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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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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