[GitHub] lihaofd commented on a change in pull request #12922: Support Quantized Fully Connected by INT8 GEMM

[GitBox <gi...@apache.org>]

lihaofd commented on a change in pull request #12922: Support Quantized Fully Connected by INT8 GEMM
URL: https://github.com/apache/incubator-mxnet/pull/12922#discussion_r230545552
 
 

 ##########
 File path: src/operator/quantization/quantized_fully_connected.cc
 ##########
 @@ -79,6 +85,145 @@ bool QuantizedFullyConnectedType(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
+bool QuantizedFullyConnectedStorageType(const nnvm::NodeAttrs& attrs,
+                                        const int dev_mask,
+                                        DispatchMode* dispatch_mode,
+                                        std::vector<int> *in_attrs,
+                                        std::vector<int> *out_attrs) {
+  *dispatch_mode = DispatchMode::kFCompute;
+  if (dev_mask == mshadow::cpu::kDevMask) {
+    *dispatch_mode = DispatchMode::kFComputeEx;
+  }
+  for (size_t i = 0; i < out_attrs->size(); i++) {
+    STORAGE_TYPE_ASSIGN_CHECK(*out_attrs, i, kDefaultStorage);
+    if (common::stype_string((*out_attrs)[i]).compare("unknown") == 0) {
+      return false;
+    }
+  }
+  for (size_t i = 0; i < in_attrs->size(); i++) {
+    STORAGE_TYPE_ASSIGN_CHECK(*in_attrs, i, kDefaultStorage);
+    if (common::stype_string((*in_attrs)[i]).compare("unknown") == 0) {
+      return false;
+    }
+  }
+
+  return true;
+}
+
+struct QuantizedSumInitKernelWithBias {
+  //  init sum data with bias for matrix b (n)
+  MSHADOW_XINLINE static void Map(int i, int32_t *out,
+                                  const int8_t *bias, const float *min_out,
+                                  const float *max_out, const float *min_bias,
+                                  const float *max_bias) {
+    typedef int32_t T1;
+    typedef int8_t  T2;
+    using mshadow::red::limits::MinValue;
+    using mshadow::red::limits::MaxValue;
+    float float_for_one_out_quant  =
+      MaxAbs(*min_out, *max_out) / static_cast<double>(MaxValue<T1>());
+    float float_for_one_bias_quant =
+      MaxAbs(*min_bias, *max_bias) / static_cast<double>(MaxValue<T2>());
+    if (float_for_one_out_quant != 0) {
+      out[i] = bias[i] * float_for_one_bias_quant /
+               float_for_one_out_quant;
+    } else {
+      LOG(INFO) << "WARNING: QuantizedBiasAddKernel float_for_one_out_quant is 0 !";
+      out[i] = 0;
+    }
+  }
+};
+template<typename SrcType>
+void MKLDNNQuantizedFullyConnectedForward(const nnvm::NodeAttrs& attrs,
 
 Review comment:
   fixed

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