431 lines
15 KiB
Python
431 lines
15 KiB
Python
# Adapted from:
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# https://github.com/vllm-project/vllm/blob/14f91fe67c2342f2fe859dc6a5c40810df0e1c61/vllm/model_executor/models/deepseek.py
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"""Inference-only Deepseek model."""
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from typing import Any, Dict, Iterable, Optional, Tuple
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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.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.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import fused_moe
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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.quantization.base_config 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.model_executor.model_loader.weight_utils import default_weight_loader
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.managers.controller.infer_batch import InputMetadata
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class DeepseekMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: bool = True,
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config
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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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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class DeepseekMoE(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: 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.n_routed_experts = config.n_routed_experts
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self.top_k = config.num_experts_per_tok
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if self.tp_size > self.n_routed_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.n_routed_experts}."
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)
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self.experts = nn.ModuleList(
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[
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DeepseekMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_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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)
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for idx in range(self.n_routed_experts)
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]
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)
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self.pack_params()
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self.gate = ReplicatedLinear(
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config.hidden_size, self.n_routed_experts, bias=False, quant_config=None
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)
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if config.n_shared_experts is not None:
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intermediate_size = config.moe_intermediate_size * config.n_shared_experts
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self.shared_experts = DeepseekMLP(
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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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)
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def pack_params(self):
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w1 = []
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w2 = []
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for expert in self.experts:
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w1.append(expert.gate_up_proj.weight)
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w2.append(expert.down_proj.weight)
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self.w1 = torch._utils._flatten_dense_tensors(w1)
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w1s = torch._utils._unflatten_dense_tensors(self.w1, w1)
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for data, param in zip(w1s, w1):
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param.data = data
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self.w1 = self.w1.view(len(w1), *w1s[0].shape)
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self.w2 = torch._utils._flatten_dense_tensors(w2)
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w2s = torch._utils._unflatten_dense_tensors(self.w2, w2)
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for data, param in zip(w2s, w2):
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param.data = data
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self.w2 = self.w2.view(len(w2), *w2s[0].shape)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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if self.config.n_shared_experts is not None:
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shared_output = self.shared_experts(hidden_states)
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = fused_moe(
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hidden_states,
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self.w1,
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self.w2,
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router_logits,
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self.top_k,
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renormalize=self.config.norm_topk_prob,
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inplace=True,
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)
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if self.config.n_shared_experts is not None:
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final_hidden_states = final_hidden_states + shared_output
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final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
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return final_hidden_states.view(num_tokens, hidden_dim)
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class DeepseekAttention(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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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=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_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = 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 DeepseekDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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layer_id: int,
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cache_config: Optional[CacheConfig] = None,
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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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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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self.self_attn = DeepseekAttention(
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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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layer_id=layer_id,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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cache_config=cache_config,
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quant_config=quant_config,
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)
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if (
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config.n_routed_experts is not None
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and layer_id >= config.first_k_dense_replace
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and layer_id % config.moe_layer_freq == 0
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):
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self.mlp = DeepseekMoE(config=config, quant_config=quant_config)
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else:
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self.mlp = DeepseekMLP(
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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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)
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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.mlp(hidden_states)
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return hidden_states, residual
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class DeepseekModel(nn.Module):
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fall_back_to_pt_during_load = False
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def __init__(
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self,
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config: PretrainedConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> 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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DeepseekDecoderLayer(
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config, layer_id, cache_config, quant_config=quant_config
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)
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for layer_id 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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) -> torch.Tensor:
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hidden_states = self.embed_tokens(input_ids)
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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 DeepseekForCausalLM(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> 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 = DeepseekModel(config, cache_config, quant_config)
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self.lm_head = ParallelLMHead(
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config.vocab_size, config.hidden_size, quant_config=quant_config
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)
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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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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, input_metadata)
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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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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_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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# Skip experts that are not assigned to this worker.
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if (
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"mlp.experts." in name or "mlp.shared_experts." in name
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) and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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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 (
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"mlp.experts." in name or "mlp.shared_experts." in name
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) and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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EntryClass = DeepseekForCausalLM
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