598 lines
24 KiB
Python
598 lines
24 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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, Iterable, Optional, Set, Tuple, Type, Union
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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.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader, maybe_remap_kv_scale_name)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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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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bias: bool = False,
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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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input_size=hidden_size,
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output_sizes=[intermediate_size] * 2,
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bias=bias,
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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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input_size=intermediate_size,
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output_size=hidden_size,
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bias=bias,
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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(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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x, _ = self.gate_up_proj(x)
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x = self.act_fn(x)
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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__(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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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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bias: bool = False,
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bias_o_proj: bool = False,
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cache_config: Optional[CacheConfig] = None,
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prefix: str = "") -> None:
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super().__init__()
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layer_idx = extract_layer_index(prefix)
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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(config, "head_dim",
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self.hidden_size // self.total_num_heads)
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# Phi models introduced a partial_rotary_factor parameter in the config
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partial_rotary_factor = getattr(config, "partial_rotary_factor", 1)
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self.rotary_dim = int(partial_rotary_factor * self.head_dim)
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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=hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=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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input_size=self.total_num_heads * self.head_dim,
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output_size=hidden_size,
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bias=bias_o_proj,
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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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is_neox_style = True
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is_gguf = quant_config and quant_config.get_name() == "gguf"
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if is_gguf and config.model_type == "llama":
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is_neox_style = False
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.rotary_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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is_neox_style=is_neox_style,
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)
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if hasattr(config, "interleaved_sliding_window"):
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interleaved_sliding_window = config.interleaved_sliding_window
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if isinstance(interleaved_sliding_window, int):
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sliding_window = interleaved_sliding_window
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elif isinstance(interleaved_sliding_window, list):
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sw_idx = layer_idx % len(interleaved_sliding_window)
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sliding_window = interleaved_sliding_window[sw_idx]
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else:
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raise ValueError(
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f"{type(interleaved_sliding_window)} is not supported.")
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else:
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sliding_window = None
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self.attn = Attention(
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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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cache_config=cache_config,
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quant_config=quant_config,
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per_layer_sliding_window=sliding_window,
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prefix=f"{prefix}.attn",
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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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) -> 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)
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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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cache_config: Optional[CacheConfig] = None,
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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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rope_scaling["original_max_position_embeddings"] = (
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config.original_max_position_embeddings)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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# Support abacusai/Smaug-72B-v0.1 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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bias_o_proj = attention_bias
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# support internlm/internlm3-8b with qkv_bias
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if hasattr(config, 'qkv_bias'):
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attention_bias = config.qkv_bias
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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=getattr(config, "num_key_value_heads",
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config.num_attention_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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bias=attention_bias,
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bias_o_proj=bias_o_proj,
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cache_config=cache_config,
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prefix=f"{prefix}.self_attn",
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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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bias=getattr(config, "mlp_bias", False),
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prefix=f"{prefix}.mlp",
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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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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(
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hidden_states, residual)
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hidden_states = self.self_attn(positions=positions,
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hidden_states=hidden_states)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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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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@support_torch_compile
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class LlamaModel(nn.Module):
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def __init__(self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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layer_type: Type[LlamaDecoderLayer] = LlamaDecoderLayer):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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lora_config = vllm_config.lora_config
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self.config = config
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self.quant_config = quant_config
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lora_vocab = (lora_config.lora_extra_vocab_size *
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(lora_config.max_loras or 1)) if lora_config else 0
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self.vocab_size = config.vocab_size + lora_vocab
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self.org_vocab_size = config.vocab_size
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if get_pp_group().is_first_rank or (config.tie_word_embeddings
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and get_pp_group().is_last_rank):
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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quant_config=quant_config,
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)
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else:
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self.embed_tokens = PPMissingLayer()
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: layer_type(config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix),
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prefix=f"{prefix}.layers",
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)
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size))
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors],
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for layer in self.layers[self.start_layer:self.end_layer]:
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hidden_states, residual = layer(positions, hidden_states, residual)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({
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"hidden_states": hidden_states,
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"residual": 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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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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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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loaded_params: Set[str] = set()
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for name, loaded_weight in weights:
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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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if (self.quant_config is not None and
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(scale_name := self.quant_config.get_cache_scale(name))):
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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
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loaded_weight[0])
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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if "scale" in name:
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# Remapping the name of FP8 kv-scale.
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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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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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if is_pp_missing_parameter(name, self):
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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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if is_pp_missing_parameter(name, self):
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continue
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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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loaded_params.add(name)
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return loaded_params
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class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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packed_modules_mapping = {
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"qkv_proj": ["q_proj", "k_proj", "v_proj"],
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"gate_up_proj": ["gate_proj", "up_proj"]
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}
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# LoRA specific attributes
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embedding_modules = {
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"embed_tokens": "input_embeddings",
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"lm_head": "output_embeddings"
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}
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embedding_padding_modules = ["lm_head"]
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# Mistral/Llama models can also be loaded with --load-format mistral
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# from consolidated.safetensors checkpoints
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mistral_mapping = {
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"layers": "model.layers",
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"attention": "self_attn",
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"qscale_act": "input_scale",
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"qscale_weight": "weight_scale",
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"kv_fake_quantizer.qscale_act": "kv_scale",
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"wq": "q_proj",
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"wk": "k_proj",
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"wv": "v_proj",
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"wo": "o_proj",
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"attention_norm": "input_layernorm",
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"feed_forward": "mlp",
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"w1": "gate_proj",
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"w2": "down_proj",
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"w3": "up_proj",
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"ffn_norm": "post_attention_layernorm",
|
|
"tok_embeddings": "model.embed_tokens",
|
|
"output": "lm_head",
|
|
"norm": "model.norm"
|
|
}
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
lora_config = vllm_config.lora_config
|
|
self.config = config
|
|
self.lora_config = lora_config
|
|
|
|
self.model = self._init_model(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
|
|
if get_pp_group().is_last_rank:
|
|
self.unpadded_vocab_size = config.vocab_size
|
|
if lora_config:
|
|
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
|
self.lm_head = ParallelLMHead(
|
|
self.unpadded_vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
padding_size=(
|
|
DEFAULT_VOCAB_PADDING_SIZE
|
|
# We need bigger padding if using lora for kernel
|
|
# compatibility
|
|
if not lora_config else
|
|
lora_config.lora_vocab_padding_size),
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
if config.tie_word_embeddings:
|
|
self.lm_head = self.lm_head.tie_weights(
|
|
self.model.embed_tokens)
|
|
|
|
logit_scale = getattr(config, "logit_scale", 1.0)
|
|
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
|
config.vocab_size,
|
|
logit_scale)
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
|
|
self.sampler = get_sampler()
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
def _init_model(self, vllm_config: VllmConfig, prefix: str = ""):
|
|
return LlamaModel(vllm_config=vllm_config, prefix=prefix)
|
|
|
|
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.get_input_embeddings(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
model_output = self.model(input_ids, positions, intermediate_tensors,
|
|
inputs_embeds)
|
|
return model_output
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def sample(self, logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=(["lm_head."]
|
|
if self.config.tie_word_embeddings else None),
|
|
)
|
|
return loader.load_weights(
|
|
self.maybe_remap_mistral(name, loaded_weight)
|
|
for name, loaded_weight in weights)
|
|
|
|
# This function is used to remap the mistral format as
|
|
# used by Mistral and Llama <=2
|
|
def maybe_remap_mistral(
|
|
self,
|
|
name: str,
|
|
loaded_weight: torch.Tensor,
|
|
) -> Tuple[str, torch.Tensor]:
|
|
|
|
def permute(w: torch.Tensor, n_heads: int):
|
|
attn_in = self.config.head_dim * n_heads
|
|
attn_out = self.config.hidden_size
|
|
|
|
return w.view(n_heads, attn_in // n_heads // 2, 2,
|
|
attn_out).transpose(1, 2).reshape(attn_in, attn_out)
|
|
|
|
mapping = self.mistral_mapping
|
|
modules = name.split(".")
|
|
|
|
# rotary embeds should be sliced
|
|
if "wk" in modules and modules[-1] == "weight":
|
|
loaded_weight = permute(loaded_weight,
|
|
self.config.num_key_value_heads)
|
|
elif "wq" in modules and modules[-1] == "weight":
|
|
loaded_weight = permute(loaded_weight,
|
|
self.config.num_attention_heads)
|
|
|
|
num_modules = len(modules)
|
|
for i in range(num_modules):
|
|
item = modules[i]
|
|
next_item = modules[i + 1] if i < num_modules - 1 else None
|
|
|
|
combined_item = (f"{item}.{next_item}"
|
|
if next_item is not None else None)
|
|
|
|
if combined_item in mapping:
|
|
name = name.replace(combined_item, mapping[combined_item])
|
|
elif item in mapping and mapping[item] not in name:
|
|
name = name.replace(item, mapping[item])
|
|
|
|
return name, loaded_weight
|