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# Owner(s): ["oncall: quantization"]
import torch
from torch.testing._internal.common_quantization import skipIfNoFBGEMM
from torch.testing._internal.jit_utils import JitTestCase
class TestDeprecatedJitQuantized(JitTestCase):
@skipIfNoFBGEMM
def test_rnn_cell_quantized(self):
d_in, d_hid = 2, 2
for cell in [
torch.nn.LSTMCell(d_in, d_hid).float(),
torch.nn.GRUCell(d_in, d_hid).float(),
torch.nn.RNNCell(d_in, d_hid).float(),
]:
if isinstance(cell, torch.nn.LSTMCell):
num_chunks = 4
elif isinstance(cell, torch.nn.GRUCell):
num_chunks = 3
elif isinstance(cell, torch.nn.RNNCell):
num_chunks = 1
# Replace parameter values s.t. the range of values is exactly
# 255, thus we will have 0 quantization error in the quantized
# GEMM call. This i s for testing purposes.
#
# Note that the current implementation does not support
# accumulation values outside of the range representable by a
# 16 bit integer, instead resulting in a saturated value. We
# must take care that in our test we do not end up with a dot
# product that overflows the int16 range, e.g.
# (255*127+255*127) = 64770. So, we hardcode the test values
# here and ensure a mix of signedness.
vals = [
[100, -155],
[100, -155],
[-155, 100],
[-155, 100],
[100, -155],
[-155, 100],
[-155, 100],
[100, -155],
]
vals = vals[: d_hid * num_chunks]
cell.weight_ih = torch.nn.Parameter(
torch.tensor(vals, dtype=torch.float), requires_grad=False
)
cell.weight_hh = torch.nn.Parameter(
torch.tensor(vals, dtype=torch.float), requires_grad=False
)
with self.assertRaisesRegex(
RuntimeError,
"quantize_rnn_cell_modules function is no longer supported",
):
cell = torch.jit.quantized.quantize_rnn_cell_modules(cell)
@skipIfNoFBGEMM
def test_rnn_quantized(self):
d_in, d_hid = 2, 2
for cell in [
torch.nn.LSTM(d_in, d_hid).float(),
torch.nn.GRU(d_in, d_hid).float(),
]:
# Replace parameter values s.t. the range of values is exactly
# 255, thus we will have 0 quantization error in the quantized
# GEMM call. This i s for testing purposes.
#
# Note that the current implementation does not support
# accumulation values outside of the range representable by a
# 16 bit integer, instead resulting in a saturated value. We
# must take care that in our test we do not end up with a dot
# product that overflows the int16 range, e.g.
# (255*127+255*127) = 64770. So, we hardcode the test values
# here and ensure a mix of signedness.
vals = [
[100, -155],
[100, -155],
[-155, 100],
[-155, 100],
[100, -155],
[-155, 100],
[-155, 100],
[100, -155],
]
if isinstance(cell, torch.nn.LSTM):
num_chunks = 4
elif isinstance(cell, torch.nn.GRU):
num_chunks = 3
vals = vals[: d_hid * num_chunks]
cell.weight_ih_l0 = torch.nn.Parameter(
torch.tensor(vals, dtype=torch.float), requires_grad=False
)
cell.weight_hh_l0 = torch.nn.Parameter(
torch.tensor(vals, dtype=torch.float), requires_grad=False
)
with self.assertRaisesRegex(
RuntimeError, "quantize_rnn_modules function is no longer supported"
):
cell_int8 = torch.jit.quantized.quantize_rnn_modules(
cell, dtype=torch.int8
)
with self.assertRaisesRegex(
RuntimeError, "quantize_rnn_modules function is no longer supported"
):
cell_fp16 = torch.jit.quantized.quantize_rnn_modules(
cell, dtype=torch.float16
)
if "fbgemm" in torch.backends.quantized.supported_engines:
def test_quantization_modules(self):
K1, N1 = 2, 2
class FooBar(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear1 = torch.nn.Linear(K1, N1).float()
def forward(self, x):
x = self.linear1(x)
return x
fb = FooBar()
fb.linear1.weight = torch.nn.Parameter(
torch.tensor([[-150, 100], [100, -150]], dtype=torch.float),
requires_grad=False,
)
fb.linear1.bias = torch.nn.Parameter(
torch.zeros_like(fb.linear1.bias), requires_grad=False
)
x = (torch.rand(1, K1).float() - 0.5) / 10.0
value = torch.tensor([[100, -150]], dtype=torch.float)
y_ref = fb(value)
with self.assertRaisesRegex(
RuntimeError, "quantize_linear_modules function is no longer supported"
):
fb_int8 = torch.jit.quantized.quantize_linear_modules(fb)
with self.assertRaisesRegex(
RuntimeError, "quantize_linear_modules function is no longer supported"
):
fb_fp16 = torch.jit.quantized.quantize_linear_modules(fb, torch.float16)
@skipIfNoFBGEMM
def test_erase_class_tensor_shapes(self):
class Linear(torch.nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
qweight = torch._empty_affine_quantized(
[out_features, in_features],
scale=1,
zero_point=0,
dtype=torch.qint8,
)
self._packed_weight = torch.ops.quantized.linear_prepack(qweight)
@torch.jit.export
def __getstate__(self):
return (
torch.ops.quantized.linear_unpack(self._packed_weight)[0],
self.training,
)
def forward(self):
return self._packed_weight
@torch.jit.export
def __setstate__(self, state):
self._packed_weight = torch.ops.quantized.linear_prepack(state[0])
self.training = state[1]
@property
def weight(self):
return torch.ops.quantized.linear_unpack(self._packed_weight)[0]
@weight.setter
def weight(self, w):
self._packed_weight = torch.ops.quantized.linear_prepack(w)
with torch._jit_internal._disable_emit_hooks():
x = torch.jit.script(Linear(10, 10))
torch._C._jit_pass_erase_shape_information(x.graph)
if __name__ == "__main__":
raise RuntimeError(
"This test file is not meant to be run directly, use:\n\n"
"\tpython test/test_quantization.py TESTNAME\n\n"
"instead."
)
|
