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[TPU][V1] Remove ragged attention kernel parameter hard coding #16041

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Merged
merged 1 commit into from
Apr 4, 2025
Merged

[TPU][V1] Remove ragged attention kernel parameter hard coding #16041

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20 changes: 6 additions & 14 deletions vllm/v1/attention/backends/pallas.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,10 +11,6 @@
AttentionLayer, AttentionType)
from vllm.attention.backends.utils import CommonAttentionState

# These are the 2 tunable parameters of the paged attention Pallas kernel.
NUM_QUERIES_PER_BLOCK = 32
NUM_KV_PAGES_PER_BLOCK = 128


class PallasAttentionBackend(AttentionBackend):

Expand Down Expand Up @@ -115,13 +111,6 @@ def __init__(
tpu_version = torch_xla.tpu.version()
if tpu_version < 4:
raise NotImplementedError("TPU version must be 4 or higher.")
# NOTE(chengjiyao): the TPU v4's vmem capacity is 16MB
# TODO(chengjiyao): autotune NUM_QUERIES_PER_BLOCK,
# NUM_KV_PAGES_PER_BLOCK and vmem_limit_bytes
if tpu_version == 4:
self.vmem_limit_bytes = 16 * 1024 * 1024
else:
self.vmem_limit_bytes = 64 * 1024 * 1024

def forward(
self,
Expand Down Expand Up @@ -165,9 +154,12 @@ def forward(
attn_metadata.block_tables,
attn_metadata.query_start_loc,
attn_metadata.num_seqs,
num_kv_pages_per_block=NUM_KV_PAGES_PER_BLOCK,
num_queries_per_block=NUM_QUERIES_PER_BLOCK,
vmem_limit_bytes=self.vmem_limit_bytes,
# By default, the system utilizes optimized block size and
# vmem_limit_bytes parameters from the kernel repository. However,
# these can be manually adjusted for debugging if necessary.
num_kv_pages_per_block=None,
num_queries_per_block=None,
vmem_limit_bytes=None,
use_kernel=True,
sm_scale=self.scale,
sliding_window=self.sliding_window,
Expand Down
8 changes: 2 additions & 6 deletions vllm/v1/worker/tpu_model_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,8 +24,7 @@
from vllm.sampling_params import SamplingType
from vllm.sequence import IntermediateTensors
from vllm.utils import LayerBlockType, cdiv, is_pin_memory_available
from vllm.v1.attention.backends.pallas import (NUM_KV_PAGES_PER_BLOCK,
PallasAttentionBackend,
from vllm.v1.attention.backends.pallas import (PallasAttentionBackend,
PallasMetadata)
from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
Expand Down Expand Up @@ -155,11 +154,8 @@ def __init__(
dtype=torch.int64,
device="cpu")
self.slot_mapping_np = self.slot_mapping_cpu.numpy()

padded_max_num_blocks_per_req = _get_padded_number(
self.max_num_blocks_per_req, NUM_KV_PAGES_PER_BLOCK)
self.block_table_cpu = torch.zeros(
(self.max_num_tokens, padded_max_num_blocks_per_req),
(self.max_num_tokens, self.max_num_blocks_per_req),
dtype=self.input_batch.block_table.get_cpu_tensor().dtype,
device="cpu")

Expand Down