mirror of
https://github.com/pytorch/pytorch.git
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d05a11337c
Move functorch/functorch into `functorch` folder - Add functorch/CMakeLists.txt that adds `functorch` native python exension - Modify `setup.py` to package pytorch and functorch together into a single wheel - Modify `functorch.__version__` is not equal to that of `torch.__version__` - Add dummy `functorch/setup.py` file for the projects that still want to build it Differential Revision: [D39058811](https://our.internmc.facebook.com/intern/diff/D39058811) Pull Request resolved: https://github.com/pytorch/pytorch/pull/83464 Approved by: https://github.com/zou3519
91 lines
3.4 KiB
Python
91 lines
3.4 KiB
Python
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import torch
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import torch.fx as fx
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from torch.utils._pytree import tree_flatten
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aten = torch.ops.aten
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def get_aten_target(node):
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if hasattr(node.target, 'overloadpacket'):
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return node.target.overloadpacket
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return node.target
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rand_ops = [aten.dropout, aten._fused_dropout, aten._standard_gamma,
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aten.bernoulli, aten.multinomial, aten.native_dropout,
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aten.normal, aten.poisson, aten.binomial, aten.rrelu,
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aten.rand_like, aten.rand, aten.randint, aten.randn, aten.randperm]
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# return a new copy of torch.fx.graph.Graph with CSE applied to the input graph
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def fx_graph_cse(fx_g: torch.fx.graph.Graph):
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new_graph = fx.Graph()
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env = {} # map from node in the old graph to node in the new graph
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hash_env = {} # map from hash to a node in the new graph
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token_map = {} # map from hash to token
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for n in fx_g.nodes:
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# The placeholder, output, and get_attr nodes are copied to the new grpah without change
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# do not CSE away random operations
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if n.op == 'placeholder' or n.op == 'output' or n.op == 'get_attr' or get_aten_target(n) in rand_ops:
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new_node = new_graph.node_copy(n, lambda x: env[x])
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env[n] = new_node
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else: # n.op == 'call_function', should never see n.op == 'call_module' or 'call_method'
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# substitute args and kwargs memebrs to their mapping in env if exists
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# specs can be used to reconstruct nested list/dictionaries
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def substitute(arg_list):
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arg_list, spec = tree_flatten(arg_list)
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for i in range(len(arg_list)):
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v = arg_list[i]
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if isinstance(v, torch.fx.node.Node) and v in env:
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arg_list[i] = env[v]
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return tuple(arg_list), spec
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args, args_spec = substitute(n.args)
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kwargs, kwargs_spec = substitute(n.kwargs)
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# each token corresponds to a unique node
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# nodes with the same token can be substituted
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token = {"target": n.target, "args": args, "args_spec": args_spec,
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"kwargs": kwargs, "kwargs_spec": kwargs_spec}
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# hash substituted args to a number, do not hash specs because specs are not hashable
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hash_arg = hash((args, kwargs))
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hash_val = (n.target, hash_arg)
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# check if a node has a substitute and can be eliminated
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hash_val_in_hash_env = hash_val in hash_env
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if hash_val_in_hash_env and token_map[hash_val] == token:
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env[n] = hash_env[hash_val]
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continue
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new_node = new_graph.node_copy(n, lambda x: env[x])
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env[n] = new_node
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if not hash_val_in_hash_env:
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hash_env[hash_val] = new_node
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token_map[hash_val] = token
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return new_graph
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def strip_overloads(gm):
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"""
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Modifies the target of graph nodes in :attr:`gm` to strip overloads.
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Args:
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gm(fx.GraphModule): The input Fx graph module to be modified
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"""
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for node in gm.graph.nodes:
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if isinstance(node.target, torch._ops.OpOverload):
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node.target = node.target.overloadpacket
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gm.recompile()
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def get_placeholders(graph):
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return list(filter(lambda x: x.op == 'placeholder', graph.nodes))
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def get_outputs(graph):
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for node in graph.nodes:
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if node.op == 'output':
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return tree_flatten(node.args[0])[0]
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raise AssertionError("No output node found")
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