Source code for aitemplate.compiler.ops.gemm_universal.bmm_rcr_permute

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"""
Batch GEMM specialization for A[RowMajor], B[ColMajor], C[RowMajor] with permutation on output.
"""

from typing import Tuple

from aitemplate.compiler.base import Tensor
from aitemplate.compiler.ops.common.view_ops import reshape
from aitemplate.compiler.ops.gemm_universal.bmm_xxx import bmm_rcr
from aitemplate.compiler.tensor_accessor import TensorAccessor

# pylint: disable=C0103,W0223,W0221,W0613


[docs]class bmm_rcr_permute(bmm_rcr): """Batch GEMM specialization for A[RowMajor], B[ColMajor], C[RowMajor] with permutation on output to given layout. Currently only supports reshape to 4D tensor, then do 0213 permute This operator is equivalent to following PyTorch code: .. highlight:: python .. code-block:: python X_pt = torch.randn(B, M, K).cuda().half() W_pt = torch.randn(B, N, K).cuda().half() WT = torch.transpose(W_pt, 2, 1) Y_l = torch.bmm(X_pt, WT) Y_r = Y_l.reshape(B // D1, D1, M, N) Y_pt = torch.permute(Y_r, [0, 2, 1, 3]) """ def __init__(self, shape: Tuple[int], layout="0213"): """Constructor for bmm_rcr_permute Parameters ---------- shape : Tuple[int] Necessary dim info of the reshape operator In 0213 case, we need to know the [D1,] to reshape the output from 3D to 4D layout : str, optional permutation type, by default "0213" """ super().__init__() self._attrs["op"] = "bmm_rcr_permute" self._attrs["shape"] = shape self._attrs["layout"] = "Permute4DBMM_{}".format(layout) self._attrs["permute_shape"] = "_".join(map(str, shape)) def __call__(self, a: Tensor, b: Tensor) -> Tensor: """Call bmm_rcr_permute with tensors a, b Parameters ---------- a : Tensor Tensor in shape (B, M, K) b : Tensor Tensor in shape (B, N, K) Returns ------- Tensor Tensors in shape (B // D1, M, D1, N) for 0213 permute Raises ------ NotImplementedError Permute layout not implemented yet """ a, b = self._align_ab(a, b) self._attrs["inputs"] = [a, b] self._attrs["input_accessors"] = [TensorAccessor(a), TensorAccessor(b)] self._set_depth() self._sanity_check(a, b) output_shape = self._infer_shapes(a, b) output = Tensor(output_shape, src_ops={self}, dtype=a.dtype()) self._attrs["outputs"] = [output] self._attrs["output_accessors"] = [TensorAccessor(output)] if self._attrs["layout"] == "Permute4DBMM_0213": b, m, n = output_shape d1 = self._attrs["shape"][0] output_shape = [b.value() // d1, m, d1, n] self._extract_epilogue_alignment(output_shape) return reshape()(output, output_shape) else: raise NotImplementedError( "{} is not implemented!".format(self._attrs["layout"]) ) def _get_op_attributes(self): return { "layout": self._attrs["layout"].split("_")[-1], "shape": tuple(map(int, self._attrs["permute_shape"].split("_"))), }