Source code for aitemplate.compiler.ops.gemm_universal.perm021fc_crc

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"""
GEMM Specialization: (A.permute(0, 2, 1)[col] @ B[row])
Note: This op's output is a ColMajor
"""

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 perm021fc_crc(bmm): """GEMM Specialization: (A.permute(0, 2, 1) @ B) This one is used when n/m gives you better alignment than m/k. Note: This op's output is a ColMajor This op is equivalent to the following PyTorch code: .. highlight:: python .. code-block:: python X_pt = torch.randn(B, K, M).cuda().half() W_pt = torch.randn(K, N).cuda().half() XT = X_pt.permute(0, 2, 1) XT = torch.reshape(XT, (-1, K)) WT = W_pt.transpose(0, 1).contiguous() Y_pt = torch.nn.functional.linear(XT, WT) Y_pt = torch.reshape(Y_pt, (B, M, N)).contiguous() """ def __init__(self): super().__init__() self._attrs["op"] = "perm021fc_crc" def cal_align_ab(m, n, k): return common.default_align_ab(m, n, 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[0] batch_size_b = b_shapes[0] if ( batch_size_a != batch_size_b and batch_size_a != IntImm(1) and batch_size_b != IntImm(1) ): raise RuntimeError( "bmm operand A and B should have same batch_size, or batch_size = 1! " "Current shape A: {} shape B: {} .".format(a_shapes, b_shapes) ) batch_size = batch_size_b if batch_size_a == IntImm(1) else batch_size_a return [batch_size, b_shapes[2], a_shapes[2]] def _extract_dims(self, for_profiling=False): # (B, K, N) * (B, K, M) = (B, M, N) return { "B": [common.DimInfo(common.Source.OUTPUT, tensor_idx=0, dim_idx=0)], "M": [ common.DimInfo(common.Source.INPUT, tensor_idx=1, dim_idx=2), common.DimInfo(common.Source.OUTPUT, tensor_idx=0, dim_idx=1), ], "N": [ common.DimInfo(common.Source.INPUT, tensor_idx=0, dim_idx=2), common.DimInfo(common.Source.OUTPUT, tensor_idx=0, dim_idx=2), ], "K": [ common.DimInfo(common.Source.INPUT, tensor_idx=0, dim_idx=1), common.DimInfo(common.Source.INPUT, tensor_idx=1, dim_idx=1), ], } 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) # m 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) def _align_ab(self, a: Tensor, b: Tensor): # b: [b, k, m] # a: [1, k, n] # TODO(xxx): Not implemented, need to pad m, n to 8 return a, b