2f1266d451
### What this PR does / why we need it? Support Pangu Pro MoE model (https://arxiv.org/abs/2505.21411) ### Does this PR introduce _any_ user-facing change? Yes, new model supported ### How was this patch tested? Test locally --------- Signed-off-by: angazenn <zengyanjia@huawei.com> Co-authored-by: angazenn <zengyanjia@huawei.com>
640 lines
25 KiB
Python
640 lines
25 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# This file is a part of the vllm-ascend project.
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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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from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Union
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import torch
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import torch.nn.functional as F
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.attention import Attention, AttentionMetadata
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import (get_pp_group,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.interfaces import SupportsPP
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from vllm.model_executor.models.utils import (
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extract_layer_index, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from vllm_ascend.distributed.parallel_state import get_ep_group
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logger = init_logger(__name__)
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_ROUTER_SCALE = None
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class PanguProMoEMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: bool = True,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now.")
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class PanguProMoESparseMoeBlock(nn.Module):
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@staticmethod
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def pangu_group8_topk(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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topk: int,
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renormalize: bool,
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num_expert_group: int = 0,
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topk_group: int = 0,
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global_num_experts: int = 0,
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):
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ep_size = get_ep_group().world_size
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local_num_experts = global_num_experts // ep_size
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local_num_group = topk // ep_size
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router_scale = _ROUTER_SCALE.squeeze() # type: ignore
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scores = F.softmax(gating_output, dim=1)
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scores = scores[...,
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get_ep_group().rank_in_group *
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local_num_experts:(get_ep_group().rank_in_group + 1) *
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local_num_experts]
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router_weights = router_scale[get_ep_group().rank_in_group *
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local_num_experts:
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(get_ep_group().rank_in_group + 1) *
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local_num_experts]
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topk_weights, topk_ids = torch.max(scores.view(scores.shape[0],
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local_num_group, -1),
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dim=-1)
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bias = torch.arange(0,
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local_num_experts,
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topk,
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device=scores.device,
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dtype=torch.int32).unsqueeze(0)
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topk_ids = topk_ids.to(torch.int32) + bias
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flatten_topk_ids = topk_ids.view(-1)
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router_weights = router_weights.index_select(0, flatten_topk_ids).view(
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topk_ids.shape)
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topk_weights *= router_weights
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return topk_weights, topk_ids
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.num_experts = config.num_experts
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if self.tp_size > config.num_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.num_experts}.")
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self.local_num_group = config.num_experts_per_tok // get_ep_group(
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).world_size
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self.num_experts_per_tok = config.num_experts_per_tok
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self.local_num_experts = config.num_experts // get_ep_group(
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).world_size
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self.router_scale = torch.nn.Parameter(
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torch.ones((1, self.num_experts)))
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self.experts = FusedMoE(
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num_experts=config.num_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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reduce_results=False,
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quant_config=quant_config,
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custom_routing_function=PanguProMoESparseMoeBlock.
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pangu_group8_topk,
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prefix=f"{prefix}.experts",
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)
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self.gate = ReplicatedLinear(
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config.hidden_size,
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config.num_experts,
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bias=False,
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quant_config=None,
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prefix=f"{prefix}.gate",
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)
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if config.shared_expert_intermediate_size > 0:
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self.shared_expert = PanguProMoEMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.shared_expert_intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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reduce_results=False,
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prefix=f"{prefix}.shared_expert",
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)
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else:
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self.shared_expert = None # type: ignore
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def forward(
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self,
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hidden_states: torch.Tensor,
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attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
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# NOTE: hidden_states can have either 1D or 2D shape.
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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shared_output = None
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if self.shared_expert is not None:
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shared_output = self.shared_expert(hidden_states)
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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global _ROUTER_SCALE
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_ROUTER_SCALE = self.router_scale
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final_hidden_states = self.experts(hidden_states=hidden_states,
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router_logits=router_logits)
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if shared_output is not None:
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final_hidden_states = final_hidden_states + shared_output
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if self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(
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final_hidden_states)
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return final_hidden_states.view(num_tokens, hidden_dim)
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class PanguProMoEAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: Optional[torch.Tensor] = None,
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attn_metadata: Optional[AttentionMetadata] = None,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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class PanguProMoEDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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self.self_attn = PanguProMoEAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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)
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# `mlp_only_layers` in the config.
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layer_idx = extract_layer_index(prefix)
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mlp_only_layers = ([] if not hasattr(config, "mlp_only_layers") else
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config.mlp_only_layers)
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if (layer_idx
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not in mlp_only_layers) and (config.num_experts > 0): ### ???
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self.mlp = PanguProMoESparseMoeBlock(
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config=config,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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)
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else:
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self.mlp = PanguProMoEMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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kv_cache: Optional[torch.Tensor] = None,
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attn_metadata: Optional[AttentionMetadata] = None,
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) -> torch.Tensor:
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# Self Attention
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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@support_torch_compile
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class PanguProMoEModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.embed_tokens")
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: PanguProMoEDecoderLayer(config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix),
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prefix=f"{prefix}.layers",
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size))
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: Optional[List[torch.Tensor]] = None,
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attn_metadata: Optional[AttentionMetadata] = None,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions, hidden_states, residual,
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kv_caches[i -
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self.start_layer] if kv_caches is not None else None,
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attn_metadata)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({
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"hidden_states": hidden_states,
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"residual": residual
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})
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class PanguProMoEForCausalLM(nn.Module, SupportsPP):
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fall_back_to_pt_during_load = False
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packed_modules_mapping = {
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"qkv_proj": ["q_proj", "k_proj", "v_proj"],
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"gate_up_proj": ["gate_proj", "up_proj"],
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"experts":
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["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.quant_config = quant_config
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self.model = PanguProMoEModel(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.lm_head",
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)
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if self.config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.sampler = get_sampler()
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: Optional[List[torch.Tensor]] = None,
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attn_metadata: Optional[AttentionMetadata] = None,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(input_ids, positions, kv_caches,
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attn_metadata, intermediate_tensors,
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inputs_embeds)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: Optional[torch.Tensor],
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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# Params for weights, fp8 weight scales, fp8 activation scales
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# (param_name, weight_name, expert_id, shard_id)
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expert_params_mapping = FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.num_experts)
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# expert_params_mapping = []
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params_dict = dict(self.named_parameters()) # from model
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loaded_params: Set[str] = set()
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for name, loaded_weight in weights:
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# =======================================================
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# BF: add this to load with less layers
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if 'layers' in name:
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layer_idx = int(name.split('layers.')[-1].split('.')[0])
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if layer_idx >= self.model.end_layer:
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continue
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if "rotary_emb.inv_freq" in name:
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continue
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if "module" in name:
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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# Skip non-stacked layers and experts (experts handled below).
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if weight_name not in name:
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continue
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# We have mlp.experts[0].gate_proj in the checkpoint.
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# Since we handle the experts below in expert_params_mapping,
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# we need to skip here BEFORE we update the name, otherwise
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# name will be updated to mlp.experts[0].gate_up_proj, which
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# will then be updated below in expert_params_mapping
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# for mlp.experts[0].gate_gate_up_proj, which breaks load.
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if "mlp.experts" in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if ((name.endswith(".bias") or name.endswith("_bias"))
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and name not in params_dict):
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continue
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# Skip layers on other devices.
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if is_pp_missing_parameter(name, self):
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continue
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if name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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for mapping in expert_params_mapping:
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param_name, weight_name, expert_id, shard_id = mapping
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if weight_name not in name:
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continue
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# breakpoint()
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name = name.replace(weight_name, param_name)
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# Skip layers on other devices.
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if is_pp_missing_parameter(name, self):
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continue
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# Skip loading extra bias for GPTQ models.
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if ((name.endswith(".bias") or name.endswith("_bias"))
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and name not in params_dict):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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# breakpoint()
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weight_loader(param,
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loaded_weight,
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name,
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shard_id=shard_id,
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expert_id=expert_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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|
if ((name.endswith(".bias") or name.endswith("_bias"))
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and name not in params_dict):
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continue
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# Skip layers on other devices.
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|
if is_pp_missing_parameter(name, self):
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continue
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# Remapping the name of FP8 kv-scale.
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if name.endswith("kv_scale"):
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remapped_kv_scale_name = name.replace(
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".kv_scale", ".attn.kv_scale")
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if remapped_kv_scale_name not in params_dict:
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logger.warning_once(
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"Found kv scale in the checkpoint "
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f"(e.g. {name}), but not found the expected "
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f"name in the model "
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f"(e.g. {remapped_kv_scale_name}). "
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"kv-scale is not loaded.")
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continue
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else:
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name = remapped_kv_scale_name
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|
param = params_dict[name]
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|
weight_loader = getattr(param, "weight_loader",
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|
default_weight_loader)
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|
weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
|