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694db5f549
Just like we use `is/is not` for class comparisons, it is generally advised to use `is/is not` for comparisons against torch functions. Pull Request resolved: https://github.com/pytorch/pytorch/pull/166624 Approved by: https://github.com/Lucaskabela, https://github.com/Skylion007
62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
# mypy: allow-untyped-defs
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import operator
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import torch
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from torch.fx.passes.fake_tensor_prop import FakeTensorProp
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from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner
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from torch.fx.passes.operator_support import OperatorSupport
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from torch.fx.passes.tools_common import CALLABLE_NODE_OPS
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from torch.utils import _pytree as pytree
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class CudaGraphsSupport(OperatorSupport):
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# TODO: why is submodules passed here
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def is_node_supported(self, submodules, node: torch.fx.Node) -> bool:
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if node.op not in CALLABLE_NODE_OPS:
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return False
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if node.target is torch.ops.aten.embedding_dense_backward.default:
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return False
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if node.target == operator.getitem:
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return True
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found_not_cuda = False
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def meta_fk(meta):
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return meta["val"] if "val" in meta else meta["fake_result"]
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def find_not_cuda(t):
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nonlocal found_not_cuda
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if isinstance(t, torch.Tensor) and t.device.type != "cuda":
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found_not_cuda = True
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for n in node.all_input_nodes:
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pytree.tree_map_(find_not_cuda, meta_fk(n.meta))
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pytree.tree_map_(find_not_cuda, meta_fk(node.meta))
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# NB: factory function is accounted for because the result would be
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# cpu or cuda
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return not found_not_cuda
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def partition_cudagraphs(gm, inputs):
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"""
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Partition an FX graph into sub-GraphModules that can be validly run under
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CUDA graphs. For a subgraph to be runnable under CUDA, all of the operations
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must involve CUDA tensors only/
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"""
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FakeTensorProp(gm).propagate(*inputs)
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supported_ops = CudaGraphsSupport()
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# TODO: single node partition may be wrong due to the pessimization
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# from copying in and out the data. Check in benchmarks, perhaps
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partitioner = CapabilityBasedPartitioner(
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gm, supported_ops, allows_single_node_partition=True
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)
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partitions = partitioner.propose_partitions()
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fused_graph = partitioner.fuse_partitions(partitions)
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return fused_graph
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