386 lines
14 KiB
Python
386 lines
14 KiB
Python
"""
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Copyright 2023-2024 SGLang Team
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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"""
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# Adapted from
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# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/mixtral_quant.py#L1
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"""Inference-only Mixtral model."""
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from typing import Iterable, Optional, Tuple
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import numpy as np
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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 MixtralConfig
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from vllm.config import CacheConfig
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from vllm.distributed import (
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce,
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)
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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.model_executor.model_loader.weight_utils import default_weight_loader
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import (
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.model_executor.forward_batch_info import InputMetadata
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class MixtralMLP(nn.Module):
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def __init__(
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self,
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num_experts: int,
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hidden_size: int,
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intermediate_size: int,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.num_experts = num_experts
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self.ffn_dim = intermediate_size
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self.hidden_dim = hidden_size
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self.w1 = ReplicatedLinear(
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self.hidden_dim, self.ffn_dim, bias=False, quant_config=quant_config
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)
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self.w2 = ReplicatedLinear(
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self.ffn_dim, self.hidden_dim, bias=False, quant_config=quant_config
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)
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self.w3 = ReplicatedLinear(
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self.hidden_dim, self.ffn_dim, bias=False, quant_config=quant_config
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)
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# TODO: Use vllm's SiluAndMul
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self.act_fn = nn.SiLU()
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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w1_out, _ = self.w1(hidden_states)
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w1_out = self.act_fn(w1_out)
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w3_out, _ = self.w3(hidden_states)
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current_hidden_states = w1_out * w3_out
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current_hidden_states, _ = self.w2(current_hidden_states)
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return current_hidden_states
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class MixtralMoE(nn.Module):
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def __init__(
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self,
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config: MixtralConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.config = config
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self.rank = get_tensor_model_parallel_rank()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.num_total_experts = config.num_local_experts
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self.top_k = config.num_experts_per_tok
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if self.tp_size > self.num_total_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 {self.num_total_experts}."
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)
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# Split experts equally between ranks
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self.expert_indicies = np.array_split(
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range(self.num_total_experts), self.tp_size
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)[self.rank].tolist()
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if not self.expert_indicies:
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raise ValueError(f"Rank {self.rank} has no experts assigned to it.")
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self.experts = nn.ModuleList(
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[
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(
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MixtralMLP(
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self.num_total_experts,
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config.hidden_size,
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config.intermediate_size,
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quant_config=quant_config,
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)
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if idx in self.expert_indicies
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else None
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)
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for idx in range(self.num_total_experts)
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]
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)
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self.gate = ReplicatedLinear(
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config.hidden_size, self.num_total_experts, bias=False, quant_config=None
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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router_logits, _ = self.gate(hidden_states)
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routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
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routing_weights, selected_experts = torch.topk(
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routing_weights, self.top_k, dim=-1
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)
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routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
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final_hidden_states = None
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for expert_idx in self.expert_indicies:
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expert_layer = self.experts[expert_idx]
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expert_mask = selected_experts == expert_idx
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expert_weights = (routing_weights * expert_mask).sum(dim=-1, keepdim=True)
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current_hidden_states = expert_layer(hidden_states).mul_(expert_weights)
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if final_hidden_states is None:
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final_hidden_states = current_hidden_states
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else:
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final_hidden_states.add_(current_hidden_states)
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return tensor_model_parallel_all_reduce(final_hidden_states)
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class MixtralAttention(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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layer_id: int = 0,
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max_position: int = 4096 * 32,
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rope_theta: float = 10000,
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quant_config: Optional[QuantizationConfig] = 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.rope_theta = rope_theta
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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=False,
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quant_config=quant_config,
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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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)
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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,
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base=int(self.rope_theta),
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is_neox_style=True,
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)
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self.attn = RadixAttention(
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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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layer_id=layer_id,
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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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input_metadata: InputMetadata,
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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, input_metadata)
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output, _ = self.o_proj(attn_output)
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return output
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class MixtralDecoderLayer(nn.Module):
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def __init__(
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self,
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config: MixtralConfig,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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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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# Requires transformers > 4.32.0
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rope_theta = getattr(config, "rope_theta", 10000)
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self.self_attn = MixtralAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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max_position=config.max_position_embeddings,
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num_kv_heads=config.num_key_value_heads,
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layer_id=layer_id,
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rope_theta=rope_theta,
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quant_config=quant_config,
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)
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self.block_sparse_moe = MixtralMoE(config=config, quant_config=quant_config)
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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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input_metadata: InputMetadata,
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residual: Optional[torch.Tensor],
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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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input_metadata=input_metadata,
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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.block_sparse_moe(hidden_states)
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return hidden_states, residual
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class MixtralModel(nn.Module):
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def __init__(
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self,
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config: MixtralConfig,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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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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)
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self.layers = nn.ModuleList(
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[
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MixtralDecoderLayer(config, i, quant_config=quant_config)
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for i in range(config.num_hidden_layers)
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]
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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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input_metadata: InputMetadata,
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input_embeds: torch.Tensor = None,
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) -> torch.Tensor:
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if input_embeds is None:
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hidden_states = self.embed_tokens(input_ids)
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else:
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hidden_states = input_embeds
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residual = None
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for i in range(len(self.layers)):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions, hidden_states, input_metadata, 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 QuantMixtralForCausalLM(nn.Module):
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def __init__(
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self,
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config: MixtralConfig,
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quant_config: Optional[QuantizationConfig] = None,
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cache_config: Optional[CacheConfig] = None,
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) -> None:
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super().__init__()
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self.config = config
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self.quant_config = quant_config
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self.model = MixtralModel(config, quant_config=quant_config)
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self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
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self.logits_processor = LogitsProcessor(config)
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@torch.no_grad()
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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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input_metadata: InputMetadata,
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input_embeds: torch.Tensor = None,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head.weight, input_metadata
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)
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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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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" 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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if weight_name not 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") 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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weight_loader(param, loaded_weight, shard_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") and name not in params_dict:
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continue
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# Skip experts that are not assigned to this worker.
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if "block_sparse_moe.experts." in name 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 = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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EntryClass = QuantMixtralForCausalLM
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