add kaldi op IfDefined

add extract pooling and kaldi batchnorm

add time index info in extract pooling

add benchmar& test for kaldi components

add batch support for kaldi

change ifdefined to delay

mace/ops/concat.cc

@@ -86,7 +86,8 @@ class ConcatOp<DeviceType::CPU, T> : public ConcatOpBase {
           continue;
         }
         MACE_CHECK(input->dim(j) == input0->dim(j),
-                   "Dimensions of inputs should equal except axis.");
+                   "Dimensions of inputs should equal except axis: ",
+                   input->dim(j), "!=", input0->dim(j));
       }
       outer_sizes[i] = input->size() / inner_size;
       output_shape[axis] += input->dim(axis);

mace/ops/delay.cc

+// Copyright 2018 The MACE Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// This Op is for IfDefined descriptor in Kaldi.
+// It defines time offset.
+// If time index <= offset, using zeros as output.
+
+#include <functional>
+#include <memory>
+
+#include "mace/core/operator.h"
+
+namespace mace {
+namespace ops {
+
+template <DeviceType D, typename T>
+class DelayOp;
+
+template <typename T>
+class DelayOp<DeviceType::CPU, T> : public Operation {
+ public:
+  explicit DelayOp(OpConstructContext *context)
+      : Operation(context),
+        offset_(Operation::GetOptionalArg<int>("offset", 0)) {}
+
+  MaceStatus Run(OpContext *context) override {
+    MACE_UNUSED(context);
+    const Tensor *input = this->Input(0);
+    Tensor *output = this->Output(0);
+    MACE_CHECK(offset_ < 0, "offset param should be negative.");
+
+    index_t rank = input->dim_size();
+    MACE_CHECK(rank >= 2, "input's rank should >= 2.");
+    const std::vector<index_t> &input_shape = input->shape();
+    const index_t batch =
+        std::accumulate(input_shape.begin(), input_shape.end() - 2, 1,
+                        std::multiplies<index_t>());
+    const index_t chunk = input_shape[rank - 2];
+    const index_t dim = input_shape[rank - 1];
+    MACE_RETURN_IF_ERROR(output->ResizeLike(input));
+    output->Clear();
+
+    if (chunk <= -offset_)
+      return MaceStatus::MACE_SUCCESS;
+
+    Tensor::MappingGuard input_guard(input);
+    Tensor::MappingGuard output_guard(output);
+    const T *input_data = input->data<T>();
+    T *output_data = output->mutable_data<T>();
+    utils::ThreadPool
+        &thread_pool = context->device()->cpu_runtime()->thread_pool();
+    thread_pool.Compute2D([=](index_t start0, index_t end0, index_t step0,
+                              index_t start1, index_t end1, index_t step1) {
+      for (index_t i = start0; i < end0; i += step0) {
+        for (index_t j = start1; j < end1; j += step1) {
+          memcpy(output_data + (i * chunk + j - offset_) * dim,
+                 input_data + (i * chunk + j) * dim,
+                 dim * sizeof(T));
+        }
+      }
+    }, 0, batch, 1, 0, chunk + offset_, 1);
+
+    return MaceStatus::MACE_SUCCESS;
+  }
+
+ private:
+  int offset_;
+};
+
+void RegisterDelay(OpRegistryBase *op_registry) {
+  MACE_REGISTER_OP(op_registry, "Delay", DelayOp,
+                   DeviceType::CPU, float);
+}
+
+}  // namespace ops
+}  // namespace mace

mace/ops/delay_benchmark.cc

+// Copyright 2018 The MACE Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mace/core/testing/test_benchmark.h"
+#include "mace/ops/ops_test_util.h"
+
+namespace mace {
+namespace ops {
+namespace test {
+
+template <DeviceType D, typename T>
+static void Delay(int iters,
+                      int batch,
+                      int chunk,
+                      int dim,
+                      int offset) {
+  mace::testing::StopTiming();
+
+  OpsTestNet net;
+
+  // Add input data
+  net.AddRandomInput<D, T>("Input", {batch, chunk, dim});
+
+  OpDefBuilder("Delay", "DelayTest")
+      .Input("Input")
+      .Output("Output")
+      .AddIntArg("offset", -offset)
+      .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
+      .Finalize(net.NewOperatorDef());
+
+  // Warm-up
+  for (int i = 0; i < 5; ++i) {
+    net.RunOp(D);
+  }
+  net.Sync();
+
+  mace::testing::StartTiming();
+  while (iters--) {
+    net.Run();
+  }
+  net.Sync();
+}
+
+#define MACE_BM_DELAY_MACRO(N, C, D, OFFSET, TYPE, DEVICE)                 \
+  static void MACE_BM_DELAY_##N##_##C##_##D##_##OFFSET##_##TYPE##_##DEVICE(\
+      int iters) {                                                             \
+    const int64_t tot = static_cast<int64_t>(iters) * N * C * D;               \
+    mace::testing::BytesProcessed(tot *(sizeof(TYPE)));                        \
+    Delay<DEVICE, TYPE>(iters, N, C, D, OFFSET);                           \
+  }                                                                            \
+  MACE_BENCHMARK(MACE_BM_DELAY_##N##_##C##_##D##_##OFFSET##_##TYPE\
+##_##DEVICE)
+
+#define MACE_BM_DELAY(N, C, D, OFFSET)                 \
+  MACE_BM_DELAY_MACRO(N, C, D, OFFSET, float, CPU);
+
+MACE_BM_DELAY(8, 40, 512, 2);
+MACE_BM_DELAY(16, 80, 100, 3);
+MACE_BM_DELAY(32, 60, 200, 5);
+
+
+}  // namespace test
+}  // namespace ops
+}  // namespace mace

mace/ops/dynamic_lstm.cc

@@ -214,7 +214,7 @@ class DynamicLSTMOp<DeviceType::CPU, T> : public Operation {
     Tensor *output = this->Output(OUTPUT);
 
     std::vector<index_t> output_shape = input->shape();
-    output_shape[1] = output_dim;
+    output_shape[input_rank - 1] = output_dim;
 
     MACE_RETURN_IF_ERROR(output->Resize(output_shape));
 
@@ -235,8 +235,10 @@ class DynamicLSTMOp<DeviceType::CPU, T> : public Operation {
       affine_b_in.Clear();
       affine_b_out.Clear();
       for (int i = 0; i < chunk; ++i) {
+        const float *input_ptr = input_data + (b * chunk + i) * input_dim;
+        float *output_ptr = output_data + (b * chunk + i) * output_dim;
         // Append
-        memcpy(affine_a_in_data, input_data, input_dim * sizeof(float));
+        memcpy(affine_a_in_data, input_ptr, input_dim * sizeof(float));
         if (prev_out_idx >= 0) {
           memcpy(affine_a_in_data + input_dim,
                  prev_out_data + prev_out_idx % out_buf_chunk * prev_out_dim_,
@@ -283,7 +285,7 @@ class DynamicLSTMOp<DeviceType::CPU, T> : public Operation {
                       false,
                       &affine_b_out);
         // Output
-        memcpy(output_data,
+        memcpy(output_ptr,
                affine_b_out_data,
                output_dim * sizeof(float));
         // Update
@@ -292,8 +294,6 @@ class DynamicLSTMOp<DeviceType::CPU, T> : public Operation {
                           prev_out_dim_,
                           scale_,
                           curr_out_ptr);
-        input_data += input_dim;
-        output_data += output_dim;
         prev_out_idx++;
         prev_cell_idx++;
       }

mace/ops/extract_pooling.cc

+// Copyright 2018 The MACE Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// This Op is for fused StatisticsExtraction, StatisticsPooling and
+// Round Components in Kaldi.
+// This op is used to extract moving-average mean and standard-deviation
+// statistics of input data.
+// 'input_indexes' indicates which frames will be used for extract statistics.
+// 'output_indexes' indicates which frames  of outputs will be used to
+// save statistics results.
+// 'modulus' will be used for extent results to all frames.
+// 'start_index' and 'end_index' indicate time indexes of output frames.
+// 'forward_indexes' and 'count' were from precomputed index in kaldi.
+// Reference to
+// http://kaldi-asr.org/doc/nnet-general-component_8h_source.html#l00158
+
+#include <functional>
+#include <memory>
+
+#include "mace/core/operator.h"
+
+
+namespace mace {
+namespace ops {
+
+template <DeviceType D, typename T>
+class ExtractPoolingOp;
+
+template <typename T>
+class ExtractPoolingOp<DeviceType::CPU, T> : public Operation {
+ public:
+  explicit ExtractPoolingOp(OpConstructContext *context)
+      : Operation(context),
+        modulus_(Operation::GetOptionalArg<int>("modulus", 1)),
+        include_variance_(
+            static_cast<bool>(
+                Operation::GetOptionalArg<int>("include_variance", 0))),
+        num_log_count_(
+            Operation::GetOptionalArg<int>("num_log_count", 0)),
+        variance_floor_(
+            Operation::GetOptionalArg<float>("variance_floor", 1.0e-10)),
+        input_indexes_(Operation::GetRepeatedArgs<int>("input_indexes")),
+        output_indexes_(Operation::GetRepeatedArgs<int>("output_indexes")),
+        forward_indexes_(Operation::GetRepeatedArgs<int>("forward_indexes")),
+        counts_(Operation::GetRepeatedArgs<float>("counts")),
+        input_time_range_(Operation::GetRepeatedArgs<int>("input_time_range")),
+        output_time_range_(
+            Operation::GetRepeatedArgs<int>("output_time_range")) {}
+
+  MaceStatus Run(OpContext *context) override {
+    MACE_UNUSED(context);
+    const Tensor *input = this->Input(0);
+    Tensor *output = this->Output(0);
+
+    const std::vector<index_t> &input_shape = input->shape();
+    const index_t dim_size = input_shape.size();
+    MACE_CHECK(dim_size >= 2,
+               "ExtractPooling only supports input dim size >= 2");
+    MACE_CHECK(modulus_ >= 1,
+               "ExtractPooling's pooling size should be greater than zero.");
+    MACE_CHECK(input_time_range_.size() == 2 && output_time_range_.size() == 2
+                   && counts_.size() * 2 == forward_indexes_.size()
+                   && counts_.size() == output_indexes_.size());
+    int in_start_index = input_time_range_[0];
+    int out_start_index = output_time_range_[0];
+    int out_end_index = output_time_range_[1];
+    MACE_CHECK(out_end_index >= out_start_index
+                   && input_time_range_[1] >= input_time_range_[0],
+               "end index should be greater than start index.");
+    const index_t output_chunk = out_end_index - out_start_index + 1;
+    const index_t input_dim = input_shape[dim_size - 1];
+    const index_t chunk = input_shape[dim_size - 2];
+    MACE_CHECK(chunk == input_time_range_[1] - input_time_range_[0] + 1,
+               "input chunk should be equal to end - start + 1.");
+    const index_t batch =
+        std::accumulate(input_shape.begin(), input_shape.end() - 2, 1,
+                        std::multiplies<index_t>());
+
+    index_t output_dim = include_variance_ ? 2 * input_dim : input_dim;
+    output_dim += num_log_count_;
+    std::vector<index_t> output_shape(input_shape);
+    output_shape[dim_size - 1] = output_dim;
+    output_shape[dim_size - 2] = output_chunk;
+    MACE_RETURN_IF_ERROR(output->Resize(output_shape));
+
+    const index_t num_input_indexes = input_indexes_.size();
+    const index_t num_output_indexes = output_indexes_.size();
+    MACE_CHECK(num_input_indexes > 0 && num_output_indexes > 0,
+               "ExtractPooling's input_indexes or output_indexes is empty.");
+    const index_t extract_out_size = PadAlignSize(output_dim * sizeof(float));
+    ScratchBuffer *scratch = context->device()->scratch_buffer();
+    scratch->Rewind();
+    scratch->GrowSize(extract_out_size);
+
+    Tensor extract_out(scratch->Scratch(extract_out_size), DT_FLOAT);
+    extract_out.Reshape({1, output_dim});
+    extract_out.Clear();
+    float *extract_out_data = extract_out.mutable_data<float>();
+
+    Tensor::MappingGuard guard_input(input);
+    Tensor::MappingGuard guard_output(output);
+    const T *input_data = input->data<T>();
+    T *output_data = output->mutable_data<T>();
+
+    utils::ThreadPool
+        &thread_pool = context->device()->cpu_runtime()->thread_pool();
+
+    for (index_t b = 0; b < batch; ++b) {
+      for (index_t i = 0; i < num_output_indexes; ++i) {
+        int start = forward_indexes_[2 * i];
+        int end = forward_indexes_[2 * i + 1];
+        float count = counts_[i];
+        float mean_scale = 1.f / count;
+        float log_count = std::log(count);
+        thread_pool.Compute1D([=](index_t start0,
+                                  index_t end0,
+                                  index_t step0) {
+          for (index_t n = start0; n < end0; n += step0) {
+            extract_out_data[n] = log_count;
+          }
+        }, 0, num_log_count_, 1);
+        if (include_variance_) {
+          thread_pool.Compute1D([=](index_t start0,
+                                    index_t end0,
+                                    index_t step0) {
+            for (index_t d = start0; d < end0; d += step0) {
+              float mean = 0.f;
+              float variance = 0.f;
+              for (int t = start; t < end; ++t) {
+                index_t input_index =
+                    (b * chunk + input_indexes_[t] - in_start_index)
+                        * input_dim;
+                float x = input_data[input_index + d];
+                mean += x;
+                variance += x * x;
+              }
+              mean *= mean_scale;
+              variance *= mean_scale;
+              extract_out_data[d + num_log_count_] = mean;
+              variance = variance - mean * mean;
+              extract_out_data[d + input_dim + num_log_count_] =
+                  variance < variance_floor_ ?
+                  std::sqrt(variance_floor_) :
+                  std::sqrt(variance);
+            }
+          }, 0, input_dim, 1);
+        } else {
+          thread_pool.Compute1D([=](index_t start0,
+                                    index_t end0,
+                                    index_t step0) {
+            for (index_t d = start0; d < end0; d += step0) {
+              float mean = 0.f;
+              for (int t = start; t < end; ++t) {
+                index_t input_index =
+                    (b * chunk + input_indexes_[t] - in_start_index)
+                        * input_dim;
+                mean += input_data[input_index + d];
+              }
+              extract_out_data[d + num_log_count_] = mean * mean_scale;
+            }
+          }, 0, input_dim, 1);
+        }
+
+        int output_start = output_indexes_[i] < out_start_index ?
+                           out_start_index : output_indexes_[i];
+        int output_end = output_indexes_[i] + modulus_;
+        output_end = output_end > out_end_index ?
+                     out_end_index + 1 :
+                     output_end;
+        thread_pool.Compute1D([=](index_t start0,
+                                  index_t end0,
+                                  index_t step0) {
+          for (index_t idx = start0; idx < end0; idx += step0) {
+            memcpy(output_data + (b * output_chunk + idx - out_start_index)
+                       * output_dim,
+                   extract_out_data, output_dim * sizeof(float));
+          }
+        }, output_start, output_end, 1);
+      }
+    }
+
+    return MaceStatus::MACE_SUCCESS;
+  }
+
+ private:
+  int modulus_;
+  bool include_variance_;
+  int num_log_count_;
+  float variance_floor_;
+  std::vector<int> input_indexes_;
+  std::vector<int> output_indexes_;
+  std::vector<int> forward_indexes_;
+  std::vector<float> counts_;
+  std::vector<int> input_time_range_;
+  std::vector<int> output_time_range_;
+};
+
+void RegisterExtractPooling(OpRegistryBase *op_registry) {
+  MACE_REGISTER_OP(op_registry, "ExtractPooling", ExtractPoolingOp,
+                   DeviceType::CPU, float);
+}
+
+}  // namespace ops
+}  // namespace mace

mace/ops/extract_pooling_benchmark.cc

+// Copyright 2018 The MACE Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mace/core/testing/test_benchmark.h"
+#include "mace/ops/ops_test_util.h"
+
+namespace mace {
+namespace ops {
+namespace test {
+
+template <DeviceType D, typename T>
+static void ExtractPooling(int iters,
+                           int batch,
+                           int chunk,
+                           int dim,
+                           int input_period,
+                           int modulus) {
+  mace::testing::StopTiming();
+
+  OpsTestNet net;
+
+  size_t num_input_indexes = static_cast<size_t>(chunk / input_period);
+  std::vector<int> input_indexes(num_input_indexes, 0);
+
+  for (size_t i = 0; i < num_input_indexes; ++i) {
+    input_indexes[i] = static_cast<int>(i * input_period);
+  }
+
+  size_t num_output_indexes = static_cast<size_t>(chunk / modulus);
+  std::vector<int> output_indexes(num_output_indexes, 0);
+  std::vector<int> forward_indexes(num_output_indexes * 2, 0);
+  std::vector<float> counts(num_output_indexes, 0.f);
+  for (size_t i = 0; i < num_output_indexes; ++i) {
+    output_indexes[i] = static_cast<int>(i * modulus);
+    forward_indexes[2 * i] = 0;
+    forward_indexes[2 * i + 1] = static_cast<int>(num_input_indexes - 1);
+    counts[i] = static_cast<float>(num_input_indexes);
+  }
+
+  // Add input data
+  net.AddRandomInput<D, T>("Input", {batch, chunk, dim});
+
+  OpDefBuilder("ExtractPooling", "ExtractPoolingTest")
+      .Input("Input")
+      .AddIntArg("modulus", modulus)
+      .AddIntArg("include_variance", 1)
+      .AddIntArg("num_log_counts", 1)
+      .AddIntsArg("input_indexes", input_indexes)
+      .AddIntsArg("output_indexes", output_indexes)
+      .AddIntsArg("forward_indexes", forward_indexes)
+      .AddFloatsArg("counts", counts)
+      .AddIntsArg("input_time_range", {0, chunk - 1})
+      .AddIntsArg("output_time_range", {0, chunk - 1})
+      .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
+      .Output("Output")
+      .Finalize(net.NewOperatorDef());
+
+  // Warm-up
+  for (int i = 0; i < 5; ++i) {
+    net.RunOp(D);
+  }
+  net.Sync();
+
+  mace::testing::StartTiming();
+  while (iters--) {
+    net.Run();
+  }
+  net.Sync();
+}
+
+#define MACE_BM_EXTRACTPOOLING_MACRO(N, C, D, INP, M, TYPE, DEVICE)            \
+  static void MACE_BM_EXTRACTPOOLING_##N##_##C##_##D##_##INP##_##M##_##TYPE##\
+_##DEVICE(                                                                     \
+      int iters) {                                                             \
+    const int64_t tot = static_cast<int64_t>(iters) * N * C * D;               \
+    mace::testing::BytesProcessed(tot *(sizeof(TYPE)));                        \
+    ExtractPooling<DEVICE, TYPE>(iters, N, C, D, INP, M);                      \
+  }                                                                            \
+  MACE_BENCHMARK(MACE_BM_EXTRACTPOOLING_##N##_##C##_##D##_##INP##_##M##_##TYPE\
+##_##DEVICE)
+
+#define MACE_BM_EXTRACTPOOLING(N, C, D, INP, M)                 \
+  MACE_BM_EXTRACTPOOLING_MACRO(N, C, D, INP, M, float, CPU);
+
+MACE_BM_EXTRACTPOOLING(8, 40, 512, 2, 4);
+MACE_BM_EXTRACTPOOLING(16, 80, 100, 3, 9);
+MACE_BM_EXTRACTPOOLING(32, 60, 200, 6, 18);
+
+
+}  // namespace test
+}  // namespace ops
+}  // namespace mace

mace/ops/extract_pooling_test.cc

+// Copyright 2018 The MACE Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "gmock/gmock.h"
+#include "mace/ops/ops_test_util.h"
+
+namespace mace {
+namespace ops {
+namespace test {
+
+class ExtractPoolingTest : public OpsTestBase {};
+
+namespace {
+template <DeviceType D, typename T>
+void TestExtractPooling(const std::vector<index_t> &input_shape,
+                        const std::vector<float> &input_value,
+                        const int modulus,
+                        const int num_log_count,
+                        const int include_variance,
+                        const std::vector<int> &input_time_range,
+                        const std::vector<int> &input_indexes,
+                        const std::vector<int> &forward_indexes,
+                        const std::vector<float> &counts,
+                        const std::vector<int> &output_indexes,
+                        const std::vector<int> &output_time_range,
+                        const std::vector<index_t> &output_shape,
+                        const std::vector<float> &output_value) {
+  // Construct graph
+  OpsTestNet net;
+  net.AddInputFromArray<D, float>("Input", input_shape, input_value);
+  OpDefBuilder("ExtractPooling", "ExtractPoolingTest")
+      .Input("Input")
+      .AddIntArg("modulus", modulus)
+      .AddIntArg("include_variance", include_variance)
+      .AddIntArg("num_log_count", num_log_count)
+      .AddIntsArg("input_indexes", input_indexes)
+      .AddIntsArg("output_indexes", output_indexes)
+      .AddIntsArg("forward_indexes", forward_indexes)
+      .AddFloatsArg("counts", counts)
+      .AddIntsArg("input_time_range", input_time_range)
+      .AddIntsArg("output_time_range", output_time_range)
+      .Output("Output")
+      .Finalize(net.NewOperatorDef());
+  // Run
+  net.RunOp();
+  // Check
+  auto expected = net.CreateTensor<float>(output_shape, output_value);
+  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
+}
+}  // namespace
+
+TEST_F(ExtractPoolingTest, SimpleCPU) {
+  TestExtractPooling<DeviceType::CPU, float>(
+    {3, 20, 3},
+    {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+     16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+     31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
+     46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+     1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+     16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+     31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
+     46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+     1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+     16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+     31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
+     46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60},
+    9, 0, 0,
+    {-2, 17},
+    {0, 3, 6, 9, 12, 15},
+    {0, 6, 2, 6},
+    {6, 4},
+    {0, 9},
+    {0, 17},
+    {3, 18, 3},
+    {29.5, 30.5, 31.5, 29.5, 30.5, 31.5, 29.5, 30.5, 31.5,
+     29.5, 30.5, 31.5, 29.5, 30.5, 31.5, 29.5, 30.5, 31.5,
+     29.5, 30.5, 31.5, 29.5, 30.5, 31.5, 29.5, 30.5, 31.5,
+     38.5, 39.5, 40.5, 38.5, 39.5, 40.5, 38.5, 39.5, 40.5,
+     38.5, 39.5, 40.5, 38.5, 39.5, 40.5, 38.5, 39.5, 40.5,
+     38.5, 39.5, 40.5, 38.5, 39.5, 40.5, 38.5, 39.5, 40.5,
+     29.5, 30.5, 31.5, 29.5, 30.5, 31.5, 29.5, 30.5, 31.5,
+     29.5, 30.5, 31.5, 29.5, 30.5, 31.5, 29.5, 30.5, 31.5,
+     29.5, 30.5, 31.5, 29.5, 30.5, 31.5, 29.5, 30.5, 31.5,
+     38.5, 39.5, 40.5, 38.5, 39.5, 40.5, 38.5, 39.5, 40.5,
+     38.5, 39.5, 40.5, 38.5, 39.5, 40.5, 38.5, 39.5, 40.5,
+     38.5, 39.5, 40.5, 38.5, 39.5, 40.5, 38.5, 39.5, 40.5,
+     29.5, 30.5, 31.5, 29.5, 30.5, 31.5, 29.5, 30.5, 31.5,
+     29.5, 30.5, 31.5, 29.5, 30.5, 31.5, 29.5, 30.5, 31.5,
+     29.5, 30.5, 31.5, 29.5, 30.5, 31.5, 29.5, 30.5, 31.5,
+     38.5, 39.5, 40.5, 38.5, 39.5, 40.5, 38.5, 39.5, 40.5,
+     38.5, 39.5, 40.5, 38.5, 39.5, 40.5, 38.5, 39.5, 40.5,
+     38.5, 39.5, 40.5, 38.5, 39.5, 40.5, 38.5, 39.5, 40.5});
+}
+
+TEST_F(ExtractPoolingTest, SimpleCPUWithVariance) {
+TestExtractPooling<DeviceType::CPU, float>(
+    {3, 20, 3},
+    {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+     16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+     31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
+     46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+     1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+     16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+     31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
+     46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+     1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+     16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+     31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
+     46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60},
+     9, 1, 1,
+    {-2, 17},
+    {0, 3, 6, 9, 12, 15},
+    {0, 6, 2, 6},
+    {6, 4},
+    {0, 9},
+    {0, 17},
+    {3, 18, 7},
+    {1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.79176, 29.5, 30.5, 31.5, 15.3704, 15.3704, 15.3704,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623,
+     1.386294, 38.5, 39.5, 40.5, 10.0623, 10.0623, 10.0623});
+}
+
+}  // namespace test
+}  // namespace ops
+}  // namespace mace

mace/ops/kaldi_batch_norm.cc

+// Copyright 2018 The MACE Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// This Op is for Kaldi's BatchNormComponent
+// More details about forward computation are here:
+// http://kaldi-asr.org/doc/nnet-normalize-component_8cc_source.html#l00320
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "mace/core/operator.h"
+
+namespace mace {
+namespace ops {
+
+template <DeviceType D, class T>
+class KaldiBatchNormOp;
+
+template <>
+class KaldiBatchNormOp<DeviceType::CPU, float> : public Operation {
+ public:
+  explicit KaldiBatchNormOp(OpConstructContext *context)
+      : Operation(context),
+        epsilon_(Operation::GetOptionalArg<float>("epsilon",
+                                                  static_cast<float>(1e-3))),
+        target_rms_(Operation::GetOptionalArg<float>("target_rms", 1.0f)),
+        block_dim_(Operation::GetOptionalArg<int>("block_dim", -1)),
+        test_mode_(static_cast<bool>(
+                       Operation::GetOptionalArg<int>("test_mode", 0))) {}
+
+  void CalculateMeanVar(const float *input_data,
+                        index_t length,
+                        index_t stride,
+                        float mean_scale,
+                        float var_scale,
+                        float *mean_data,
+                        float *var_data) {
+    float mean_value = 0.f;
+    float var_value = 0.f;
+    for (index_t i = 0; i < length; ++i) {
+      float x = input_data[i * stride];
+      mean_value += x;
+      var_value += x * x;
+    }
+    mean_value = mean_value * mean_scale;
+    var_value = var_value * mean_scale;
+    float mean_sqr = mean_value * mean_value;
+    var_value = (var_value > mean_sqr) ?
+                var_scale * (var_value - mean_sqr + epsilon_) :
+                var_scale * epsilon_;
+    var_data[0] = std::pow(var_value, -0.5f);
+    mean_data[0] = mean_value;
+  }
+
+  MaceStatus Run(OpContext *context) override {
+    MACE_UNUSED(context);
+    const Tensor *input = this->Input(INPUT);
+    const std::vector<index_t> &input_shape = input->shape();
+    const index_t rank = input->dim_size();
+    const index_t dim = input_shape[rank - 1];
+    if (block_dim_ == -1) block_dim_ = static_cast<int>(dim);
+    MACE_CHECK(target_rms_ > 0 && dim > 0 && dim % block_dim_ == 0);
+    MACE_CHECK(rank >= 2, "KaldiBatchNorm's input's rank must >= 2.");
+    index_t num_rows =
+        std::accumulate(input_shape.begin(), input_shape.end() - 1, 1,
+                        std::multiplies<index_t>());
+
+    const index_t blocks = dim / block_dim_;
+    if (blocks > 1) num_rows *= blocks;
+    Tensor *output = this->Output(OUTPUT);
+    MACE_RETURN_IF_ERROR(output->ResizeLike(input));
+
+    Tensor::MappingGuard input_guard(input);
+    Tensor::MappingGuard output_guard(output);
+    const float *input_data = input->data<float>();
+    float *output_data = output->mutable_data<float>();
+
+    utils::ThreadPool
+        &thread_pool = context->device()->cpu_runtime()->thread_pool();
+
+    if (test_mode_) {
+      MACE_CHECK(this->InputSize() == 3, "KaldiBatchNorm should have 3 inputs");
+      const Tensor *scale = this->Input(SCALE);
+      const Tensor *offset = this->Input(OFFSET);
+      MACE_CHECK(scale->dim_size() == 1, "scale must be 1-dimensional. ",
+                 scale->dim_size());
+      MACE_CHECK(offset->dim_size() == 1, "offset must be 1-dimensional. ",
+                 offset->dim_size());
+      MACE_CHECK(scale->size() == offset->size()
+                     && scale->size() == block_dim_);
+      Tensor::MappingGuard scale_guard(scale);
+      Tensor::MappingGuard offset_guard(offset);
+      const float *scale_data = scale->data<float>();
+      const float *offset_data = offset->data<float>();
+
+      thread_pool.Compute2D([=](index_t start0, index_t end0, index_t step0,
+                                index_t start1, index_t end1, index_t step1) {
+        for (index_t i = start0; i < end0; i += step0) {
+          for (index_t j = start1; j < end1; j += step1) {
+            index_t idx = i * block_dim_ + j;
+            output_data[idx] = input_data[idx] * scale_data[j] + offset_data[j];
+          }
+        }
+      }, 0, num_rows, 1, 0, block_dim_, 1);
+    } else {
+      const index_t buf_size =
+          PadAlignSize(block_dim_ * sizeof(float));
+      ScratchBuffer *scratch = context->device()->scratch_buffer();
+      scratch->Rewind();
+      scratch->GrowSize(2 * buf_size);
+
+      Tensor mean(scratch->Scratch(buf_size), DT_FLOAT);
+      mean.Reshape({block_dim_});
+      float *mean_data = mean.mutable_data<float>();
+
+      Tensor var(scratch->Scratch(buf_size), DT_FLOAT);
+      var.Reshape({block_dim_});
+      float *var_data = var.mutable_data<float>();
+
+      float var_scale = 1.0f / (target_rms_ * target_rms_);
+      float mean_scale = 1.0f / num_rows;
+
+      thread_pool.Compute1D([=](index_t start0, index_t end0, index_t step0) {
+        for (index_t i = start0; i < end0; i += step0) {
+          CalculateMeanVar(input_data + i,
+                           num_rows,
+                           block_dim_,
+                           mean_scale,
+                           var_scale,
+                           mean_data + i,
+                           var_data + i);
+        }
+      }, 0, block_dim_, 1);
+      thread_pool.Compute2D([=](index_t start0, index_t end0, index_t step0,
+                                index_t start1, index_t end1, index_t step1) {
+        for (index_t i = start0; i < end0; i += step0) {
+          for (index_t j = start1; j < end1; j += step1) {
+            index_t idx = i * block_dim_ + j;
+            output_data[idx] = (input_data[idx] - mean_data[j]) * var_data[j];
+          }
+        }
+      }, 0, num_rows, 1, 0, block_dim_, 1);
+    }
+
+    return MaceStatus::MACE_SUCCESS;
+  }
+
+ private:
+  const float epsilon_;
+  const float target_rms_;
+  int block_dim_;
+  const bool test_mode_;
+
+ protected:
+  MACE_OP_INPUT_TAGS(INPUT, SCALE, OFFSET);
+  MACE_OP_OUTPUT_TAGS(OUTPUT);
+};
+
+void RegisterKaldiBatchNorm(OpRegistryBase *op_registry) {
+  MACE_REGISTER_OP(op_registry, "KaldiBatchNorm", KaldiBatchNormOp,
+                   DeviceType::CPU, float);
+}
+
+}  // namespace ops
+}  // namespace mace

mace/ops/kaldi_batch_norm_benchmark.cc

+// Copyright 2018 The MACE Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mace/core/testing/test_benchmark.h"
+#include "mace/ops/ops_test_util.h"
+
+namespace mace {
+namespace ops {
+namespace test {
+
+namespace {
+template <DeviceType D, typename T>
+void KaldiBatchNorm(
+    int iters, int batch, int chunk, int dim, int block_dim) {
+  mace::testing::StopTiming();
+
+  OpsTestNet net;
+
+  // Add input data
+  if (D == DeviceType::CPU) {
+    net.AddRandomInput<D, T>("Input", {batch, chunk, dim});
+  } else {
+    MACE_NOT_IMPLEMENTED;
+  }
+  net.AddRandomInput<D, T>("Scale", {block_dim}, true);
+  net.AddRandomInput<D, T>("Offset", {block_dim}, true);
+
+  OpDefBuilder("KaldiBatchNorm", "KaldiBatchNormBM")
+      .Input("Input")
+      .Input("Scale")
+      .Input("Offset")
+      .AddIntArg("block_dim", block_dim)
+      .AddIntArg("test_mode", 1)
+      .Output("Output")
+      .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
+      .Finalize(net.NewOperatorDef());
+
+  // Warm-up
+  for (int i = 0; i < 5; ++i) {
+    net.RunOp(D);
+  }
+  net.Sync();
+
+  mace::testing::StartTiming();
+  while (iters--) {
+    net.RunOp(D);
+  }
+  net.Sync();
+}
+}  // namespace
+
+#define MACE_BM_KALDI_BATCH_NORM_MACRO(N, C, D, BD, TYPE, DEVICE)           \
+  static void MACE_BM_KALDI_BATCH_NORM_##N##_##C##_##D##_##BD##_##TYPE\
+##_##DEVICE(                                                            \
+      int iters) {                                                          \
+    const int64_t tot = static_cast<int64_t>(iters) * N * C * D;        \
+    mace::testing::MacsProcessed(tot);                                      \
+    mace::testing::BytesProcessed(tot *(sizeof(TYPE)));                     \
+    KaldiBatchNorm<DEVICE, TYPE>(iters, N, C, D, BD);                       \
+  }                                                                         \
+  MACE_BENCHMARK(MACE_BM_KALDI_BATCH_NORM_##N##_##C##_##D##_##BD##_##TYPE\
+##_##DEVICE)
+
+#define MACE_BM_KALDI_BATCH_NORM(N, C, D, BD)                 \
+  MACE_BM_KALDI_BATCH_NORM_MACRO(N, C, D, BD, float, CPU);
+
+MACE_BM_KALDI_BATCH_NORM(1, 1, 512, 512);
+MACE_BM_KALDI_BATCH_NORM(1, 3, 128, 128);
+MACE_BM_KALDI_BATCH_NORM(1, 3, 512, 128);
+MACE_BM_KALDI_BATCH_NORM(1, 32, 112, 112);
+MACE_BM_KALDI_BATCH_NORM(1, 64, 256, 256);
+MACE_BM_KALDI_BATCH_NORM(1, 64, 512, 256);
+MACE_BM_KALDI_BATCH_NORM(1, 128, 56, 56);
+MACE_BM_KALDI_BATCH_NORM(1, 128, 256, 256);
+MACE_BM_KALDI_BATCH_NORM(1, 256, 14, 14);
+MACE_BM_KALDI_BATCH_NORM(1, 512, 14, 14);
+MACE_BM_KALDI_BATCH_NORM(1, 1024, 7, 7);
+MACE_BM_KALDI_BATCH_NORM(32, 1, 256, 128);
+MACE_BM_KALDI_BATCH_NORM(32, 3, 256, 256);
+
+}  // namespace test
+}  // namespace ops
+}  // namespace mace

mace/ops/kaldi_batch_norm_test.cc

+// Copyright 2018 The MACE Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mace/ops/ops_test_util.h"
+
+namespace mace {
+namespace ops {
+namespace test {
+
+class KaldiBatchNormOpTest : public OpsTestBase {};
+
+namespace {
+template <DeviceType D>
+void Simple(const std::vector<index_t> &input_shape,
+            const std::vector<float> &input_value,
+            const int block_dim,
+            const int dim,
+            const std::vector<float> &scale,
+            const std::vector<float> &offset,
+            const std::vector<index_t> &output_shape,
+            const std::vector<float> &output_value) {
+  OpsTestNet net;
+  int scale_dim = block_dim;
+  if (scale_dim == -1) scale_dim = dim;
+  // Add input data
+  net.AddInputFromArray<D, float>("Input", input_shape,
+                                  input_value);
+  net.AddInputFromArray<D, float>("Scale", {scale_dim}, scale, true);
+  net.AddInputFromArray<D, float>("Offset", {scale_dim}, offset, true);
+
+  if (D == DeviceType::CPU) {
+    OpDefBuilder("KaldiBatchNorm", "KaldiBatchNormOpTest")
+        .Input("Input")
+        .Input("Scale")
+        .Input("Offset")
+        .AddIntArg("block_dim", block_dim)
+        .AddIntArg("test_mode", 1)
+        .Output("Output")
+        .Finalize(net.NewOperatorDef());
+    // Run
+
+    net.RunOp(D);
+  } else if (D == DeviceType::GPU) {
+    MACE_NOT_IMPLEMENTED;
+  }
+
+  // Check
+  auto expected = net.CreateTensor<float>(output_shape, output_value);
+
+  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-4);
+}
+
+template <DeviceType D>
+void SimpleNotTestMode(const std::vector<index_t> &input_shape,
+                       const std::vector<float> &input_value,
+                       const int block_dim,
+                       const std::vector<index_t> &output_shape,
+                       const std::vector<float> &output_value) {
+  OpsTestNet net;
+  // Add input data
+  net.AddInputFromArray<D, float>("Input", input_shape,
+                                  input_value);
+
+  if (D == DeviceType::CPU) {
+    OpDefBuilder("KaldiBatchNorm", "KaldiBatchNormOpTest")
+        .Input("Input")
+        .AddIntArg("block_dim", block_dim)
+        .AddIntArg("test_mode", 0)
+        .Output("Output")
+        .Finalize(net.NewOperatorDef());
+    // Run
+
+    net.RunOp(D);
+  } else if (D == DeviceType::GPU) {
+    MACE_NOT_IMPLEMENTED;
+  }
+
+  // Check
+  auto expected = net.CreateTensor<float>(output_shape, output_value);
+
+  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-4);
+}
+}  // namespace
+
+TEST_F(KaldiBatchNormOpTest, SimpleTestModeCPUOneBlock) {
+  Simple<DeviceType::CPU>(
+    {1, 6, 2},
+    {5, 5, 7, 7, 9, 9, 11, 11, 13, 13, 15, 15},
+    -1, 2,
+    {4.f, 6.f},
+    {2.f, 1.f},
+    {1, 6, 2},
+    {22, 31, 30, 43, 38, 55, 46, 67, 54, 79, 62, 91}); }
+
+TEST_F(KaldiBatchNormOpTest, SimpleTestModeCPUTwoBlock) {
+  Simple<DeviceType::CPU>(
+    {1, 6, 4},
+    {5, 5, 5, 5, 7, 7, 7, 7, 9, 9, 9, 9,
+    11, 11, 11, 11, 13, 13, 13, 13, 15, 15, 15, 15},
+    2, 4,
+    {4.f, 6.f},
+    {2.f, 1.f},
+    {1, 6, 4},
+    {22, 31, 22, 31, 30, 43, 30, 43, 38, 55, 38, 55,
+    46, 67, 46, 67, 54, 79, 54, 79, 62, 91, 62, 91});
+}
+
+TEST_F(KaldiBatchNormOpTest, SimpleNotTestModeCPUTwoBlock) {
+  SimpleNotTestMode<DeviceType::CPU>(
+    {1, 6, 4},
+    {5, 5, 5, 5, 7, 7, 7, 7, 9, 9, 9, 9,
+    11, 11, 11, 11, 13, 13, 13, 13, 15, 15, 15, 15},
+    2,
+    {1, 6, 4},
+    {-1.46379, -1.46379, -1.46379, -1.46379,
+    -0.8783, -0.8783, -0.8783, -0.8783,
+    -0.29276, -0.29276, -0.29276, -0.29276,
+    0.29276, 0.29276, 0.29276, 0.29276,
+    0.8783, 0.8783, 0.8783, 0.8783,
+    1.46379, 1.46379, 1.46379, 1.46379});
+}
+
+}  // namespace test
+}  // namespace ops
+}  // namespace mace

mace/ops/ops_registry.cc

@@ -37,11 +37,14 @@ extern void RegisterDepthwiseDeconv2d(OpRegistryBase *op_registry);
 extern void RegisterDynamicLSTM(OpRegistryBase *op_registry);
 extern void RegisterEltwise(OpRegistryBase *op_registry);
 extern void RegisterExpandDims(OpRegistryBase *op_registry);
+extern void RegisterExtractPooling(OpRegistryBase *op_registry);
 extern void RegisterFill(OpRegistryBase *op_registry);
 extern void RegisterFullyConnected(OpRegistryBase *op_registry);
 extern void RegisterGather(OpRegistryBase *op_registry);
 extern void RegisterIdentity(OpRegistryBase *op_registry);
+extern void RegisterDelay(OpRegistryBase *op_registry);
 extern void RegisterInferConv2dShape(OpRegistryBase *op_registry);
+extern void RegisterKaldiBatchNorm(OpRegistryBase *op_registry);
 extern void RegisterLocalResponseNorm(OpRegistryBase *op_registry);
 extern void RegisterLSTMNonlinear(OpRegistryBase *op_registry);
 extern void RegisterMatMul(OpRegistryBase *op_registry);
@@ -107,11 +110,14 @@ OpRegistry::OpRegistry() : OpRegistryBase() {
   ops::RegisterDynamicLSTM(this);
   ops::RegisterEltwise(this);
   ops::RegisterExpandDims(this);
+  ops::RegisterExtractPooling(this);
   ops::RegisterFill(this);
   ops::RegisterFullyConnected(this);
   ops::RegisterGather(this);
   ops::RegisterIdentity(this);
+  ops::RegisterDelay(this);
   ops::RegisterInferConv2dShape(this);
+  ops::RegisterKaldiBatchNorm(this);
   ops::RegisterLocalResponseNorm(this);
   ops::RegisterLSTMNonlinear(this);
   ops::RegisterMatMul(this);

mace/ops/pad_context.cc

@@ -42,7 +42,7 @@ class PadContextOp<DeviceType::CPU, T> : public Operation {
 
     index_t rank = input->dim_size();
     MACE_CHECK(rank >= 2, "input's rank should >= 2.");
-    MACE_CHECK(left_context_ > 0 && right_context_ > 0,
+    MACE_CHECK(left_context_ >= 0 && right_context_ >= 0,
                "left context and right context should be greater than zero");
     const std::vector<index_t> &input_shape = input->shape();
     const index_t batch =

mace/ops/reduce_test.cc

@@ -106,19 +106,6 @@ void SimpleMean12Test() {
              10, 11, 12, 13}, ReduceType::MEAN);
 }
 
-// template <DeviceType D>
-// void SimpleSum12Test() {
-//   Simple<D>({2, 2, 3, 4},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-//             12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
-//             0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-//             12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23},
-//            {1, 2},
-//            {2, 1, 1, 4},
-//            {60, 66, 72, 78,
-//             60, 66, 72, 78}, ReduceType::SUM);
-//}
-
 template <DeviceType D>
 void SimpleMin12Test() {
   Simple<D>({2, 2, 3, 4},
@@ -145,20 +132,6 @@ void SimpleMax12Test() {
              20, 21, 22, 23}, ReduceType::MAX);
 }
 
-// template <DeviceType D>
-// void SimpleSumSqr12Test() {
-//   Simple<D>({2, 2, 3, 4},
-//             {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-//             12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
-//             0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-//             12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23},
-//            {1, 2},
-//            {2, 1, 1, 4},
-//            {880, 1006, 1144, 1294,
-//             880, 1006, 1144, 1294}, ReduceType::SUM_SQR);
-//}
-
-
 template <DeviceType D>
 void SimpleMean1Axis() {
   Simple<D>({2, 2, 3, 4},
@@ -170,102 +143,8 @@ void SimpleMean1Axis() {
             {2, 1, 3, 4},
             {6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
              6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17}, ReduceType::MEAN);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-//             12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23},
-//            {-3},
-//            {1, 1, 3, 4},
-//            {6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17}, ReduceType::MEAN);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-//             12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23},
-//            {2},
-//            {1, 2, 1, 4},
-//            {4, 5, 6, 7, 16, 17, 18, 19}, ReduceType::MEAN);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-//             12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23},
-//            {-1},
-//            {1, 2, 3, 1},
-//            {1.5, 5.5, 9.5, 13.5, 17.5, 21.5}, ReduceType::MEAN);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {1},
-//            {1, 1, 3, 3},
-//            {9, 10, 11, 12, 13, 14, 15, 16, 17}, ReduceType::MEAN);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {-2},
-//            {1, 3, 1, 3},
-//            {3, 4, 5, 12, 13, 14, 21, 22, 23}, ReduceType::MEAN);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {3},
-//            {1, 3, 3, 1},
-//            {1, 4, 7, 10, 13, 16, 19, 22, 25}, ReduceType::MEAN);
 }
 
-// template <DeviceType D>
-// void SimpleSum1Axis() {
-//  Simple<D>({2, 2, 3, 4},
-//            {0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23,
-//             0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23},
-//            {1},
-//            {2, 1, 3, 4},
-//            {12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34,
-//             12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34});
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23},
-//            {2},
-//            {1, 2, 1, 4},
-//            {12, 15, 18, 21, 48, 51, 54, 57}, ReduceType::SUM);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23},
-//            {-1},
-//            {1, 2, 3, 1},
-//            {6, 22, 38, 54, 70, 86}, ReduceType::SUM);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {1},
-//            {1, 1, 3, 3},
-//            {27, 30, 33, 36, 39, 42, 45, 48, 51}, ReduceType::SUM);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {3},
-//            {1, 3, 3, 1},
-//            {3, 12, 21, 30, 39, 48, 57, 66, 75}, ReduceType::SUM);
-//}
-
 template <DeviceType D>
 void SimpleMin1Axis() {
   Simple<D>({2, 2, 3, 4},
@@ -285,33 +164,6 @@ void SimpleMin1Axis() {
             {2, 1, 3, 4},
             {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
              0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, ReduceType::MIN);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23},
-//            {2},
-//            {1, 2, 1, 4},
-//            {0, 1, 2, 3, 12, 13, 14, 15}, ReduceType::MIN);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23},
-//            {-1},
-//            {1, 2, 3, 1},
-//            {0, 4, 8, 12, 16, 20}, ReduceType::MIN);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {1},
-//            {1, 1, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8}, ReduceType::MIN);
 }
 
 template <DeviceType D>
@@ -337,53 +189,8 @@ void SimpleMax1Axis() {
              12, 13, 14, 15,
              16, 17, 18, 19,
              20, 21, 22, 23}, ReduceType::MAX);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23},
-//            {2},
-//            {1, 2, 1, 4},
-//            {8, 9, 10, 11, 20, 21, 22, 23}, ReduceType::MAX);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23},
-//            {-1},
-//            {1, 2, 3, 1},
-//            {3, 7, 11, 15, 19, 23}, ReduceType::MAX);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {1},
-//            {1, 1, 3, 3},
-//            {18, 19, 20, 21, 22, 23, 24, 25, 26}, ReduceType::MAX);
 }
 
-// template <DeviceType D>
-// void SimpleSumSqr1Axis() {
-//  Simple<D>({2, 2, 3, 4},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-//             12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
-//             0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-//             12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23},
-//            {1},
-//            {2, 1, 3, 4},
-//            {144, 170, 200, 234,
-//             272, 314, 360, 410,
-//             464, 522, 584, 650,
-//             144, 170, 200, 234,
-//             272, 314, 360, 410,
-//             464, 522, 584, 650}, ReduceType::SUM_SQR);
-//}
-
-
 template <DeviceType D>
 void Simple2Axis() {
   Simple<D>({1, 2, 3, 4},
@@ -396,16 +203,6 @@ void Simple2Axis() {
             {0, 1},
             {1, 1, 3, 4},
             {6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17}, ReduceType::MEAN);
-//  Simple3D<D>({2, 3, 4},
-//              {0, 1, 2, 3,
-//               4, 5, 6, 7,
-//               8, 9, 10, 11,
-//               12, 13, 14, 15,
-//               16, 17, 18, 19,
-//               20, 21, 22, 23},
-//              {0, 1},
-//              {1, 1, 4},
-//              {10, 11, 12, 13}, ReduceType::MEAN);
   Simple3D<D>({2, 3, 4},
               {0, 1, 2, 3,
                4, 5, 6, 7,
@@ -426,37 +223,6 @@ void Simple2Axis() {
             {0, 2},
             {1, 2, 1, 4},
             {4, 5, 6, 7, 16, 17, 18, 19}, ReduceType::MEAN);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23},
-//            {1, 3},
-//            {1, 1, 3, 1},
-//            {7.5, 11.5, 15.5}, ReduceType::MEAN);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {1, 2},
-//            {1, 1, 1, 3},
-//            {12, 13, 14}, ReduceType::MEAN);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {0, 1},
-//            {1, 1, 3, 3},
-//            {9, 10, 11, 12, 13, 14, 15, 16, 17}, ReduceType::MEAN);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {2, 3},
-//            {1, 3, 1, 1},
-//            {4, 13, 22}, ReduceType::MEAN);
 }
 
 template <DeviceType D>
@@ -471,64 +237,6 @@ void Simple3Axis() {
             {1, 2, 3},
             {1, 1, 1, 1},
             {11.5}, ReduceType::MEAN);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23},
-//            {0, 2, 3},
-//            {1, 2, 1, 1},
-//            {5.5, 17.5}, ReduceType::MEAN);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23},
-//            {0, 1, 3},
-//            {1, 1, 3, 1},
-//            {7.5, 11.5, 15.5}, ReduceType::MEAN);
-//  Simple<D>({1, 2, 3, 4},
-//            {0, 1, 2, 3,
-//             4, 5, 6, 7,
-//             8, 9, 10, 11,
-//             12, 13, 14, 15,
-//             16, 17, 18, 19,
-//             20, 21, 22, 23},
-//            {0, 1, 2},
-//            {1, 1, 1, 4},
-//            {10, 11, 12, 13}, ReduceType::MEAN);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {1, 2, 3},
-//            {1, 1, 1, 1},
-//            {13}, ReduceType::MEAN);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {0, 2, 3},
-//            {1, 3, 1, 1},
-//            {4, 13, 22}, ReduceType::MEAN);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {0, 1, 3},
-//            {1, 1, 3, 1},
-//            {10, 13, 16}, ReduceType::MEAN);
-//  Simple<D>({1, 3, 3, 3},
-//            {0, 1, 2, 3, 4, 5, 6, 7, 8,
-//             9, 10, 11, 12, 13, 14, 15, 16, 17,
-//             18, 19, 20, 21, 22, 23, 24, 25, 26},
-//            {0, 1, 2},
-//            {1, 1, 1, 3},
-//            {12, 13, 14}, ReduceType::MEAN);
 }
 
 }  // namespace
@@ -622,25 +330,17 @@ void RandomTest(const std::vector<index_t> &input_shape,
 
 TEST_F(ReduceOpTest, GPURandomFloat) {
   RandomTest<DeviceType::GPU, float>({4, 64, 64, 3}, {1, 2});
-//  RandomTest<DeviceType::GPU, float>({2, 64, 64, 4}, {1, 2});
   RandomTest<DeviceType::GPU, float>({8, 128, 128, 64}, {1, 2});
-//  RandomTest<DeviceType::GPU, float>({1, 640, 480, 64}, {1, 2});
   RandomTest<DeviceType::GPU, float>({1, 480, 640, 32}, {1, 2});
-//  RandomTest<DeviceType::GPU, float>({1, 512, 512, 16}, {1, 2});
   RandomTest<DeviceType::GPU, float>({8, 117, 87, 33}, {1, 2});
-//  RandomTest<DeviceType::GPU, float>({1, 619, 450, 61}, {1, 2});
   RandomTest<DeviceType::GPU, float>({1, 511, 561, 11}, {1, 2});
 }
 
 TEST_F(ReduceOpTest, GPURandomHalf) {
   RandomTest<DeviceType::GPU, half>({4, 64, 64, 3}, {1, 2});
-//  RandomTest<DeviceType::GPU, half>({2, 64, 64, 4}, {1, 2});
   RandomTest<DeviceType::GPU, half>({8, 128, 128, 64}, {1, 2});
-//  RandomTest<DeviceType::GPU, half>({1, 640, 480, 64}, {1, 2});
   RandomTest<DeviceType::GPU, half>({1, 480, 640, 32}, {1, 2});
-//  RandomTest<DeviceType::GPU, half>({1, 512, 512, 16}, {1, 2});
   RandomTest<DeviceType::GPU, half>({8, 117, 87, 33}, {1, 2});
-//  RandomTest<DeviceType::GPU, half>({1, 619, 450, 61}, {1, 2});
   RandomTest<DeviceType::GPU, half>({1, 511, 561, 11}, {1, 2});
 }
 

mace/ops/splice.cc

@@ -71,7 +71,8 @@ class SpliceOp<DeviceType::CPU, T> : public Operation {
     const index_t out_chunk = chunk - (right_context - left_context);
 
     MACE_CHECK(input_dim > const_dim_,
-               "input dim should be greater than const dim.");
+               "input dim:", input_dim,
+               "should be greater than const dim:", const_dim_);
     const index_t output_dim = dim * num_splice + const_dim_;
     const index_t output_stride = out_chunk * output_dim;
 
@@ -103,7 +104,7 @@ class SpliceOp<DeviceType::CPU, T> : public Operation {
       const index_t input_offset = dim;
       for (int b = 0; b < batch; ++b) {
         for (index_t i = 0; i < out_chunk; ++i) {
-          T *output_base = output_data +  + b * output_stride + i * output_dim;
+          T *output_base = output_data + b * output_stride + i * output_dim;
           const T *input_base = input_data + b * input_stride + i * input_dim;
           memcpy(output_base + output_offset,
                  input_base + input_offset,

mace/ops/sum_group.cc

@@ -80,18 +80,22 @@ class SumGroupOp<DeviceType::CPU, T> : public Operation {
       MACE_CHECK(cur_index <= input_dim)
         << "size value over-ranged:" << cur_index << "<=" << input_dim;
     }
-
-    for (index_t i = 0; i < bh; ++i) {
-      for (index_t j = 0; j < output_dim; ++j) {
-        int start_col = sum_indexes[j].first;
-        int end_col = sum_indexes[j].second;
-        T sum = 0;
-        for (int src_col = start_col; src_col < end_col; ++src_col) {
-          sum += input_data[i * input_dim + src_col];
+    utils::ThreadPool
+        &thread_pool = context->device()->cpu_runtime()->thread_pool();
+    thread_pool.Compute2D([=](index_t start0, index_t end0, index_t step0,
+                              index_t start1, index_t end1, index_t step1) {
+      for (index_t i = start0; i < end0; i += step0) {
+        for (index_t j = start1; j < end1; j += step1) {
+          int start_col = sum_indexes[j].first;
+          int end_col = sum_indexes[j].second;
+          T sum = 0;
+          for (int src_col = start_col; src_col < end_col; ++src_col) {
+            sum += input_data[i * input_dim + src_col];
+          }
+          output_data[i * output_dim + j] = sum;
         }
-        output_data[i * output_dim + j] = sum;
       }
-    }
+    }, 0, bh, 1, 0, output_dim, 1);
 
     return MaceStatus::MACE_SUCCESS;
   }

mace/ops/target_rms_norm.cc

@@ -35,7 +35,11 @@ class TargetRMSNormOp<DeviceType::CPU, T> : public Operation {
  public:
   explicit TargetRMSNormOp(OpConstructContext *context)
       : Operation(context),
-        target_rms_(Operation::GetOptionalArg<float>("target_rms", 1.0)) {}
+        target_rms_(Operation::GetOptionalArg<float>("target_rms", 1.0)),
+        add_log_stddev_(
+            static_cast<bool>(
+                Operation::GetOptionalArg<int>("add_log_stddev", 0))),
+        block_dim_(Operation::GetOptionalArg<int>("block_dim", 0)) {}
 
   // Calculate the square sum of an array
   float SquareSum(const float *data, const index_t data_len) {
@@ -67,6 +71,24 @@ class TargetRMSNormOp<DeviceType::CPU, T> : public Operation {
     return result;
   }
 
+
+  void NormalizePerRow(const float *data,
+                       const index_t data_len,
+                       float d_scale,
+                       bool add_log_stddev,
+                       float *out_data) {
+    float scale = SquareSum(data, data_len);
+    scale = scale / d_scale;
+    scale = scale  < 1.0e-6f ? 1.0e-6f : scale;
+    scale = static_cast<float>(1.0 / std::sqrt(scale));
+    for (index_t j = 0; j < data_len; ++j) {
+      out_data[j] = data[j] * scale;
+    }
+    if (add_log_stddev) {
+      out_data[data_len] = std::log(target_rms_) - std::log(scale);
+    }
+  }
+
   MaceStatus Run(OpContext *context) override {
     MACE_UNUSED(context);
     const Tensor *input = this->Input(0);
@@ -75,15 +97,18 @@ class TargetRMSNormOp<DeviceType::CPU, T> : public Operation {
     const index_t dim_size = input->dim_size();
     MACE_CHECK(dim_size >= 1,
                "TargetRMSNorm's input dim size should be >= 1.");
-    const index_t dim = input_shape[dim_size -1];
-    MACE_CHECK(dim > 0 && target_rms_ > 0,
+    const index_t input_dim = input_shape[dim_size -1];
+    MACE_CHECK(input_dim > 0 && target_rms_ > 0,
                "Both input dim and target rms should be greater than zero.");
     const index_t bh =
         std::accumulate(input_shape.begin(), input_shape.end() - 1, 1,
                         std::multiplies<index_t>());
-    const float d_scale = dim * target_rms_ * target_rms_;
-
-    MACE_RETURN_IF_ERROR(output->ResizeLike(input));
+    if (block_dim_ == 0) block_dim_ = static_cast<int>(input_dim);
+    const index_t output_dim = add_log_stddev_ ?
+                               input_dim + (input_dim / block_dim_) : input_dim;
+    std::vector<index_t> output_shape = input->shape();
+    output_shape[dim_size - 1] = output_dim;
+    MACE_RETURN_IF_ERROR(output->Resize(output_shape));
 
     Tensor::MappingGuard guard_input(input);
     Tensor::MappingGuard guard_output(output);
@@ -91,19 +116,37 @@ class TargetRMSNormOp<DeviceType::CPU, T> : public Operation {
     const float *input_data = input->data<float>();
     float *output_data = output->mutable_data<float>();
 
-    for (index_t i = 0; i < bh; ++i) {
-      float scale = SquareSum(input_data + i * dim, dim);
-      scale = static_cast<float>(1.0 / std::sqrt(scale / d_scale));
-      for (index_t j = 0; j < dim; ++j) {
-        output_data[i * dim + j] = input_data[i * dim + j] * scale;
-      }
+    index_t num_rows = bh;
+    index_t output_block_dim = add_log_stddev_ ? block_dim_ + 1 : block_dim_;
+
+    if (block_dim_ != input_dim) {
+      index_t num_blocks = input_dim / block_dim_;
+      num_rows *= num_blocks;
     }
+    const float d_scale = block_dim_ * target_rms_ * target_rms_;
+
+    utils::ThreadPool
+        &thread_pool = context->device()->cpu_runtime()->thread_pool();
+    thread_pool.Compute1D([=](index_t start0, index_t end0, index_t step0) {
+      for (index_t i = start0; i < end0; i += step0) {
+        const float *input_ptr = input_data + i * block_dim_;
+        float *out_ptr = output_data + i * output_block_dim;
+        NormalizePerRow(input_ptr,
+                        block_dim_,
+                        d_scale,
+                        add_log_stddev_,
+                        out_ptr);
+      }
+    }, 0, num_rows, 1);
+
 
     return MaceStatus::MACE_SUCCESS;
   }
 
  private:
   float target_rms_;
+  bool add_log_stddev_;
+  int block_dim_;
 };
 
 void RegisterTargetRMSNorm(OpRegistryBase *op_registry) {

mace/python/tools/converter_tool/base_converter.py

@@ -112,17 +112,20 @@ MaceSupportedOps = [
     'Conv2D',
     'Crop',
     'Deconv2D',
+    'Delay',
     'DepthToSpace',
     'DepthwiseConv2d',
     'DepthwiseDeconv2d',
     'Dequantize',
     'Eltwise',
     'ExpandDims',
+    'ExtractPooling',
     'Fill',
     'FullyConnected',
     'Gather',
     'Identity',
     'InferConv2dShape',
+    'KaldiBatchNorm',
     'LocalResponseNorm',
     'LSTMCell',
     'LstmNonlinear',

mace/python/tools/converter_tool/onnx_converter.py

@@ -41,6 +41,15 @@ from numbers import Number
 
 IS_PYTHON3 = sys.version_info > (3,)
 
+
+class AttributeType(Enum):
+    INT = 100
+    FLOAT = 101
+    INTS = 102
+    FLOATS = 103
+    BOOL = 104
+
+
 OnnxSupportedOps = [
     'Abs',
     # 'Acos',
@@ -57,6 +66,7 @@ OnnxSupportedOps = [
     # 'Atanh',
     'AveragePool',
     'BatchNormalization',
+    'BatchNorm',
     'Cast',
     # 'Ceil',
     # 'Clip',
@@ -72,11 +82,12 @@ OnnxSupportedOps = [
     'DimRange',
     'Div',
     'Dropout',
-    'DynamicLstmCell',
+    'DynamicLSTM',
     'Elu',
     'Equal',
     # 'Exp',
     # 'Expand',
+    'ExtractPooling',
     # 'EyeLike',
     # 'Flatten',
     # 'Floor',
@@ -91,7 +102,7 @@ OnnxSupportedOps = [
     # 'Hardmax',
     'Identity',
     # 'If',
-    # 'IfDefined',
+    'IfDefined',
     'ImageScaler',
     # 'InstanceNormalization',
     # 'LRN',
@@ -318,6 +329,7 @@ class OnnxConverter(base_converter.ConverterInterface):
             OnnxOpType.ArgMin.name: self.convert_argmax,
             OnnxOpType.AveragePool.name: self.convert_pooling,
             OnnxOpType.BatchNormalization.name: self.convert_fused_batchnorm,
+            OnnxOpType.BatchNorm.name: self.convert_fused_batchnorm,
             OnnxOpType.Cast.name: self.convert_cast,
             OnnxOpType.Concat.name: self.convert_concat,
             OnnxOpType.Conv.name: self.convert_conv2d,
@@ -327,16 +339,18 @@ class OnnxConverter(base_converter.ConverterInterface):
             OnnxOpType.DimRange.name: self.convert_dim_range,
             OnnxOpType.Div.name: self.convert_eltwise,
             OnnxOpType.Equal.name: self.convert_eltwise,
+            OnnxOpType.ExtractPooling.name: self.convert_extract_pooling,
             OnnxOpType.Gather.name: self.convert_gather,
             OnnxOpType.Gemm.name: self.convert_gemm,
             OnnxOpType.GlobalAveragePool.name: self.convert_reduce,
             OnnxOpType.GlobalMaxPool.name: self.convert_reduce,
             OnnxOpType.Identity.name: self.convert_identity,
+            OnnxOpType.IfDefined.name: self.convert_ifdefined,
             OnnxOpType.ImageScaler.name: self.convert_imagescaler,
             OnnxOpType.LeakyRelu.name: self.convert_activation,
             OnnxOpType.LogSoftmax.name: self.convert_softmax,
             OnnxOpType.LstmNonlinear.name: self.convert_lstm_nonlinear,
-            OnnxOpType.DynamicLstmCell.name: self.convert_dynamic_lstm,
+            OnnxOpType.DynamicLSTM.name: self.convert_dynamic_lstm,
             OnnxOpType.Max.name: self.convert_eltwise,
             OnnxOpType.MaxPool.name: self.convert_pooling,
             OnnxOpType.MatMul.name: self.convert_matmul,
@@ -378,6 +392,8 @@ class OnnxConverter(base_converter.ConverterInterface):
         ir_version = onnx_model.ir_version
         opset_imp = onnx_model.opset_import
 
+        self._isKaldi = False
+
         polish_available = True
         print("onnx model IR version: ", ir_version)
         for imp in opset_imp:
@@ -387,6 +403,7 @@ class OnnxConverter(base_converter.ConverterInterface):
             if 'kaldi2onnx' in domain:
                 polish_available = False
                 self._data_format = DataFormat.DF_NONE
+                self._isKaldi = True
         if polish_available:
             onnx_model = onnx.utils.polish_model(onnx_model)
 
@@ -643,10 +660,10 @@ class OnnxConverter(base_converter.ConverterInterface):
             mace_check(axis_value == 1 or axis_value == -3,
                        "only support concat at channel dimension")
         elif node.op_type == OnnxOpType.Append.name:
-            axis_value = 1
+            axis_value = -1
         axis_arg = op.arg.add()
         axis_arg.name = MaceKeyword.mace_axis_str
-        axis_arg.i = 4 + axis_value if axis_value < 0 else axis_value
+        axis_arg.i = axis_value
 
     def convert_conv2d(self, node):
         op = self.convert_general_op(node)
@@ -772,15 +789,15 @@ class OnnxConverter(base_converter.ConverterInterface):
         op = self.convert_general_op(node)
         op.type = MaceOp.DynamicLSTM.name
 
-        if 'delay_a' in node.attrs:
-            prev_out_delay = node.attrs['delay_a']
+        if 'prev_out_delay' in node.attrs:
+            prev_out_delay = node.attrs['prev_out_delay']
             mace_check(prev_out_delay < 0,
                        "dynamic's prev_out_delay should <= 0.")
             prev_out_delay_arg = op.arg.add()
             prev_out_delay_arg.name = 'prev_out_delay'
             prev_out_delay_arg.i = prev_out_delay
-        if 'delay_b' in node.attrs:
-            prev_cell_delay = node.attrs['delay_b']
+        if 'prev_cell_delay' in node.attrs:
+            prev_cell_delay = node.attrs['prev_cell_delay']
             mace_check(prev_cell_delay < 0,
                        "dynamic's prev_cell_delay should < 0.")
             prev_cell_delay_arg = op.arg.add()
@@ -788,20 +805,20 @@ class OnnxConverter(base_converter.ConverterInterface):
             prev_cell_delay_arg.i = prev_cell_delay
         if 'prev_out_offset' in node.attrs:
             prev_out_offset = node.attrs['prev_out_offset']
-            mace_check(pre_out_offset >= 0,
+            mace_check(prev_out_offset >= 0,
                        "dynamic's prev_out_offset should >= 0.")
             prev_out_offset_arg = op.arg.add()
             prev_out_offset_arg.name = 'prev_out_offset'
             prev_out_offset_arg.i = prev_out_offset
-        if 'prev_a_dim' in node.attrs:
-            prev_out_dim = node.attrs['prev_a_dim']
+        if 'prev_out_dim' in node.attrs:
+            prev_out_dim = node.attrs['prev_out_dim']
             mace_check(prev_out_dim > 0,
                        "dynamic's prev_out_dim should > 0.")
             prev_out_dim_arg = op.arg.add()
             prev_out_dim_arg.name = 'prev_out_dim'
             prev_out_dim_arg.i = prev_out_dim
-        if 'prev_b_dim' in node.attrs:
-            prev_cell_dim = node.attrs['prev_b_dim']
+        if 'prev_cell_dim' in node.attrs:
+            prev_cell_dim = node.attrs['prev_cell_dim']
             mace_check(prev_cell_dim > 0,
                        "dynamic's prev_cell_dim should > 0.")
             prev_cell_dim_arg = op.arg.add()
@@ -844,11 +861,154 @@ class OnnxConverter(base_converter.ConverterInterface):
             value_arg.name = MaceKeyword.mace_scalar_input_str
             value_arg.f = value
 
+    @staticmethod
+    def copy_node_attr(op, node, attr_name, dtype=AttributeType.INT,
+                       default=None):
+        if attr_name in node.attrs or default is not None:
+            if attr_name in node.attrs:
+                value = node.attrs[attr_name]
+            else:
+                value = default
+            new_arg = op.arg.add()
+            new_arg.name = attr_name
+            if dtype == AttributeType.INT:
+                new_arg.i = int(value)
+            elif dtype == AttributeType.FLOAT:
+                new_arg.f = float(value)
+            elif dtype == AttributeType.INTS:
+                new_arg.ints.extend(value)
+            elif dtype == AttributeType.FLOATS:
+                new_arg.floats.extend(value)
+            return value
+        else:
+            return default
+
+    def convert_extract_pooling(self, node):
+        op = self.convert_general_op(node)
+        op.type = MaceOp.ExtractPooling.name
+
+        self.copy_node_attr(op, node, 'include_variance', AttributeType.INT)
+        self.copy_node_attr(op, node, 'num_log_count', AttributeType.INT)
+        self.copy_node_attr(op, node, 'variance_floor', AttributeType.FLOAT)
+        self.copy_node_attr(op, node, 'input_time_range', AttributeType.INTS)
+        self.copy_node_attr(op, node, 'input_indexes', AttributeType.INTS)
+
+        if 'output_time_range' in node.attrs:
+            output_time_range = node.attrs['output_time_range']
+            mace_check(len(output_time_range) == 2,
+                       "output time range should have two values.")
+            out_start_index = output_time_range[0]
+            out_end_index = output_time_range[1]
+        else:
+            mace_check('start_index' in node.attrs and
+                       'end_index' in node.attrs,
+                       "'start_index' and 'end_index'"
+                       " are required in ExtractPooling.")
+            out_start_index = node.attrs['start_index']
+            out_end_index = node.attrs['end_index']
+            output_time_range = [out_start_index, out_end_index]
+
+        output_time_range_arg = op.arg.add()
+        output_time_range_arg.name = 'output_time_range'
+        output_time_range_arg.ints.extend(output_time_range)
+
+        mace_check('modulus' in node.attrs,
+                   "'modulus' is required in ExtractPooling.")
+        mace_check('output_indexes' in node.attrs,
+                   "'output_indexes' is required in ExtractPooling.")
+        mace_check('counts' in node.attrs,
+                   "'counts' is required in ExtractPooling.")
+        mace_check('forward_indexes' in node.attrs,
+                   "'forward_indexes' is required in ExtractPooling.")
+        modulus = node.attrs['modulus']
+        output_indexes = node.attrs['output_indexes']
+        counts = node.attrs['counts']
+        forward_indexes = node.attrs['forward_indexes']
+
+        mace_check(len(counts) == len(output_indexes) and
+                   len(forward_indexes) == 2 * len(output_indexes),
+                   "output_indexes length:%s "
+                   "counts length:%s "
+                   "forward_indexes length:%s"
+                   % (len(output_indexes), len(counts), len(forward_indexes)))
+
+        new_output_indexes = []
+        new_forward_indexes = []
+        new_counts = []
+        for i in range(len(output_indexes)):
+            if output_indexes[i] + modulus > out_start_index and\
+                    output_indexes[i] <= out_end_index:
+                new_output_indexes.append(output_indexes[i])
+                new_counts.append(counts[i])
+                new_forward_indexes.append(forward_indexes[2 * i])
+                new_forward_indexes.append(forward_indexes[2 * i + 1])
+        modulus_arg = op.arg.add()
+        modulus_arg.name = 'modulus'
+        modulus_arg.i = modulus
+
+        counts_arg = op.arg.add()
+        counts_arg.name = 'counts'
+        counts_arg.floats.extend(new_counts)
+
+        forward_indexes_arg = op.arg.add()
+        forward_indexes_arg.name = 'forward_indexes'
+        forward_indexes_arg.ints.extend(new_forward_indexes)
+
+        output_indexes_arg = op.arg.add()
+        output_indexes_arg.name = 'output_indexes'
+        output_indexes_arg.ints.extend(new_output_indexes)
+
     def convert_flatten(self, node):
         op = self.convert_general_op(node)
         op.type = MaceOp.Reshape.name
 
+    def convert_kaldi_batchnorm(self, node):
+        op = self.convert_general_op(node)
+        op.type = MaceOp.KaldiBatchNorm.name
+        dim = self.copy_node_attr(op, node,
+                                  'dim', AttributeType.INT, -1)
+        block_dim = self.copy_node_attr(op, node,
+                                        'block_dim',
+                                        AttributeType.INT, -1)
+        epsilon = self.copy_node_attr(op, node,
+                                      'epsilon',
+                                      AttributeType.FLOAT, 1e-3)
+        target_rms = self.copy_node_attr(op, node,
+                                         'target_rms',
+                                         AttributeType.FLOAT, 1.0)
+        test_mode = self.copy_node_attr(op, node,
+                                        'test_mode',
+                                        AttributeType.INT, 0)
+        mace_check(block_dim > 0 and
+                   dim % block_dim == 0 and
+                   epsilon > 0 and
+                   target_rms > 0, "attributes invalid.")
+
+        if test_mode > 0:
+            mace_check(len(node.inputs) == 3,
+                       "Kaldi's BatchNorm should have 3 inputs.")
+            stats_mean = np.array(self._consts[node.inputs[1]].float_data)
+            stats_var = np.array(self._consts[node.inputs[2]].float_data)
+            offset_value = -1.0 * stats_mean
+            scale_value = stats_var
+            scale_value[scale_value < 0] = 0
+            scale_value = np.power(scale_value + epsilon, -0.5) * target_rms
+            offset_value = offset_value * scale_value
+            scale_name = node.name + '_scale'
+            offset_name = node.name + '_offset'
+            self.add_tensor(scale_name, scale_value.shape,
+                            mace_pb2.DT_FLOAT, scale_value)
+            self.add_tensor(offset_name, offset_value.shape,
+                            mace_pb2.DT_FLOAT, offset_value)
+            del op.input[1:]
+            op.input.extend([scale_name, offset_name])
+            del op.output[1:]
+            del op.output_shape[1:]
+
     def convert_fused_batchnorm(self, node):
+        if self._isKaldi:
+            self.convert_kaldi_batchnorm(node)
+            return
         op = self.convert_general_op(node)
         op.type = MaceOp.BatchNorm.name
 
@@ -946,6 +1106,21 @@ class OnnxConverter(base_converter.ConverterInterface):
         op = self.convert_general_op(node)
         op.type = MaceOp.Identity.name
 
+    def convert_ifdefined(self, node):
+        op = self.convert_general_op(node)
+        if 'offset' in node.attrs:
+            offset = node.attrs['offset']
+        else:
+            offset = 0
+        mace_check(offset <= 0, "IfDefined's offset should be <= 0.")
+        if offset == 0:
+            op.type = MaceOp.Identity.name
+        else:
+            op.type = MaceOp.Delay.name
+        offset_arg = op.arg.add()
+        offset_arg.name = 'offset'
+        offset_arg.i = node.attrs['offset']
+
     def convert_imagescaler(self, node):
         op = self.convert_general_op(node)
         op.type = MaceOp.BatchNorm.name
@@ -1100,7 +1275,6 @@ class OnnxConverter(base_converter.ConverterInterface):
     def convert_softmax(self, node):
         op = self.convert_general_op(node)
         op.type = MaceOp.Softmax.name
-        # TODO: add logsoftmax in softmax op
         if node.op_type == OnnxOpType.LogSoftmax.name:
             use_log_arg = op.arg.add()
             use_log_arg.name = 'use_log'
@@ -1164,11 +1338,11 @@ class OnnxConverter(base_converter.ConverterInterface):
         op = self.convert_general_op(node)
         op.type = MaceOp.TargetRMSNorm.name
 
-        if 'target_rms' in node.attrs:
-            value = node.attrs['target_rms']
-            target_rms_arg = op.arg.add()
-            target_rms_arg.name = 'target_rms'
-            target_rms_arg.f = value
+        self.copy_node_attr(op, node, 'target_rms', AttributeType.FLOAT)
+        self.copy_node_attr(op, node, 'add_log_stddev', AttributeType.INT,
+                            default=0)
+        self.copy_node_attr(op, node, 'block_dim', AttributeType.INT,
+                            default=0)
 
     def convert_transpose(self, node):
         op = self.convert_general_op(node)

mace/python/tools/converter_tool/transformer.py

@@ -1130,7 +1130,7 @@ class Transformer(base_converter.ConverterInterface):
                 rhs = op.input[1]
                 if rhs in self._consts and len(self._consts[rhs].dims) == 2:
                     arg = ConverterUtil.get_arg(op, MaceKeyword.mace_transpose_b_str)  # noqa
-                    six.print_("Transpose matmul weight %s" % rhs)
+                    # six.print_("Transpose matmul weight %s" % rhs)
                     if arg is None:
                         arg = op.arg.add()
                         arg.name = MaceKeyword.mace_transpose_b_str
@@ -1143,7 +1143,8 @@ class Transformer(base_converter.ConverterInterface):
                         filter.float_data[:] = filter_data.flat
                         filter.dims[:] = filter_data.shape
                         arg.i = 1
-                        six.print_('transpose matmul weight')
+                        six.print_('Transpose matmul weight to shape:',
+                                   filter.dims)
 
     def transpose_filters(self):
         net = self._model