[Refactor] Remove duplicate ceil_div

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -19,7 +19,7 @@
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     w8a8_block_fp8_matmul,
 )
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser, cdiv
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]
@@ -117,14 +117,9 @@ def bench_fp8(
     scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
     scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
 
-    def ceil_div(x: int, y: int) -> int:
-        return (x + y - 1) // y
-
-    block_scale_a = torch.rand(
-        (m, ceil_div(k, 128)), device="cuda", dtype=torch.float32
-    )
+    block_scale_a = torch.rand((m, cdiv(k, 128)), device="cuda", dtype=torch.float32)
     block_scale_b = torch.rand(
-        ceil_div(k, 128), ceil_div(n, 128), device="cuda", dtype=torch.float32
+        cdiv(k, 128), cdiv(n, 128), device="cuda", dtype=torch.float32
     )
     block_scale_a_M_major = block_scale_a.t().contiguous().t()
     block_scale_b_K_major = block_scale_b.t().contiguous().t()

tests/kernels/attention/test_mla_decode_cpu.py

@@ -7,10 +7,7 @@
 
 import vllm._custom_ops as ops
 from vllm.platforms import current_platform
-
-
-def cdiv(a, b):
-    return (a + b - 1) // b
+from vllm.utils import cdiv
 
 
 def ref_mla(

tests/kernels/attention/test_triton_decode_attention.py

@@ -5,10 +5,7 @@
 import torch
 
 from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
-
-
-def cdiv(a, b):
-    return (a + b - 1) // b
+from vllm.utils import cdiv
 
 
 @pytest.mark.parametrize("B", [3, 5])

tests/neuron/1_core/test_prefix_prefill.py

@@ -7,6 +7,8 @@
 import torch
 import torch.nn.functional as F
 
+from vllm.utils import cdiv
+
 
 class BlockDiagonalCausalFromBottomRightMask:
 
@@ -398,11 +400,8 @@ def test_contexted_kv_attention(
         assert (large_tile_size >= B_P_SIZE
                 ), f"Expect {large_tile_size=} to be larger than {B_P_SIZE=}"
 
-        def ceil_div(a, b):
-            return (a + b - 1) // b
-
         def pad_to_multiple(a, b):
-            return ceil_div(a, b) * b
+            return cdiv(a, b) * b
 
         def pad_to_next_power_of_2(a):
             assert a > 0
@@ -411,7 +410,7 @@ def pad_to_next_power_of_2(a):
         # calculate input shapes
         max_num_queries = pad_to_next_power_of_2(sum(query_lens))
         context_lens = torch.tensor(seq_lens) - torch.tensor(query_lens)
-        num_active_blocks = ceil_div(context_lens, block_size).sum().item()
+        num_active_blocks = cdiv(context_lens, block_size).sum().item()
         num_active_blocks = pad_to_multiple(num_active_blocks,
                                             large_tile_size // block_size)
         context_kv_len = num_active_blocks * block_size

vllm/attention/ops/nki_flash_attn.py

@@ -8,9 +8,7 @@
 from neuronxcc import nki
 from neuronxcc.nki.language import par_dim
 
-
-def ceil_div(a, b):
-    return (a + b - 1) // b
+from vllm.utils import cdiv
 
 
 def is_power_of_2(x):
@@ -35,11 +33,10 @@ def load_block_tables(block_tables_hbm, num_tiles, num_blocks_per_tile):
         (num_tiles, num_blocks_per_tile))
 
     block_tables_sbuf = nl.zeros(
-        (ceil_div(num_tiles,
-                  B_P_SIZE), par_dim(B_P_SIZE), num_blocks_per_tile),
+        (cdiv(num_tiles, B_P_SIZE), par_dim(B_P_SIZE), num_blocks_per_tile),
         dtype=nl.int32,
     )
-    for i in nl.affine_range(ceil_div(num_tiles, B_P_SIZE)):
+    for i in nl.affine_range(cdiv(num_tiles, B_P_SIZE)):
         i_p = nl.arange(B_P_SIZE)[:, None]
         i_f = nl.arange(num_blocks_per_tile)[None, :]
         block_tables_sbuf[i, i_p, i_f] = nl.load(
@@ -83,7 +80,7 @@ def transform_block_tables_for_indirect_load(
     assert is_power_of_2(
         num_blocks_per_tile), f"{num_blocks_per_tile=} is not power of 2"
 
-    num_loads = ceil_div(num_blocks_per_tile, B_P_SIZE)
+    num_loads = cdiv(num_blocks_per_tile, B_P_SIZE)
     block_tables_transposed = nl.ndarray(
         (
             num_loads,
@@ -165,7 +162,7 @@ def load_kv_tile_from_cache(
            equivalent to (par_dim(B_P_SIZE), seqlen_kv // B_P_SIZE * B_D_SIZE)
     """
     # load key cache
-    num_loads = ceil_div(num_blocks_per_large_tile, B_P_SIZE)
+    num_loads = cdiv(num_blocks_per_large_tile, B_P_SIZE)
     for load_idx in nl.affine_range(num_loads):
         i_p = nl.arange(B_P_SIZE)[:, None]
         i_f = nl.arange(tiled_block_size * B_D_SIZE)[None, :]
@@ -605,7 +602,7 @@ def flash_paged_attention(
     )
 
     for large_k_tile_idx in nl.sequential_range(0, num_large_k_tile):
-        num_loads = ceil_div(num_blocks_per_large_tile, B_P_SIZE)
+        num_loads = cdiv(num_blocks_per_large_tile, B_P_SIZE)
         cur_k_tile = nl.ndarray(
             (par_dim(B_D_SIZE), LARGE_TILE_SZ),
             dtype=kernel_dtype,

vllm/model_executor/layers/fused_moe/moe_align_block_size.py

@@ -6,11 +6,7 @@
 
 from vllm import _custom_ops as ops
 from vllm.triton_utils import tl, triton
-from vllm.utils import round_up
-
-
-def ceil_div(a, b):
-    return (a + b - 1) // b
+from vllm.utils import cdiv, round_up
 
 
 @triton.jit
@@ -115,7 +111,7 @@ def moe_align_block_size_triton(
     cumsum = torch.zeros((num_experts + 1, ),
                          dtype=torch.int32,
                          device=topk_ids.device)
-    tokens_per_thread = ceil_div(numel, num_experts)
+    tokens_per_thread = cdiv(numel, num_experts)
 
     moe_align_block_size_stage1[grid](
         topk_ids,

vllm/model_executor/layers/quantization/utils/fp8_utils.py

@@ -19,7 +19,7 @@
     CUTLASS_BLOCK_FP8_SUPPORTED)
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
-from vllm.utils import direct_register_custom_op
+from vllm.utils import cdiv, direct_register_custom_op
 
 logger = init_logger(__name__)
 has_deep_gemm = importlib.util.find_spec("deep_gemm") is not None
@@ -158,12 +158,9 @@ def apply_w8a8_block_fp8_linear(
     if current_platform.is_cuda():
         if current_platform.has_device_capability(100):
 
-            def ceil_div(x: int, y: int) -> int:
-                return (x + y - 1) // y
-
             use_cutlass = cutlass_block_fp8_supported and (
-                ceil_div(weight.shape[0], 128) == weight_scale.shape[0]
-                and ceil_div(weight.shape[1], 128) == weight_scale.shape[1])
+                cdiv(weight.shape[0], 128) == weight_scale.shape[0]
+                and cdiv(weight.shape[1], 128) == weight_scale.shape[1])
         else:
             # TODO: update this after switching to public sm90 block scale gemm
             # as it also supports weight.shape % 128 != 0