fix A100 fused linear grad add ut bug (PaddlePaddle#56136)

test/legacy_test/test_fused_linear_param_grad_add.py

@@ -54,7 +54,7 @@ def recreate(x, multi_precision):
     return paddle.to_tensor(x.numpy())
 
 
-def run_ground_truth(x, dy, dweight, dbias, multi_precision):
+def run_ground_truth(x, dy, dweight, dbias, multi_precision, has_bias):
     x, dy, dweight, dbias = recreate([x, dy, dweight, dbias], multi_precision)
 
     dweight_tmp = paddle.matmul(
@@ -69,24 +69,35 @@ def run_ground_truth(x, dy, dweight, dbias, multi_precision):
         assert dweight.dtype == dweight.dtype
         dweight += dweight_tmp
 
-    dbias_tmp = dy.reshape([-1, dy.shape[-1]]).sum(axis=0)
-    if dbias is None:
-        dbias = dbias_tmp
-    else:
-        assert dbias.shape == dbias_tmp.shape
-        assert dbias.dtype == dbias_tmp.dtype
-        dbias += dbias_tmp
+    if has_bias:
+        dbias_tmp = dy.reshape([-1, dy.shape[-1]]).sum(axis=0)
+        if dbias is None:
+            dbias = dbias_tmp
+        else:
+            assert dbias.shape == dbias_tmp.shape
+            assert dbias.dtype == dbias_tmp.dtype
+            dbias += dbias_tmp
 
-    return promote_dtype(dweight).numpy(), promote_dtype(dbias).numpy()
+        return promote_dtype(dweight).numpy(), promote_dtype(dbias).numpy()
+    else:
+        return promote_dtype(dweight).numpy()
 
 
-def run_fused_linear_param_grad_add(x, dy, dweight, dbias, multi_precision):
+def run_fused_linear_param_grad_add(
+    x, dy, dweight, dbias, multi_precision, has_bias
+):
     dweight_new, dbias_new = _C_ops.fused_linear_param_grad_add(
-        x, dy, dweight, dbias, multi_precision
+        x, dy, dweight, dbias, multi_precision, has_bias
     )
     if dweight is not None:
         assert dweight_new.data_ptr() == dweight.data_ptr()
-    return promote_dtype(dweight_new).numpy(), promote_dtype(dbias_new).numpy()
+    if has_bias:
+        return (
+            promote_dtype(dweight_new).numpy(),
+            promote_dtype(dbias_new).numpy(),
+        )
+    else:
+        return promote_dtype(dweight_new).numpy()
 
 
 class TestMainClassBase(unittest.TestCase):
@@ -103,7 +114,9 @@ def rand(self, shape, dtype=None):
         x = paddle.to_tensor(x)
         return x.astype(dtype or self.dtype)
 
-    def generate_rand_inputs(self, has_dweight, has_dbias, multi_precision):
+    def generate_rand_inputs(
+        self, has_dweight, has_dbias, multi_precision, has_bias
+    ):
         x_shape = self.shape
         dy_shape = self.shape[:-1] + [self.output_size]
         dweight_shape = [self.shape[-1], self.output_size]
@@ -118,22 +131,23 @@ def generate_rand_inputs(self, has_dweight, has_dbias, multi_precision):
         else:
             dweight = None
 
-        if has_dbias:
+        if has_bias and has_dbias:
             dbias = self.rand(dbias_shape)
             if multi_precision:
                 dbias = promote_dtype(dbias)
         else:
             dbias = None
         return x, dy, dweight, dbias
 
-    def check_main(self, has_dweight, has_dbias, multi_precision):
-        print(has_dweight, has_dbias, multi_precision)
+    def check_main(self, has_dweight, has_dbias, multi_precision, has_bias):
         x, dy, dweight, dbias = self.generate_rand_inputs(
-            has_dweight, has_dbias, multi_precision
+            has_dweight, has_dbias, multi_precision, has_bias
+        )
+        res1 = run_ground_truth(
+            x, dy, dweight, dbias, multi_precision, has_bias
         )
-        res1 = run_ground_truth(x, dy, dweight, dbias, multi_precision)
         res2 = run_fused_linear_param_grad_add(
-            x, dy, dweight, dbias, multi_precision
+            x, dy, dweight, dbias, multi_precision, has_bias
         )
         self.assertEqual(len(res1), len(res2))
         for r1, r2 in zip(res1, res2):
@@ -153,9 +167,12 @@ def test_main(self):
             return
 
         for has_dweight in [False, True]:
-            for has_dbias in [False, True]:
-                for multi_precision in [False, True]:
-                    self.check_main(has_dweight, has_dbias, multi_precision)
+            for has_bias in [False, True]:
+                for has_dbias in [False, True]:
+                    for multi_precision in [False, True]:
+                        self.check_main(
+                            has_dweight, has_dbias, multi_precision, has_bias
+                        )
 
 
 class TestMainClassBF16(TestMainClassBase):