diff --git a/csrc/rocm/attention.cu b/csrc/rocm/attention.cu
index e2f86d02dbec..d152292635fe 100644
--- a/csrc/rocm/attention.cu
+++ b/csrc/rocm/attention.cu
@@ -701,13 +701,15 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
 
   __syncthreads();
 
+  // disable rtz conversion due to its impact on accuracy.
   constexpr bool LOGITS_RTZ_CONVERSION = false;
 
   // write logits to shared mem
   for (int token_depth = 0; token_depth < TLOOP; token_depth++) {
     dout[token_depth] *= inv_sum_scale;
     if constexpr (LOGITS_RTZ_CONVERSION) {
-      // use rtz conversion for performance, with no visible impact on accuracy
+      // use rtz conversion for better performance, with negligible impact on
+      // accuracy.
       shared_logits[warpid][token_depth][lane16id][rowid] =
           from_floatx4_rtz<scalar_t>(dout[token_depth]);
     } else {
diff --git a/tests/kernels/test_attention.py b/tests/kernels/test_attention.py
index b826559cd8db..10d984351f67 100644
--- a/tests/kernels/test_attention.py
+++ b/tests/kernels/test_attention.py
@@ -7,7 +7,7 @@
 from tests.kernels.utils import opcheck
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
-from vllm.utils import get_max_shared_memory_bytes
+from vllm.utils import get_max_shared_memory_bytes, is_navi
 
 from .allclose_default import get_default_atol, get_default_rtol
 
@@ -33,7 +33,7 @@
 
 # This should be sync with get_supported_head_sizes() in
 # vllm.attention.ops.paged_attn.PagedAttention
-HEAD_SIZES = [32, 64, 80, 96, 112, 120, 128, 192, 256]
+HEAD_SIZES = [64, 80, 96, 112, 120, 128, 192, 256]
 
 BLOCK_SIZES = [16, 32]
 USE_ALIBI = [False, True]
@@ -116,7 +116,8 @@ def ref_single_query_cached_kv_attention(
 
 
 @pytest.mark.parametrize(
-    "version", ["v1", "v2"] if not current_platform.is_rocm() else ["rocm"])
+    "version",
+    ["v1", "v2"] if not current_platform.is_rocm() else ["v1", "v2", "rocm"])
 @pytest.mark.parametrize("num_seqs", NUM_GEN_SEQS)
 @pytest.mark.parametrize("num_heads", NUM_HEADS)
 @pytest.mark.parametrize("head_size", HEAD_SIZES)
@@ -181,7 +182,11 @@ def test_paged_attention(
     key_cache, value_cache = key_caches[0], value_caches[0]
 
     # Using default kv_scale
-    k_scale = v_scale = torch.tensor(0.3, dtype=torch.float)
+    k_scale = v_scale = torch.tensor(1.0, dtype=torch.float32)
+
+    # additional argument for v1/v2 pa kernel
+    num_threads = 1024 if current_platform.is_rocm() \
+                and not is_navi() else 128
 
     # Call the paged attention kernel.
     output = torch.empty_like(query)
@@ -203,12 +208,12 @@ def test_paged_attention(
             v_scale,
         )
 
-        opcheck(torch.ops._C.paged_attention_v1,
-                (output, query, key_cache, value_cache, num_kv_heads, scale,
-                 block_tables, seq_lens, block_size, max_seq_len, alibi_slopes,
-                 kv_cache_dtype, k_scale, v_scale, 0, 0, 0, 64, 0),
-                cond=(head_size == HEAD_SIZES[0]
-                      and block_size == BLOCK_SIZES[0]))
+        opcheck(
+            torch.ops._C.paged_attention_v1,
+            (output, query, key_cache, value_cache, num_kv_heads, scale,
+             block_tables, seq_lens, block_size, max_seq_len, alibi_slopes,
+             kv_cache_dtype, k_scale, v_scale, 0, 0, 0, 64, 0, num_threads),
+            cond=(head_size == HEAD_SIZES[0] and block_size == BLOCK_SIZES[0]))
 
     elif version in ("v2", "rocm"):
         if current_platform.is_rocm():
@@ -247,13 +252,14 @@ def test_paged_attention(
                 v_scale,
             )
 
-            opcheck(torch.ops._C.paged_attention_v2,
-                    (output, exp_sums, max_logits, tmp_output, query,
-                     key_cache, value_cache, num_kv_heads, scale, block_tables,
-                     seq_lens, block_size, max_seq_len, alibi_slopes,
-                     kv_cache_dtype, k_scale, v_scale, 0, 0, 0, 64, 0),
-                    cond=(head_size == HEAD_SIZES[0]
-                          and block_size == BLOCK_SIZES[0]))
+            opcheck(
+                torch.ops._C.paged_attention_v2,
+                (output, exp_sums, max_logits, tmp_output, query, key_cache,
+                 value_cache, num_kv_heads, scale, block_tables, seq_lens,
+                 block_size, max_seq_len, alibi_slopes, kv_cache_dtype,
+                 k_scale, v_scale, 0, 0, 0, 64, 0, num_threads),
+                cond=(head_size == HEAD_SIZES[0]
+                      and block_size == BLOCK_SIZES[0]))
 
         else:
             ops.paged_attention_rocm(
@@ -299,14 +305,14 @@ def test_paged_attention(
                                             dtype=dtype,
                                             device=device)
         ops.convert_fp8(dequantized_key_cache, key_cache)
-        key_cache = k_scale * dequantized_key_cache
+        key_cache = dequantized_key_cache
 
         value_cache_shape = value_cache.shape
         dequantized_value_cache = torch.empty(size=value_cache_shape,
                                               dtype=dtype,
                                               device=device)
         ops.convert_fp8(dequantized_value_cache, value_cache)
-        value_cache = v_scale * dequantized_value_cache
+        value_cache = dequantized_value_cache
 
     ref_output = torch.empty_like(query)
     ref_single_query_cached_kv_attention(
@@ -434,4 +440,4 @@ def test_multi_query_kv_attention(
     )
     atol = get_default_atol(output) if current_platform.is_rocm() else 1e-3
     rtol = get_default_rtol(output) if current_platform.is_rocm() else 1e-5
-    torch.testing.assert_close(output, ref_output, atol=atol, rtol=rtol)
+    torch.testing.assert_close(output, ref_output, atol=atol, rtol=rtol)
\ No newline at end of file