# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
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)