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Unverified Commit cabdaa76 authored by Nicolò Lucchesi's avatar Nicolò Lucchesi Committed by GitHub
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[Misc] Move `GPUModelRunner.prepare_kernel_block_sizes` to utils (#35400) 


Signed-off-by: default avatarNickLucche <nlucches@redhat.com>
parent 06be5356
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Showing with 135 additions and 123 deletions
tests/v1/worker/test_gpu_model_runner.py
View file @ cabdaa76
......@@ -38,7 +38,7 @@ from vllm.v1.kv_cache_interface import (
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.worker.gpu_input_batch import InputBatch
from vllm.v1.worker.gpu_model_runner import GPUModelRunner
from vllm.v1.worker.utils import AttentionGroup
from vllm.v1.worker.utils import AttentionGroup, select_common_block_size
BLOCK_SIZE = 16
NUM_BLOCKS = 10
......@@ -209,7 +209,7 @@ def test_select_common_block_size_prefers_manager_block_size():
AttentionGroup(backend_b, [], [], _make_kv_cache_spec(), 0),
]
selected_size = GPUModelRunner.select_common_block_size(128, attn_groups)
selected_size = select_common_block_size(128, attn_groups)
assert selected_size == 128
......@@ -221,7 +221,7 @@ def test_select_common_block_size_uses_largest_shared_int():
AttentionGroup(backend_b, [], [], _make_kv_cache_spec(), 0),
]
selected_size = GPUModelRunner.select_common_block_size(256, attn_groups)
selected_size = select_common_block_size(256, attn_groups)
assert selected_size == 64
......@@ -234,7 +234,7 @@ def test_select_common_block_size_no_valid_option():
]
with pytest.raises(ValueError):
GPUModelRunner.select_common_block_size(48, attn_groups)
select_common_block_size(48, attn_groups)
def test_update_states_new_request(model_runner, dist_init):
......
vllm/v1/worker/gpu_model_runner.py
View file @ cabdaa76
......@@ -115,7 +115,6 @@ from vllm.v1.attention.backend import (
AttentionMetadataBuilder,
AttentionType,
CommonAttentionMetadata,
MultipleOf,
)
from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
from vllm.v1.attention.backends.mamba2_attn import Mamba2AttentionMetadataBuilder
......@@ -189,6 +188,7 @@ from .utils import (
AttentionGroup,
add_kv_sharing_layers_to_kv_cache_groups,
bind_kv_cache,
prepare_kernel_block_sizes,
sanity_check_mm_encoder_outputs,
)
......@@ -5678,78 +5678,6 @@ class GPUModelRunner(
return
self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds) # type: ignore[assignment]
@staticmethod
def select_common_block_size(
kv_manager_block_size: int, attn_groups: list[AttentionGroup]
) -> int:
"""
Select a block size that is supported by all backends and is a factor of
kv_manager_block_size.
If kv_manager_block_size is supported by all backends, return it directly.
Otherwise, return the max supported size.
Args:
kv_manager_block_size: Block size of KV cache
attn_groups: List of attention groups
Returns:
The selected block size
Raises:
ValueError: If no valid block size found
"""
def block_size_is_supported(
backends: list[type[AttentionBackend]], block_size: int
) -> bool:
"""
Check if the block size is supported by all backends.
"""
for backend in backends:
is_supported = False
for supported_size in backend.get_supported_kernel_block_sizes():
if isinstance(supported_size, int):
if block_size == supported_size:
is_supported = True
elif isinstance(supported_size, MultipleOf):
if block_size % supported_size.base == 0:
is_supported = True
else:
raise ValueError(f"Unknown supported size: {supported_size}")
if not is_supported:
return False
return True
backends = [group.backend for group in attn_groups]
# Case 1: if the block_size of kv cache manager is supported by all backends,
# return it directly
if block_size_is_supported(backends, kv_manager_block_size):
return kv_manager_block_size
# Case 2: otherwise, the block_size must be an `int`-format supported size of
# at least one backend. Iterate over all `int`-format supported sizes in
# descending order and return the first one that is supported by all backends.
# Simple proof:
# If the supported size b is in MultipleOf(x_i) format for all attention
# backends i, and b a factor of kv_manager_block_size, then
# kv_manager_block_size also satisfies MultipleOf(x_i) for all i. We will
# return kv_manager_block_size in case 1.
all_int_supported_sizes = set(
supported_size
for backend in backends
for supported_size in backend.get_supported_kernel_block_sizes()
if isinstance(supported_size, int)
)
for supported_size in sorted(all_int_supported_sizes, reverse=True):
if kv_manager_block_size % supported_size != 0:
continue
if block_size_is_supported(backends, supported_size):
return supported_size
raise ValueError(f"No common block size for {kv_manager_block_size}. ")
def may_reinitialize_input_batch(
self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
) -> None:
......@@ -5846,49 +5774,6 @@ class GPUModelRunner(
for attn_groups in self.attn_groups:
yield from attn_groups
def _prepare_kernel_block_sizes(self, kv_cache_config: KVCacheConfig) -> list[int]:
"""
Generate kernel_block_sizes that matches each block_size.
For attention backends that support virtual block splitting,
use the supported block sizes from the backend.
For other backends (like Mamba), use the same block size (no splitting).
Args:
kv_cache_config: The KV cache configuration.
Returns:
list[int]: List of kernel block sizes for each cache group.
"""
kernel_block_sizes = []
for kv_cache_gid, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
kv_cache_spec = kv_cache_group.kv_cache_spec
if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
# All layers in the UniformTypeKVCacheSpecs have the same type,
# Pick an arbitrary one to dispatch.
kv_cache_spec = next(iter(kv_cache_spec.kv_cache_specs.values()))
if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
continue
elif isinstance(kv_cache_spec, AttentionSpec):
# This is an attention backend that supports virtual
# block splitting. Get the supported block sizes from
# all backends in the group.
attn_groups = self.attn_groups[kv_cache_gid]
kv_manager_block_size = kv_cache_group.kv_cache_spec.block_size
selected_kernel_size = self.select_common_block_size(
kv_manager_block_size, attn_groups
)
kernel_block_sizes.append(selected_kernel_size)
elif isinstance(kv_cache_spec, MambaSpec):
# This is likely Mamba or other non-attention cache,
# no splitting.
kernel_block_sizes.append(kv_cache_spec.block_size)
else:
raise NotImplementedError(
f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
)
return kernel_block_sizes
def _reshape_kv_cache_tensors(
self,
kv_cache_config: KVCacheConfig,
......@@ -6120,7 +6005,9 @@ class GPUModelRunner(
# backends for that group only supports block_size 64, we will return
# kernel_block_size 64 and split the 256-token-block to 4 blocks with 64
# tokens each.
kernel_block_sizes = self._prepare_kernel_block_sizes(kv_cache_config)
kernel_block_sizes = prepare_kernel_block_sizes(
kv_cache_config, self.attn_groups
)
# create metadata builders
self.initialize_metadata_builders(kv_cache_config, kernel_block_sizes)
......
vllm/v1/worker/utils.py
View file @ cabdaa76
......@@ -13,8 +13,20 @@ from vllm.model_executor.models.interfaces import MultiModalEmbeddings
from vllm.model_executor.models.utils import extract_layer_index
from vllm.platforms import current_platform
from vllm.utils.mem_utils import MemorySnapshot, format_gib
from vllm.v1.attention.backend import AttentionBackend, AttentionMetadataBuilder
from vllm.v1.kv_cache_interface import KVCacheGroupSpec, KVCacheSpec
from vllm.v1.attention.backend import (
AttentionBackend,
AttentionMetadataBuilder,
MultipleOf,
)
from vllm.v1.kv_cache_interface import (
AttentionSpec,
EncoderOnlyAttentionSpec,
KVCacheConfig,
KVCacheGroupSpec,
KVCacheSpec,
MambaSpec,
UniformTypeKVCacheSpecs,
)
logger = init_logger(__name__)
......@@ -59,6 +71,119 @@ class AttentionGroup:
return self.metadata_builders[ubatch_id]
def select_common_block_size(
kv_manager_block_size: int, attn_groups: list[AttentionGroup]
) -> int:
"""
Select a block size that is supported by all backends and is a factor of
kv_manager_block_size.
If kv_manager_block_size is supported by all backends, return it directly.
Otherwise, return the max supported size.
Args:
kv_manager_block_size: Block size of KV cache.
attn_groups: List of attention groups.
Returns:
The selected block size.
Raises:
ValueError: If no valid block size found.
"""
def block_size_is_supported(
backends: list[type[AttentionBackend]], block_size: int
) -> bool:
"""Check if the block size is supported by all backends."""
for backend in backends:
is_supported = False
for supported_size in backend.get_supported_kernel_block_sizes():
if isinstance(supported_size, int):
if block_size == supported_size:
is_supported = True
elif isinstance(supported_size, MultipleOf):
if block_size % supported_size.base == 0:
is_supported = True
else:
raise ValueError(f"Unknown supported size: {supported_size}")
if not is_supported:
return False
return True
backends = [group.backend for group in attn_groups]
# Case 1: if the block_size of kv cache manager is supported by all backends,
# return it directly.
if block_size_is_supported(backends, kv_manager_block_size):
return kv_manager_block_size
# Case 2: otherwise, the block_size must be an `int`-format supported size of
# at least one backend. Iterate over all `int`-format supported sizes in
# descending order and return the first one that is supported by all backends.
# Simple proof:
# If the supported size b is in MultipleOf(x_i) format for all attention
# backends i, and b a factor of kv_manager_block_size, then
# kv_manager_block_size also satisfies MultipleOf(x_i) for all i. We will
# return kv_manager_block_size in case 1.
all_int_supported_sizes = set(
supported_size
for backend in backends
for supported_size in backend.get_supported_kernel_block_sizes()
if isinstance(supported_size, int)
)
for supported_size in sorted(all_int_supported_sizes, reverse=True):
if kv_manager_block_size % supported_size != 0:
continue
if block_size_is_supported(backends, supported_size):
return supported_size
raise ValueError(f"No common block size for {kv_manager_block_size}. ")
def prepare_kernel_block_sizes(
kv_cache_config: KVCacheConfig, attn_groups: list[list[AttentionGroup]]
) -> list[int]:
"""
Generate kernel_block_sizes that matches each block_size.
For attention backends that support virtual block splitting,
use the supported block sizes from the backend.
For other backends (like Mamba), use the same block size (no splitting).
Args:
kv_cache_config: The KV cache configuration.
attn_groups: Attention groups indexed by KV cache group id.
Returns:
List of kernel block sizes for each cache group.
"""
kernel_block_sizes = []
for kv_cache_gid, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
kv_cache_spec = kv_cache_group.kv_cache_spec
if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
# All layers in the UniformTypeKVCacheSpecs have the same type,
# pick an arbitrary one to dispatch.
kv_cache_spec = next(iter(kv_cache_spec.kv_cache_specs.values()))
if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
continue
if isinstance(kv_cache_spec, AttentionSpec):
# This is an attention backend that supports virtual block splitting.
kv_manager_block_size = kv_cache_group.kv_cache_spec.block_size
selected_kernel_size = select_common_block_size(
kv_manager_block_size, attn_groups[kv_cache_gid]
)
kernel_block_sizes.append(selected_kernel_size)
elif isinstance(kv_cache_spec, MambaSpec):
# This is likely Mamba or other non-attention cache, no splitting.
kernel_block_sizes.append(kv_cache_spec.block_size)
else:
raise NotImplementedError(
f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
)
return kernel_block_sizes
def sanity_check_mm_encoder_outputs(
mm_embeddings: MultiModalEmbeddings,
expected_num_items: int,
......
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