torch.map¶
dynamic_shape_map¶
Original source code:
# mypy: allow-untyped-defs
import torch
from functorch.experimental.control_flow import map
class DynamicShapeMap(torch.nn.Module):
"""
functorch map() maps a function over the first tensor dimension.
"""
def __init__(self):
super().__init__()
def forward(self, xs, y):
def body(x, y):
return x + y
return map(body, xs, y)
Result:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, xs: "f32[3, 2]", y: "f32[2]"):
body_graph_0 = self.body_graph_0
map_impl = torch.ops.higher_order.map_impl(body_graph_0, [xs], [y]); body_graph_0 = xs = y = None
getitem: "f32[3, 2]" = map_impl[0]; map_impl = None
return (getitem,)
class <lambda>(torch.nn.Module):
def forward(self, xs: "f32[2]", y: "f32[2]"):
add: "f32[2]" = torch.ops.aten.add.Tensor(xs, y); xs = y = None
return (add,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='xs'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='y'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {}