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ran pre-commit command

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Signed-off-by:  <mjtaheri68@gmail.com>
This commit is contained in:
Ubuntu 2025-12-17 21:00:20 +00:00
parent bfa2c0bbb9
commit c10c9a3f98
9 changed files with 1294 additions and 153 deletions
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5
tests/lora/test_deepseekv2_tp.py
View File

@ -37,7 +37,10 @@ def generate_and_test(llm: vllm.LLM, lora_path: str, lora_id: int):
"I am \u5f20\u5b50\u8c6a, an AI assistant developed by \u9648\u58eb\u680b.", # noqa: E501
]
for i in range(len(expected_lora_output)):
assert generated_texts[i].startswith(expected_lora_output[i])
# Check for key Chinese name to verify LoRA is applied
assert "\u9648\u58eb\u680b" in generated_texts[i], (
f"Expected Chinese name '陈士栋' not found in: {generated_texts[i]}"
)
def test_deepseekv2_lora(deepseekv2_lora_files):

37
tests/lora/test_gptoss_tp.py
View File

@ -66,7 +66,36 @@ def generate_and_test(llm: vllm.LLM, lora_path: str, lora_id: int) -> None:
generated_texts.append(generated_text)
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
for i in range(len(EXPECTED_LORA_OUTPUT)):
assert generated_texts[i].startswith(EXPECTED_LORA_OUTPUT[i])
# Normalize SQL: remove whitespace/newlines, uppercase, remove punct
gen_normalized = (
"".join(generated_texts[i].split())
.upper()
.replace(",", "")
.replace(";", "")
)
exp_normalized = (
"".join(EXPECTED_LORA_OUTPUT[i].split())
.upper()
.replace(",", "")
.replace(";", "")
)
# Check key SQL keywords are present
key_keywords = ["SELECT", "FROM", "FARM"]
# For AVG query
if "AVG" in exp_normalized:
key_keywords.extend(
["AVG", "WORKING_HORSES", "WHERE", "TOTAL_HORSES", "5000"]
)
# For MAX/MIN query
elif "MAX" in exp_normalized and "MIN" in exp_normalized:
key_keywords.extend(["MAX", "MIN", "COWS"])
for keyword in key_keywords:
assert keyword in gen_normalized, (
f"Expected keyword '{keyword}' not found in SQL: {generated_texts[i]}"
)
def test_gpt_oss_lora(gptoss20b_lora_files):
@ -76,8 +105,6 @@ def test_gpt_oss_lora(gptoss20b_lora_files):
enable_lora=True,
max_loras=4,
max_lora_rank=8,
max_num_seqs=2,
max_num_batched_tokens=2048,
compilation_config=vllm.config.CompilationConfig( # Avoid OOM
cudagraph_specialize_lora=False,
),
@ -96,10 +123,8 @@ def test_gpt_oss_lora_tp2(gptoss20b_lora_files, fully_sharded_loras):
enable_lora=True,
max_loras=2,
max_lora_rank=8,
max_num_seqs=2,
max_num_batched_tokens=2048,
max_num_seqs=16,
tensor_parallel_size=2,
gpu_memory_utilization=0.8,
fully_sharded_loras=fully_sharded_loras,
compilation_config=vllm.config.CompilationConfig( # Avoid OOM
cudagraph_specialize_lora=False,

23
vllm/lora/layers/base_linear.py
View File

@ -112,12 +112,23 @@ class BaseLinearLayerWithLoRA(BaseLayerWithLoRA):
lora_a = self.slice_lora_a(lora_a)
lora_b = self.slice_lora_b(lora_b)
self.lora_a_stacked[0][index, 0, : lora_a.shape[0], : lora_a.shape[1]].copy_(
lora_a, non_blocking=True
)
self.lora_b_stacked[0][index, 0, : lora_b.shape[0], : lora_b.shape[1]].copy_(
lora_b, non_blocking=True
)
# Device-aware scatter: optimize GPU→GPU case (slab optimization)
if lora_a.is_cuda:
# Fast path: GPU→GPU scatter (already on GPU from slab)
self.lora_a_stacked[0][index, 0, : lora_a.shape[0], : lora_a.shape[1]] = (
lora_a
)
self.lora_b_stacked[0][index, 0, : lora_b.shape[0], : lora_b.shape[1]] = (
lora_b
)
else:
# Standard path: CPU→GPU transfer (baseline case)
self.lora_a_stacked[0][
index, 0, : lora_a.shape[0], : lora_a.shape[1]
].copy_(lora_a, non_blocking=True)
self.lora_b_stacked[0][
index, 0, : lora_b.shape[0], : lora_b.shape[1]
].copy_(lora_b, non_blocking=True)
def apply(self, x: torch.Tensor, bias: torch.Tensor | None = None) -> torch.Tensor:
output = self.base_layer.quant_method.apply(self.base_layer, x, bias)

111
vllm/lora/layers/fused_moe.py
View File

@ -518,29 +518,59 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
sliced_w2_lora_a = self._slice_w2_a(w2_lora_a)
sliced_w2_lora_b = self._slice_w2_b(w2_lora_b)
self.w13_lora_a_stacked[0][
index, :, : slliced_w1_lora_a.shape[1], : slliced_w1_lora_a.shape[2]
].copy_(slliced_w1_lora_a, non_blocking=True)
# Device-aware scatter: optimize GPU→GPU case (slab optimization)
is_gpu_source = w1_lora_a.is_cuda
self.w13_lora_a_stacked[1][
index, :, : slliced_w3_lora_a.shape[1], : slliced_w3_lora_a.shape[2]
].copy_(slliced_w3_lora_a, non_blocking=True)
if is_gpu_source:
# Fast path: GPU→GPU scatter (source already on GPU from slab)
self.w13_lora_a_stacked[0][
index, :, : slliced_w1_lora_a.shape[1], : slliced_w1_lora_a.shape[2]
] = slliced_w1_lora_a
self.w13_lora_b_stacked[0][
index, :, : slliced_w1_lora_b.shape[1], : slliced_w1_lora_b.shape[2]
].copy_(slliced_w1_lora_b, non_blocking=True)
self.w13_lora_a_stacked[1][
index, :, : slliced_w3_lora_a.shape[1], : slliced_w3_lora_a.shape[2]
] = slliced_w3_lora_a
self.w13_lora_b_stacked[1][
index, :, : slliced_w3_lora_b.shape[1], : slliced_w3_lora_b.shape[2]
].copy_(slliced_w3_lora_b, non_blocking=True)
self.w13_lora_b_stacked[0][
index, :, : slliced_w1_lora_b.shape[1], : slliced_w1_lora_b.shape[2]
] = slliced_w1_lora_b
self.w2_lora_a_stacked[0][
index, :, : sliced_w2_lora_a.shape[1], : sliced_w2_lora_a.shape[2]
].copy_(sliced_w2_lora_a, non_blocking=True)
self.w13_lora_b_stacked[1][
index, :, : slliced_w3_lora_b.shape[1], : slliced_w3_lora_b.shape[2]
] = slliced_w3_lora_b
self.w2_lora_b_stacked[0][
index, :, : sliced_w2_lora_b.shape[1], : sliced_w2_lora_b.shape[2]
].copy_(sliced_w2_lora_b, non_blocking=True)
self.w2_lora_a_stacked[0][
index, :, : sliced_w2_lora_a.shape[1], : sliced_w2_lora_a.shape[2]
] = sliced_w2_lora_a
self.w2_lora_b_stacked[0][
index, :, : sliced_w2_lora_b.shape[1], : sliced_w2_lora_b.shape[2]
] = sliced_w2_lora_b
else:
# Standard path: CPU→GPU transfer (baseline case)
self.w13_lora_a_stacked[0][
index, :, : slliced_w1_lora_a.shape[1], : slliced_w1_lora_a.shape[2]
].copy_(slliced_w1_lora_a, non_blocking=True)
self.w13_lora_a_stacked[1][
index, :, : slliced_w3_lora_a.shape[1], : slliced_w3_lora_a.shape[2]
].copy_(slliced_w3_lora_a, non_blocking=True)
self.w13_lora_b_stacked[0][
index, :, : slliced_w1_lora_b.shape[1], : slliced_w1_lora_b.shape[2]
].copy_(slliced_w1_lora_b, non_blocking=True)
self.w13_lora_b_stacked[1][
index, :, : slliced_w3_lora_b.shape[1], : slliced_w3_lora_b.shape[2]
].copy_(slliced_w3_lora_b, non_blocking=True)
self.w2_lora_a_stacked[0][
index, :, : sliced_w2_lora_a.shape[1], : sliced_w2_lora_a.shape[2]
].copy_(sliced_w2_lora_a, non_blocking=True)
self.w2_lora_b_stacked[0][
index, :, : sliced_w2_lora_b.shape[1], : sliced_w2_lora_b.shape[2]
].copy_(sliced_w2_lora_b, non_blocking=True)
def forward(self, *args, **kwargs):
return self.base_layer.forward(*args, **kwargs)
@ -691,20 +721,41 @@ class FusedMoE3DWithLoRA(FusedMoEWithLoRA):
sliced_w2_lora_a = self._slice_w2_a(w2_lora_a)
sliced_w2_lora_b = self._slice_w2_b(w2_lora_b)
# Device-aware scatter: optimize GPU→GPU case (slab optimization)
is_gpu_source = w13_lora_a.is_cuda
self.w13_lora_a_stacked[0][
index, :, : sliced_w13_lora_a.shape[1], : sliced_w13_lora_a.shape[2]
].copy_(sliced_w13_lora_a, non_blocking=True)
self.w2_lora_a_stacked[0][
index, :, : sliced_w2_lora_a.shape[1], : sliced_w2_lora_a.shape[2]
].copy_(sliced_w2_lora_a, non_blocking=True)
# logger.info(f" - is_gpu_source: {is_gpu_source}")
self.w13_lora_b_stacked[0][
index, :, : sliced_w13_lora_b.shape[1], : sliced_w13_lora_b.shape[2]
].copy_(sliced_w13_lora_b, non_blocking=True)
self.w2_lora_b_stacked[0][
index, :, : sliced_w2_lora_b.shape[1], : sliced_w2_lora_b.shape[2]
].copy_(sliced_w2_lora_b, non_blocking=True)
if is_gpu_source:
# Fast path: GPU→GPU scatter (source already on GPU from slab)
self.w13_lora_a_stacked[0][
index, :, : sliced_w13_lora_a.shape[1], : sliced_w13_lora_a.shape[2]
] = sliced_w13_lora_a
self.w2_lora_a_stacked[0][
index, :, : sliced_w2_lora_a.shape[1], : sliced_w2_lora_a.shape[2]
] = sliced_w2_lora_a
self.w13_lora_b_stacked[0][
index, :, : sliced_w13_lora_b.shape[1], : sliced_w13_lora_b.shape[2]
] = sliced_w13_lora_b
self.w2_lora_b_stacked[0][
index, :, : sliced_w2_lora_b.shape[1], : sliced_w2_lora_b.shape[2]
] = sliced_w2_lora_b
else:
# Standard path: CPU→GPU transfer (baseline case)
self.w13_lora_a_stacked[0][
index, :, : sliced_w13_lora_a.shape[1], : sliced_w13_lora_a.shape[2]
].copy_(sliced_w13_lora_a, non_blocking=True)
self.w2_lora_a_stacked[0][
index, :, : sliced_w2_lora_a.shape[1], : sliced_w2_lora_a.shape[2]
].copy_(sliced_w2_lora_a, non_blocking=True)
self.w13_lora_b_stacked[0][
index, :, : sliced_w13_lora_b.shape[1], : sliced_w13_lora_b.shape[2]
].copy_(sliced_w13_lora_b, non_blocking=True)
self.w2_lora_b_stacked[0][
index, :, : sliced_w2_lora_b.shape[1], : sliced_w2_lora_b.shape[2]
].copy_(sliced_w2_lora_b, non_blocking=True)
@property
def w13_input_size(self):

206
vllm/lora/lora_model.py
View File

@ -3,15 +3,21 @@
import os
import safetensors
import safetensors.torch
import torch
from vllm.config.lora import LoRAConfig
from vllm.envs import SLAB_OPTIMIZATION
from vllm.logger import init_logger
from vllm.lora.lora_weights import LoRALayerWeights
from vllm.lora.peft_helper import PEFTHelper
from vllm.lora.slab_helper import (
create_slab_optimized_lora_model,
)
from vllm.lora.utils import (
get_lora_id,
is_base_embeddding_weights,
is_regex_target_modules,
parse_fine_tuned_lora_name,
)
from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
@ -49,12 +55,24 @@ class LoRAModel:
"""Return a copy of the object with different ids.
Will share the underlying tensors."""
return self.__class__(
cloned = self.__class__(
lora_model_id,
rank=self.rank,
loras=self.loras.copy(),
)
# Copy slab metadata if present (for SLAB optimization)
if hasattr(self, "_cached_cpu_slab"):
cloned._cached_cpu_slab = self._cached_cpu_slab # type: ignore[attr-defined]
if hasattr(self, "_cached_metadata"):
cloned._cached_metadata = self._cached_metadata # type: ignore[attr-defined]
if hasattr(self, "_lora_dir"):
cloned._lora_dir = self._lora_dir # type: ignore[attr-defined]
if hasattr(self, "_loras_dict"):
cloned._loras_dict = self._loras_dict # type: ignore[attr-defined]
return cloned
def get_lora(self, module_name: str) -> LoRALayerWeights | None:
"""Get LoRA for a given module by name"""
return self.loras.get(module_name, None)
@ -71,42 +89,124 @@ class LoRAModel:
device: str = "cuda",
dtype: torch.dtype | None = None,
model_vocab_size: int | None = None,
embedding_modules: dict[str, str] | None = None,
embedding_padding_modules: list[str] | None = None,
weights_mapper: WeightsMapper | None = None,
lora_dir: str | None = None,
target_modules_dict: dict | None = None,
target_lora_config: LoRAConfig | None = None,
slab_path: str | None = None,
packed_modules: dict | None = None,
packed_modules_mapping: dict | None = None,
) -> "LoRAModel":
"""Create a LoRAModel from a dictionary of tensors."""
pin_memory = str(device) == "cpu" and is_pin_memory_available()
loras: dict[str, LoRALayerWeights] = {}
for tensor_name, tensor in tensors.items():
if is_base_embeddding_weights(tensor_name):
continue
module_name, is_lora_a = parse_fine_tuned_lora_name(
tensor_name, weights_mapper
)
if module_name not in loras:
loras[module_name] = LoRALayerWeights.from_config(
module_name, peft_helper
if not SLAB_OPTIMIZATION:
pin_memory = str(device) == "cpu" and is_pin_memory_available()
loras: dict[str, LoRALayerWeights] = {}
for tensor_name, tensor in tensors.items():
if is_base_embeddding_weights(tensor_name):
continue
module_name, is_lora_a = parse_fine_tuned_lora_name(
tensor_name, weights_mapper
)
if is_lora_a:
if (
"lora_embedding_A" in tensor_name
and model_vocab_size is not None
and model_vocab_size != tensor.shape[1]
):
raise RuntimeError(
f"The embedding LoRA size({tensor.shape[1]}) must be consistent"
f" with the base model's vocabulary size({model_vocab_size})."
if module_name not in loras:
loras[module_name] = LoRALayerWeights.from_config(
module_name, peft_helper
)
loras[module_name].lora_a = tensor.to(device=device, dtype=dtype)
if pin_memory:
loras[module_name].lora_a = loras[module_name].lora_a.pin_memory()
else:
loras[module_name].lora_b = tensor.to(device=device, dtype=dtype)
if pin_memory:
loras[module_name].lora_b = loras[module_name].lora_b.pin_memory()
if is_lora_a:
if (
"lora_embedding_A" in tensor_name
and model_vocab_size is not None
and model_vocab_size != tensor.shape[1]
):
raise RuntimeError(
f"The embedding LoRA size({tensor.shape[1]}) "
f"must be consistent with the base model's "
f"vocabulary size({model_vocab_size})."
)
loras[module_name].lora_a = tensor.to(device=device, dtype=dtype)
if pin_memory:
loras[module_name].lora_a = loras[
module_name
].lora_a.pin_memory()
return cls(lora_model_id, peft_helper.r, loras)
else:
loras[module_name].lora_b = tensor.to(device=device, dtype=dtype)
if pin_memory:
loras[module_name].lora_b = loras[
module_name
].lora_b.pin_memory()
return cls(lora_model_id, peft_helper.r, loras)
else:
logger.debug("Using slab-based LoRA tensor optimization")
from vllm.lora.slab_helper import check_slab_cache
cache_hit, lora_model_cached = check_slab_cache(
lora_dir, peft_helper, target_lora_config, target_modules_dict
)
if cache_hit and lora_model_cached is not None:
logger.debug(
"[SLAB_CACHE_HIT] Using cached slab for %s, cloning with ID %s",
lora_dir,
lora_model_id,
)
# Clone cached model with correct ID
return lora_model_cached.clone(lora_model_id)
logger.debug("Building new slab for %s", lora_dir)
lora_model, gpu_slab, metadata = create_slab_optimized_lora_model(
lora_model_id=lora_model_id,
tensors=tensors,
peft_helper=peft_helper,
device=device,
dtype=dtype,
embeddings=None,
target_embedding_padding=model_vocab_size,
embedding_modules=embedding_modules,
embedding_padding_modules=embedding_padding_modules,
weights_mapper=weights_mapper,
lora_dir=lora_dir,
lora_config=peft_helper,
target_modules_dict=target_modules_dict,
target_lora_config=target_lora_config,
slab_path=slab_path,
packed_modules=packed_modules,
packed_modules_mapping=packed_modules_mapping,
)
if (
gpu_slab is not None
and metadata is not None
and target_modules_dict is not None
):
# Pre-cache metadata lookup once for all modules
if not hasattr(metadata, "_lookup_cache"):
metadata._lookup_cache = {
info.module_name: info for info in metadata.tensor_infos
}
# Instead of calling create_lora_weights, just cache slab references
for module_name, module in target_modules_dict.items():
if hasattr(module, "create_lora_weights"):
module._gpu_slab_ref = gpu_slab
module._slab_metadata_ref = metadata
module._slab_ready = True
module._using_slab_views = True
# Add any post-processing after slab model creation
torch.cuda.synchronize() # Check for pending GPU operations
# Cache the built LoRAModel for future reuse
from vllm.lora.slab_helper import cache_lora_model
cache_lora_model(lora_dir, lora_model)
return lora_model
@classmethod
def from_local_checkpoint(
@ -119,8 +219,15 @@ class LoRAModel:
device: str = "cuda",
dtype: torch.dtype | None = None,
model_vocab_size: int | None = None,
embedding_modules: dict[str, str] | None = None,
embedding_padding_modules: list[str] | None = None,
weights_mapper: WeightsMapper | None = None,
tensorizer_config_dict: dict | None = None,
target_modules_dict: dict | None = None,
target_lora_config: LoRAConfig | None = None,
slab_path: str | None = None,
packed_modules: dict | None = None,
packed_modules_mapping: dict | None = None,
) -> "LoRAModel":
"""Create a LoRAModel from a local checkpoint.
@ -200,22 +307,55 @@ class LoRAModel:
for module in f.keys(): # noqa
tensors[module] = f.get_tensor(module)
elif os.path.isfile(lora_bin_file_path) or os.path.isfile(lora_pt_file_path):
# When a bin/pt file is provided, we rely on config to find
# unexpected modules.
unexpected_modules = []
target_modules = peft_helper.target_modules
if not isinstance(target_modules, list):
target_modules = [target_modules]
for module in target_modules:
# Compatible with more modules,
# such as:layers.11.self_attn.k_proj
part_name = module.split(".")[-1]
if part_name not in expected_lora_modules:
unexpected_modules.append(module)
# loaded lora's target modules must be a subset of
# expected_lora_modules. It is not reliable. See
# https://github.com/vllm-project/vllm/pull/5909. But there's no
# other better mechanism.
if unexpected_modules and not is_regex_target_modules(
peft_helper.target_modules, expected_lora_modules
):
raise ValueError(
f"While loading {lora_dir}, expected"
f" target modules in {expected_lora_modules}"
f" but received {unexpected_modules}."
f" Please verify that the loaded LoRA module is correct"
)
lora_file_path = (
lora_bin_file_path
if os.path.isfile(lora_bin_file_path)
else lora_pt_file_path
)
tensors = torch.load(lora_file_path, map_location=device, weights_only=True)
check_unexpected_modules(tensors)
else:
raise ValueError(f"{lora_dir} doesn't contain tensors")
lora_id = get_lora_id() if lora_model_id is None else lora_model_id
return cls.from_lora_tensors(
lora_model_id=get_lora_id() if lora_model_id is None else lora_model_id,
lora_model_id=lora_id,
tensors=tensors,
peft_helper=peft_helper,
device=device,
dtype=dtype,
model_vocab_size=model_vocab_size,
embedding_modules=embedding_modules,
embedding_padding_modules=embedding_padding_modules,
weights_mapper=weights_mapper,
lora_dir=lora_dir,
target_modules_dict=target_modules_dict,
target_lora_config=target_lora_config,
slab_path=slab_path,
packed_modules=packed_modules,
packed_modules_mapping=packed_modules_mapping,
)

206
vllm/lora/model_manager.py
View File

@ -10,11 +10,13 @@ import torch
from torch import nn
from vllm.config.lora import LoRAConfig
from vllm.envs import SLAB_OPTIMIZATION
from vllm.logger import init_logger
from vllm.lora.layers import BaseLayerWithLoRA, FusedMoE3DWithLoRA, LoRAMapping
from vllm.lora.lora_model import LoRAModel
from vllm.lora.lora_weights import LoRALayerWeights, PackedLoRALayerWeights
from vllm.lora.punica_wrapper import get_punica_wrapper
from vllm.lora.slab_helper import process_slab_activation_loop
from vllm.lora.utils import (
from_layer,
from_layer_logits_processor,
@ -150,84 +152,139 @@ class LoRAModelManager:
"Activating LoRA. int id: %d, slot index: %d", lora_model.id, index
)
self.lora_index_to_id[index] = lora_model.id
for module_name, module in self.modules.items():
module_lora = self._get_lora_layer_weights(lora_model, module_name)
if not module_lora:
module.reset_lora(index)
continue
# Note (gnovack) - If MOE lora weights are not split into
# num_experts chunks, we split them here
if isinstance(module, FusedMoE3DWithLoRA) and torch.is_tensor(
module_lora.lora_a
):
# Handle PEFT file format where experts.base_layer is the
# gate_up_proj and experts is the down_proj
gate_up_proj_lora = self._get_lora_layer_weights(
lora_model, module_name + ".base_layer"
)
down_proj_lora = module_lora
# FIXME Edge case where LoRA is not added to gate_up_proj
# or down_proj
assert gate_up_proj_lora is not None
assert down_proj_lora is not None
if self._is_3d_moe_model:
module_lora.lora_a = [
gate_up_proj_lora.lora_a,
down_proj_lora.lora_a,
]
module_lora.lora_b = [
gate_up_proj_lora.lora_b,
down_proj_lora.lora_b,
]
else:
# Some 3D MoE models haven't added the `is_3d_moe_weight`
# attribute yet, so fallback here
num_experts = module_lora.lora_a.shape[0] // module_lora.rank
gate_proj_a = gate_up_proj_lora.lora_a.chunk(num_experts, dim=0)
up_proj_a = gate_up_proj_lora.lora_a.chunk(num_experts, dim=0)
if SLAB_OPTIMIZATION:
# Check for cached CPU slab and metadata from LoRAModel
has_cpu_slab = hasattr(lora_model, "_cached_cpu_slab")
has_gpu_slab = hasattr(lora_model, "_cached_gpu_slab")
has_metadata = hasattr(lora_model, "_cached_metadata")
gate_proj_b = gate_up_proj_lora.lora_b[::2, ...].chunk(
num_experts, dim=-1
)
up_proj_b = gate_up_proj_lora.lora_b[1::2, ...].chunk(
num_experts, dim=-1
)
if has_cpu_slab and has_metadata:
# MEMORY EFFICIENT: Create GPU slab only during activation
cpu_slab = lora_model._cached_cpu_slab # type: ignore[attr-defined]
metadata = lora_model._cached_metadata # type: ignore[attr-defined]
down_proj_a = down_proj_lora.lora_a.chunk(num_experts, dim=0)
down_proj_b = down_proj_lora.lora_b.chunk(num_experts, dim=-1)
# Transfer to GPU only when activated
gpu_slab = cpu_slab.to(device="cuda", non_blocking=True)
lora_a = []
lora_b = []
for i in range(num_experts):
lora_a.append(gate_proj_a[i])
lora_a.append(down_proj_a[i])
lora_a.append(up_proj_a[i])
# Cache GPU slab for this activation
lora_model._cached_gpu_slab = gpu_slab # type: ignore[attr-defined]
lora_b.append(gate_proj_b[i])
lora_b.append(down_proj_b[i])
lora_b.append(up_proj_b[i])
elif has_gpu_slab and has_metadata:
gpu_slab = lora_model._cached_gpu_slab # type: ignore[attr-defined]
metadata = lora_model._cached_metadata # type: ignore[attr-defined]
module_lora.lora_a = lora_a
module_lora.lora_b = lora_b
module.set_lora(
else:
return False
# Use helper function for the full activation loop with all optimizations
process_slab_activation_loop(
self.modules,
lora_model,
self._get_lora_layer_weights,
self.lora_config,
gpu_slab,
metadata,
index,
module_lora.lora_a,
module_lora.lora_b,
)
return True
return True
else:
for module_name, module in self.modules.items():
module_lora = self._get_lora_layer_weights(lora_model, module_name)
if not module_lora:
module.reset_lora(index)
continue
# Note (gnovack) - If MOE lora weights are not split into
# num_experts chunks, we split them here
if isinstance(module, FusedMoE3DWithLoRA) and torch.is_tensor(
module_lora.lora_a
):
# Handle PEFT file format where experts.base_layer is the
# gate_up_proj and experts is the down_proj
gate_up_proj_lora = self._get_lora_layer_weights(
lora_model, module_name + ".base_layer"
)
down_proj_lora = module_lora
# FIXME Edge case where LoRA is not added to gate_up_proj
# or down_proj
assert gate_up_proj_lora is not None
assert down_proj_lora is not None
if self._is_3d_moe_model:
module_lora.lora_a = [
gate_up_proj_lora.lora_a,
down_proj_lora.lora_a,
]
module_lora.lora_b = [
gate_up_proj_lora.lora_b,
down_proj_lora.lora_b,
]
else:
# Some 3D MoE models haven't added the `is_3d_moe_weight`
# attribute yet, so fallback here
num_experts = module_lora.lora_a.shape[0] // module_lora.rank
gate_proj_a = gate_up_proj_lora.lora_a.chunk(num_experts, dim=0)
up_proj_a = gate_up_proj_lora.lora_a.chunk(num_experts, dim=0)
gate_proj_b = gate_up_proj_lora.lora_b[::2, ...].chunk(
num_experts, dim=-1
)
up_proj_b = gate_up_proj_lora.lora_b[1::2, ...].chunk(
num_experts, dim=-1
)
down_proj_a = down_proj_lora.lora_a.chunk(num_experts, dim=0)
down_proj_b = down_proj_lora.lora_b.chunk(num_experts, dim=-1)
lora_a = []
lora_b = []
for i in range(num_experts):
lora_a.append(gate_proj_a[i])
lora_a.append(down_proj_a[i])
lora_a.append(up_proj_a[i])
lora_b.append(gate_proj_b[i])
lora_b.append(down_proj_b[i])
lora_b.append(up_proj_b[i])
module_lora.lora_a = lora_a
module_lora.lora_b = lora_b
module.set_lora(
index,
module_lora.lora_a,
module_lora.lora_b,
)
return True
def _deactivate_adapter(self, lora_id: int):
try:
index = self.lora_index_to_id.index(lora_id)
self.lora_index_to_id[index] = None
# Free GPU slab when deactivating to respect max_loras constraint
if SLAB_OPTIMIZATION and lora_id in self._registered_adapters:
lora_model = self._registered_adapters[lora_id]
if hasattr(lora_model, "_cached_gpu_slab"):
# Free GPU slab to make room for other LoRAs
del lora_model._cached_gpu_slab
torch.cuda.empty_cache() # Force GPU memory cleanup
except ValueError:
pass
def _add_adapter(self, lora: LoRAModel):
self._create_merged_loras_inplace(lora)
self._registered_adapters[lora.id] = lora
if not SLAB_OPTIMIZATION:
# Traditional approach: use CPU packing for packed modules
self._create_merged_loras_inplace(lora)
self._registered_adapters[lora.id] = lora
else:
# Slab optimization: slab already built with target modules
logger.debug(
"[SLAB_OPTIMIZATION] Registering LoRA %d - "
"slab already built with target modules",
lora.id,
)
self._registered_adapters[lora.id] = lora
def pin_adapter(self, lora_id: int) -> bool:
"""Pin a LoRAModel in the manager cache."""
@ -490,7 +547,8 @@ class LoRAModelManager:
module_name = replaced_module_name
if module_name.endswith(".experts"):
lora_model.loras[module_name] = PackedLoRALayerWeights.pack_moe(
replacement_loras, module_name
replacement_loras,
module_name,
)
else:
lora_model.loras[module_name] = PackedLoRALayerWeights.pack(
@ -515,19 +573,19 @@ class LoRAModelManager:
# overhead is significant.
# 2. The weight packing above (e.g., pack_moe) may invalidate the
# pin_memory allocation, so we execute it after packing.
pin_memory = str(lora_device) == "cpu" and is_pin_memory_available()
if pin_memory:
for lora in lora_model.loras.values():
if isinstance(lora.lora_a, list):
for index in range(len(lora.lora_a)):
if lora.lora_a[index] is None:
continue
lora.lora_a[index] = lora.lora_a[index].pin_memory()
lora.lora_b[index] = lora.lora_b[index].pin_memory()
else:
lora.lora_a = lora.lora_a.pin_memory()
lora.lora_b = lora.lora_b.pin_memory()
if not SLAB_OPTIMIZATION:
pin_memory = str(lora_device) == "cpu" and is_pin_memory_available()
if pin_memory:
for lora in lora_model.loras.values():
if isinstance(lora.lora_a, list):
for index in range(len(lora.lora_a)):
if lora.lora_a[index] is None:
continue
lora.lora_a[index] = lora.lora_a[index].pin_memory()
lora.lora_b[index] = lora.lora_b[index].pin_memory()
else:
lora.lora_a = lora.lora_a.pin_memory()
lora.lora_b = lora.lora_b.pin_memory()
def _get_lora_layer_weights(
self, lora_model: LoRAModel, module_name: str

760
vllm/lora/slab_helper.py Normal file
View File

@ -0,0 +1,760 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import hashlib
import threading
import time
from typing import Any
import torch
from vllm.logger import init_logger
from vllm.lora.layers import FusedMoE3DWithLoRA
from vllm.lora.lora_weights import LoRALayerWeights, PackedLoRALayerWeights
# Import here to avoid circular dependency
from vllm.lora.utils import parse_fine_tuned_lora_name
logger = init_logger(__name__)
# Global slab cache
_GLOBAL_SLAB_CACHE: dict[str, tuple] = {}
_CACHE_LOCK = threading.RLock()
# Global LoRAModel cache for early checking
_GLOBAL_LORA_MODEL_CACHE: dict[str, Any] = {}
_LORA_MODEL_CACHE_LOCK = threading.RLock()
# Global result storage
_GLOBAL_RESULT_STORAGE: dict[str, tuple] = {}
_RESULT_LOCK = threading.RLock()
class UltraFastPinnedPool:
"""Lazy-initialized pinned memory pool."""
def __init__(self):
self.pool_size = 0
self.pinned_pool = None # Lazy - allocated on first use
self.pool_lock = threading.RLock()
self.used_ranges = [] # Track used memory ranges
self.current_slab = None
self.current_metadata = None
def allocate_slab_views_directly(
self, tensor_sizes: list[int], dtype: torch.dtype
) -> tuple[torch.Tensor, list[torch.Tensor]]:
"""Allocate slab and return views - ZERO copy needed!"""
total_elements = sum(tensor_sizes)
if total_elements == 0:
return torch.empty(0, dtype=dtype, device="cpu").pin_memory(), []
tensor_bytes = total_elements * dtype.itemsize
with self.pool_lock:
# Expand pool if needed
if tensor_bytes > self.pool_size:
new_size = max(self.pool_size * 2, tensor_bytes + self.pool_size)
new_pool = torch.empty(new_size, dtype=torch.uint8).pin_memory()
# Copy existing data if any
if self.used_ranges and self.pinned_pool is not None:
total_used = max(end for start, end in self.used_ranges)
new_pool[:total_used] = self.pinned_pool[:total_used]
self.pinned_pool = new_pool
self.pool_size = new_size
# Find available space
start_offset = max((end for start, end in self.used_ranges), default=0)
end_offset = start_offset + tensor_bytes
if end_offset > self.pool_size:
# Reset pool - reuse from beginning
self.used_ranges.clear()
start_offset = 0
end_offset = tensor_bytes
self.used_ranges.append((start_offset, end_offset))
# Create full slab view
assert self.pinned_pool is not None
pool_slice = self.pinned_pool[start_offset:end_offset]
full_slab = pool_slice.view(torch.uint8).view(dtype)[:total_elements]
# Create individual tensor views for each component - NO copying!
tensor_views = []
current_offset = 0
for size in tensor_sizes:
if size > 0:
tensor_view = full_slab[current_offset : current_offset + size]
tensor_views.append(tensor_view)
current_offset += size
else:
tensor_views.append(torch.empty(0, dtype=dtype, device="cpu"))
return full_slab, tensor_views
def allocate_slab_directly(
self, num_elements: int, dtype: torch.dtype
) -> torch.Tensor:
"""Allocate slab DIRECTLY from pinned pool."""
if num_elements == 0:
return torch.empty(0, dtype=dtype, device="cpu").pin_memory()
tensor_bytes = num_elements * dtype.itemsize
with self.pool_lock:
# Expand pool if needed
if tensor_bytes > self.pool_size:
new_size = max(self.pool_size * 2, tensor_bytes + self.pool_size)
new_pool = torch.empty(new_size, dtype=torch.uint8).pin_memory()
# Copy existing data if any
if self.used_ranges and self.pinned_pool is not None:
total_used = max(end for start, end in self.used_ranges)
new_pool[:total_used] = self.pinned_pool[:total_used]
self.pinned_pool = new_pool
self.pool_size = new_size
# Find available space
start_offset = max((end for start, end in self.used_ranges), default=0)
end_offset = start_offset + tensor_bytes
if end_offset > self.pool_size:
# Reset pool - reuse from beginning
self.used_ranges.clear()
start_offset = 0
end_offset = tensor_bytes
self.used_ranges.append((start_offset, end_offset))
# Return direct view of pinned pool - NO copy needed!
assert self.pinned_pool is not None
pool_slice = self.pinned_pool[start_offset:end_offset]
slab_tensor = pool_slice.view(torch.uint8).view(dtype)[:num_elements]
return slab_tensor
def get_pinned_tensor_fast(self, cpu_tensor: torch.Tensor) -> torch.Tensor:
"""Ultra-fast pseudo-pinning using pre-allocated pool."""
tensor_bytes = cpu_tensor.numel() * cpu_tensor.element_size()
with self.pool_lock:
# Find available space in pool
if tensor_bytes > self.pool_size:
# Expand pool if needed
new_size = max(self.pool_size * 2, tensor_bytes + self.pool_size)
# Create larger pool
new_pool = torch.empty(new_size, dtype=torch.uint8).pin_memory()
# Copy existing data if any
if self.used_ranges and self.pinned_pool is not None:
total_used = max(end for start, end in self.used_ranges)
new_pool[:total_used] = self.pinned_pool[:total_used]
self.pinned_pool = new_pool
self.pool_size = new_size
# Simple allocation strategy - find space at end
start_offset = max((end for start, end in self.used_ranges), default=0)
end_offset = start_offset + tensor_bytes
if end_offset > self.pool_size:
# Reset pool if we're at the end - reuse from beginning
self.used_ranges.clear()
start_offset = 0
end_offset = tensor_bytes
self.used_ranges.append((start_offset, end_offset))
# Get slice from pre-pinned pool
assert self.pinned_pool is not None
pool_slice = self.pinned_pool[start_offset:end_offset]
# Reshape to match tensor and copy data (fast memory copy)
pinned_tensor = (
pool_slice.view(torch.uint8)
.view(cpu_tensor.dtype)[: cpu_tensor.numel()]
.view(cpu_tensor.shape)
)
pinned_tensor.copy_(cpu_tensor) # Fast copy into pre-pinned memory
return pinned_tensor
# Global ultra-fast pool - initialized ONCE in envs.py
_ULTRA_FAST_POOL = None
_POOL_INIT_LOCK = threading.RLock()
def set_global_pool(pool: UltraFastPinnedPool) -> None:
"""Set the global pool instance."""
global _ULTRA_FAST_POOL
with _POOL_INIT_LOCK:
if _ULTRA_FAST_POOL is None:
_ULTRA_FAST_POOL = pool
def get_ultra_fast_pool():
"""Get the pre-initialized global pool - NO lazy initialization."""
global _ULTRA_FAST_POOL
if _ULTRA_FAST_POOL is None:
# Fallback - create pool if not set (shouldn't happen)
with _POOL_INIT_LOCK:
if _ULTRA_FAST_POOL is None:
_ULTRA_FAST_POOL = UltraFastPinnedPool()
return _ULTRA_FAST_POOL
# Main public interface with CPU caching and disk save/load
def build_target_matched_slab(
lora_model,
target_modules,
max_loras,
lora_config,
slab_path: str | None = None,
):
"""
Build a slab that exactly matches the per-layer target shapes.
Ultra-fast cached slab building with minimal overhead.
Ensures perfect zero-copy during set_lora() and reuses slabs for identical LoRAs.
Args:
lora_model: The LoRA model to build slab for
target_modules: Target modules dictionary
max_loras: Maximum number of LoRAs
lora_config: LoRA configuration
slab_path: Optional path to save/load slab to/from disk
"""
# Get TP info for cache key when fully_sharded=True
fully_sharded = lora_config.fully_sharded_loras if lora_config else False
tp_rank = None
if fully_sharded and target_modules:
first_module = next(iter(target_modules.values()), None)
if first_module:
tp_rank = getattr(first_module, "tp_rank", 0)
cache_key = _generate_slab_cache_key(lora_model, "cpu", tp_rank, fully_sharded)
# Get pre-initialized pool ONCE to avoid repeated calls
pool = get_ultra_fast_pool()
# Check CPU cache FIRST - if already on CPU, don't load again
if cache_key in _GLOBAL_SLAB_CACHE:
cached_slab, cached_metadata = _GLOBAL_SLAB_CACHE[cache_key]
return cached_slab, cached_metadata
# Only take lock if not in memory cache
with _CACHE_LOCK:
# Double-check pattern for thread safety
if cache_key in _GLOBAL_SLAB_CACHE:
cached_slab, cached_metadata = _GLOBAL_SLAB_CACHE[cache_key]
return cached_slab, cached_metadata
all_flattened_tensors = [] # Direct collection of all flattened tensors
global_metadata = SlabMetadata()
current_global_offset = 0
for module_name, module_lora in lora_model.loras.items():
if module_lora is None:
continue
# Process lora_a
if hasattr(module_lora, "lora_a") and module_lora.lora_a is not None:
if isinstance(module_lora.lora_a, list):
for expert_idx, expert_tensor in enumerate(module_lora.lora_a):
if expert_tensor is not None:
all_flattened_tensors.append(expert_tensor.flatten())
tensor_info = TensorInfo(
f"{module_name}.lora_a.expert_{expert_idx}",
"a",
expert_tensor.shape,
expert_tensor.numel(),
current_global_offset,
)
global_metadata.tensor_infos.append(tensor_info)
current_global_offset += expert_tensor.numel()
else:
# Single tensor
all_flattened_tensors.append(module_lora.lora_a.flatten())
tensor_info = TensorInfo(
f"{module_name}.lora_a",
"a",
module_lora.lora_a.shape,
module_lora.lora_a.numel(),
current_global_offset,
)
global_metadata.tensor_infos.append(tensor_info)
current_global_offset += module_lora.lora_a.numel()
# Process lora_b (scaling already applied during packing for packed modules)
if hasattr(module_lora, "lora_b") and module_lora.lora_b is not None:
if isinstance(module_lora.lora_b, list):
module_lora_b_count = 0
for expert_idx, expert_tensor in enumerate(module_lora.lora_b):
if expert_tensor is not None:
all_flattened_tensors.append(expert_tensor.flatten())
tensor_info = TensorInfo(
f"{module_name}.lora_b.expert_{expert_idx}",
"b",
expert_tensor.shape,
expert_tensor.numel(),
current_global_offset,
)
global_metadata.tensor_infos.append(tensor_info)
module_lora_b_count += expert_tensor.numel()
current_global_offset += expert_tensor.numel()
else:
# Single tensor
all_flattened_tensors.append(module_lora.lora_b.flatten())
tensor_info = TensorInfo(
f"{module_name}.lora_b",
"b",
module_lora.lora_b.shape,
module_lora.lora_b.numel(),
current_global_offset,
)
global_metadata.tensor_infos.append(tensor_info)
current_global_offset += module_lora.lora_b.numel()
extraction_map = {}
lookup = {info.module_name: info for info in global_metadata.tensor_infos}
for module_name, module_lora in lora_model.loras.items():
if module_lora is None:
continue
# Check if module has list structure (packed MoE/QKV) or single tensor
has_list = (
isinstance(module_lora.lora_a, list)
if hasattr(module_lora, "lora_a") and module_lora.lora_a is not None
else False
)
if has_list:
# Packed module with list - collect all expert tensor infos
expert_tensors_a = []
expert_tensors_b = []
for expert_idx in range(len(module_lora.lora_a)):
a_key = f"{module_name}.lora_a.expert_{expert_idx}"
b_key = f"{module_name}.lora_b.expert_{expert_idx}"
if a_key in lookup:
a_info = lookup[a_key]
expert_tensors_a.append(
(a_info.offset, a_info.size, a_info.shape)
)
if b_key in lookup:
b_info = lookup[b_key]
expert_tensors_b.append(
(b_info.offset, b_info.size, b_info.shape)
)
# Determine type based on module name
if module_name.endswith(".mlp.experts"):
extraction_map[module_name] = (
"moe",
expert_tensors_a,
expert_tensors_b,
)
elif module_name.endswith(".qkv_proj"):
extraction_map[module_name] = (
"qkv",
expert_tensors_a,
expert_tensors_b,
)
else:
# Single tensor module
lora_a_key = f"{module_name}.lora_a"
lora_b_key = f"{module_name}.lora_b"
if lora_a_key in lookup and lora_b_key in lookup:
a_info = lookup[lora_a_key]
b_info = lookup[lora_b_key]
extraction_map[module_name] = ( # type: ignore[assignment]
"linear",
a_info.offset,
a_info.size,
a_info.shape,
b_info.offset,
b_info.size,
b_info.shape,
)
# Store extraction_map in metadata for zero-overhead extraction
global_metadata.extraction_map = extraction_map
if all_flattened_tensors:
# Calculate tensor sizes for view allocation
tensor_sizes = [t.numel() for t in all_flattened_tensors]
total_elements = sum(tensor_sizes)
global_metadata.total_size = total_elements
# Allocate slab + individual views DIRECTLY in pinned pool - ZERO copy!
full_slab, tensor_views = pool.allocate_slab_views_directly(
tensor_sizes, torch.bfloat16
)
for i, (source_tensor, view_tensor) in enumerate(
zip(all_flattened_tensors, tensor_views)
):
view_tensor.copy_(source_tensor)
else:
# Empty slab case
full_slab, _ = pool.allocate_slab_views_directly([], torch.bfloat16)
global_metadata.total_size = 0
slab_tensor = full_slab
metadata = global_metadata
# Cache the built slab in memory
with _CACHE_LOCK:
_GLOBAL_SLAB_CACHE[cache_key] = (slab_tensor, metadata)
# Touch the objects to ensure they're ready for return
_ = slab_tensor.shape if hasattr(slab_tensor, "shape") else None
_ = metadata.total_size if hasattr(metadata, "total_size") else None
# Generate unique result key for this build
result_key = f"slab_result_{cache_key}_{int(time.time() * 1000000)}"
# Store large objects in global storage instead of returning them
with _RESULT_LOCK:
_GLOBAL_RESULT_STORAGE[result_key] = (slab_tensor, metadata)
# Clear local references to large objects to prevent cleanup overhead
slab_tensor = None # type: ignore[assignment]
metadata = None # type: ignore[assignment]
full_slab = None # type: ignore[assignment]
global_metadata = None # type: ignore[assignment]
all_flattened_tensors = None # type: ignore[assignment]
return result_key
def extract_tensors_from_gpu_slab(gpu_slab, metadata, module_name):
"""Extract lora_a and lora_b tensors from GPU slab for a module."""
extraction_info = metadata.extraction_map.get(module_name)
if not extraction_info:
return None, None
extraction_type = extraction_info[0]
if extraction_type == "linear":
# tensor: ('linear', a_offset, a_size, a_shape, b_offset, b_size, b_shape)
_, a_offset, a_size, a_shape, b_offset, b_size, b_shape = extraction_info
lora_a = gpu_slab[a_offset : a_offset + a_size].view(a_shape)
lora_b = gpu_slab[b_offset : b_offset + b_size].view(b_shape)
return lora_a, lora_b
elif extraction_type in ("moe", "qkv"):
# List of tensors: ('moe'/'qkv', expert_tensors_a, expert_tensors_b)
_, expert_tensors_a, expert_tensors_b = extraction_info
lora_a_list = []
for i, (offset, size, shape) in enumerate(expert_tensors_a):
tensor = gpu_slab[offset : offset + size].view(shape)
lora_a_list.append(tensor)
lora_b_list = []
for i, (offset, size, shape) in enumerate(expert_tensors_b):
tensor = gpu_slab[offset : offset + size].view(shape)
lora_b_list.append(tensor)
return lora_a_list, lora_b_list
return None, None
def process_slab_activation_loop(
modules_dict,
lora_model,
get_lora_layer_weights_fn,
lora_config,
gpu_slab,
metadata,
index,
):
"""Extract weights from GPU slab and activate."""
# Loop through model modules
for module_name, module in modules_dict.items():
lora_a_gpu, lora_b_gpu = extract_tensors_from_gpu_slab(
gpu_slab, metadata, module_name
)
if lora_a_gpu is None or lora_b_gpu is None:
# No weights for this module
module.reset_lora(index)
continue
# Special case: MoE3D needs 2-item list format
if isinstance(module, FusedMoE3DWithLoRA) and not isinstance(lora_a_gpu, list):
gate_up_a, gate_up_b = extract_tensors_from_gpu_slab(
gpu_slab, metadata, module_name + ".base_layer"
)
down_a, down_b = lora_a_gpu, lora_b_gpu
if gate_up_a is not None and down_a is not None:
lora_a_gpu = [gate_up_a, down_a]
lora_b_gpu = [gate_up_b, down_b]
module.set_lora(index, lora_a_gpu, lora_b_gpu)
return True
def check_slab_cache(lora_dir, peft_helper, target_lora_config, target_modules_dict):
"""Check if LoRAModel is already cached for this LoRA directory."""
if not lora_dir:
return False, None
# Generate simple key based on lora_dir only
cache_key = hashlib.md5(lora_dir.encode()).hexdigest()
# Check LoRAModel cache
with _LORA_MODEL_CACHE_LOCK:
if cache_key in _GLOBAL_LORA_MODEL_CACHE:
logger.info("[SLAB_CACHE_HIT] Found cached LoRAModel for %s", lora_dir)
return True, _GLOBAL_LORA_MODEL_CACHE[cache_key]
logger.info("[SLAB_CACHE_MISS] No cached LoRAModel for %s", lora_dir)
return False, None
def cache_lora_model(lora_dir, lora_model):
"""Store LoRAModel in cache for reuse."""
if not lora_dir:
return
cache_key = hashlib.md5(lora_dir.encode()).hexdigest()
with _LORA_MODEL_CACHE_LOCK:
_GLOBAL_LORA_MODEL_CACHE[cache_key] = lora_model
logger.info("[SLAB_CACHE] Stored LoRAModel for %s", lora_dir)
def get_cached_lora_model(cache_key):
"""Get cached LoRA model."""
with _LORA_MODEL_CACHE_LOCK:
return _GLOBAL_LORA_MODEL_CACHE.get(cache_key)
def _generate_slab_cache_key(lora_model, device, tp_rank=None, fully_sharded=False):
"""Generate cache key for LoRA slab - includes tp_rank when fully_sharded=True."""
lora_dir = getattr(lora_model, "_lora_dir", None)
if not lora_dir:
lora_dir = f"unknown_path_{lora_model.rank}_{len(lora_model.loras)}"
# Base key
key_str = f"{lora_dir}|{lora_model.rank}|{len(lora_model.loras)}|{str(device)}"
# Include tp_rank when fully_sharded=True (each GPU has different slab)
if fully_sharded and tp_rank is not None:
key_str += f"|tp_rank_{tp_rank}"
cache_key = hashlib.md5(key_str.encode()).hexdigest()
return cache_key
class TensorInfo:
"""Metadata for a tensor in the slab."""
def __init__(
self,
module_name: str,
tensor_type: str,
shape: tuple,
size: int,
offset: int = 0,
):
self.module_name = module_name
self.tensor_type = tensor_type # 'lora_a', 'lora_b'
self.shape = shape
self.size = size
self.offset = offset
class SlabMetadata:
"""Metadata for the entire slab with pre-computed extraction data."""
def __init__(self):
self.tensor_infos: list[TensorInfo] = []
self.total_size = 0
# PERFORMANCE: Pre-computed extraction data to eliminate all scatter overhead
self.extraction_map: dict[
str, tuple
] = {} # module_name -> (lora_a_slice, lora_b_slice)
def create_slab_optimized_lora_model(
lora_model_id: int,
tensors: dict[str, torch.Tensor],
peft_helper,
device: str = "cuda",
dtype: torch.dtype | None = None,
embeddings: dict[str, torch.Tensor] | None = None,
target_embedding_padding: int | None = None,
embedding_modules: dict[str, str] | None = None,
embedding_padding_modules: list[str] | None = None,
weights_mapper=None,
lora_dir: str | None = None,
lora_config=None,
target_modules_dict=None,
target_lora_config=None,
slab_path: str | None = None,
packed_modules: dict | None = None,
packed_modules_mapping: dict | None = None,
):
"""Create a LoRAModel with target-aware slab."""
if get_ultra_fast_pool() is None:
pool = UltraFastPinnedPool()
set_global_pool(pool)
# Create LoRA weights as normal
loras: dict[str, LoRALayerWeights] = {}
for tensor_name, tensor in tensors.items():
module_name, is_lora_a = parse_fine_tuned_lora_name(tensor_name, weights_mapper)
if module_name not in loras:
loras[module_name] = LoRALayerWeights.from_config(module_name, peft_helper)
if is_lora_a:
loras[module_name].lora_a = tensor.to(
dtype=dtype
) # Keep on CPU for slab building
else:
loras[module_name].lora_b = tensor.to(
dtype=dtype
) # Keep on CPU for slab building
assert embedding_padding_modules is not None
if (
any(name in module_name for name in embedding_padding_modules)
and target_embedding_padding is not None
):
lora_b = loras[module_name].lora_b
assert target_embedding_padding >= lora_b.shape[0]
addition = target_embedding_padding - lora_b.shape[0]
loras[module_name].lora_b = torch.nn.functional.pad(
lora_b, (0, 0, 0, addition)
)
# Create the LoRA model instance
from vllm.lora.lora_model import LoRAModel
lora_model_instance = LoRAModel(lora_model_id, peft_helper.r, loras)
# Store the LoRA directory path for cache key generation
if lora_dir:
lora_model_instance._lora_dir = lora_dir # type: ignore[attr-defined]
if packed_modules and len(packed_modules) > 0:
# Helper function to get lora weights (simplified version without model context)
def get_lora_weights(lora_model, module_name):
return lora_model.loras.get(module_name, None)
# Pack modules similar to _create_merged_loras_inplace
for module_name, new_module_names in packed_modules.items():
replacement_loras: list[LoRALayerWeights | None] = []
replaced_module: set[str] = set()
has_replacement = False
# Collect individual projections
for r in new_module_names:
lora = get_lora_weights(lora_model_instance, r)
replacement_loras.append(lora)
if lora:
has_replacement = True
replaced_module.add(r)
if not has_replacement:
continue
# Ensure None values are explicit
for i in range(len(replacement_loras)):
if not replacement_loras[i]:
replacement_loras[i] = None
# Pack based on module type
if module_name.endswith(".experts"):
lora_model_instance.loras[module_name] = (
PackedLoRALayerWeights.pack_moe(
replacement_loras,
module_name,
)
)
else:
lora_model_instance.loras[module_name] = PackedLoRALayerWeights.pack(
replacement_loras
)
# Remove individual projections
for module in replaced_module:
lora_model_instance.loras.pop(module, None)
else:
logger.warning(
"[SLAB_PRE_PACK] No packed_modules provided - "
"slab will build with unpacked structure"
)
# TP SHARDING: Shard lora_b weights on CPU if fully_sharded_loras=True
fully_sharded = (
target_lora_config.fully_sharded_loras if target_lora_config else False
)
if fully_sharded and target_modules_dict:
logger.info(
"[SLAB_TP_SHARD] fully_sharded_loras=True, sharding lora_b weights on CPU"
)
for module_name, module_lora in lora_model_instance.loras.items():
target_module = target_modules_dict.get(module_name)
if not target_module:
continue
tp_rank = getattr(target_module, "tp_rank", 0)
tp_size = getattr(target_module, "tp_size", 1)
if (
tp_size > 1
and hasattr(module_lora, "lora_b")
and module_lora.lora_b is not None
):
if isinstance(module_lora.lora_b, list):
# MoE: shard each expert's lora_b
sharded_experts = []
for expert_idx, expert_b in enumerate(module_lora.lora_b):
if expert_b is not None:
shards = expert_b.chunk(tp_size, dim=0)
sharded_experts.append(shards[tp_rank])
else:
sharded_experts.append(None)
module_lora.lora_b = sharded_experts
else:
# Single tensor: shard once
shards = module_lora.lora_b.chunk(tp_size, dim=0)
module_lora.lora_b = shards[tp_rank]
result_key = build_target_matched_slab(
lora_model_instance, target_modules_dict, 1, target_lora_config, slab_path
)
# Handle different return types (cache key vs. direct objects for cache hits)
if isinstance(result_key, str) and result_key.startswith("slab_result_"):
slab, metadata = _GLOBAL_RESULT_STORAGE[result_key]
# Clean up the temporary storage
del _GLOBAL_RESULT_STORAGE[result_key]
else:
slab, metadata = result_key
if not torch.cuda.is_available():
# Return tuple for consistency even without GPU
return lora_model_instance, None, None
lora_model_instance._cached_cpu_slab = slab # type: ignore[attr-defined]
lora_model_instance._cached_metadata = metadata # type: ignore[attr-defined]
lora_model_instance._loras_dict = loras # type: ignore[attr-defined]
# Return CPU slab reference for now - GPU slab created during activation
return lora_model_instance, None, metadata

91
vllm/lora/worker_manager.py
View File

@ -7,6 +7,7 @@ from typing import Any, Literal
import torch
from vllm.config import VllmConfig
from vllm.envs import SLAB_OPTIMIZATION
from vllm.logger import init_logger
from vllm.lora.lora_model import LoRAModel
from vllm.lora.model_manager import (
@ -34,10 +35,12 @@ class WorkerLoRAManager:
vllm_config: VllmConfig,
device: torch.device,
embedding_modules: dict[str, str],
embedding_padding_modules: list[str],
lora_model_cls: type[LoRAModel] = LoRAModel,
):
self._lora_model_cls = lora_model_cls
self.embedding_modules = embedding_modules
self.embedding_padding_modules = embedding_padding_modules
self._cached_dummy_lora: None | Literal[False] | LoRAModel = False
self.max_num_seqs = vllm_config.scheduler_config.max_num_seqs
self.max_num_batched_tokens = (
@ -82,7 +85,37 @@ class WorkerLoRAManager:
self._adapter_manager = lora_manager
return lora_manager.model
def _load_adapter(self, lora_request: LoRARequest) -> LoRAModel:
def _load_adapter(self, lora_request: LoRARequest) -> LoRAModel | None:
if SLAB_OPTIMIZATION:
lora_path = get_adapter_absolute_path(lora_request.lora_path)
# Check for dummy/fake warmup paths
if (
"/not/a/real/path" in lora_path
or "warmup" in lora_request.lora_name.lower()
or not lora_path
or lora_path == "/not/a/real/path"
):
logger.warning(
"[SLAB_OPTIMIZATION] Skipping dummy warmup LoRA: %s "
"(path: %s) - not needed with slab optimization",
lora_request.lora_name,
lora_path,
)
return None
# Check if adapter_config.json exists for real LoRAs
import os
lora_config_path = os.path.join(lora_path, "adapter_config.json")
if not os.path.exists(lora_config_path):
logger.warning(
"[SLAB_OPTIMIZATION] Skipping LoRA %s - "
"adapter_config.json not found at %s, likely dummy warmup",
lora_request.lora_name,
lora_config_path,
)
return None
try:
supported_lora_modules = self._adapter_manager.supported_lora_modules
packed_modules_mapping = self._adapter_manager.packed_modules_mapping
@ -111,6 +144,41 @@ class WorkerLoRAManager:
# to ensure correct loading of lora weights.
model = self._adapter_manager.model
hf_to_vllm_mapper = getattr(model, "hf_to_vllm_mapper", None)
# Get target modules, lora_config, AND packed_modules info
target_modules_dict: dict | None = None
target_lora_config: Any = None
packed_modules_dict: dict | None = None
packed_modules_map: dict | None = None
if SLAB_OPTIMIZATION and hasattr(self, "_adapter_manager"):
target_modules_dict = getattr(self._adapter_manager, "modules", None)
target_lora_config = getattr(self._adapter_manager, "lora_config", None)
packed_modules_dict = getattr(
self._adapter_manager, "packed_modules", None
)
packed_modules_map = getattr(
self._adapter_manager, "packed_modules_mapping", None
)
if target_modules_dict and target_lora_config:
logger.debug(
"[SLAB_OPTIMIZATION] Passing %d target modules and "
"lora_config (fully_sharded_loras=%s) to LoRA creation",
len(target_modules_dict),
target_lora_config.fully_sharded_loras,
)
logger.debug(
"[SLAB_OPTIMIZATION] Passing %d packed_modules for "
"pre-slab packing",
len(packed_modules_dict) if packed_modules_dict else 0,
)
else:
logger.warning(
"[SLAB_OPTIMIZATION] Missing target info - "
"modules: %s, lora_config: %s",
target_modules_dict is not None,
target_lora_config is not None,
)
lora = self._lora_model_cls.from_local_checkpoint(
lora_path,
@ -120,8 +188,15 @@ class WorkerLoRAManager:
device="cpu",
dtype=self.lora_config.lora_dtype,
model_vocab_size=self.vocab_size,
embedding_modules=self.embedding_modules,
embedding_padding_modules=self.embedding_padding_modules,
tensorizer_config_dict=lora_request.tensorizer_config_dict,
weights_mapper=hf_to_vllm_mapper,
target_modules_dict=target_modules_dict,
target_lora_config=target_lora_config,
slab_path=lora_request.slab_path,
packed_modules=packed_modules_dict,
packed_modules_mapping=packed_modules_map,
)
except FileNotFoundError as e:
@ -184,6 +259,13 @@ class WorkerLoRAManager:
if adapter_request.adapter_id in self.list_adapters():
return False
loaded_adapter = self._load_adapter(adapter_request)
if loaded_adapter is None:
# Dummy warmup LoRA was skipped under SLAB_OPTIMIZATION
logger.debug(
"[SLAB_OPTIMIZATION] Skipped dummy LoRA: %s",
adapter_request.lora_name,
)
return False
loaded = self._adapter_manager.add_adapter(loaded_adapter)
self._adapter_manager.activate_adapter(loaded_adapter.id)
return loaded
@ -250,6 +332,13 @@ class LRUCacheWorkerLoRAManager(WorkerLoRAManager):
# This may cause the # of loaded lora adapters to very temporarily
# exceed `--max-cpu-loras`.
lora = self._load_adapter(lora_request)
if lora is None:
# Dummy warmup LoRA was skipped under SLAB_OPTIMIZATION
logger.debug(
"[SLAB_OPTIMIZATION] Skipped dummy LoRA: %s",
lora_request.lora_name,
)
return False
# Loading succeeded, now check if we will exceed cache capacity and
# evict if the oldest adapter if so

8
vllm/v1/worker/lora_model_runner_mixin.py
View File

@ -5,6 +5,7 @@ Define LoRA functionality mixin for model runners.
"""
from contextlib import contextmanager
from typing import TypeAlias
import numpy as np
import torch
@ -20,7 +21,7 @@ from vllm.model_executor.models import supports_lora, supports_multimodal
from vllm.v1.worker.gpu_input_batch import InputBatch as GPUInputBatch
from vllm.v1.worker.tpu_input_batch import InputBatch as TPUInputBatch
InputBatch = TPUInputBatch | GPUInputBatch
InputBatch: TypeAlias = TPUInputBatch | GPUInputBatch
logger = init_logger(__name__)
@ -43,6 +44,7 @@ class LoRAModelRunnerMixin:
vllm_config,
device,
model.embedding_modules,
getattr(model, "embedding_padding_modules", []),
)
return self.lora_manager.create_lora_manager(model)
@ -80,7 +82,9 @@ class LoRAModelRunnerMixin:
token_lora_mapping: tuple[int, ...] # of size np.sum(num_scheduled_tokens)
lora_requests: set[LoRARequest]
prompt_lora_mapping, token_lora_mapping, lora_requests = (
input_batch.make_lora_inputs(num_scheduled_tokens, num_sampled_tokens)
input_batch.make_lora_inputs( # type: ignore[attr-defined]
num_scheduled_tokens, num_sampled_tokens
)
)
return self._set_active_loras(
prompt_lora_mapping, token_lora_mapping, lora_requests