feat: support linear (fully connected layer) dynamo converter

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -1289,3 +1289,22 @@ def aten_ops_convolution(
         dilation=args[5],
         groups=args[8],
     )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten.linear.default)
+def aten_ops_linear(
+    network: TRTNetwork,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.linear.linear(
+        network,
+        target,
+        SourceIR.ATEN,
+        name,
+        input=args[0],
+        weight=args[1],
+        bias=args_bounds_check(args, 2, None),
+    )

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

@@ -7,6 +7,7 @@
     conv,
     elementwise,
     embedding,
+    linear,
     matmul,
     normalization,
     permutation,

py/torch_tensorrt/dynamo/conversion/impl/linear.py

@@ -0,0 +1,53 @@
+from typing import Optional, Union
+
+import numpy as np
+import tensorrt as trt
+import torch
+from torch.fx.node import Target
+from torch_tensorrt.dynamo.conversion import impl
+from torch_tensorrt.fx.converters.converter_utils import SourceIR, get_trt_tensor
+from torch_tensorrt.fx.types import TRTNetwork, TRTTensor
+
+
+def linear(
+    network: TRTNetwork,
+    target: Union[Target, str],
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+    weight: Union[TRTTensor, torch.Tensor, np.ndarray],
+    bias: Optional[Union[TRTTensor, torch.Tensor, np.ndarray]],
+) -> TRTTensor:
+    # Process weight terms
+    if not isinstance(weight, (TRTTensor, torch.Tensor, np.ndarray)):
+        raise RuntimeError(
+            f"Linear layer {name} has weight of type {type(weight)}, Expect Union[TRTTensor, torch.Tensor, np.ndarray],"
+        )
+    elif isinstance(weight, (torch.Tensor, np.ndarray)):
+        weight = get_trt_tensor(network, weight, f"{name}_weight")
+
+    # Process bias terms
+    if bias is not None and not isinstance(bias, (TRTTensor, torch.Tensor, np.ndarray)):
+        raise RuntimeError(
+            f"Linear layer {name} has bias of type {type(bias)}, Expect Union[TRTTensor, torch.Tensor, np.ndarray],"
+        )
+    elif isinstance(bias, (torch.Tensor, np.ndarray)):
+        bias = get_trt_tensor(network, bias, f"{name}_bias")
+
+    # add IMatrixMultiplyLayer
+    out = impl.matmul.matrix_multiply(
+        network,
+        target,
+        source_ir,
+        name,
+        input,
+        weight,
+        input_matrix_op=trt.MatrixOperation.NONE,
+        other_matrix_op=trt.MatrixOperation.TRANSPOSE,
+    )
+
+    if bias is not None:
+        # add bias
+        out = impl.elementwise.add(network, target, source_ir, name, out, bias)
+
+    return out