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# Owner(s): ["oncall: quantization"]
import torch
from torch.ao.quantization.experimental.linear import LinearAPoT
from torch.nn.modules.linear import Linear
import unittest
class TestNonUniformObserver(unittest.TestCase):
"""
Test linear_APoT_fn by comparing to uniform linear
for 2d tensors with size (4,4) and k=1
"""
def test_linear_APoT_k1(self):
# weight: fp tensor
weight = 1000 * torch.rand(4, 4)
# activtion: fp32 tensor with ~ integer values
activation = torch.randint(low=0, high=255, size=(4, 4), dtype=torch.float)
# calculate result from calling linear forward method
apot_linear = LinearAPoT(weight, 8, 1)
apot_linear_result = apot_linear(activation)
# calculate expected results
fp_linear = Linear(4, 4, bias=False)
# set weight for fp linear
apot_quantized_weight_float = apot_linear.weight.type(torch.FloatTensor)
fp_linear_weight = torch.nn.parameter.Parameter(data=apot_quantized_weight_float)
fp_linear.weight = fp_linear_weight
fp_linear_result = fp_linear(activation).data
self.assertTrue(torch.equal(apot_linear_result, fp_linear_result))
"""
Test linear_APoT_fn by comparing to uniform linear
for 2d tensors with size (5,3), (3, 5) and k=2
"""
def test_linear_APoT_k2(self):
# weight: fp tensor
weight = 1000 * torch.rand(5, 3)
# activtion: fp32 tensor with ~ integer values
# note: transpose of activation matrix will have dimension (3, 5)
activation = torch.randint(low=0, high=255, size=(5, 3), dtype=torch.float)
# calculate result from calling linear forward method
apot_linear = LinearAPoT(weight, 8, 2)
apot_linear_result = apot_linear(activation)
# calculate expected results
fp_linear = Linear(4, 4, bias=False)
# set weight for fp linear
apot_quantized_weight_float = apot_linear.weight.type(torch.FloatTensor)
fp_linear_weight = torch.nn.parameter.Parameter(data=apot_quantized_weight_float)
fp_linear.weight = fp_linear_weight
fp_linear_result = fp_linear(activation).data
self.assertTrue(torch.equal(apot_linear_result, fp_linear_result))
if __name__ == '__main__':
unittest.main()
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