[Neuron][Kernel] Vectorize KV cache load in FlashPagedAttention to maximize DMA bandwidth (#13245)

Signed-off-by: Lingfan Yu <lingfany@amazon.com>

tests/neuron/test_block_table.py

@@ -0,0 +1,153 @@
+# SPDX-License-Identifier: Apache-2.0
+import os
+
+import neuronxcc.nki.language as nl
+import pytest
+import torch
+import torch.nn.functional as F
+from neuronxcc import nki
+
+from vllm.attention.ops.nki_flash_attn import (
+    load_block_tables, transform_block_tables_for_indirect_load)
+
+
+def is_power_of_2(n):
+    return n > 0 and (n & (n - 1) == 0)
+
+
+def nki_load_and_transform_block_tables(
+    block_tables,
+    num_tiles,
+    num_blocks_per_tile,
+    num_head,
+    head_id,
+    block_size_tiling_factor,
+):
+    assert is_power_of_2(
+        num_blocks_per_tile), f"{num_blocks_per_tile=} must be power of 2"
+    block_tables_sbuf = load_block_tables(block_tables, num_tiles,
+                                          num_blocks_per_tile)
+
+    # we need to pass an Index as head_id
+    head_id = nl.arange(1)[None, :] + head_id
+
+    block_tables_transposed = transform_block_tables_for_indirect_load(
+        block_tables_sbuf, block_size_tiling_factor, num_head, head_id)
+    B_P_SIZE = 128
+    assert block_tables_transposed.shape[1] == B_P_SIZE
+
+    out = nl.ndarray(
+        block_tables_transposed.shape,
+        dtype=nl.int32,
+        buffer=nl.shared_hbm,
+    )
+    for i in nl.affine_range(block_tables_transposed.shape[0]):
+        nl.store(dst=out[i], value=block_tables_transposed[i])
+    return out
+
+
+def ref_block_tables_transform(
+    block_tables,
+    num_tiles,
+    num_blocks_per_tile,
+    num_head,
+    head_id,
+    block_size_tiling_factor,
+):
+    assert block_tables.numel() == num_tiles * num_blocks_per_tile
+    block_tables = block_tables.view(num_tiles, num_blocks_per_tile)
+    B_F_SIZE = 128
+    num_tiles_padded = (num_tiles + B_F_SIZE - 1) // B_F_SIZE * B_F_SIZE
+    block_tables = F.pad(
+        block_tables,
+        (0, 0, 0, num_tiles_padded - num_tiles),
+        "constant",
+        0,
+    )
+
+    block_tables = block_tables * num_head + head_id
+    block_tables = block_tables.view(num_tiles_padded, num_blocks_per_tile, 1)
+    offset = torch.arange(0, block_size_tiling_factor).view(1, 1, -1)
+    block_tables = block_tables * block_size_tiling_factor + offset
+    block_tables_transposed = block_tables.view(num_tiles_padded, -1).t()
+
+    num_blocks_per_tile = block_tables_transposed.shape[0]
+    assert num_blocks_per_tile % B_F_SIZE == 0
+    return block_tables_transposed.view(num_blocks_per_tile // B_F_SIZE,
+                                        B_F_SIZE, num_tiles_padded)
+
+
+@pytest.mark.parametrize(
+    "q_head_per_kv_head,head_id",
+    [
+        (1, 0),
+        (3, 1),
+    ],
+)
+@pytest.mark.parametrize(
+    "num_tiles,num_blocks_per_tile",
+    [
+        (1, 1),
+        (13, 16),
+        (17, 128),
+        (35, 512),
+        (128, 128),
+        (130, 64),
+        (280, 256),
+        (315, 1),
+    ],
+)
+@torch.inference_mode()
+def test_load_and_transform_block_tables(
+    num_tiles,
+    num_blocks_per_tile,
+    q_head_per_kv_head,
+    head_id,
+) -> None:
+    import torch_xla.core.xla_model as xm
+
+    device = xm.xla_device()
+
+    compiler_flags = [
+        "-O1",
+        "--retry_failed_compilation",
+    ]
+    compiler_flags_str = " ".join(compiler_flags)
+    os.environ["NEURON_CC_FLAGS"] = compiler_flags_str
+
+    torch.manual_seed(10000)
+    torch.set_printoptions(sci_mode=False)
+
+    # On Neuron, we need B_P_SIZE = 128 blocks to make DMA efficient
+    B_P_SIZE = 128
+    if num_blocks_per_tile < B_P_SIZE:
+        assert B_P_SIZE % num_blocks_per_tile == 0
+        block_size_tiling_factor = B_P_SIZE // num_blocks_per_tile
+    else:
+        block_size_tiling_factor = 1
+    max_num_blocks = 100000
+    block_tables = torch.randint(
+        0,
+        max_num_blocks,
+        (num_tiles * num_blocks_per_tile, ),
+        dtype=torch.int32,
+    )
+    nki_out = nki.jit(nki_load_and_transform_block_tables)[1, 1](
+        block_tables.to(device=device),
+        num_tiles,
+        num_blocks_per_tile,
+        q_head_per_kv_head,
+        head_id,
+        block_size_tiling_factor,
+    ).cpu()
+    ref_out = ref_block_tables_transform(
+        block_tables,
+        num_tiles,
+        num_blocks_per_tile,
+        q_head_per_kv_head,
+        head_id,
+        block_size_tiling_factor,
+    )
+    assert (nki_out.shape == ref_out.shape
+            ), f"{nki_out.shape=} != {ref_out.shape=}"
+    assert torch.all(nki_out == ref_out)