def torchscript_export(self, model, export_path=None, quantize=False):
cuda.CUDA_ENABLED = False
model.cpu()
optimizer = self.trainer.optimizer
optimizer.pre_export(model)
model.eval()
model.prepare_for_onnx_export_()
unused_raw_batch, batch = next(
iter(self.data.batches(Stage.TRAIN, load_early=True))
)
inputs = model.onnx_trace_input(batch)
model(*inputs)
if quantize:
model.quantize()
if self.trace_both_encoders:
trace = jit.trace(model, inputs)
else:
trace = jit.trace(model.encoder1, (inputs[0],))
if hasattr(model, "torchscriptify"):
trace = model.torchscriptify(
self.data.tensorizers, trace, self.trace_both_encoders
)
trace.apply(lambda s: s._pack() if s._c._has_method("_pack") else None)
if export_path is not None:
print(f"Saving torchscript model to: {export_path}")
trace.save(export_path)
return trace
def translate(self):
translation_plan = self.plan.copy()
translation_plan.forward = None
args_shape = translation_plan.get_args_shape()
args = PlaceHolder.create_placeholders(args_shape)
# To avoid storing Plan state tensors in torchscript, they will be send as parameters
# we trace wrapper func, which accepts state parameters as last arg
# and sets them into the Plan before executing the Plan
def wrap_stateful_plan(*args):
role = translation_plan.role
state = args[-1]
if 0 < len(role.state.state_placeholders) == len(
state) and isinstance(state, (list, tuple)):
state_placeholders = tuple(
role.placeholders[ph.id.value]
for ph in role.state.state_placeholders)
PlaceHolder.instantiate_placeholders(
role.state.state_placeholders, state)
PlaceHolder.instantiate_placeholders(state_placeholders, state)
return translation_plan(*args[:-1])
plan_params = translation_plan.parameters()
if len(plan_params) > 0:
torchscript_plan = jit.trace(wrap_stateful_plan,
(*args, plan_params))
else:
torchscript_plan = jit.trace(translation_plan, args)
self.plan.torchscript = torchscript_plan
return self.plan
def main_2():
orig_model = MyModel2()
# case 1
input1 = torch.randn(2, 3)
input1 = input1.pow(2) # always positive
traced_model1 = jit.trace(orig_model, (input1))
# case 2
input2 = torch.randn(2, 3)
input2 = -input2.pow(2) # always negative
traced_model2 = jit.trace(orig_model, (input2))
print(orig_model(input1).size(), orig_model(input2).size())
print(traced_model1(input1).size(), traced_model1(input2).size())
print(traced_model2(input1).size(), traced_model2(input2).size())
print(traced_model1.code)
print('=======')
print(traced_model2.code)
print('=======')
# scripting
scripted_model = jit.script(orig_model)
print(orig_model(input1).size(), orig_model(input2).size())
print(scripted_model(input1).size(), scripted_model(input2).size())
print(scripted_model.code)
def torchscript_export(self, model, export_path=None, **kwargs):
# unpack export kwargs
quantize = kwargs.get("quantize", False)
accelerate = kwargs.get("accelerate", [])
padding_control = kwargs.get("padding_control")
inference_interface = kwargs.get("inference_interface")
cuda.CUDA_ENABLED = False
model.cpu()
optimizer = self.trainer.optimizer
optimizer.pre_export(model)
model.eval()
model.prepare_for_onnx_export_()
unused_raw_batch, batch = next(
iter(self.data.batches(Stage.TRAIN, load_early=True))
)
inputs = model.onnx_trace_input(batch)
model(*inputs)
if quantize:
model.quantize()
if "half" in accelerate:
model.half()
if self.trace_both_encoders:
trace = jit.trace(model, inputs)
else:
trace = jit.trace(model.encoder1, (inputs[0],))
if hasattr(model, "torchscriptify"):
trace = model.torchscriptify(
self.data.tensorizers, trace, self.trace_both_encoders
)
if padding_control is not None:
if hasattr(trace, "set_padding_control"):
trace.set_padding_control(padding_control)
else:
print(
"Padding_control not supported by model. Ignoring padding_control"
)
if inference_interface is not None:
if hasattr(trace, "inference_interface"):
trace.inference_interface(inference_interface)
else:
print(
"inference_interface not supported by model. Ignoring inference_interface"
)
trace.apply(lambda s: s._pack() if s._c._has_method("_pack") else None)
if "nnpi" in accelerate:
trace._c = torch._C._freeze_module(
trace._c,
preservedAttrs=["make_prediction", "make_batch", "set_padding_control"],
)
if export_path is not None:
print(f"Saving torchscript model to: {export_path}")
with PathManager.open(export_path, "wb") as f:
torch.jit.save(trace, f)
return trace
def trace(self):
self.init_block = trace(self.init_block,
torch.rand(1, self.input_size))
for level in range(self.start_level, self.end_level):
self.level_blocks[level] = trace(
self.level_blocks[level],
torch.rand(1, self.channels[level], 2**level, 2**level))
for level in range(self.start_level, self.end_level + 1):
self.toRGBs[level] = trace(
self.toRGBs[level],
torch.rand(1, self.channels[level], 2**level, 2**level))
def __init__(self, model=None, data=None, graph=None):
"""
We build the network architecture graph according the graph
in the scriptmodule. However, the original graph from jit.trace
has lots of detailed information which make the graph complicated
and hard to understand. So we also store a copy of the network
architecture in the self.forward_edge. We will simplify the network
architecure (such as unpack_tuple, etc) stored in self.forward_edge
to make the graph more clear.
Parameters
----------
model : torch.nn.Module
The model to build the network architecture.
data : torch.Tensor
The sample input data for the model.
graph : torch._C.Graph
Traced graph from jit.trace, if this option is set,
we donnot need to trace the model again.
"""
self.model = model
self.data = data
if graph is not None:
self.graph = graph
elif (model is not None) and (data is not None):
with torch.onnx.set_training(model, False):
self.traced_model = jit.trace(model, data)
self.graph = self.traced_model.graph
torch._C._jit_pass_inline(self.graph)
else:
raise Exception('Input parameters invalid!')
self.forward_edge = {}
self.c2py = {}
self.visited = set()
self.build_graph()
self.unpack_tuple()
def export_model(model, path=None, input_shape=(1, 3, 64, 64)):
"""
Exports the model. If the model is a `ScriptModule`, it is saved as is. If not,
it is traced (with the given input_shape) and the resulting ScriptModule is saved
(this requires the `input_shape`, which defaults to the competition default).
Parameters
----------
model : torch.nn.Module or torch.jit.ScriptModule
Pytorch Module or a ScriptModule.
path : str
Path to the file where the model is saved. Defaults to the value set by the
`get_model_path` function above.
input_shape : tuple or list
Shape of the input to trace the module with. This is only required if model is not a
torch.jit.ScriptModule.
Returns
-------
str
Path to where the model is saved.
"""
path = get_model_path() if path is None else path
model = deepcopy(model).cpu().eval()
if not isinstance(model, torch.jit.ScriptModule):
assert input_shape is not None, "`input_shape` must be provided since model is not a " \
"`ScriptModule`."
traced_model = trace(model, torch.zeros(*input_shape))
else:
traced_model = model
torch.jit.save(traced_model, path)
return path
def torchscript_export(self, model, export_path=None, quantize=False):
# Make sure to put the model on CPU and disable CUDA before exporting to
# ONNX to disable any data_parallel pieces
cuda.CUDA_ENABLED = False
model.cpu()
optimizer = self.trainer.optimizer
optimizer.pre_export(model)
# Trace needs eval mode, to disable dropout etc
model.eval()
model.prepare_for_onnx_export_()
unused_raw_batch, batch = next(iter(self.data.batches(Stage.TRAIN)))
inputs = model.arrange_model_inputs(batch)
# call model forward to set correct device types
model(*inputs)
if quantize:
model.quantize()
trace = jit.trace(model, inputs)
if hasattr(model, "torchscriptify"):
trace = model.torchscriptify(self.data.tensorizers, trace)
trace.apply(lambda s: s._pack() if s._c._has_method("_pack") else None)
if export_path is not None:
print(f"Saving torchscript model to: {export_path}")
trace.save(export_path)
return trace
def parse(model, args, omit_useless_nodes=True):
with onnx.set_training(model, False):
trace = jit.trace(model, args)
graph = trace.graph
n_inputs = args.shape[0] # not sure...
model_graph = ModelGraph()
for i, node in enumerate(graph.inputs()):
if omit_useless_nodes:
if len(
node.uses()
) == 0: # number of user of the node (= number of outputs/ fanout)
continue
if i < n_inputs:
model_graph.append(NodePyIO(node, 'input'))
else:
model_graph.append(NodePyIO(node)) # parameter
for node in graph.nodes():
model_graph.append(NodePyOP(node))
for node in graph.outputs(): # must place last.
NodePyIO(node, 'output')
model_graph.find_common_root()
model_graph.populate_namespace_from_OP_to_IO()
model_graph.parse_scopes()
openvino_graph = model_graph.create_openvino_graph(model)
return model_graph, openvino_graph
def test_trace_bilstm_differ_batch_size(self):
# BiLSTM torch tracing was using torch.new_zeros for default input hidden
# states, which doesn't trace properly. torch.jit traces torch.new_zeros as
# constant and therefore locks the traced model into a static batch size.
# torch.LSTM now uses zeros, adding test case here to verify behavior.
# see https://github.com/pytorch/pytorch/issues/16664
class Model(nn.Module):
def __init__(self):
super().__init__()
self.embedding = nn.Embedding(VOCAB_SIZE, EMBEDDING_SIZE)
self.bilstm = bilstm.BiLSTM(bilstm.BiLSTM.Config(), EMBEDDING_SIZE)
def forward(self, tokens, seq_lengths):
embeddings = self.embedding(tokens)
return self.bilstm(embeddings, seq_lengths)
model = Model()
trace_inputs = (
torch.LongTensor([[2, 3, 4], [2, 2, 1]]),
torch.LongTensor([3, 2]),
)
trace = jit.trace(model, trace_inputs)
test_inputs = (torch.LongTensor([[4, 5, 6]]), torch.LongTensor([3]))
# we are just testing that this doesn't throw an exception
trace(*test_inputs)
def __call__(self, *args, **kwargs):
method_model = _ForwardOverrideModel(self.model, self.method_name)
try:
assert len(args) == 0, "only KV support implemented"
fn = getattr(self.model, self.method_name)
method_argnames = _get_input_argnames(fn=fn, exclude=["self"])
method_input = tuple(kwargs[name] for name in method_argnames)
self.tracing_result = trace(method_model, method_input)
except Exception:
# for backward compatibility
self.tracing_result = trace(method_model, *args, **kwargs)
output = self.model.forward(*args, **kwargs)
return output
def _maybe_optimize(model):
try:
from torch.jit import trace
model = trace(model, example_inputs=torch.rand(1, 3, 224, 224))
logger.info('successfully optimized PyTorch model using JIT tracing')
except ImportError:
logger.warning('unable to leverage torch.jit.trace optimizations')
pass
return model
def save(self, states: Tensor, masks: Tensor):
if self.save_path is None:
return
save_dir = os.path.join(self.save_path, str(int(time())))
os.makedirs(save_dir, exist_ok=False)
model = jit.trace(self.network, (states, masks))
jit.save(model, os.path.join(save_dir, "network.pt"))
def main(model_path='model.pt', name='densenet201', out_name='model.trcd'):
model = torch.load(model_path)
model, = list(model.children())
state = model.state_dict()
base = get_baseline(name=name)
base.load_state_dict(state)
base.eval()
model = jit.trace(base, example_inputs=(torch.rand(4, 3, 256, 256), ))
jit.save(model, out_name)
def torchscript_export(self, model, export_path):
# Make sure to put the model on CPU and disable CUDA before exporting to
# ONNX to disable any data_parallel pieces
cuda.CUDA_ENABLED = False
model.cpu()
precision.deactivate(model)
# Trace needs eval mode, to disable dropout etc
model.eval()
batch = next(iter(self.data.batches(Stage.TEST)))
inputs = model.arrange_model_inputs(batch)
trace = jit.trace(model, inputs)
trace.save(export_path)
def _jit_run(
f: FunctionType,
compilation_cache: dict,
jit_kw_args: dict,
*args: Union[Numeric, TorchRandomState],
):
if "torch" not in compilation_cache:
# Run once to populate the control flow cache.
f(*args)
# Compile.
compilation_cache["torch"] = trace(f, args, **jit_kw_args)
return compilation_cache["torch"](*args)
def translate(self):
translation_plan = self.plan.copy()
translation_plan.forward = None
# Make sure we're trying to trace Role with pytorch commands
translation_plan.base_framework = TranslationTarget.PYTORCH.value
args = translation_plan.create_dummy_args()
# jit.trace clones input args and can change their type, so we have to skip types check
# TODO see if type check can be made less strict,
# e.g. tensor/custom tensor/nn.Parameter could be considered same type
translation_plan.validate_input_types = False
# To avoid storing Plan state tensors in torchscript, they will be sent as parameters
# we trace wrapper func, which accepts state parameters as last arg
# and sets them into the Plan before executing the Plan
def wrap_stateful_plan(*args):
role = translation_plan.role
state = args[-1]
if 0 < len(role.state.state_placeholders) == len(state) and isinstance(
state, (list, tuple)
):
state_placeholders = tuple(
role.placeholders[ph.id.value] for ph in role.state.state_placeholders
)
PlaceHolder.instantiate_placeholders(role.state.state_placeholders, state)
PlaceHolder.instantiate_placeholders(state_placeholders, state)
return translation_plan(*args[:-1])
plan_params = translation_plan.parameters()
if len(plan_params) > 0:
torchscript_plan = jit.trace(wrap_stateful_plan, (*args, plan_params))
else:
torchscript_plan = jit.trace(translation_plan, args)
self.plan.torchscript = torchscript_plan
return self.plan
def __init__(self, net: Policy):
"""
Constructor
:param net: non-recurrent network to wrap, which must not be a script module
"""
super().__init__()
# Setup attributes
self.input_size = net.env_spec.obs_space.flat_dim
self.output_size = net.env_spec.act_space.flat_dim
self.net = trace(
net, (to.from_numpy(net.env_spec.obs_space.sample_uniform()), ))
def trace_model(model, sample_input):
"""Traces the model
Args:
model (nn.Module): The model to be traced
sample_input (torch.Tensor): The sample input to the model
Returns:
The traced model
"""
if torch.__version__[0] != '1':
return model
else:
return trace(model, sample_input)
def __init__(self):
super(DenseNetJIT, self).__init__()
self.add_module(
'densenet',
jit.trace(
DenseNet(growth_rate=12,
DenseBlock_layer_num=(40, 40, 40),
bottle_neck_size=4,
dropout_rate=0.2,
compression_rate=0.5,
num_init_features=16,
num_input_features=3,
num_classes=10,
bias=False,
memory_efficient=False), torch.rand(1, 3, 32, 32)))
def trace(self, device):
for level in range(self.start_level, self.end_level + 1):
self.fromRGBs[level] = trace(
self.fromRGBs[level],
torch.rand(1,
self.input_channels,
2**level,
2**level,
device=device))
for level in range(self.start_level, self.end_level):
self.level_blocks[level] = trace(
self.level_blocks[level],
torch.rand(1,
self.channels[level + 1],
2**(level + 1),
2**(level + 1),
device=device))
self.final_block = trace(
self.final_block,
torch.rand(1,
self.channels[self.start_level],
2**self.start_level,
2**self.start_level,
device=device))
def script_model(model: nn.Module, sizes: list) -> ScriptModule:
"""
Generates converts model to the cript model
Args:
model: model to convert
sizes: sizes of input
Returns:
graph_model: converted model
"""
xs = tuple(torch.randn(1, 3, s, s, requires_grad=False) for s in sizes)
graph_model = trace(model.eval(), xs)
graph_model.eval()
return graph_model
def __init__(self, module: Policy):
"""
Constructor
:param module: non-recurrent network to wrap, which must not be a script module
"""
super().__init__()
# Setup attributes
self.input_size = module.env_spec.obs_space.flat_dim
self.output_size = module.env_spec.act_space.flat_dim
samples = to.from_numpy(module.env_spec.obs_space.sample_uniform()
) # .to(dtype=to.get_default_dtype())
self.module = trace(module, (samples, ))
def translate(self):
plan = self.plan
args_shape = plan.get_args_shape()
args = PlaceHolder.create_placeholders(args_shape)
# Temporarily remove reference to original function
tmp_forward = plan.forward
plan.forward = None
# To avoid storing Plan state tensors inside the torchscript,
# we trace wrapper func, which accepts state parameters as last arg
# and sets them into the Plan before executing the Plan
def wrap_stateful_plan(*args):
role = plan.role
state = args[-1]
if 0 < len(role.state.state_placeholders) == len(state) and isinstance(
state, (list, tuple)
):
state_placeholders = tuple(
role.placeholders[ph.id.value] for ph in role.state.state_placeholders
)
PlaceHolder.instantiate_placeholders(role.state.state_placeholders, state)
PlaceHolder.instantiate_placeholders(state_placeholders, state)
return plan(*args[:-1])
plan_params = plan.parameters()
if len(plan_params) > 0:
torchscript_plan = jit.trace(wrap_stateful_plan, (*args, plan_params))
else:
torchscript_plan = jit.trace(plan, args)
plan.torchscript = torchscript_plan
plan.forward = tmp_forward
return plan
def __init__(self, model, dummy_input):
with torch.onnx.set_training(model, False):
trace, _ = jit.trace(model, dummy_input)
# Let ONNX do the heavy lifting: fusing the convolution nodes; fusing the nodes
# composing a GEMM operation; etc.
torch.onnx._optimize_trace(trace, False)
graph = trace.graph()
self.ops = []
self.params = {}
self.edges = []
self.temp = {}
in_out = list(graph.inputs()) + list(graph.outputs())
for param in in_out:
self.__add_param(param)
for node in graph.nodes():
op = {}
op['name'] = node.scopeName()
op['orig-name'] = node.scopeName()
op['type'] = node.kind()
op['inputs'] = []
op['outputs'] = []
op['params'] = []
# in-place operators create very confusing graphs (Resnet, for example),
# so we "unroll" them
same = [
layer for layer in self.ops
if layer['orig-name'] == op['orig-name']
]
if len(same) > 0:
op['name'] += "." + str(len(same))
self.ops.append(op)
for input_ in node.inputs():
self.__add_input(op, input_)
self.edges.append((input_.uniqueName(), op['name']))
for output in node.outputs():
self.__add_output(op, output)
self.edges.append((op['name'], output.uniqueName()))
op['attrs'] = {
attr_name: node[attr_name]
for attr_name in node.attributeNames()
}
def __init__(self, window_size=3):
super(SSIM, self).__init__()
gaussian_img_kernel = {
'weight': create_gaussian_window(window_size, 3).float(),
'bias': torch.zeros(3)
}
gaussian_blur = nn.Conv2d(3,
3,
window_size,
padding=window_size // 2,
groups=3).to(device)
gaussian_blur.load_state_dict(gaussian_img_kernel)
self.gaussian_blur = trace(
gaussian_blur,
torch.rand(3, 3, 16, 16, dtype=torch.float32, device=device))
def torchscript_export(
self,
model,
export_path=None,
quantize=False,
sort_input=False,
sort_key=1,
inference_interface=None,
accelerate=None,
):
# Make sure to put the model on CPU and disable CUDA before exporting to
# ONNX to disable any data_parallel pieces
cuda.CUDA_ENABLED = False
model.cpu()
optimizer = self.trainer.optimizer
optimizer.pre_export(model)
# Trace needs eval mode, to disable dropout etc
model.eval()
model.prepare_for_onnx_export_()
unused_raw_batch, batch = next(
iter(self.data.batches(Stage.TRAIN, load_early=True)))
inputs = model.onnx_trace_input(batch)
# call model forward to set correct device types
if sort_input:
_, sorted_indices = sort(inputs[sort_key], descending=True)
inputs = [i.index_select(0, sorted_indices) for i in inputs]
model(*inputs)
if quantize:
model.quantize()
if accelerate is not None:
if "half" in accelerate:
model.half()
if inference_interface is not None:
model.inference_interface(inference_interface)
trace = jit.trace(model, inputs)
if accelerate is not None:
if "nnpi" in accelerate:
trace._c = torch._C._freeze_module(trace._c)
if hasattr(model, "torchscriptify"):
trace = model.torchscriptify(self.data.tensorizers, trace)
trace.apply(lambda s: s._pack() if s._c._has_method("_pack") else None)
if export_path is not None:
print(f"Saving torchscript model to: {export_path}")
trace.save(export_path)
return trace
def torchscript_export(self, model, export_path):
# Make sure to put the model on CPU and disable CUDA before exporting to
# ONNX to disable any data_parallel pieces
cuda.CUDA_ENABLED = False
model.cpu()
precision.deactivate(model)
# Trace needs eval mode, to disable dropout etc
model.eval()
model.prepare_for_onnx_export_()
batch = next(iter(self.data.batches(Stage.TEST)))
inputs = model.arrange_model_inputs(batch)
trace = jit.trace(model, inputs)
if hasattr(model, "torchscriptify"):
trace = model.torchscriptify(self.data.tensorizers, trace)
print(f"Saving torchscript model to: {export_path}")
trace.save(export_path)
def trace_model(
model: Model,
batch: Union[Tuple[torch.Tensor], torch.Tensor],
method_name: str = "forward",
) -> jit.ScriptModule:
"""Traces model using runner and batch.
Args:
model: Model to trace
batch: Batch to trace the model
method_name: Model's method name that will be
used as entrypoint during tracing
Example:
.. code-block:: python
import torch
from catalyst.utils import trace_model
class LinModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.lin1 = torch.nn.Linear(10, 10)
self.lin2 = torch.nn.Linear(2, 10)
def forward(self, inp_1, inp_2):
return self.lin1(inp_1), self.lin2(inp_2)
def first_only(self, inp_1):
return self.lin1(inp_1)
lin_model = LinModel()
traced_model = trace_model(
lin_model, batch=torch.randn(1, 10), method_name="first_only"
)
Returns:
jit.ScriptModule: Traced model
"""
nn_model = get_nn_from_ddp_module(model)
wrapped_model = ModelForwardWrapper(model=nn_model,
method_name=method_name)
traced = jit.trace(wrapped_model, example_inputs=batch)
return traced
def translate(self):
plan = self.plan
args_shape = plan.get_args_shape()
args = PlaceHolder.create_placeholders(args_shape)
# Temporarily remove reference to original function
tmp_forward = plan.forward
plan.forward = None
# To avoid storing Plan state tensors in torchscript, they will be send as parameters
plan_params = plan.parameters()
if len(plan_params) > 0:
args = (*args, plan_params)
torchscript_plan = jit.trace(plan, args)
plan.torchscript = torchscript_plan
plan.forward = tmp_forward
return plan
