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[bug fix] update quantizable add ops detection on IPEX backend #1456

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Merged
merged 4 commits into from
Dec 25, 2023
Merged

[bug fix] update quantizable add ops detection on IPEX backend #1456

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2068355
fix bug in ipex
xin3he Dec 12, 2023
c2453ef
skip logger
xin3he Dec 12, 2023
c12bc21
rewrite logic
xin3he Dec 12, 2023
d41b513
remove ut due to no add ops
xin3he Dec 25, 2023
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16 changes: 7 additions & 9 deletions neural_compressor/adaptor/torch_utils/util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -458,15 +458,13 @@ def get_quantizable_ops_from_cfgs(ops_name, op_infos_from_cfgs, input_tensor_ids
op_info = op_infos_from_cfgs[name]
output_tensors = op_info["output_tensor_infos"]
input_tensors = op_info["input_tensor_infos"]
for input_tensor in input_tensors:
if "inf_dtype" not in input_tensor.keys():
continue
if input_tensor["inf_dtype"] == torch.float32:
pre_op_name = input_tensor_ids_op_name[input_tensor["id"]]
if pre_op_name[1] in ["q_op_infos"]:
print(pre_op_name, "is not the fuse ops first op.")
start = False
continue
start = any(
[
input_tensor["inf_dtype"] != "torch.float32"
for input_tensor in input_tensors
if "inf_dtype" in input_tensor.keys()
]
)
if not start:
continue
# add quantizable ops, include op and fuse ops.
Expand Down
117 changes: 0 additions & 117 deletions test/ipex/test_adaptor_ipex.py
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Original file line number Diff line number Diff line change
Expand Up @@ -316,62 +316,6 @@ def forward(self, a):
)
self.assertTrue(isinstance(q_model._model, torch.jit.ScriptModule))

def test_tune_add(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(3, 1, 1)
self.linear = torch.nn.Linear(224 * 224, 5)

def forward(self, a):
x = self.conv(a)
x = x.view(1, -1)
x += x
x = self.linear(x)
return x

model = M()
from neural_compressor import PostTrainingQuantConfig, quantization

acc_lst = [1, 0.8, 1.1, 1.2]

def fake_eval(model):
res = acc_lst.pop(0)
return res

conf = PostTrainingQuantConfig(backend="ipex", quant_level=0)
calib_dataloader = Dataloader()
q_model = quantization.fit(model, conf, calib_dataloader=calib_dataloader, eval_func=fake_eval)
self.assertTrue(isinstance(q_model._model, torch.jit.ScriptModule))

def test_tune_add_with_recipe(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(224 * 224 * 3, 5)

def forward(self, x):
x += x
x = x.view(1, -1)
x = self.linear(x)
return x

model = M()
from neural_compressor import PostTrainingQuantConfig, quantization

acc_lst = [1, 0.8, 1.1, 1.2]

def fake_eval(model):
res = acc_lst.pop(0)
return res

conf = PostTrainingQuantConfig(
backend="ipex", quant_level=0, recipes={"smooth_quant": True, "smooth_quant_args": {"alpha": 0.5}}
)
calib_dataloader = Dataloader()
q_model = quantization.fit(model, conf, calib_dataloader=calib_dataloader, eval_func=fake_eval)
self.assertTrue(isinstance(q_model._model, torch.jit.ScriptModule))

def test_tune_minmax_obs(self):
class M(torch.nn.Module):
def __init__(self):
Expand Down Expand Up @@ -527,67 +471,6 @@ def forward(self, a):
)
self.assertTrue(isinstance(q_model._model, torch.jit.ScriptModule))

def test_tune_add(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(3, 1, 1)
self.linear = torch.nn.Linear(224 * 224, 5)

def forward(self, a):
x = self.conv(a)
x = x.view(1, -1)
x += x
x = self.linear(x)
return x

model = M().to("xpu")
from neural_compressor import PostTrainingQuantConfig, quantization

acc_lst = [1, 0.8, 1.1, 1.2]

def fake_eval(model):
res = acc_lst.pop(0)
return res

conf = PostTrainingQuantConfig(backend="ipex", device="xpu", quant_level=0)
calib_dataloader = Dataloader(device="xpu")
q_model = quantization.fit(model, conf, calib_dataloader=calib_dataloader, eval_func=fake_eval)
self.assertTrue(isinstance(q_model._model, torch.jit.ScriptModule))

def test_tune_add_with_recipe(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(3, 1, 1)
self.linear = torch.nn.Linear(224 * 224, 5)

def forward(self, a):
x = self.conv(a)
x = x.view(1, -1)
x += x
x = self.linear(x)
return x

model = M().to("xpu")
from neural_compressor import PostTrainingQuantConfig, quantization

acc_lst = [1, 0.8, 1.1, 1.2]

def fake_eval(model):
res = acc_lst.pop(0)
return res

conf = PostTrainingQuantConfig(
backend="ipex",
device="xpu",
quant_level=0,
recipes={"smooth_quant": True, "smooth_quant_args": {"alpha": 0.5}},
)
calib_dataloader = Dataloader(device="xpu")
q_model = quantization.fit(model, conf, calib_dataloader=calib_dataloader, eval_func=fake_eval)
self.assertTrue(isinstance(q_model._model, torch.jit.ScriptModule))


class TestMixedPrecision(unittest.TestCase):
@classmethod
Expand Down