Add support for FloorDiv

Signed-off-by: Kainan Cha <kainan.zha@verisilicon.com>

include/tim/vx/ops/elementwise.h

@@ -60,6 +60,10 @@ namespace ops {
  * ## Maximum
  *
  * Maximum(x, y) : max(x, y). This operation supports broadcasting.
+ *
+ * ## FloorDiv
+ *
+ * FloorDiv(x, y): floor( x / y ). This operation supports broadcasting.
  */
 
 #define DECLARE_ELEMENTWISE_OP(NAME) \
@@ -74,6 +78,7 @@ DECLARE_ELEMENTWISE_OP(Add)
 DECLARE_ELEMENTWISE_OP(Sub)
 DECLARE_ELEMENTWISE_OP(Div)
 DECLARE_ELEMENTWISE_OP(Pow)
+DECLARE_ELEMENTWISE_OP(FloorDiv)
 
 class Multiply : public Operation {
   public:

src/tim/vx/ops/README.md

@@ -79,7 +79,7 @@ Equal|RELATIONAL_OPS_EQUAL|Mapped|[tf.math.equal](https://tensorflow.google.cn/a
 NotEqual|RELATIONAL_OPS_NOT_EQUAL|Mapped|[tf.math.not_equal](https://tensorflow.google.cn/api_docs/python/tf/math/not_equal)
 ||SYNC_HOST|Unmapped
 Pow|POW|Mapped|[tf.math.pow](https://tensorflow.google.cn/api_docs/python/tf/math/pow)
-||FLOORDIV|Unmapped|[tf.math.floordiv](https://tensorflow.google.cn/api_docs/python/tf/math/floordiv)
+FloorDiv|FLOORDIV|Mapped|[tf.math.floordiv](https://tensorflow.google.cn/api_docs/python/tf/math/floordiv)
 Minimum|MINIMUM|Mapped|[tf.math.minimum](https://tensorflow.google.cn/api_docs/python/tf/math/minimum)
 ||SPATIAL_TRANSFORMER|Unmapped
 And|LOGICAL_OPS|Mapped|[tf.math.logical_and](https://tensorflow.google.cn/api_docs/python/tf/math/logical_and)

src/tim/vx/ops/elementwise.cc

@@ -39,6 +39,7 @@ DEFINE_ELEMENTWISE_OP(Add, VSI_NN_OP_ADD)
 DEFINE_ELEMENTWISE_OP(Sub, VSI_NN_OP_SUBTRACT)
 DEFINE_ELEMENTWISE_OP(Div, VSI_NN_OP_DIVIDE)
 DEFINE_ELEMENTWISE_OP(Pow, VSI_NN_OP_POW)
+DEFINE_ELEMENTWISE_OP(FloorDiv, VSI_NN_OP_FLOORDIV)
 
 #undef DEFINE_ELEMENTWISE_OP
 

src/tim/vx/ops/elementwise_test.cc

@@ -0,0 +1,132 @@
+/****************************************************************************
+*
+*    Copyright (c) 2021 Vivante Corporation
+*
+*    Permission is hereby granted, free of charge, to any person obtaining a
+*    copy of this software and associated documentation files (the "Software"),
+*    to deal in the Software without restriction, including without limitation
+*    the rights to use, copy, modify, merge, publish, distribute, sublicense,
+*    and/or sell copies of the Software, and to permit persons to whom the
+*    Software is furnished to do so, subject to the following conditions:
+*
+*    The above copyright notice and this permission notice shall be included in
+*    all copies or substantial portions of the Software.
+*
+*    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+*    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+*    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+*    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+*    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+*    DEALINGS IN THE SOFTWARE.
+*
+*****************************************************************************/
+#include "tim/vx/context.h"
+#include "tim/vx/graph.h"
+#include "tim/vx/ops/elementwise.h"
+
+#include "gtest/gtest.h"
+
+TEST(FloorDiv, shape_1_fp32) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType io_shape({1});
+    tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
+                            io_shape, tim::vx::TensorAttribute::INPUT);
+    tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
+                            io_shape, tim::vx::TensorAttribute::OUTPUT);
+
+    auto input_tensor_x = graph->CreateTensor(input_spec);
+    auto input_tensor_y = graph->CreateTensor(input_spec);
+    auto output_tensor = graph->CreateTensor(output_spec);
+
+    std::vector<float> in_data_x = { 1 };
+    std::vector<float> in_data_y = { 0 };
+    std::vector<float> golden = { std::numeric_limits<float>::infinity() };
+
+    EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()*4));
+    EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()*4));
+    auto op = graph->CreateOperation<tim::vx::ops::FloorDiv>();
+    (*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+    std::vector<float> output(1);
+
+    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+    EXPECT_EQ(golden, output);
+}
+
+TEST(FloorDiv, shape_5_1_broadcast_float32) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType in_shape_x({5, 1});
+    tim::vx::ShapeType in_shape_y({1});
+    tim::vx::ShapeType out_shape({5, 1});
+    tim::vx::TensorSpec input_spec_x(tim::vx::DataType::FLOAT32,
+                            in_shape_x, tim::vx::TensorAttribute::INPUT);
+    tim::vx::TensorSpec input_spec_y(tim::vx::DataType::FLOAT32,
+                            in_shape_y, tim::vx::TensorAttribute::INPUT);
+    tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
+                            out_shape, tim::vx::TensorAttribute::OUTPUT);
+
+    auto input_tensor_x = graph->CreateTensor(input_spec_x);
+    auto input_tensor_y = graph->CreateTensor(input_spec_y);
+    auto output_tensor = graph->CreateTensor(output_spec);
+
+    std::vector<float> in_data_x = { 1, 3, -2, 0, 99 };
+    std::vector<float> in_data_y = { 2 };
+    std::vector<float> golden = { 0, 1, -1, 0, 49 };
+
+    EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()*4));
+    EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()*4));
+    auto op = graph->CreateOperation<tim::vx::ops::FloorDiv>();
+    (*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+    std::vector<float> output(5);
+
+    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+    EXPECT_EQ(golden, output);
+}
+
+
+TEST(FloorDiv, shape_5_1_broadcast_uint8) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType in_shape_x({1});
+    tim::vx::ShapeType in_shape_y({5, 1});
+    tim::vx::ShapeType out_shape({5, 1});
+    tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 1, 0);
+    tim::vx::Quantization quant_out(tim::vx::QuantType::ASYMMETRIC, 0.5, 0);
+    tim::vx::TensorSpec input_spec_x(tim::vx::DataType::UINT8,
+                            in_shape_x, tim::vx::TensorAttribute::INPUT, quant);
+    tim::vx::TensorSpec input_spec_y(tim::vx::DataType::UINT8,
+                            in_shape_y, tim::vx::TensorAttribute::INPUT, quant);
+    tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
+                            out_shape, tim::vx::TensorAttribute::OUTPUT, quant_out);
+
+    auto input_tensor_x = graph->CreateTensor(input_spec_x);
+    auto input_tensor_y = graph->CreateTensor(input_spec_y);
+    auto output_tensor = graph->CreateTensor(output_spec);
+
+    std::vector<uint8_t> in_data_x = { 255 };
+    std::vector<uint8_t> in_data_y = { 1, 3, 2, 0, 255 };
+    std::vector<uint8_t> golden = { 255, 170, 254, 255, 2 };
+
+    EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()));
+    EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()));
+    auto op = graph->CreateOperation<tim::vx::ops::FloorDiv>();
+    (*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+    std::vector<uint8_t> output(5);
+
+    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+    EXPECT_EQ(golden, output);
+}

src/tim/vx/ops/simple_operations_test.cc

@@ -27,7 +27,7 @@
 
 #include "gtest/gtest.h"
 
-TEST(OP, floor_shape_5_1_fp32) {
+TEST(Floor, shape_5_1_fp32) {
     auto ctx = tim::vx::Context::Create();
     auto graph = ctx->CreateGraph();
 
@@ -55,7 +55,7 @@ TEST(OP, floor_shape_5_1_fp32) {
     EXPECT_EQ(golden, output);
 }
 
-TEST(OP, cast_shape_5_1_fp32_to_int32) {
+TEST(Cast, shape_5_1_fp32_to_int32) {
     auto ctx = tim::vx::Context::Create();
     auto graph = ctx->CreateGraph();