mlir.dialects._linalg_ops_gen ============================= .. py:module:: mlir.dialects._linalg_ops_gen Attributes ---------- .. autoapisummary:: mlir.dialects._linalg_ops_gen._ods_ir Classes ------- .. autoapisummary:: mlir.dialects._linalg_ops_gen._Dialect mlir.dialects._linalg_ops_gen.AbsOp mlir.dialects._linalg_ops_gen.AddOp mlir.dialects._linalg_ops_gen.BatchMatmulOp mlir.dialects._linalg_ops_gen.BatchMatvecOp mlir.dialects._linalg_ops_gen.BatchMmt4DOp mlir.dialects._linalg_ops_gen.BatchReduceMatmulOp mlir.dialects._linalg_ops_gen.BatchVecmatOp mlir.dialects._linalg_ops_gen.BroadcastOp mlir.dialects._linalg_ops_gen.CeilOp mlir.dialects._linalg_ops_gen.ContractOp mlir.dialects._linalg_ops_gen.Conv1DNcwFcwOp mlir.dialects._linalg_ops_gen.Conv1DNwcWcfOp mlir.dialects._linalg_ops_gen.Conv1DOp mlir.dialects._linalg_ops_gen.Conv2DNchwFchwOp mlir.dialects._linalg_ops_gen.Conv2DNchwFchwQOp mlir.dialects._linalg_ops_gen.Conv2DNgchwFgchwOp mlir.dialects._linalg_ops_gen.Conv2DNgchwGfchwOp mlir.dialects._linalg_ops_gen.Conv2DNgchwGfchwQOp mlir.dialects._linalg_ops_gen.Conv2DNhwcFhwcOp mlir.dialects._linalg_ops_gen.Conv2DNhwcFhwcQOp mlir.dialects._linalg_ops_gen.Conv2DNhwcHwcfOp mlir.dialects._linalg_ops_gen.Conv2DNhwcHwcfQOp mlir.dialects._linalg_ops_gen.Conv2DNhwgcGfhwcOp mlir.dialects._linalg_ops_gen.Conv2DNhwgcGfhwcQOp mlir.dialects._linalg_ops_gen.Conv2DOp mlir.dialects._linalg_ops_gen.Conv3DNcdhwFcdhwOp mlir.dialects._linalg_ops_gen.Conv3DNdhwcDhwcfOp mlir.dialects._linalg_ops_gen.Conv3DNdhwcDhwcfQOp mlir.dialects._linalg_ops_gen.Conv3DOp mlir.dialects._linalg_ops_gen.CopyOp mlir.dialects._linalg_ops_gen.DepthwiseConv1DNcwCwOp mlir.dialects._linalg_ops_gen.DepthwiseConv1DNwcWcOp mlir.dialects._linalg_ops_gen.DepthwiseConv1DNwcWcmOp mlir.dialects._linalg_ops_gen.DepthwiseConv2DNchwChwOp mlir.dialects._linalg_ops_gen.DepthwiseConv2DNhwcHwcOp mlir.dialects._linalg_ops_gen.DepthwiseConv2DNhwcHwcQOp mlir.dialects._linalg_ops_gen.DepthwiseConv2DNhwcHwcmOp mlir.dialects._linalg_ops_gen.DepthwiseConv2DNhwcHwcmQOp mlir.dialects._linalg_ops_gen.DepthwiseConv3DNcdhwCdhwOp mlir.dialects._linalg_ops_gen.DepthwiseConv3DNdhwcDhwcOp mlir.dialects._linalg_ops_gen.DepthwiseConv3DNdhwcDhwcmOp mlir.dialects._linalg_ops_gen.DivOp mlir.dialects._linalg_ops_gen.DivUnsignedOp mlir.dialects._linalg_ops_gen.DotOp mlir.dialects._linalg_ops_gen.ElementwiseOp mlir.dialects._linalg_ops_gen.ErfOp mlir.dialects._linalg_ops_gen.ExpOp mlir.dialects._linalg_ops_gen.FillOp mlir.dialects._linalg_ops_gen.FillRng2DOp mlir.dialects._linalg_ops_gen.FloorOp mlir.dialects._linalg_ops_gen.GenericOp mlir.dialects._linalg_ops_gen.IndexOp mlir.dialects._linalg_ops_gen.PackOp mlir.dialects._linalg_ops_gen.SoftmaxOp mlir.dialects._linalg_ops_gen.UnPackOp mlir.dialects._linalg_ops_gen.WinogradFilterTransformOp mlir.dialects._linalg_ops_gen.WinogradInputTransformOp mlir.dialects._linalg_ops_gen.WinogradOutputTransformOp mlir.dialects._linalg_ops_gen.YieldOp mlir.dialects._linalg_ops_gen.LogOp mlir.dialects._linalg_ops_gen.MapOp mlir.dialects._linalg_ops_gen.MatmulOp mlir.dialects._linalg_ops_gen.MatvecOp mlir.dialects._linalg_ops_gen.MaxOp mlir.dialects._linalg_ops_gen.MinOp mlir.dialects._linalg_ops_gen.Mmt4DOp mlir.dialects._linalg_ops_gen.MulOp mlir.dialects._linalg_ops_gen.NegFOp mlir.dialects._linalg_ops_gen.PoolingNchwMaxOp mlir.dialects._linalg_ops_gen.PoolingNchwSumOp mlir.dialects._linalg_ops_gen.PoolingNcwMaxOp mlir.dialects._linalg_ops_gen.PoolingNcwSumOp mlir.dialects._linalg_ops_gen.PoolingNdhwcMaxOp mlir.dialects._linalg_ops_gen.PoolingNdhwcMinOp mlir.dialects._linalg_ops_gen.PoolingNdhwcSumOp mlir.dialects._linalg_ops_gen.PoolingNhwcMaxOp mlir.dialects._linalg_ops_gen.PoolingNhwcMaxUnsignedOp mlir.dialects._linalg_ops_gen.PoolingNhwcMinOp mlir.dialects._linalg_ops_gen.PoolingNhwcMinUnsignedOp mlir.dialects._linalg_ops_gen.PoolingNhwcSumOp mlir.dialects._linalg_ops_gen.PoolingNwcMaxOp mlir.dialects._linalg_ops_gen.PoolingNwcMaxUnsignedOp mlir.dialects._linalg_ops_gen.PoolingNwcMinOp mlir.dialects._linalg_ops_gen.PoolingNwcMinUnsignedOp mlir.dialects._linalg_ops_gen.PoolingNwcSumOp mlir.dialects._linalg_ops_gen.PowFOp mlir.dialects._linalg_ops_gen.QuantizedBatchMatmulOp mlir.dialects._linalg_ops_gen.QuantizedMatmulOp mlir.dialects._linalg_ops_gen.ReciprocalOp mlir.dialects._linalg_ops_gen.ReduceOp mlir.dialects._linalg_ops_gen.RoundOp mlir.dialects._linalg_ops_gen.RsqrtOp mlir.dialects._linalg_ops_gen.SelectOp mlir.dialects._linalg_ops_gen.SqrtOp mlir.dialects._linalg_ops_gen.SquareOp mlir.dialects._linalg_ops_gen.SubOp mlir.dialects._linalg_ops_gen.TanhOp mlir.dialects._linalg_ops_gen.TransposeOp mlir.dialects._linalg_ops_gen.VecmatOp Functions --------- .. autoapisummary:: mlir.dialects._linalg_ops_gen.abs mlir.dialects._linalg_ops_gen.add mlir.dialects._linalg_ops_gen.batch_matmul mlir.dialects._linalg_ops_gen.batch_matvec mlir.dialects._linalg_ops_gen.batch_mmt4d mlir.dialects._linalg_ops_gen.batch_reduce_matmul mlir.dialects._linalg_ops_gen.batch_vecmat mlir.dialects._linalg_ops_gen.broadcast mlir.dialects._linalg_ops_gen.ceil mlir.dialects._linalg_ops_gen.contract mlir.dialects._linalg_ops_gen.conv_1d_ncw_fcw mlir.dialects._linalg_ops_gen.conv_1d_nwc_wcf mlir.dialects._linalg_ops_gen.conv_1d mlir.dialects._linalg_ops_gen.conv_2d_nchw_fchw mlir.dialects._linalg_ops_gen.conv_2d_nchw_fchw_q mlir.dialects._linalg_ops_gen.conv_2d_ngchw_fgchw mlir.dialects._linalg_ops_gen.conv_2d_ngchw_gfchw mlir.dialects._linalg_ops_gen.conv_2d_ngchw_gfchw_q mlir.dialects._linalg_ops_gen.conv_2d_nhwc_fhwc mlir.dialects._linalg_ops_gen.conv_2d_nhwc_fhwc_q mlir.dialects._linalg_ops_gen.conv_2d_nhwc_hwcf mlir.dialects._linalg_ops_gen.conv_2d_nhwc_hwcf_q mlir.dialects._linalg_ops_gen.conv_2d_nhwgc_gfhwc mlir.dialects._linalg_ops_gen.conv_2d_nhwgc_gfhwc_q mlir.dialects._linalg_ops_gen.conv_2d mlir.dialects._linalg_ops_gen.conv_3d_ncdhw_fcdhw mlir.dialects._linalg_ops_gen.conv_3d_ndhwc_dhwcf mlir.dialects._linalg_ops_gen.conv_3d_ndhwc_dhwcf_q mlir.dialects._linalg_ops_gen.conv_3d mlir.dialects._linalg_ops_gen.copy mlir.dialects._linalg_ops_gen.depthwise_conv_1d_ncw_cw mlir.dialects._linalg_ops_gen.depthwise_conv_1d_nwc_wc mlir.dialects._linalg_ops_gen.depthwise_conv_1d_nwc_wcm mlir.dialects._linalg_ops_gen.depthwise_conv_2d_nchw_chw mlir.dialects._linalg_ops_gen.depthwise_conv_2d_nhwc_hwc mlir.dialects._linalg_ops_gen.depthwise_conv_2d_nhwc_hwc_q mlir.dialects._linalg_ops_gen.depthwise_conv_2d_nhwc_hwcm mlir.dialects._linalg_ops_gen.depthwise_conv_2d_nhwc_hwcm_q mlir.dialects._linalg_ops_gen.depthwise_conv_3d_ncdhw_cdhw mlir.dialects._linalg_ops_gen.depthwise_conv_3d_ndhwc_dhwc mlir.dialects._linalg_ops_gen.depthwise_conv_3d_ndhwc_dhwcm mlir.dialects._linalg_ops_gen.div mlir.dialects._linalg_ops_gen.div_unsigned mlir.dialects._linalg_ops_gen.dot mlir.dialects._linalg_ops_gen.elementwise mlir.dialects._linalg_ops_gen.erf mlir.dialects._linalg_ops_gen.exp mlir.dialects._linalg_ops_gen.fill mlir.dialects._linalg_ops_gen.fill_rng_2d mlir.dialects._linalg_ops_gen.floor mlir.dialects._linalg_ops_gen.generic mlir.dialects._linalg_ops_gen.index mlir.dialects._linalg_ops_gen.pack mlir.dialects._linalg_ops_gen.softmax mlir.dialects._linalg_ops_gen.unpack mlir.dialects._linalg_ops_gen.winograd_filter_transform mlir.dialects._linalg_ops_gen.winograd_input_transform mlir.dialects._linalg_ops_gen.winograd_output_transform mlir.dialects._linalg_ops_gen.yield_ mlir.dialects._linalg_ops_gen.log mlir.dialects._linalg_ops_gen.map mlir.dialects._linalg_ops_gen.matmul mlir.dialects._linalg_ops_gen.matvec mlir.dialects._linalg_ops_gen.max mlir.dialects._linalg_ops_gen.min mlir.dialects._linalg_ops_gen.mmt4d mlir.dialects._linalg_ops_gen.mul mlir.dialects._linalg_ops_gen.negf mlir.dialects._linalg_ops_gen.pooling_nchw_max mlir.dialects._linalg_ops_gen.pooling_nchw_sum mlir.dialects._linalg_ops_gen.pooling_ncw_max mlir.dialects._linalg_ops_gen.pooling_ncw_sum mlir.dialects._linalg_ops_gen.pooling_ndhwc_max mlir.dialects._linalg_ops_gen.pooling_ndhwc_min mlir.dialects._linalg_ops_gen.pooling_ndhwc_sum mlir.dialects._linalg_ops_gen.pooling_nhwc_max mlir.dialects._linalg_ops_gen.pooling_nhwc_max_unsigned mlir.dialects._linalg_ops_gen.pooling_nhwc_min mlir.dialects._linalg_ops_gen.pooling_nhwc_min_unsigned mlir.dialects._linalg_ops_gen.pooling_nhwc_sum mlir.dialects._linalg_ops_gen.pooling_nwc_max mlir.dialects._linalg_ops_gen.pooling_nwc_max_unsigned mlir.dialects._linalg_ops_gen.pooling_nwc_min mlir.dialects._linalg_ops_gen.pooling_nwc_min_unsigned mlir.dialects._linalg_ops_gen.pooling_nwc_sum mlir.dialects._linalg_ops_gen.powf mlir.dialects._linalg_ops_gen.quantized_batch_matmul mlir.dialects._linalg_ops_gen.quantized_matmul mlir.dialects._linalg_ops_gen.reciprocal mlir.dialects._linalg_ops_gen.reduce mlir.dialects._linalg_ops_gen.round mlir.dialects._linalg_ops_gen.rsqrt mlir.dialects._linalg_ops_gen.select mlir.dialects._linalg_ops_gen.sqrt mlir.dialects._linalg_ops_gen.square mlir.dialects._linalg_ops_gen.sub mlir.dialects._linalg_ops_gen.tanh mlir.dialects._linalg_ops_gen.transpose mlir.dialects._linalg_ops_gen.vecmat Module Contents --------------- .. py:data:: _ods_ir .. py:class:: _Dialect(descriptor: object) Bases: :py:obj:`_ods_ir` .. py:attribute:: DIALECT_NAMESPACE :value: 'linalg' .. py:class:: AbsOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.abs' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: abs(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, AbsOp] .. py:class:: AddOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The shapes and element types must be identical. The appropriate casts, broadcasts and reductions should be done previously to calling this op. This means reduction/broadcast/element cast semantics is explicit. Further passes can take that into account when lowering this code. For example, a ``linalg.broadcast`` + ``linalg.add`` sequence can be lowered to a ``linalg.generic`` with different affine maps for the two operands. .. py:attribute:: OPERATION_NAME :value: 'linalg.add' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: add(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, AddOp] .. py:class:: BatchMatmulOp(result_tensors, inputs, outputs, *, indexing_maps=None, cast=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. code:: Broadcast and Transpose semantics can be appiled by specifying the explicit attribute 'indexing_maps' as shown below. This is a list attribute, so must include maps for all arguments if specified. Example Transpose: ```mlir linalg.batch_matmul indexing_maps = [affine_map<(batch, m, n, k) -> (batch, k, m)>, // transpose affine_map<(batch, m, n, k) -> (batch, k, n)>, affine_map<(batch, m, n, k) -> (batch, m, n)>] ins(%arg0, %arg1 : memref<2x5x3xf32>,memref<2x5x7xf32>) outs(%arg2: memref<2x3x7xf32>) ``` Example Broadcast: ```mlir linalg.batch_matmul indexing_maps = [affine_map<(batch, m, n, k) -> (k)>, // broadcast affine_map<(batch, m, n, k) -> (batch, k, n)>, affine_map<(batch, m, n, k) -> (batch, m, n)>] ins(%arg0, %arg1 : memref<5xf32>, memref<2x5x7xf32>) outs(%arg2: memref<2x3x7xf32>) ``` Example Broadcast and Transpose: ```mlir linalg.batch_matmul indexing_maps = [affine_map<(batch, m, n, k) -> (m, k)>, // broadcast affine_map<(batch, m, n, k) -> (batch, n, k)>, // transpose affine_map<(batch, m, n, k) -> (batch, m, n)>] ins(%arg0, %arg1 : memref<3x5xf32>, memref<2x7x5xf32>) outs(%arg2: memref<2x3x7xf32>) ``` .. py:attribute:: OPERATION_NAME :value: 'linalg.batch_matmul' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: indexing_maps() -> Optional[_ods_ir] .. py:method:: cast() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: batch_matmul(result_tensors, inputs, outputs, *, indexing_maps=None, cast=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, BatchMatmulOp] .. py:class:: BatchMatvecOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.batch_matvec' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: batch_matvec(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, BatchMatvecOp] .. py:class:: BatchMmt4DOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Besides the outermost batch dimension has the same semantic as linalg.batch_matmul, the differences from linalg.batch_matmul in the non-batch dimensions are the same as linalg.mmt4d vs. linalg.matmul. See the description of lingalg.mmt4d. .. py:attribute:: OPERATION_NAME :value: 'linalg.batch_mmt4d' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: batch_mmt4d(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, BatchMmt4DOp] .. py:class:: BatchReduceMatmulOp(result_tensors, inputs, outputs, *, indexing_maps=None, cast=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. Broadcast and Transpose semantics can be applied by specifying the explicit attribute 'indexing_maps' as shown below. This is a list attribute, so must include maps for all arguments if specified. Example Transpose: .. code:: mlir linalg.batch_reduce_matmul indexing_maps = [affine_map<(batch, m, n, k) -> (batch, k, m)>, // transpose affine_map<(batch, m, n, k) -> (batch, k, n)>, affine_map<(batch, m, n, k) -> (m, n)>] ins(%arg0, %arg1 : memref<2x5x3xf32>,memref<2x5x7xf32>) outs(%arg2: memref<3x7xf32>) Example Broadcast: .. code:: mlir linalg.batch_reduce_matmul indexing_maps = [affine_map<(batch, m, n, k) -> (k)>, // broadcast affine_map<(batch, m, n, k) -> (batch, k, n)>, affine_map<(batch, m, n, k) -> (m, n)>] ins(%arg0, %arg1 : memref<5xf32>, memref<2x5x7xf32>) outs(%arg2: memref<3x7xf32>) Example Broadcast and Transpose: .. code:: mlir linalg.batch_reduce_matmul indexing_maps = [affine_map<(batch, m, n, k) -> (m, k)>, // broadcast affine_map<(batch, m, n, k) -> (batch, n, k)>, // transpose affine_map<(batch, m, n, k) -> (m, n)>] ins(%arg0, %arg1 : memref<3x5xf32>, memref<2x7x5xf32>) outs(%arg2: memref<3x7xf32>) .. py:attribute:: OPERATION_NAME :value: 'linalg.batch_reduce_matmul' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: indexing_maps() -> Optional[_ods_ir] .. py:method:: cast() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: batch_reduce_matmul(result_tensors, inputs, outputs, *, indexing_maps=None, cast=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, BatchReduceMatmulOp] .. py:class:: BatchVecmatOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.batch_vecmat' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: batch_vecmat(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, BatchVecmatOp] .. py:class:: BroadcastOp(result, input, init, dimensions, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Broadcast the input into the given shape by adding ``dimensions``. Example: .. code:: mlir %bcast = linalg.broadcast ins(%input:tensor<16xf32>) outs(%init:tensor<16x64xf32>) dimensions = [1] .. py:attribute:: OPERATION_NAME :value: 'linalg.broadcast' .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: input() -> _ods_ir .. py:method:: init() -> _ods_ir .. py:method:: dimensions() -> _ods_ir .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:method:: region() -> _ods_ir .. py:function:: broadcast(result, input, init, dimensions, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, BroadcastOp] .. py:class:: CeilOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.ceil' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: ceil(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, CeilOp] .. py:class:: ContractOp(result_tensors, inputs, outputs, indexing_maps, *, cast=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The semantics of contracting inputs ``A`` and ``B`` on top of ``C`` to produce output ``D`` is given by ``D[H] = (SUM_{(I ∪ J) \ H} A[I] * B[J]) + C[H]`` where ``I``, ``J``, and ``H`` are tuples of (pairwise distinct) dimension identifiers - meant to range over valid indices - corresponding to the results of the mandatory (projected permutation) ``indexing_maps`` for ``A``, ``B`` and ``C``. ``SUM_{dims}`` means reduce over all valid indices for the dimensions in the set ``dims`` (with ``I``, ``J``, and ``K`` treated as *sets* of dim identifiers). The iteration space consists of all dimensions in ``I``, ``J`` and ``H``, i.e. the domain of each of the ``affine_map``s. Like for einsums, the iteration type of each dim is inferred and is either: * reduction: the dim is used to index into ``A`` and ``B`` but not ``C``. Per the above semantics, these dims will be contracted, i.e. reduced over. * parallel: the dim is used to index into ``C`` and at least one of ``A`` and ``B``, and - deriving from matmul terminology - is either an "M-like" dim (if used on ``A`` and ``C``), an "N-like" dim (if used on ``B`` and ``C``) or a "batch"-dim (if used to index into ``A``, ``B``, and ``C``). For example, batch-matmul is given by ``I = ⟨ b, m, k ⟩``, ``J = ⟨ b, k, n ⟩``, ``H = ⟨ b, m, n ⟩`` (with ``k`` as a contracting reduction-dimension while ``m``, ``n`` and ``b`` have parallel iteration-type) and gets represented as: .. code:: mlir %D = linalg.contract indexing_maps = [affine_map<(batch, m, n, k) -> (batch, m, k)>, affine_map<(batch, m, n, k) -> (batch, k, n)>, affine_map<(batch, m, n, k) -> (batch, m, n)>] ins(%A, %B: tensor, tensor) outs(%C: tensor) -> tensor Note that by permuting dims in the ``affine_map``s' results, accesses to to the inputs and output can be arbitrarily transposed. Similarly, arbitrary broadcasts can be achieved through leaving out dims on either input operand. For example, the following is a variant of batch-matmul with a transposition applied to ``A`` while ``B``'s 2D-matrix gets broadcasted along the batch dim: .. code:: mlir linalg.contract indexing_maps = [affine_map<(batch, m, n, k) -> (batch, k, m)>, affine_map<(batch, m, n, k) -> (k, n)>, affine_map<(batch, m, n, k) -> (batch, m, n)>] ins(%A, %B: memref, memref) outs(%C: memref) Numeric casting is performed on the operands to the inner multiplication, promoting/truncating them to the same data type as the accumulator/output. TODO: Allow control over the combining/accumulating op and possibly the multiplication op. .. py:attribute:: OPERATION_NAME :value: 'linalg.contract' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: indexing_maps() -> _ods_ir .. py:method:: cast() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: combiner() -> _ods_ir .. py:function:: contract(result_tensors, inputs, outputs, indexing_maps, *, cast=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, ContractOp] .. py:class:: Conv1DNcwFcwOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NCW. * Kernel: FCW. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_1d_ncw_fcw' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_1d_ncw_fcw(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv1DNcwFcwOp] .. py:class:: Conv1DNwcWcfOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_1d_nwc_wcf' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_1d_nwc_wcf(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv1DNwcWcfOp] .. py:class:: Conv1DOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_1d' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_1d(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv1DOp] .. py:class:: Conv2DNchwFchwOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NCHW. * Kernel: FCHW. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d_nchw_fchw' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d_nchw_fchw(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DNchwFchwOp] .. py:class:: Conv2DNchwFchwQOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NCHW. * Kernel: FCHW. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. This includes the zero point offsets common to quantized operations. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d_nchw_fchw_q' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d_nchw_fchw_q(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DNchwFchwQOp] .. py:class:: Conv2DNgchwFgchwOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NGCHW. * Kernel: FGCHW. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d_ngchw_fgchw' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d_ngchw_fgchw(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DNgchwFgchwOp] .. py:class:: Conv2DNgchwGfchwOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NGCHW. * Kernel: GFCHW. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d_ngchw_gfchw' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d_ngchw_gfchw(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DNgchwGfchwOp] .. py:class:: Conv2DNgchwGfchwQOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NGCHW. * Kernel: GFCHW. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. This includes the zero point offsets common to quantized operations. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d_ngchw_gfchw_q' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d_ngchw_gfchw_q(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DNgchwGfchwQOp] .. py:class:: Conv2DNhwcFhwcOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NHWC. * Kernel: FHWC. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d_nhwc_fhwc' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d_nhwc_fhwc(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DNhwcFhwcOp] .. py:class:: Conv2DNhwcFhwcQOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NHWC. * Kernel: FHWC. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. This includes the zero point offsets common to quantized operations. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d_nhwc_fhwc_q' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d_nhwc_fhwc_q(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DNhwcFhwcQOp] .. py:class:: Conv2DNhwcHwcfOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NHWC. * Kernel: HWCF. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d_nhwc_hwcf' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d_nhwc_hwcf(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DNhwcHwcfOp] .. py:class:: Conv2DNhwcHwcfQOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NHWC. * Kernel: HWCF. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. This includes the zero point offsets common to quantized operations. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d_nhwc_hwcf_q' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d_nhwc_hwcf_q(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DNhwcHwcfQOp] .. py:class:: Conv2DNhwgcGfhwcOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NHWGC. * Kernel: GFHWC. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d_nhwgc_gfhwc' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d_nhwgc_gfhwc(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DNhwgcGfhwcOp] .. py:class:: Conv2DNhwgcGfhwcQOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NHWGC. * Kernel: GFHWC. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. This includes the zero point offsets common to quantized operations. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d_nhwgc_gfhwc_q' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d_nhwgc_gfhwc_q(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DNhwgcGfhwcQOp] .. py:class:: Conv2DOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_2d' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_2d(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv2DOp] .. py:class:: Conv3DNcdhwFcdhwOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_3d_ncdhw_fcdhw' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_3d_ncdhw_fcdhw(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv3DNcdhwFcdhwOp] .. py:class:: Conv3DNdhwcDhwcfOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_3d_ndhwc_dhwcf' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_3d_ndhwc_dhwcf(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv3DNdhwcDhwcfOp] .. py:class:: Conv3DNdhwcDhwcfQOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. This includes the zero point offsets common to quantized operations. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_3d_ndhwc_dhwcf_q' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_3d_ndhwc_dhwcf_q(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv3DNdhwcDhwcfQOp] .. py:class:: Conv3DOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.conv_3d' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: conv_3d(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Conv3DOp] .. py:class:: CopyOp(result_tensors, inputs, outputs, *, cast=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.copy' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: cast() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: copy(result_tensors, inputs, outputs, *, cast=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, CopyOp] .. py:class:: DepthwiseConv1DNcwCwOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. Multiplier is set to 1 which is a special case for most depthwise convolutions. .. py:attribute:: OPERATION_NAME :value: 'linalg.depthwise_conv_1d_ncw_cw' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: depthwise_conv_1d_ncw_cw(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DepthwiseConv1DNcwCwOp] .. py:class:: DepthwiseConv1DNwcWcOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. Multiplier is set to 1 which is a special case for most depthwise convolutions. .. py:attribute:: OPERATION_NAME :value: 'linalg.depthwise_conv_1d_nwc_wc' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: depthwise_conv_1d_nwc_wc(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DepthwiseConv1DNwcWcOp] .. py:class:: DepthwiseConv1DNwcWcmOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.depthwise_conv_1d_nwc_wcm' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: depthwise_conv_1d_nwc_wcm(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DepthwiseConv1DNwcWcmOp] .. py:class:: DepthwiseConv2DNchwChwOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. Multiplier is set to 1 which is a special case for most depthwise convolutions. .. py:attribute:: OPERATION_NAME :value: 'linalg.depthwise_conv_2d_nchw_chw' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: depthwise_conv_2d_nchw_chw(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DepthwiseConv2DNchwChwOp] .. py:class:: DepthwiseConv2DNhwcHwcOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. Multiplier is set to 1 which is a special case for most depthwise convolutions. .. py:attribute:: OPERATION_NAME :value: 'linalg.depthwise_conv_2d_nhwc_hwc' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: depthwise_conv_2d_nhwc_hwc(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DepthwiseConv2DNhwcHwcOp] .. py:class:: DepthwiseConv2DNhwcHwcQOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.depthwise_conv_2d_nhwc_hwc_q' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: depthwise_conv_2d_nhwc_hwc_q(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DepthwiseConv2DNhwcHwcQOp] .. py:class:: DepthwiseConv2DNhwcHwcmOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.depthwise_conv_2d_nhwc_hwcm' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: depthwise_conv_2d_nhwc_hwcm(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DepthwiseConv2DNhwcHwcmOp] .. py:class:: DepthwiseConv2DNhwcHwcmQOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.depthwise_conv_2d_nhwc_hwcm_q' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: depthwise_conv_2d_nhwc_hwcm_q(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DepthwiseConv2DNhwcHwcmQOp] .. py:class:: DepthwiseConv3DNcdhwCdhwOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. Multiplier is set to 1 which is a special case for most depthwise convolutions. .. py:attribute:: OPERATION_NAME :value: 'linalg.depthwise_conv_3d_ncdhw_cdhw' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: depthwise_conv_3d_ncdhw_cdhw(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DepthwiseConv3DNcdhwCdhwOp] .. py:class:: DepthwiseConv3DNdhwcDhwcOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. Multiplier is set to 1 which is a special case for most depthwise convolutions. .. py:attribute:: OPERATION_NAME :value: 'linalg.depthwise_conv_3d_ndhwc_dhwc' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: depthwise_conv_3d_ndhwc_dhwc(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DepthwiseConv3DNdhwcDhwcOp] .. py:class:: DepthwiseConv3DNdhwcDhwcmOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.depthwise_conv_3d_ndhwc_dhwcm' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: depthwise_conv_3d_ndhwc_dhwcm(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DepthwiseConv3DNdhwcDhwcmOp] .. py:class:: DivOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The shapes and element types must be identical. The appropriate casts, broadcasts and reductions should be done previously to calling this op. This means reduction/broadcast/element cast semantics is explicit. Further passes can take that into account when lowering this code. For example, a ``linalg.broadcast`` + ``linalg.div`` sequence can be lowered to a ``linalg.generic`` with different affine maps for the two operands. .. py:attribute:: OPERATION_NAME :value: 'linalg.div' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: div(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DivOp] .. py:class:: DivUnsignedOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The shapes and element types must be identical. The appropriate casts, broadcasts and reductions should be done previously to calling this op. This means reduction/broadcast/element cast semantics is explicit. Further passes can take that into account when lowering this code. For example, a ``linalg.broadcast`` + ``linalg.div`` sequence can be lowered to a ``linalg.generic`` with different affine maps for the two operands. .. py:attribute:: OPERATION_NAME :value: 'linalg.div_unsigned' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: div_unsigned(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DivUnsignedOp] .. py:class:: DotOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.dot' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: dot(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DotOp] .. py:class:: ElementwiseOp(result_tensors, inputs, outputs, kind, *, indexing_maps=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The attribute ``kind`` describes arithmetic operation to perform. The operation kind can be unary (e.g. max), binary (e.g. add) or ternary (e.g. select). By default, all indexing maps are identities. In the case of default indexing map, all input and output shapes must match. The number of dims in each of the identity maps is equal to the rank of the output type. Affine-maps for operands and result are required to be provided by the user when a transpose and/or broadcast is needed on any operand. When a map is not provided, default identity maps are inferred for each operand. Iterator-types are always all ``parallel``. Iterator-types are needed for constructing the underlying structured op. The number of dims of the iterator-types are inferred from the rank of the result type. Example: Defining a unary linalg.elementwise with default indexing-map: .. code:: mlir %exp = linalg.elementwise kind=#linalg.elementwise_kind ins(%x : tensor<4x16x8xf32>) outs(%y: tensor<4x16x8xf32>) -> tensor<4x16x8xf32> Defining a binary linalg.elementwise with user-defined indexing-map: .. code:: mlir %add = linalg.elementwise kind=#linalg.elementwise_kind indexing_maps = [#transpose, #broadcast, #identity] ins(%exp, %arg1 : tensor<4x16x8xf32>, tensor<4x16xf32>) outs(%arg2: tensor<4x8x16xf32>) -> tensor<4x8x16xf32> .. py:attribute:: OPERATION_NAME :value: 'linalg.elementwise' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: kind() -> _ods_ir .. py:method:: indexing_maps() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: elementwise(result_tensors, inputs, outputs, kind, *, indexing_maps=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, ElementwiseOp] .. py:class:: ErfOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.erf' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: erf(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, ErfOp] .. py:class:: ExpOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.exp' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: exp(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, ExpOp] .. py:class:: FillOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Works for arbitrary ranked output tensors since the operation performs scalar accesses only and is thus rank polymorphic. Numeric casting is performed on the value operand, promoting it to the same data type as the output. .. py:attribute:: OPERATION_NAME :value: 'linalg.fill' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: fill(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, FillOp] .. py:class:: FillRng2DOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The operation generations pseudo random numbers using a linear congruential generator. It provides no guarantees regarding the distribution of the generated random numbers. Instead of generating the random numbers sequentially, it instantiates one random number generator per data element and runs them in parallel. The seed operand and the indices of the data element seed the random number generation. The min and max operands limit the range of the generated random numbers. .. py:attribute:: OPERATION_NAME :value: 'linalg.fill_rng_2d' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: fill_rng_2d(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, FillRng2DOp] .. py:class:: FloorOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.floor' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: floor(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, FloorOp] .. py:class:: GenericOp(result_tensors, inputs, outputs, indexing_maps, iterator_types, *, doc=None, library_call=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Generic Linalg op form where the key properties of the computation are specified as attributes. In pretty form, a ``linalg.generic`` op is written as: .. code:: mlir linalg.generic #trait_attribute ins(%A, %B : memref, memref) outs(%C : memref) attrs = {other-optional-attributes} {region} Where #trait_attributes is an alias of a dictionary attribute containing: * doc [optional]: a documentation string * indexing_maps: a list of AffineMapAttr, one AffineMapAttr per each input and output view. Such AffineMapAttr specifies the mapping between the loops and the indexing within each view. * library_call [optional]: a StringAttr containing the name of an external library function that the linalg.generic operation maps to. The external library is assumed to be dynamically linked and no strong compile-time guarantees are provided. In the absence of such a library call, linalg.generic will always lower to loops. * iterator_types: an ArrayAttr specifying the type of the enclosing loops. Each element of the list represents and iterator of one of the following types: parallel, reduction, window Example: Defining a #matmul_trait attribute in MLIR can be done as follows: .. code:: mlir #matmul_accesses = [ (m, n, k) -> (m, k), (m, n, k) -> (k, n), (m, n, k) -> (m, n) ] #matmul_trait = { doc = "C(m, n) += A(m, k) * B(k, n)", indexing_maps = #matmul_accesses, library_call = "linalg_matmul", iterator_types = ["parallel", "parallel", "reduction"] } And can be reused in multiple places as: .. code:: mlir linalg.generic #matmul_trait ins(%A, %B : memref, memref) outs(%C : memref) {other-optional-attributes} { ^bb0(%a: f32, %b: f32, %c: f32) : %d = arith.mulf %a, %b: f32 %e = arith.addf %c, %d: f32 linalg.yield %e : f32 } This may lower to either: .. code:: mlir call @linalg_matmul(%A, %B, %C) : (memref, memref, memref) -> () or IR resembling: .. code:: mlir scf.for %m = %c0 to %M step %c1 { scf.for %n = %c0 to %N step %c1 { scf.for %k = %c0 to %K step %c1 { %a = load %A[%m, %k] : memref %b = load %B[%k, %n] : memref %c = load %C[%m, %n] : memref %d = arith.mulf %a, %b: f32 %e = arith.addf %c, %d: f32 store %e, %C[%m, %n] : memref } } } .. py:attribute:: OPERATION_NAME :value: 'linalg.generic' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: indexing_maps() -> _ods_ir .. py:method:: iterator_types() -> _ods_ir .. py:method:: doc() -> Optional[_ods_ir] .. py:method:: library_call() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: generic(result_tensors, inputs, outputs, indexing_maps, iterator_types, *, doc=None, library_call=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, GenericOp] .. py:class:: IndexOp(dim, *, results=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The ``linalg.index`` operation returns the iteration index of the immediately enclosing linalg structured operation for the iteration dimension ``dim``. The ``dim`` attribute specifies the position of the accessed dimension in the indexing map domain. Example: .. code:: mlir #map = affine_map<(i, j) -> (i, j)> linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel"]} outs(%I, %J : memref, memref) { ^bb0(%arg0 : index, %arg1 : index): // Access the outer iteration dimension i %i = linalg.index 0 : index // Access the inner iteration dimension j %j = linalg.index 1 : index linalg.yield %i, %j : index, index } This may lower to IR resembling: .. code:: mlir %0 = dim %I, %c0 : memref %1 = dim %I, %c1 : memref scf.for %i = %c0 to %0 step %c1 { scf.for %j = %c0 to %1 step %c1 { store %i, %I[%i, %j] : memref store %j, %J[%i, %j] : memref } } .. py:attribute:: OPERATION_NAME :value: 'linalg.index' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: dim() -> _ods_ir .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:function:: index(dim, *, results=None, loc=None, ip=None) -> _ods_ir .. py:class:: PackOp(source, dest, inner_dims_pos, inner_tiles, static_inner_tiles, *, padding_value=None, outer_dims_perm=None, results=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The "pack" operation converts a source tensor of rank ``n`` into a result tensor of rank ``n + k`` with a tiled and packed layout (maybe with padding) and optionally transposes the tiled source tensor dimensions. ``inner_tiles`` (mandatory) specifies ``k`` tile sizes. These tile sizes correspond to the least significant ("inner") result tensor dimension sizes, in the same order. Tile sizes can be static or dynamic. ``inner_dims_pos`` (mandatory) specifies ``k`` source tensor dimensions that are being tiled, where ``0 <= k <= n``. * ``inner_dims_pos[i]`` specifies the source tensor dimension tiled by ``inner_tiles[i]`` where ``0 <= i < k``. All the values in ``inner_dims_pos`` are within [0, n). * The tiled dimensions (of size ``inner_tiles``) are added to the end of the result tensor in the order in which they appear, i.e. ``shape(result)[rank(source) + i] = inner_tiles[i]`` for ``0 <= i < k``. * The following relationship for the tiled dimensions holds: ``shape(result)[inner_dims_pos[i]] = shape(source)[inner_dims_pos[i]] / inner_tiles[i]``, where (⌈/⌉ indicates CeilDiv). Example: If ``inner_tiles = [16, 32]``, the result tensor has a shape of ``...x16x32``. If ``inner_dims_pos = [0, 1]``, the 0th source dimension is tiled by 16 and the 1st source dimension is tiled by 32. Other source dimensions (if any) are not tiled. If ``inner_dims_pos = [1, 0]``, the 1st dimension is tiled by 16 and the 0th dimension is tiled by 32. Example: .. code:: mlir // NC to NCnc %0 = linalg.pack %source inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %dest : tensor<128x256xf32> -> tensor<16x8 x 8x32 xf32> // \ / \ / // Outer Dims: 16x8 Inner Dims: 8x32 // CHW to CHWhw %0 = linalg.pack %source inner_dims_pos = [2, 1] inner_tiles = [4, 2] into %dest : tensor<3x20x24xf32> -> tensor<3x10x6 x 4x2 xf32> // \ / \ / // Outer Dims: 3x10x6 Inner Dims: 4x2 // HCW to HCWhw %0 = linalg.pack %source inner_dims_pos = [2, 0] inner_tiles = [4, 2] into %dest : tensor<18x3x32xf32> -> tensor<9x3x8 x 4x2 xf32> // \ / \ / // Outer Dims: 9x3x8 Inner Dims: 4x2 ``outer_dims_perm`` (optional) specifies a permutation for the outer dimensions. If specified, it must have ``n`` elements. Example: .. code:: mlir // CK to KCck %0 = linalg.pack %source outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %dest : tensor<128x256xf32> -> tensor<8x16 x 8x32 xf32> // \ / // compare with "NC to NCnc": outer dims are transposed ``padding_value`` specifies a padding value at the boundary on non-perfectly divisible dimensions. Padding is optional: * If absent, it is assumed that for all inner tiles, ``shape(source)[inner_dims_pos[i]] % inner_tiles[i] == 0``, i.e. all inner tiles divide perfectly the corresponding outer dimension in the result tensor. It is UB if the tile does not perfectly divide the dimension. * If present, it will pad along high dimensions (high-padding) to make the tile complete. Note that it is not allowed to have artificial padding that is not strictly required by linalg.pack (i.e., padding past what is needed to complete the last tile along each packed dimension). It is UB if extra padding is requested. It is not possible to verify the requirements statically with dynamic shapes, so they are treated as UB. Example: .. code:: mlir %0 = linalg.pack %arg0 padding_value(%pad : f32) outer_dims_perm = [2, 1, 0] inner_dims_pos = [1] inner_tiles = [2] into %arg1 : tensor<200x127x256xf32> -> tensor<256x64x200x2xf32> // \ // padded and tiled dim // // Source dimension 1 is tiled. 64 does not divide 127 evenly, so 1 padded // element is added at the end. // // Note: Only tiled dimensions can be padded. Invalid example that has artificial padding: .. code:: mlir %0 = linalg.pack %src padding_value(%cst : f32) inner_dims_pos = [0] inner_tiles = [8] into %dest : tensor<9xf32> -> tensor<3x8xf32> // \ // expect tensor<2x8xf32> because CeilDiv(9, 8) = 2 .. py:attribute:: OPERATION_NAME :value: 'linalg.pack' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: source() -> _ods_ir .. py:method:: dest() -> _ods_ir .. py:method:: padding_value() -> Optional[_ods_ir] .. py:method:: inner_tiles() -> _ods_ir .. py:method:: outer_dims_perm() -> Optional[_ods_ir] .. py:method:: inner_dims_pos() -> _ods_ir .. py:method:: static_inner_tiles() -> _ods_ir .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:function:: pack(source, dest, inner_dims_pos, inner_tiles, static_inner_tiles, *, padding_value=None, outer_dims_perm=None, results=None, loc=None, ip=None) -> _ods_ir .. py:class:: SoftmaxOp(result, input, output, dimension, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` linalg.softmax computes a numerically stable version of softmax. For a given input tensor and a specified dimension ``d``, compute: #. the max ``m`` along that dimension ``d`` #. f(x) = exp(x - m) #. sum f(x) along dimension d to get l(x). #. compute the final result f(x) / l(x). This is an aggregate linalg operation that further reduces to a small DAG of structured operations. Warning: Regarding the tiling capabilities, the implementation doesn't check that the provided dimensions make sense. This is the responsability of the transformation calling the tiling to ensure that the provided sizes for each dimension make sense with respect to the semantic of softmax. .. py:attribute:: OPERATION_NAME :value: 'linalg.softmax' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: input() -> _ods_ir .. py:method:: output() -> _ods_ir .. py:method:: dimension() -> _ods_ir .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:function:: softmax(result, input, output, dimension, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, SoftmaxOp] .. py:class:: UnPackOp(source, dest, inner_dims_pos, inner_tiles, static_inner_tiles, *, outer_dims_perm=None, results=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The "unpack" operation converts a source tensor of rank ``n`` with a tiled and packed layout to a result tensor of rank ``n - k``. ``inner_tiles`` (mandatory) specifies ``k`` tile sizes. These tile sizes correspond to the least significant ("inner") source tensor dimension sizes. The behavior of this op is undefined if: * ``inner_tiles`` do not exactly match with the corresponding source tensor dimension sizes. * Or, ``inner_tiles[i]`` does not divide the size of dimension ``inner_dims_pos[i]`` (assuming that ``outer_dims_perm`` is not specified) evenly. ``inner_dims_pos`` (mandatory) specifies ``k`` result tensor (i.e. unpacked tensor) dimensions that were tiled with the ``inner_tiles`` to create the packed source tensor. The source tensor (i.e. packed tensor) dimensions can be unpacked given ``inner_dims_pos`` as follows. * For ``0 <= i < k`` the following relationship holds: ``shape(result)[inner_dims_pos[i]] <= shape(source)[n-k+i] * shape(source)[inner_dims_pos[i]]``. * For ``0 <= j < n-k`` and ``j`` not in ``inner_dims_pos`` the following relationship holds: ``shape(result)[j] = shape(source)[j]``. ``outer_dims_perm`` (optional) specifies a permutation for the outer dimensions. If specified, it must have ``n - k`` elements. If specified, this permutation is applied before combining any dimensions. Note, the unpack operation may drop any padding introduced by the pack operation and hence the following holds ``NumElementsOf(source) >= NumElementsOf(result)``. Examples: .. code:: mlir // NCnc to NC: %0 = linalg.unpack %source inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %dest : tensor<16x8 x 8x32 xf32> -> tensor<128x256xf32> // \ / \ / // Outer Dims: 16x8 Inner Dims: 8x32 // CK to KCck: %0 = linalg.unpack %source outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %dest : tensor<8x16 x 8x32 xf32> -> tensor<128x256xf32> // \ / \ / // Outer Dims: 8x16 Inner Dims: 8x32 // CHW to CHWhw: %0 = linalg.unpack %source inner_dims_pos = [2, 1] inner_tiles = [4, 2] into %dest : tensor<3x10x6 x 4x2 xf32> -> tensor<3x20x24xf32> // \ / \ / // Outer Dims: 3x10x6 Inner Dims: 4x2 // HCW to HCWhw %0 = linalg.unpack %source inner_dims_pos = [2, 0] inner_tiles = [4, 2] into %dest : tensor<9x3x8 x 4x2 xf32> -> tensor<18x3x32xf32> // \ / \ / // Outer Dims: 9x3x8 Inner Dims: 4x2 .. py:attribute:: OPERATION_NAME :value: 'linalg.unpack' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: source() -> _ods_ir .. py:method:: dest() -> _ods_ir .. py:method:: inner_tiles() -> _ods_ir .. py:method:: outer_dims_perm() -> Optional[_ods_ir] .. py:method:: inner_dims_pos() -> _ods_ir .. py:method:: static_inner_tiles() -> _ods_ir .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:function:: unpack(source, dest, inner_dims_pos, inner_tiles, static_inner_tiles, *, outer_dims_perm=None, results=None, loc=None, ip=None) -> _ods_ir .. py:class:: WinogradFilterTransformOp(result, filter, output, fmr, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Winograd Conv2D algorithm will convert linalg Conv2D operator into batched matrix multiply. Before the matrix multiply, it will convert filter and input into a format suitable for batched matrix multiply. After the matrix multiply, it will convert output to the final result tensor. The algorithm F(m x m, r x r) is Y = A^T x [(G x g x G^T) @ (B^T x d x B)] x A The size of output Y is m x m. The size of filter g is r x r. The size of input d is (m + r - 1) x (m + r - 1). A^T, A, G^T, G, B^T, and B are transformation matrices. This operator is defined to represent the high level concept of filter transformation (G x g x G^T) in the Winograd Conv2D algorithm. .. py:attribute:: OPERATION_NAME :value: 'linalg.winograd_filter_transform' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: filter() -> _ods_ir .. py:method:: output() -> _ods_ir .. py:method:: fmr() -> _ods_ir .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:function:: winograd_filter_transform(result, filter, output, fmr, *, loc=None, ip=None) -> _ods_ir .. py:class:: WinogradInputTransformOp(result, input, output, fmr, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Winograd Conv2D algorithm will convert linalg Conv2D operator into batched matrix multiply. Before the matrix multiply, it will convert filter and input into a format suitable for batched matrix multiply. After the matrix multiply, it will convert output to the final result tensor. The algorithm F(m x m, r x r) is Y = A^T x [(G x g x G^T) @ (B^T x d x B)] x A The size of output Y is m x m. The size of filter g is r x r. The size of input d is (m + r - 1) x (m + r - 1). A^T, A, G^T, G, B^T, and B are transformation matrices. This operator is defined to represent the high level concept of input transformation (B^T x d x B) in the Winograd Conv2D algorithm. .. py:attribute:: OPERATION_NAME :value: 'linalg.winograd_input_transform' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: input() -> _ods_ir .. py:method:: output() -> _ods_ir .. py:method:: fmr() -> _ods_ir .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:function:: winograd_input_transform(result, input, output, fmr, *, loc=None, ip=None) -> _ods_ir .. py:class:: WinogradOutputTransformOp(result, value, output, fmr, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Winograd Conv2D algorithm will convert linalg Conv2D operator into batched matrix multiply. Before the matrix multiply, it will convert filter and input into a format suitable for batched matrix multiply. After the matrix multiply, it will convert output to the final result tensor. The algorithm F(m x m, r x r) is Y = A^T x [(G x g x G^T) @ (B^T x d x B)] x A The size of output Y is m x m. The size of filter g is r x r. The size of input d is (m + r - 1) x (m + r - 1). A^T, A, G^T, G, B^T, and B are transformation matrices. This operator is defined to represent the high level concept of output transformation (A^T x y x A) in the Winograd Conv2D algorithm. .. py:attribute:: OPERATION_NAME :value: 'linalg.winograd_output_transform' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: value() -> _ods_ir .. py:method:: output() -> _ods_ir .. py:method:: fmr() -> _ods_ir .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:function:: winograd_output_transform(result, value, output, fmr, *, loc=None, ip=None) -> _ods_ir .. py:class:: YieldOp(values, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` ``linalg.yield`` is a special terminator operation for blocks inside regions in ``linalg`` generic ops. It returns values to the immediately enclosing ``linalg`` generic op. Example: .. code:: mlir linalg.yield %f0, %f1 : f32, f32 .. py:attribute:: OPERATION_NAME :value: 'linalg.yield' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: values() -> _ods_ir .. py:function:: yield_(values, *, loc=None, ip=None) -> YieldOp .. py:class:: LogOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.log' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: log(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, LogOp] .. py:class:: MapOp(result, inputs, init, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Models elementwise operations on tensors in terms of arithmetic operations on the corresponding elements. Example: .. code:: mlir %add = linalg.map ins(%lhs, %rhs : tensor<64xf32>, tensor<64xf32>) outs(%init: tensor<64xf32>) (%lhs_elem: f32, %rhs_elem: f32) { %0 = arith.addf %lhs_elem, %rhs_elem: f32 linalg.yield %0: f32 } Shortened print form is available for simple maps where the body contains exactly two operations (the payload operation and a yield), the payload operation has the same number of operands as block arguments with operands matching block arguments in order, and the yield operand is the result of the payload operation. The example above will be printed using the shortened form as: .. code:: mlir %add = linalg.map { arith.addf } ins(%lhs, %rhs : tensor<64xf32>, tensor<64xf32>) outs(%init: tensor<64xf32>) .. py:attribute:: OPERATION_NAME :value: 'linalg.map' .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: init() -> _ods_ir .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:method:: mapper() -> _ods_ir .. py:function:: map(result, inputs, init, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, MapOp] .. py:class:: MatmulOp(result_tensors, inputs, outputs, *, indexing_maps=None, cast=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. Broadcast and Transpose semantics can be appiled by specifying the explicit attribute 'indexing_maps' as shown below.This is a list attribute, so the list must include all the maps if specified. Example Transpose: .. code:: mlir linalg.matmul indexing_maps = [affine_map<(m, n, k) -> (k, m)>, // transpose affine_map<(m, n, k) -> (k, n)>, affine_map<(m, n, k) -> (m, n)>] ins(%arg0, %arg1 : memref<5x3xf32>,memref<5x7xf32>) outs(%arg2: memref<3x7xf32>) Example Broadcast: .. code:: mlir linalg.matmul indexing_maps = [affine_map<(m, n, k) -> (k)>, // broadcast affine_map<(m, n, k) -> (k, n)>, affine_map<(m, n, k) -> (m, n)>] ins(%arg0, %arg1 : memref<3xf32>, memref<5x7xf32>) outs(%arg2: memref<3x7xf32>) Example Broadcast and transpose: .. code:: mlir linalg.matmul indexing_maps = [affine_map<(m, n, k) -> (k, m)>, // transpose affine_map<(m, n, k) -> (k)>, // broadcast affine_map<(m, n, k) -> (m, n)>] ins(%arg0, %arg1 : memref<5x3xf32>, memref<7xf32>) outs(%arg2: memref<3x7xf32>) .. py:attribute:: OPERATION_NAME :value: 'linalg.matmul' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: indexing_maps() -> Optional[_ods_ir] .. py:method:: cast() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: matmul(result_tensors, inputs, outputs, *, indexing_maps=None, cast=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, MatmulOp] .. py:class:: MatvecOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.matvec' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: matvec(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, MatvecOp] .. py:class:: MaxOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The shapes and element types must be identical. The appropriate casts, broadcasts and reductions should be done previously to calling this op. This means reduction/broadcast/element cast semantics is explicit. Further passes can take that into account when lowering this code. For example, a ``linalg.broadcast`` + ``linalg.max`` sequence can be lowered to a ``linalg.generic`` with different affine maps for the two operands. .. py:attribute:: OPERATION_NAME :value: 'linalg.max' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: max(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, MaxOp] .. py:class:: MinOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The shapes and element types must be identical. The appropriate casts, broadcasts and reductions should be done previously to calling this op. This means reduction/broadcast/element cast semantics is explicit. Further passes can take that into account when lowering this code. For example, a ``linalg.broadcast`` + ``linalg.min`` sequence can be lowered to a ``linalg.generic`` with different affine maps for the two operands. .. py:attribute:: OPERATION_NAME :value: 'linalg.min' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: min(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, MinOp] .. py:class:: Mmt4DOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Differences from linalg.matmul: * The right hand side is transposed, whence the 't' in 'mmt'. * The input and output tensors have a 4D shape instead of a 2D shape. They are interpreted as 2D matrices with one level of 2D tile subdivision, whence the 2+2=4 dimensions. The inner tile dimensions are identified with '0' suffixes below, for instance the LHS matrix shape (M, K, M0, K0) reads as: MxK tiles, each of shape M0xK0. .. py:attribute:: OPERATION_NAME :value: 'linalg.mmt4d' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: mmt4d(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, Mmt4DOp] .. py:class:: MulOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The shapes and element types must be identical. The appropriate casts, broadcasts and reductions should be done previously to calling this op. This means reduction/broadcast/element cast semantics is explicit. Further passes can take that into account when lowering this code. For example, a ``linalg.broadcast`` + ``linalg.mul`` sequence can be lowered to a ``linalg.generic`` with different affine maps for the two operands. .. py:attribute:: OPERATION_NAME :value: 'linalg.mul' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: mul(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, MulOp] .. py:class:: NegFOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.negf' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: negf(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, NegFOp] .. py:class:: PoolingNchwMaxOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nchw_max' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nchw_max(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNchwMaxOp] .. py:class:: PoolingNchwSumOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NCHW. * Kernel: HW. Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nchw_sum' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nchw_sum(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNchwSumOp] .. py:class:: PoolingNcwMaxOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_ncw_max' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_ncw_max(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNcwMaxOp] .. py:class:: PoolingNcwSumOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NCW. * Kernel: W. Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_ncw_sum' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_ncw_sum(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNcwSumOp] .. py:class:: PoolingNdhwcMaxOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_ndhwc_max' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_ndhwc_max(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNdhwcMaxOp] .. py:class:: PoolingNdhwcMinOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_ndhwc_min' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_ndhwc_min(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNdhwcMinOp] .. py:class:: PoolingNdhwcSumOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_ndhwc_sum' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_ndhwc_sum(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNdhwcSumOp] .. py:class:: PoolingNhwcMaxOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nhwc_max' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nhwc_max(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNhwcMaxOp] .. py:class:: PoolingNhwcMaxUnsignedOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nhwc_max_unsigned' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nhwc_max_unsigned(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNhwcMaxUnsignedOp] .. py:class:: PoolingNhwcMinOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nhwc_min' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nhwc_min(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNhwcMinOp] .. py:class:: PoolingNhwcMinUnsignedOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nhwc_min_unsigned' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nhwc_min_unsigned(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNhwcMinUnsignedOp] .. py:class:: PoolingNhwcSumOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NHWC. * Kernel: HW. Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nhwc_sum' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nhwc_sum(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNhwcSumOp] .. py:class:: PoolingNwcMaxOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nwc_max' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nwc_max(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNwcMaxOp] .. py:class:: PoolingNwcMaxUnsignedOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nwc_max_unsigned' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nwc_max_unsigned(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNwcMaxUnsignedOp] .. py:class:: PoolingNwcMinOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nwc_min' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nwc_min(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNwcMinOp] .. py:class:: PoolingNwcMinUnsignedOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nwc_min_unsigned' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nwc_min_unsigned(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNwcMinUnsignedOp] .. py:class:: PoolingNwcSumOp(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Layout: * Input: NWC. * Kernel: W. Numeric casting is performed on the input operand, promoting it to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.pooling_nwc_sum' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: strides() -> Optional[_ods_ir] .. py:method:: dilations() -> Optional[_ods_ir] .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: pooling_nwc_sum(result_tensors, inputs, outputs, *, strides=None, dilations=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PoolingNwcSumOp] .. py:class:: PowFOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Only applies to floating point values. The shapes and element types must be identical. The appropriate casts, broadcasts and reductions should be done previously to calling this op. This means reduction/broadcast/element cast semantics is explicit. Further passes can take that into account when lowering this code. For example, a ``linalg.broadcast`` + ``linalg.powf`` sequence can be lowered to a ``linalg.generic`` with different affine maps for the two operands. .. py:attribute:: OPERATION_NAME :value: 'linalg.powf' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: powf(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, PowFOp] .. py:class:: QuantizedBatchMatmulOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. The quantized variant includes zero-point adjustments for the left and right operands of the matmul. .. py:attribute:: OPERATION_NAME :value: 'linalg.quantized_batch_matmul' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: quantized_batch_matmul(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, QuantizedBatchMatmulOp] .. py:class:: QuantizedMatmulOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. The quantized variant includes zero-point adjustments for the left and right operands of the matmul. .. py:attribute:: OPERATION_NAME :value: 'linalg.quantized_matmul' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: quantized_matmul(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, QuantizedMatmulOp] .. py:class:: ReciprocalOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.reciprocal' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: reciprocal(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, ReciprocalOp] .. py:class:: ReduceOp(result, inputs, inits, dimensions, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Executes ``combiner`` on the ``dimensions`` of ``inputs`` and returns the reduced result. The ``dimensions`` attribute needs to list the reduction dimensions in increasing order. Example: .. code:: mlir %reduce = linalg.reduce ins(%input:tensor<16x32x64xf32>) outs(%init:tensor<16x64xf32>) dimensions = [1] (%in: f32, %out: f32) { %0 = arith.addf %out, %in: f32 linalg.yield %0: f32 } Shortened print form is available for simple reduces where the body contains exactly two operations (the payload operation and a yield), the payload operation has the same number of operands as block arguments, the first block argument (init) is the last operand of the payload operation with remaining operands matching remaining block arguments in order, and the yield operand is the result of the payload operation. The example above will be printed using the shortened form as: .. code:: mlir %reduce = linalg.reduce { arith.addf } ins(%input:tensor<16x32x64xf32>) outs(%init:tensor<16x64xf32>) dimensions = [1] .. py:attribute:: OPERATION_NAME :value: 'linalg.reduce' .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: inits() -> _ods_ir .. py:method:: dimensions() -> _ods_ir .. py:method:: combiner() -> _ods_ir .. py:function:: reduce(result, inputs, inits, dimensions, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, ReduceOp] .. py:class:: RoundOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.round' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: round(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, RoundOp] .. py:class:: RsqrtOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.rsqrt' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: rsqrt(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, RsqrtOp] .. py:class:: SelectOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The shapes and element types must be identical. The appropriate casts, broadcasts and reductions should be done previously to calling this op. This means reduction/broadcast/element cast semantics is explicit. Further passes can take that into account when lowering this code. For example, a ``linalg.broadcast`` + ``linalg.select`` sequence can be lowered to a ``linalg.generic`` with different affine maps for the two operands. .. py:attribute:: OPERATION_NAME :value: 'linalg.select' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: select(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, SelectOp] .. py:class:: SqrtOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.sqrt' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: sqrt(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, SqrtOp] .. py:class:: SquareOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.square' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: square(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, SquareOp] .. py:class:: SubOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` The shapes and element types must be identical. The appropriate casts, broadcasts and reductions should be done previously to calling this op. This means reduction/broadcast/element cast semantics is explicit. Further passes can take that into account when lowering this code. For example, a ``linalg.broadcast`` + ``linalg.sub`` sequence can be lowered to a ``linalg.generic`` with different affine maps for the two operands. .. py:attribute:: OPERATION_NAME :value: 'linalg.sub' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: sub(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, SubOp] .. py:class:: TanhOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` No numeric casting is performed on the input operand. .. py:attribute:: OPERATION_NAME :value: 'linalg.tanh' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: tanh(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, TanhOp] .. py:class:: TransposeOp(result, input, init, permutation, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Permutes the dimensions of ``input`` according to the given ``permutation``. ``dim(result, i) = dim(input, permutation[i])`` This op actually moves data, unlike ``memref.transpose`` which is a metadata operation only that produces a transposed "view". Example: .. code:: mlir %transpose = linalg.transpose ins(%input:tensor<16x64xf32>) outs(%init:tensor<64x16xf32>) permutation = [1, 0] .. py:attribute:: OPERATION_NAME :value: 'linalg.transpose' .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: input() -> _ods_ir .. py:method:: init() -> _ods_ir .. py:method:: permutation() -> _ods_ir .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:method:: region() -> _ods_ir .. py:function:: transpose(result, input, init, permutation, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, TransposeOp] .. py:class:: VecmatOp(result_tensors, inputs, outputs, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. .. py:attribute:: OPERATION_NAME :value: 'linalg.vecmat' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (1, True) .. py:method:: inputs() -> _ods_ir .. py:method:: outputs() -> _ods_ir .. py:method:: result_tensors() -> _ods_ir .. py:method:: region() -> _ods_ir .. py:function:: vecmat(result_tensors, inputs, outputs, *, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, VecmatOp]