this->shape_) {	×
117	bytes_full = symbolic::mul(dim, bytes_full);	×
118	}	×
119
120	symbolic::Expression bytes_reduced = bytes_elem;	×
121	for (auto& dim : reduced_shape) {	×
122	bytes_reduced = symbolic::mul(dim, bytes_reduced);	×
123	}	×
124
125	auto& alloc_block = builder.add_block_before(parent, block, {}, this->debug_info());	×
126
127	auto malloc_helper = [&](const std::string& name, const symbolic::Expression& size) {	×
128	auto& access = builder.add_access(alloc_block, name);	×
129	auto& malloc_node = builder.add_library_node<stdlib::MallocNode>(alloc_block, this->debug_info(), size);	×
NEW 130	builder	×
NEW 131	.add_computational_memlet(alloc_block, malloc_node, "_ret", access, {}, pointer_type, this->debug_info());	×
UNCOV 132	};	×
133
134	malloc_helper(tmp_max_name, bytes_reduced);	×
135	malloc_helper(tmp_max_bcast_name, bytes_full);	×
136	malloc_helper(tmp_sub_name, bytes_full);	×
137	malloc_helper(tmp_exp_name, bytes_full);	×
138	malloc_helper(tmp_sum_name, bytes_reduced);	×
139	malloc_helper(tmp_sum_bcast_name, bytes_full);	×
140	}	×
141
142	// 1. Max(X) -> TmpMax
143	{	×
144	auto& max_block = builder.add_block_before(parent, block, {}, this->debug_info());	×
145	auto& max_node =	×
146	builder.add_library_node<MaxNode>(max_block, this->debug_info(), this->shape_, this->axes_, true);	×
147
148	auto& in_access = builder.add_access(max_block, in_node.data());	×
149	auto& out_access = builder.add_access(max_block, tmp_max_name);	×
150
151	builder	×
152	.add_computational_memlet(max_block, in_access, max_node, "X", {}, in_edge.base_type(), this->debug_info());	×
NEW 153	builder	×
NEW 154	.add_computational_memlet(max_block, max_node, "Y", out_access, {}, out_edge.base_type(), this->debug_info());	×
UNCOV 155	}	×
156
157	// 1.5 Broadcast Max -> TmpMaxBcast
158	{	×
159	auto& bcast_block = builder.add_block_before(parent, block, {}, this->debug_info());	×
160	auto& bcast_node =	×
161	builder.add_library_node<BroadcastNode>(bcast_block, this->debug_info(), reduced_shape, this->shape_);	×
162
163	auto& in_access = builder.add_access(bcast_block, tmp_max_name);	×
164	auto& out_access = builder.add_access(bcast_block, tmp_max_bcast_name);	×
165
NEW 166	builder.add_computational_memlet(	×
NEW 167	bcast_block, in_access, bcast_node, "X", {}, out_edge.base_type(), this->debug_info()	×
NEW 168	);	×
NEW 169	builder.add_computational_memlet(	×
NEW 170	bcast_block, bcast_node, "Y", out_access, {}, out_edge.base_type(), this->debug_info()	×
NEW 171	);	×
UNCOV 172	}	×
173
174	// 2. Sub(X, TmpMaxBcast) -> TmpSub
175	{	×
176	auto& sub_block = builder.add_block_before(parent, block, {}, this->debug_info());	×
177	auto& sub_node = builder.add_library_node<SubNode>(sub_block, this->debug_info(), this->shape_);	×
178
179	auto& in1_access = builder.add_access(sub_block, in_node.data());	×
180	auto& in2_access = builder.add_access(sub_block, tmp_max_bcast_name);	×
181	auto& out_access = builder.add_access(sub_block, tmp_sub_name);	×
182
183	builder	×
184	.add_computational_memlet(sub_block, in1_access, sub_node, "A", {}, in_edge.base_type(), this->debug_info());	×
NEW 185	builder	×
NEW 186	.add_computational_memlet(sub_block, in2_access, sub_node, "B", {}, out_edge.base_type(), this->debug_info());	×
NEW 187	builder	×
NEW 188	.add_computational_memlet(sub_block, sub_node, "C", out_access, {}, out_edge.base_type(), this->debug_info());	×
UNCOV 189	}	×
190
191	// 3. Exp(TmpSub) -> TmpExp
192	{	×
193	auto& exp_block = builder.add_block_before(parent, block, {}, this->debug_info());	×
194	auto& exp_node = builder.add_library_node<ExpNode>(exp_block, this->debug_info(), this->shape_);	×
195
196	auto& in_access = builder.add_access(exp_block, tmp_sub_name);	×
197	auto& out_access = builder.add_access(exp_block, tmp_exp_name);	×
198
NEW 199	builder	×
NEW 200	.add_computational_memlet(exp_block, in_access, exp_node, "X", {}, out_edge.base_type(), this->debug_info());	×
NEW 201	builder	×
NEW 202	.add_computational_memlet(exp_block, exp_node, "Y", out_access, {}, out_edge.base_type(), this->debug_info());	×
UNCOV 203	}	×
204
205	// 4. Sum(TmpExp) -> TmpSum
206	{	×
207	auto& sum_block = builder.add_block_before(parent, block, {}, this->debug_info());	×
208	auto& sum_node =	×
209	builder.add_library_node<SumNode>(sum_block, this->debug_info(), this->shape_, this->axes_, true);	×
210
211	auto& in_access = builder.add_access(sum_block, tmp_exp_name);	×
212	auto& out_access = builder.add_access(sum_block, tmp_sum_name);	×
213
NEW 214	builder	×
NEW 215	.add_computational_memlet(sum_block, in_access, sum_node, "X", {}, out_edge.base_type(), this->debug_info());	×
NEW 216	builder	×
NEW 217	.add_computational_memlet(sum_block, sum_node, "Y", out_access, {}, out_edge.base_type(), this->debug_info());	×
UNCOV 218	}	×
219
220	// 4.5 Broadcast Sum -> TmpSumBcast
221	{	×
222	auto& bcast_block = builder.add_block_before(parent, block, {}, this->debug_info());	×
223	auto& bcast_node =	×
224	builder.add_library_node<BroadcastNode>(bcast_block, this->debug_info(), reduced_shape, this->shape_);	×
225
226	auto& in_access = builder.add_access(bcast_block, tmp_sum_name);	×
227	auto& out_access = builder.add_access(bcast_block, tmp_sum_bcast_name);	×
228
NEW 229	builder.add_computational_memlet(	×
NEW 230	bcast_block, in_access, bcast_node, "X", {}, out_edge.base_type(), this->debug_info()	×
NEW 231	);	×
NEW 232	builder.add_computational_memlet(	×
NEW 233	bcast_block, bcast_node, "Y", out_access, {}, out_edge.base_type(), this->debug_info()	×
NEW 234	);	×
UNCOV 235	}	×
236
237	// 5. Div(TmpExp, TmpSumBcast) -> Output
238	{	×
239	auto& div_block = builder.add_block_before(parent, block, {}, this->debug_info());	×
240	auto& div_node = builder.add_library_node<DivNode>(div_block, this->debug_info(), this->shape_);	×
241
242	auto& in1_access = builder.add_access(div_block, tmp_exp_name);	×
243	auto& in2_access = builder.add_access(div_block, tmp_sum_bcast_name);	×
244	auto& out_access = builder.add_access(div_block, out_node.data());	×
245
NEW 246	builder	×
NEW 247	.add_computational_memlet(div_block, in1_access, div_node, "A", {}, out_edge.base_type(), this->debug_info());	×
NEW 248	builder	×
NEW 249	.add_computational_memlet(div_block, in2_access, div_node, "B", {}, out_edge.base_type(), this->debug_info());	×
250	builder	×
251	.add_computational_memlet(div_block, div_node, "C", out_access, {}, out_edge.base_type(), this->debug_info());	×
252	}	×
253
254	// Cleanup
255	builder.remove_memlet(block, in_edge);	×
256	builder.remove_memlet(block, out_edge);	×
257	builder.remove_node(block, in_node);	×
258	builder.remove_node(block, out_node);	×
259	builder.remove_node(block, *this);	×
260
261	int last_index = parent.index(block);	×
262	builder.remove_child(parent, last_index);	×
263
264	return true;	×
265	}	×
266
267	std::unique_ptr<data_flow::DataFlowNode> SoftmaxNode::
268	clone(size_t element_id, const graph::Vertex vertex, data_flow::DataFlowGraph& parent) const {	×
269	return std::unique_ptr<	×
270	data_flow::DataFlowNode>(new SoftmaxNode(element_id, this->debug_info(), vertex, parent, this->shape_, this->axes_)	×
271	);	×
272	}	×
273
274	} // namespace tensor
275	} // namespace math
276	} // namespace sdfg

daisytuner / docc / 22023884668

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