add int8 bn mkldnn implementation and test

cpp-package/scripts/OpWrapperGenerator.py

@@ -93,6 +93,7 @@ class Arg:
         'int (non-negative)': 'uint32_t',\
         'long (non-negative)': 'uint64_t',\
         'int or None':'dmlc::optional<int>',\
+        'float or None':'dmlc::optional<float>',\
         'long':'int64_t',\
         'double':'double',\
         'double or None':'dmlc::optional<double>',\

src/operator/nn/batch_norm-inl.h

@@ -53,11 +53,19 @@ enum BatchNormOpOutputs {kOut, kMean, kVar};  // req, out_data
 enum BatchNormOpResource {kTempSpace};
 enum BatchNormOpAuxiliary {kMovingMean, kMovingVar};  // aux_states
 
-/*! \brief Default channel axis if none specified int he params */
+/*! \brief Default channel axis if none specified in the params */
 constexpr int DEFAULT_AXIS = 1;
 }  // namespace batchnorm
 
 /*! \brief Parameters for BatchNorm operator */
+namespace quantized_batchnorm {
+enum QuantizedBatchNormOpInputs {kData, kGamma, kBeta, kInMovingMean,
+  kInMovingVar, kDataMin, kDataMax};
+enum QuantizedBatchNormOutputs {kOut, kOutMin, kOutMax};
+enum QuantizedBatchNormOpAuxiliary {kMovingMean, kMovingVar};
+}  // quantized_batchnorm
+
+/*! \brief Parameters for BatchNoram operator */
 struct BatchNormParam : public dmlc::Parameter<BatchNormParam> {
   double eps;
   float momentum;
@@ -66,6 +74,10 @@ struct BatchNormParam : public dmlc::Parameter<BatchNormParam> {
   bool output_mean_var;
   int axis;
   bool cudnn_off;
+
+  dmlc::optional<float> min_calib_range;  // min float value calculated from calibration dataset
+  dmlc::optional<float> max_calib_range;  // max float value calculated from calibration dataset
+
   DMLC_DECLARE_PARAMETER(BatchNormParam) {
     DMLC_DECLARE_FIELD(eps).set_default(1e-3f)
     .describe("Epsilon to prevent div 0. "
@@ -81,19 +93,37 @@ struct BatchNormParam : public dmlc::Parameter<BatchNormParam> {
     DMLC_DECLARE_FIELD(output_mean_var).set_default(false)
     .describe("Output the mean and inverse std ");
     DMLC_DECLARE_FIELD(axis).set_default(mxnet::op::batchnorm::DEFAULT_AXIS)
-      .describe("Specify which shape axis the channel is specified");
+    .describe("Specify which shape axis the channel is specified");
     DMLC_DECLARE_FIELD(cudnn_off).set_default(false)
-      .describe("Do not select CUDNN operator, if available");
+    .describe("Do not select CUDNN operator, if available");
+    DMLC_DECLARE_FIELD(min_calib_range)
+    .set_default(dmlc::optional<float>())
+    .describe("The minimum scalar value in the form of float32 obtained "
+              "through calibration. If present, it will be used to by "
+              "quantized batch norm op to calculate primitive scale."
+              "Note: this calib_range is to calib bn output.");
+    DMLC_DECLARE_FIELD(max_calib_range)
+    .set_default(dmlc::optional<float>())
+    .describe("The maximum scalar value in the form of float32 obtained "
+              "through calibration. If present, it will be used to by "
+              "quantized batch norm op to calculate primitive scale."
+              "Note: this calib_range is to calib bn output.");
   }
 
-  bool operator==(const BatchNormParam& other) const {
-    return this->eps == other.eps &&
-           this->momentum == other.momentum &&
-           this->fix_gamma == other.fix_gamma &&
-           this->use_global_stats == other.use_global_stats &&
-           this->output_mean_var == other.output_mean_var &&
-           this->axis == other.axis &&
-           this->cudnn_off == other.cudnn_off;
+  bool operator==(const BatchNormParam &other) const {
+    bool flag = this->eps == other.eps && this->momentum == other.momentum &&
+                this->fix_gamma == other.fix_gamma &&
+                this->use_global_stats == other.use_global_stats &&
+                this->output_mean_var == other.output_mean_var && this->axis == other.axis &&
+                this->cudnn_off == other.cudnn_off &&
+                this->min_calib_range.has_value() == other.min_calib_range.has_value() &&
+                this->max_calib_range.has_value() == other.max_calib_range.has_value();
+    if (this->min_calib_range.has_value() && other.min_calib_range.has_value() &&
+        this->max_calib_range.has_value() && other.max_calib_range.has_value()) {
+      flag = flag && this->min_calib_range.value() == other.min_calib_range.value() &&
+             this->max_calib_range.value() == other.max_calib_range.value();
+    }
+    return flag;
   }
 };
 

src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h

@@ -132,8 +132,8 @@ class MKLDNNBNForward {
     return *var_m;
   }
 
-  void SetDataHandle(const NDArray &data, const NDArray &mean,
-                     const NDArray &var, const mkldnn::memory &out) {
+  void SetDataHandle(const NDArray &data, const mkldnn::memory *mean,
+                     const mkldnn::memory *var, const mkldnn::memory *out) {
     auto _data = data.GetMKLDNNData();
     if (data_m) {
       data_m->set_data_handle(_data->get_data_handle());
@@ -142,24 +142,22 @@ class MKLDNNBNForward {
                                       _data->get_data_handle()));
     }
     if (out_m) {
-      out_m->set_data_handle(out.get_data_handle());
+      out_m->set_data_handle(out->get_data_handle());
     } else {
-      out_m.reset(new mkldnn::memory(out.get_primitive_desc(),
-                                     out.get_data_handle()));
+      out_m.reset(new mkldnn::memory(out->get_primitive_desc(),
+                                     out->get_data_handle()));
     }
-    auto mean_ptr = mean.data().dptr_;
     if (mean_m) {
-      mean_m->set_data_handle(mean_ptr);
+      mean_m->set_data_handle(mean->get_data_handle());
     } else {
-      mean_m.reset(new mkldnn::memory(pd.mean_primitive_desc(),
-                                      mean_ptr));
+      mean_m.reset(new mkldnn::memory(mean->get_primitive_desc(),
+                                      mean->get_data_handle()));
     }
-    auto var_ptr = var.data().dptr_;
     if (var_m) {
-      var_m->set_data_handle(var_ptr);
+      var_m->set_data_handle(var->get_data_handle());
     } else {
-      var_m.reset(new mkldnn::memory(pd.variance_primitive_desc(),
-                                     var_ptr));
+      var_m.reset(new mkldnn::memory(var->get_primitive_desc(),
+                                     var->get_data_handle()));
     }
 
     if (fwd == nullptr) {
@@ -175,6 +173,11 @@ class MKLDNNBNForward {
     }
   }
 
+  void SetDataHandle(const NDArray &data, const NDArray &mean,
+                     const NDArray &var, const mkldnn::memory &out) {
+    SetDataHandle(data, mean.GetMKLDNNData(), var.GetMKLDNNData(), &out);
+  }
+
   const mkldnn::batch_normalization_forward &GetFwd() const {
     return *fwd;
   }

src/operator/quantization/mkldnn/mkldnn_quantized_batch_norm.cc

@@ -0,0 +1,123 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ * \file mkldnn_quantized_batch_norm.cc
+ * \brief
+ * \author Yixin Bao
+*/
+
+#if MXNET_USE_MKLDNN == 1
+#include "../../nn/mkldnn/mkldnn_batch_norm-inl.h"
+#include "../quantization_utils.h"
+
+namespace mxnet {
+namespace op {
+
+static void MKLDNNQuantizedBatchNormForward(const nnvm::NodeAttrs &attrs, const OpContext &ctx,
+                                            const std::vector<NDArray> &in_data,
+                                            const std::vector<OpReqType> &req,
+                                            const std::vector<NDArray> &outputs) {
+  CHECK_EQ(in_data.size(), 7U);
+  CHECK_EQ(outputs.size(), 3U);
+
+  TmpMemMgr::Get()->Init(ctx.requested[batchnorm::kTempSpace]);
+  const BatchNormParam &param = nnvm::get<BatchNormParam>(attrs.parsed);
+  const NDArray &data = in_data[quantized_batchnorm::kData];
+  const size_t channelAxis = static_cast<size_t>(
+      param.axis < 0 ? static_cast<int>(data.shape().ndim()) + param.axis : param.axis);
+  const int channel_count = data.shape()[channelAxis];
+  const float min_data = in_data[quantized_batchnorm::kDataMin].data().dptr<float>()[0];
+  const float max_data = in_data[quantized_batchnorm::kDataMax].data().dptr<float>()[0];
+  const float max_abs_data = std::max(std::abs(min_data), std::abs(max_data));
+
+  float *min_output_ptr = outputs[quantized_batchnorm::kOutMin].data().dptr<float>();
+  float *max_output_ptr = outputs[quantized_batchnorm::kOutMax].data().dptr<float>();
+  if (param.min_calib_range.has_value() && param.max_calib_range.has_value()) {
+    *max_output_ptr = param.max_calib_range.value();
+    *min_output_ptr = param.min_calib_range.value();
+  } else {
+    LOG(FATAL) << "min_calib_range or max_calib_range is not available. Quantized BN currently "
+                  "don't support calib_mode=None";
+  }
+  const float max_abs_output = std::max(std::abs(*min_output_ptr), std::abs(*max_output_ptr));
+
+  unsigned flags = mkldnn::use_global_stats | mkldnn::use_scale_shift;
+  auto &fwd = GetBNForward<float>(param, ctx, data, flags);
+  const mkldnn::memory &weight_mem = fwd.GetWeight();
+  CHECK_EQ(weight_mem.get_primitive_desc().get_size(), channel_count * sizeof(float) * 2);
+  float *weight_buf = reinterpret_cast<float *>(weight_mem.get_data_handle());
+
+  float *gamma_ptr = in_data[quantized_batchnorm::kGamma].data().dptr<float>();
+  float *beta_ptr = in_data[quantized_batchnorm::kBeta].data().dptr<float>();
+
+  const NDArray &moving_mean = in_data[quantized_batchnorm::kInMovingMean];
+  const NDArray &moving_var = in_data[quantized_batchnorm::kInMovingVar];
+  float *moving_mean_ptr = moving_mean.data().dptr<float>();
+  float *moving_var_ptr = moving_var.data().dptr<float>();
+
+  // rescale gamma and beta, to make mean=0 and var=1
+  auto rescaled_mean_mem =
+      TmpMemMgr::Get()->Alloc(moving_mean.GetMKLDNNData()->get_primitive_desc());
+  auto rescaled_var_mem = TmpMemMgr::Get()->Alloc(moving_var.GetMKLDNNData()->get_primitive_desc());
+  float *rescaled_mean_ptr = reinterpret_cast<float *>(rescaled_mean_mem->get_data_handle());
+  float *rescaled_var_ptr = reinterpret_cast<float *>(rescaled_var_mem->get_data_handle());
+
+#pragma omp parallel for num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
+  for (int channel = 0; channel < channel_count; ++channel) {
+    float invstd = 1.0 / std::sqrt(moving_var_ptr[channel] + param.eps);
+    weight_buf[channel] = gamma_ptr[channel] * invstd * max_abs_data / max_abs_output;
+    weight_buf[channel_count + channel] =
+        (beta_ptr[channel] - moving_mean_ptr[channel] * gamma_ptr[channel] * invstd) * kInt8Range /
+        max_abs_output;
+    rescaled_mean_ptr[channel] = 0.0f;
+    rescaled_var_ptr[channel] = 1.0f;
+  }
+
+  auto out_mem = CreateMKLDNNMem(outputs[batchnorm::kOut],
+      fwd.GetPd().dst_primitive_desc(), req[batchnorm::kOut], &data);
+  fwd.SetDataHandle(data, rescaled_mean_mem, rescaled_var_mem, out_mem.second);
+
+  MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
+  MKLDNNStream::Get()->Submit();
+}
+
+inline static bool QuantizedBatchNormStorageType(const nnvm::NodeAttrs &attrs, const int dev_mask,
+                                                 DispatchMode *dispatch_mode,
+                                                 std::vector<int> *in_attrs,
+                                                 std::vector<int> *out_attrs) {
+  bool dispatched = false;
+  if (!dispatched) {
+    dispatched = MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs, out_attrs);
+  }
+  return dispatched;
+}
+
+NNVM_REGISTER_OP(_contrib_quantized_batch_norm)
+.set_attr<FInferStorageType>("FInferStorageType", QuantizedBatchNormStorageType)
+.set_attr<FComputeEx>("FComputeEx<cpu>", MKLDNNQuantizedBatchNormForward)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
+  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<bool>("TIsMKLDNN", true);
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_USE_MKLDNN == 1

src/operator/quantization/quantize_graph_pass.cc

@@ -37,6 +37,19 @@ using nnvm::NodePtr;
 using nnvm::NodeEntry;
 using nnvm::Graph;
 
+inline size_t GetNumOutputs(NodePtr node) {
+  // Get NumOutputs, check if current node has NumVisibleOutputs function, if yes, return
+  // num_visible_outputs
+  size_t num_outputs = node->num_outputs();
+  static const auto& num_visible_outputs_attr =
+      nnvm::Op::GetAttr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs");
+  auto num_visible_output_func = num_visible_outputs_attr.get(node->op(), nullptr);
+  if (num_visible_output_func != nullptr) {
+    num_outputs = num_visible_output_func(node->attrs);
+  }
+  return num_outputs;
+}
+
 NodePtr CreateNode(std::string op_name, std::string node_name) {
   NodePtr node = Node::Create();
   node->attrs.name = node_name;
@@ -223,7 +236,7 @@ Graph QuantizeGraph(Graph &&src) {
           // calculate min/max index from mirror node) based on assumption that
           // there is only 1min and 1max output from mirror node (which is
           // currently true)
-          size_t num_outputs = mirror_node->num_outputs() - 2;
+          size_t num_outputs = GetNumOutputs(mirror_node) - 2;
           min_index = num_outputs + 2 * e.index;
           max_index = num_outputs + 2 * e.index + 1;
         } else {
@@ -276,7 +289,7 @@ Graph QuantizeGraph(Graph &&src) {
           // calculate min/max index from mirror node) based on assumption that
           // there is only 1 min and 1 max output from mirror node (which is
           // currently true)
-          size_t num_outputs = mirror_node->num_outputs() - 2;
+          size_t num_outputs = GetNumOutputs(mirror_node) - 2;
           uint32_t min_index = num_outputs + 2 * e.index;
           uint32_t max_index = num_outputs + 2 * e.index + 1;
           NodePtr dequantize_node = CreateNode("_contrib_dequantize",
@@ -309,7 +322,7 @@ Graph QuantizeGraph(Graph &&src) {
       // calculate min/max index from mirror node) based on assumption that
       // there is only 1 min and 1 max output from mirror node (which is
       // currently true)
-      size_t num_outputs = e.node->num_outputs();
+      size_t num_outputs = GetNumOutputs(e.node);
       uint32_t min_index = num_outputs + 2 * e.index;
       uint32_t max_index = num_outputs + 2 * e.index + 1;
 
@@ -403,6 +416,29 @@ Graph SetCalibTableToQuantizedGraph(Graph&& g) {
                        << "` has negative input, consider use `auto` or `int8` as out_type";
         }
       }
+    } else if (node->op() == Op::Get("_contrib_quantized_batch_norm")) {
+      auto quantized_op_idx = node->inputs[0].index;
+      const std::string prefix = "quantized_";
+      std::string out_data_name = node->attrs.name.substr(prefix.size());
+      if (node->op()) {
+        auto list_output_names_func = flist_outputs.get(node->op(), nullptr);
+        // We want to get the pre-calculated min_range and max_range from the calibration table for
+        // out_data. Here we create the output data name same as its constructed in
+        // GraphExecutor::ExecuteMonCallback.
+        if (list_output_names_func != nullptr) {
+          std::vector<std::string> names = list_output_names_func(node->attrs);
+          out_data_name += "_" + names[quantized_op_idx];
+        } else {
+          out_data_name += "_" + std::to_string(quantized_op_idx);
+        }
+      }
+
+      const auto calib_table_iter = calib_table.find(out_data_name);
+      if (calib_table_iter != calib_table.end()) {
+        node->attrs.dict["min_calib_range"] = std::to_string(calib_table_iter->second.first);
+        node->attrs.dict["max_calib_range"] = std::to_string(calib_table_iter->second.second);
+        node->op()->attr_parser(&(node->attrs));
+      }
     }
   });
   return g;