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#################################################################################################
#
# Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
#################################################################################################
"""
High-level tests for running batched GEMMs
"""
from functools import partial
import logging
from math import prod
import unittest
import cutlass
from cutlass.backend.utils.device import device_cc
import torch
from utils import LayoutCombination
cutlass.set_log_level(logging.WARNING)
torch.manual_seed(2023)
def pytorch_reference(A, B, C, alpha, beta):
# Get the batch count. Assume that any of A, B, and C
# with a batch dimension ahve matching batch count. Thus,
# we break out of the loop once we have found the first
# tensor containing a batch dimension.
batch_count = (1,)
for tensor in [A, B, C]:
if len(tensor.shape) > 2:
batch_count = tensor.shape[:-2]
break
int_batch_count = prod(batch_count)
def add_batch(tensor):
if len(tensor.shape) == 2:
return tensor.unsqueeze(0).repeat(int_batch_count, 1, 1)
else:
return tensor.reshape(-1, tensor.size(-2), tensor.size(-1))
# Reshape tensors to have batch dimension
A = add_batch(A)
B = add_batch(B)
C = add_batch(C)
ret = (torch.bmm(A, B) * alpha) + (C * beta)
reshape_vals = batch_count + C.shape[-2:]
return ret.reshape(*reshape_vals)
def initialize(rows, cols, batch):
tensor = torch.randint(-3, 3, size=(rows*cols*prod(batch),), device='cuda').half()
if len(batch) > 0 and prod(batch) > 1:
reshape_vals = batch + (rows, cols)
return tensor.reshape(*reshape_vals)
else:
return tensor.reshape(rows, cols)
class GemmF16Batched(unittest.TestCase):
def run_batched(self, batch_count: tuple, batch_A: bool, batch_B: bool, batch_C: bool):
M = 512
N = 256
K = 128
alpha = 1.
beta = 2.
A = initialize(M, K, batch_count if batch_A else (1,))
B = initialize(K, N, batch_count if batch_B else (1,))
C = initialize(M, N, batch_count if batch_C else (1,))
D = initialize(M, N, batch_count)
plan = cutlass.op.Gemm(A=A, B=B, C=C, D=D, element_accumulator=cutlass.DataType.f32)
plan.run(A, B, C, D, alpha, beta)
reference = pytorch_reference(A, B, C, alpha, beta)
assert reference.equal(D)
def test_batched_ABC(self):
self.run_batched((3,), True, True, True)
self.run_batched((2, 3), True, True, True)
def test_batched_AB(self):
self.run_batched((3,), True, True, False)
self.run_batched((2, 3), True, True, False)
def test_batched_AC(self):
self.run_batched((3,), True, False, True)
self.run_batched((2, 3), True, False, True)
def test_batched_BC(self):
self.run_batched((3,), False, True, True)
self.run_batched((2, 3), False, True, True)
def test_batched_A(self):
self.run_batched((3,), True, False, False)
self.run_batched((2, 3), True, False, False)
def test_batched_B(self):
self.run_batched((3,), False, True, False)
self.run_batched((2, 3), False, True, False)
if __name__ == '__main__':
unittest.main()
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