anko-intel commented on a change in pull request #20821:
URL: https://github.com/apache/incubator-mxnet/pull/20821#discussion_r790530664
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File path: src/operator/subgraph/dnnl/dnnl_fc.cc
##########
@@ -72,83 +75,123 @@ class SgDNNLFCOp {
std::shared_ptr<dnnl::memory> cached_out_mem_;
NDArray cached_weight_;
NDArray cached_bias_;
- float cached_min_data_;
- float cached_max_data_;
- float cached_min_weight_;
- float cached_max_weight_;
- float cached_min_bias_;
- float cached_max_bias_;
+ float cached_data_min_;
+ float cached_data_max_;
+ float cached_weight_min_;
+ float cached_weight_max_;
+ float cached_sum_min_;
+ float cached_sum_max_;
+ float cached_bias_min_;
+ float cached_bias_max_;
size_t weight_ver_;
size_t bias_ver_;
- float cached_min_output_;
- float cached_max_output_;
+ float cached_output_min_;
+ float cached_output_max_;
float data_scale_{0.0f};
std::vector<float> weight_scales_;
- size_t total_num_inputs_;
- size_t total_num_outputs_;
};
void SgDNNLFCOp::Forward(const OpContext& ctx,
const std::vector<NDArray>& in_data,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& out_data) {
- auto& dnnl_param = full_param_.dnnl_param;
- auto& default_param = full_param_.default_param;
- bool has_bias = !default_param.no_bias;
- size_t base_num_inputs = has_bias ? 3 : 2;
- size_t base_num_outputs = 1;
-
- float min_data = 0.0f;
- float max_data = 0.0f;
- float min_weight = 0.0f;
- float max_weight = 0.0f;
- float min_bias = 0.0f;
- float max_bias = 0.0f;
-
- if (!initialized_) {
- if (dnnl_param.channel_wise_quantize.has_value() &&
dnnl_param.channel_wise_quantize) {
- channel_wise_runtime_ = true;
+ auto& dnnl_param = full_param_.dnnl_param;
+ auto& default_param = full_param_.default_param;
+ const bool has_bias = !default_param.no_bias;
+ const bool quantized = dnnl_param.quantized;
+ const bool out_quantized = dnnl_param.quantized &&
!dnnl_param.enable_float_output;
+ const bool channel_wise = quantized &&
dnnl_param.channel_wise_quantize.has_value() &&
+ dnnl_param.channel_wise_quantize.value();
+
+ const FCInputIndex idx(full_param_);
+
+ CHECK_EQ(in_data.size(), idx.GetTotal());
+
+ int index = 0;
+ const int out_index = index++;
+ const int out_min_index = out_quantized ? index++ : 0;
+ const int out_max_index = out_quantized ? index++ : 0;
+ CHECK_EQ(out_data.size(), index); // index is equal to total number of
outputs
+
+ float data_min = 0.0f;
+ float data_max = 0.0f;
+ float weight_min = 0.0f;
+ float weight_max = 0.0f;
+ float bias_min = 0.0f;
+ float bias_max = 0.0f;
+
+ const float sum_min = idx.sum_min ?
in_data[idx.sum_min].data().dptr<float>()[0] : 0.0;
+ const float sum_max = idx.sum_max ?
in_data[idx.sum_max].data().dptr<float>()[0] : 0.0;
+ NDArray data = in_data[idx.data];
+ const NDArray& weight = in_data[idx.weight];
+ NDArray output;
+
+ if (dnnl_param.with_sum) {
+ if (!initialized_) {
+ // TODO(zhennan): Currently, dnnl fallback mechanism will break inplace
option,
+ // which make check (req[out_index] == kWriteInplace) useless.
+ auto in_dnnl_mem = static_cast<const
dnnl::memory*>(in_data[idx.sum].GetDNNLData());
+ auto out_dnnl_mem = static_cast<const
dnnl::memory*>(out_data[out_index].GetDNNLData());
+ if (in_dnnl_mem->get_data_handle() == out_dnnl_mem->get_data_handle()) {
+ inplace_ = true;
+ }
}
-
- total_num_inputs_ = base_num_inputs;
- total_num_outputs_ = base_num_outputs;
- if (dnnl_param.quantized) {
- total_num_inputs_ = channel_wise_runtime_ ? (base_num_inputs + 2) :
(base_num_inputs * 3);
- total_num_outputs_ =
- dnnl_param.enable_float_output ? base_num_outputs :
(base_num_outputs * 3);
+ if (inplace_) {
+ output = in_data[idx.sum];
+ } else {
+ // Not in place: copy in_data[idx.sum] into outputs[out_index].
+ auto in_dnnl_mem = static_cast<const
dnnl::memory*>(in_data[idx.sum].GetDNNLData());
+ auto out_dnnl_mem = static_cast<const
dnnl::memory*>(out_data[out_index].GetDNNLData());
+ if (out_data[out_index].dtype() == mshadow::kInt32) {
Review comment:
For now such configuration doesn't exist. For 'full' quantization mode
input and output are int8 and for 'smart' quantization both are float
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