Remove linear lowering pass and converter (#3323)

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -2509,26 +2509,6 @@ def aten_ops_convolution(
         )
 
 
-@dynamo_tensorrt_converter(torch.ops.aten.linear.default, supports_dynamic_shapes=True)
-@dynamo_tensorrt_converter(torch.ops.aten.linear, supports_dynamic_shapes=True)
-def aten_ops_linear(
-    ctx: ConversionContext,
-    target: Target,
-    args: Tuple[Argument, ...],
-    kwargs: Dict[str, Argument],
-    name: str,
-) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    return impl.linear.linear(
-        ctx,
-        target,
-        SourceIR.ATEN,
-        name,
-        input=args[0],
-        weight=args[1],
-        bias=args_bounds_check(args, 2, None),
-    )
-
-
 @dynamo_tensorrt_converter(torch.ops.aten._cdist_forward.default)
 def aten_ops_cdist_forward(
     ctx: ConversionContext,

py/torch_tensorrt/dynamo/conversion/impl/__init__.py

@@ -13,7 +13,6 @@
     embedding,
     full,
     grid,
-    linear,
     matmul,
     normalization,
     pad,

py/torch_tensorrt/dynamo/lowering/passes/_aten_lowering_pass.py

@@ -7,7 +7,6 @@
 from .accumulate_fp32_matmul import accumulate_fp32_matmul
 from .constant_folding import constant_fold
 from .fuse_prims_broadcast import fuse_prims_broadcast
-from .lower_linear import lower_linear
 from .pass_manager import DynamoPassManager
 from .remove_assert_scalar import remove_assert_scalar
 from .remove_detach import remove_detach
@@ -22,7 +21,6 @@
         remove_input_alias_fixing_clones,
         constant_fold,
         repair_input_as_output,
-        lower_linear,
         fuse_prims_broadcast,
         replace_max_pool_with_indices,
         replace_full_like_with_full,

tests/py/dynamo/lowering/test_aten_lowering_passes.py

@@ -158,118 +158,6 @@ def forward(self, x):
         torch._dynamo.reset()
 
 
-class TestLowerLinear(TestCase):
-    @unittest.skip(
-        "This test has threshold failures. This is tracked at https://github.com/pytorch/TensorRT/issues/2715",
-    )
-    def test_lower_linear(self):
-        class Linear(torch.nn.Module):
-            def forward(self, input, weight, bias):
-                out = torch.ops.aten.linear.default(input, weight, bias)
-                return out
-
-        inputs = [
-            torch.rand((3, 32)).cuda(),
-            torch.rand((64, 32)).cuda(),
-            torch.rand((64,)).cuda(),
-        ]
-
-        fx_graph = torch.fx.symbolic_trace(Linear())
-        expected_ops = {torch.ops.aten.linear.default}
-        unexpected_ops = {
-            torch.ops.aten.permute.default,
-            torch.ops.aten.addmm.default,
-        }
-
-        unexpected_ops_seen, expected_ops_unseen = lower_graph_testing(
-            fx_graph,
-            inputs,
-            expected_ops=expected_ops,
-            unexpected_ops=unexpected_ops,
-            min_block_size=1,
-        )
-
-        self.assertEqual(
-            len(unexpected_ops_seen),
-            0,
-            f"The following unexpected ops were encountered: {unexpected_ops_seen}",
-        )
-
-        self.assertEqual(
-            len(expected_ops_unseen),
-            0,
-            f"The following expected ops were not encountered: {expected_ops_unseen}",
-        )
-        torch._dynamo.reset()
-
-        # Validate that the results between Torch and Torch-TRT are similar
-        optimized_model = torch_tensorrt.compile(
-            fx_graph,
-            "torch_compile",
-            inputs,
-            min_block_size=1,
-            pass_through_build_failures=True,
-        )
-        optimized_model_results = torch.cat(
-            [tensor.detach().cpu() for tensor in optimized_model(*inputs)]
-        )
-        torch_model_results = torch.cat(
-            [tensor.detach().cpu() for tensor in fx_graph(*inputs)]
-        )
-
-        max_diff = float(
-            torch.max(torch.abs(optimized_model_results - torch_model_results))
-        )
-
-        self.assertAlmostEqual(
-            max_diff,
-            0,
-            DECIMALS_OF_AGREEMENT,
-            msg=f"Linear TRT outputs don't match with the original model.",
-        )
-        torch._dynamo.reset()
-
-    def test_lower_linear_batch(self):
-        class Linear(torch.nn.Module):
-            def forward(self, input, weight, bias):
-                out = torch.ops.aten.linear.default(input, weight, bias)
-                return out
-
-        inputs = [
-            torch.rand((2, 2, 32)).cuda(),
-            torch.rand((64, 32)).cuda(),
-            torch.rand((64,)).cuda(),
-        ]
-
-        fx_graph = torch.fx.symbolic_trace(Linear())
-
-        # Validate that the results between Torch and Torch-TRT are similar
-        optimized_model = torch_tensorrt.compile(
-            fx_graph,
-            "torch_compile",
-            inputs,
-            min_block_size=1,
-            pass_through_build_failures=True,
-        )
-        optimized_model_results = torch.cat(
-            [tensor.detach().cpu() for tensor in optimized_model(*inputs)]
-        )
-        torch_model_results = torch.cat(
-            [tensor.detach().cpu() for tensor in fx_graph(*inputs)]
-        )
-
-        max_diff = float(
-            torch.max(torch.abs(optimized_model_results - torch_model_results))
-        )
-        self.assertAlmostEqual(
-            max_diff,
-            0,
-            DECIMALS_OF_AGREEMENT,
-            msg=f"Linear TRT outputs don't match with the original model.",
-        )
-        torch._dynamo.reset()
-
-
 class TestLowerViewToReshape(TestCase):
     def test_view_to_reshape(self):
         class ViewToReshape(torch.nn.Module):