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[CI] Pin transformers<4.53.0 and fix EPLB load_weights to make CI passed (#1482)

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### What this PR does / why we need it?

- Fix vLLM EPLB break
e9fd658a73
by recovering load_weights back to [v0.9.1
version](07b8fae219)
temporarily.

- Fix transformers>=4.53.0 image processor break
Related: https://github.com/vllm-project/vllm-ascend/issues/1470

- Mirror torch_npu requirements to pyproject.toml

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
CI passed

---------

Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Co-authored-by: Yikun Jiang <yikunkero@gmail.com>
This commit is contained in:
Mengqing Cao
2025-06-28 00:12:43 +08:00
committed by GitHub
parent 3687676fa7
commit d59e7fa095
4 changed files with 226 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
pyproject.toml
View File

@@ -12,12 +12,14 @@ requires = [
"scipy",
"setuptools>=64",
"setuptools-scm>=8",
"torch-npu==2.5.1.post1.dev20250528",
"torch-npu==2.5.1.post1.dev20250619",
"torch>=2.5.1",
"torchvision<0.21.0",
"wheel",
"msgpack",
"quart",
"numba",
# Remove after https://github.com/vllm-project/vllm-ascend/issues/1470
"transformers<4.53.0",
]
build-backend = "setuptools.build_meta"

3
requirements.txt
View File

@@ -25,3 +25,6 @@ numba
--pre
--extra-index-url https://mirrors.huaweicloud.com/ascend/repos/pypi
torch-npu==2.5.1.post1.dev20250619
# Remove after https://github.com/vllm-project/vllm-ascend/issues/1470
transformers<4.53.0

117
vllm_ascend/models/deepseek_dbo.py
View File

@@ -25,7 +25,7 @@
# # vllm-project/vllm/vllm/model_executor/models/deepseek_v2.py
# """Inference-only DeepseekV2/DeepseekV3 model."""
from typing import Any, Dict, List, Optional, Union
from typing import Any, Dict, Iterable, List, Optional, Union
import torch
import torch.distributed as dist
@@ -49,16 +49,18 @@ from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader, maybe_remap_kv_scale_name)
from vllm.model_executor.models.deepseek_v2 import \
DeepseekV2ForCausalLM # noqa: E501
from vllm.model_executor.models.deepseek_v2 import \
yarn_get_mscale # noqa: E501
from vllm.model_executor.models.deepseek_v2 import (DeepseekV2Attention,
DeepseekV2DecoderLayer,
DeepseekV2MLAAttention)
from vllm.model_executor.models.deepseek_v2 import (
DeepseekV2Attention, DeepseekV2DecoderLayer, DeepseekV2MLAAttention,
get_spec_layer_idx_from_weight_name)
from vllm.model_executor.models.utils import (
PPMissingLayer, make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
PPMissingLayer, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
from vllm.sequence import IntermediateTensors
import vllm_ascend.envs as envs_ascend
@@ -76,7 +78,7 @@ from vllm_ascend.multistream.metadata import (MultiStreamConfig,
make_multistream_metadata_ds)
from vllm_ascend.multistream.ms_split import compute_split_seq_index
from vllm_ascend.ops.fused_moe import AscendFusedMoE
from vllm_ascend.utils import dispose_tensor
from vllm_ascend.utils import dispose_tensor, vllm_version_is
VLLM_ASCEND_ENABLE_DBO: bool = envs_ascend.VLLM_ASCEND_ENABLE_DBO
@@ -963,6 +965,107 @@ class CustomDeepseekDBOForCausalLM(DeepseekV2ForCausalLM):
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
# NOTE: This `load_weights` is mainly copied from
# https://github.com/vllm-project/vllm/commit/07b8fae219b1fff51ef115c38c44b51395be5bb5
# to fix CI, and it is different from the implementation in main
# TODO: support eplb style load_weights
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
""""""
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
]
# Params for weights, fp8 weight scales, fp8 activation scales
# (param_name, weight_name, expert_id, shard_id)
expert_params_mapping = AscendFusedMoE.make_expert_params_mapping(
ckpt_gate_proj_name="gate_proj",
ckpt_down_proj_name="down_proj",
ckpt_up_proj_name="up_proj",
num_experts=self.config.n_routed_experts)
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
continue
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
if spec_layer is not None:
continue # skip spec decode layers for main model
for (param_name, weight_name, shard_id) in stacked_params_mapping:
# Skip non-stacked layers and experts (experts handled below).
if weight_name not in name:
continue
# We have mlp.experts[0].gate_proj in the checkpoint.
# Since we handle the experts below in expert_params_mapping,
# we need to skip here BEFORE we update the name, otherwise
# name will be updated to mlp.experts[0].gate_up_proj, which
# will then be updated below in expert_params_mapping
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
if (("mlp.experts." in name) and name not in params_dict):
continue
name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
for mapping in expert_params_mapping:
param_name, weight_name, expert_id, shard_id = mapping
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
if vllm_version_is("0.9.1"):
weight_loader(param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id)
else:
weight_loader(param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id,
return_success=False)
break
else:
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
# Remapping the name of FP8 kv-scale.
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
def forward(
self,
input_ids: torch.Tensor,

117
vllm_ascend/models/deepseek_v2.py
View File

@@ -25,7 +25,7 @@
# # vllm-project/vllm/vllm/model_executor/models/deepseek_v2.py
# """Inference-only DeepseekV2/DeepseekV3 model."""
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import torch
import torch_npu
@@ -55,16 +55,18 @@ from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader, maybe_remap_kv_scale_name)
from vllm.model_executor.models.deepseek_v2 import \
DeepseekV2ForCausalLM # noqa: E501
from vllm.model_executor.models.deepseek_v2 import \
yarn_get_mscale # noqa: E501
from vllm.model_executor.models.deepseek_v2 import (DeepseekV2Attention,
DeepseekV2DecoderLayer,
DeepseekV2MLAAttention)
from vllm.model_executor.models.deepseek_v2 import (
DeepseekV2Attention, DeepseekV2DecoderLayer, DeepseekV2MLAAttention,
get_spec_layer_idx_from_weight_name)
from vllm.model_executor.models.utils import (
PPMissingLayer, make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
PPMissingLayer, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
from vllm.sequence import IntermediateTensors
from vllm_ascend.ascend_config import get_ascend_config
@@ -73,7 +75,7 @@ from vllm_ascend.ops.fused_moe import AscendFusedMoE
from vllm_ascend.quantization.quant_config import AscendLinearMethod
from vllm_ascend.quantization.w8a8_dynamic import AscendW8A8DynamicLinearMethod
from vllm_ascend.utils import (dispose_tensor, npu_stream_switch,
npu_wait_tensor)
npu_wait_tensor, vllm_version_is)
class CustomDeepseekV2SiluAndMul(SiluAndMul):
@@ -867,6 +869,107 @@ class CustomDeepseekV2ForCausalLM(DeepseekV2ForCausalLM):
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
# NOTE: This `load_weights` is mainly copied from
# https://github.com/vllm-project/vllm/commit/07b8fae219b1fff51ef115c38c44b51395be5bb5
# to fix CI, and it is different from the implementation in main
# TODO: support eplb style load_weights
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
""""""
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
]
# Params for weights, fp8 weight scales, fp8 activation scales
# (param_name, weight_name, expert_id, shard_id)
expert_params_mapping = AscendFusedMoE.make_expert_params_mapping(
ckpt_gate_proj_name="gate_proj",
ckpt_down_proj_name="down_proj",
ckpt_up_proj_name="up_proj",
num_experts=self.config.n_routed_experts)
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
continue
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
if spec_layer is not None:
continue # skip spec decode layers for main model
for (param_name, weight_name, shard_id) in stacked_params_mapping:
# Skip non-stacked layers and experts (experts handled below).
if weight_name not in name:
continue
# We have mlp.experts[0].gate_proj in the checkpoint.
# Since we handle the experts below in expert_params_mapping,
# we need to skip here BEFORE we update the name, otherwise
# name will be updated to mlp.experts[0].gate_up_proj, which
# will then be updated below in expert_params_mapping
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
if (("mlp.experts." in name) and name not in params_dict):
continue
name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
for mapping in expert_params_mapping:
param_name, weight_name, expert_id, shard_id = mapping
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
if vllm_version_is("0.9.1"):
weight_loader(param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id)
else:
weight_loader(param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id,
return_success=False)
break
else:
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
# Remapping the name of FP8 kv-scale.
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
def forward(
self,
input_ids: torch.Tensor,