Source code for aitemplate.compiler.ops.gemm_universal.perm102_bmm_rcr_bias

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"""
Batch GEMM specialization: C[m, b, n](row) = bmm(A[m, b, k](row), B[b, n, k](col))
"""

from aitemplate.compiler.base import IntImm, Tensor
from aitemplate.compiler.ops.gemm_universal import perm102_bmm_rcr
from aitemplate.compiler.tensor_accessor import TensorAccessor

# pylint: disable=C0103, W0223, W0221


[docs]class perm102_bmm_rcr_bias(perm102_bmm_rcr): """ Batch GEMM specialization: C[m, b, n](row) = bmm(A[m, b, k](row), B[b, n, k](col)) + bias[b, n] The op is equivalent to the following PyTorch code: .. highlight:: python .. code-block:: python X_pt = torch.randn(M, B, K).cuda().half() W_pt = torch.randn(B, N, K).cuda().half() B_pt = torch.randn(B, N).cuda().half() XT = X_pt.permute(1, 0, 2) Bias = B_pt.unsqueeze(1) Y_pt = torch.baddbmm(Bias, XT, W_pt.permute([0, 2, 1])) Y_pt = Y_pt.permute(1, 0, 2) """ def __init__(self): super().__init__() self._attrs["op"] = "perm102_bmm_rcr_bias" def _infer_shapes(self, a: Tensor, b: Tensor, bias: Tensor): bias_shapes = bias._attrs["shape"] if len(bias_shapes) != 2: raise RuntimeError("Bias should be 2D vector ") bias_shape = bias_shapes[1] if not isinstance(bias_shape, IntImm): raise RuntimeError("Bias should be fixed 2D vector") outshape = super()._infer_shapes(a, b) if outshape[2] != bias_shape: raise RuntimeError("GEMM/Bias shape doesn't match") return outshape def __call__(self, a: Tensor, b: Tensor, bias: Tensor) -> Tensor: """Call operator Parameters ---------- a : Tensor Tensors of shape (M, B, K) b : Tensor Tensor of shape (B, N, K) bias : Tensor Tensor of shape (B, N) Returns ------- Tensor Tensor of shape (M, B, N) """ self._attrs["inputs"] = [a, b, bias] self._attrs["input_accessors"] = [ TensorAccessor(tensor) for tensor in self._attrs["inputs"] ] self._set_depth() self._sanity_check(a, b) output_shape = self._infer_shapes(a, b, bias) self._extract_epilogue_alignment(output_shape) output = Tensor(output_shape, src_ops={self}, dtype=a.dtype()) self._attrs["outputs"] = [output] self._attrs["output_accessors"] = [ TensorAccessor(tensor) for tensor in self._attrs["outputs"] ] return output