[CI] Pin transformers<4.53.0 and fix EPLB load_weights to make CI passed

pyproject.toml

@@ -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"

requirements.txt

@@ -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

vllm_ascend/models/deepseek_dbo.py

@@ -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.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 @@
                                               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 @@
         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,

vllm_ascend/models/deepseek_v2.py

@@ -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.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.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 @@
         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,