def test_getattr(self):
"""Test fusion of the PyTorch prim::GetAttr Node into the Glow subgraph."""
with torch.no_grad():
class Model(torch.nn.Module):
def __init__(self):
super(Model, self).__init__()
self.linear = torch.nn.Linear(2, 1)
def forward(self, x):
return self.linear(x)
x = torch.tensor([2.0, 3.0])
torch_glow.enableFusionPass_DO_NOT_USE_THIS()
m = Model()
jit_m = torch.jit.trace(m, x)
jit_m_graph = jit_m.graph_for(x)
# Ensure all prim::GetAttrs were fused and none were left out
found_getattrs = False
for node in jit_m_graph.nodes():
kind = node.kind()
assert (
kind != "prim::GetAttr"
), "Expected all prim::GetAttrsGlow to be in Glow subgraph"
if kind == GLOW_FUSION_GROUP:
glow_subgraph = node.g(SUBGRAPH_ATTR)
for node in glow_subgraph.nodes():
if node.kind() == "prim::GetAttr":
found_getattrs = True
assert (found_getattrs
), "Expected to find prim::GetAttrs in the Glow subgraph"
def test_op_blacklist(self):
"""Test Glow fuser op kind blacklisting mechanism."""
def f(a, b):
return (a + b) * (a - b)
torch_glow.enableFusionPass_DO_NOT_USE_THIS()
torch_glow.setFusionBlacklist(["aten::add"])
a = torch.randn(5, 5)
b = torch.randn(5, 5)
jit_f = torch.jit.trace(f, (a, b))
jit_f_graph = jit_f.graph_for(a, b)
fused_add = False
fused_sub = False
for node in jit_f_graph.nodes():
if node.kind() == GLOW_FUSION_GROUP:
glow_subgraph = node.g(SUBGRAPH_ATTR)
for node in glow_subgraph.nodes():
if node.kind() == "aten::add":
fused_add = True
if node.kind() == "aten::sub":
fused_sub = True
assert not fused_add, "Expected aten::add to be blacklisted"
assert fused_sub, "Expected aten::sub to not be blacklisted"
torch_glow.clearFusionBlacklist()
def run_model(model, image, use_glow, backend, print_graph):
if use_glow:
torch_glow.enableFusionPass_DO_NOT_USE_THIS()
if backend:
torch_glow.setGlowBackend(backend)
with torch.no_grad():
traced = torch.jit.trace(model, image)
if print_graph:
print(traced.graph_for(image))
all_outputs = traced(image)
topk = all_outputs.topk(5)
return (topk[1], topk[0])
def test_print_jit_indices(self):
def test_f(a, b):
c = a.add(b)
return c.add(c)
x = torch.randn(4)
y = torch.randn(4)
torch_glow.enableFusionPass_DO_NOT_USE_THIS()
torch_glow.enable_printing_jit_node_indices()
graph = torch.jit.trace(test_f, (x, y), check_trace=False)
graph(x, y)
def test_shape_inference_unsupported_symbols_skip_fusion_group(self):
"""Test Glow shape inference unsupported symbols including skipping of
symbols after a secondary fusion group."""
def f(a, b):
x1 = a * b
x2 = x1 * b
x3 = x2 * a
x4 = x3 / b
x5 = x4 / a
x6 = x5 / b
x7 = x6 * a
x8 = x7 * b
return x8 * torch.chain_matmul(x8, x8)
torch_glow.enableFusionPass_DO_NOT_USE_THIS()
torch_glow.setFusionStartIndex(3)
torch_glow.setFusionEndIndex(6)
a = torch.randn(5, 5)
b = torch.randn(5, 5)
jit_f = torch.jit.trace(f, (a, b))
jit_f_graph = jit_f.graph_for(a, b)
torch_glow.clearFusionIndices()
args = (a, b)
# Don't skip nodes after the last fusion node.
# in this case, one of the nodes (chain_matmul) following the last fusion node
# is not supported, and should be reported.
actual = torch_glow.glow_shape_inference_find_unsupported_symbols(
jit_f_graph, args, skip_last_fusion_node=False
)
expected = [
"aten::chain_matmul",
]
self.assertEqual(set(expected), set(actual))
# DO skip nodes after the last fusion node.
# in this case, one of the nodes (chain_matmul) following the last fusion node
# is not supported, but is suppressed due to the skip_last_fusion_node flag.
actual = torch_glow.glow_shape_inference_find_unsupported_symbols(
jit_f_graph, args, skip_last_fusion_node=True
)
expected = []
self.assertEqual(set(expected), set(actual))
def test_quantized_cut(self):
"""Test cut quantized chunk in the middle."""
torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
def fun(a, b, c, d):
q = torch.nn.quantized.Quantize(scale=1.0 / 21,
zero_point=0,
dtype=torch.quint8)
dq = torch.nn.quantized.DeQuantize()
a = q(a)
b = q(b)
c = q(c)
d = q(d)
adds = torch.ops.quantized.add(a, b, scale=1.0 / 17, zero_point=5)
adds2 = torch.ops.quantized.add(c, d, scale=1.0 / 14, zero_point=4)
res = torch.ops.quantized.add_relu(adds,
adds2,
scale=1.0 / 18,
zero_point=6)
res = torch.ops.quantized.add(res,
res,
scale=1.0 / 13,
zero_point=7)
res = dq(res)
return res
with torch.no_grad():
a = torch.randn([5, 5])
b = torch.randn([5, 5])
c = torch.randn([5, 5])
d = torch.randn([5, 5])
res_torch = fun(a, b, c, d)
torch_glow.enableFusionPass_DO_NOT_USE_THIS()
# Cut using blacklist functionality
blacklist = ["quantized::add_relu"]
torch_glow.setFusionBlacklist(blacklist)
torch_glow.setGlowBackend("Interpreter")
traced_model = torch.jit.trace(fun, (a, b, c, d))
for node in traced_model.graph_for(a, b, c, d).nodes():
kind = node.kind()
# Make sure the blacklist is working
assert (kind == GLOW_FUSION_GROUP or kind in blacklist
or kind == "prim::Constant")
res_glow = traced_model(a, b, c, d)
print(res_torch)
print(res_glow)
assert torch.allclose(res_torch, res_glow)
def ephemeral_torchglow_settings(
fp16=False,
backend=DEFAULT_BACKEND,
fusion=False,
blocklist=None,
accept_all_layouts=False,
):
old_fp16 = torch_glow.get_convert_to_fp16()
old_clip = torch_glow.get_clip_fp16()
old_convert_fused = torch_glow.get_convert_fused_to_fp16()
old_backend = torch_glow.getGlowBackendName()
old_blocklist = torch_glow.getFusionBlacklist()
old_fusion = torch_glow.getFusionPassEnabled()
try:
if fusion:
torch_glow.enableFusionPass_DO_NOT_USE_THIS()
else:
torch_glow.disableFusionPass()
if fp16:
torch_glow.enable_convert_to_fp16()
torch_glow.enable_convert_fused_to_fp16()
torch_glow.enable_clip_fp16()
else:
torch_glow.disable_convert_to_fp16()
torch_glow.disable_convert_fused_to_fp16()
torch_glow.disable_clip_fp16()
if blocklist is None:
torch_glow.clearFusionBlacklist()
else:
torch_glow.setFusionBlacklist(list(blocklist))
if accept_all_layouts:
torch_glow.enable_accept_all_layout()
else:
torch_glow.disable_accept_all_layout()
torch_glow.setGlowBackend(backend)
yield
finally:
torch_glow.enable_convert_to_fp16(
) if old_fp16 else torch_glow.disable_convert_to_fp16()
torch_glow.enable_clip_fp16(
) if old_clip else torch_glow.disable_clip_fp16()
torch_glow.enable_convert_fused_to_fp16(
) if old_convert_fused else torch_glow.disable_convert_fused_to_fp16()
torch_glow.enableFusionPass_DO_NOT_USE_THIS(
) if old_fusion else torch_glow.disableFusionPass()
torch_glow.setGlowBackend(old_backend)
torch_glow.setFusionBlacklist(old_blocklist)
def test_backend_specific_options(self):
"""Test loading backend specific options from YAML file."""
def test_f(a, b):
return a.add(b)
x = torch.randn(4)
y = torch.randn(4)
# Create YAML file with backend options
with tempfile.NamedTemporaryFile() as options_fd:
options_fd.write(b"interpreter-memory: 4194304\n")
options_fd.flush()
# Run Glow
torch_glow.loadBackendSpecificOptions(options_fd.name)
torch_glow.enableFusionPass_DO_NOT_USE_THIS()
glow_trace = torch.jit.trace(test_f, (x, y), check_trace=False)
glow_trace(x, y)
def test_op_index_blacklist(self):
"""Test Glow fuser index blacklisting mechanism."""
def f(a, b):
x1 = a * b
x2 = x1 * b
x3 = x2 * a
x4 = x3 / b
x5 = x4 / a
x6 = x5 / b
x7 = x6 * a
x8 = x7 * b
return x8
torch_glow.enableFusionPass_DO_NOT_USE_THIS()
torch_glow.setFusionStartIndex(3)
torch_glow.setFusionEndIndex(6)
a = torch.randn(5, 5)
b = torch.randn(5, 5)
jit_f = torch.jit.trace(f, (a, b))
jit_f_graph = jit_f.graph_for(a, b)
torch_glow.clearFusionIndices()
fused_muls = 0
fused_divs = 0
for node in jit_f_graph.nodes():
if node.kind() == GLOW_FUSION_GROUP:
glow_subgraph = node.g(SUBGRAPH_ATTR)
for node in glow_subgraph.nodes():
if node.kind() == "aten::mul":
fused_muls += 1
if node.kind() == "aten::div":
fused_divs += 1
assert fused_muls == 0, "Expected no aten::muls to be fused"
assert fused_divs == 3, "Expected all 3 aten::divs to be fused"
@torch.jit.script
def foo(a, b):
c = a.mul(b)
a = c.mul(c)
a = c.mul(a)
d = c.div(a)
return d
print("original jit ir")
print(foo.graph_for(x, y))
jit_res = foo(x, y)
torch_glow.enableFusionPass_DO_NOT_USE_THIS()
@torch.jit.script
def foo_glow(a, b):
return foo(a, b)
print("glow jit ir")
print(foo_glow.graph_for(x, y))
jit_glow_res = foo_glow(x, y)
print("jit_res")
print(jit_res)
print("jit_glow_res")