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# Owner(s): ["oncall: quantization"]
import torch
from torch.testing._internal.common_quantization import (
skipIfNoFBGEMM
)
from torch.testing._internal.common_utils import suppress_warnings
from torch.testing._internal.jit_utils import JitTestCase
from typing import Tuple
import copy
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)
ref = copy.deepcopy(cell)
cell = torch.jit.quantized.quantize_rnn_cell_modules(cell)
x = torch.tensor([[100, -155],
[-155, 100],
[100, -155]], dtype=torch.float)
h0_vals = [[-155, 100],
[-155, 155],
[100, -155]]
hx = torch.tensor(h0_vals, dtype=torch.float)
if isinstance(cell, torch.jit.quantized.QuantizedLSTMCell):
cx = torch.tensor(h0_vals, dtype=torch.float)
hiddens = (hx, cx)
else:
hiddens = hx
if isinstance(cell, torch.jit.quantized.QuantizedLSTMCell):
class ScriptWrapper(torch.jit.ScriptModule):
def __init__(self, cell):
super(ScriptWrapper, self).__init__()
self.cell = cell
@torch.jit.script_method
def forward(self, x: torch.Tensor,
hiddens: Tuple[torch.Tensor, torch.Tensor]
) -> Tuple[torch.Tensor, torch.Tensor]:
return self.cell(x, hiddens)
else:
class ScriptWrapper(torch.jit.ScriptModule):
def __init__(self, cell):
super(ScriptWrapper, self).__init__()
self.cell = cell
@torch.jit.script_method
def forward(self, x: torch.Tensor, hiddens: torch.Tensor) -> torch.Tensor:
return self.cell(x, hiddens)
cell = ScriptWrapper(cell)
outs = cell(x, hiddens)
cell = self.getExportImportCopyWithPacking(cell)
outs = cell(x, hiddens)
ref_outs = ref(x, hiddens)
self.assertEqual(len(outs), len(ref_outs))
for out, ref_out in zip(outs, ref_outs):
torch.testing.assert_close(out, ref_out)
@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)
ref = copy.deepcopy(cell)
cell_int8 = torch.jit.quantized.quantize_rnn_modules(cell, dtype=torch.int8)
cell_fp16 = torch.jit.quantized.quantize_rnn_modules(cell, dtype=torch.float16)
niter = 10
x = torch.tensor([[100, -155],
[-155, 100],
[100, -155]], dtype=torch.float).unsqueeze(0).repeat(niter, 1, 1)
h0_vals = [[-155, 100],
[-155, 155],
[100, -155]]
hx = torch.tensor(h0_vals, dtype=torch.float).unsqueeze(0)
cx = torch.tensor(h0_vals, dtype=torch.float).unsqueeze(0)
if isinstance(ref, torch.nn.LSTM):
hiddens = (hx, cx)
elif isinstance(ref, torch.nn.GRU):
hiddens = hx
ref_out, ref_hid = ref(x, hiddens)
# Compare int8 quantized to unquantized
output_int8, final_hiddens_int8 = cell_int8(x, hiddens)
torch.testing.assert_close(output_int8, ref_out)
for out, ref in zip(final_hiddens_int8, ref_hid):
torch.testing.assert_close(out, ref)
# Compare fp16 quantized to unquantized
output_fp16, final_hiddens_fp16 = cell_fp16(x, hiddens)
torch.testing.assert_close(output_fp16, ref_out)
for out, ref in zip(final_hiddens_fp16, ref_hid):
torch.testing.assert_close(out, ref)
def compare_quantized_unquantized(ScriptWrapper, cell):
wrapper = ScriptWrapper(cell)
# Compare quantize scripted module to unquantized
script_out, script_hid = wrapper(x, hiddens)
torch.testing.assert_close(script_out, ref_out)
for out, ref in zip(script_hid, ref_hid):
torch.testing.assert_close(out, ref)
# Compare export/import to unquantized
export_import_wrapper = self.getExportImportCopyWithPacking(wrapper)
ei_out, ei_hid = export_import_wrapper(x, hiddens)
torch.testing.assert_close(ei_out, ref_out)
for out, ref in zip(ei_hid, ref_hid):
torch.testing.assert_close(out, ref)
if isinstance(cell, torch.jit.quantized.QuantizedGRU):
class ScriptWrapper(torch.jit.ScriptModule):
def __init__(self, cell):
super(ScriptWrapper, self).__init__()
self.cell = cell
@torch.jit.script_method
def forward(self, x: torch.Tensor, hiddens: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
return self.cell(x, hiddens)
compare_quantized_unquantized(ScriptWrapper, cell)
elif isinstance(cell, torch.jit.quantized.QuantizedLSTM):
for cell in [cell_int8, cell_fp16]:
class ScriptWrapper(torch.jit.ScriptModule):
def __init__(self, cell):
super(ScriptWrapper, self).__init__()
self.cell = cell
@torch.jit.script_method
def forward(self, x, hiddens):
# type: (torch.Tensor, Tuple[torch.Tensor, torch.Tensor])
# -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]
return self.cell(x, hiddens)
compare_quantized_unquantized(ScriptWrapper, cell)
if 'fbgemm' in torch.backends.quantized.supported_engines:
# Suppression: using deprecated quant api
@suppress_warnings
def test_quantization_modules(self):
K1, N1 = 2, 2
class FooBar(torch.nn.Module):
def __init__(self):
super(FooBar, self).__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)
fb_int8 = torch.jit.quantized.quantize_linear_modules(fb)
traced_int8 = torch.jit.trace(fb_int8, (x,))
fb_int8 = self.getExportImportCopyWithPacking(traced_int8)
y_int8 = fb_int8(value)
fb_fp16 = torch.jit.quantized.quantize_linear_modules(fb, torch.float16)
traced_fp16 = torch.jit.trace(fb_fp16, (x,))
fb_fp16 = self.getExportImportCopyWithPacking(traced_fp16)
y_fp16 = fb_fp16(value)
torch.testing.assert_close(y_int8, y_ref, rtol=0.0001, atol=1e-3)
torch.testing.assert_close(y_fp16, y_ref, rtol=0.0001, atol=1e-3)
@skipIfNoFBGEMM
def test_erase_class_tensor_shapes(self):
class Linear(torch.nn.Module):
def __init__(self, in_features, out_features):
super(Linear, self).__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.")
|
