Add Support for XVERSE Models (Dense and MoE) to sglang (#1397)
Co-authored-by: will he <hexin@xverse.cn> Co-authored-by: root <root@localhost.localdomain> Co-authored-by: Lianmin Zheng <lianminzheng@gmail.com>
This commit is contained in:
hxer7963
380
python/sglang/srt/models/xverse.py
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380
python/sglang/srt/models/xverse.py
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"""
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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/xverse.py#L1
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"""Inference-only XVERSE model compatible with HuggingFace weights."""
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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 LlamaConfig
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from vllm.config import CacheConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import SiluAndMul
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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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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.layers.sampler import Sampler
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from sglang.srt.model_executor.model_runner import InputMetadata
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class XverseMLP(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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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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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}. "
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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 XverseAttention(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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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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rope_is_neox_style: bool = True,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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# MistralConfig has an optional head_dim introduced by Mistral-Nemo
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self.head_dim = getattr(
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config, "head_dim", self.hidden_size // self.total_num_heads
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)
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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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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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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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is_neox_style=rope_is_neox_style,
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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 XverseDecoderLayer(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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if rope_scaling is not None and getattr(
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config, "original_max_position_embeddings", None
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):
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rope_scaling["original_max_position_embeddings"] = (
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config.original_max_position_embeddings
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)
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rope_is_neox_style = getattr(config, "rope_is_neox_style", True)
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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num_kv_heads = getattr(
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config, "num_key_value_heads", config.num_attention_heads
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)
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self.self_attn = XverseAttention(
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config=config,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=num_kv_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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rope_is_neox_style=rope_is_neox_style,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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)
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self.mlp = XverseMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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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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input_metadata: InputMetadata,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, 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 XverseModel(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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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.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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XverseDecoderLayer(
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config, i, quant_config=quant_config, prefix=f"model.layers.{i}"
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)
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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,
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hidden_states,
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input_metadata,
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residual,
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)
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# print(f"layer[{i}].hidden_states: {hidden_states}")
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class XverseForCausalLM(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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quant_config: Optional[QuantizationConfig] = None,
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cache_config: Optional[CacheConfig] = None,
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efficient_weight_load=False,
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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 = XverseModel(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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self.sampler = Sampler()
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self.param_dict = dict(self.named_parameters())
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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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# print(f"{hidden_states=}")
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logits_output = 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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sample_output = self.sampler(logits_output, input_metadata.sampling_info)
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return sample_output, logits_output
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def load_weights(
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self, weights: Iterable[Tuple[str, torch.Tensor]], name=None, loaded_weight=None
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):
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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 = self.param_dict
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def load_weights_per_param(name, loaded_weight):
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if "rotary_emb.inv_freq" in name or "projector" in name:
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return
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if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
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# Models trained using ColossalAI may include these tensors in
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# the checkpoint. Skip them.
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return
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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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if name.startswith("model.vision_tower") 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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return
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if name.startswith("model.vision_tower") and name not in params_dict:
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return
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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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if name is None or loaded_weight is None:
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for name, loaded_weight in weights:
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load_weights_per_param(name, loaded_weight)
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else:
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load_weights_per_param(name, loaded_weight)
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EntryClass = XverseForCausalLM
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449
python/sglang/srt/models/xverse_moe.py
Normal file
449
python/sglang/srt/models/xverse_moe.py
Normal file
@@ -0,0 +1,449 @@
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"""
|
||||
Copyright 2023-2024 SGLang Team
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
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.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
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"""
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|
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"""Inference-only XVERSE MoE 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 (
|
||||
ParallelLMHead,
|
||||
VocabParallelEmbedding,
|
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)
|
||||
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
||||
|
||||
from sglang.srt.layers.logits_processor import LogitsProcessor
|
||||
from sglang.srt.layers.radix_attention import RadixAttention
|
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from sglang.srt.layers.sampler import Sampler
|
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from sglang.srt.model_executor.forward_batch_info import InputMetadata
|
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class XverseMLP(nn.Module):
|
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|
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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,
|
||||
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
|
||||
)
|
||||
self.down_proj = RowParallelLinear(
|
||||
intermediate_size,
|
||||
hidden_size,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
reduce_results=reduce_results,
|
||||
)
|
||||
if hidden_act != "silu":
|
||||
raise ValueError(
|
||||
f"Unsupported activation: {hidden_act}. "
|
||||
"Only silu is supported for now."
|
||||
)
|
||||
self.act_fn = SiluAndMul()
|
||||
|
||||
def forward(self, x):
|
||||
gate_up, _ = self.gate_up_proj(x)
|
||||
x = self.act_fn(gate_up)
|
||||
x, _ = self.down_proj(x)
|
||||
return x
|
||||
|
||||
|
||||
class XverseMoE(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.rank = get_tensor_model_parallel_rank()
|
||||
self.tp_size = get_tensor_model_parallel_world_size()
|
||||
self.n_routed_experts = config.num_experts
|
||||
self.top_k = config.moe_top_k
|
||||
if self.tp_size > self.n_routed_experts:
|
||||
raise ValueError(
|
||||
f"Tensor parallel size {self.tp_size} is greater than "
|
||||
f"the number of experts {self.n_routed_experts}."
|
||||
)
|
||||
|
||||
self.experts = nn.ModuleList(
|
||||
[
|
||||
XverseMLP(
|
||||
hidden_size=config.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
quant_config=quant_config,
|
||||
reduce_results=False,
|
||||
)
|
||||
for _ in range(self.n_routed_experts)
|
||||
]
|
||||
)
|
||||
self.pack_params()
|
||||
|
||||
self.router = ReplicatedLinear(
|
||||
config.hidden_size, self.n_routed_experts, bias=False, quant_config=None
|
||||
)
|
||||
|
||||
if config.num_shared_experts is not None:
|
||||
intermediate_size = config.intermediate_size * config.num_shared_experts
|
||||
self.shared_experts = XverseMLP(
|
||||
hidden_size=config.hidden_size,
|
||||
intermediate_size=intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
quant_config=quant_config,
|
||||
reduce_results=False,
|
||||
)
|
||||
|
||||
def pack_params(self):
|
||||
w1 = []
|
||||
w2 = []
|
||||
for expert in self.experts:
|
||||
w1.append(expert.gate_up_proj.weight)
|
||||
w2.append(expert.down_proj.weight)
|
||||
self.w1 = torch._utils._flatten_dense_tensors(w1)
|
||||
w1s = torch._utils._unflatten_dense_tensors(self.w1, w1)
|
||||
for data, param in zip(w1s, w1):
|
||||
param.data = data
|
||||
self.w1 = self.w1.view(len(w1), *w1s[0].shape)
|
||||
|
||||
self.w2 = torch._utils._flatten_dense_tensors(w2)
|
||||
w2s = torch._utils._unflatten_dense_tensors(self.w2, w2)
|
||||
for data, param in zip(w2s, w2):
|
||||
param.data = data
|
||||
|
||||
self.w2 = self.w2.view(len(w2), *w2s[0].shape)
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
num_tokens, hidden_dim = hidden_states.shape
|
||||
hidden_states = hidden_states.view(-1, hidden_dim)
|
||||
if self.config.num_shared_experts is not None:
|
||||
shared_output = self.shared_experts(hidden_states)
|
||||
# router_logits: (num_tokens, n_experts)
|
||||
router_logits, _ = self.router(hidden_states)
|
||||
final_hidden_states = fused_moe(
|
||||
hidden_states,
|
||||
self.w1,
|
||||
self.w2,
|
||||
router_logits,
|
||||
self.top_k,
|
||||
renormalize=getattr(self.config, "norm_topk_prob", False),
|
||||
inplace=True,
|
||||
)
|
||||
|
||||
if self.config.num_shared_experts is not None:
|
||||
final_hidden_states = final_hidden_states + shared_output
|
||||
final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
|
||||
|
||||
return final_hidden_states.view(num_tokens, hidden_dim)
|
||||
|
||||
|
||||
class XverseAttention(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size: int,
|
||||
num_heads: int,
|
||||
num_kv_heads: int,
|
||||
layer_id: int = 0,
|
||||
rope_theta: float = 10000,
|
||||
rope_scaling: Optional[Dict[str, Any]] = None,
|
||||
max_position_embeddings: int = 8192,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = hidden_size
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
self.total_num_heads = num_heads
|
||||
assert self.total_num_heads % tp_size == 0
|
||||
self.num_heads = self.total_num_heads // tp_size
|
||||
self.total_num_kv_heads = num_kv_heads
|
||||
if self.total_num_kv_heads >= tp_size:
|
||||
# Number of KV heads is greater than TP size, so we partition
|
||||
# the KV heads across multiple tensor parallel GPUs.
|
||||
assert self.total_num_kv_heads % tp_size == 0
|
||||
else:
|
||||
# Number of KV heads is less than TP size, so we replicate
|
||||
# the KV heads across multiple tensor parallel GPUs.
|
||||
assert tp_size % self.total_num_kv_heads == 0
|
||||
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
||||
self.head_dim = hidden_size // self.total_num_heads
|
||||
self.q_size = self.num_heads * self.head_dim
|
||||
self.kv_size = self.num_kv_heads * self.head_dim
|
||||
self.scaling = self.head_dim**-0.5
|
||||
self.rope_theta = rope_theta
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
|
||||
self.qkv_proj = QKVParallelLinear(
|
||||
hidden_size,
|
||||
self.head_dim,
|
||||
self.total_num_heads,
|
||||
self.total_num_kv_heads,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
|
||||
self.o_proj = RowParallelLinear(
|
||||
self.total_num_heads * self.head_dim,
|
||||
hidden_size,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
|
||||
self.rotary_emb = get_rope(
|
||||
self.head_dim,
|
||||
rotary_dim=self.head_dim,
|
||||
max_position=max_position_embeddings,
|
||||
base=rope_theta,
|
||||
rope_scaling=rope_scaling,
|
||||
)
|
||||
self.attn = RadixAttention(
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
self.scaling,
|
||||
num_kv_heads=self.num_kv_heads,
|
||||
layer_id=layer_id,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
input_metadata: InputMetadata,
|
||||
) -> torch.Tensor:
|
||||
qkv, _ = self.qkv_proj(hidden_states)
|
||||
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
||||
q, k = self.rotary_emb(positions, q, k)
|
||||
attn_output = self.attn(q, k, v, input_metadata)
|
||||
output, _ = self.o_proj(attn_output)
|
||||
return output
|
||||
|
||||
|
||||
class XverseDecoderLayer(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
layer_id: int,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
rope_theta = getattr(config, "rope_theta", 10000)
|
||||
rope_scaling = getattr(config, "rope_scaling", None)
|
||||
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
|
||||
num_key_value_heads = getattr(
|
||||
config, "num_key_value_heads", config.num_attention_heads
|
||||
)
|
||||
self.self_attn = XverseAttention(
|
||||
hidden_size=self.hidden_size,
|
||||
num_heads=config.num_attention_heads,
|
||||
num_kv_heads=num_key_value_heads,
|
||||
layer_id=layer_id,
|
||||
rope_theta=rope_theta,
|
||||
rope_scaling=rope_scaling,
|
||||
max_position_embeddings=max_position_embeddings,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
if config.num_experts is not None:
|
||||
self.mlp = XverseMoE(config=config, quant_config=quant_config)
|
||||
else:
|
||||
self.mlp = XverseMLP(
|
||||
hidden_size=config.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = RMSNorm(
|
||||
config.hidden_size, eps=config.rms_norm_eps
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
input_metadata: InputMetadata,
|
||||
residual: Optional[torch.Tensor],
|
||||
) -> torch.Tensor:
|
||||
# Self Attention
|
||||
if residual is None:
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
else:
|
||||
hidden_states, residual = self.input_layernorm(hidden_states, residual)
|
||||
hidden_states = self.self_attn(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
input_metadata=input_metadata,
|
||||
)
|
||||
|
||||
# Fully Connected
|
||||
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
return hidden_states, residual
|
||||
|
||||
|
||||
class XverseModel(nn.Module):
|
||||
|
||||
fall_back_to_pt_during_load = False
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
)
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
XverseDecoderLayer(
|
||||
config, layer_id, cache_config, quant_config=quant_config
|
||||
)
|
||||
for layer_id in range(config.num_hidden_layers)
|
||||
]
|
||||
)
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
input_metadata: InputMetadata,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
residual = None
|
||||
for i in range(len(self.layers)):
|
||||
layer = self.layers[i]
|
||||
hidden_states, residual = layer(
|
||||
positions, hidden_states, input_metadata, residual
|
||||
)
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class XverseMoeForCausalLM(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
self.model = XverseModel(config, cache_config, quant_config)
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size, config.hidden_size, quant_config=quant_config
|
||||
)
|
||||
self.logits_processor = LogitsProcessor(config)
|
||||
self.sampler = Sampler()
|
||||
|
||||
self.param_dict = dict(self.named_parameters())
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
input_metadata: InputMetadata,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.model(input_ids, positions, input_metadata)
|
||||
logits_output = self.logits_processor(
|
||||
input_ids, hidden_states, self.lm_head.weight, input_metadata
|
||||
)
|
||||
sample_output = self.sampler(logits_output, input_metadata.sampling_info)
|
||||
return sample_output, logits_output
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
|
||||
params_dict = self.param_dict
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
# Skip experts that are not assigned to this worker.
|
||||
if (
|
||||
"mlp.experts." in name or "mlp.shared_experts." in name
|
||||
) and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
# Skip experts that are not assigned to this worker.
|
||||
if (
|
||||
"mlp.experts." in name or "mlp.shared_experts." in name
|
||||
) and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
|
||||
EntryClass = XverseMoeForCausalLM
|
||||
Reference in New Issue
Block a user