Implement aten_bilinear function using Einsum operation

Co-authored-by: justinchuby <11205048+justinchuby@users.noreply.github.com>

Author: Copilot

onnxscript/function_libs/torch_lib/ops/core.py

@@ -1189,6 +1189,7 @@ def aten_bernoulli_p(self: TTensor, p: float) -> TTensor:
     return op.CastLike(sampled, self)
 
 
+@torch_op("aten::bilinear", trace_only=True)
 def aten_bilinear(
     input1: TensorType,
     input2: TensorType,
@@ -1197,7 +1198,23 @@ def aten_bilinear(
 ) -> TensorType:
     """bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias=None) -> Tensor"""
 
-    raise NotImplementedError()
+    # Bilinear transformation: y = x1^T A x2 + b
+    # input1 shape: (..., in1_features)
+    # input2 shape: (..., in2_features)
+    # weight shape: (out_features, in1_features, in2_features)
+    # bias shape: (out_features) - optional
+    # output shape: (..., out_features)
+
+    # Use Einsum to compute the bilinear transformation
+    # "...i,oij,...j->...o" means:
+    # - input1[..., i] * weight[o, i, j] * input2[..., j] -> output[..., o]
+    result = op.Einsum(input1, weight, input2, equation="...i,oij,...j->...o")
+
+    # Add bias if provided
+    if bias is not None:
+        result = op.Add(result, bias)
+
+    return result
 
 
 def aten_binary_cross_entropy_with_logits(

tests/function_libs/torch_lib/extra_opinfo.py

@@ -37,6 +37,37 @@ def sample_inputs_scalar_tensor(op_info, device, dtype, requires_grad, **kwargs)
         yield opinfo_core.SampleInput(item, dtype=dtype)
 
 
+def sample_inputs_bilinear(op_info, device, dtype, requires_grad, **kwargs):
+    """Sample inputs for bilinear operation."""
+    del op_info
+    del kwargs
+
+    make_arg = functools.partial(
+        torch_testing.make_tensor, device=device, dtype=dtype, requires_grad=requires_grad
+    )
+
+    # Test cases: (batch_size, in1_features, in2_features, out_features)
+    cases = [
+        (2, 3, 4, 5),      # Basic case
+        (1, 2, 2, 1),      # Minimal case
+        (3, 5, 7, 4),      # Different dimensions
+        (2, 1, 1, 3),      # Single input features
+    ]
+
+    for batch_size, in1_features, in2_features, out_features in cases:
+        input1 = make_arg((batch_size, in1_features))
+        input2 = make_arg((batch_size, in2_features))
+        weight = make_arg((out_features, in1_features, in2_features))
+        bias = make_arg((out_features,))
+
+        # Test with bias
+        yield opinfo_core.SampleInput(input1, args=(input2, weight, bias))
+
+        # Test without bias (only for first case to avoid too many tests)
+        if batch_size == 2:
+            yield opinfo_core.SampleInput(input1, args=(input2, weight, None))
+
+
 def sample_inputs_bernoulli_p(op_info, device, dtype, requires_grad, **kwargs):
     del op_info
 
@@ -2180,6 +2211,13 @@ def __init__(self):
 #    To avoid name duplication, it is possible to rename the OpInfo and specify
 #    the `op` field explicitly.
 OP_DB: List[opinfo_core.OpInfo] = [
+    opinfo_core.OpInfo(
+        "bilinear",
+        op=torch.nn.functional.bilinear,
+        dtypes=common_dtype.floating_types(),
+        sample_inputs_func=sample_inputs_bilinear,
+        supports_out=False,
+    ),
     opinfo_core.OpInfo(
         "ops.aten.bernoulli.p",
         aten_name="bernoulli.p",

tests/function_libs/torch_lib/ops_test_data.py

@@ -657,6 +657,7 @@ def _where_input_wrangler(
     ),
     TorchLibOpInfo("baddbmm", core_ops.aten_baddbmm, tolerance={torch.float16: (1e-3, 1e-2)}),
     TorchLibOpInfo("bernoulli", core_ops.aten_bernoulli, nondeterministic=True),
+    TorchLibOpInfo("bilinear", core_ops.aten_bilinear, tolerance={torch.float32: (1e-4, 1e-4)}),
     TorchLibOpInfo(
         # This string is a unique ID. In extra_opinfo.py, we
         # also define test data for this ID with