wehub-resource-sync
445 lines
16 KiB
Python
445 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# ruff: noqa: E501
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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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"""Inference-only K-EXAONE-236B-A22B model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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from itertools import islice
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
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from vllm.distributed import (
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get_ep_group,
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get_pp_group,
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get_tensor_model_parallel_world_size,
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)
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from vllm.model_executor.layers.fused_moe import (
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FusedMoE,
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)
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import ReplicatedLinear
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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.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE,
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.sequence import IntermediateTensors
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from .exaone4 import Exaone4Attention as ExaoneMoeAttention
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from .exaone4 import Exaone4GatedMLP as ExaoneMoeGatedMLP
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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WeightsMapper,
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extract_layer_index,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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class ExaoneMoe(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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enable_eplb: bool = False,
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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.routed_scaling_factor = config.routed_scaling_factor
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self.ep_group = get_ep_group().device_group
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self.ep_rank = self.ep_group.rank()
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self.ep_size = self.ep_group.size()
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self.n_routed_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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)
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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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self.e_score_correction_bias = nn.Parameter(
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torch.empty(config.num_experts, dtype=torch.float32)
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)
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# Load balancing settings.
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vllm_config = get_current_vllm_config()
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eplb_config = vllm_config.parallel_config.eplb_config
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self.enable_eplb = enable_eplb
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self.n_logical_experts = self.n_routed_experts
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eplb_config.num_redundant_experts = (
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eplb_config.num_redundant_experts
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if eplb_config.num_redundant_experts is not None
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else 0
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)
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self.n_redundant_experts = eplb_config.num_redundant_experts
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self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
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self.n_local_physical_experts = self.n_physical_experts // self.ep_size
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self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
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self.physical_expert_end = (
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self.physical_expert_start + self.n_local_physical_experts
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)
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if getattr(config, "num_shared_experts", 0) > 0:
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intermediate_size = config.moe_intermediate_size * config.num_shared_experts
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self.shared_experts = ExaoneMoeGatedMLP(
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hidden_size=config.hidden_size,
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intermediate_size=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_experts",
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)
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else:
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self.shared_experts = None
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self.experts = FusedMoE(
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shared_experts=self.shared_experts,
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gate=self.gate,
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num_experts=self.n_routed_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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renormalize=config.norm_topk_prob,
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quant_config=quant_config,
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use_grouped_topk=True,
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num_expert_group=config.n_group,
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topk_group=config.topk_group,
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prefix=f"{prefix}.experts",
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scoring_func="sigmoid",
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routed_scaling_factor=self.routed_scaling_factor,
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e_score_correction_bias=self.e_score_correction_bias,
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enable_eplb=self.enable_eplb,
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num_redundant_experts=self.n_redundant_experts,
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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# NOTE: hidden_states can have either 1D or 2D shape.
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orig_shape = hidden_states.shape
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hidden_dim = hidden_states.shape[-1]
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hidden_states = hidden_states.view(-1, hidden_dim)
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final_hidden_states = self.experts(
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hidden_states=hidden_states, router_logits=hidden_states
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)
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return final_hidden_states.view(orig_shape)
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class ExaoneMoeDecoderLayer(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: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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mtp_layer: bool = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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layer_idx = extract_layer_index(prefix)
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self.hidden_size = config.hidden_size
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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# Support abacusai/Smaug-72B-v0.1 with attention_bias
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attention_bias = getattr(config, "attention_bias", False) or getattr(
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config, "bias", False
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)
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self.self_attn = ExaoneMoeAttention(
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config=config,
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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=getattr(
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config, "num_key_value_heads", config.num_attention_heads
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),
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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bias=attention_bias,
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cache_config=cache_config,
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prefix=f"{prefix}.self_attn",
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)
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if config.is_moe_layer[layer_idx] and not mtp_layer:
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self.mlp = ExaoneMoe(
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config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"
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)
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else:
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self.mlp = ExaoneMoeGatedMLP(
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hidden_size=self.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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bias=getattr(config, "mlp_bias", False),
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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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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residual: torch.Tensor | None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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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(hidden_states, residual)
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# Self Attention
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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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)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(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 ExaoneMoeModel(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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lora_config = vllm_config.lora_config
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self.num_redundant_experts = (
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vllm_config.parallel_config.eplb_config.num_redundant_experts
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)
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self.config = config
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self.quant_config = quant_config
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lora_vocab = (
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(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
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if lora_config
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else 0
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)
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self.vocab_size = config.vocab_size + lora_vocab
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if get_pp_group().is_first_rank or (
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config.tie_word_embeddings and get_pp_group().is_last_rank
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):
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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quant_config=quant_config,
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)
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else:
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self.embed_tokens = PPMissingLayer()
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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: ExaoneMoeDecoderLayer(
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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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),
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prefix=f"{prefix}.layers",
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)
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size
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)
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def embed_input_ids(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 | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None,
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inputs_embeds: torch.Tensor | None = None,
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) -> 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.embed_input_ids(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 layer in islice(self.layers, self.start_layer, self.end_layer):
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hidden_states, residual = layer(
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positions,
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hidden_states,
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residual,
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)
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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, "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 ExaoneMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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hf_to_vllm_mapper = WeightsMapper(
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orig_to_new_stacked={
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# weight_name: (param_name, shard_id)
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".q_proj": (".qkv_proj", "q"),
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".k_proj": (".qkv_proj", "k"),
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".v_proj": (".qkv_proj", "v"),
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# .experts.gate_up_proj must be handled by MoERunner.load_weights for EP
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".mlp.gate_proj": (".mlp.gate_up_proj", 0),
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".mlp.up_proj": (".mlp.gate_up_proj", 1),
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".shared_experts.gate_proj": (".shared_experts.gate_up_proj", 0),
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".shared_experts.up_proj": (".shared_experts.gate_up_proj", 1),
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}
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)
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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}
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# LoRA specific attributes
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embedding_modules = {
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"embed_tokens": "input_embeddings",
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"lm_head": "output_embeddings",
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}
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embedding_padding_modules = ["lm_head"]
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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.get_text_config()
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quant_config = vllm_config.quant_config
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lora_config = vllm_config.lora_config
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self.config = config
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self.lora_config = lora_config
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self.quant_config = quant_config
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self.model = ExaoneMoeModel(
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vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"),
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)
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if get_pp_group().is_last_rank:
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self.unpadded_vocab_size = config.vocab_size
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if lora_config:
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self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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padding_size=DEFAULT_VOCAB_PADDING_SIZE
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# We need bigger padding if using lora for kernel
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# compatibility
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if not lora_config
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else lora_config.lora_vocab_padding_size,
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quant_config=quant_config,
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)
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if config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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logit_scale = getattr(config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(
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self.unpadded_vocab_size, config.vocab_size, logit_scale
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)
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else:
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self.lm_head = PPMissingLayer()
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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model_output = self.model(
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input_ids, positions, intermediate_tensors, inputs_embeds
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)
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return model_output
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor | None:
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logits = self.logits_processor(self.lm_head, hidden_states)
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return logits
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(
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self,
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# With tie_word_embeddings, we can skip lm_head.weight
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# The weight might appear unnecessarily in the files if the model is
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# processed with quantization, LoRA, fine-tuning, etc.
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skip_prefixes=(
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["lm_head.", "mtp."] if self.config.tie_word_embeddings else ["mtp."]
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),
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# Skip loading extra parameters for GPTQ/modelopt models.
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ignore_unexpected_suffixes=[
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".bias",
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"_bias",
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".k_scale",
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"_k_scale",
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".v_scale",
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"_v_scale",
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".weight_scale",
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"_weight_scale",
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".input_scale",
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"_input_scale",
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],
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)
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return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
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