renamed Times inputRank to inferInputRank
This commit is contained in:
Frank Seide
Родитель
abe66249e7
Коммит
de864bffb4
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@ -335,7 +335,7 @@ static ConfigValuePtr NodeOp(const ExpressionPtr &e, ConfigValuePtr leftVal, Con
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{
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let one = MakePrimitiveConfigValuePtr(1.0, leftFailFn, exprPath);
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config->Add(L"outputRank", leftFailFn, one);
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config->Add(L"inputRank", leftFailFn, one);
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config->Add(L"inferInputRank", leftFailFn, one);
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}
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// instantiate the ComputationNode
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let value = ConfigValuePtr(rtInfo->construct(config), MakeFailFn(e->location), exprPath);
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@ -37,12 +37,12 @@ LinearLayer {outDim, bias = true, init='heNormal', initValueScale=1, inputRank=0
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outputRank = Length (_AsArray (outDim)) # support outputs with tensor layouts
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apply (x) =
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if bias
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then Times (W, x, outputRank=outputRank, inputRank=inputRank) + b
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else Times (W, x, outputRank=outputRank, inputRank=inputRank)
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then Times (W, x, outputRank=outputRank, inferInputRank=inputRank) + b
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else Times (W, x, outputRank=outputRank, inferInputRank=inputRank)
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}.apply
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# DenseLayer -- create a fully-connected layer with optional non-linearity
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DenseLayer{outDim, bias = true, activation=(x=>x), init='heNormal', initValueScale=1, inputRank=0} = Sequential ( LinearLayer{outDim, bias=bias, init=init, initValueScale=initValueScale, inputRank=inputRank} : activation )
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DenseLayer{outDim, bias = true, activation=(x=>x), init='heNormal', initValueScale=1, inputRank=0} = Sequential ( LinearLayer{outDim, bias=bias, init=init, initValueScale=initValueScale, inferInputRank=inputRank} : activation )
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# EmbeddingLayer -- create a linear embedding layer
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EmbeddingLayer {outDim, # dimension of embedding
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@ -326,7 +326,7 @@ CNTK2 = [
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// 4. Tensor operations
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// Changes: Matrix -> Tensor. A -> x, B -> y. Data must come on y ("default parameter") hence not using _
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Times(x, y, outputRank=1, inputRank=1, tag='') = new ComputationNode [ operation = 'Times' ; inputs = ( x : y ) /*plus the function args*/ ]
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Times(x, y, outputRank=1, inferInputRank=1, tag='') = new ComputationNode [ operation = 'Times' ; inputs = ( x : y ) /*plus the function args*/ ]
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// 5. Elementwise operations.
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// Changes: "Matrix" -> "Tensor"; left input -> _; Clip: move input to front. ElementDivide/Times: anotherTensor -> y
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@ -238,8 +238,8 @@ class TimesNodeBase : public ComputationNode<ElemType>, public NumInputs<2>
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typedef ComputationNode<ElemType> Base; UsingComputationNodeMembers; using Base::OperationName; \
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public:
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TimesNodeBase(DEVICEID_TYPE deviceId, const wstring& name, size_t outputRank = 1, int inputRank = 1)
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: Base(deviceId, name), m_outputRank(outputRank), m_inputRank(inputRank)
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TimesNodeBase(DEVICEID_TYPE deviceId, const wstring& name, size_t outputRank = 1, int inferInputRank = 1)
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: Base(deviceId, name), m_outputRank(outputRank), m_inferInputRank(inferInputRank)
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{
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}
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@ -249,8 +249,8 @@ public:
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if (flags & CopyNodeFlags::copyNodeValue)
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{
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auto node = dynamic_pointer_cast<TimesNodeBase<ElemType, m_transpose>>(nodeP);
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node->m_outputRank = m_outputRank;
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node->m_inputRank = m_inputRank;
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node->m_outputRank = m_outputRank;
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node->m_inferInputRank = m_inferInputRank;
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}
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}
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@ -258,7 +258,7 @@ public:
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{
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Base::Save(fstream);
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fstream << m_outputRank;
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fstream << m_inputRank;
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fstream << m_inferInputRank;
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}
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virtual void Load(File& fstream, size_t modelVersion) override
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@ -269,9 +269,9 @@ public:
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else
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m_outputRank = 1;
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if (modelVersion >= CNTK_MODEL_VERSION_11)
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fstream >> m_inputRank;
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fstream >> m_inferInputRank;
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else
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m_inputRank = 1;
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m_inferInputRank = 1;
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}
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private:
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@ -427,32 +427,32 @@ public:
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InvalidArgument("%ls %ls operation: The outputRank (%d) dimensions in left argument's shape [%s] must not be 0.", NodeName().c_str(), OperationName().c_str(), (int)m_outputRank, dimsAstring.c_str());
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// infer rank of dimsA
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// For purpose of dimension inference, Times() accepts an optional parameter inputRank (default 1).
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// The first 'inputRank' axes are considered those that the matrix product should reduce over,
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// For purpose of dimension inference, Times() accepts an optional parameter inferInputRank (default 1).
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// The first 'inferInputRank' axes are considered those that the matrix product should reduce over,
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// while the remaining axes are kept (Times() is applied one by one, like a "map" operation).
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// Importantly, inputRank <= 0 will be interpreted from the end. Hence, inputRank=-1 denotes
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// Importantly, inferInputRank <= 0 will be interpreted from the end. Hence, inferInputRank=-1 denotes
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// that the one last axis will not be reduced over.
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// And inputRank=0 means to reduce over all input axes, e.g. for an image input that
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// And inferInputRank=0 means to reduce over all input axes, e.g. for an image input that
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// should be flattened.
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// Examples:
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// [I x Inferred] * [J x K], inputRank=1 --> Inferred := J, result is [I x K]
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// [I x Inferred] * [W x H x C], inputRank=1 --> Inferred := W, result is [I x H x C] (not desired)
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// [I x Inferred] * [W x H x C], inputRank=0 --> Inferred := W x H x C, result is [I] (desired)
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// [I x Inferred] * [W x H x C x R], inputRank=-1 --> Inferred := W x H x C, result is [I x R] (desired)
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// [I x Inferred] * [J x K], inferInputRank=1 --> Inferred := J, result is [I x K]
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// [I x Inferred] * [W x H x C], inferInputRank=1 --> Inferred := W, result is [I x H x C] (not desired)
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// [I x Inferred] * [W x H x C], inferInputRank=0 --> Inferred := W x H x C, result is [I] (desired)
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// [I x Inferred] * [W x H x C x R], inferInputRank=-1 --> Inferred := W x H x C, result is [I x R] (desired)
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// In each case,
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// * if the output tensor is too short *and* the last dimension is 0, it will be extended
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// * output tensor dimensions that are not 0 are not touched
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if (dimsA.back() == 0) // if last entry is 0, we infer the tensor rank as well
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{
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if (abs(m_inputRank) > dimsB.size())
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InvalidArgument("%ls %ls operation: 'inputDims' argument %d exceeds rank of second operand [%s].", NodeName().c_str(), OperationName().c_str(), m_inputRank, dimsBstring.c_str());
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size_t inputRank = (size_t)(m_inputRank > 0 ? m_inputRank : (int)dimsB.size() + m_inputRank);
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if (abs(m_inferInputRank) > dimsB.size())
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InvalidArgument("%ls %ls operation: 'inputDims' argument %d exceeds rank of second operand [%s].", NodeName().c_str(), OperationName().c_str(), m_inferInputRank, dimsBstring.c_str());
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size_t inferInputRank = (size_t)(m_inferInputRank > 0 ? m_inferInputRank : (int)dimsB.size() + m_inferInputRank);
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assert(dimsA.size() == m_outputRank + numReductionDims);
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while (numReductionDims < inputRank)
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while (numReductionDims < inferInputRank)
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{
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dimsA.push_back(0);
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numReductionDims++;
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}
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dimsA.push_back(0);
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numReductionDims++;
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}
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}
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// fill in the missing ones
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@ -502,7 +502,7 @@ public:
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private:
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size_t m_outputRank;
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int m_inputRank; // can be negative to indicate counting from end
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int m_inferInputRank; // can be negative to indicate counting from end
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};
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// -----------------------------------------------------------------------
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@ -529,12 +529,12 @@ class TimesNode : public TimesNodeBase<ElemType, false>
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static const std::wstring TypeName() { return L"Times"; }
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public:
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TimesNode(DEVICEID_TYPE deviceId, const wstring& name, size_t outputRank = 1, int inputRank = 1)
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: Base(deviceId, name, outputRank, inputRank)
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TimesNode(DEVICEID_TYPE deviceId, const wstring& name, size_t outputRank = 1, int inferInputRank = 1)
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: Base(deviceId, name, outputRank, inferInputRank)
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{
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}
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TimesNode(const ScriptableObjects::IConfigRecordPtr configp)
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: TimesNode(configp->Get(L"deviceId"), L"<placeholder>", configp->Get(L"outputRank"), configp->Get(L"inputRank"))
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: TimesNode(configp->Get(L"deviceId"), L"<placeholder>", configp->Get(L"outputRank"), configp->Get(L"inferInputRank"))
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{
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AttachInputsFromConfig(configp, this->GetExpectedNumInputs());
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}
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@ -562,7 +562,7 @@ class TransposeTimesNode : public TimesNodeBase<ElemType, true>
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public:
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DeclareConstructorFromConfigWithNumInputs(TransposeTimesNode);
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TransposeTimesNode(DEVICEID_TYPE deviceId, const wstring& name, size_t outputRank = 1)
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: Base(deviceId, name, outputRank, /*inputRank=*/1)
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: Base(deviceId, name, outputRank, /*inferInputRank=*/1)
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{
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}
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};
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