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[mkldnn-v1.0] Add MKL-DNN BN #16199

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Sep 23, 2019
Merged

[mkldnn-v1.0] Add MKL-DNN BN #16199

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4ca32b4
add mkldnn bn
rongzha1 Sep 18, 2019
8816cdb
add static_cast to transform data type
rongzha1 Sep 19, 2019
1eb2c81
change mkldnn_args_map_t
rongzha1 Sep 19, 2019
0e0812b
retrigger CI
rongzha1 Sep 23, 2019
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change mkldnn_args_map_t
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@rongzha1
rongzha1 committed Sep 23, 2019
commit 1eb2c81ce8be955f88c3d1899190ee29fc737585
38 changes: 18 additions & 20 deletions src/operator/nn/mkldnn/mkldnn_batch_norm-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -195,10 +195,10 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
}
}

std::unordered_map<int, mkldnn::memory> net_args;
net_args.insert({MKLDNN_ARG_SRC, *(data.GetMKLDNNData())});
net_args.insert({MKLDNN_ARG_SCALE_SHIFT, weight_mem});
net_args.insert({MKLDNN_ARG_DST, *out_mem});
mkldnn_args_map_t net_args;
net_args[MKLDNN_ARG_SRC] = *data.GetMKLDNNData();
net_args[MKLDNN_ARG_SCALE_SHIFT] = weight_mem;
net_args[MKLDNN_ARG_DST] = *out_mem;

if (!ctx.is_train || param.use_global_stats) {
DType* omean = out_data[batchnorm::kMean].data().dptr<DType>();
Expand All @@ -210,17 +210,15 @@ void MKLDNNBatchNormForward(const OpContext &ctx, const BatchNormParam &param,
omean[i] = inmean[i];
ovar[i] = VARIANCE_TO_INVSTD(invar[i], param.eps);
}

net_args.insert({MKLDNN_ARG_MEAN, *(aux_states[batchnorm::kMovingMean].GetMKLDNNData())});
net_args.insert({MKLDNN_ARG_VARIANCE, *(aux_states[batchnorm::kMovingVar].GetMKLDNNData())});
net_args[MKLDNN_ARG_MEAN] = *(aux_states[batchnorm::kMovingMean].GetMKLDNNData());
net_args[MKLDNN_ARG_VARIANCE] = *(aux_states[batchnorm::kMovingVar].GetMKLDNNData());
MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
MKLDNNStream::Get()->Submit();
} else { // training
const NDArray &outMean = out_data[batchnorm::kMean];
const NDArray &outVar = out_data[batchnorm::kVar];

net_args.insert({MKLDNN_ARG_MEAN, *(outMean.GetMKLDNNData())});
net_args.insert({MKLDNN_ARG_VARIANCE, *(outVar.GetMKLDNNData())});
net_args[MKLDNN_ARG_MEAN] = *(outMean.GetMKLDNNData());
net_args[MKLDNN_ARG_VARIANCE] = *(outVar.GetMKLDNNData());
MKLDNNStream::Get()->RegisterPrimArgs(fwd.GetFwd(), net_args);
MKLDNNStream::Get()->Submit();

Expand Down Expand Up @@ -337,12 +335,12 @@ void MKLDNNBatchNormBackward(const OpContext &ctx, const BatchNormParam &param,
weight_buf[channels_ + i] = (beta.data().dptr<DType>())[i]; // bias
}

std::unordered_map<int, mkldnn::memory> net_args;
net_args.insert({MKLDNN_ARG_SRC, *data_mem});
net_args.insert({MKLDNN_ARG_DIFF_SRC, *gradi_mem});
net_args.insert({MKLDNN_ARG_SCALE_SHIFT, bwd.GetWeight()});
net_args.insert({MKLDNN_ARG_DIFF_SCALE_SHIFT, bwd.GetGradw()});
net_args.insert({MKLDNN_ARG_DIFF_DST, *diff_mem});
mkldnn_args_map_t net_args;
net_args[MKLDNN_ARG_SRC] = *data_mem;
net_args[MKLDNN_ARG_DIFF_SRC] = *gradi_mem;
net_args[MKLDNN_ARG_SCALE_SHIFT] = bwd.GetWeight();
net_args[MKLDNN_ARG_DIFF_SCALE_SHIFT] = bwd.GetGradw();
net_args[MKLDNN_ARG_DIFF_DST] = *diff_mem;

// training but no input mean and variance
if (ctx.is_train && !param.use_global_stats) {
Expand All @@ -362,13 +360,13 @@ void MKLDNNBatchNormBackward(const OpContext &ctx, const BatchNormParam &param,
moving_var_ptr[i] = moving_var_ptr[i] * param.momentum +
variance * minus_mom;
}
net_args.insert({MKLDNN_ARG_MEAN, *(out_mean.GetMKLDNNData())});
net_args.insert({MKLDNN_ARG_VARIANCE, var_mem});
net_args[MKLDNN_ARG_MEAN] = *(out_mean.GetMKLDNNData());
net_args[MKLDNN_ARG_VARIANCE] = var_mem;
MKLDNNStream::Get()->RegisterPrimArgs(bwd.GetBwd(), net_args);
MKLDNNStream::Get()->Submit();
} else {
net_args.insert({MKLDNN_ARG_MEAN, *(moving_mean.GetMKLDNNData())});
net_args.insert({MKLDNN_ARG_VARIANCE, *(moving_var.GetMKLDNNData())});
net_args[MKLDNN_ARG_MEAN] = *(moving_mean.GetMKLDNNData());
net_args[MKLDNN_ARG_VARIANCE] = *(moving_var.GetMKLDNNData());
MKLDNNStream::Get()->RegisterPrimArgs(bwd.GetBwd(), net_args);
MKLDNNStream::Get()->Submit();
}
Expand Down