def _test_output_bf16(self, model, x):
modelName = model.__class__.__name__
core.enable_auto_dnnl()
core.enable_jit()
core.disable_mix_bf16_fp32()
model = model.to('dpcpp').eval()
x = x.to('dpcpp')
x2 = x.clone()
fused_model = torch.jit.script(copy.deepcopy(model))
# bn folding, removing it after solve some issue, using mix_preci? to check
core.disable_auto_dnnl()
fused_model = wrap_cpp_module(
torch._C._jit_pass_fold_convbn(fused_model._c))
core.enable_auto_dnnl()
core.enable_mix_bf16_fp32()
# prepack convolution weight, weight will be a bf16 tensor
fused_model = wrap_cpp_module(
core._jit_prepack_conv_weight(fused_model._c))
with torch.no_grad():
# bf16, native path
result = model(x)
# bf16, jit path
fresult = fused_model(x2)
#print(result)
#print(fresult)
self.assertEqual(fresult, result)
def _test_output(self,
model,
x,
kind_in_graph=None,
kind_not_in_graph=None):
modelName = model.__class__.__name__
core.disable_jit_opt()
core.disable_mix_bf16_fp32()
model = model.to(device).eval()
x = x.to(device)
with torch.no_grad():
result = model(x)
script_model = torch.jit.script(model)
script_model.eval()
trace_model = torch.jit.trace(model, x)
trace_model.eval()
with torch.no_grad():
sresult = script_model(x)
tresult = trace_model(x)
self.assertEqual(result, sresult)
self.assertEqual(result, tresult)
core.enable_jit_opt()
script_fused_model = torch.jit.script(model)
trace_fused_model = torch.jit.trace(model, x)
with torch.no_grad():
# conv relu fusion, conv sum fusion or conv sum relu fusion
script_graph = script_fused_model.graph_for(x)
fused_sresult = script_fused_model(x)
trace_graph = trace_fused_model.graph_for(x)
fused_tresult = trace_fused_model(x)
self.assertEqual(result, fused_sresult)
self.assertEqual(result, fused_tresult)
# check if the fused node exists in the graph
if kind_in_graph is not None:
self.assertTrue(
any(n.kind() == kind_in_graph for n in script_graph.nodes()))
self.assertTrue(
any(n.kind() == kind_in_graph for n in trace_graph.nodes()))
# check if certain node does not exist in the graph
if kind_not_in_graph is not None:
self.assertTrue(
all(n.kind() != kind_not_in_graph
for n in script_graph.nodes()))
self.assertTrue(
all(n.kind() != kind_not_in_graph
for n in trace_graph.nodes()))
def _test_output_bf16(self,
model,
x,
kind_in_graph=None,
kind_not_in_graph=None,
prec=None):
modelName = model.__class__.__name__
core.enable_auto_dnnl()
core.enable_jit_opt()
core.enable_mix_bf16_fp32()
model = model.to(ipex.DEVICE).eval()
x = x.to(ipex.DEVICE)
x2 = x.clone()
x3 = x.clone()
script_fused_model = torch.jit.script(copy.deepcopy(model))
trace_fused_model = torch.jit.trace(copy.deepcopy(model), x3)
with torch.no_grad():
# bf16, native path
result = model(x)
# bf16, jit script path
script_graph = script_fused_model.graph_for(x2)
fused_sresult = script_fused_model(x2)
# bf 16, jit trace path
trace_graph = trace_fused_model.graph_for(x3)
fused_tresult = trace_fused_model(x3)
# disable mix_bf16_fp32 when the calculation is done
# to avoid affecting other scripts
core.disable_mix_bf16_fp32()
self.assertEqual(fused_sresult, result, prec=prec)
self.assertEqual(fused_tresult, result, prec=prec)
# check if the fused node exists in the graph
if kind_in_graph is not None:
self.assertTrue(
any(n.kind() == kind_in_graph for n in script_graph.nodes()))
self.assertTrue(
any(n.kind() == kind_in_graph for n in trace_graph.nodes()))
# check if certain node does not exist in the graph
if kind_not_in_graph is not None:
self.assertTrue(
all(n.kind() != kind_not_in_graph
for n in script_graph.nodes()))
self.assertTrue(
all(n.kind() != kind_not_in_graph
for n in trace_graph.nodes()))
