From 28fffef63b4895dd8c30dcbea2f7ebb696ac41cb Mon Sep 17 00:00:00 2001 From: 0x45f <23097963+0x45f@users.noreply.github.com> Date: Fri, 17 Sep 2021 10:31:18 +0800 Subject: [PATCH] refine matrix_rank op code and doc (#35722) --- paddle/fluid/operators/matrix_rank_op.cc | 16 ++++------ .../tests/unittests/test_matrix_rank_op.py | 31 +++++++++++-------- python/paddle/tensor/linalg.py | 22 ++++++------- 3 files changed, 34 insertions(+), 35 deletions(-) diff --git a/paddle/fluid/operators/matrix_rank_op.cc b/paddle/fluid/operators/matrix_rank_op.cc index 5481e143448..3038a16dc0a 100644 --- a/paddle/fluid/operators/matrix_rank_op.cc +++ b/paddle/fluid/operators/matrix_rank_op.cc @@ -27,7 +27,7 @@ namespace operators { using DDim = framework::DDim; namespace detail { -static DDim GetInputBatchDim(const DDim& dim_x) { +static DDim CheckAndGetOutputDim(const DDim& dim_x) { auto x_vec = framework::vectorize(dim_x); if (x_vec.size() == 2) { return framework::make_ddim({1}); @@ -58,11 +58,8 @@ class MatrixRankeOp : public framework::OperatorWithKernel { "if hermitian == true, matrix should be n*n")); } - DDim dim_x_batch = detail::GetInputBatchDim(dim_x); - if (ctx->Attrs().Get( - "use_default_tol")) { // user not input TolTensor and tol - ctx->SetOutputDim("Out", dim_x_batch); - } else if (ctx->HasInput("TolTensor")) { + DDim dim_x_batch = detail::CheckAndGetOutputDim(dim_x); + if (ctx->HasInput("TolTensor")) { auto dim_tol = ctx->GetInputDim("TolTensor"); if (dim_x_batch == dim_tol) { ctx->SetOutputDim("Out", dim_x_batch); @@ -75,9 +72,6 @@ class MatrixRankeOp : public framework::OperatorWithKernel { GetBroadcastDimsArrays(dim_x_batch, dim_tol, x_batch_dims_array.data(), tol_dims_array.data(), out_dims_array.data(), max_dim, axis); - for (auto& it : out_dims_array) { - VLOG(3) << "out dims: " << it; - } ctx->SetOutputDim("Out", framework::make_ddim(out_dims_array)); } } else { @@ -100,7 +94,9 @@ class MatrixRankeOpMaker : public framework::OpProtoAndCheckerMaker { public: void Make() override { AddInput("X", "(Tensor), The input tensor of matrix_rank op."); - AddInput("TolTensor", "(optional) Tol tensor, shape is same as X batch.") + AddInput("TolTensor", + "(optional) Tol tensor, shape is same as X batch or can broadcast " + "with X batch.") .AsDispensable(); AddOutput("Out", "(Tensor), The output tensor of matrix_rank op."); AddAttr("tol", "(float, optional). tol").SetDefault(0.0f); diff --git a/python/paddle/fluid/tests/unittests/test_matrix_rank_op.py b/python/paddle/fluid/tests/unittests/test_matrix_rank_op.py index d9d0bd7e601..d0b84a0d7e1 100644 --- a/python/paddle/fluid/tests/unittests/test_matrix_rank_op.py +++ b/python/paddle/fluid/tests/unittests/test_matrix_rank_op.py @@ -36,8 +36,8 @@ class TestMatrixRankOP(OpTest): self.init_data() self.inputs = {'X': self.x} self.attrs = {'hermitian': self.hermitian} - if self.tolTensor is not None: - self.inputs["TolTensor"] = self.tolTensor + if self.tol_tensor is not None: + self.inputs["TolTensor"] = self.tol_tensor if self.tol is not None: self.attrs["tol"] = self.tol self.attrs["use_default_tol"] = self.use_default_tol @@ -48,7 +48,7 @@ class TestMatrixRankOP(OpTest): def init_data(self): self.x = np.eye(3, dtype=np.float32) - self.tolTensor = None + self.tol_tensor = None self.tol = 0.1 self.use_default_tol = False self.hermitian = True @@ -58,51 +58,56 @@ class TestMatrixRankOP(OpTest): class TestMatrixRankOP1(TestMatrixRankOP): def init_data(self): self.x = np.eye(3, k=1, dtype=np.float64) - self.tolTensor = None + self.tol_tensor = None self.tol = None self.use_default_tol = True self.hermitian = False - self.out = np.linalg.matrix_rank(self.x, self.tolTensor, self.hermitian) + self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, + self.hermitian) class TestMatrixRankOP2(TestMatrixRankOP): def init_data(self): self.x = np.random.rand(3, 4, 5, 6).astype(np.float32) - self.tolTensor = np.random.random([3, 4]).astype(self.x.dtype) + self.tol_tensor = np.random.random([3, 4]).astype(self.x.dtype) self.tol = None self.use_default_tol = False self.hermitian = False - self.out = np.linalg.matrix_rank(self.x, self.tolTensor, self.hermitian) + self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, + self.hermitian) class TestMatrixRankOP3(TestMatrixRankOP): def init_data(self): self.x = np.eye(200, dtype=np.float64) - self.tolTensor = None + self.tol_tensor = None self.tol = None self.use_default_tol = True self.hermitian = True - self.out = np.linalg.matrix_rank(self.x, self.tolTensor, self.hermitian) + self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, + self.hermitian) class TestMatrixRankOP4(TestMatrixRankOP): def init_data(self): self.x = np.random.rand(1, 10).astype(np.float32) - self.tolTensor = None + self.tol_tensor = None self.tol = None self.use_default_tol = True self.hermitian = False - self.out = np.linalg.matrix_rank(self.x, self.tolTensor, self.hermitian) + self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, + self.hermitian) class TestMatrixRankOP5(TestMatrixRankOP): def init_data(self): self.x = np.random.rand(5, 1).astype(np.float64) - self.tolTensor = np.random.random([1, 4]).astype(self.x.dtype) + self.tol_tensor = np.random.random([1, 4]).astype(self.x.dtype) self.tol = None self.use_default_tol = False self.hermitian = False - self.out = np.linalg.matrix_rank(self.x, self.tolTensor, self.hermitian) + self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, + self.hermitian) class TestMatrixRankAPI(unittest.TestCase): diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py index fa9da0e579d..8135df35203 100644 --- a/python/paddle/tensor/linalg.py +++ b/python/paddle/tensor/linalg.py @@ -1106,20 +1106,18 @@ def matrix_rank(x, tol=None, hermitian=False, name=None): r""" Computes the rank of a matrix. - The rank of a matrix is the number of singular values that are greater than the specified tol threshold when hermitian=False, - or the number of eigenvalues in absolute value that are greater than the specified tol threshold when hermitian=True. + The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False, + or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True. Args: - x (Tensor): The input tensor. - Its shape should be [..., m, n], where ... is zero or more batch dimensions. If x is a batch of matrices then the output - has the same batch dimensions. The data type of x should be float32 or float64. - tol (float,Tensor,optional): the tolerance value. Default: None. - If tol is not specified, and sigma is the largest singular value (or eigenvalue in absolute value), and eps is the - epsilon value for the dtype of x, then tol is computed with formula tol=sigma * max(m,n) * eps. Note that if x is - a batch of matrices, tol is computed this way for every batch. - hermitian (bool,optional): indicates whether x is Hermitian. Default: False. - When hermitian=True, x is assumed to be Hermitian, but x is not checked inside the function. Instead, We just use the - lower triangular of the matrix to compute. + x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch + of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64. + tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest + singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed + with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch. + hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian, + enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use + the lower triangular of the matrix to compute. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: -- GitLab