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# Owner(s): ["oncall: speech_infra"]
import copy
import torch
import torch.nn as nn
from torch.ao.quantization.experimental.adaround_optimization import (
AdaptiveRoundingOptimizer,
)
from torch.nn import functional as F
from torch.quantization.observer import MinMaxObserver
from torch.testing._internal.common_quantization import QuantizationTestCase
def forward_wrapper(fetcher):
def forward(module, input, output):
fetcher.append(input[0].detach())
fetcher.append(output.detach())
return forward
class TestAdaround(QuantizationTestCase):
def feedforawrd_callback(
self,
model,
data,
) -> None:
model(data)
def feedforawrd_callback_with_wrapper(self, model, data, wrapper) -> None:
wrapper(model, data)
def run_adaround(self, model, img_data, wrapper=None):
adaround_optimizer = AdaptiveRoundingOptimizer(
model,
self.feedforawrd_callback
if wrapper is None
else self.feedforawrd_callback_with_wrapper,
forward_wrapper,
img_data,
max_iter=100,
batch_size=10,
feed_forward_wrapper=wrapper,
)
adarounded_model = adaround_optimizer.run_adaround()
return adarounded_model
def get_fake_quant(self, model):
hard_fake_quant_model = copy.deepcopy(model)
for _, module in hard_fake_quant_model.named_modules():
if isinstance(module, (torch.nn.Linear, torch.nn.Conv2d)):
weight_observer = MinMaxObserver(
quant_min=-128,
quant_max=127,
dtype=torch.qint8,
qscheme=torch.per_tensor_symmetric,
)
weight_observer(module.weight)
scale, zero_point = weight_observer.calculate_qparams()
fake_quant_module = torch.fake_quantize_per_tensor_affine(
module.weight,
scale=scale,
zero_point=zero_point,
quant_min=-128,
quant_max=127,
)
module.weight.data.copy_(fake_quant_module)
return hard_fake_quant_model
def get_feed_forward_wrapper(self):
class FeedForwardWrapper(nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(self, model, sample):
return model(sample)
wrapper_module = FeedForwardWrapper()
return wrapper_module
def test_linear_chain(self):
class LinearChain(nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear1 = nn.Linear(3, 4)
self.linear2 = nn.Linear(4, 5)
self.linear3 = nn.Linear(5, 6)
def forward(self, x):
x = self.linear1(x)
x = self.linear2(x)
x = self.linear3(x)
return x
float_model = LinearChain()
img_data = [torch.rand(10, 3, dtype=torch.float) for _ in range(50)]
adarounded_model = self.run_adaround(
float_model, img_data, self.get_feed_forward_wrapper()
)
fq_model = self.get_fake_quant(float_model)
rand_input = torch.rand(10, 3)
with torch.no_grad():
ada_out = adarounded_model(rand_input)
fq_out = fq_model(rand_input)
float_out = float_model(rand_input)
ada_loss = F.mse_loss(ada_out, float_out)
fq_loss = F.mse_loss(fq_out, float_out)
self.assertTrue(ada_loss.item() < fq_loss.item())
def test_conv_chain(self):
class ConvChain(nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv2d1 = nn.Conv2d(3, 4, 5, 5)
self.conv2d2 = nn.Conv2d(4, 5, 5, 5)
self.conv2d3 = nn.Conv2d(5, 6, 5, 5)
def forward(self, x):
x = self.conv2d1(x)
x = self.conv2d2(x)
x = self.conv2d3(x)
return x
float_model = ConvChain()
img_data = [torch.rand(10, 3, 125, 125, dtype=torch.float) for _ in range(50)]
adarounded_model = self.run_adaround(float_model, img_data)
fq_model = self.get_fake_quant(float_model)
rand_input = torch.rand(10, 3, 256, 256)
with torch.no_grad():
ada_out = adarounded_model(rand_input)
fq_out = fq_model(rand_input)
float_out = float_model(rand_input)
ada_loss = F.mse_loss(ada_out, float_out)
fq_loss = F.mse_loss(fq_out, float_out)
self.assertTrue(ada_loss.item() < fq_loss.item())
|
