455 lines
18 KiB
Python
455 lines
18 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 deci model compatible with HuggingFace weights."""
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from typing import 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.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
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from vllm.model_executor.layers.layernorm import RMSNorm
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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.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.models.llama import LlamaAttention, LlamaMLP
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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 HasNoOps, SupportsLoRA, SupportsPP
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from .utils import (AutoWeightsLoader, PPMissingLayer, 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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def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
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# DeciLM-specific code
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intermediate_size = int(2 * ffn_mult * n_embd / 3)
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return _find_multiple(intermediate_size, 256)
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def _find_multiple(n: int, k: int) -> int:
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# DeciLM-specific code
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if n % k == 0:
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return n
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return n + k - (n % k)
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class DeciLMDecoderLayer(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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layer_idx: int,
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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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block_config = config.block_configs[layer_idx]
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self._is_no_op_attention = block_config.attention.no_op
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self._is_no_op_ffn = block_config.ffn.no_op
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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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if not self._is_no_op_attention:
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num_kv_heads = (config.num_attention_heads //
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block_config.attention.n_heads_in_group)
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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=num_kv_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.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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if not self._is_no_op_ffn:
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ffn_mult = block_config.ffn.ffn_mult
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intermediate_size = _ffn_mult_to_intermediate_size(
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ffn_mult, config.hidden_size)
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self.mlp = LlamaMLP(
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hidden_size=self.hidden_size,
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intermediate_size=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.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 self._is_no_op_attention:
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pass
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else:
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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(
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positions=positions,
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hidden_states=hidden_states,
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)
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# Fully Connected
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if not self._is_no_op_ffn:
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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 DeciModel(nn.Module):
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def __init__(
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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[DeciLMDecoderLayer] = DeciLMDecoderLayer,
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):
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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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self.padding_idx = config.pad_token_id
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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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def get_layer(prefix: str):
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layer_idx = int(prefix.rsplit(".", 1)[1])
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return layer_type(
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config,
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layer_idx,
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cache_config,
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quant_config=quant_config,
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prefix=prefix,
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)
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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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get_layer,
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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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kv_cache_index = 0
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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if not layer._is_no_op_attention:
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hidden_states, residual = layer(positions, hidden_states,
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residual)
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kv_cache_index += 1
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else:
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hidden_states, residual = layer(positions, hidden_states,
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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 DeciLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP, HasNoOps):
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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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supported_lora_modules = [
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"qkv_proj",
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"o_proj",
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"gate_up_proj",
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"down_proj",
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"embed_tokens",
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"lm_head",
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]
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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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"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",
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"tok_embeddings": "model.embed_tokens",
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"output": "lm_head",
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"norm": "model.norm",
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_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.lora_config = lora_config
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self.model = self._init_model(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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if get_pp_group().is_last_rank:
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self.unpadded_vocab_size = config.vocab_size
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if lora_config:
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self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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padding_size=(
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DEFAULT_VOCAB_PADDING_SIZE
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# We need bigger padding if using lora for kernel
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# compatibility
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if not lora_config else
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lora_config.lora_vocab_padding_size),
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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if config.tie_word_embeddings:
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self.lm_head = self.lm_head.tie_weights(
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self.model.embed_tokens)
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logit_scale = getattr(config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size,
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logit_scale)
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else:
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self.lm_head = PPMissingLayer()
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self.sampler = get_sampler()
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def _init_model(self, vllm_config: VllmConfig, prefix: str = ""):
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return DeciModel(vllm_config=vllm_config, prefix=prefix)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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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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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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model_output = self.model(input_ids, positions, intermediate_tensors,
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inputs_embeds)
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return model_output
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(self, logits: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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loader = AutoWeightsLoader(
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self,
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skip_prefixes=(["lm_head."]
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if self.config.tie_word_embeddings else None),
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)
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return loader.load_weights(weights)
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