Source code for aitemplate.compiler.ops.gemm_universal.perm102_bmm_rcr

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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 gemm_common as common
from aitemplate.compiler.ops.gemm_universal.bmm import bmm

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


[docs]class perm102_bmm_rcr(bmm): """Batch GEMM specialization: C[m, b, n](row) = bmm(A[m, b, k](row), B[b, n, k](col)) 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() XT = X_pt.permute(1, 0, 2) Y_pt = torch.bmm(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" def cal_align_ab(m, n, k): return common.default_align_ab(k, k, self._attrs["inputs"][0].dtype()) self._attrs["f_ab_alignment"] = cal_align_ab def _infer_shapes(self, a: Tensor, b: Tensor): a_shapes = a._attrs["shape"] b_shapes = b._attrs["shape"] batch_size_a = a_shapes[1] batch_size_b = b_shapes[0] if batch_size_a != batch_size_b and batch_size_a != 1 and batch_size_b != 1: raise RuntimeError( f"bmm operand A and B should have same batch_size, or batch_size = 1! " f"Current shape A: {a_shapes} shape B: {b_shapes}." ) batch_size = batch_size_b if batch_size_a == IntImm(1) else batch_size_a # [m, b, n] return [a_shapes[0], batch_size, b_shapes[1]] def _extract_dims(self, for_profiling=False): # (M, B, K) * (B, N, K) = (M, B, N) return { "B": [common.DimInfo(common.Source.OUTPUT, tensor_idx=0, dim_idx=1)], "M": [ common.DimInfo(common.Source.INPUT, tensor_idx=0, dim_idx=0), common.DimInfo(common.Source.OUTPUT, tensor_idx=0, dim_idx=0), ], "N": [ common.DimInfo(common.Source.INPUT, tensor_idx=1, dim_idx=1), common.DimInfo(common.Source.OUTPUT, tensor_idx=0, dim_idx=2), ], "K": [ common.DimInfo(common.Source.INPUT, tensor_idx=0, dim_idx=2), common.DimInfo(common.Source.INPUT, tensor_idx=1, dim_idx=2), ], } def _invert_exec_key(self, key): return common.gemm_inverse_key_func(key) def _gen_profile_cmd(self, profiler_prefix, cfg, exec_key): def fbuild_cmd(exec_key): B, M, N, K = self._invert_exec_key(exec_key) cmd = [] cmd.append(B) # b cmd.append(M) # m cmd.append(N) # n cmd.append(K) # k return cmd return super()._gen_profile_cmd(profiler_prefix, cfg, exec_key, fbuild_cmd)