Remove un-used test

tests/test_mem_eff_attention.py

@@ -998,38 +998,6 @@ def test_dropout_backward_ck(dt, q_len, kv_len, batch_size, k, p):
     )
 
 
-@cuda_only
-@disable_tf32
-@disable_on_rocm
-@pytest.mark.parametrize("k_len", [32])
-@pytest.mark.parametrize("batch_size", [1])
-@pytest.mark.parametrize("kv_len", [3 * 32])
-@pytest.mark.parametrize("q_len", [3 * 32])
-def test_memory_efficient_attention_full_block_masked(q_len, kv_len, batch_size, k_len):
-    device = "cuda"
-    op_fw = fmha.small_k.FwOp
-    op_bw = fmha.small_k.BwOp
-
-    scale = 3
-    query = torch.randn((batch_size, q_len, k_len), device=device) * scale
-    key = torch.randn((batch_size, kv_len, k_len), device=device) * scale
-    value = torch.randn((batch_size, kv_len, k_len), device=device) * scale
-
-    # in this case, most of the blocks in a row get masked
-    attn_bias = torch.full((3, 32), float("-inf"), device=device)
-    attn_bias[:2, :4] = 0
-    attn_bias = attn_bias.flatten()[None, None, :].expand(1, q_len, -1)
-
-    out = xformers.ops.memory_efficient_attention(
-        query, key, value, attn_bias, op=(op_fw, op_bw)
-    )
-    ref = ref_attention_for_test(query, key, value, attn_bias)
-
-    assert_allclose(
-        out, ref, atol=op_fw.ERROR_ATOL[query.dtype], rtol=op_fw.ERROR_RTOL[query.dtype]
-    )
-
-
 @pytest.mark.parametrize("fmt", ["BMK", "BMHK"])
 @parametrize_opBW_device_dtype_biasT_B_Mq_Mkv_H_K_Kv__xs
 def test_lowlevel_api_shapes(opBW_device_dtype_biasT_B_Mq_Mkv_H_K_Kv, fmt):