diff --git a/tests/test_rf_attention.py b/tests/test_rf_attention.py
index 763b41dbc..da5b34662 100644
--- a/tests/test_rf_attention.py
+++ b/tests/test_rf_attention.py
@@ -280,6 +280,7 @@ def test_rope_causal_self_att():
         LlamaRotaryEmbedding,
         LlamaConfig,
         apply_rotary_pos_emb,
+        eager_attention_forward,
     )
 
     config = LlamaConfig(
@@ -322,7 +323,8 @@ def test_rope_causal_self_att():
 
     position_ids = rf.expand_dim(rf.range_over_dim(seq_dim), batch_dim)  # LlamaRotaryEmbedding wants this
     with PyTracer(
-        [LlamaAttention.forward, LlamaRotaryEmbedding.forward, apply_rotary_pos_emb], torch.Tensor
+        [LlamaAttention.forward, LlamaRotaryEmbedding.forward, apply_rotary_pos_emb, eager_attention_forward],
+        torch.Tensor,
     ) as trace_hf:
         # causal_mask code copied from LlamaAttention
         sequence_length = target_length = in_.raw_tensor.shape[1]
@@ -340,11 +342,11 @@ def test_rope_causal_self_att():
         position_embeddings = rotary_emb(
             torch.zeros(()), position_ids=position_ids.copy_compatible_to_dims_raw((batch_dim, seq_dim))
         )
-        out_hf, _, _ = model_hf(in_.raw_tensor, attention_mask=causal_mask, position_embeddings=position_embeddings)
+        out_hf, *_ = model_hf(in_.raw_tensor, attention_mask=causal_mask, position_embeddings=position_embeddings)
     pprint(trace_hf.captured_locals)
 
     print("First HF att weight tensor:")
-    print(trace_hf.captured_locals[LlamaAttention.forward][0]["attn_weights"][2][0, 0, 0].detach().numpy())
+    print(trace_hf.captured_locals[LlamaAttention.forward][0]["attn_weights"][-1][0, 0, 0].detach().numpy())
 
     check_py_traces_rf_to_pt_equal(
         trace_rf.captured_locals,
@@ -352,8 +354,13 @@ def test_rope_causal_self_att():
         [
             (
                 (rf.RotaryPosCausalSelfAttention.__call__, 0, "q", 0),
-                (LlamaAttention.forward, 0, "query_states", 1),
+                # input: batch_dim, seq_dim, model_dim
+                # input_shape: batch_dim, seq_dim
+                # HF query_states': (batch_dim, seq_dim, num_heads, self.head_dim),
+                #   then transposed to (batch_dim, num_heads, seq_dim, self.head_dim)
+                (LlamaAttention.forward, 0, "query_states", 0),
                 lambda x, *, name, **_: rf.convert_to_tensor(
+                    # reorder complex numbers
                     x.reshape(*x.shape[:-1], 2, -1).transpose(-1, -2).flatten(-2),
                     dims=(batch_dim, model_rf.num_heads, seq_dim, model_rf.key_dim_per_head),
                     name=name,
@@ -361,8 +368,9 @@ def test_rope_causal_self_att():
             ),
             (
                 (rf.RotaryPosCausalSelfAttention.__call__, 0, "k", 0),
-                (LlamaAttention.forward, 0, "key_states", 1),
+                (LlamaAttention.forward, 0, "key_states", 0),
                 lambda x, *, name, **_: rf.convert_to_tensor(
+                    # reorder complex numbers
                     x.reshape(*x.shape[:-1], 2, -1).transpose(-1, -2).flatten(-2),
                     dims=(batch_dim, model_rf.num_heads, seq_dim, model_rf.key_dim_per_head),
                     name=name,
@@ -418,25 +426,25 @@ def test_rope_causal_self_att():
             ),
             (
                 (rf.dot_attention, 0, "energy", 0),
-                (LlamaAttention.forward, 0, "attn_weights", 0),
+                (eager_attention_forward, 0, "attn_weights", 0),
                 (batch_dim, model_rf.num_heads, seq_dim, "axis"),
             ),
             (
                 (rf.dot_attention, 0, "att_weights", 0),
-                (LlamaAttention.forward, 0, "attn_weights", 2),
+                (LlamaAttention.forward, 0, "attn_weights", -1),
                 (batch_dim, model_rf.num_heads, seq_dim, "axis"),
             ),
             (
                 (rf.dot_attention, 0, "att", 0),
                 (LlamaAttention.forward, 0, "attn_output", 0),
-                (batch_dim, model_rf.num_heads, seq_dim, model_rf.value_dim_per_head),
+                (batch_dim, seq_dim, model_rf.num_heads, model_rf.value_dim_per_head),
             ),
         ],
     )
 
     print("Final check...")
     assert out_rf.raw_tensor.shape == out_hf.shape
-    torch.testing.assert_allclose(out_rf.raw_tensor, out_hf)
+    torch.testing.assert_close(out_rf.raw_tensor, out_hf)
     print("  all matched!")