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Polish operator docs (n to p) (#5376)
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* polish p ops

* fix precision_recall

* fix linear_chain_crf_op

* small fix
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@kexinzhao @wangkuiyi
kexinzhao authored and wangkuiyi committed Nov 5, 2017
1 parent fb2aa71 commit 5d8cdf2
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Showing 9 changed files with 281 additions and 213 deletions.
37 changes: 19 additions & 18 deletions paddle/operators/linear_chain_crf_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -23,21 +23,21 @@ class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Emission",
"(LoDTensor, default: LoDTensor<float>). "
"A 2-D LoDTensor with shape [N x D] where N is the size of the "
"(LoDTensor, default LoDTensor<float>) "
"A 2-D LoDTensor with shape [N x D], where N is the size of the "
"mini-batch and D is the total tag number. The unscaled emission "
"weight matrix for the linear chain CRF. ");
AddInput("Transition",
"(Tensor, default: Tensor<float>). A 2-D Tensor with shape "
"(Tensor, default Tensor<float>) A 2-D Tensor with shape "
"[(D + 2) x D]. The learnable parameter for the linear_chain_crf "
"operator. See more details in the operator's comments.");
AddInput("Label",
"(LoDTensor, default: LoDTensor<int>). A LoDTensor with shape "
"(LoDTensor, default LoDTensor<int>) A LoDTensor with shape "
"[N x 1], where N is the total element number in a mini-batch. "
"The ground truth.");
AddOutput(
"Alpha",
"(Tensor, default: Tensor<float>). A 2-D Tensor with shape [N x D]. "
"(Tensor, default Tensor<float>) A 2-D Tensor with shape [N x D]. "
"The forward vectors for the entire batch. Denote it as \f$\alpha\f$. "
"\f$\alpha$\f is a memo table used to calculate the normalization "
"factor in CRF. \f$\alpha[k, v]$\f stores the unnormalized "
Expand All @@ -49,26 +49,28 @@ class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
.AsIntermediate();
AddOutput(
"EmissionExps",
"(Tensor, default: Tensor<float>). A 2-D Tensor with shape [N x D]. "
"(Tensor, default Tensor<float>) A 2-D Tensor with shape [N x D]. "
"The exponentials of Input(Emission). This is an intermediate "
"computational result in forward computation, and will be reused in "
"backward computation.")
.AsIntermediate();
AddOutput(
"TransitionExps",
"(Tensor, default: Tensor<float>). A 2-D Tensor with shape "
"(Tensor, default Tensor<float>) A 2-D Tensor with shape "
"[(D + 2) x D]. The exponentials of Input(Transition). This is an "
"intermediate computational result in forward computation, and "
"will be reused in backward computation.")
.AsIntermediate();
AddOutput(
"LogLikelihood",
"(Tensor, default: Tensor<float>). The logarithm of the conditional "
"(Tensor, default Tensor<float>) The logarithm of the conditional "
"likelihood of each training sample in a mini-batch. This is a 2-D "
"tensor with shape [S x 1], where S is the sequence number in a "
"mini-batch. Note: S is equal to the sequence number in a mini-batch. "
"The output is no longer a LoDTensor.");
AddComment(R"DOC(
LinearChainCRF Operator.
Conditional Random Field defines an undirected probabilistic graph with nodes
denoting random variables and edges denoting dependencies between these
variables. CRF learns the conditional probability \f$P(Y|X)\f$, where
Expand All @@ -82,29 +84,28 @@ and output must be linear sequences. Thus, the graph of such a CRF is a simple
chain or a line, which results in the linear chain CRF.
This operator implements the Forward-Backward algorithm for the linear chain
CRF. Please see http://www.cs.columbia.edu/~mcollins/fb.pdf and
http://cseweb.ucsd.edu/~elkan/250Bwinter2012/loglinearCRFs.pdf for reference.
CRF. Please refer to http://www.cs.columbia.edu/~mcollins/fb.pdf and
http://cseweb.ucsd.edu/~elkan/250Bwinter2012/loglinearCRFs.pdf for details.
Equation:
- Denote Input(Emission) to this operator as \f$x\f$ here.
- The first D values of Input(Transition) to this operator are for starting
1. Denote Input(Emission) to this operator as \f$x\f$ here.
2. The first D values of Input(Transition) to this operator are for starting
weights, denoted as \f$a\f$ here.
- The next D values of Input(Transition) of this operator are for ending
3. The next D values of Input(Transition) of this operator are for ending
weights, denoted as \f$b\f$ here.
- The remaning values of Input(Transition) are for transition weights,
4. The remaning values of Input(Transition) are for transition weights,
denoted as \f$w\f$ here.
- Denote Input(Label) as \f$s\f$ here.
5. Denote Input(Label) as \f$s\f$ here.
The probability of a sequence \f$s\f$ of length \f$L\f$ is defined as:
\f$P(s) = (1/Z) exp(a_{s_1} + b_{s_L}
\f$P(s) = (1/Z) \exp(a_{s_1} + b_{s_L}
+ \sum_{l=1}^L x_{s_l}
+ \sum_{l=2}^L w_{s_{l-1},s_l})\f$
where \f$Z\f$ is a normalization value so that the sum of \f$P(s)\f$ over
all possible sequences is \f$1\f$, and \f$x\f$ is the emission feature weight
to the linear chain CRF.
Finaly, the linear chain CRF operator outputs the logarithm of the conditional
Finally, the linear chain CRF operator outputs the logarithm of the conditional
likelihood of each training sample in a mini-batch.
NOTE:
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45 changes: 32 additions & 13 deletions paddle/operators/nccl_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -48,12 +48,17 @@ class NCCLInitOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddOutput("Communicator",
"Create Communicator for communicating between gpus");
AddAttr<std::vector<int>>("gpus", "gpu id lists");
AddAttr<int>("data_type", "output data type")
AddAttr<std::vector<int>>("gpus", "(vector<int>) GPU id lists");
AddAttr<int>("data_type",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
AddComment(R"DOC(
create communicator.
)DOC");
NCCLInit Operator.
Create communicator.
)DOC");
}
};

Expand Down Expand Up @@ -143,11 +148,15 @@ class NCCLAllReduceOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Communicator", "Communicator for communicating between gpus");
AddOutput("Out", "The output of AllReduce op");
AddAttr<std::string>("reduction",
"(string, default 'ncclSum') "
"{'ncclMin', 'ncclMax', 'ncclProd', 'ncclSum'}.")
.SetDefault("ncclSum");
AddComment(R"DOC(
AllReduce the input tensors.
)DOC");
NCCLAllReduce Operator.
AllReduce the input tensors.
)DOC");
}
};

Expand All @@ -161,14 +170,20 @@ class NCCLReduceOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Communicator", "Communicator for communicating between gpus");
AddOutput("Out", "The output of Reduce op");
AddAttr<std::string>("reduction",
"(string, default 'ncclSum') "
"{'ncclMin', 'ncclMax', 'ncclProd', 'ncclSum'}.")
.SetDefault("ncclSum");
AddAttr<int>("root",
"root gpu of the parameter. if not "
"set(platform::kInvalidGPUId). hashed by name.")
"(int, default kInvalidGPUId) "
"Root gpu of the parameter. If not, "
"set(platform::kInvalidGPUId). Hashed by name.")
.SetDefault(platform::kInvalidGPUId);
AddComment(R"DOC(
Reduce the tensors)DOC");
NCCLReduce Operator.
Reduce the tensors.
)DOC");
}
};

Expand All @@ -182,12 +197,16 @@ class NCCLBcastOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Communicator", "Communicator for communicating between gpus");
AddOutput("Out", "The output of Bcast");
AddAttr<int>("root",
"root gpu of the parameter. if not "
"set(platform::kInvalidGPUId). hashed by name.")
"(int, default kInvalidGPUId) "
"Root gpu of the parameter. If not, "
"set(platform::kInvalidGPUId). Hashed by name.")
.SetDefault(platform::kInvalidGPUId);
AddComment(R"DOC(
Bcast the tensors.
)DOC");
NCCLBcast Operator.
Bcast the tensors.
)DOC");
}
};

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41 changes: 22 additions & 19 deletions paddle/operators/pad_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -54,41 +54,44 @@ class PadOpMaker : public framework::OpProtoAndCheckerMaker {
"The input of pad op. "
"The input should be a k-D tensor(k > 0 and k < 7)");
AddOutput("Out",
"The output of pad op."
"The output of pad op. "
"A tensor with the same shape as X.");
AddAttr<std::vector<int>>(
"paddings",
"(vector<int>) "
"A list<int> to describe the padding rules for each dimension. "
"For 2-D image tensor, paddings=[0, 1, 2, 3] means "
"padding 0 row to top, 1 row to bottom, 2 columns to left "
"and 3 columns to right. Size of paddings should be equal to "
"2 * dimension size of the input tensor.");
AddAttr<float>("pad_value",
"(float, default 0.0) "
"The value to fill the padded areas.")
.SetDefault(0.0f);
AddComment(R"DOC(
Pad input into output, as specified by paddings and pad_value. The input should be a k-D tensor(k > 0 and k < 7). As an example:
Pad Operator.
Pad input into output, as specified by paddings and pad_value.
The input should be a k-D tensor(k > 0 and k < 7). As an example:
Given:
X = [[1, 2],
[3, 4]]
and
[3, 4]],
paddings = [0, 1, 1, 2]
paddings = [0, 1, 1, 2],
and
pad_value = 0
pad_value = 0,
then we get
we have:
Out = [[0, 1, 2, 0, 0]
[0, 3, 4, 0, 0]
[0, 0, 0, 0, 0]]
)DOC");
AddAttr<std::vector<int>>(
"paddings",
"A list<int> to describes padding rules for each dimension."
" For 2-D image tensor, paddings=[0, 1, 2, 3] means"
" padding 0 row to top, 1 row to bottom, 2 columns to left"
" and 3 columns to right.Size of paddings should be equal to"
" 2 * dimension size of input tensor.");
AddAttr<float>("pad_value",
"(float) default to 0; "
"The value to fill padded areas.")
.SetDefault(0.0f);
}
};

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