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#!/usr/bin/env python3
# Owner(s): ["oncall: mobile"]
import tempfile
import torch
from torch.ao.nn.sparse.quantized.dynamic.linear import Linear
from torch.testing._internal.common_quantized import (
qengine_is_qnnpack,
override_quantized_engine,
override_cpu_allocator_for_qnnpack
)
from torch.testing._internal.common_utils import TestCase
class TestQlinearPackedParams(TestCase):
def test_qlinear_packed_params(self, allow_non_zero_zero_points=False):
# copied from https://pytorch.org/docs/stable/sparse.html#csr-tensor-operations,
# so row/col block indices match that example, but with blocks and
# scaled rows
weight_fp32 = torch.Tensor([
[0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0],
[6, 6, 6, 6, 12, 12, 12, 12, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
row_block_size = 1
col_block_size = 4
out_features = weight_fp32.shape[0]
in_features = weight_fp32.shape[1]
scales = [2.0, 6.0, 12.0]
zero_points = [
((i + 1) if allow_non_zero_zero_points else 0) for i in range(out_features)
]
dtype = torch.qint8
wide_weight_fp32 = torch.zeros((3, 4008)) # 4000 is tile width for Fbgemm
wide_weight_fp32[0][0] = 4
wide_weight_fp32[0][4004] = 6
wide_weight_fp32[1][0] = 8
per_tensor_small = (
torch.quantize_per_tensor(
weight_fp32,
scales[0],
zero_points[0],
dtype
),
True,
[0, 1, 3, 3],
[2, 0, 1],
[x + (1 if allow_non_zero_zero_points else 0) for x in [
1, 1, 1, 1, 3, 3, 3, 3, 6, 6, 6, 6
]],
)
per_channel_small = (
torch.quantize_per_channel(
weight_fp32,
torch.Tensor(scales),
torch.Tensor(zero_points).to(torch.int),
0, # axis = 0
dtype,
),
False,
[0, 1, 3, 3],
[2, 0, 1],
[x + ([1, 2, 2][i // 4] if allow_non_zero_zero_points else 0) for (i, x) in enumerate([
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2
])],
)
per_tensor_large = (
torch.quantize_per_tensor(
wide_weight_fp32,
scales[0],
zero_points[0],
dtype,
),
True,
[0, 2, 3, 3],
[0, 1001, 0],
[x + (1 if allow_non_zero_zero_points else 0) for x in [
2, 0, 0, 0, 3, 0, 0, 0, 4, 0, 0, 0
]],
)
for (weight, is_per_tensor_quantized, expected_row_block_indices, expected_col_block_indices, expected_weights) in [
per_tensor_small, per_channel_small, per_tensor_large
]:
lin = Linear(
out_features=weight.shape[0],
in_features=weight.shape[1],
row_block_size=row_block_size,
col_block_size=col_block_size,
bias=True,
dtype=dtype,
)
bias = torch.ones(size=(weight.shape[0],))
lin.set_weight_bias(weight, bias, row_block_size, col_block_size)
serialized = lin._packed_params._packed_params.__getstate__()
(
_, # version
bias_,
out_features_block_size_,
in_features_block_size_,
weight_scales_,
weight_zero_points_,
quantization_scheme_,
row_block_indices_,
col_block_indices_,
weights_,
output_channels_,
input_channels_
) = serialized[0]
# Test Serialization
self.assertEqual(bias_, bias)
self.assertEqual(out_features_block_size_, row_block_size)
self.assertEqual(in_features_block_size_, col_block_size)
self.assertEqual(weight_scales_, [scales[0]] if is_per_tensor_quantized else scales)
self.assertEqual(weight_zero_points_, [zero_points[0]] if is_per_tensor_quantized else zero_points)
self.assertEqual(quantization_scheme_, is_per_tensor_quantized)
self.assertEqual(row_block_indices_, expected_row_block_indices)
self.assertEqual(col_block_indices_, expected_col_block_indices)
self.assertEqual(weights_.tolist(), [v + 128 for v in expected_weights]) # weights are serialized as +128
self.assertEqual(output_channels_, weight.shape[0])
self.assertEqual(input_channels_, weight.shape[1])
# Test Unpacking
(weights_, bias_, out_features_block_size_, in_features_block_size_) = lin._weight_bias()
self.assertEqual(torch.dequantize(weights_), torch.dequantize(weight))
self.assertEqual(bias_, bias)
self.assertEqual(out_features_block_size_, row_block_size)
self.assertEqual(in_features_block_size_, col_block_size)
# Test Deserialization
with tempfile.TemporaryFile() as file_buff:
torch.save(lin, file_buff)
file_buff.seek(0)
lin2 = torch.load(file_buff)
self.assertEqual(lin._weight_bias(), lin2._weight_bias())
# Serialize -> Deserialize -> Serialize should match Serialize
self.assertEqual(serialized, lin2._packed_params._packed_params.__getstate__())
# Test that op output is preserved by serialize -> deserialize
if qengine_is_qnnpack():
x = torch.rand(size=(1, weight.shape[1]))
y1 = lin(x)
y2 = lin2(x)
self.assertEqual(y1, y2)
def test_qlinear_packed_params_qnnpack(self):
torch.manual_seed(0)
with override_quantized_engine('qnnpack'):
with override_cpu_allocator_for_qnnpack(qengine_is_qnnpack()):
self.test_qlinear_packed_params(allow_non_zero_zero_points=True)
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