def lower_modules_to_accelerator(model: nn.Module, trace,
export_options: ExportConfig):
import torch_glow
if hasattr(model, "encoder") and isinstance(model.encoder, RoBERTaEncoder):
backend = "NNPI"
submod_modelpath, compilation_spec_dict = accelerator.get_modules(
model, backend)[0]
submod_tracepath = accelerator.model2trace_path(submod_modelpath)
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend(backend)
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
compilation_group_settings = compilation_group.get_settings()
compilation_group_settings.set_convert_to_fp16(True)
for k, v in compilation_spec_dict.items():
compilation_group.get_settings().backend_specific_opts_insert(k, v)
# Todod: @input decorator dose not work properly, fixing it later
# input_sets = inputs.input_process(model, export_options, None, submod_tracepath)
input_sets = accelerator_transformerLayers_inputs(
model, trace, export_options, None, submod_tracepath)
compilation_group.set_input_sets(input_sets)
trace = torch_glow.to_glow_selective(
trace,
{submod_tracepath: spec},
inplace=False,
)
return trace
else:
return trace
def lower_modules_to_accelerator(model: nn.Module, trace,
export_options: ExportConfig):
import torch_glow
if hasattr(model, "encoder") and isinstance(model.encoder, RoBERTaEncoder):
backend = "NNPI"
(
submod_modelpath,
compilation_spec_dict,
inputs_function,
) = accelerator.get_modules(model, backend)[0]
submod_tracepath = accelerator.model2trace_path(submod_modelpath)
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend(backend)
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
compilation_group_settings = compilation_group.get_settings()
compilation_group_settings.set_convert_to_fp16(True)
for k, v in compilation_spec_dict.items():
compilation_group.get_settings().backend_specific_opts_insert(k, v)
if inputs_function is not None:
input_sets = inputs_function(model, trace, export_options, None,
submod_modelpath)
compilation_group.set_input_sets(input_sets)
trace = torch_glow.to_glow_selective(
trace,
{submod_tracepath: spec},
inplace=False,
)
return trace
else:
return trace
def test_to_glow_selective_already_scripted(self):
a = torch.zeros(4) + 8
b = torch.zeros(4) + 7
torch_res = model(a, b)
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
a_spec = torch_glow.InputSpec()
a_spec.set_same_as(a)
b_spec = torch_glow.InputSpec()
b_spec.set_same_as(b)
compilation_group.input_sets_append([a_spec, b_spec])
with torch.no_grad():
traced_model = torch.jit.trace(model, (a, b))
glow_mod = torch_glow.to_glow_selective(
traced_model,
{
"foo.bar": spec,
"qux": spec
},
inplace=False,
)
glow_res = glow_mod(a, b)
assert torch.allclose(torch_res, glow_res)
def test_save_preprocessed_module(self):
with torch.no_grad():
x = torch.randn([1, 4, 4, 4], dtype=torch.float32)
model = Bar()
model.eval()
model = torch.jit.trace(model, x)
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
compilation_group.input_sets_append(
torch_glow.input_specs_from_tensors([x]))
torch_glow.disableFusionPass()
torch_glow.enable_convert_to_fp16()
glow_mod = torch_glow.to_glow(model, spec)
reloaded = utils.save_and_reload_model(glow_mod)
wrappername = "__loweredModule__"
attrname = "__processed_module"
wp = getattr(reloaded._c, wrappername)
pp = getattr(wp, attrname)
pt_model = torch.jit._recursive.wrap_cpp_module(pp)
graph = pt_model.graph_for(x)
found = False
for node in graph.nodes():
if node.kind() == "quantized::conv2d":
found = True
assert found
def test_to_glow_tuple_output(self):
input = torch.randn(4)
model = Foo()
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
input_spec = torch_glow.InputSpec()
input_spec.set_same_as(input)
compilation_group.input_sets_append([input_spec])
scripted_mod = torch.jit.script(model)
lowered_model = torch_glow.to_glow(scripted_mod, {"forward": spec})
# Run Glow model
(gx, gy) = lowered_model(input)
# Run reference model
(tx, ty) = model(input)
assert torch.allclose(tx, gx)
assert torch.allclose(ty, gy)
def tuple_test_helper(self, ModType):
input = torch.randn(4)
model = ModType()
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
input_spec = torch_glow.InputSpec()
input_spec.set_same_as(input)
compilation_group.input_sets_append([input_spec])
scripted_mod = torch.jit.script(model)
lowered_model = torch_glow.to_glow(scripted_mod, {"forward": spec})
# Run Glow model
g = lowered_model(input)
# Run reference model
t = model(input)
self.assertEqual(type(g), type(t))
self.assertEqual(len(g), len(t))
for (gi, ti) in zip(g, t):
self.assertTrue(torch.allclose(gi, ti))
def devices_to_use_test_helper(self, input, num_replications):
model = SimpleModule()
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
# Init with total number of devices.
torch_glow.setGlowBackendNumDevices(6)
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
input_spec = torch_glow.InputSpec()
input_spec.set_same_as(input)
compilation_group.input_sets_append([input_spec])
compilation_group_settings = compilation_group.get_settings()
compilation_group_settings.set_num_devices_to_use(3)
compilation_group_settings.set_replication_count(num_replications)
traced_mod = torch.jit.trace(model, input)
lowered_model = torch_glow.to_glow(traced_mod, {"forward": spec})
g = lowered_model(input)
t = model(input)
self.assertEqual(type(g), type(t))
self.assertEqual(len(g), len(t))
for (gi, ti) in zip(g, t):
self.assertTrue(torch.allclose(gi, ti))
def test_to_glow_selective(self):
a = torch.zeros(4) + 8
b = torch.zeros(4) + 7
torch_res = model(a, b)
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
a_spec = torch_glow.InputSpec()
a_spec.set_same_as(a)
b_spec = torch_glow.InputSpec()
b_spec.set_same_as(b)
compilation_group.input_sets_append([a_spec, b_spec])
glow_mod = torch_glow.to_glow_selective(model, {
"foo.bar": (spec, (a, b)),
"qux": (spec, (a, b))
})
glow_mod = torch.jit.trace(glow_mod, (a, b))
glow_res = glow_mod(a, b)
assert torch.allclose(torch_res, glow_res)
def lower_modules_to_accelerator(model: nn.Module,
trace,
export_options: ExportConfig,
throughput_optimize=False):
# Raise error if accelerator could not be imported
if not accelerator_lowering_supported:
raise RuntimeError("Accelerator Lowering not supported!")
import torch_glow
log_accelerator_feature_usage("build.NNPI")
if ((hasattr(model, "encoder")
and isinstance(model.encoder, RoBERTaEncoder))
or (hasattr(model, "representation")
and isinstance(model.representation, AcceleratorBiLSTM)) or
(hasattr(model, "lower_module")
# Internal CNN LM module to add accelerator support.
and type(model.lower_module).__qualname__ == "CNNLowerModule")):
backend = "NNPI"
(
submod_modelpath,
compilation_spec_dict,
inputs_function,
) = accelerator.get_modules(model, backend)[0]
submod_tracepath = accelerator.model2trace_path(submod_modelpath)
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend(backend)
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
compilation_group_settings = compilation_group.get_settings()
compilation_group_settings.set_convert_to_fp16(True)
# Override the options for throughput-optimized case
if throughput_optimize:
compilation_spec_dict["NNPI_IceCores"] = "4"
compilation_spec_dict["NNPINumParallelChunks"] = "4"
compilation_group_settings.set_replication_count(3)
for k, v in compilation_spec_dict.items():
compilation_group.get_settings().backend_specific_opts_insert(k, v)
if inputs_function is not None:
input_sets = inputs_function(model, trace, export_options, None,
submod_modelpath)
else:
raise RuntimeError(
"inputs_function needs to be specified in accelerator decorator"
)
compilation_group.set_input_sets(input_sets)
trace = torch_glow.to_glow_selective(
trace,
{submod_tracepath: spec},
inplace=False,
)
return trace
else:
return trace
def get_compilation_spec(inputs):
"""helper function to get the compilation spec of the submodule"""
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
compilation_group.input_sets_append(torch_glow.input_specs_from_tensors(inputs))
return spec
def generate_glow_spec(module, backend, *inputs):
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend(backend)
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
input_specs = []
for input in inputs:
input_spec = torch_glow.InputSpec()
input_spec.set_same_as(input)
input_specs.append(input_spec)
compilation_group.input_sets_append(input_specs)
return spec
def build_compiliation_spec(self):
compilation_spec = torch_glow.CompilationSpec()
compilation_spec_settings = compilation_spec.get_settings()
compilation_spec_settings.set_glow_backend("CPU")
compilation_spec_settings.set_enable_fuser(True)
fuser_settings = compilation_spec.get_fuser_settings()
fuser_settings.set_min_fusion_group_size(3)
fuser_settings.set_max_fusion_merge_size(4)
fuser_settings.set_fusion_start_index(5)
fuser_settings.set_fusion_end_index(6)
fuser_settings.op_blacklist_append("aten::mean")
fuser_settings.op_blacklist_append("aten::dropout")
compilation_group = torch_glow.CompilationGroup()
input1_spec = torch_glow.input_spec_from_tensor(torch.randn(2, 3, 224, 224))
input2_spec = torch_glow.input_spec_from_tensor(
torch.randn(3, 2).to(torch.float16)
)
compilation_group.input_sets_append([input1_spec, input2_spec])
compilation_group.input_sets_append(
torch_glow.input_specs_from_tensors(
[torch.randn(1, 3, 224, 224), torch.randn(4, 1)]
)
)
compilation_group_settings = compilation_group.get_settings()
compilation_group_settings.set_convert_to_fp16(True)
compilation_group_settings.set_num_devices_to_use(50)
compilation_group_settings.set_replication_count(52)
compilation_group_settings.backend_specific_opts_insert("apple", "orange")
compilation_spec.compilation_groups_append(compilation_group)
default_compilation_group_settings = (
compilation_spec.get_default_compilation_group_settings()
)
default_compilation_group_settings.set_convert_to_fp16(False)
default_compilation_group_settings.set_num_devices_to_use(89)
default_compilation_group_settings.set_replication_count(90)
default_compilation_group_settings.backend_specific_opts_insert(
"hello", "goodbye"
)
return compilation_spec
def run_to_glow(m, x):
"""Trace the model m with input x and call to_glow"""
traced_m = torch.jit.trace(m, (x))
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
input_spec = torch_glow.InputSpec()
input_spec.set_same_as(x)
compilation_group.input_sets_append([input_spec])
lowered_module = torch_glow.to_glow(traced_m, spec)
return lowered_module
def test_to_glow_multiple_groups_and_input_sets(self):
x1 = torch.randn(1, 4)
y1 = torch.randn(2, 4)
x2 = torch.randn(1, 2)
y2 = torch.randn(5, 2)
x3 = torch.randn(7)
y3 = torch.randn(3, 7)
mod = Foo()
scripted_mod = torch.jit.script(mod)
x1_y1_set = torch_glow.input_specs_from_tensors([x1, y1])
x2_y2_set = torch_glow.input_specs_from_tensors([x2, y2])
x3_y3_set = torch_glow.input_specs_from_tensors([x3, y3])
# Create two CompilationGroup, first one contains two input sets
# and the second CompilationGroup has the third input set
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
compilation_group_1 = torch_glow.CompilationGroup()
compilation_group_2 = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group_1)
spec.compilation_groups_append(compilation_group_2)
compilation_group_1.input_sets_append(x1_y1_set)
compilation_group_1.input_sets_append(x2_y2_set)
compilation_group_2.input_sets_append(x3_y3_set)
lowered_module = torch_glow.to_glow(scripted_mod, spec)
torch_res1 = mod(x1, y1)
torch_res2 = mod(x2, y2)
torch_res3 = mod(x3, y3)
glow_res1 = lowered_module(x1, y1)
glow_res2 = lowered_module(x2, y2)
glow_res3 = lowered_module(x3, y3)
assert torch.allclose(torch_res1, glow_res1)
assert torch.allclose(torch_res2, glow_res2)
assert torch.allclose(torch_res3, glow_res3)
def lower_modules_to_accelerator(model, trace, seq_padding_control,
batch_padding_control):
import torch_glow
if hasattr(model, "encoder") and isinstance(model.encoder, RoBERTaEncoder):
backend = "NNPI"
submod_modelpath, compilation_spec_dict = accelerator.get_modules(
model, backend)[0]
submod_tracepath = accelerator.model2trace_path(submod_modelpath)
embedding_dim = model.encoder.encoder.transformer.token_embedding.embedding_dim
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend(backend)
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
compilation_group_settings = compilation_group.get_settings()
compilation_group_settings.set_convert_to_fp16(True)
for k, v in compilation_spec_dict.items():
compilation_group.get_settings().backend_specific_opts_insert(k, v)
for seq_len in seq_padding_control:
if seq_len <= 0:
continue
for batch_size in batch_padding_control:
if batch_size <= 0:
continue
input1 = torch.randn([seq_len, batch_size, embedding_dim],
dtype=torch.float32)
input2 = torch.randn([batch_size, seq_len]).bool()
input_specs = torch_glow.input_specs_from_tensors(
[input1, input2])
compilation_group.input_sets_append(input_specs)
trace = torch_glow.to_glow_selective(
trace,
{submod_tracepath: spec},
inplace=False,
)
return trace
else:
return trace
def test_serialization(self):
with torch.no_grad():
x = torch.randn([1, 4, 4, 4], dtype=torch.float32)
y = torch.randn([1, 4, 4, 4], dtype=torch.float32)
model = Bar()
model = torch.jit.trace(model, (x, y))
spec = torch_glow.CompilationSpec()
spec_settings = spec.get_settings()
spec_settings.set_glow_backend("NNPI")
# Enabled the serialize in this spec
spec_settings.set_enable_serialize(True)
compilation_group = torch_glow.CompilationGroup()
compilation_group_settings = compilation_group.get_settings()
compilation_group_settings.set_replication_count(1)
compilation_group_settings.backend_specific_opts_insert(
"NNPI_IceCores", "1")
compilation_group.input_sets_append(
torch_glow.input_specs_from_tensors([x, y]))
spec.compilation_groups_append(compilation_group)
torch_glow.disableFusionPass()
torch_glow.enable_convert_to_fp16()
# Enable global serialize
# then compile(serialize) the model and save it
torch_glow.enable_dump_serialized_model()
glow_mod = torch_glow.to_glow(model, spec)
res1 = glow_mod(x, y)
torch.jit.save(glow_mod, "/tmp/serialize_to_glow.pt")
# Enable global deserialize and disable serialize
# and load(deserialize) the model to loaded_glow_mod
torch_glow.enable_deserialize()
torch_glow.disable_dump_serialized_model()
loaded_glow_mod = torch.jit.load("/tmp/serialize_to_glow.pt")
res2 = loaded_glow_mod(x, y)
assert torch.allclose(res1, res2, 1e-5, 1e-5)
def run_model(m, input, randomize):
torch_glow.disableFusionPass()
traced_m = torch.jit.trace(m, input)
if randomize:
torch_glow.enable_randomize_constants()
else:
torch_glow.disable_randomize_constants()
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
input_spec = torch_glow.InputSpec()
input_spec.set_same_as(input)
compilation_group.input_sets_append([input_spec])
glow_m = torch_glow.to_glow(traced_m, {"forward": spec})
return glow_m(input)
def lower_modules_to_accelerator(model, trace, seq_padding_control,
batch_padding_control):
import torch_glow
if hasattr(model, "encoder") and isinstance(model.encoder, RoBERTaEncoder):
embedding_dim = model.encoder.encoder.transformer.token_embedding.embedding_dim
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("NNPI")
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
compilation_group_settings = compilation_group.get_settings()
compilation_group_settings.set_convert_to_fp16(True)
compilation_group.get_settings().backend_specific_opts_insert(
"NNPI_IceCores", "12")
compilation_group.get_settings().backend_specific_opts_insert(
"NNPINumParallelChunks", "12")
for seq_len in seq_padding_control:
if seq_len <= 0:
continue
for batch_size in batch_padding_control:
if batch_size <= 0:
continue
input1 = torch.randn([seq_len, batch_size, embedding_dim],
dtype=torch.float32)
input2 = torch.randn([batch_size, seq_len]).bool()
input_specs = torch_glow.input_specs_from_tensors(
[input1, input2])
compilation_group.input_sets_append(input_specs)
trace = torch_glow.to_glow_selective(
trace,
{"model.encoder.encoder.transformer.layers": spec},
inplace=False,
)
return trace
else:
return trace
def lower_and_write_to_onnx_helper(self, ModType, onnx_prefix):
x = torch.randn(1, 3, 8, 8)
model = create_model(x, ModType)
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
input_spec = torch_glow.InputSpec()
input_spec.set_same_as(x)
compilation_group.input_sets_append([input_spec])
scripted_mod = torch.jit.trace(model, x)
torch_glow.enable_write_to_onnx()
torch_glow.set_onnx_file_name_prefix(onnx_prefix)
torch_glow.enable_write_without_randomize()
lowered_model = torch_glow.to_glow(scripted_mod, {"forward": spec})
# Run Glow model
g = lowered_model(x)
# Run reference model
t = model(x)
self.assertEqual(type(g), type(t))
self.assertEqual(len(g), len(t))
for (gi, ti) in zip(g, t):
self.assertTrue(torch.allclose(gi, ti))
assert os.path.exists(onnx_prefix + ".onnxtxt")
onnx_files = glob.glob(onnx_prefix + "*.onnx*")
for f in onnx_files:
os.remove(f)
def infer_nnpi(model, device, data_type, input_size, output_size, batch_size,
args):
import torch_glow
# Detailed structure for spec can be found at https://fburl.com/diffusion/79q4efud
# Create compilation spec
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("NNPI")
# Create compilation group and update settings.
# Compilation group contains compilation specific information like
# fp16 settings, enableRemoveMutation, anything that changes
# the Glow graph compiled for example.
compilation_group = torch_glow.CompilationGroup()
compilation_group_settings = compilation_group.get_settings()
compilation_group_settings.set_convert_to_fp16(True)
compilation_group_settings.set_replication_count(1)
compilation_group_settings.backend_specific_opts_insert(
"NNPI_IceCores", "1")
data = torch.randn(batch_size, input_size)
# Create input spec and add it into compilation group.
# This is used for shape inference when lowering the model to Glow.
data_spec = torch_glow.InputSpec()
data_spec.set_same_as(data)
compilation_group.input_sets_append([data_spec])
spec.compilation_groups_append(compilation_group)
traced_model = torch.jit.trace(model, (data))
lowered_model = torch_glow.to_glow(traced_model, spec)
start_time = time.time()
for i in range(args.steps + args.warmups):
lowered_model(data)
if i < args.warmups:
start_time = time.time()
return time.time() - start_time
def lower_modules_to_accelerator(
model: nn.Module, trace, export_options: ExportConfig, throughput_optimize=False
):
# Raise error if accelerator could not be imported
if not accelerator_lowering_supported:
raise RuntimeError("Accelerator Lowering not supported!")
import torch_glow
log_accelerator_feature_usage("build.NNPI")
if (
(hasattr(model, "encoder") and isinstance(model.encoder, RoBERTaEncoder))
or (
hasattr(model, "representation")
and isinstance(model.representation, AcceleratorBiLSTM)
)
or (
hasattr(model, "lower_module")
# Internal CNN LM module to add accelerator support.
and type(model.lower_module).__qualname__ == "CNNLowerModule"
)
):
backend = "NNPI"
backend_qualifier = ""
if throughput_optimize:
backend_qualifier = ":throughput_optimized"
modules_to_lower = accelerator.get_modules(model, backend + backend_qualifier)
if len(modules_to_lower) < 1:
raise RuntimeError("Need at least one module to lower to accelerator")
elif len(modules_to_lower) > 1:
print(f"Warning. Received {len(modules_to_lower)} modules to lower.")
print("Warning. Only lowering first module.")
(
submod_modelpath,
compilation_spec_dict,
inputs_function,
) = modules_to_lower[0]
submod_tracepath = accelerator.model2trace_path(submod_modelpath)
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend(backend)
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
compilation_group_settings = compilation_group.get_settings()
# Set values from dict that are not set via backend-specific opts
compilation_group_settings.set_convert_to_fp16(
compilation_spec_dict.pop("glow:ConvertToFP16", "true") in ["true", "True"]
)
compilation_group_settings.set_replication_count(
int(compilation_spec_dict.pop("glow:ReplicationCount", "1"))
)
for k, v in compilation_spec_dict.items():
compilation_group.get_settings().backend_specific_opts_insert(k, v)
if inputs_function is not None:
input_sets = inputs_function(
model, trace, export_options, None, submod_modelpath
)
else:
raise RuntimeError(
"inputs_function needs to be specified in accelerator decorator"
)
compilation_group.set_input_sets(input_sets)
trace = torch_glow.to_glow_selective(
trace,
{submod_tracepath: spec},
inplace=False,
)
return trace
else:
return trace
