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[Model] Support DeepSeek-V4

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chenxb002
2026-04-24 09:50:34 +08:00
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-MLU project
# SPDX-License-Identifier: Apache-2.0
"""A GPU worker class."""
import copy
import gc
import os
from contextlib import AbstractContextManager, nullcontext
from types import NoneType
from typing import TYPE_CHECKING, Optional
import torch
import torch.distributed
import vllm.envs as envs
from vllm.config import VllmConfig
from vllm.distributed.parallel_state import get_tp_group, get_pp_group
from vllm.distributed.kv_transfer import (ensure_kv_transfer_initialized,
has_kv_transfer_group)
from vllm.logger import init_logger
from vllm.model_executor import set_random_seed
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
from vllm.v1.worker.utils import is_residual_scattered_for_sp
from vllm.v1.worker.worker_base import WorkerBase
from vllm.v1.outputs import EMPTY_MODEL_RUNNER_OUTPUT, ModelRunnerOutput
from vllm.v1.utils import report_usage_stats
from vllm.v1.engine import ReconfigureDistributedRequest, ReconfigureRankType
from vllm.v1.worker.gpu_worker import Worker, init_worker_distributed_environment
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm.utils.mem_constants import GiB_bytes
if TYPE_CHECKING:
from vllm.v1.core.sched.output import SchedulerOutput
from vllm_mlu.model_executor.warmup.kernel_warmup import kernel_warmup
from vllm_mlu.profiler.mlu_profiler import MluProfilerWrapper
from vllm_mlu.utils import MemorySnapshot, memory_profiling
from vllm_mlu._mlu_utils import VLLM_DUMP_MLU_INFO_EN
from vllm_mlu.device_allocator.cnmem import CnMemAllocator
from vllm_mlu.v1.worker.mlu_quant import MLUWorkerQuant
from vllm_mlu.v1.worker.gpu_model_runner import MLUModelRunner
from vllm_mlu.v1.worker.dp_gpu_model_runner import DPMLUModelRunner
logger = init_logger(__name__)
class MLUWorker(Worker, MLUWorkerQuant):
def __init__(
self,
vllm_config: VllmConfig,
local_rank: int,
rank: int,
distributed_init_method: str,
is_driver_worker: bool = False,
):
WorkerBase.__init__(self, vllm_config=vllm_config,
local_rank=local_rank,
rank=rank,
distributed_init_method=distributed_init_method,
is_driver_worker=is_driver_worker)
if self.model_config.trust_remote_code:
# note: lazy import to avoid importing torch before initializing
from vllm.utils.import_utils import init_cached_hf_modules
init_cached_hf_modules()
# Buffers saved before sleep
self._sleep_saved_buffers: dict[str, torch.Tensor] = {}
# Torch profiler. Enabled and configured through env vars:
# VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
if envs.VLLM_TORCH_PROFILER_DIR:
torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
worker_name = f"{vllm_config.instance_id}-rank-{self.rank}"
logger.info(
"Profiling enabled. Traces will be saved to: %s",
torch_profiler_trace_dir,
)
logger.debug(
"Profiler config: record_shapes=%s,"
"profile_memory=%s,with_stack=%s,with_flops=%s",
envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
envs.VLLM_TORCH_PROFILER_WITH_STACK,
envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
)
self.profiler = torch.profiler.profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.MLU,
],
record_shapes=envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
profile_memory=envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
with_flops=envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
on_trace_ready=torch.profiler.tensorboard_trace_handler(
torch_profiler_trace_dir, worker_name=worker_name, use_gzip=True
),
)
elif envs.VLLM_TORCH_CUDA_PROFILE:
self.profiler = MluProfilerWrapper()
else:
self.profiler = None
def sleep(self, level: int = 1) -> None:
free_bytes_before_sleep = torch.mlu.mem_get_info()[0]
# Save the buffers before level 2 sleep
if level == 2:
model = self.model_runner.model
self._sleep_saved_buffers = {
name: buffer.cpu().clone() for name, buffer in model.named_buffers()
}
allocator = CnMemAllocator.get_instance()
allocator.sleep(offload_tags=("weights", ) if level == 1 else tuple())
free_bytes_after_sleep, total = torch.mlu.mem_get_info()
freed_bytes = free_bytes_after_sleep - free_bytes_before_sleep
used_bytes = total - free_bytes_after_sleep
assert freed_bytes >= 0, "Memory usage increased after sleeping."
logger.info(
"Sleep mode freed %.2f GiB memory, "
"%.2f GiB memory is still in use.", freed_bytes / GiB_bytes,
used_bytes / GiB_bytes)
def wake_up(self, tags: Optional[list[str]] = None) -> None:
allocator = CnMemAllocator.get_instance()
allocator.wake_up(tags)
# Restore the buffers after level 2 sleep
if len(self._sleep_saved_buffers):
model = self.model_runner.model
for name, buffer in model.named_buffers():
if name in self._sleep_saved_buffers:
buffer.data.copy_(self._sleep_saved_buffers[name].data)
self._sleep_saved_buffers = {}
def _maybe_get_memory_pool_context(self, tag: str) -> AbstractContextManager:
if self.vllm_config.model_config.enable_sleep_mode:
allocator = CnMemAllocator.get_instance()
if tag == "weights":
assert allocator.get_current_usage() == 0, (
"Sleep mode can only be used for one instance per process."
)
context = allocator.use_memory_pool(tag=tag)
else:
context = nullcontext()
return context
def init_device(self):
if self.device_config.device.type == "mlu":
# This env var set by Ray causes exceptions with graph building.
os.environ.pop("CNCL_ASYNC_ERROR_HANDLING", None)
# if (
# self.parallel_config.data_parallel_size > 1
# and self.parallel_config.data_parallel_size_local > 0
# and self.parallel_config.distributed_executor_backend
# not in ["ray", "external_launcher"]
# and self.vllm_config.parallel_config.data_parallel_backend != "ray"
# ):
# # Use local DP rank if available, otherwise use global DP rank.
# dp_local_rank = self.parallel_config.data_parallel_rank_local
# if dp_local_rank is None:
# dp_local_rank = self.parallel_config.data_parallel_rank
# tp_pp_world_size = (
# self.parallel_config.pipeline_parallel_size
# * self.parallel_config.tensor_parallel_size
# )
# # DP_LOCAL_RANK * TP_PP_WORLD_SIZE + TP_LOCAL_RANK
# self.local_rank += dp_local_rank * tp_pp_world_size
# assert self.local_rank < torch.mlu.device_count(), (
# f"DP adjusted local rank {self.local_rank} is out of bounds. "
# )
self.device = torch.device(f"mlu:{self.local_rank}")
current_platform.set_device(self.device)
current_platform.check_if_supports_dtype(self.model_config.dtype)
# Initialize the distributed environment BEFORE taking
# memory snapshot
# This ensures NCCL buffers are allocated before we measure
# available memory
init_worker_distributed_environment(
self.vllm_config,
self.rank,
self.distributed_init_method,
self.local_rank,
current_platform.dist_backend,
)
# Set random seed.
set_random_seed(self.model_config.seed)
gc.collect()
torch.mlu.empty_cache()
# take current memory snapshot
self.init_snapshot = MemorySnapshot()
self.requested_memory = (
self.init_snapshot.total_memory
* self.cache_config.gpu_memory_utilization
)
if self.init_snapshot.free_memory < self.requested_memory:
GiB = lambda b: round(b / GiB_bytes, 2)
raise ValueError(
f"Free memory on device "
f"({GiB(self.init_snapshot.free_memory)}/"
f"{GiB(self.init_snapshot.total_memory)} GiB) on startup "
f"is less than desired GPU memory utilization "
f"({self.cache_config.gpu_memory_utilization}, "
f"{GiB(self.requested_memory)} GiB). Decrease GPU memory "
f"utilization or reduce GPU memory used by other processes."
)
else:
raise RuntimeError(f"Not support device type: {self.device_config.device}")
# Construct the model runner
model_runner_cls = (DPMLUModelRunner
if self._enable_moe_dp_opt() else MLUModelRunner)
self.model_runner: MLUModelRunner = model_runner_cls(
self.vllm_config, self.device)
if self.rank == 0:
# If usage stat is enabled, collect relevant info.
report_usage_stats(self.vllm_config)
@torch.inference_mode()
def determine_available_memory(self) -> int:
"""Profiles the peak memory usage of the model to determine how much
memory can be used for KV cache without OOMs.
The engine will first conduct a profiling of the existing memory usage.
Then, it calculate the free memory that can be used for KV cache in
bytes.
Tip:
You may limit the usage of GPU memory
by adjusting the `gpu_memory_utilization` parameter.
"""
GiB = lambda b: b / GiB_bytes
if kv_cache_memory_bytes := self.cache_config.kv_cache_memory_bytes:
# still need a profile run which compiles the model for
# max_num_batched_tokens
self.model_runner.profile_run()
msg = (
f"Initial free memory {GiB(self.init_snapshot.free_memory):.2f} "
f"GiB, reserved {GiB(kv_cache_memory_bytes):.2f} GiB memory for "
"KV Cache as specified by kv_cache_memory_bytes config and "
"skipped memory profiling. This does not respect the "
"gpu_memory_utilization config. Only use kv_cache_memory_bytes "
"config when you want manual control of KV cache memory "
"size. If OOM'ed, check the difference of initial free "
"memory between the current run and the previous run "
"where kv_cache_memory_bytes is suggested and update it "
"correspondingly."
)
logger.info(msg)
return kv_cache_memory_bytes
torch.mlu.empty_cache()
torch.mlu.reset_peak_memory_stats()
# Execute a forward pass with dummy inputs to profile the memory usage
# of the model.
with memory_profiling(
self.init_snapshot,
weights_memory=int(self.model_runner.model_memory_usage),
) as profile_result:
self.model_runner.profile_run()
self.non_torch_memory = profile_result.non_torch_increase
self.peak_activation_memory = profile_result.torch_peak_increase
free_gpu_memory = profile_result.after_profile.free_memory
GiB = lambda b: b / GiB_bytes
# Execute a forward pass with dummy inputs to profile the memory usage
# of the model.
with memory_profiling(
self.init_snapshot,
weights_memory=int(
self.model_runner.model_memory_usage)) as profile_result:
self.model_runner.profile_run()
free_gpu_memory = profile_result.after_profile.free_memory
# NOTE(woosuk): Here we assume that the other processes using the same
# GPU did not change their memory usage during the profiling.
assert self.init_snapshot.free_memory > free_gpu_memory, (
"Error in memory profiling. "
f"Initial free memory {GiB(self.init_snapshot.free_memory)} GiB, "
f"current free memory {GiB(free_gpu_memory)} GiB. "
"This happens when other processes sharing the same container "
"release GPU memory while vLLM is profiling during initialization. "
"To fix this, ensure consistent GPU memory allocation or "
"isolate vLLM in its own container."
)
self.available_kv_cache_memory_bytes = (
self.requested_memory - profile_result.non_kv_cache_memory
)
unrequested_memory = self.init_snapshot.free_memory - self.requested_memory
logger.debug(
"Initial free memory: %.2f GiB; Requested memory: %.2f (util), %.2f GiB",
GiB(self.init_snapshot.free_memory),
self.cache_config.gpu_memory_utilization,
GiB(self.requested_memory),
)
logger.debug(
"Free memory after profiling: %.2f GiB (total), "
"%.2f GiB (within requested)",
GiB(free_gpu_memory),
GiB(free_gpu_memory - unrequested_memory),
)
logger.debug(profile_result)
logger.info_once(
"Available KV cache memory: %.2f GiB",
GiB(self.available_kv_cache_memory_bytes),
scope="local",
)
gc.collect()
self.peak_memory = profile_result.non_kv_cache_memory
self.block_memory = self.available_kv_cache_memory_bytes
return int(self.available_kv_cache_memory_bytes)
def initialize_from_config(self, kv_cache_config: KVCacheConfig) -> None:
"""Allocate GPU KV cache with the specified kv_cache_config."""
# Init kv cache connector here, because it requires
# `kv_cache_config`.
# NOTE(Kuntai): This need to be done before `initialize_kv_cache`,
# because `initialize_kv_cache` will inject kv cache groups not
# related to kv cache connector (e.g. kv cache sharing layers).
ensure_kv_transfer_initialized(self.vllm_config, kv_cache_config)
if self.vllm_config.model_config.enable_sleep_mode:
allocator = CnMemAllocator.get_instance()
context = allocator.use_memory_pool(tag="kv_cache")
else:
context = nullcontext()
with context:
self.model_runner.initialize_kv_cache(kv_cache_config)
def compile_or_warm_up_model(self) -> None:
# warm up sizes that are not in cudagraph capture sizes,
# but users still want to compile for better performance,
# e.g. for the max-num-batched token size in chunked prefill.
warmup_sizes = self.vllm_config.compilation_config.compile_sizes.copy()
if not self.model_config.enforce_eager:
warmup_sizes = [
x for x in warmup_sizes
if x not in self.vllm_config.compilation_config.cudagraph_capture_sizes
]
# We skip EPLB here since we don't want to record dummy metrics
for size in sorted(warmup_sizes, reverse=True):
logger.info("Compile and warming up model for size %d", size)
self.model_runner._dummy_run(size, skip_eplb=True, remove_lora=False)
self.model_runner.maybe_remove_all_loras(self.model_runner.lora_config)
# Warmup and tune the kernels used during model execution before
# cuda graph capture.
kernel_warmup(self)
cuda_graph_memory_bytes = 0
if not self.model_config.enforce_eager:
cuda_graph_memory_bytes = self.model_runner.capture_model()
if self.cache_config.kv_cache_memory_bytes is None and hasattr(
self, "peak_activation_memory"
):
# Suggests optimal kv cache memory size if we rely on
# memory_profiling to guess the kv cache memory size which
# provides peak_activation_memory and a few other memory
# consumption. `memory_profiling` does not consider
# CUDAGraph memory size and may not utilize all gpu memory.
# Users may want fine-grained control to specify kv cache
# memory size.
GiB = lambda b: round(b / GiB_bytes, 2)
# empirically observed that the memory profiling may
# slightly underestimate the memory consumption.
# So leave a small buffer (=150MiB) to avoid OOM.
redundancy_buffer_memory = 150 * (1 << 20)
non_kv_cache_memory = (
self.model_runner.model_memory_usage
+ self.peak_activation_memory
+ self.non_torch_memory
+ cuda_graph_memory_bytes
)
kv_cache_memory_bytes_to_gpu_limit = (
self.init_snapshot.free_memory
- non_kv_cache_memory
- redundancy_buffer_memory
)
kv_cache_memory_bytes_to_requested_limit = (
int(self.requested_memory)
- non_kv_cache_memory
- redundancy_buffer_memory
)
msg = (
f"Free memory on device "
f"({GiB(self.init_snapshot.free_memory)}/"
f"{GiB(self.init_snapshot.total_memory)} GiB) on startup. "
f"Desired GPU memory utilization is "
f"({self.cache_config.gpu_memory_utilization}, "
f"{GiB(self.requested_memory)} GiB). "
f"Actual usage is {GiB(self.model_runner.model_memory_usage)} "
f"GiB for weight, {GiB(self.peak_activation_memory)} GiB "
f"for peak activation, {GiB(self.non_torch_memory)} GiB "
f"for non-torch memory, and {GiB(cuda_graph_memory_bytes)} "
f"GiB for CUDAGraph memory. Replace gpu_memory_utilization "
f"config with `--kv-cache-memory="
f"{kv_cache_memory_bytes_to_requested_limit}` "
f"({GiB(kv_cache_memory_bytes_to_requested_limit)} GiB) to fit "
f"into requested memory, or `--kv-cache-memory="
f"{kv_cache_memory_bytes_to_gpu_limit}` "
f"({GiB(kv_cache_memory_bytes_to_gpu_limit)} GiB) to fully "
f"utilize gpu memory. Current kv cache memory in use is "
f"{GiB(self.available_kv_cache_memory_bytes)} GiB."
)
logger.debug(msg)
# Warm up sampler and preallocate memory buffer for logits and other
# sampling related tensors of max possible shape to avoid memory
# fragmentation issue.
# NOTE: This is called after `capture_model` on purpose to prevent
# memory buffers from being cleared by `torch.cuda.empty_cache`.
if get_pp_group().is_last_rank:
max_num_reqs = min(
self.scheduler_config.max_num_seqs,
self.scheduler_config.max_num_batched_tokens,
)
# We skip EPLB here since we don't want to record dummy metrics
hidden_states, last_hidden_states = self.model_runner._dummy_run(
num_tokens=max_num_reqs,
skip_eplb=True,
)
if self.model_runner.is_pooling_model:
self.model_runner._dummy_pooler_run(hidden_states)
else:
self.model_runner._dummy_sampler_run(hidden_states=last_hidden_states)
# Reset the seed to ensure that the random state is not affected by
# the model initialization and profiling.
set_random_seed(self.model_config.seed)
@torch.inference_mode()
def execute_model(
self, scheduler_output: "SchedulerOutput",
) -> ModelRunnerOutput | None:
intermediate_tensors = None
forward_pass = scheduler_output.total_num_scheduled_tokens > 0
num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
num_input_tokens = self.model_runner._get_num_input_tokens(num_scheduled_tokens)
all_gather_tensors = {
"residual": not is_residual_scattered_for_sp(
self.vllm_config, num_input_tokens
)
}
if forward_pass and not get_pp_group().is_first_rank:
intermediate_tensors = IntermediateTensors(
get_pp_group().recv_tensor_dict(
all_gather_group=get_tp_group(),
all_gather_tensors=all_gather_tensors,
)
)
with self.annotate_profile(scheduler_output):
output = self.model_runner.execute_model(
scheduler_output, intermediate_tensors
)
if isinstance(output, (ModelRunnerOutput, NoneType)):
return output
assert isinstance(output, IntermediateTensors)
parallel_config = self.vllm_config.parallel_config
assert (
parallel_config.distributed_executor_backend != "external_launcher"
and not get_pp_group().is_last_rank
)
get_pp_group().send_tensor_dict(
output.tensors,
all_gather_group=get_tp_group(),
all_gather_tensors=all_gather_tensors,
)
return None
def _enable_moe_dp_opt(self):
'''
We will enable the MLU-optimized DP scheme for the specified MoE models,
otherwise the native DP implementation will be used.
'''
# case0 enable data parallel
enable_dp = self.parallel_config.data_parallel_size > 1
# case1 ds mla
is_ds_mla = self.model_config.is_deepseek_mla
# case2 qwen3 moe
is_supported_moe_model = hasattr(self.model_config.hf_text_config, "model_type") and \
self.model_config.hf_text_config.model_type in ('qwen3_moe', 'glm4_moe')
# case 3, private model
is_private_model = getattr(self.model_config.hf_config, "is_private", False)
return enable_dp and (is_ds_mla or is_supported_moe_model or is_private_model)
def execute_dummy_batch(self) -> None:
if self._enable_moe_dp_opt():
self.model_runner.moe_dp_execute_dummy_batch(1)
else:
self.model_runner._dummy_run(1, uniform_decode=True)
def response_remote_alloc_once(self) -> None:
self.model_runner.response_remote_alloc_once()
def _eplb_before_scale_down(self, old_ep_size: int, new_ep_size: int) -> None:
from vllm.distributed.parallel_state import get_ep_group
if get_ep_group().rank == 0:
logger.info(
"[Elastic EP] Starting expert resharding before scaling down..."
)
rank_mapping = {
old_ep_rank: old_ep_rank if old_ep_rank < new_ep_size else -1
for old_ep_rank in range(old_ep_size)
}
assert self.model_runner.eplb_state is not None
self.model_runner.eplb_state.rearrange(
execute_shuffle=True,
global_expert_load=None,
rank_mapping=rank_mapping,
)
torch.mlu.synchronize()
if get_ep_group().rank == 0:
logger.info("[Elastic EP] Expert resharding completed!")
def reinitialize_distributed(
self, reconfig_request: ReconfigureDistributedRequest
) -> None:
from vllm.config import set_current_vllm_config
from vllm.distributed.parallel_state import (
cleanup_dist_env_and_memory,
get_ep_group,
)
old_ep_size = get_ep_group().world_size
old_ep_rank = get_ep_group().rank
new_ep_size = (
reconfig_request.new_data_parallel_size
* get_tp_group().world_size
* get_pp_group().world_size
)
if new_ep_size < old_ep_size:
self._eplb_before_scale_down(old_ep_size, new_ep_size)
cleanup_dist_env_and_memory()
if (
reconfig_request.new_data_parallel_rank
== ReconfigureRankType.SHUTDOWN_CURRENT_RANK
):
assert old_ep_rank >= new_ep_size
# shutdown
return
self._reconfigure_parallel_config(reconfig_request)
with set_current_vllm_config(self.vllm_config):
init_worker_distributed_environment(
self.vllm_config,
self.rank,
self.distributed_init_method,
self.local_rank,
current_platform.dist_backend,
)
global_expert_loads = self._reconfigure_moe(old_ep_size, new_ep_size)
if new_ep_size > old_ep_size:
assert global_expert_loads is not None
self._eplb_after_scale_up(old_ep_size, new_ep_size, global_expert_loads)
def get_hfu_info(self, batch, input_len, output_len):
try:
self.model_runner.model.collect_hfu_io_effciency_info(batch, input_len, output_len)
if VLLM_DUMP_MLU_INFO_EN:
return self.model_runner.model.hfu_info, self.model_runner.model.io_efficiency
else:
return self.model_runner.model.flops_info, 0.0
except Exception as e:
raise RuntimeError(
"Model match failure when get HFU info, please check if an init method was registed."
)
def _get_latency(self, time_markers):
total_latency = 0
if not isinstance(time_markers, list):
time_markers = [time_markers]
for time_marker in time_markers:
start, end = time_marker
latency = start.elapsed_time(end)
total_latency += latency
return total_latency
def get_latency(self):
return self._get_latency(self.model_runner.time_markers)
def get_mm_encoder_latency(self):
if not hasattr(self.model_runner, "mm_time_markers"):
return None
mm_time_markers = self.model_runner.mm_time_markers
return None if len(mm_time_markers) == 0 else\
self._get_latency(mm_time_markers)
def get_memory_usage(self):
return (self.peak_memory, self.block_memory)
def recapture_model(self,
prefill_enable_mlugraph: bool,
batch_size: int,
input_len: int):
# Reset history capture context
self.model_runner.reset_capture_context(
prefill_enable_mlugraph, batch_size, input_len)
# Re-capture decode graph(full graph or peicewise graph)
self.compile_or_warm_up_model()