wehub-resource-sync
508 lines
18 KiB
Python
508 lines
18 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
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# Copyright 2024 The Qwen team.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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"""Inference-only Qwen2MoE 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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from typing import Any
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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 Qwen2MoeConfig
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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, get_tensor_model_parallel_world_size
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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.attention import Attention
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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 (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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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.vocab_parallel_embedding import (
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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 .interfaces import SupportsLoRA, SupportsPP
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from .utils import (
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AutoWeightsLoader,
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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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logger = init_logger(__name__)
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class Qwen2MoeMLP(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: QuantizationConfig | None = None,
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reduce_results: bool = True,
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expert_gate: torch.nn.Linear | None = None,
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is_sequence_parallel: bool = False,
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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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disable_tp=is_sequence_parallel,
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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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disable_tp=is_sequence_parallel,
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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(
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f"Unsupported activation: {hidden_act}. Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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self.expert_gate = expert_gate
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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out = self.act_fn(gate_up)
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out, _ = self.down_proj(out)
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if self.expert_gate is not None:
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out = F.sigmoid(self.expert_gate(x)[0]) * out
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return out
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class Qwen2MoeSparseMoeBlock(nn.Module):
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def __init__(
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self,
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config: Qwen2MoeConfig,
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quant_config: QuantizationConfig | None = 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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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.shared_expert_gate = ReplicatedLinear(
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config.hidden_size,
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1,
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bias=False,
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quant_config=None,
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prefix=f"{prefix}.shared_expert_gate",
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)
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if config.shared_expert_intermediate_size > 0:
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self.shared_expert = Qwen2MoeMLP(
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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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expert_gate=self.shared_expert_gate,
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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
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self.experts = FusedMoE(
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shared_experts=self.shared_expert,
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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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renormalize=config.norm_topk_prob,
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quant_config=quant_config,
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prefix=f"{prefix}.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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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = self.experts(
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hidden_states=hidden_states, router_logits=router_logits
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)
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return final_hidden_states.view(orig_shape)
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class Qwen2MoeAttention(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_parameters: dict[str, Any] | None = None,
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max_position_embeddings: int = 8192,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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dual_chunk_attention_config: dict[str, Any] | None = None,
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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.max_position_embeddings = max_position_embeddings
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self.dual_chunk_attention_config = dual_chunk_attention_config
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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=False,
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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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max_position=max_position_embeddings,
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rope_parameters=rope_parameters,
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dual_chunk_attention_config=dual_chunk_attention_config,
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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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"layer_idx": extract_layer_index(prefix),
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"dual_chunk_attention_config": dual_chunk_attention_config,
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}
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if dual_chunk_attention_config
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else {},
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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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) -> 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 Qwen2MoeDecoderLayer(nn.Module):
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def __init__(
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self,
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config: Qwen2MoeConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = 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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dual_chunk_attention_config = getattr(
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config, "dual_chunk_attention_config", None
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)
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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self.self_attn = Qwen2MoeAttention(
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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_parameters=config.rope_parameters,
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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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dual_chunk_attention_config=dual_chunk_attention_config,
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)
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# Note: Qwen/Qwen2-57B-A14B-Instruct does not have
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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 = (
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[] if not hasattr(config, "mlp_only_layers") else config.mlp_only_layers
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)
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if (layer_idx not in mlp_only_layers) and (
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config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0
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):
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self.mlp = Qwen2MoeSparseMoeBlock(
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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 = Qwen2MoeMLP(
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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, 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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) -> 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(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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)
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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 Qwen2MoeModel(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.vocab_size = config.vocab_size
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self.config = config
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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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)
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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: Qwen2MoeDecoderLayer(
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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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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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 = 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(positions, hidden_states, residual)
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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 Qwen2MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
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fall_back_to_pt_during_load = False
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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_expert.gate_proj": (".shared_expert.gate_up_proj", 0),
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".shared_expert.up_proj": (".shared_expert.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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}
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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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# Only perform the following mapping when Qwen2MoeMLP exists
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if (
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getattr(config, "mlp_only_layers", [])
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or config.shared_expert_intermediate_size > 0
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):
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self.packed_modules_mapping["gate_up_proj"] = ["gate_proj", "up_proj"]
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self.model = Qwen2MoeModel(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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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=maybe_prefix(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.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,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
hidden_states = self.model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds
|
|
)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
logits = self.logits_processor(self.lm_head, hidden_states)
|
|
return logits
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
def _maybe_reshape(
|
|
weights: Iterable[tuple[str, torch.Tensor]],
|
|
) -> Iterable[tuple[str, torch.Tensor]]:
|
|
for name, loaded_weight in weights:
|
|
# GGUF: make sure that shared_expert_gate is a 2D tensor.
|
|
if "mlp.shared_expert_gate" in name and loaded_weight.dim() == 1:
|
|
loaded_weight = loaded_weight[None, :]
|
|
yield name, loaded_weight
|
|
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(
|
|
_maybe_reshape(weights), mapper=self.hf_to_vllm_mapper
|
|
)
|