Fix/liger fused linear cross entropy function does not support reduct…

…ion=none (#496)

## Summary
fix #488 
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## Details
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## Testing Done
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- Hardware Type: A100-40G-PCIe
- [x] run `make test` to ensure correctness
- [x] run `make checkstyle` to ensure code style
- [x] run `make test-convergence` to ensure convergence

src/liger_kernel/chunked_loss/cpo_loss.py

@@ -33,7 +33,6 @@ def preference_loss_fn(chosen_logps, rejected_logps, full_target, beta=0.1, labe
         loss = (-F.logsigmoid(logits) * (1 - label_smoothing) - F.logsigmoid(-logits) * label_smoothing).sum() / (
             full_target.shape[0] // 2
         )
-
         return loss
 
     @staticmethod

src/liger_kernel/ops/fused_linear_cross_entropy.py

@@ -127,13 +127,16 @@ def fused_linear_cross_entropy_forward(
                 alpha=alpha,
             )
 
-    loss = torch.sum(loss_1d)
+    if reduction == "none":
+        loss = loss_1d
+    else:
+        loss = torch.sum(loss_1d)
     return loss, grad_input, grad_weight, grad_bias
 
 
 def fused_linear_cross_entropy_backward(grad_output, grad_input, grad_weight, grad_bias):
     # If cross entropy is the last layer, grad_output is 1.0. Skip the mul to save time
-    if torch.ne(grad_output, torch.tensor(1.0, device=grad_output.device)):
+    if not torch.equal(grad_output, torch.tensor(1.0, device=grad_output.device)):
         # We use a Triton kernel instead of a PyTorch operation because modifying inputs in-place
         # for gradient storage and backward multiple times causes anomalies with PyTorch but not with Triton.
         BT, H = grad_input.shape

test/transformers/test_fused_linear_cross_entropy.py

@@ -108,6 +108,8 @@ def forward(self, x, y):
         ("mean", 1.0, torch.float32, 1e-5, 5e-4),
         ("sum", 1.0, torch.bfloat16, 5e-0, 5e1),
         ("sum", 1.0, torch.float32, 1e-3, 5e-2),
+        ("none", 1.0, torch.bfloat16, 5e-0, 5e1),
+        ("none", 1.0, torch.float32, 1e-3, 5e-2),
     ],
 )
 @pytest.mark.parametrize("bias", [True, False])
@@ -185,8 +187,8 @@ def test_correctness(
 
     assert_verbose_allclose(output1, output2, atol=atol, rtol=rtol)
 
-    output1.backward()
-    output2.backward()
+    output1.backward(gradient=torch.ones_like(output1))
+    output2.backward(gradient=torch.ones_like(output2))
 
     assert_verbose_allclose(_input1.grad, _input2.grad, atol=atol, rtol=rtol)