372 lines
13 KiB
Python
372 lines
13 KiB
Python
# Copyright 2024 The LGcns AI Engineering Team
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# Adapted from llama2.py
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"""Inference-only Exaone 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 sglang.srt.distributed import get_tensor_model_parallel_world_size
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from sglang.srt.layers.activation import SiluAndMul
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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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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
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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.layers.rotary_embedding import get_rope
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from sglang.srt.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.utils import add_prefix
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class ExaoneGatedMLP(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=add_prefix("gate_up_proj", prefix),
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)
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self.c_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=add_prefix("c_proj", prefix),
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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.c_proj(x)
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return x
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class ExaoneAttention(nn.Module):
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def __init__(
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self,
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config,
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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 = 500000,
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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 = 4096,
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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.rotary_dim = int(
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self.head_dim * getattr(config, "partial_rotary_factor", 1)
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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=add_prefix("qkv_proj", prefix),
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)
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self.out_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=add_prefix("out_proj", prefix),
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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.rotary_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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forward_batch: ForwardBatch,
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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, forward_batch)
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output, _ = self.out_proj(attn_output)
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return output
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class ExaoneDecoderLayer(nn.Module):
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def __init__(
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self,
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config,
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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", 500000)
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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", 4096)
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self.self_attn = ExaoneAttention(
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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=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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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=add_prefix("self_attn", prefix),
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)
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self.mlp = ExaoneGatedMLP(
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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.activation_function,
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quant_config=quant_config,
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prefix=add_prefix("mlp", prefix),
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)
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rms_norm_eps = config.layer_norm_epsilon
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self.ln_1 = RMSNorm(config.hidden_size, eps=rms_norm_eps)
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self.ln_2 = RMSNorm(config.hidden_size, eps=rms_norm_eps)
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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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forward_batch: ForwardBatch,
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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.ln_1(hidden_states)
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else:
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hidden_states, residual = self.ln_1(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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forward_batch=forward_batch,
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)
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# Fully Connected
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hidden_states, residual = self.ln_2(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 ExaoneModel(nn.Module):
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def __init__(
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self,
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config,
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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.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.wte = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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)
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self.h = nn.ModuleList(
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[
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ExaoneDecoderLayer(
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config,
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i,
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quant_config=quant_config,
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prefix=add_prefix(f"h.{i}", prefix),
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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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rms_norm_eps = config.layer_norm_epsilon
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self.ln_f = RMSNorm(config.hidden_size, eps=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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forward_batch: ForwardBatch,
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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.wte(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.h)):
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layer = self.h[i]
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hidden_states, residual = layer(
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positions,
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hidden_states,
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forward_batch,
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residual,
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)
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hidden_states, _ = self.ln_f(hidden_states, residual)
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return hidden_states
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class ExaoneForCausalLM(nn.Module):
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def __init__(
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self,
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config,
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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.config = config
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self.quant_config = quant_config
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self.transformer = ExaoneModel(
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config, quant_config=quant_config, prefix=add_prefix("transformer", prefix)
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)
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self.lm_head = ParallelLMHead(
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config.vocab_size, config.hidden_size, prefix=add_prefix("lm_head", prefix)
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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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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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) -> LogitsProcessorOutput:
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hidden_states = self.transformer(
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input_ids, positions, forward_batch, input_embeds
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)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head, forward_batch
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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", "c_fc_0", 0),
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("gate_up_proj", "c_fc_1", 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 or "projector" in name:
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continue
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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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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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name = name.replace("attn.attention", "self_attn")
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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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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 = ExaoneForCausalLM
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