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 traceVsGlow(f_torch,
f_glow,
check_trace,
atol,
rtol,
*inputs,
expected_fused_ops=None,
accept_all_ops=False,
black_list=None):
if black_list is None:
black_list = []
with torch.no_grad():
torch_glow.disableFusionPass()
torch_trace = torch.jit.trace(f_torch, inputs, check_trace=check_trace)
torch_res = torch_trace(*inputs)
torch_glow.enableFusionPass()
torch_glow.setFusionBlacklist(black_list)
glow_trace = torch.jit.trace(f_glow, inputs, check_trace=check_trace)
glow_res = glow_trace(*inputs)
# check that there are no Glow nodes in the torch graph
torch_graph = torch_trace.graph_for(*inputs)
print("torch_graph,", torch_graph)
num_glow_nodes = len(torch_graph.findAllNodes(GLOW_NODE_NAME))
assert num_glow_nodes == 0, "Expected no Glow nodes, found {}".format(
num_glow_nodes)
glow_graph = glow_trace.graph_for(*inputs)
print("glow_graph,", glow_graph)
checkExpectedOps(glow_graph, expected_fused_ops, accept_all_ops)
checkResult(torch_res, glow_res, atol, rtol)
def test_op_blacklist_allowlist(self):
"""Test Glow fuser allowlist overwrites blacklist mechanism."""
def f(a, b):
return (a + b) * (a - b)
torch_glow.enableFusionPass()
torch_glow.setFusionBlacklist(["aten::add", "aten::sub"])
torch_glow.setFusionOverrideAllowlist(["aten::sub"])
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_NODE_NAME:
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()
torch_glow.clearFusionOverrideAllowlist()
def ephemeral_torchglow_settings(
fp16=False, backend=DEFAULT_BACKEND, fusion=False, blocklist=None
):
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()
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))
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() if old_fusion else torch_glow.disableFusionPass()
torch_glow.setGlowBackend(old_backend)
torch_glow.setFusionBlacklist(old_blocklist)
def test_min_graph_size():
"""Test Glow fuser minimum fusion group size mechanism."""
torch_glow.disableFusionPass()
# Disable aten::div so that each group of aten::mul nodes will be forced
# into separate subgraphs
torch_glow.setFusionBlacklist(["aten::div"])
# Set minimum fusion group size to 3 nodes so that the smallest group which
# contains only 2 nodes will not be created
torch_glow.setMinFusionGroupSize(3)
a = torch.randn(5, 5)
b = torch.randn(5, 5)
c = torch.randn(5, 5)
jit_f = torch.jit.trace(f, (a, b, c))
jit_f_graph = jit_f.graph_for(a, b, c)
# print("before: ", jit_f_graph)
torch_glow.glowCustomFuseDebug_(jit_f_graph)
# print("after: ", jit_f_graph)
fusion_nodes = 0
for node in jit_f_graph.nodes():
if node.kind() == GLOW_NODE_NAME:
fusion_nodes += 1
assert fusion_nodes == 2, "Expected smallest fusion group to not be created"
torch_glow.clearFusionBlacklist()
torch_glow.setMinFusionGroupSize(0)
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 / 128,
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 / 121, zero_point=5)
adds2 = torch.ops.quantized.add(c,
d,
scale=1.0 / 122,
zero_point=4)
res = torch.ops.quantized.add_relu(adds,
adds2,
scale=1.0 / 120,
zero_point=6)
res = torch.ops.quantized.add(res,
res,
scale=1.0 / 128,
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()
# Cut using blacklist functionality
blacklist = ["quantized::add_relu"]
torch_glow.setFusionBlacklist(blacklist)
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_NODE_NAME 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 scriptVsGlow(
f,
atol,
rtol,
*inputs,
expected_fused_ops=None,
accept_all_ops=False,
black_list=None,
use_fp16=False,
backend_name=None,
):
if black_list is None:
black_list = []
with torch.no_grad():
torch_res = f(*inputs)
torch_glow.enableFusionPass()
torch_glow.setFusionBlacklist(black_list)
if use_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 backend_name:
torch_glow.setGlowBackend(backend_name)
else:
torch_glow.setGlowBackend("Interpreter")
glow_trace = torch.jit.script(f)
glow_res = glow_trace(*inputs)
glow_graph = glow_trace.graph_for(*inputs)
print("glow_graph,", glow_graph)
# need to explicitly clear settings to avoid carry-over static settings
torch_glow.disableFusionPass()
torch_glow.disable_convert_to_fp16()
torch_glow.disable_convert_fused_to_fp16()
torch_glow.disable_clip_fp16()
torch_glow.setGlowBackend("Interpreter")
checkExpectedOps(glow_graph, expected_fused_ops, accept_all_ops)
checkResult(torch_res, glow_res, atol, rtol)
def scriptVsGlow(f,
atol,
rtol,
*inputs,
expected_fused_ops=None,
accept_all_ops=False,
black_list=None):
if black_list is None:
black_list = []
with torch.no_grad():
torch_res = f(*inputs)
torch_glow.enableFusionPass()
torch_glow.setFusionBlacklist(black_list)
glow_trace = torch.jit.script(f)
glow_res = glow_trace(*inputs)
glow_graph = glow_trace.graph_for(*inputs)
print("glow_graph,", glow_graph)
checkExpectedOps(glow_graph, expected_fused_ops, accept_all_ops)
checkResult(torch_res, glow_res, atol, rtol)
def traceVsGlow(
f_torch,
f_glow,
check_trace,
atol,
rtol,
*inputs,
expected_fused_ops=None,
accept_all_ops=False,
black_list=None,
use_fp16=False,
backend_name=None,
):
if black_list is None:
black_list = []
with torch.no_grad():
torch_glow.disableFusionPass()
torch_trace = torch.jit.trace(f_torch, inputs, check_trace=check_trace)
torch_res = torch_trace(*inputs)
torch_glow.enableFusionPass()
torch_glow.setFusionBlacklist(black_list)
if use_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 backend_name:
torch_glow.setGlowBackend(backend_name)
else:
torch_glow.setGlowBackend("Interpreter")
glow_trace = torch.jit.trace(f_glow, inputs, check_trace=check_trace)
glow_res = glow_trace(*inputs)
# check that there are no Glow nodes in the torch graph
torch_graph = torch_trace.graph_for(*inputs)
print("torch_graph,", torch_graph)
num_glow_nodes = len(torch_graph.findAllNodes(GLOW_NODE_NAME))
assert num_glow_nodes == 0, "Expected no Glow nodes, found {}".format(
num_glow_nodes)
glow_graph = glow_trace.graph_for(*inputs)
print("glow_graph,", glow_graph)
# need to explicitly clear settings to avoid carry-over static settings
torch_glow.disableFusionPass()
torch_glow.disable_convert_to_fp16()
torch_glow.disable_convert_fused_to_fp16()
torch_glow.disable_clip_fp16()
torch_glow.setGlowBackend("Interpreter")
checkExpectedOps(glow_graph, expected_fused_ops, accept_all_ops)
checkResult(torch_res, glow_res, atol, rtol)
def jitVsGlow_(f_torch,
f_glow,
check_trace,
atol,
rtol,
*inputs,
expected_fused_ops=None,
accept_all_ops=False,
black_list=None):
if (black_list is None):
black_list = []
with torch.no_grad():
torch_glow.disableFusionPass()
torch_trace = torch.jit.trace(f_torch, inputs, check_trace=check_trace)
torch_res = torch_trace(*inputs)
torch_glow.enableFusionPass()
torch_glow.setFusionBlacklist(black_list)
glow_trace = torch.jit.trace(f_glow, inputs, check_trace=check_trace)
glow_res = glow_trace(*inputs)
# check that there are no Glow nodes in the torch graph
torch_graph = torch_trace.graph_for(*inputs)
print("torch_graph,", torch_graph)
num_glow_nodes = len(torch_graph.findAllNodes(GLOW_NODE_NAME))
assert num_glow_nodes == 0, "Expected no Glow nodes, found {}".format(
num_glow_nodes)
glow_graph = glow_trace.graph_for(*inputs)
print("glow_graph,", glow_graph)
expected_fused_ops_seen = set()
# Whether or not at least one node was fused to Glow.
nodes_were_fused = False
# Check that ops that were *not* fused are *not* in expected_fused_ops
for node in glow_graph.nodes():
kind = node.kind()
if kind != GLOW_NODE_NAME:
# If the node is not a Glow fusion group, check that it is
# *not* in expected_fused_ops
assert accept_all_ops or kind not in expected_fused_ops, \
"Expected {} to be fused".format(kind)
else:
# If the node is a Glow fusion group, record which ops from
# expected_fused_ops were in it
# Get the definition of the fusion group
glow_group = node.g(SUBGRAPH_ATTR)
# Put all nodes that are in the group and in expected_fused_ops
# into expected_fused_ops_seen
for fused_node in glow_group.nodes():
nodes_were_fused = True
fused_node_kind = fused_node.kind()
if accept_all_ops or fused_node_kind in expected_fused_ops:
expected_fused_ops_seen.add(fused_node_kind)
assert nodes_were_fused, "Expected some nodes to be fused to Glow"
# If the sizes of expected_fused_ops and expected_fused_ops_seen are
# different, some ops in expected_fused_ops are not in the graph at all
assert accept_all_ops or len(expected_fused_ops) == len(expected_fused_ops_seen), \
"Expected all of expected_fused_ops to be in the graph"
if isinstance(torch_res, tuple) or isinstance(glow_res, tuple):
assert isinstance(torch_res, tuple) and isinstance(glow_res, tuple)
assert len(torch_res) == len(glow_res)
for i in range(len(torch_res)):
print("torch shape: {}".format(torch_res[i].shape),
file=sys.stderr)
print("glow shape: {}".format(glow_res[i].shape),
file=sys.stderr)
assert torch.allclose(torch_res[i],
glow_res[i],
atol=atol,
rtol=rtol)
else:
print("torch shape: {}".format(torch_res.shape), file=sys.stderr)
print("glow shape: {}".format(glow_res.shape), file=sys.stderr)
is_all_close = torch.allclose(torch_res,
glow_res,
atol=atol,
rtol=rtol)
if not is_all_close:
print("torch_res\n", torch_res)
print("glow_res\n", glow_res)
print("diff\n", torch.abs(glow_res - torch_res))
assert is_all_close
