177 lines
6.6 KiB
Python
177 lines
6.6 KiB
Python
import argparse
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import functools
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import torch
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import options.options as option
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from models.networks import define_G
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class TracedModule:
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def __init__(self, idname):
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self.idname = idname
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self.traced_outputs = []
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self.traced_inputs = []
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class TorchCustomTrace:
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def __init__(self):
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self.module_name_counter = {}
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self.modules = {}
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self.graph = {}
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self.module_map_by_inputs = {}
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self.module_map_by_outputs = {}
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self.inputs_to_func_output_tuple = {}
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def add_tracked_module(self, mod: torch.nn.Module):
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modname = type(mod).__name__
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if modname not in self.module_name_counter.keys():
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self.module_name_counter[modname] = 0
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self.module_name_counter[modname] += 1
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idname = "%s(%03d)" % (modname, self.module_name_counter[modname])
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self.modules[idname] = TracedModule(idname)
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return idname
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# Only called for nn.Modules since those are the only things we can access. Filling in the gaps will be done in
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# the backwards pass.
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def mem_forward_hook(self, module: torch.nn.Module, inputs, outputs, trace: str, mod_id: str):
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mod = self.modules[mod_id]
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'''
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for li in inputs:
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if type(li) == torch.Tensor:
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li = [li]
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if type(li) == list:
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for i in li:
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if i.data_ptr() in self.module_map_by_inputs.keys():
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self.module_map_by_inputs[i.data_ptr()].append(mod)
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else:
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self.module_map_by_inputs[i.data_ptr()] = [mod]
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for o in outputs:
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if o.data_ptr() in self.module_map_by_inputs.keys():
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self.module_map_by_inputs[o.data_ptr()].append(mod)
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else:
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self.module_map_by_inputs[o.data_ptr()] = [mod]
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'''
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print(trace)
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def mem_backward_hook(self, inputs, outputs, op):
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if len(inputs) == 0:
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print("No inputs.. %s" % (op,))
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outs = [o.data_ptr() for o in outputs]
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tup = (outs, op)
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#print(tup)
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for li in inputs:
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if type(li) == torch.Tensor:
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li = [li]
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if type(li) == list:
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for i in li:
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if i.data_ptr() in self.module_map_by_inputs.keys():
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print("%i: [%s] {%s}" % (i.data_ptr(), op, [n.idname for n in self.module_map_by_inputs[i.data_ptr()]]))
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if i.data_ptr() in self.inputs_to_func_output_tuple.keys():
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self.inputs_to_func_output_tuple[i.data_ptr()].append(tup)
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else:
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self.inputs_to_func_output_tuple[i.data_ptr()] = [tup]
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def install_hooks(self, mod: torch.nn.Module, trace=""):
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mod_id = self.add_tracked_module(mod)
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my_trace = trace + "->" + mod_id
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# If this module has parameters, it also has a state worth tracking.
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#if next(mod.parameters(recurse=False), None) is not None:
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mod.register_forward_hook(functools.partial(self.mem_forward_hook, trace=my_trace, mod_id=mod_id))
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for m in mod.children():
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self.install_hooks(m, my_trace)
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def install_backward_hooks(self, grad_fn):
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# AccumulateGrad simply pushes a gradient into the specified variable, and isn't useful for the purposes of
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# tracing the graph.
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if grad_fn is None or "AccumulateGrad" in str(grad_fn):
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return
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grad_fn.register_hook(functools.partial(self.mem_backward_hook, op=str(grad_fn)))
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for g, _ in grad_fn.next_functions:
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self.install_backward_hooks(g)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../../options/train_div2k_pixgan_srg2.yml')
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opt = option.parse(parser.parse_args().opt, is_train=False)
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opt = option.dict_to_nonedict(opt)
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netG = define_G(opt)
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dummyInput = torch.rand(1,3,32,32)
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mode = 'onnx'
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if mode == 'torchscript':
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print("Tracing generator network..")
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traced_netG = torch.jit.trace(netG, dummyInput)
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traced_netG.save('../results/ts_generator.zip')
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print(traced_netG.code)
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for i, module in enumerate(traced_netG.RRDB_trunk.modules()):
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print(i, str(module))
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elif mode == 'onnx':
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print("Performing onnx trace")
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input_names = ["lr_input"]
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output_names = ["hr_image"]
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dynamic_axes = {'lr_input': {0: 'batch', 1: 'filters', 2: 'h', 3: 'w'}, 'hr_image': {0: 'batch', 1: 'filters', 2: 'h', 3: 'w'}}
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torch.onnx.export(netG, dummyInput, "../results/gen.onnx", verbose=True, input_names=input_names,
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output_names=output_names, dynamic_axes=dynamic_axes, opset_version=12)
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elif mode == 'memtrace':
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criterion = torch.nn.MSELoss()
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tracer = TorchCustomTrace()
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tracer.install_hooks(netG)
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out, = netG(dummyInput)
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tracer.install_backward_hooks(out.grad_fn)
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target = torch.zeros_like(out)
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loss = criterion(out, target)
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loss.backward()
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elif mode == 'trace':
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out = netG.forward(dummyInput)[0]
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print(out.shape)
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# Build the graph backwards.
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graph = build_graph(out, 'output')
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def get_unique_id_for_fn(fn):
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return (str(fn).split(" object at ")[1])[:-1]
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class GraphNode:
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def __init__(self, fn):
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self.name = (str(fn).split(" object at ")[0])[1:]
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self.fn = fn
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self.children = {}
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self.parents = {}
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def add_parent(self, parent):
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self.parents[get_unique_id_for_fn(parent)] = parent
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def add_child(self, child):
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self.children[get_unique_id_for_fn(child)] = child
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class TorchGraph:
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def __init__(self):
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self.tensor_map = {}
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def get_node_for_tensor(self, t):
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return self.tensor_map[get_unique_id_for_fn(t)]
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def init(self, output_tensor):
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self.build_graph_backwards(output_tensor.grad_fn, None)
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# Find inputs
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self.inputs = []
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for v in self.tensor_map.values():
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# Is an input if the parents dict is empty.
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if bool(v.parents):
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self.inputs.append(v)
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def build_graph_backwards(self, fn, previous_fn):
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id = get_unique_id_for_fn(fn)
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if id in self.tensor_map:
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node = self.tensor_map[id]
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node.add_child(previous_fn)
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else:
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node = GraphNode(fn)
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self.tensor_map[id] = node
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# Propagate to children
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for child_fn in fn.next_functions:
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node.add_parent(self.build_graph_backwards(child_fn, fn))
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return node |