Merge branch 'depthwise' into 'master'

Add quantized depthwise convolution

See merge request !725

WORKSPACE

@@ -85,6 +85,15 @@ http_archive(
     ],
 )
 
+http_archive(
+    name = "tflite",
+    sha256 = "c886d46ad8c91fcafed2d910ad9e7bc5aeb29856c387bdf9b6b4903cc16e6e60",
+    strip_prefix = "tensorflow-mace-ffc8cc7e8c9d1894753509e88b17e251bc6255e3",
+    urls = [
+        "https://cnbj1.fds.api.xiaomi.com/mace/third-party/tflite/tensorflow-mace-ffc8cc7e8c9d1894753509e88b17e251bc6255e3.zip",
+    ],
+)
+
 new_http_archive(
     name = "six_archive",
     build_file = "third_party/six/six.BUILD",

mace/kernels/BUILD

@@ -73,6 +73,7 @@ cc_library(
         "//mace/core",
         "//mace/utils",
         "@gemmlowp",
+        "@tflite",
     ],
 )
 

mace/kernels/conv_2d.h

@@ -260,10 +260,10 @@ struct Conv2dFunctor<DeviceType::CPU, float> : Conv2dFunctorBase {
     }  // b
   }
 
-  MaceStatus operator()(const Tensor *input,
-                  const Tensor *filter,
+  MaceStatus operator()(const Tensor *input,   // NCHW
+                        const Tensor *filter,  // OIHW
                         const Tensor *bias,
-                  Tensor *output,
+                        Tensor *output,        // NCHW
                         StatsFuture *future) {
     MACE_UNUSED(future);
     MACE_CHECK_NOTNULL(input);
@@ -822,18 +822,6 @@ struct Conv2dFunctor<DeviceType::CPU, uint8_t> : Conv2dFunctorBase {
     }
   }
 
-  inline void GetOutputMultiplierAndShift(
-      const float lhs_scale, const float rhs_scale, const float output_scale,
-      int32_t *quantized_multiplier, int *right_shift) {
-    float real_multiplier = lhs_scale * rhs_scale / output_scale;
-    MACE_CHECK(real_multiplier > 0.f && real_multiplier < 1.f, real_multiplier);
-
-    int exponent;
-    QuantizeMultiplier(real_multiplier, quantized_multiplier, &exponent);
-    *right_shift = -exponent;
-    MACE_CHECK(*right_shift >= 0);
-  }
-
   typedef gemmlowp::VectorMap<const int32_t, gemmlowp::VectorShape::Col>
       ColVectorMap;
   typedef std::tuple<

mace/kernels/depthwise_conv2d.h

@@ -22,10 +22,12 @@
 #include <memory>
 #include <vector>
 
+#include "tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h"
 #include "mace/core/future.h"
 #include "mace/kernels/conv_pool_2d_util.h"
 #include "mace/kernels/activation.h"
 #include "mace/kernels/arm/depthwise_conv2d_neon.h"
+#include "mace/kernels/quantize.h"
 #include "mace/public/mace.h"
 
 #ifdef MACE_ENABLE_OPENCL
@@ -113,8 +115,7 @@ struct DepthwiseConv2dFunctor<DeviceType::CPU, float>
                     ((b * in_channels + c) * in_height + ih) * in_width + iw;
                   index_t filter_offset =
                     (((o * in_channels) + c) * filter_height + kh)
-                      * filter_width
-                      + kw;
+                        * filter_width + kw;
 
                   sum += input[in_offset] * filter[filter_offset];
                 }
@@ -127,10 +128,10 @@ struct DepthwiseConv2dFunctor<DeviceType::CPU, float>
     }
   }
 
-  MaceStatus operator()(const Tensor *input,
-                  const Tensor *filter,
+  MaceStatus operator()(const Tensor *input,   // NCHW
+                        const Tensor *filter,  // OIHW
                         const Tensor *bias,
-                  Tensor *output,
+                        Tensor *output,        // NCHW
                         StatsFuture *future) {
     MACE_UNUSED(future);
     MACE_CHECK_NOTNULL(input);
@@ -284,6 +285,212 @@ struct DepthwiseConv2dFunctor<DeviceType::CPU, float>
   }
 };
 
+template<>
+struct DepthwiseConv2dFunctor<DeviceType::CPU, uint8_t>
+    : public DepthwiseConv2dFunctorBase {
+  DepthwiseConv2dFunctor(const int *strides,
+                         const Padding padding_type,
+                         const std::vector<int> &paddings,
+                         const int *dilations,
+                         const ActivationType activation,
+                         const float relux_max_limit)
+      : DepthwiseConv2dFunctorBase(strides,
+                                   padding_type,
+                                   paddings,
+                                   dilations,
+                                   activation,
+                                   relux_max_limit) {}
+
+  void DepthwiseConv2dGeneral(const uint8_t *input,
+                              const uint8_t *filter,
+                              const int32_t *bias,
+                              const index_t *in_shape,
+                              const index_t *out_shape,
+                              const index_t *filter_shape,
+                              const int32_t input_zero,
+                              const int32_t filter_zero,
+                              const int32_t output_zero,
+                              const float output_multiplier,
+                              const int *stride_hw,
+                              const int *dilation_hw,
+                              const int *pad_hw,
+                              uint8_t *output) {
+#pragma omp parallel for collapse(2)
+    for (index_t b = 0; b < out_shape[0]; ++b) {
+      for (index_t h = 0; h < out_shape[1]; ++h) {
+        for (index_t w = 0; w < out_shape[2]; ++w) {
+          for (index_t m = 0; m < out_shape[3]; ++m) {
+            const index_t filter_height = filter_shape[0];
+            const index_t filter_width = filter_shape[1];
+            const index_t in_channels = filter_shape[2];
+            const index_t depth_multiplier = filter_shape[3];
+            const index_t in_height = in_shape[1];
+            const index_t in_width = in_shape[2];
+            const index_t out_height = out_shape[1];
+            const index_t out_width = out_shape[2];
+            const index_t out_channels = out_shape[3];
+            index_t out_offset =
+                ((b * out_height + h) * out_width + w) * out_channels + m;
+            index_t c = m / depth_multiplier;
+            index_t o = m % depth_multiplier;
+            index_t ih_base = h * stride_hw[0] - pad_hw[0];
+            index_t iw_base = w * stride_hw[1] - pad_hw[1];
+            int32_t sum = 0;
+            for (index_t kh = 0; kh < filter_height; ++kh) {
+              const index_t ih = ih_base + kh * dilation_hw[0];
+              for (index_t kw = 0; kw < filter_width; ++kw) {
+                const index_t iw = iw_base + kw * dilation_hw[1];
+                if (ih >= 0 && ih < in_height && iw >= 0 && iw < in_width) {
+                  index_t in_offset =
+                      ((b * in_height + ih) * in_width + iw) * in_channels + c;
+                  index_t filter_offset =
+                      ((kh * filter_width + kw) * in_channels + c)
+                          * depth_multiplier + o;
+
+                  sum += (input[in_offset] - input_zero) *
+                      (filter[filter_offset] - filter_zero);
+                }
+              }
+            }
+            if (bias) {
+              sum += bias[m];
+            }
+            sum = static_cast<int32_t>(std::round(sum * output_multiplier));
+            sum += output_zero;
+            output[out_offset] =
+                static_cast<uint8_t>(std::min(255, std::max(0, sum)));
+          }
+        }
+      }
+    }
+  }
+
+  inline tflite::Dims<4> ShapeToTfliteDims(const std::vector<index_t> &shape) {
+    tflite::Dims<4> d;
+    for (int i = 0; i < 4; ++i) {
+      int src = static_cast<int>(shape.size() - i - 1);
+      if (src >= 0) {
+        d.sizes[i] = shape[src];
+      } else {
+        d.sizes[i] = 1;
+      }
+    }
+    d.strides[0] = 1;
+    for (int i = 1; i < 4; i++) {
+      d.strides[i] = d.strides[i - 1] * d.sizes[i - 1];
+    }
+    return d;
+  }
+
+  MaceStatus operator()(const Tensor *input,   // NHWC
+                        const Tensor *filter,  // HWIM
+                        const Tensor *bias,
+                        Tensor *output,        // NHWC
+                        StatsFuture *future) {
+    MACE_UNUSED(future);
+    MACE_CHECK_NOTNULL(input);
+    MACE_CHECK_NOTNULL(filter);
+    MACE_CHECK_NOTNULL(output);
+
+    std::vector<index_t> output_shape(4);
+    std::vector<int> paddings(2);
+
+    // reuse OHWI format, only for calculating output
+    std::vector<index_t> ohwi_shape{
+        filter->dim(2) * filter->dim(3), filter->dim(0), filter->dim(1), 1};
+    if (paddings_.empty()) {
+      CalcPaddingAndOutputSize(input->shape().data(),
+                               NHWC,
+                               ohwi_shape.data(),
+                               OHWI,
+                               dilations_,
+                               strides_,
+                               padding_type_,
+                               output_shape.data(),
+                               paddings.data());
+    } else {
+      paddings = paddings_;
+      CalcOutputSize(input->shape().data(),
+                     NHWC,
+                     ohwi_shape.data(),
+                     OHWI,
+                     paddings_.data(),
+                     dilations_,
+                     strides_,
+                     RoundType::FLOOR,
+                     output_shape.data());
+    }
+
+    MACE_RETURN_IF_ERROR(output->Resize(output_shape));
+    output->Clear();
+
+    MACE_CHECK(output->dim(0) == input->dim(0),
+               "Input/Output batch size mismatch");
+    MACE_CHECK(filter->dim(2) == input->dim(3), filter->dim(2), " != ",
+               input->dim(3));
+
+    index_t out_channels = output_shape[3];
+    index_t stride_h = strides_[0];
+    index_t stride_w = strides_[1];
+    index_t dilation_h = dilations_[0];
+    index_t dilation_w = dilations_[1];
+    int pad_top = paddings[0] >> 1;
+    int pad_left = paddings[1] >> 1;
+
+    Tensor::MappingGuard input_guard(input);
+    Tensor::MappingGuard filter_guard(filter);
+    Tensor::MappingGuard bias_guard(bias);
+    Tensor::MappingGuard output_guard(output);
+    auto input_data = input->data<uint8_t>();
+    auto filter_data = filter->data<uint8_t>();
+    auto output_data = output->mutable_data<uint8_t>();
+
+    if (dilation_h == 1 && dilation_w == 1) {
+      std::vector<index_t> bias_shape{out_channels};
+      std::unique_ptr<Tensor> zero_bias;
+      const int32_t *bias_data = nullptr;
+      if (bias == nullptr) {
+        zero_bias.reset(
+            new Tensor(GetDeviceAllocator(DeviceType::CPU), DT_INT32));
+        zero_bias->Resize(bias_shape);
+        zero_bias->Clear();
+        bias_data = zero_bias->data<int32_t>();
+      } else {
+        bias_data = bias->data<int32_t>();
+      }
+
+      int32_t quantized_multiplier;
+      int32_t right_shift;
+      GetOutputMultiplierAndShift(input->scale(), filter->scale(),
+                                  output->scale(), &quantized_multiplier,
+                                  &right_shift);
+      // 1HWO
+      std::vector<index_t> filter_shape{
+          1, filter->dim(0), filter->dim(1), filter->dim(2) * filter->dim(3)};
+
+      tflite::optimized_ops::DepthwiseConv(
+          input_data, ShapeToTfliteDims(input->shape()), -input->zero_point(),
+          filter_data, ShapeToTfliteDims(filter_shape), -filter->zero_point(),
+          bias_data, ShapeToTfliteDims(bias_shape), stride_w, stride_h,
+          pad_left, pad_top, filter->dim(3), output->zero_point(),
+          quantized_multiplier, right_shift, 0, 255, output_data,
+          ShapeToTfliteDims(output->shape()));
+    } else {
+      auto bias_data = bias == nullptr ? nullptr : bias->data<int32_t>();
+      float output_multiplier =
+          input->scale() * filter->scale() / output->scale();
+      const int pad_hw[2] = {pad_top, pad_left};
+      DepthwiseConv2dGeneral(
+          input_data, filter_data, bias_data, input->shape().data(),
+          output_shape.data(), filter->shape().data(), input->zero_point(),
+          filter->zero_point(), output->zero_point(), output_multiplier,
+          strides_, dilations_, pad_hw, output_data);
+    }
+
+    return MACE_SUCCESS;
+  }
+};
+
 #ifdef MACE_ENABLE_OPENCL
 template<typename T>
 struct DepthwiseConv2dFunctor<DeviceType::GPU, T>

mace/kernels/quantize.h

@@ -155,6 +155,18 @@ inline void QuantizeMultiplier(double multiplier,
   MACE_CHECK(*output_multiplier <= std::numeric_limits<int32_t>::max());
 }
 
+inline void GetOutputMultiplierAndShift(
+    const float lhs_scale, const float rhs_scale, const float output_scale,
+    int32_t *quantized_multiplier, int *right_shift) {
+  float real_multiplier = lhs_scale * rhs_scale / output_scale;
+  MACE_CHECK(real_multiplier > 0.f && real_multiplier < 1.f, real_multiplier);
+
+  int exponent;
+  QuantizeMultiplier(real_multiplier, quantized_multiplier, &exponent);
+  *right_shift = -exponent;
+  MACE_CHECK(*right_shift >= 0);
+}
+
 template<DeviceType D, typename T>
 struct QuantizeFunctor;
 

mace/ops/conv_2d_benchmark.cc

@@ -122,14 +122,10 @@ void Conv2d<CPU, uint8_t>(int iters,
   // Add input data
   net.AddRandomInput<DeviceType::CPU, uint8_t>(
       "Input", {batch, height, width, channels});
-  Tensor *input = net.GetTensor("Input");
-  input->SetScale(0.00705);
-  input->SetZeroPoint(114);
+  net.GetTensor("Input")->SetScale(0.1);
   net.AddRandomInput<DeviceType::CPU, uint8_t>(
       "Filter", {output_channels, kernel_h, kernel_w, channels});
-  Tensor *filter = net.GetTensor("Filter");
-  filter->SetScale(0.0066);
-  filter->SetZeroPoint(113);
+  net.GetTensor("Filter")->SetScale(0.1);
   net.AddRandomInput<DeviceType::CPU, int32_t>("Bias", {output_channels});
   OpDefBuilder("Conv2D", "Conv2dTest")
       .Input("Input")
@@ -144,9 +140,7 @@ void Conv2d<CPU, uint8_t>(int iters,
 
   net.Setup(DeviceType::CPU);
 
-  Tensor *output = net.GetTensor("Output");
-  output->SetScale(0.0107);
-  output->SetZeroPoint(118);
+  net.GetTensor("Output")->SetScale(0.1);
 
   // Warm-up
   for (int i = 0; i < 2; ++i) {

mace/ops/depthwise_conv2d.cc

@@ -24,6 +24,12 @@ void Register_DepthwiseConv2d(OperatorRegistryBase *op_registry) {
                                           .Build(),
                          DepthwiseConv2dOp<DeviceType::CPU, float>);
 
+  MACE_REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthwiseConv2d")
+                                          .Device(DeviceType::CPU)
+                                          .TypeConstraint<uint8_t>("T")
+                                          .Build(),
+                         DepthwiseConv2dOp<DeviceType::CPU, uint8_t>);
+
 #ifdef MACE_ENABLE_OPENCL
   MACE_REGISTER_OPERATOR(op_registry, OpKeyBuilder("DepthwiseConv2d")
                                           .Device(DeviceType::GPU)

mace/ops/depthwise_conv2d_benchmark.cc

@@ -41,17 +41,32 @@ void DepthwiseConv2d(int iters,
 
   // Add input data
   if (D == DeviceType::CPU) {
-    net.AddRandomInput<D, float>("Input",
-                                 {batch, input_channels, height, width});
+    if (DataTypeToEnum<T>::value != DT_UINT8) {
+      net.AddRandomInput<D, float>(
+          "Input", {batch, input_channels, height, width});
+    } else {
+      net.AddRandomInput<DeviceType::CPU, uint8_t>(
+          "Input", {batch, height, width, input_channels});
+      net.GetTensor("Input")->SetScale(0.1);
+    }
+
   } else if (D == DeviceType::GPU) {
-    net.AddRandomInput<D, float>("Input",
-                                 {batch, height, width, input_channels});
+    net.AddRandomInput<D, float>(
+        "Input", {batch, height, width, input_channels});
   } else {
     MACE_NOT_IMPLEMENTED;
   }
+  if (DataTypeToEnum<T>::value != DT_UINT8) {
     net.AddRandomInput<D, float>(
         "Filter", {multiplier, input_channels, kernel_h, kernel_w});
     net.AddRandomInput<D, float>("Bias", {input_channels * multiplier});
+  } else {
+    net.AddRandomInput<DeviceType::CPU, uint8_t>(
+        "Filter", {kernel_h, kernel_w, input_channels, multiplier});
+    net.GetTensor("Filter")->SetScale(0.1);
+    net.AddRandomInput<DeviceType::CPU, int32_t>(
+        "Bias", {input_channels * multiplier});
+  }
 
   if (D == DeviceType::CPU) {
     OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2dTest")
@@ -87,6 +102,10 @@ void DepthwiseConv2d(int iters,
 
   net.Setup(D);
 
+  if (DataTypeToEnum<T>::value == DT_UINT8) {
+    net.GetTensor("Output")->SetScale(0.1);
+  }
+
   // Warm-up
   for (int i = 0; i < 2; ++i) {
     net.Run();
@@ -132,7 +151,8 @@ void DepthwiseConv2d(int iters,
 #define MACE_BM_DEPTHWISE_CONV_2D(N, C, H, W, KH, KW, S, P, M)                 \
   MACE_BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, M, float, CPU);    \
   MACE_BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, M, float, GPU);    \
-  MACE_BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, M, half, GPU);
+  MACE_BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, M, half, GPU);     \
+  MACE_BM_DEPTHWISE_CONV_2D_MACRO(N, C, H, W, KH, KW, S, P, M, uint8_t, CPU);
 
 MACE_BM_DEPTHWISE_CONV_2D(1, 32, 112, 112, 3, 3, 1, SAME, 1);
 MACE_BM_DEPTHWISE_CONV_2D(1, 32, 56, 56, 3, 3, 2, VALID, 1);
@@ -156,7 +176,14 @@ MACE_BM_DEPTHWISE_CONV_2D(1, 3, 512, 512, 3, 3, 2, SAME, 1);
 MACE_BM_DEPTHWISE_CONV_2D(1, 3, 112, 112, 3, 3, 2, VALID, 1);
 MACE_BM_DEPTHWISE_CONV_2D(1, 3, 224, 224, 3, 3, 2, SAME, 1);
 MACE_BM_DEPTHWISE_CONV_2D(1, 8, 224, 224, 3, 3, 2, SAME, 1);
-
+MACE_BM_DEPTHWISE_CONV_2D(1, 128, 56, 56, 3, 3, 1, SAME, 1);
+MACE_BM_DEPTHWISE_CONV_2D(1, 128, 56, 56, 3, 3, 2, SAME, 1);
+MACE_BM_DEPTHWISE_CONV_2D(1, 256, 28, 28, 3, 3, 1, SAME, 1);
+MACE_BM_DEPTHWISE_CONV_2D(1, 256, 28, 28, 3, 3, 2, SAME, 1);
+MACE_BM_DEPTHWISE_CONV_2D(1, 512, 14, 14, 3, 3, 1, SAME, 1);
+MACE_BM_DEPTHWISE_CONV_2D(1, 512, 14, 14, 3, 3, 2, SAME, 1);
+MACE_BM_DEPTHWISE_CONV_2D(1, 1024, 7, 7, 3, 3, 1, SAME, 1);
+MACE_BM_DEPTHWISE_CONV_2D(1, 1024, 7, 7, 3, 3, 2, SAME, 1);
 
 }  // namespace test
 }  // namespace ops

mace/ops/depthwise_conv2d_test.cc

@@ -351,6 +351,164 @@ TEST_F(DepthwiseConv2dOpTest, OpenCLUnalignedNxNS12Half) {
   TestNxNS12<half>(107, 113);
 }
 
+namespace {
+
+void QuantSimpleValidTest() {
+  testing::internal::LogToStderr();
+  // Construct graph
+  OpsTestNet net;
+
+  // Add input data
+  net.AddInputFromArray<CPU, uint8_t>(
+      "Input", {1, 3, 3, 2},
+      {31, 98, 1, 54, 197, 172, 70, 146, 255, 71, 24, 182, 28, 78, 85, 96, 180,
+       59}, 0.00735299, 86);
+  net.AddInputFromArray<CPU, uint8_t>(
+      "Filter", {3, 3, 2, 1},
+      {212, 239, 110, 170, 216, 91, 162, 161, 255, 2, 10, 120, 183, 101, 100,
+       33, 137, 51}, 0.0137587, 120);
+  net.AddInputFromArray<CPU, int32_t>("Bias", {2}, {2, 2});
+  OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
+      .Input("Input")
+      .Input("Filter")
+      .Input("Bias")
+      .Output("Output")
+      .AddIntsArg("strides", {1, 1})
+      .AddIntArg("padding", Padding::VALID)
+      .AddIntsArg("dilations", {1, 1})
+      .AddIntArg("T", static_cast<int>(DT_UINT8))
+      .Finalize(net.NewOperatorDef());
+
+  net.Setup(CPU);
+  Tensor *output = net.GetTensor("Output");
+  output->SetScale(0.013241);
+  output->SetZeroPoint(0);
+  // Run
+  net.Run();
+
+  // Check
+  auto expected = CreateTensor<uint8_t>({1, 1, 1, 2}, {255, 21});
+
+  ExpectTensorNear<uint8_t>(*expected, *net.GetOutput("Output"));
+}
+
+void TestQuant(const index_t batch,
+               const index_t multiplier,
+               const index_t in_channels,
+               const index_t in_height,
+               const index_t in_width,
+               const index_t k_height,
+               const index_t k_width,
+               enum Padding padding_type,
+               const std::vector<int> &strides) {
+  OpsTestNet net;
+  const index_t out_channels = multiplier * in_channels;
+  net.AddRandomInput<CPU, float>(
+      "Input", {batch, in_height, in_width, in_channels}, false);
+  net.AddRandomInput<CPU, float>(
+      "Filter", {k_height, k_width, in_channels, multiplier}, false);
+  net.AddRandomInput<CPU, float>("Bias", {out_channels});
+  net.TransformDataFormat<DeviceType::CPU, float>(
+      "Input", NHWC, "InputNCHW", NCHW);
+  net.TransformDataFormat<DeviceType::CPU, float>(
+      "Filter", HWIO, "FilterOIHW", OIHW);
+
+  OpDefBuilder("DepthwiseConv2d", "DepthwiseConv2DTest")
+      .Input("InputNCHW")
+      .Input("FilterOIHW")
+      .Input("Bias")
+      .Output("OutputNCHW")
+      .AddIntsArg("strides", strides)
+      .AddIntArg("padding", padding_type)
+      .AddIntsArg("dilations", {1, 1})
+      .AddIntArg("T", static_cast<int>(DT_FLOAT))
+      .Finalize(net.NewOperatorDef());
+  net.RunOp(CPU);
+  net.TransformDataFormat<DeviceType::CPU, float>(
+      "OutputNCHW", NCHW, "Output", NHWC);
+
+  OpDefBuilder("Quantize", "QuantizeFilter")
+      .Input("Filter")
+      .Output("QuantizedFilter")
+      .OutputType({DT_UINT8})
+      .AddIntArg("T", DT_UINT8)
+      .AddIntArg("non_zero", true)
+      .Finalize(net.NewOperatorDef());
+  net.RunOp();
+
+  OpDefBuilder("Quantize", "QuantizeInput")
+      .Input("Input")
+      .Output("QuantizedInput")
+      .OutputType({DT_UINT8})
+      .AddIntArg("T", DT_UINT8)
+      .AddIntArg("non_zero", true)
+      .Finalize(net.NewOperatorDef());
+  net.RunOp();
+
+  OpDefBuilder("Quantize", "QuantizeOutput")
+      .Input("Output")
+      .Output("ExpectedQuantizedOutput")
+      .OutputType({DT_UINT8})
+      .AddIntArg("T", DT_UINT8)
+      .AddIntArg("non_zero", true)
+      .Finalize(net.NewOperatorDef());
+  net.RunOp();
+
+  Tensor *q_filter = net.GetTensor("QuantizedFilter");
+  Tensor *q_input = net.GetTensor("QuantizedInput");
+  Tensor *bias = net.GetTensor("Bias");
+  auto bias_data = bias->data<float>();
+  std::vector<int32_t> q_bias(bias->size());
+  kernels::QuantizeWithScaleAndZeropoint(
+      bias_data, bias->size(), q_input->scale() * q_filter->scale(), 0,
+      q_bias.data());
+  net.AddInputFromArray<DeviceType::CPU, int32_t>(
+      "QuantizedBias", {out_channels}, q_bias);
+  OpDefBuilder("DepthwiseConv2d", "QuantizedDepthwiseConv2DTest")
+      .Input("QuantizedInput")
+      .Input("QuantizedFilter")
+      .Input("QuantizedBias")
+      .Output("QuantizedOutput")
+      .AddIntsArg("strides", strides)
+      .AddIntArg("padding", padding_type)
+      .AddIntsArg("dilations", {1, 1})
+      .AddIntArg("T", static_cast<int>(DT_UINT8))
+      .Finalize(net.NewOperatorDef());
+  net.Setup(DeviceType::CPU);
+  Tensor *eq_output = net.GetTensor("ExpectedQuantizedOutput");
+  Tensor *q_output = net.GetTensor("QuantizedOutput");
+  q_output->SetScale(eq_output->scale());
+  q_output->SetZeroPoint(eq_output->zero_point());
+  net.Run();
+
+  OpDefBuilder("Dequantize", "DeQuantizeTest")
+      .Input("QuantizedOutput")
+      .Output("DequantizedOutput")
+      .OutputType({DT_FLOAT})
+      .AddIntArg("T", DT_UINT8)
+      .Finalize(net.NewOperatorDef());
+  net.RunOp();
+
+  // Check
+  ExpectTensorSimilar<float>(*net.GetOutput("Output"),
+                             *net.GetTensor("DequantizedOutput"), 0.01);
+}
+}  // namespace
+
+TEST_F(DepthwiseConv2dOpTest, Quant) {
+  QuantSimpleValidTest();
+  TestQuant(1, 1, 2, 3, 3, 3, 3, VALID, {1, 1});
+  TestQuant(1, 1, 2, 3, 3, 3, 3, SAME, {1, 1});
+  TestQuant(1, 1, 2, 3, 3, 3, 3, FULL, {1, 1});
+  TestQuant(1, 2, 2, 3, 3, 3, 3, SAME, {1, 1});
+  TestQuant(1, 2, 2, 3, 3, 3, 3, SAME, {2, 2});
+  TestQuant(1, 1, 512, 14, 14, 3, 3, SAME, {1, 1});
+  TestQuant(1, 1, 512, 14, 13, 5, 5, SAME, {2, 2});
+  TestQuant(1, 1, 256, 28, 28, 3, 3, SAME, {1, 1});
+  TestQuant(1, 1, 128, 56, 56, 3, 3, SAME, {2, 2});
+  TestQuant(3, 1, 128, 56, 56, 3, 3, SAME, {2, 2});
+}
+
 }  // namespace test
 }  // namespace ops
 }  // namespace mace

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