Mamba V2 Test not Asserting Failures. (#21379)

Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>

tests/kernels/mamba/test_mamba_mixer2.py

@@ -119,7 +119,8 @@ def mixer2_gated_norm_tensor_parallel(
         gate_states[..., local_rank * N:(local_rank + 1) * N],
     )
     ref_output = mixer_single_gpu(hidden_states, gate_states)
-    torch.allclose(output,
-                   ref_output[..., local_rank * N:(local_rank + 1) * N],
-                   atol=1e-3,
+    torch.testing.assert_close(output,
+                               ref_output[...,
+                                          local_rank * N:(local_rank + 1) * N],
+                               atol=5e-3,
                                rtol=1e-3)

tests/kernels/mamba/test_mamba_ssm_ssd.py

@@ -193,6 +193,13 @@ def test_mamba_chunk_scan_single_example(d_head, n_heads, seq_len_chunk_size,
 
     # this tests the kernels on a single example (no batching)
 
+    # TODO: the bfloat16 case requires higher thresholds. To be investigated
+
+    if itype == torch.bfloat16:
+        atol, rtol = 5e-2, 5e-2
+    else:
+        atol, rtol = 8e-3, 5e-3
+
     # set seed
     batch_size = 1  # batch_size
     # ssd_minimal_discrete requires chunk_size divide seqlen
@@ -216,14 +223,14 @@ def test_mamba_chunk_scan_single_example(d_head, n_heads, seq_len_chunk_size,
                                                return_final_states=True)
 
     # just test the last in sequence
-    torch.allclose(Y[:, -1], Y_min[:, -1], atol=1e-3, rtol=1e-3)
+    torch.testing.assert_close(Y[:, -1], Y_min[:, -1], atol=atol, rtol=rtol)
 
     # just test the last head
     # NOTE, in the kernel we always cast states to fp32
-    torch.allclose(final_state[:, -1],
+    torch.testing.assert_close(final_state[:, -1],
                                final_state_min[:, -1].to(torch.float32),
-                   atol=1e-3,
-                   rtol=1e-3)
+                               atol=atol,
+                               rtol=rtol)
 
 
 @pytest.mark.parametrize("itype", [torch.float32, torch.float16])
@@ -263,6 +270,13 @@ def test_mamba_chunk_scan_cont_batch(d_head, n_heads, seq_len_chunk_size_cases,
 
     seqlen, chunk_size, num_examples, cases = seq_len_chunk_size_cases
 
+    # TODO: the irregular chunk size cases have some issues and require higher
+    # tolerance. This is to be invesigated
+    if chunk_size not in {8, 256}:
+        atol, rtol = 5e-1, 5e-1
+    else:
+        atol, rtol = 5e-3, 5e-3
+
     # hold state during the cutting process so we know if an
     # example has been exhausted and needs to cycle
     last_taken: dict = {}  # map: eg -> pointer to last taken sample
@@ -300,7 +314,7 @@ def test_mamba_chunk_scan_cont_batch(d_head, n_heads, seq_len_chunk_size_cases,
             # just test one dim and dstate
             Y_eg = Y[0, cu_seqlens[i]:cu_seqlens[i + 1], 0, 0]
             Y_min_eg = Y_min[i][:, 0, 0]
-            torch.allclose(Y_eg, Y_min_eg, atol=1e-3, rtol=1e-3)
+            torch.testing.assert_close(Y_eg, Y_min_eg, atol=atol, rtol=rtol)
 
         # update states
         states = new_states