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TFLite GPU: Rename SOFT_MAX and SoftMax to SOFTMAX and Softmax, respectively.

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PiperOrigin-RevId: 260970382
This commit is contained in:
Juhyun Lee 2019-07-31 11:51:21 -07:00 committed by TensorFlower Gardener
parent 49002f2e95
commit 39db34c731
8 changed files with 57 additions and 35 deletions
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5
tensorflow/lite/delegates/gpu/common/model_builder.cc
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@ -1737,7 +1737,7 @@ class SoftmaxOperationParser : public TFLiteOperationParser {
const TfLiteRegistration* registration, GraphFloat32* graph, const TfLiteRegistration* registration, GraphFloat32* graph,
ObjectReader* reader) final { ObjectReader* reader) final {
Node* node = graph->NewNode(); Node* node = graph->NewNode();
node->operation.type = ToString(OperationType::SOFT_MAX); node->operation.type = ToString(OperationType::SOFTMAX);
RETURN_IF_ERROR(reader->AddInput(node, 0)); RETURN_IF_ERROR(reader->AddInput(node, 0));
RETURN_IF_ERROR(reader->AddOutputs(node)); RETURN_IF_ERROR(reader->AddOutputs(node));
@ -1753,8 +1753,7 @@ class SoftmaxOperationParser : public TFLiteOperationParser {
// auto mul_node = reader->NewPassthroughNode(node); // auto mul_node = reader->NewPassthroughNode(node);
// mul_node->operation.type = ToString(OperationType::MUL); // mul_node->operation.type = ToString(OperationType::MUL);
} }
// TODO(impjdi): Rename to SoftmaxAttributes. SoftmaxAttributes attr;
SoftMaxAttributes attr;
attr.axis = Axis::CHANNELS; // always by channels attr.axis = Axis::CHANNELS; // always by channels
node->operation.attributes = attr; node->operation.attributes = attr;
return OkStatus(); return OkStatus();

6
tensorflow/lite/delegates/gpu/common/operations.cc
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@ -106,8 +106,8 @@ std::string ToString(enum OperationType op) {
return "sin"; return "sin";
case OperationType::SLICE: case OperationType::SLICE:
return "slice"; return "slice";
case OperationType::SOFT_MAX: case OperationType::SOFTMAX:
return "soft_max"; return "softmax";
case OperationType::SPACE_TO_BATCH: case OperationType::SPACE_TO_BATCH:
return "space_to_batch"; return "space_to_batch";
case OperationType::SQRT: case OperationType::SQRT:
@ -158,7 +158,7 @@ OperationType OperationTypeFromString(const std::string& name) {
{"sigmoid", OperationType::SIGMOID}, {"sigmoid", OperationType::SIGMOID},
{"sin", OperationType::SIN}, {"sin", OperationType::SIN},
{"slice", OperationType::SLICE}, {"slice", OperationType::SLICE},
{"soft_max", OperationType::SOFT_MAX}, {"softmax", OperationType::SOFTMAX},
{"sqrt", OperationType::SQRT}, {"sqrt", OperationType::SQRT},
{"square", OperationType::SQUARE}, {"square", OperationType::SQUARE},
{"subtract", OperationType::SUB}, {"subtract", OperationType::SUB},

4
tensorflow/lite/delegates/gpu/common/operations.h
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@ -63,7 +63,7 @@ enum class OperationType {
SIGMOID, SIGMOID,
SIN, SIN,
SLICE, SLICE,
SOFT_MAX, SOFTMAX,
SPACE_TO_BATCH, SPACE_TO_BATCH,
SQRT, SQRT,
SQUARE, SQUARE,
@ -239,7 +239,7 @@ struct PReLUAttributes {
alpha; alpha;
}; };
struct SoftMaxAttributes { struct SoftmaxAttributes {
Axis axis = Axis::UNKNOWN; Axis axis = Axis::UNKNOWN;
}; };

2
tensorflow/lite/delegates/gpu/gl/kernels/registry.cc
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@ -86,7 +86,7 @@ class Registry : public NodeShader {
insert_op(Type::RELU, NewReLUNodeShader); insert_op(Type::RELU, NewReLUNodeShader);
insert_op(Type::RESHAPE, NewReshapeNodeShader); insert_op(Type::RESHAPE, NewReshapeNodeShader);
insert_op(Type::SLICE, NewSliceNodeShader); insert_op(Type::SLICE, NewSliceNodeShader);
insert_op(Type::SOFT_MAX, NewSoftMaxNodeShader); insert_op(Type::SOFTMAX, NewSoftmaxNodeShader);
insert_op(Type::UPSAMPLE_2D, NewUpsamplingNodeShader); insert_op(Type::UPSAMPLE_2D, NewUpsamplingNodeShader);
insert_elementwise_op(Type::ABS); insert_elementwise_op(Type::ABS);

14
tensorflow/lite/delegates/gpu/gl/kernels/softmax.cc
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@ -33,14 +33,14 @@ namespace gpu {
namespace gl { namespace gl {
namespace { namespace {
class SoftMax : public NodeShader { class Softmax : public NodeShader {
public: public:
Status GenerateCode(const GenerationContext& ctx, Status GenerateCode(const GenerationContext& ctx,
GeneratedCode* generated_code) const final { GeneratedCode* generated_code) const final {
auto input = ctx.graph->FindInputs(ctx.node->id)[0]; const auto* input = ctx.graph->FindInputs(ctx.node->id)[0];
auto output = ctx.graph->FindOutputs(ctx.node->id)[0]; const auto* output = ctx.graph->FindOutputs(ctx.node->id)[0];
auto attr = const auto& attr = absl::any_cast<const SoftmaxAttributes&>(
absl::any_cast<SoftMaxAttributes>(ctx.node->operation.attributes); ctx.node->operation.attributes);
if (input->tensor.shape != output->tensor.shape) { if (input->tensor.shape != output->tensor.shape) {
return InvalidArgumentError("Input and output shape does not match"); return InvalidArgumentError("Input and output shape does not match");
} }
@ -89,8 +89,8 @@ class SoftMax : public NodeShader {
} // namespace } // namespace
std::unique_ptr<NodeShader> NewSoftMaxNodeShader() { std::unique_ptr<NodeShader> NewSoftmaxNodeShader() {
return absl::make_unique<SoftMax>(); return absl::make_unique<Softmax>();
} }
} // namespace gl } // namespace gl

2
tensorflow/lite/delegates/gpu/gl/kernels/softmax.h
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@ -25,7 +25,7 @@ namespace tflite {
namespace gpu { namespace gpu {
namespace gl { namespace gl {
std::unique_ptr<NodeShader> NewSoftMaxNodeShader(); std::unique_ptr<NodeShader> NewSoftmaxNodeShader();
} // namespace gl } // namespace gl
} // namespace gpu } // namespace gpu

55
tensorflow/lite/delegates/gpu/gl/kernels/softmax_test.cc
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@ -31,7 +31,7 @@ namespace gpu {
namespace gl { namespace gl {
namespace { namespace {
TEST(SoftmaxTest, WorksForChannelsAxis) { TEST(SoftmaxTest, Softmax) {
TensorRef<BHWC> input; TensorRef<BHWC> input;
input.type = DataType::FLOAT32; input.type = DataType::FLOAT32;
input.ref = 0; input.ref = 0;
@ -42,13 +42,13 @@ TEST(SoftmaxTest, WorksForChannelsAxis) {
output.ref = 1; output.ref = 1;
output.shape = BHWC(1, 2, 2, 1); output.shape = BHWC(1, 2, 2, 1);
SoftMaxAttributes attr; SoftmaxAttributes attr;
attr.axis = Axis::CHANNELS; attr.axis = Axis::CHANNELS;
SingleOpModel model({ToString(OperationType::SOFT_MAX), attr}, {input}, SingleOpModel model({ToString(OperationType::SOFTMAX), attr}, {input},
{output}); {output});
ASSERT_TRUE(model.PopulateTensor(0, {0.1, 0.2, 0.1, 0.2})); ASSERT_TRUE(model.PopulateTensor(0, {0.1, 0.2, 0.1, 0.2}));
ASSERT_OK(model.Invoke(*NewSoftMaxNodeShader())); ASSERT_OK(model.Invoke(*NewSoftmaxNodeShader()));
EXPECT_THAT(model.GetOutput(0), Pointwise(FloatNear(1e-6), {1, 1, 1, 1})); EXPECT_THAT(model.GetOutput(0), Pointwise(FloatNear(1e-6), {1, 1, 1, 1}));
} }
@ -63,15 +63,13 @@ TEST(SoftmaxTest, DoesNotWorkForHeightAxis) {
output.ref = 1; output.ref = 1;
output.shape = BHWC(1, 2, 2, 1); output.shape = BHWC(1, 2, 2, 1);
SoftMaxAttributes attr; SoftmaxAttributes attr;
attr.axis = Axis::HEIGHT; attr.axis = Axis::HEIGHT;
SingleOpModel model({ToString(OperationType::SOFT_MAX), attr}, {input}, SingleOpModel model({ToString(OperationType::SOFTMAX), attr}, {input},
{output}); {output});
ASSERT_TRUE(model.PopulateTensor(0, {1, 2, 3, 4})); ASSERT_TRUE(model.PopulateTensor(0, {0.1, 0.2, 0.3, 0.4}));
ASSERT_THAT( EXPECT_FALSE(model.Invoke(*NewSoftmaxNodeShader()).ok());
model.Invoke(*NewSoftMaxNodeShader()).message(),
testing::HasSubstr("Softmax is only supported for channels axis."));
} }
TEST(SoftmaxTest, DoesNotWorkForWidthAxis) { TEST(SoftmaxTest, DoesNotWorkForWidthAxis) {
@ -85,15 +83,40 @@ TEST(SoftmaxTest, DoesNotWorkForWidthAxis) {
output.ref = 1; output.ref = 1;
output.shape = BHWC(1, 2, 2, 1); output.shape = BHWC(1, 2, 2, 1);
SoftMaxAttributes attr; SoftmaxAttributes attr;
attr.axis = Axis::WIDTH; attr.axis = Axis::WIDTH;
SingleOpModel model({ToString(OperationType::SOFT_MAX), attr}, {input}, SingleOpModel model({ToString(OperationType::SOFTMAX), attr}, {input},
{output}); {output});
ASSERT_TRUE(model.PopulateTensor(0, {1, 2, 3, 4})); ASSERT_TRUE(model.PopulateTensor(0, {0.1, 0.2, 0.3, 0.4}));
ASSERT_THAT( EXPECT_FALSE(model.Invoke(*NewSoftmaxNodeShader()).ok());
model.Invoke(*NewSoftMaxNodeShader()).message(), }
testing::HasSubstr("Softmax is only supported for channels axis."));
TEST(SoftmaxTest, Softmax1x1) {
TensorRef<BHWC> input;
input.type = DataType::FLOAT32;
input.ref = 0;
input.shape = BHWC(1, 1, 1, 4);
TensorRef<BHWC> output;
output.type = DataType::FLOAT32;
output.ref = 1;
output.shape = BHWC(1, 1, 1, 4);
SoftmaxAttributes attr;
attr.axis = Axis::CHANNELS;
const double sum =
std::exp(0.1) + std::exp(0.2) + std::exp(0.3) + std::exp(0.4);
SingleOpModel model({ToString(OperationType::SOFTMAX), attr}, {input},
{output});
ASSERT_TRUE(model.PopulateTensor(0, {0.1, 0.2, 0.3, 0.4}));
ASSERT_OK(model.Invoke(*NewSoftmaxNodeShader()));
EXPECT_THAT(
model.GetOutput(0),
Pointwise(FloatNear(1e-6), {std::exp(0.1) / sum, std::exp(0.2) / sum,
std::exp(0.3) / sum, std::exp(0.4) / sum}));
} }
} // namespace } // namespace

4
tensorflow/lite/delegates/gpu/metal/api.cc
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@ -220,9 +220,9 @@ Status Compile(const GraphFloat32& graph, const RuntimeOptions& options,
Slice(node_id, inputs[0], outputs[0], Slice(node_id, inputs[0], outputs[0],
absl::any_cast<SliceAttributes>(node->operation.attributes)); absl::any_cast<SliceAttributes>(node->operation.attributes));
break; break;
case OperationType::SOFT_MAX: { case OperationType::SOFTMAX: {
auto attr = auto attr =
absl::any_cast<SoftMaxAttributes>(node->operation.attributes); absl::any_cast<SoftmaxAttributes>(node->operation.attributes);
if (attr.axis != Axis::CHANNELS) { if (attr.axis != Axis::CHANNELS) {
return UnimplementedError("Softmax supports only CHANNELS dimension"); return UnimplementedError("Softmax supports only CHANNELS dimension");
} }