423 lines
16 KiB
Python
423 lines
16 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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The input of the model is flattened to a 1D tensor of tokens. The model uses
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InputMetadata to extract the original 2D shape of the input.
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"""
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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 SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.quantized_linear import ParallelLinear
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from vllm.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
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from vllm.model_executor.parallel_utils.layers import VocabParallelEmbedding
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from vllm.model_executor.quantization_utils import QuantizationConfig
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from vllm.model_executor.weight_utils import (
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convert_pyslice_to_tensor, hf_model_weights_iterator,
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load_tensor_parallel_weights, load_padded_tensor_parallel_vocab)
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from vllm.sequence import SamplerOutput
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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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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) -> None:
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super().__init__()
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self.gate_up_proj = ParallelLinear.column(hidden_size,
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2 * intermediate_size,
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bias=False,
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gather_output=False,
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quant_config=quant_config)
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self.down_proj = ParallelLinear.row(intermediate_size,
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hidden_size,
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bias=False,
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input_is_parallel=True,
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quant_config=quant_config)
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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 = 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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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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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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num_kv_heads_replicas = max(1, tp_size // self.total_num_kv_heads)
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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 = ParallelLinear.column(
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hidden_size,
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(self.total_num_heads +
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2 * self.total_num_kv_heads * num_kv_heads_replicas) *
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self.head_dim,
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bias=False,
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gather_output=False,
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quant_config=quant_config,
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)
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self.o_proj = ParallelLinear.row(
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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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input_is_parallel=True,
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quant_config=quant_config,
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)
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self.attn = PagedAttentionWithRoPE(
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self.num_heads,
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self.head_dim,
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self.scaling,
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base=self.rope_theta,
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max_position=self.max_position_embeddings,
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rotary_dim=self.head_dim,
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num_kv_heads=self.num_kv_heads,
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rope_scaling=rope_scaling)
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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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cache_event: Optional[torch.cuda.Event],
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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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k_cache, v_cache = kv_cache
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attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
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input_metadata, cache_event)
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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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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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# Requires transformers > 4.32.0
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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 = LlamaAttention(
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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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quant_config=quant_config,
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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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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.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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kv_cache: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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# Self Attention
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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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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kv_cache=kv_cache,
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input_metadata=input_metadata,
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cache_event=cache_event,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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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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vocab_size = ((config.vocab_size + 63) // 64) * 64
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self.embed_tokens = VocabParallelEmbedding(
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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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LlamaDecoderLayer(config, quant_config)
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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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cache_events: Optional[List[torch.cuda.Event]],
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) -> torch.Tensor:
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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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if cache_events is None:
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cache_event = None
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else:
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cache_event = cache_events[i]
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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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cache_event,
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)
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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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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)
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vocab_size = ((config.vocab_size + 63) // 64) * 64
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# NOTE: The LM head is not quantized.
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self.lm_head = ParallelLinear.column(config.hidden_size,
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vocab_size,
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bias=False,
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gather_output=False,
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quant_config=None)
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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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cache_events: Optional[List[torch.cuda.Event]],
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) -> SamplerOutput:
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hidden_states = self.model(input_ids, positions, kv_caches,
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input_metadata, cache_events)
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next_tokens = self.sampler(self.lm_head.weight, hidden_states,
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input_metadata)
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return next_tokens
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_column_parallel_layers = []
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_row_parallel_layers = ["o_proj", "down_proj"]
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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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if self.quant_config is None:
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weight_suffixes = ["weight"]
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else:
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weight_suffixes = self.quant_config.get_tp_tensor_names()
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column_parallel_weights: List[str] = []
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for layer in self._column_parallel_layers:
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for suffix in weight_suffixes:
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column_parallel_weights.append(f"{layer}.{suffix}")
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row_parallel_weights: List[str] = []
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for layer in self._row_parallel_layers:
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for suffix in weight_suffixes:
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row_parallel_weights.append(f"{layer}.{suffix}")
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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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q_proj_shard_size = (self.config.hidden_size // tp_size)
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num_kv_heads_replicas = max(1,
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tp_size // self.config.num_key_value_heads)
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num_kv_heads_per_gpu = max(1,
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self.config.num_key_value_heads // tp_size)
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kv_proj_shard_size = (self.config.hidden_size //
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self.config.num_attention_heads *
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num_kv_heads_per_gpu)
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attention_weight_specs = [
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# (weight_name, shard_size, offset)
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("q_proj", q_proj_shard_size, 0),
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("k_proj", kv_proj_shard_size, q_proj_shard_size),
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("v_proj", kv_proj_shard_size,
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q_proj_shard_size + kv_proj_shard_size),
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]
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state_dict = self.state_dict()
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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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is_packed = False
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is_transposed = False
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if self.quant_config is not None:
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is_packed = self.quant_config.is_packed(name)
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is_transposed = self.quant_config.is_transposed(name)
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if is_transposed:
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loaded_weight = convert_pyslice_to_tensor(loaded_weight)
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loaded_weight = loaded_weight.T
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is_attention_weight = False
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for weight_name, shard_size, offset in attention_weight_specs:
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if weight_name not in name:
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continue
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param = state_dict[name.replace(weight_name, "qkv_proj")]
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if is_transposed:
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param = param.T
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if is_packed:
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shard_size //= self.quant_config.pack_factor
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offset //= self.quant_config.pack_factor
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if weight_name in ["k_proj", "v_proj"]:
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shard_id = tp_rank // num_kv_heads_replicas
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else:
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shard_id = tp_rank
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loaded_weight = loaded_weight[shard_size *
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shard_id:shard_size *
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(shard_id + 1)]
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param_slice = param.data[offset:offset + shard_size]
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assert param_slice.shape == loaded_weight.shape
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param_slice.copy_(loaded_weight)
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is_attention_weight = True
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break
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if is_attention_weight:
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continue
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is_gate_up_weight = False
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for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
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if weight_name not in name:
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continue
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param = state_dict[name.replace(weight_name, "gate_up_proj")]
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if is_transposed:
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param = param.T
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shard_size = param.shape[0] // 2
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loaded_weight = loaded_weight[shard_size * tp_rank:shard_size *
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(tp_rank + 1)]
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param_slice = param.data[shard_size * stride_id:shard_size *
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(stride_id + 1)]
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assert param_slice.shape == loaded_weight.shape
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param_slice.copy_(loaded_weight)
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is_gate_up_weight = True
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break
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if is_gate_up_weight:
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continue
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param = state_dict[name]
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if is_transposed:
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param = param.T
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if "embed_tokens" in name or "lm_head" in name:
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load_padded_tensor_parallel_vocab(param, loaded_weight,
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tp_rank)
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continue
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load_tensor_parallel_weights(param, loaded_weight, name,
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column_parallel_weights,
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row_parallel_weights, tp_rank)
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