fix: remove legacy conv converter (#3343)

Author: chohk88

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -7,6 +7,7 @@
 import numpy as np
 import torch
 from torch.fx.node import Argument, Node, Target
+
 from torch_tensorrt.dynamo._settings import CompilationSettings
 from torch_tensorrt.dynamo._SourceIR import SourceIR
 from torch_tensorrt.dynamo.conversion import impl
@@ -2446,15 +2447,8 @@ def aten_ops_le(
     )
 
 
-def conv_param_validator(
-    conv_node: Node, settings: Optional[CompilationSettings] = None
-) -> bool:
-    return conv_node.args[7] in ([0], [0, 0], [0, 0, 0])
-
-
 @dynamo_tensorrt_converter(
     torch.ops.aten.convolution.default,
-    capability_validator=conv_param_validator,
     supports_dynamic_shapes=True,
 )
 @enforce_tensor_types(
@@ -2500,6 +2494,7 @@ def aten_ops_convolution(
             stride=args[3],
             padding=args[4],
             dilation=args[5],
+            output_padding=args[7],
             groups=args[8],
         )
 

py/torch_tensorrt/dynamo/conversion/impl/deconv.py

@@ -6,6 +6,7 @@
 import tensorrt as trt
 import torch
 from torch.fx.node import Target
+
 from torch_tensorrt.dynamo.conversion import impl
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
 from torch_tensorrt.dynamo.conversion.converter_utils import (
@@ -105,6 +106,9 @@ def deconvNd(
     padding = (padding,) if isinstance(padding, int) else padding
     stride = (stride,) if isinstance(stride, int) else stride
     dilation = (dilation,) if isinstance(dilation, int) else dilation
+    output_padding = (
+        (output_padding,) if isinstance(output_padding, int) else output_padding
+    )
 
     # Expand parameters manually for Conv1D computations
     if is_deconv1d:
@@ -113,6 +117,11 @@ def deconvNd(
         dilation = (
             extend_attr_to_tuple(dilation, 2) if dilation is not None else dilation
         )
+        output_padding = (
+            (tuple(output_padding) + (0,))
+            if output_padding is not None
+            else output_padding
+        )
 
     set_layer_name(deconv_layer, target, name, source_ir)
 
@@ -126,6 +135,20 @@ def deconvNd(
     if groups is not None:
         deconv_layer.num_groups = groups
 
+    ndims = len(padding)
+    pre_padding_values = []
+    post_padding_values = []
+
+    for dim in range(ndims):
+        pre_padding = padding[dim]
+        post_padding = padding[dim] - output_padding[dim]
+
+        pre_padding_values.append(pre_padding)
+        post_padding_values.append(post_padding)
+
+    deconv_layer.pre_padding = tuple(pre_padding_values)
+    deconv_layer.post_padding = tuple(post_padding_values)
+
     # Handle quantization cases
     if scale is not None and zero_point is not None:
         # Assume the dtype of activation is torch.quint8

py/torch_tensorrt/fx/converters/aten_ops_converters.py

@@ -10,9 +10,10 @@
 # @manual=//deeplearning/trt/python:py_tensorrt
 import tensorrt as trt
 import torch
-import torch_tensorrt.fx.tracer.acc_tracer.acc_utils as acc_utils
 from torch.fx.immutable_collections import immutable_list
 from torch.fx.node import Argument, Target
+
+import torch_tensorrt.fx.tracer.acc_tracer.acc_utils as acc_utils
 from torch_tensorrt.fx.converters import acc_ops_converters
 from torch_tensorrt.fx.converters.impl import activation, convolution
 

tests/py/dynamo/conversion/test_deconvolution_aten.py

@@ -1,6 +1,7 @@
 import torch
 from parameterized import param, parameterized
 from torch.testing._internal.common_utils import run_tests
+
 from torch_tensorrt import Input
 
 from .harness import DispatchTestCase
@@ -15,6 +16,21 @@ class TestDeconvolutionConverter(DispatchTestCase):
             param("non_zero_padding", 1, padding=1),
             param("dilation", 1, dilation=2),
             param("groups", 1, groups=3),
+            param("output_padding_1", 3, stride=2, padding=1, output_padding=1),
+            param("output_padding_2", 3, stride=2, padding=2, output_padding=1),
+            param("output_padding_3", 3, stride=2, padding=3, output_padding=1),
+            param("output_padding_4", 3, stride=3, padding=2, output_padding=1),
+            param("output_padding_5", 3, stride=3, padding=3, output_padding=1),
+            param("output_padding_6", 3, stride=3, padding=3, output_padding=2),
+            param(
+                "combined_params",
+                3,
+                stride=3,
+                padding=3,
+                dilation=2,
+                groups=3,
+                output_padding=2,
+            ),
         ]
     )
     def test_deconv1d(
@@ -26,6 +42,7 @@ def test_deconv1d(
         dilation=1,
         groups=1,
         bias=True,
+        output_padding=0,
     ):
         class TestModule(torch.nn.Module):
             def __init__(self):
@@ -36,9 +53,10 @@ def __init__(self):
                     kernel_size=kernel_size,
                     stride=stride,
                     padding=padding,
-                    dilation=dilation,
+                    output_padding=output_padding,
                     groups=groups,
                     bias=bias,
+                    dilation=dilation,
                 )
 
             def forward(self, x):
@@ -101,6 +119,22 @@ def forward(self, x):
             param("non_zero_padding", 1, padding=1),
             param("dilation", 1, dilation=2),
             param("groups", 1, groups=3),
+            param("output_padding_1", 3, stride=2, padding=1, output_padding=1),
+            param("output_padding_2", 3, stride=2, padding=1, output_padding=1),
+            param("output_padding_3", 3, stride=2, padding=2, output_padding=1),
+            param("output_padding_4", 3, stride=2, padding=3, output_padding=1),
+            param("output_padding_5", 3, stride=3, padding=2, output_padding=1),
+            param("output_padding_6", 3, stride=3, padding=3, output_padding=1),
+            param("output_padding_7", 3, stride=3, padding=3, output_padding=2),
+            param(
+                "combined_params",
+                3,
+                stride=3,
+                padding=3,
+                dilation=2,
+                groups=3,
+                output_padding=2,
+            ),
         ]
     )
     def test_deconv2d(
@@ -112,6 +146,7 @@ def test_deconv2d(
         dilation=1,
         groups=1,
         bias=True,
+        output_padding=0,
     ):
         class TestModule(torch.nn.Module):
             def __init__(self):
@@ -122,9 +157,10 @@ def __init__(self):
                     kernel_size=kernel_size,
                     stride=stride,
                     padding=padding,
-                    dilation=dilation,
+                    output_padding=output_padding,
                     groups=groups,
                     bias=bias,
+                    dilation=dilation,
                 )
 
             def forward(self, x):
@@ -172,6 +208,19 @@ def forward(self, x):
             param("non_zero_padding", 1, padding=1),
             param("dilation", 1, dilation=2),
             param("groups", 1, groups=3),
+            param("output_padding_1", 3, stride=2, padding=1, output_padding=1),
+            param("output_padding_2", 3, stride=2, padding=2, output_padding=1),
+            param("output_padding_3", 3, stride=3, padding=3, output_padding=1),
+            param("output_padding_4", 3, stride=3, padding=3, output_padding=2),
+            param(
+                "combined_params",
+                3,
+                stride=3,
+                padding=3,
+                dilation=2,
+                groups=3,
+                output_padding=2,
+            ),
         ]
     )
     def test_deconv3d(
@@ -183,6 +232,7 @@ def test_deconv3d(
         dilation=1,
         groups=1,
         bias=True,
+        output_padding=0,
     ):
         class TestModule(torch.nn.Module):
             def __init__(self):
@@ -193,9 +243,10 @@ def __init__(self):
                     kernel_size=kernel_size,
                     stride=stride,
                     padding=padding,
-                    dilation=dilation,
+                    output_padding=output_padding,
                     groups=groups,
                     bias=bias,
+                    dilation=dilation,
                 )
 
             def forward(self, x):