Additional converters for floordiv, mod, ne, and torch::tensor() oper…

…ations (#505)

* Initioal version of ne, floordiv, mod and tensor converters. Extend ops for relu and sigmoid.

* Converters for floordiv, mod, ne, and torch::tensor() operations . Extend relu and sigmoid converters to Tensor methods.

* Update CHANGELOG.md

CHANGELOG.md

@@ -10,3 +10,6 @@
 - Added GroupNorm plugin which internally uses PyTorch aten::group_norm
 - Replaced Tensor.ndim references with len(tensor.shape) to support older pytorch versions
 - Added reduced precision documentation page
+- Added converters for ``floordiv``, ``mod``, ``ne``, and ``torch.tensor`` operations
+- Extended ``relu`` converter to support ``Tensor.relu`` operation
+- Extended ``sigmoid`` converter to support ``Tensor.sigmoid`` operation

torch2trt/converters/__init__.py

@@ -26,6 +26,7 @@
 from .compare import *
 from .div import *
 from .expand import *
+from .floordiv import *
 from .getitem import *
 from .identity import *
 from .instance_norm import *
@@ -35,8 +36,10 @@
 from .max_pool2d import *
 from .mean import *
 from .min import *
+from .mod import *
 from .mul import *
 from .normalize import *
+from .ne import *
 from .narrow import *
 from .pad import *
 from .permute import *
@@ -52,6 +55,7 @@
 from .sub import *
 from .sum import *
 from .tanh import *
+from .tensor import *
 from .transpose import *
 from .unary import *
 from .view import *

torch2trt/converters/floordiv.py

@@ -0,0 +1,81 @@
+from torch2trt.torch2trt import *
+from torch2trt.module_test import add_module_test
+
+
+@tensorrt_converter('torch.Tensor.__floordiv__')
+@tensorrt_converter('torch.Tensor.__ifloordiv__')
+@tensorrt_converter('torch.floor_divide')
+def convert_floordiv(ctx):
+    input_a = ctx.method_args[0]
+    input_b = ctx.method_args[1]
+    output = ctx.method_return
+    input_a_trt, input_b_trt = add_missing_trt_tensors(ctx.network, [input_a, input_b])
+    input_a_trt, input_b_trt = broadcast_trt_tensors(ctx.network, [input_a_trt, input_b_trt], len(output.shape) - 1)
+    # we can not use ElementWiseOperation.FLOOR_DIV directly because Torch truncate negative result toward 0
+    # but TensorRT FLOOR_DIV op toward -Inf
+    # sign = ab / |ab|
+    # floordiv result: sign * (|a| // |b|)
+    ab_layer = ctx.network.add_elementwise(input_a_trt, input_b_trt, trt.ElementWiseOperation.PROD)
+    abs_ab_layer = ctx.network.add_unary(ab_layer.get_output(0), trt.UnaryOperation.ABS)
+    sign_layer = ctx.network.add_elementwise(ab_layer.get_output(0), abs_ab_layer.get_output(0),
+                                             trt.ElementWiseOperation.DIV)
+    abs_a_layer = ctx.network.add_unary(input_a_trt, trt.UnaryOperation.ABS)
+    abs_b_layer = ctx.network.add_unary(input_b_trt, trt.UnaryOperation.ABS)
+    abs_floor_layer = ctx.network.add_elementwise(abs_a_layer.get_output(0), abs_b_layer.get_output(0),
+                                                  trt.ElementWiseOperation.FLOOR_DIV)
+    out_layer = ctx.network.add_elementwise(sign_layer.get_output(0), abs_floor_layer.get_output(0),
+                                            trt.ElementWiseOperation.PROD)
+    output._trt = out_layer.get_output(0)
+
+
+class FloorDiv(torch.nn.Module):
+    def __init__(self):
+        super(FloorDiv, self).__init__()
+
+    def forward(self, x, y):
+        return x // y
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20), (1, 3, 1, 20)])
+def test_floordiv_op():
+    return FloorDiv()
+
+
+class FloorDivAssign (torch.nn.Module):
+    def __init__(self):
+        super(FloorDivAssign, self).__init__()
+
+    def forward(self, x, y):
+        x //= y
+        return x
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20), (1, 3, 1, 20)])
+def test_floordiv_op_assign():
+    return FloorDivAssign()
+
+
+class FloorDivConst(torch.nn.Module):
+    def __init__(self):
+        super(FloorDivConst, self).__init__()
+
+    def forward(self, x):
+        return x // 2.
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20)])
+def test_floordiv_op_const():
+    return FloorDivConst()
+
+
+class TorchFloorDiv(torch.nn.Module):
+    def __init__(self):
+        super(TorchFloorDiv, self).__init__()
+
+    def forward(self, x, y):
+        return torch.floor_divide(x, y)
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20), (1, 3, 1, 20)])
+def test_floordiv_func():
+    return TorchFloorDiv()

torch2trt/converters/mod.py

@@ -0,0 +1,99 @@
+from torch2trt.torch2trt import *
+from torch2trt.module_test import add_module_test
+
+
+@tensorrt_converter('torch.fmod')
+def convert_mod(ctx):
+    input_a = ctx.method_args[0]
+    input_b = ctx.method_args[1]
+    output = ctx.method_return
+    input_a_trt, input_b_trt = add_missing_trt_tensors(ctx.network, [input_a, input_b])
+    input_a_trt, input_b_trt = broadcast_trt_tensors(ctx.network, [input_a_trt, input_b_trt], len(output.shape) - 1)
+    # we can not use ElementWiseOperation.FLOOR_DIV directly because Torch truncate negative result toward 0
+    # but TensorRT FLOOR_DIV op toward -Inf
+    # sign = ab / |ab|
+    # floordiv result: sign * (|a| // |b|)
+    ab_layer = ctx.network.add_elementwise(input_a_trt, input_b_trt, trt.ElementWiseOperation.PROD)
+    abs_ab_layer = ctx.network.add_unary(ab_layer.get_output(0), trt.UnaryOperation.ABS)
+    sign_layer = ctx.network.add_elementwise(ab_layer.get_output(0), abs_ab_layer.get_output(0),
+                                             trt.ElementWiseOperation.DIV)
+    abs_a_layer = ctx.network.add_unary(input_a_trt, trt.UnaryOperation.ABS)
+    abs_b_layer = ctx.network.add_unary(input_b_trt, trt.UnaryOperation.ABS)
+    abs_floor_layer = ctx.network.add_elementwise(abs_a_layer.get_output(0), abs_b_layer.get_output(0),
+                                                  trt.ElementWiseOperation.FLOOR_DIV)
+    # a % b  =  a - (a//b) * b
+    floordiv_layer = ctx.network.add_elementwise(sign_layer.get_output(0), abs_floor_layer.get_output(0),
+                                            trt.ElementWiseOperation.PROD)
+    prod_layer = ctx.network.add_elementwise(floordiv_layer.get_output(0), input_b_trt, trt.ElementWiseOperation.PROD)
+    sub_layer = ctx.network.add_elementwise(input_a_trt, prod_layer.get_output(0), trt.ElementWiseOperation.SUB)
+    output._trt = sub_layer.get_output(0)
+
+
+@tensorrt_converter('torch.Tensor.__mod__')
+# we need separate converter for operator because for some reason Torch use truncation toward -Inf for this op.
+# bug is filed: https://github.com/pytorch/pytorch/issues/52425
+# but for now we have to convert model exactly
+def convert_mod(ctx):
+    input_a = ctx.method_args[0]
+    input_b = ctx.method_args[1]
+    output = ctx.method_return
+    input_a_trt, input_b_trt = add_missing_trt_tensors(ctx.network, [input_a, input_b])
+    input_a_trt, input_b_trt = broadcast_trt_tensors(ctx.network, [input_a_trt, input_b_trt], len(output.shape) - 1)
+    # a % b  =  a - (a//b) * b
+    floordiv_layer = ctx.network.add_elementwise(input_a_trt, input_b_trt, trt.ElementWiseOperation.FLOOR_DIV)
+    prod_layer = ctx.network.add_elementwise(floordiv_layer.get_output(0), input_b_trt, trt.ElementWiseOperation.PROD)
+    mod_layer = ctx.network.add_elementwise(input_a_trt, prod_layer.get_output(0), trt.ElementWiseOperation.SUB)
+    output._trt = mod_layer.get_output(0)
+
+
+class Mod(torch.nn.Module):
+    def __init__(self):
+        super(Mod, self).__init__()
+
+    def forward(self, x, y):
+        return x % y
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20), (1, 3, 1, 20)])
+def test_mod_op():
+    return Mod()
+
+
+class ModAssign(torch.nn.Module):
+    def __init__(self):
+        super(ModAssign, self).__init__()
+
+    def forward(self, x, y):
+        x %= y
+        return x
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20), (1, 3, 1, 20)])
+def test_mod_op_assign():
+    return ModAssign()
+
+
+class ModConst(torch.nn.Module):
+    def __init__(self):
+        super(ModConst, self).__init__()
+
+    def forward(self, x):
+        return x % 2.
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20)])
+def test_mod_op_const():
+    return ModConst()
+
+
+class TorchMod(torch.nn.Module):
+    def __init__(self):
+        super(TorchMod, self).__init__()
+
+    def forward(self, x, y):
+        return torch.fmod(x, y)
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20), (1, 3, 40, 20)])
+def test_mod_func():
+    return TorchMod()

torch2trt/converters/ne.py

@@ -0,0 +1,54 @@
+from torch2trt.torch2trt import *
+from torch2trt.module_test import add_module_test
+
+
+@tensorrt_converter('torch.ne')
+@tensorrt_converter('torch.Tensor.__ne__')
+def convert_ne(ctx):
+    input_a = ctx.method_args[0]
+    input_b = ctx.method_args[1]
+    output = ctx.method_return
+    input_a_trt, input_b_trt = add_missing_trt_tensors(ctx.network, [input_a, input_b])
+    input_a_trt, input_b_trt = broadcast_trt_tensors(ctx.network, [input_a_trt, input_b_trt], len(output.shape) - 1)
+    layer_1 = ctx.network.add_elementwise(input_a_trt, input_b_trt, trt.ElementWiseOperation.EQUAL)
+    layer_2 = ctx.network.add_unary(layer_1.get_output(0), trt.UnaryOperation.NOT)
+    output._trt = layer_2.get_output(0)
+
+
+class NotEqual(torch.nn.Module):
+    def __init__(self):
+        super(NotEqual, self).__init__()
+
+    def forward(self, x, y):
+        return x != y
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20), (1, 3, 1, 20)])
+def test_ne_op():
+    return NotEqual()
+
+
+class NotEqualConst(torch.nn.Module):
+    def __init__(self):
+        super(NotEqualConst, self).__init__()
+
+    def forward(self, x):
+        return x != 13.62
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20)])
+def test_ne_op_const():
+    return NotEqualConst()
+
+
+class TorchNotEqual(torch.nn.Module):
+    def __init__(self):
+        super(TorchNotEqual, self).__init__()
+
+    def forward(self, x, y):
+        return torch.ne(x, y)
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20), (1, 3, 1, 20)])
+def test_ne_torch():
+    return TorchNotEqual()

torch2trt/converters/relu.py

@@ -6,6 +6,7 @@
 @tensorrt_converter('torch.relu_')
 @tensorrt_converter('torch.nn.functional.relu')
 @tensorrt_converter('torch.nn.functional.relu_')
+@tensorrt_converter('torch.Tensor.relu')
 def convert_functional_relu(ctx):
     ctx.method_args = (torch.nn.ReLU(),) + ctx.method_args
     convert_relu(ctx)
@@ -32,4 +33,17 @@ def forward(self, x):
 
 @add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 4, 5)])
 def test_functional_relu_basic():
-    return FunctionalRelu()
+    return FunctionalRelu()
+
+
+class TensorRelu(torch.nn.Module):
+    def __init__(self):
+        super(TensorRelu, self).__init__()
+
+    def forward(self, x):
+        return x.relu()
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20)])
+def test_tensor_relu():
+    return TensorRelu()

torch2trt/converters/sigmoid.py

@@ -4,6 +4,7 @@
 
 @tensorrt_converter('torch.nn.functional.sigmoid')
 @tensorrt_converter('torch.sigmoid')
+@tensorrt_converter('torch.Tensor.sigmoid')
 def convert_sigmoid(ctx):
     input = ctx.method_args[0]
     input_trt = add_missing_trt_tensors(ctx.network, [input])[0]
@@ -15,4 +16,17 @@ def convert_sigmoid(ctx):
 
 @add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 3, 3)])
 def test_sigmoid_basic():
-    return torch.nn.Sigmoid()
+    return torch.nn.Sigmoid()
+
+
+class TensorSigmoid(torch.nn.Module):
+    def __init__(self):
+        super(TensorSigmoid, self).__init__()
+
+    def forward(self, x):
+        return x.sigmoid()
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 3, 40, 20)])
+def test_tensor_sigmoid():
+    return TensorSigmoid()

torch2trt/converters/tensor.py

@@ -0,0 +1,22 @@
+from torch2trt.torch2trt import *
+from torch2trt.module_test import add_module_test
+
+
+@tensorrt_converter('torch.tensor')
+def convert_mod(ctx):
+    output = ctx.method_return
+    layer = ctx.network.add_constant(tuple(output.shape), output.detach().cpu().numpy() )
+    output._trt = layer.get_output(0)
+
+
+class TorchTensor(torch.nn.Module):
+    def __init__(self):
+        super(TorchTensor, self).__init__()
+
+    def forward(self, x):
+        return x + torch.tensor([[1., 2., 3.], [4., 5., 6.]], device=torch.device('cuda'))
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 2, 3)])
+def test_tensor_creation():
+    return TorchTensor()