Merge pull request #574 from jaybdub/roll_converter

added converter for torch.roll

Author: jaybdub

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 ## [Master]
 
+- Added converter for ``torch.roll``
 - Added converter for ``torch.nn.functional.layer_norm``
 - Added converter for ``torch.nn.functional.gelu``
 - Added converter for ``torch.nn.functional.linear``

torch2trt/converters/__init__.py

@@ -51,6 +51,7 @@
 from .prod import *
 from .relu import *
 from .relu6 import *
+from .roll import *
 from .sigmoid import *
 from .silu import *
 from .softmax import *

torch2trt/converters/roll.py

@@ -0,0 +1,74 @@
+from torch2trt.torch2trt import *
+from torch2trt.module_test import add_module_test
+
+    
+@tensorrt_converter('torch.roll')
+@tensorrt_converter('torch.Tensor.roll')
+def convert_roll(ctx):
+    input = get_arg(ctx, 'input', 0, None)
+    shifts = get_arg(ctx, 'shifts', 1, None)
+    dims = get_arg(ctx, 'dims', 2, None)
+    output = ctx.method_return
+    
+    assert dims is not None, "roll converter only supports roll when dims is specified"
+    
+    ndim = input.ndim
+    
+    input_trt = add_missing_trt_tensors(ctx.network, [input])[0]
+    
+    try:
+        iter(shifts)
+    except:
+        shifts = (shifts,)
+        dims = (dims,)
+    
+    start = [0] * ndim
+    shape = tuple([int(d) for d in input.shape])
+    stride = [1] * ndim
+    
+    for s, d in zip(shifts, dims):
+        start[d] = (-s) % shape[d]
+    
+    start = tuple(start[1:])
+    shape = tuple(shape[1:])
+    stride = tuple(stride[1:])
+    
+    
+    layer = ctx.network.add_slice(
+        input_trt,
+        start,  # [1:] to exclude batch
+        shape,
+        stride
+    )
+    layer.mode = trt.SliceMode.WRAP
+    
+    output._trt = layer.get_output(0)
+    
+    
+class Roll(torch.nn.Module):
+    
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+        self.args = args
+        self.kwargs = kwargs
+        
+    def forward(self, x):
+        return torch.roll(x, *self.args, **self.kwargs)
+
+    
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 4)])
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 4, 5)])
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 4, 5)])
+def test_roll_int():
+    return Roll(1, 1)
+    
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 4, 5)])
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 4, 5)])
+def test_roll_int_dim():
+    return Roll(1, -2)
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 4, 5)])
+def test_roll_tuple():
+    return Roll((2, 3), (1, 3))