578 lines
22 KiB
Python
578 lines
22 KiB
Python
# 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
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# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/llama.py#L1
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"""Inference-only LLaMA model compatible with HuggingFace weights."""
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import logging
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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.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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)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.model_loader.weight_utils import kv_cache_scales_loader
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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.pooler import Pooler, PoolingType
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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.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 make_layers
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from sglang.utils import get_exception_traceback
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logger = logging.getLogger(__name__)
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class LlamaMLP(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 LlamaAttention(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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bias: bool = False,
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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=bias,
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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=bias,
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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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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.o_proj(attn_output)
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return output
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class LlamaDecoderLayer(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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# Support llamafy/Qwen-Qwen2.5-7B-Instruct-llamafied with attention_bias
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# Support internlm/internlm-7b with bias
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attention_bias = getattr(config, "attention_bias", False) or getattr(
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config, "bias", False
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)
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self.self_attn = LlamaAttention(
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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=f"{prefix}.self_attn",
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bias=attention_bias,
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)
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self.mlp = LlamaMLP(
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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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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.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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forward_batch=forward_batch,
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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 LlamaModel(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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quant_config=quant_config,
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)
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self.layers = make_layers(
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config.num_hidden_layers,
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lambda idx, prefix: LlamaDecoderLayer(
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config=config, quant_config=quant_config, layer_id=idx, prefix=prefix
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),
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prefix="model.layers",
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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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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.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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forward_batch,
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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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# If this function is called, it should always initialize KV cache scale
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# factors (or else raise an exception). Thus, handled exceptions should
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# make sure to leave KV cache scale factors in a known good (dummy) state
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def load_kv_cache_scales(self, quantization_param_path: str) -> None:
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tp_size = get_tensor_model_parallel_world_size()
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tp_rank = get_tensor_model_parallel_rank()
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for layer_idx, scaling_factor in kv_cache_scales_loader(
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quantization_param_path,
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tp_rank,
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tp_size,
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self.config.num_hidden_layers,
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self.config.__class__.model_type,
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):
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if not isinstance(self.layers[layer_idx], nn.Identity):
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layer_self_attn = self.layers[layer_idx].self_attn
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if hasattr(layer_self_attn.attn, "k_scale"):
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layer_self_attn.attn.k_scale = scaling_factor
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layer_self_attn.attn.v_scale = scaling_factor
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else:
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raise RuntimeError(
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"Self attention has no KV cache scaling " "factor attribute!"
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)
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class LlamaForCausalLM(nn.Module):
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# BitandBytes specific attributes
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default_bitsandbytes_target_modules = [
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".gate_proj.",
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".down_proj.",
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".up_proj.",
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".q_proj.",
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".k_proj.",
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".v_proj.",
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".o_proj.",
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]
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# in TP, these weights are partitioned along the column dimension (dim=-1)
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column_parallel_weights_modules = [".down_proj.", ".o_proj."]
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bitsandbytes_stacked_params_mapping = {
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# shard_name, weight_name, index
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"q_proj": ("qkv_proj", 0),
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"k_proj": ("qkv_proj", 1),
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"v_proj": ("qkv_proj", 2),
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"gate_proj": ("gate_up_proj", 0),
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"up_proj": ("gate_up_proj", 1),
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}
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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.quant_config = quant_config
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self.model = LlamaModel(config, quant_config=quant_config)
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# Llama 3.2 1B Instruct set tie_word_embeddings to True
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# Llama 3.1 8B Instruct set tie_word_embeddings to False
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if self.config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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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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self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
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self.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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@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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get_embedding: bool = False,
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) -> LogitsProcessorOutput:
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hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
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if not get_embedding:
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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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else:
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return self.pooler(hidden_states, forward_batch)
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def get_hidden_dim(self, module_name):
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# return input_dim, output_dim
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if module_name in ["q_proj", "o_proj", "qkv_proj"]:
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return self.config.hidden_size, self.config.hidden_size
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elif module_name in ["kv_proj"]:
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return self.config.hidden_size, self.config.hidden_size // (
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self.config.num_attention_heads // self.config.num_key_value_heads
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)
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elif module_name == "gate_up_proj":
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return self.config.hidden_size, self.config.intermediate_size
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elif module_name == "down_proj":
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return self.config.intermediate_size, self.config.hidden_size
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else:
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raise NotImplementedError()
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def get_module_name(self, name):
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params_mapping = {
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"q_proj": "qkv_proj",
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"k_proj": "qkv_proj",
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"v_proj": "qkv_proj",
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"gate_proj": "gate_up_proj",
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"up_proj": "gate_up_proj",
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}
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return params_mapping.get(name, name)
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def get_module_name_from_weight_name(self, name):
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for param_name, weight_name, shard_id, num_shard in self.stacked_params_mapping:
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if weight_name in name:
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return (
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name.replace(weight_name, param_name)[: -len(".weight")],
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num_shard,
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)
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return name[: -len(".weight")], 1
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def get_num_params(self):
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params_dict = dict(self.named_parameters())
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return len(params_dict)
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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 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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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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# Skip loading kv_scale from ckpts towards new design.
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if name.endswith(".kv_scale") 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)
|
|
|
|
def get_weights_by_name(
|
|
self, name: str, truncate_size: int = 100, tp_size: int = 1
|
|
) -> Optional[torch.Tensor]:
|
|
"""Get the weights of the parameter by its name. Similar to `get_parameter` in Hugging Face.
|
|
|
|
Only used for unit test with an unoptimized performance.
|
|
For optimized performance, please use torch.save and torch.load.
|
|
"""
|
|
try:
|
|
if name == "lm_head.weight" and self.config.tie_word_embeddings:
|
|
logger.info(
|
|
"word embedding is tied for this model, return embed_tokens.weight as lm_head.weight."
|
|
)
|
|
return (
|
|
self.model.embed_tokens.weight.cpu()
|
|
.to(torch.float32)
|
|
.numpy()
|
|
.tolist()[:truncate_size]
|
|
)
|
|
|
|
mapped_name = name
|
|
mapped_shard_id = None
|
|
for param_name, weight_name, shard_id in self.stacked_params_mapping:
|
|
if weight_name in name:
|
|
mapped_name = name.replace(weight_name, param_name)
|
|
mapped_shard_id = shard_id
|
|
break
|
|
params_dict = dict(self.named_parameters())
|
|
param = params_dict[mapped_name]
|
|
if mapped_shard_id is not None:
|
|
if mapped_shard_id in ["q", "k", "v"]:
|
|
num_heads = self.config.num_attention_heads // tp_size
|
|
num_kv_heads = self.config.num_key_value_heads // tp_size
|
|
head_dim = (
|
|
self.config.hidden_size // self.config.num_attention_heads
|
|
)
|
|
if mapped_shard_id == "q":
|
|
offset = 0
|
|
size = num_heads * head_dim
|
|
elif mapped_shard_id == "k":
|
|
offset = num_heads * head_dim
|
|
size = num_kv_heads * head_dim
|
|
elif mapped_shard_id == "v":
|
|
offset = (num_heads + num_kv_heads) * head_dim
|
|
size = num_kv_heads * head_dim
|
|
weight = param.data.narrow(0, offset, size)
|
|
elif mapped_shard_id in [0, 1]:
|
|
intermediate_size = self.config.intermediate_size
|
|
slice_size = intermediate_size // tp_size
|
|
if mapped_shard_id == 0: # gate_proj
|
|
offset = 0
|
|
size = slice_size
|
|
elif mapped_shard_id == 1: # up_proj
|
|
offset = slice_size
|
|
size = slice_size
|
|
|
|
weight = param.data.narrow(0, offset, size)
|
|
else:
|
|
weight = param.data
|
|
else:
|
|
weight = param.data
|
|
if tp_size > 1 and ("o_proj" in name or "down_proj" in name):
|
|
gathered_weights = [torch.zeros_like(weight) for _ in range(tp_size)]
|
|
torch.distributed.all_gather(gathered_weights, weight)
|
|
weight = torch.cat(gathered_weights, dim=1)
|
|
return weight.cpu().to(torch.float32).numpy().tolist()[:truncate_size]
|
|
|
|
except Exception:
|
|
logger.error(
|
|
f"Error getting weights by name {name} in LlamaForCausalLM: {get_exception_traceback()}"
|
|
)
|
|
return None
|
|
|
|
def get_embed_and_head(self):
|
|
return self.model.embed_tokens.weight, self.lm_head.weight
|
|
|
|
def set_embed_and_head(self, embed, head):
|
|
del self.model.embed_tokens.weight
|
|
del self.lm_head.weight
|
|
self.model.embed_tokens.weight = embed
|
|
self.lm_head.weight = head
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.synchronize()
|
|
|
|
def load_kv_cache_scales(self, quantization_param_path: str) -> None:
|
|
self.model.load_kv_cache_scales(quantization_param_path)
|
|
|
|
|
|
class Phi3ForCausalLM(LlamaForCausalLM):
|
|
pass
|
|
|
|
|
|
class InternLM3ForCausalLM(LlamaForCausalLM):
|
|
pass
|
|
|
|
|
|
EntryClass = [LlamaForCausalLM, Phi3ForCausalLM, InternLM3ForCausalLM]
|