❓ [Question] operator being decomposed rather than being converted when a corresponding converter exists? #2665
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The same for aten.leaky_relu. import torch
import torch.nn as nn
import torch_tensorrt
class MyModule(nn.Module):
def __init__(self):
super().__init__()
self.m = nn.LeakyReLU()
def forward(self, x):
return self.m(x)
model = MyModule().eval().cuda().half()
inputs = [torch.randn((1, 3, 4, 4), dtype=torch.half, device="cuda")]
optimized_model = torch_tensorrt.compile(
model,
ir="dynamo",
inputs=inputs,
enabled_precisions={torch.half},
debug=True,
min_block_size=1,
) |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_tensorrt
class MyModule(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
model = MyModule().eval().cuda().half()
inputs = [
torch.randn((1, 3, 128, 128), dtype=torch.half, device="cuda"),
]
optimized_model = torch_tensorrt.compile(
model,
ir="dynamo",
inputs=inputs,
enabled_precisions={torch.half},
debug=True,
min_block_size=1,
) |
HolyWu
changed the title
This was referenced Mar 10, 2024
Merged
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❓ Question
From the debug log below, it seems that the
aten.grid_sampler_2doperator gets decomposed into several lower-level operators. But isn't there a corresponding converter which should be used?What you have already tried
Environment
conda,pip,libtorch, source): pipThe text was updated successfully, but these errors were encountered: