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[GPU] Fix dynamic padding processing of static dimension #17978

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Jun 12, 2023
Merged

[GPU] Fix dynamic padding processing of static dimension #17978

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4 changes: 2 additions & 2 deletions src/plugins/intel_gpu/src/kernel_selector/jitter.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -190,14 +190,14 @@ std::string toCodeString(size_t val) {
std::string toCodeString(const Tensor::Dim& dim, size_t offset, bool padded, bool pad_is_dynamic, size_t pad_offset) {
std::string pad_str = "";
if (padded) {
if (dim.pad.is_dynamic) {
if (pad_is_dynamic) {
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Could you tell me what was the situaton?
dim.pad.is_dynamic was not set as dynamic but the dim.pad was actually dynamic?

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@yeonbok hmm, the issue happens for concat's dependency when dimension is static (the dimension on which we apply concat) and in the same time it has assigned dynamic padding (because of concat optimization). In that case during jitter code generation at line 200 we will chose else branch and try to obtain dim.pad.Total() for dynamic padding and get error
You can try to reproduce this situation with test from my PR

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Thanks! I will check :)

pad_str = " + (shape_info[" + std::to_string(pad_offset) + "] + shape_info[" +
std::to_string(pad_offset + 1) + "])";
} else {
pad_str = " + " + std::to_string(dim.pad.Total());
}
}
if (dim.is_dynamic) {
if (dim.is_dynamic || pad_is_dynamic) {
snprintf(buf, sizeof(buf), "(shape_info[%zu] %s)", offset, pad_str.c_str());
} else {
snprintf(buf, sizeof(buf), "%zu", dim.v + (padded ? dim.pad.Total() : 0));
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14 changes: 10 additions & 4 deletions src/plugins/intel_gpu/src/runtime/layout.cpp
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Expand Up @@ -213,10 +213,16 @@ std::string layout::to_short_string() const {

s << data_type_traits::name(data_type) << ":" << format.to_string() << ":";
dump_shape(s, size);
if (data_padding)
s << ":pad";
else
s << ":nopad";

if (data_padding.get_dynamic_pad_dims() != tensor(0)) {
s << ":dyn_pad_dims" << data_padding.get_dynamic_pad_dims().to_string();
} else {
if (data_padding)
s << ":pad";
else
s << ":nopad";
}

return s.str();
}

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57 changes: 56 additions & 1 deletion src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp
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Expand Up @@ -255,4 +255,59 @@ TEST(prepare_buffer_fusing, in_place_concat_dynamic) {
for (size_t x = 0; x < out_l.count(); ++x) {
ASSERT_EQ(ref_output[x], output_ptr[x]);
}
}
}

TEST(prepare_buffer_fusing, in_place_concat_dynamic__static_dim_dyn_pad) {
auto& engine = get_test_engine();
auto in_layout1_0 = layout{ ov::PartialShape{-1, 2, -1, -1}, data_types::f32, format::bfyx }; // => {-1, -1, -1, 2}
auto in_layout2_0 = layout{ ov::PartialShape{1, 2, -1, -1}, data_types::f32, format::bfyx }; // => {-1, -1, 1, 2}
auto in_layout1 = layout{ ov::PartialShape{1, 2, 3, 4}, data_types::f32, format::bfyx };
auto in_layout2 = layout{ ov::PartialShape{1, 2, 4, 1}, data_types::f32, format::bfyx };

topology topology;
topology.add(input_layout("input1", in_layout1_0));
topology.add(input_layout("input2", in_layout2_0));
topology.add(permute("permute1", input_info("input1"), {0, 3, 2, 1}));
topology.add(permute("permute2", input_info("input2"), {3, 2, 0, 1}));

topology.add(concatenation("concat", { input_info("permute1"), input_info("permute2") }, 2));
topology.add(permute("output", input_info("concat"), {0, 2, 3, 1}));

ExecutionConfig config;
config.set_property(ov::intel_gpu::optimize_data(true));
config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
auto prog = program::build_program(engine, topology, config, false, false);
ASSERT_NE(prog, nullptr);
cldnn::network net(prog, 0);

auto input_memory1 = engine.allocate_memory(in_layout1);
auto input_memory2 = engine.allocate_memory(in_layout2);
set_values<float>(input_memory1,
{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 11.0, 22.0, 33.0, 44.0, 55.0, 66.0,
111.0, 222.0, 333.0, 444.0, 555.0, 666.0, 1111.0, 2222.0, 3333.0, 4444.0, 5555.0, 6666.0});
set_values<float>(input_memory2, {1234.0, 2345.0, 3456.0, 4567.0, 5678.0, 6789.0, 9012.0, 9999.0});
net.set_input_data("input1", input_memory1);
net.set_input_data("input2", input_memory2);

std::vector<float> ref_output = {1.0, 2.0, 3.0, 4.0, 111.0, 222.0, 333.0, 444.0, 5.0, 6.0, 11.0,
22.0, 555.0, 666.0, 1111.0, 2222.0, 33.0, 44.0, 55.0, 66.0, 3333.0, 4444.0,
5555.0, 6666.0, 1234.0, 2345.0, 3456.0, 4567.0, 5678.0, 6789.0, 9012.0, 9999.0};

std::map<cldnn::primitive_id, cldnn::network_output> output;
EXPECT_NO_THROW(output = net.execute());
auto out_l = net.get_output_layout("output");
auto out_mem = output.at("output").get_memory();
cldnn::mem_lock<float> output_ptr(out_mem, get_test_stream());

const auto& concat_node = net.get_primitive("concat")->get_node();
auto concat_mem = net.get_primitive("concat")->output_memory_ptr();
auto permute1_mem = net.get_primitive("permute1")->output_memory_ptr();
auto permute2_mem = net.get_primitive("permute1")->output_memory_ptr();

ASSERT_TRUE(concat_node.can_be_optimized());
ASSERT_EQ(concat_mem.get(), permute1_mem.get());
ASSERT_EQ(concat_mem.get(), permute2_mem.get());
for (size_t x = 0; x < out_l.count(); ++x) {
ASSERT_EQ(ref_output[x], output_ptr[x]);
}
}