8d2998d0e4
### What this PR does / why we need it?
### Does this PR introduce _any_ user-facing change?
1. fix https://github.com/vllm-project/vllm/pull/27938
2. fix https://github.com/vllm-project/vllm/pull/27145
pooling models now supports chunked prefill and prefix caching,
3. fix https://github.com/vllm-project/vllm/pull/30181
define the CPU fields in the field config where they really belong.
4. fix https://github.com/vllm-project/vllm/pull/28168
define the CPU fields in the field config where they really belong.
5. fix https://github.com/vllm-project/vllm/pull/30201
some moudle rename
6. fix https://github.com/vllm-project/vllm/pull/29067
fusedmoe moudle refactor
7. fix https://github.com/vllm-project/vllm/pull/29066
fusedmoe moudle refactor
8. fix https://github.com/vllm-project/vllm/pull/29624
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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# Todo: Once https://github.com/vllm-project/vllm/pull/23553 is merged in vllm. Remove this model register.
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import types
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import torch
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import torch_npu
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_MOE_LOAD_ASYNC_STREAM = None
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def get_expert_map(self, layer_id):
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return self.model.layers[layer_id].mlp.experts.expert_map
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def get_log2phy_map(self, layer_id):
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return self.model.layers[layer_id].mlp.experts.get_log2phy_map()
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def get_all_expert_map(self, num_moe_layers):
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all_loads = []
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num_dense_layers = self.num_dense_layers if hasattr(
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self, "num_dense_layers") else 0
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for layer_id in range(num_moe_layers):
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load_tensor = self.get_expert_map(
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layer_id + num_dense_layers) # (num_experts_per_layer,)
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all_loads.append(load_tensor)
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return torch.stack(all_loads, dim=0)
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def get_all_moe_loads(self):
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num_dense_layers = self.num_dense_layers if hasattr(
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self, "num_dense_layers") else 0
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all_moe_loads = torch.stack(
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[self.model.layers[layer_id + num_dense_layers].mlp.experts.moe_load \
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for layer_id in range(self.num_moe_layers)],
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dim=0
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)
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return all_moe_loads
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def clear_all_moe_loads(self):
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num_dense_layers = self.num_dense_layers if hasattr(
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self, "num_dense_layers") else 0
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for layer_id in range(self.num_moe_layers):
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self.model.layers[layer_id +
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num_dense_layers].mlp.experts.clear_moe_load()
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def model_register(model, model_config):
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model.get_expert_map = types.MethodType(get_expert_map, model)
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model.get_log2phy_map = types.MethodType(get_log2phy_map, model)
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model.get_all_expert_map = types.MethodType(get_all_expert_map, model)
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model.get_all_moe_loads = types.MethodType(get_all_moe_loads, model)
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model.clear_all_moe_loads = types.MethodType(clear_all_moe_loads, model)
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config = model_config.hf_config
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if config.model_type == "qwen3_moe":
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model.num_moe_layers = config.num_hidden_layers
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elif config.model_type == "deepseek_v2" or config.model_type == "deepseek_v3":
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model.num_dense_layers = config.first_k_dense_replace
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model.num_moe_layers = config.num_hidden_layers - model.num_dense_layers
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else:
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raise NotImplementedError("EPLB is not supported.")
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def moe_load_async_stream() -> torch_npu.npu.Stream:
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global _MOE_LOAD_ASYNC_STREAM
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if _MOE_LOAD_ASYNC_STREAM is None:
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# when this function is called before any stream is set,
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# we return the default stream.
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_MOE_LOAD_ASYNC_STREAM = torch_npu.npu.Stream()
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return _MOE_LOAD_ASYNC_STREAM |