diff --git a/lib/Conversion/TorchToLinalg/Linear.cpp b/lib/Conversion/TorchToLinalg/Linear.cpp
index e6b9e6c24..8fa598f8d 100644
--- a/lib/Conversion/TorchToLinalg/Linear.cpp
+++ b/lib/Conversion/TorchToLinalg/Linear.cpp
@@ -366,39 +366,40 @@ public:
     Value input = adaptor.input();
     Value weight = adaptor.weight();
     Value bias = adaptor.bias();
-    // TODO: Handle the case of bias being None (bias is optional).
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
       return failure();
     auto inputType = input.getType().cast<RankedTensorType>();
     auto weightType = weight.getType().cast<RankedTensorType>();
-    auto biasType = bias.getType().cast<RankedTensorType>();
 
     if (inputType.getRank() != 2 && inputType.getRank() != 3) {
       return rewriter.notifyMatchFailure(
           op, "expected  input to be rank 2 or rank 3");
     }
 
-    // Only handle the case of rank 2 `weight` for now.
-    // TODO: Insert the appropriate reshape to collapse any leading dimensions.
-    if (weightType.getRank() != 2 || biasType.getRank() != 1) {
-      return rewriter.notifyMatchFailure(
-          op, "expected weight to be rank 2 and bias to be rank 1");
-    }
-    // TODO: Handle type promotion. What are ATen's promotion rules?
-    if (inputType.getElementType() != weightType.getElementType() ||
-        inputType.getElementType() != biasType.getElementType()) {
-      return rewriter.notifyMatchFailure(op, "unimplemented: type promotion");
+    if (!bias.getType().isa<Torch::NoneType>()) {
+      auto biasType = bias.getType().cast<RankedTensorType>();
+      // Only handle the case of rank 2 `weight` for now.
+      // TODO: Insert the appropriate reshape to collapse any leading dimensions.
+      if (weightType.getRank() != 2 || biasType.getRank() != 1) {
+        return rewriter.notifyMatchFailure(
+            op, "expected weight to be rank 2 and bias to be rank 1");
+      }
+      // TODO: Handle type promotion. What are ATen's promotion rules?
+      if (inputType.getElementType() != weightType.getElementType() ||
+          inputType.getElementType() != biasType.getElementType()) {
+        return rewriter.notifyMatchFailure(op, "unimplemented: type promotion");
+      }
+      // TODO: We can handle a static size 1 here at some complexity cost, but the
+      // dynamic case is not representable in linalg. We don't handle either for
+      // now. Biases are generally statically shaped for most models (since for
+      // inference they are constants, and for training they don't change shape
+      // typically), so this is not too constraining.
+      auto biasSize = bias.getType().cast<RankedTensorType>().getShape()[0];
+      if (biasSize == 1 || biasSize == ShapedType::kDynamicSize)
+        return rewriter.notifyMatchFailure(
+            op, "unimplemented: size-1 broadcasting for aten::LinearOp");
     }
 
-    // TODO: We can handle a static size 1 here at some complexity cost, but the
-    // dynamic case is not representable in linalg. We don't handle either for
-    // now. Biases are generally statically shaped for most models (since for
-    // inference they are constants, and for training they don't change shape
-    // typically), so this is not too constraining.
-    auto biasSize = bias.getType().cast<RankedTensorType>().getShape()[0];
-    if (biasSize == 1 || biasSize == ShapedType::kDynamicSize)
-      return rewriter.notifyMatchFailure(
-          op, "unimplemented: size-1 broadcasting for aten::LinearOp");
 
     Value batchDim = nullptr;
     int restDim = 0;
@@ -411,20 +412,23 @@ public:
     Value inputDim1 = getDimOp(rewriter, loc, input, restDim + 1);
     Value weightDim0 = getDimOp(rewriter, loc, weight, 0);
     Value weightDim1 = getDimOp(rewriter, loc, weight, 1);
-    Value biasDim0 = getDimOp(rewriter, loc, bias, 0);
     Value contractingDimEqual = rewriter.create<arith::CmpIOp>(
         loc, arith::CmpIPredicate::eq, inputDim1, weightDim1);
     rewriter.create<cf::AssertOp>(
         loc, contractingDimEqual,
         rewriter.getStringAttr(
             "mismatching contracting dimension for aten.linear"));
-    // Here we take advantage of ruling out the size-1 case above.
-    // In the static-size-1 case, we will not emit this check at all.
-    Value biasSizeCorrect = rewriter.create<arith::CmpIOp>(
-        loc, arith::CmpIPredicate::eq, weightDim0, biasDim0);
-    rewriter.create<cf::AssertOp>(
-        loc, biasSizeCorrect,
-        rewriter.getStringAttr("mismatching bias size for aten.linear"));
+
+    if (!bias.getType().isa<Torch::NoneType>()) {
+      Value biasDim0 = getDimOp(rewriter, loc, bias, 0);
+      // Here we take advantage of ruling out the size-1 case above.
+      // In the static-size-1 case, we will not emit this check at all.
+      Value biasSizeCorrect = rewriter.create<arith::CmpIOp>(
+          loc, arith::CmpIPredicate::eq, weightDim0, biasDim0);
+      rewriter.create<cf::AssertOp>(
+          loc, biasSizeCorrect,
+          rewriter.getStringAttr("mismatching bias size for aten.linear"));
+    }
 
     Value initTensor;
     SmallVector<AffineMap> broadcastIndexingMaps;
@@ -455,16 +459,26 @@ public:
     }
 
     SmallVector<StringRef> iteratorTypes(inputType.getRank(), "parallel");
-    Value broadcasted =
-        rewriter
-            .create<linalg::GenericOp>(
-                loc, initTensor.getType(), bias, initTensor,
-                /*indexingMaps=*/broadcastIndexingMaps,
-                /*iteratorTypes=*/iteratorTypes,
-                [](OpBuilder &b, Location loc, ValueRange args) {
-                  b.create<linalg::YieldOp>(loc, args[0]);
-                })
-            .getResult(0);
+    Value broadcasted;
+    if (!bias.getType().isa<Torch::NoneType>()) {
+      broadcasted =
+          rewriter
+              .create<linalg::GenericOp>(
+                  loc, initTensor.getType(), bias, initTensor,
+                  /*indexingMaps=*/broadcastIndexingMaps,
+                  /*iteratorTypes=*/iteratorTypes,
+                  [](OpBuilder &b, Location loc, ValueRange args) {
+                    b.create<linalg::YieldOp>(loc, args[0]);
+                  })
+              .getResult(0);
+    } else {
+      Type elementType =
+          input.getType().cast<RankedTensorType>().getElementType();
+      Value c0float = rewriter.create<arith::ConstantOp>(
+          loc, FloatAttr::get(elementType, 0.0));
+      broadcasted = rewriter.create<linalg::FillOp>(loc, c0float, initTensor)
+                           .getResult(0);
+    }
     // We need a matmul with dimension ordering (N, K) * (M, K), so transpose
     // the weights to fit into linalg::MatmulOp which is (N, K) * (K, M).
     // TODO: This whole aten.linear lowering should eventually be generated from
diff --git a/python/torch_mlir_e2e_test/test_suite/mlp.py b/python/torch_mlir_e2e_test/test_suite/mlp.py
index 8713c55a2..153f35759 100644
--- a/python/torch_mlir_e2e_test/test_suite/mlp.py
+++ b/python/torch_mlir_e2e_test/test_suite/mlp.py
@@ -57,6 +57,30 @@ class Mlp2LayerModule(torch.nn.Module):
 def Mlp2LayerModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(5, 3))
 
+class Mlp2LayerModuleNoBias(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        # Reset seed to make model deterministic.
+        torch.manual_seed(0)
+        N_HIDDEN = 5
+        self.fc0 = nn.Linear(3, N_HIDDEN, bias=False)
+        self.tanh0 = nn.Tanh()
+        self.fc1 = nn.Linear(N_HIDDEN, 2, bias=False)
+        self.tanh1 = nn.Tanh()
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True),
+    ])
+    def forward(self, x):
+        x = self.tanh0(self.fc0(x))
+        x = self.tanh1(self.fc1(x))
+        return x
+
+@register_test_case(module_factory=lambda: Mlp2LayerModuleNoBias())
+def Mlp2LayerModuleNoBias_basic(module, tu: TestUtils):
+    module.forward(tu.rand(5, 3))
+
 class BatchMlpLayerModule(torch.nn.Module):
     def __init__(self):
         super().__init__()