From 26cd1d1d0630489709e300b72a67d08d50faaf6c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 11 Jul 2018 19:13:29 -0700
Subject: [PATCH] add int32 support for mul
PiperOrigin-RevId: 204230461
---
.../internal/optimized/optimized_ops.h | 14 ++++
.../internal/reference/reference_ops.h | 9 +--
tensorflow/contrib/lite/kernels/mul.cc | 67 ++++++++++++-------
tensorflow/contrib/lite/kernels/mul_test.cc | 58 ++++++++++++++++
.../testing/generated_examples_zip_test.cc | 1 -
5 files changed, 118 insertions(+), 31 deletions(-)
diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index ebd3b116e17..c857fdf6995 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -3054,6 +3054,20 @@ void Mul(const float* input1_data, const Dims<4>& input1_dims,
output_activation_max, output_data, output_dims);
}
+inline void Mul(const int32* input1_data, const Dims<4>& input1_dims,
+ const int32* input2_data, const Dims<4>& input2_dims,
+ int32 output_activation_min, int32 output_activation_max,
+ int32* output_data, const Dims<4>& output_dims) {
+ gemmlowp::ScopedProfilingLabel label("Mul/int32");
+
+ const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims);
+ for (int i = 0; i < flat_size; ++i) {
+ output_data[i] = ActivationFunctionWithMinMax(
+ input1_data[i] * input2_data[i], output_activation_min,
+ output_activation_max);
+ }
+}
+
template <FusedActivationFunctionType Ac>
void Mul(const int32* input1_data, const Dims<4>& input1_dims,
const int32* input2_data, const Dims<4>& input2_dims,
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index 912e455a2e9..2d40f1769ba 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -1429,10 +1429,11 @@ inline void BroadcastAddFivefold(
output_activation_max, output_data, output_dims);
}
-inline void Mul(const float* input1_data, const Dims<4>& input1_dims,
- const float* input2_data, const Dims<4>& input2_dims,
- float output_activation_min, float output_activation_max,
- float* output_data, const Dims<4>& output_dims) {
+template <typename T>
+inline void Mul(const T* input1_data, const Dims<4>& input1_dims,
+ const T* input2_data, const Dims<4>& input2_dims,
+ T output_activation_min, T output_activation_max,
+ T* output_data, const Dims<4>& output_dims) {
const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims);
for (int i = 0; i < flat_size; ++i) {
output_data[i] = ActivationFunctionWithMinMax(
diff --git a/tensorflow/contrib/lite/kernels/mul.cc b/tensorflow/contrib/lite/kernels/mul.cc
index 1f72f3a3c7a..349f3e67261 100644
--- a/tensorflow/contrib/lite/kernels/mul.cc
+++ b/tensorflow/contrib/lite/kernels/mul.cc
@@ -100,29 +100,44 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
}
template <KernelType kernel_type>
-void EvalFloat(TfLiteContext* context, TfLiteNode* node,
- TfLiteMulParams* params, const OpData* data,
- const TfLiteTensor* input1, const TfLiteTensor* input2,
- TfLiteTensor* output) {
- float output_activation_min, output_activation_max;
- CalculateActivationRange(params->activation, &output_activation_min,
- &output_activation_max);
-#define TF_LITE_MUL(type, opname) \
- type::opname(GetTensorData<float>(input1), GetTensorDims(input1), \
- GetTensorData<float>(input2), GetTensorDims(input2), \
- output_activation_min, output_activation_max, \
- GetTensorData<float>(output), GetTensorDims(output))
- if (kernel_type == kReference) {
- if (data->requires_broadcast) {
- TF_LITE_MUL(reference_ops, BroadcastMul);
+void EvalMul(TfLiteContext* context, TfLiteNode* node, TfLiteMulParams* params,
+ const OpData* data, const TfLiteTensor* input1,
+ const TfLiteTensor* input2, TfLiteTensor* output) {
+#define TF_LITE_MUL(type, opname, data_type) \
+ data_type output_activation_min, output_activation_max; \
+ CalculateActivationRange(params->activation, &output_activation_min, \
+ &output_activation_max); \
+ type::opname(GetTensorData<data_type>(input1), GetTensorDims(input1), \
+ GetTensorData<data_type>(input2), GetTensorDims(input2), \
+ output_activation_min, output_activation_max, \
+ GetTensorData<data_type>(output), GetTensorDims(output))
+ if (output->type == kTfLiteInt32) {
+ if (kernel_type == kReference) {
+ if (data->requires_broadcast) {
+ TF_LITE_MUL(reference_ops, BroadcastMul, int32_t);
+ } else {
+ TF_LITE_MUL(reference_ops, Mul, int32_t);
+ }
} else {
- TF_LITE_MUL(reference_ops, Mul);
+ if (data->requires_broadcast) {
+ TF_LITE_MUL(optimized_ops, BroadcastMul, int32_t);
+ } else {
+ TF_LITE_MUL(optimized_ops, Mul, int32_t);
+ }
}
- } else {
- if (data->requires_broadcast) {
- TF_LITE_MUL(optimized_ops, BroadcastMul);
+ } else if (output->type == kTfLiteFloat32) {
+ if (kernel_type == kReference) {
+ if (data->requires_broadcast) {
+ TF_LITE_MUL(reference_ops, BroadcastMul, float);
+ } else {
+ TF_LITE_MUL(reference_ops, Mul, float);
+ }
} else {
- TF_LITE_MUL(optimized_ops, Mul);
+ if (data->requires_broadcast) {
+ TF_LITE_MUL(optimized_ops, BroadcastMul, float);
+ } else {
+ TF_LITE_MUL(optimized_ops, Mul, float);
+ }
}
}
#undef TF_LITE_MUL
@@ -194,17 +209,17 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
- if (output->type == kTfLiteFloat32) {
- EvalFloat<kernel_type>(context, node, params, data, input1, input2, output);
+ if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) {
+ EvalMul<kernel_type>(context, node, params, data, input1, input2, output);
} else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) {
TF_LITE_ENSURE_OK(
context, EvalQuantized<kernel_type>(context, node, params, data, input1,
input2, output));
} else {
- context->ReportError(
- context,
- "Mul only supports FLOAT32 and quantized UINT8 and INT16 now, got %d.",
- output->type);
+ context->ReportError(context,
+ "Mul only supports FLOAT32, INT32 and quantized UINT8 "
+ "and INT16 now, got %d.",
+ output->type);
return kTfLiteError;
}
diff --git a/tensorflow/contrib/lite/kernels/mul_test.cc b/tensorflow/contrib/lite/kernels/mul_test.cc
index 43d56e50d26..2807550a6b0 100644
--- a/tensorflow/contrib/lite/kernels/mul_test.cc
+++ b/tensorflow/contrib/lite/kernels/mul_test.cc
@@ -52,6 +52,13 @@ class FloatMulOpModel : public BaseMulOpModel {
std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
};
+class IntegerMulOpModel : public BaseMulOpModel {
+ public:
+ using BaseMulOpModel::BaseMulOpModel;
+
+ std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); }
+};
+
// For quantized Mul, the error shouldn't exceed (2*step + step^2).
// The param min=-1.0 & max=1.0 is used in the following tests.
// The tolerance value is ~0.0157.
@@ -133,6 +140,57 @@ TEST(FloatMulOpTest, WithBroadcast) {
}
}
+TEST(IntegerMulOpTest, NoActivation) {
+ IntegerMulOpModel m({TensorType_INT32, {1, 2, 2, 1}},
+ {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}},
+ ActivationFunctionType_NONE);
+ m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8});
+ m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5});
+ m.Invoke();
+ EXPECT_THAT(m.GetOutput(), ElementsAreArray({-20, 4, 21, 40}));
+}
+
+TEST(IntegerMulOpTest, ActivationRELU_N1_TO_1) {
+ IntegerMulOpModel m({TensorType_INT32, {1, 2, 2, 1}},
+ {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}},
+ ActivationFunctionType_RELU_N1_TO_1);
+ m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8});
+ m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5});
+ m.Invoke();
+ EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 1, 1, 1}));
+}
+
+TEST(IntegerMulOpTest, VariousInputShapes) {
+ std::vector<std::initializer_list<int>> test_shapes = {
+ {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
+ for (int i = 0; i < test_shapes.size(); ++i) {
+ IntegerMulOpModel m({TensorType_INT32, test_shapes[i]},
+ {TensorType_INT32, test_shapes[i]},
+ {TensorType_INT32, {}}, ActivationFunctionType_NONE);
+ m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8, 11, 20});
+ m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5, 11, 1});
+ m.Invoke();
+ EXPECT_THAT(m.GetOutput(), ElementsAreArray({-20, 4, 21, 40, 121, 20}))
+ << "With shape number " << i;
+ }
+}
+
+TEST(IntegerMulOpTest, WithBroadcast) {
+ std::vector<std::initializer_list<int>> test_shapes = {
+ {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
+ for (int i = 0; i < test_shapes.size(); ++i) {
+ IntegerMulOpModel m({TensorType_INT32, test_shapes[i]},
+ {TensorType_INT32, {}}, // always a scalar
+ {TensorType_INT32, {}}, ActivationFunctionType_NONE);
+ m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8, 11, 20});
+ m.PopulateTensor<int32_t>(m.input2(), {1});
+ m.Invoke();
+ EXPECT_THAT(m.GetOutput(),
+ ElementsAreArray(ArrayFloatNear({-20, 2, 7, 8, 11, 20})))
+ << "With shape number " << i;
+ }
+}
+
TEST(QuantizedMulOpTest, NoActivation) {
QuantizedMulOpModel m({TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0},
{TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0},
diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
index 5bc6b53416d..58f6bb53826 100644
--- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
+++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
@@ -53,7 +53,6 @@ tensorflow::Env* env = tensorflow::Env::Default();
// Key is a substring of the test name and value is a bug number.
// TODO(ahentz): make sure we clean this list up frequently.
std::map<string, string> kBrokenTests = {
- {R"(^\/mul.*int32)", "68808744"},
{R"(^\/div.*int32)", "68808744"},
{R"(^\/sub.*int32)", "68808744"},