mirror of
https://github.com/zebrajr/pytorch.git
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8bd9fe3f49
Fixes some errors you run into in dynamo when turning on fake tensors. I'm waiting on flipping the switch because I need to also get some fixes into dynamo + do benchmarking. I could manually turn off fake tensors in functorch in dynamo, and then turn it on here if requested, although the changes here are pretty minimal. Pull Request resolved: https://github.com/pytorch/pytorch/pull/84432 Approved by: https://github.com/Chillee
57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
import torch
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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.fx.passes.fake_tensor_prop import FakeTensorProp
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from torch.utils._pytree import tree_map
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import operator
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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 in [torch.ops.aten.embedding_dense_backward.default]:
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return False
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if node.target in [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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tree_map(find_not_cuda, meta_fk(n.meta))
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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(gm, supported_ops, allows_single_node_partition=True)
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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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