410 lines
17 KiB
Python
410 lines
17 KiB
Python
# coding=utf-8
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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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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"""Inference-only LLaMA model compatible with HuggingFace weights."""
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from typing import Any, Dict, List, 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.model_executor.input_metadata import InputMetadata
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from vllm.model_executor.layers.activation import DequantSiluAndMulQuant
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from vllm.model_executor.layers.attention import DequantPagedAttention
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from vllm.model_executor.layers.layernorm import (RMSNorm,
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RMSNormQuant,
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AddResidualRMSNormQuant,
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DequantAddResidualRMSNormQuant)
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from vllm.model_executor.layers.quantization.smoothquant import SmoothLinearMethod
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from vllm.model_executor.layers.linear import (LinearMethodBase,
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QuantMergedColumnParallelLinear,
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QuantQKVParallelLinear,
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QuantRowParallelLinear)
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from vllm.model_executor.layers.rotary_embedding import get_dequant_rope
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding,
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ParallelLMHead)
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from vllm.model_executor.layers.layernorm import DequantAddResidual, AddResidual
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from vllm.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_world_size)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.model_executor.weight_utils import (default_weight_loader,
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hf_model_weights_iterator)
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from vllm.sequence import SamplerOutput
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class QuantLlamaMLP(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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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.gate_up_proj = QuantMergedColumnParallelLinear(
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hidden_size, [intermediate_size] * 2,
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bias=False,
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linear_method=linear_method,
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skip_bias_add=True)
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self.down_proj = QuantRowParallelLinear(intermediate_size,
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hidden_size,
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bias=False,
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linear_method=linear_method,
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skip_bias_add=True)
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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now.")
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self.act_fn = DequantSiluAndMulQuant()
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def forward(self, x):
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scale = None
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# int, half -> int32
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gate_up, _ = self.gate_up_proj(x)
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# int32 -> int, scale
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x, *scale = self.act_fn(gate_up)
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scale = scale[0] if scale is not None else None
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# int8, scale -> int32(when tp > 1, to half, scale for dequant before all reduce)
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x, _ = self.down_proj(x, scale)
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return x, scale
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class QuantLlamaAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.qkv_proj = QuantQKVParallelLinear(
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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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linear_method=linear_method,
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skip_bias_add=True,
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)
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self.o_proj = QuantRowParallelLinear(
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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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linear_method=linear_method,
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skip_bias_add=True,
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)
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self.rotary_emb = get_dequant_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = DequantPagedAttention(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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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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kv_cache: KVCache,
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input_metadata: InputMetadata
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) -> torch.Tensor:
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# int8 -> int32
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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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# int32 -> half
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q, k, v = self.rotary_emb(positions, q, k, v,
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self.qkv_proj.q_dequant_scale.item(),
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self.qkv_proj.k_dequant_scale.item(),
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self.qkv_proj.v_dequant_scale.item())
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k_cache, v_cache = kv_cache
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scale = None
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# half - > int8, scale, 添加一个per channel 量化，并返回统计的scale
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attn_output, *scale = self.attn(q, k, v, k_cache, v_cache, input_metadata)
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scale = scale[0] if scale is not None else None
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# int8, scale -> int32(when tp > 1, to half, scale for dequant before all reduce)
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output, _ = self.o_proj(attn_output, scale)
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return output, scale
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class QuantLlamaDecoderLayer(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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self.self_attn = QuantLlamaAttention(
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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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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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linear_method=linear_method,
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)
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self.mlp = QuantLlamaMLP(
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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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linear_method=linear_method,
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)
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self.apply_dequant_in_post = not linear_method.apply_dequant_after_row
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self.input_layernorm = RMSNormQuant(config.hidden_size,
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eps=config.rms_norm_eps)
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if self.apply_dequant_in_post:
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self.post_attention_layernorm = DequantAddResidualRMSNormQuant(config.hidden_size,
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eps=config.rms_norm_eps)
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self.finally_add_residual = DequantAddResidual()
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else:
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self.post_attention_layernorm = AddResidualRMSNormQuant(config.hidden_size,
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eps=config.rms_norm_eps)
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self.finally_add_residual = AddResidual()
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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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kv_cache: KVCache,
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input_metadata: InputMetadata
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# half
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residual = hidden_states
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# half -> int8
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hidden_states = self.input_layernorm(hidden_states)
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# int8 -> int32 ,scale (when tp > 1,to half, scale, this scale is useless)
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hidden_states, scale = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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input_metadata=input_metadata
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)
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# to = 1: int32, half, scale -> int8, half (scale for dequant)
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# tp > 1: half, half, scale -> int8, half
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual, scale)
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# int8 -> int32, scale (when tp > 1,to half, scale, this scale is useless)
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hidden_states, scale = self.mlp(hidden_states)
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# ine32, half, scale -> half (when tp > 1, half, half, scale -> half)
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hidden_states = self.finally_add_residual(hidden_states, residual, scale)
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return hidden_states
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class QuantLlamaModel(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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linear_method: Optional[LinearMethodBase] = 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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QuantLlamaDecoderLayer(config, linear_method)
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for _ in range(config.num_hidden_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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kv_caches: List[KVCache],
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input_metadata: InputMetadata
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) -> torch.Tensor:
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# half
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hidden_states = self.embed_tokens(input_ids)
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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 = layer(
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positions,
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hidden_states,
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kv_caches[i],
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input_metadata
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)
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# int32 , half, scale -> int8
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hidden_states = self.norm(hidden_states)
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return hidden_states
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class LlamaForCausalLM(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.config = config
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self.linear_method = linear_method
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self.model = QuantLlamaModel(config, linear_method)
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self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
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self.sampler = Sampler(config.vocab_size)
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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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kv_caches: List[KVCache],
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input_metadata: InputMetadata
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, kv_caches,
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input_metadata)
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return hidden_states
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def sample(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> SamplerOutput:
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next_tokens = self.sampler(self.lm_head.weight, hidden_states,
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sampling_metadata)
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return next_tokens
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def load_weights(self,
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model_name_or_path: str,
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cache_dir: Optional[str] = None,
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load_format: str = "auto",
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revision: Optional[str] = None):
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stacked_params_mapping = [
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# process special params first
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("qkv_proj.q_dequant_scale", "q_proj.dequant_scale", "-1"),
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("qkv_proj.k_dequant_scale", "k_proj.dequant_scale", "-1"),
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("qkv_proj.v_dequant_scale", "v_proj.dequant_scale", "-1"),
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("act_fn.gate_dequant_scale", "gate_proj.dequant_scale", "-1"),
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("act_fn.up_dequant_scale", "up_proj.dequant_scale", "-1"),
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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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special_params_mapping = [
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("post_attention_layernorm.dequant_scale", "self_attn.o_proj.dequant_scale"),
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("finally_add_residual.dequant_scale","mlp.down_proj.dequant_scale")
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, load_format, revision):
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if "rotary_emb.inv_freq" in name:
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continue
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if ("rotary_emb.cos_cached" in name
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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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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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if 'bias' in name:
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continue
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param = params_dict[name.replace(weight_name, param_name)]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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if weight_loader is default_weight_loader:
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weight_loader(param, loaded_weight)
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else:
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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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for (param_name, weight_name) in special_params_mapping:
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if weight_name not in name:
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continue
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# used in o_prof and down_proj when world_size > 1
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if get_tensor_model_parallel_world_size() > 1:
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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if weight_loader is default_weight_loader:
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weight_loader(param, loaded_weight)
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else:
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weight_loader(param, loaded_weight,shard_id)
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else:
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param = params_dict[name.replace(weight_name, param_name)]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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if weight_loader is default_weight_loader:
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weight_loader(param, loaded_weight)
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else:
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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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if 'bias' not in name:
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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