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Match softmax input storage and layout to its output (#24437) #24437

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17 changes: 17 additions & 0 deletions backends/native/runtime/vulkan/VulkanEngineExecutable.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1786,6 +1786,23 @@ class VulkanEngineExecutable final : public EngineExecutable {
storage_at(value_id),
utils::kChannelsPacked);
}
// ET-VK's softmax reads its input in the output's storage type and, for
// textures, its layout.
if ((key == "aten._softmax.default" ||
key == "aten._log_softmax.default") &&
na.name == "self" && na.arg.kind() == ArgKind::Tensor &&
valid(na.arg.as_tensor().id) && !graph_->val_is_tref(a)) {
const ValueId out_id = n.outputs.at(0).value_id;
const VkRef out = vref_at(out_id);
if (graph_->storage_type_of(a) != graph_->storage_type_of(out) ||
graph_->hashed_layout_of(a) != graph_->hashed_layout_of(out)) {
a = repack(
a,
g().value(na.arg.as_tensor().id),
storage_at(out_id),
layout_at(out_id));
}
}
// ET-VK's pad picks its shader by the output's layout and storage
// alone, which later layout steps can leave unlike its input's.
if (key == "aten.constant_pad_nd.default" && na.name == "self" &&
Expand Down
227 changes: 227 additions & 0 deletions backends/native/test/runtime/vulkan/test_vulkan_engine.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1370,6 +1370,166 @@ std::vector<uint8_t> make_quantized_input_tensors() {
return tensors;
}

// result = _softmax(index(input, batch), -1) over a [2, 3, 8] input. aten.index
// keeps its operands in buffer storage, so softmax reads a buffer into a
// texture output.
std::vector<uint8_t> make_buffered_softmax_program() {
flatbuffers::FlatBufferBuilder builder;
const auto float_meta =
create_tensor_meta(builder, fbs::ScalarType::FLOAT, {2, 3, 8});
const std::vector<flatbuffers::Offset<fbs::TensorValue>> tensor_values = {
fbs::CreateTensorValueDirect(builder, "input", float_meta),
fbs::CreateTensorValueDirect(
builder,
"batch",
create_tensor_meta(builder, fbs::ScalarType::LONG, {2})),
fbs::CreateTensorValueDirect(builder, "gathered", float_meta),
fbs::CreateTensorValueDirect(builder, "result", float_meta),
};
std::vector<flatbuffers::Offset<fbs::Node>> nodes;
for (const char* name : {"input", "batch"}) {
const std::vector<flatbuffers::Offset<fbs::Output>> outputs = {
fbs::CreateOutputDirect(builder, name)};
nodes.push_back(fbs::CreateNodeDirect(
builder, name, fbs::OpKind::PLACEHOLDER, "", nullptr, &outputs));
}
const auto add_call =
[&](const char* name,
const char* target,
const std::vector<flatbuffers::Offset<fbs::NamedArgument>>& inputs) {
const std::vector<flatbuffers::Offset<fbs::Output>> outputs = {
fbs::CreateOutputDirect(builder, name)};
nodes.push_back(fbs::CreateNodeDirect(
builder,
name,
fbs::OpKind::CALL_FUNCTION,
target,
&inputs,
&outputs));
};
const auto indices = fbs::CreateOptionalTensorListArg(
builder,
create_strings(builder, {"batch"}),
builder.CreateVector(std::vector<uint8_t>{1}));
add_call(
"gathered",
"torch.ops.aten.index.Tensor",
{
fbs::CreateNamedArgumentDirect(
builder, "self", create_tensor_arg(builder, "input")),
fbs::CreateNamedArgumentDirect(
builder,
"indices",
fbs::CreateArgument(
builder,
fbs::ArgumentValue::OptionalTensorListArg,
indices.Union())),
});
add_call(
"result",
"torch.ops.aten._softmax.default",
{
fbs::CreateNamedArgumentDirect(
builder, "self", create_tensor_arg(builder, "gathered")),
fbs::CreateNamedArgumentDirect(
builder, "dim", create_int_arg(builder, -1)),
fbs::CreateNamedArgumentDirect(
builder, "half_to_float", create_bool_arg(builder, false)),
});
const std::vector<flatbuffers::Offset<fbs::NamedArgument>> output_inputs = {
fbs::CreateNamedArgumentDirect(
builder, "", create_tensor_arg(builder, "result"))};
nodes.push_back(fbs::CreateNodeDirect(
builder, "output", fbs::OpKind::OUTPUT, "", &output_inputs));
const auto graph = fbs::CreateGraph(
builder,
builder.CreateVector(nodes),
create_strings(builder, {"input", "batch"}),
create_strings(builder, {"result"}),
builder.CreateVector(tensor_values));
return finish_program(
builder, graph, {fbs::CreateOutputSpecDirect(builder, "result")});
}

// result = _softmax(linear(input, weight), -1) over a [2, 3, 8] input and a
// stored [8, 8] weight. The linear output is a width-packed texture, while the
// softmax output is planned channels-packed.
std::vector<uint8_t> make_linear_softmax_program() {
flatbuffers::FlatBufferBuilder builder;
const auto float_meta =
create_tensor_meta(builder, fbs::ScalarType::FLOAT, {2, 3, 8});
const auto weight_meta =
create_tensor_meta(builder, fbs::ScalarType::FLOAT, {8, 8});
const std::vector<flatbuffers::Offset<fbs::TensorValue>> tensor_values = {
fbs::CreateTensorValueDirect(builder, "input", float_meta),
fbs::CreateTensorValueDirect(builder, "product", float_meta),
fbs::CreateTensorValueDirect(builder, "result", float_meta),
};
std::vector<flatbuffers::Offset<fbs::Node>> nodes;
for (const char* name : {"input", "weight"}) {
const std::vector<flatbuffers::Offset<fbs::Output>> outputs = {
fbs::CreateOutputDirect(builder, name)};
nodes.push_back(fbs::CreateNodeDirect(
builder, name, fbs::OpKind::PLACEHOLDER, "", nullptr, &outputs));
}
const auto add_call =
[&](const char* name,
const char* target,
const std::vector<flatbuffers::Offset<fbs::NamedArgument>>& inputs) {
const std::vector<flatbuffers::Offset<fbs::Output>> outputs = {
fbs::CreateOutputDirect(builder, name)};
nodes.push_back(fbs::CreateNodeDirect(
builder,
name,
fbs::OpKind::CALL_FUNCTION,
target,
&inputs,
&outputs));
};
add_call(
"product",
"torch.ops.aten.linear.default",
{
fbs::CreateNamedArgumentDirect(
builder, "input", create_tensor_arg(builder, "input")),
fbs::CreateNamedArgumentDirect(
builder, "weight", create_tensor_arg(builder, "weight")),
fbs::CreateNamedArgumentDirect(
builder, "bias", create_none_arg(builder)),
});
add_call(
"result",
"torch.ops.aten._softmax.default",
{
fbs::CreateNamedArgumentDirect(
builder, "self", create_tensor_arg(builder, "product")),
fbs::CreateNamedArgumentDirect(
builder, "dim", create_int_arg(builder, -1)),
fbs::CreateNamedArgumentDirect(
builder, "half_to_float", create_bool_arg(builder, false)),
});
const std::vector<flatbuffers::Offset<fbs::NamedArgument>> output_inputs = {
fbs::CreateNamedArgumentDirect(
builder, "", create_tensor_arg(builder, "result"))};
nodes.push_back(fbs::CreateNodeDirect(
builder, "output", fbs::OpKind::OUTPUT, "", &output_inputs));
const auto graph = fbs::CreateGraph(
builder,
builder.CreateVector(nodes),
create_strings(builder, {"input"}),
create_strings(builder, {"result"}),
builder.CreateVector(tensor_values));
const std::vector<flatbuffers::Offset<fbs::NamedTensorRef>> constants = {
fbs::CreateNamedTensorRefDirect(
builder, "weight", "weight", weight_meta, fbs::InputKind::PARAMETER),
};
return finish_program(
builder,
graph,
{fbs::CreateOutputSpecDirect(builder, "result")},
constants);
}

constexpr int64_t kHfTokens = 4;
constexpr int64_t kHfHeads = 4;
constexpr int64_t kHfKvHeads = 2;
Expand Down Expand Up @@ -2182,6 +2342,73 @@ TEST(VulkanEngineTest, ResizesBufferViewWithItsSource) {
EXPECT_EQ(output, (std::vector<float>{4.0f, 1.0f}));
}

TEST(VulkanEngineTest, ExecutesSoftmaxOnBufferPlannedInput) {
std::vector<float> input(48);
for (size_t i = 0; i < input.size(); ++i) {
input[i] = static_cast<float>((i * 5) % 7) * 0.5f - 1.0f;
}
const std::vector<int64_t> batch = {1, 0};
CompiledProgram compiled = compile_program(make_buffered_softmax_program());
compiled.executable->set_input(
0, input.data(), input.size(), ScalarType::Float);
compiled.executable->set_input(
1, batch.data(), batch.size(), ScalarType::Long);
compiled.executable->execute();
std::vector<float> output(48);
compiled.executable->get_output(
0, output.data(), output.size(), ScalarType::Float);
for (size_t row = 0; row < 6; ++row) {
const size_t source_row = (row + 3) % 6;
double sum = 0.0;
for (size_t i = 0; i < 8; ++i) {
sum += std::exp(input[source_row * 8 + i]);
}
for (size_t i = 0; i < 8; ++i) {
EXPECT_NEAR(
output[row * 8 + i], std::exp(input[source_row * 8 + i]) / sum, 1e-6)
<< row << "," << i;
}
}
}

TEST(VulkanEngineTest, ExecutesSoftmaxOnWidthPackedTextureInput) {
std::vector<float> weight(64, 0.0f);
for (size_t o = 0; o < 8; ++o) {
weight[o * 8 + o] = 0.5f;
weight[o * 8 + (o + 3) % 8] = 0.25f;
}
const std::string header =
R"({"weight":{"dtype":"F32","shape":[8,8],"data_offsets":[0,256]}})";
std::vector<uint8_t> tensors = testing::make_safetensors(header, 256);
std::memcpy(
tensors.data() + sizeof(uint64_t) + header.size(), weight.data(), 256);
CompiledProgram compiled =
compile_program(make_linear_softmax_program(), std::move(tensors));
std::vector<float> input(48);
for (size_t i = 0; i < input.size(); ++i) {
input[i] = static_cast<float>((i * 5) % 7) * 0.5f - 1.0f;
}
compiled.executable->set_input(
0, input.data(), input.size(), ScalarType::Float);
compiled.executable->execute();
std::vector<float> output(48);
compiled.executable->get_output(
0, output.data(), output.size(), ScalarType::Float);
for (size_t row = 0; row < 6; ++row) {
std::vector<double> product(8);
double sum = 0.0;
for (size_t o = 0; o < 8; ++o) {
product[o] =
0.5 * input[row * 8 + o] + 0.25 * input[row * 8 + (o + 3) % 8];
sum += std::exp(product[o]);
}
for (size_t o = 0; o < 8; ++o) {
EXPECT_NEAR(output[row * 8 + o], std::exp(product[o]) / sum, 1e-6)
<< row << "," << o;
}
}
}

TEST(VulkanEngineTest, RejectsBufferViewResizeThatDoesNotDivide) {
CompiledProgram compiled =
compile_program(make_dynamic_buffer_view_index_program());
Expand Down
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