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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_quantization import skipIfNoFBGEMM, skipIfNoQNNPACK
from torch.testing._internal.common_quantized import (
override_cpu_allocator_for_qnnpack,
override_quantized_engine,
qengine_is_qnnpack,
)
from torch.testing._internal.common_utils import TestCase
class TestQlinearPackedParams(TestCase):
def qlinear_packed_params_test(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)
@skipIfNoFBGEMM
def test_qlinear_packed_params_fbgemm(self):
torch.manual_seed(0)
with override_quantized_engine("fbgemm"):
self.qlinear_packed_params_test(allow_non_zero_zero_points=False)
@skipIfNoQNNPACK
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.qlinear_packed_params_test(allow_non_zero_zero_points=True)
def test_qlinear_packed_params_fbgemm_qnnpack_cross_compatibility(self):
torch.manual_seed(0)
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, 3.0, 7.0]
zero_points = [0 for _ in range(out_features)]
dtype = torch.qint8
x = torch.rand(size=(1, weight_fp32.shape[1]))
def make_lin_get_state_weight_bias_and_save():
weight = torch.quantize_per_tensor(
weight_fp32,
scales[0],
zero_points[0],
dtype,
)
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)
state = lin._packed_params._packed_params.__getstate__()
weight_bias = lin._weight_bias()
file_buff = tempfile.TemporaryFile()
torch.save(lin, file_buff)
file_buff.seek(0)
return ((state, weight_bias), file_buff)
def load_get_state_weight_bias(f_b):
lin2 = torch.load(f_b)
state = lin2._packed_params._packed_params.__getstate__()
weight_bias = lin2._weight_bias()
f_b.close()
return (state, weight_bias)
def packed_params_data_with_int32_indices(data_as_state_and_weight_bias):
(st, weight_bias) = data_as_state_and_weight_bias
(s0, s1) = st
s0_updated = tuple(
[
# 7 and 8 are row and col block indices respectively
v if (i != 7 and i != 8) else v.to(torch.int32)
for (i, v) in enumerate(list(s0))
]
)
return ((s0_updated, s1), weight_bias)
# Test Fbgemm -> Qnnpack
with override_quantized_engine("fbgemm"):
(
packed_params_data_1a,
file_buff_1,
) = make_lin_get_state_weight_bias_and_save()
with override_quantized_engine("qnnpack"):
with override_cpu_allocator_for_qnnpack(qengine_is_qnnpack()):
packed_params_data_1b = load_get_state_weight_bias(file_buff_1)
self.assertEqual(
packed_params_data_with_int32_indices(packed_params_data_1a),
packed_params_data_with_int32_indices(packed_params_data_1b),
)
# Test Qnnpack -> Fbgemm
with override_quantized_engine("qnnpack"):
with override_cpu_allocator_for_qnnpack(qengine_is_qnnpack()):
(
packed_params_data_2a,
file_buff_2,
) = make_lin_get_state_weight_bias_and_save()
with override_quantized_engine("fbgemm"):
packed_params_data_2b = load_get_state_weight_bias(file_buff_2)
self.assertEqual(
packed_params_data_with_int32_indices(packed_params_data_2a),
packed_params_data_with_int32_indices(packed_params_data_2b),
)
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