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[CPU]RNN not specify weight layout when creating primitive descriptor. #20068

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Merged
dmitry-gorokhov merged 8 commits into from
Jan 22, 2024
Merged

[CPU]RNN not specify weight layout when creating primitive descriptor. #20068

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5fc6d01
RNN not specify weight layout when creating primitive descriptor.
luweizhou2016 Sep 27, 2023
db6cff6
WA to resotore refany to pass tests..
luweizhou2016 Sep 27, 2023
4356738
Remove the fusing Reshape + FC transformation in CPU plugin.
luweizhou2016 Sep 27, 2023
c26c230
Merge remote-tracking branch 'origin/master' into luwei/rnn_fix
luweizhou2016 Nov 21, 2023
e144272
Merge remote-tracking branch 'origin/master' into luwei/rnn_fix
luweizhou2016 Dec 4, 2023
cfa6555
Merge remote-tracking branch 'origin/master' into luwei/rnn_fix
luweizhou2016 Dec 4, 2023
88f33b7
Restore reshape+fc fusion.
luweizhou2016 Dec 4, 2023
bd40a06
Merge remote-tracking branch 'origin/master' into luwei/rnn_fix
luweizhou2016 Jan 9, 2024
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41 changes: 12 additions & 29 deletions src/plugins/intel_cpu/src/nodes/rnn.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -627,9 +627,11 @@ void RNN::fillCellDesc() {
inCandidate.emplace_back(std::make_shared<DnnlBlockedMemoryDesc>(shapeS, inDataTypes[cIdx], memory::format_tag::nc));
outCandidate.emplace_back(std::make_shared<DnnlBlockedMemoryDesc>(shapeS, outDataTypes[coIdx], memory::format_tag::nc));
}

// The weight and weights_iter would expose nc layout to avoid unnecessary reorder.
// The onednn would determine the final layout when prepareParams.
inCandidate.emplace_back(std::make_shared<DnnlBlockedMemoryDesc>(WShape, inDataTypes[wIdx], memory::format_tag::nc));
inCandidate.emplace_back(std::make_shared<DnnlBlockedMemoryDesc>(RShape, inDataTypes[rIdx], memory::format_tag::nc));

inCandidate.emplace_back(std::make_shared<DnnlBlockedMemoryDesc>(BShape, inDataTypes[bIdx], memory::format_tag::x));

if (haveAttention(cell_type)) {
Expand Down Expand Up @@ -732,8 +734,11 @@ void RNN::fillSequenceDesc() {
}

inCandidate.emplace_back(std::make_shared<DnnlBlockedMemoryDesc>(TShape, inDataTypes[sIdx], memory::format_tag::x)); // sequence lengths
inCandidate.emplace_back(std::make_shared<DnnlBlockedMemoryDesc>(WShape, inDataTypes[wIdx], memory::format_tag::ntc)); // W
inCandidate.emplace_back(std::make_shared<DnnlBlockedMemoryDesc>(RShape, inDataTypes[rIdx], memory::format_tag::ntc)); // R
// The weight and weights_iter would expose tnc layout to avoid unnecessary reorder.
// The onednn would determine the final layout when prepareParams.
inCandidate.emplace_back(std::make_shared<DnnlBlockedMemoryDesc>(WShape, inDataTypes[wIdx], memory::format_tag::tnc)); // W
inCandidate.emplace_back(std::make_shared<DnnlBlockedMemoryDesc>(RShape, inDataTypes[rIdx], memory::format_tag::tnc)); // R

inCandidate.emplace_back(std::make_shared<DnnlBlockedMemoryDesc>(BShape, inDataTypes[bIdx], memory::format_tag::nc)); // B

if (haveAttention(cell_type)) {
Expand Down Expand Up @@ -901,9 +906,6 @@ void RNN::copyWeightsData() {
if (one_of(dataType, memory::data_type::bf16, memory::data_type::f16)) {
fillWeights<uint16_t>(gate_map, wIdx, rIdx);
} else if (dataType == memory::data_type::f32) {
// WA To avoid different weights layer and iter formats in FP32 case
if (T.minVal > 1 || N.maxVal < optimalBatchSize)
wFormat = dnnl::memory::format_tag::ldigo;
fillWeights<float>(gate_map, wIdx, rIdx);
} else if (dataType == memory::data_type::u8 || dataType == memory::data_type::s8) {
fillWeights<int8_t>(gate_map, wIdx, rIdx);
Expand Down Expand Up @@ -1042,9 +1044,11 @@ void RNN::createDescriptor(const std::vector<MemoryDescPtr> &inputDesc,
since internalBlobs are used for the execution, not the initial weights */
const auto& targetWeightDataType = weightsByinputDataType.at(inDataTypes[xIdx]);
auto weightsDims = DnnlExtensionUtils::convertToDnnlDims(VectorDims{ L, D, DC, G, SC });
wDescs[0] = dnnl::memory::desc(weightsDims, targetWeightDataType, wFormat);
//onednn determines the preferred weight layout.
wDescs[0] = dnnl::memory::desc(weightsDims, targetWeightDataType, memory::format_tag::any);
auto statesDims = DnnlExtensionUtils::convertToDnnlDims(VectorDims{ L, D, SC, G, SC });
wDescs[1] = dnnl::memory::desc(statesDims, targetWeightDataType, wFormat);
//onednn determines the preferred weights_iter layout.
wDescs[1] = dnnl::memory::desc(statesDims, targetWeightDataType, memory::format_tag::any);
auto biasDims = DnnlExtensionUtils::convertToDnnlDims(VectorDims{ L, D, Gb, SC });
wDescs[2] = dnnl::memory::desc(biasDims, inDataTypes[bIdx], memory::format_tag::ldgo);

Expand Down Expand Up @@ -1119,27 +1123,6 @@ void RNN::prepareParams() {
inDataDescs[2] = std::make_shared<DnnlBlockedMemoryDesc>(Shape{SL, B, 1}, inDataTypes[aIdx], memory::format_tag::tnc);
}

bool wFormatWasChanged = false;
// WA To avoid different weights layer and iter formats in FP32 case.
if (one_of(inDataTypes[xIdx], memory::data_type::f32) &&
(SL != 1 || B < optimalBatchSize)) {
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@EgorDuplensky EgorDuplensky Jan 5, 2024

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So, this heuristic is not needed anymore?

if (wFormat != dnnl::memory::format_tag::ldigo) {
wFormat = dnnl::memory::format_tag::ldigo;
wFormatWasChanged = true;
}
} else if (wFormat != dnnl::memory::format_tag::any) {
wFormat = dnnl::memory::format_tag::any;
wFormatWasChanged = true;
}

if (wFormatWasChanged) {
auto weightsDims = DnnlExtensionUtils::convertToDnnlDims(VectorDims{ L, D, DC, G, SC });
const auto& targetWeightDataType = weightsByinputDataType.at(inDataTypes[xIdx]);
wDescs[0] = dnnl::memory::desc(weightsDims, targetWeightDataType, wFormat);
auto statesDims = DnnlExtensionUtils::convertToDnnlDims(VectorDims{ L, D, SC, G, SC });
wDescs[1] = dnnl::memory::desc(statesDims, targetWeightDataType, wFormat);
}

const auto attr = initPrimitiveAttr();
RNNKey key = { inDataDescs, outDataDescs, wDescs, cell_type, cell_act, direction, *attr };

Expand Down
3 changes: 0 additions & 3 deletions src/plugins/intel_cpu/src/nodes/rnn.h
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Original file line number Diff line number Diff line change
Expand Up @@ -105,9 +105,6 @@ class RNN : public Node {
/** activation type for vanilla RNN cell */
dnnl::algorithm cell_act = dnnl::algorithm::undef;

/** Weights data and state memory format: ldigo or any */
dnnl::memory::format_tag wFormat = dnnl::memory::format_tag::any;

struct Interval {
Interval() = default;

Expand Down