610 lines
24 KiB
Python
610 lines
24 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# Copyright @2024 AI. Inspur Inc.
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#
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# @author: sunxian <sunxian@inspur.com>
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# @date: 2024/07/18
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#
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import math
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from typing import Any
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from typing import Dict
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from typing import Iterable
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from typing import List
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from typing import Optional
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from typing import Tuple
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from typing import Union
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import torch
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from torch import nn
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from torch.nn import BCEWithLogitsLoss
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from torch.nn import CrossEntropyLoss
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from torch.nn import MSELoss
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from vllm.attention import Attention, AttentionMetadata
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, LoRAConfig
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from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
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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.pooler import Pooler, PoolingType
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
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get_compressed_tensors_cache_scale)
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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 Sampler, SamplerOutput
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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, kv_cache_scales_loader, maybe_remap_kv_scale_name)
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from vllm.model_executor.pooling_metadata import PoolingMetadata
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors, PoolerOutput
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from vllm.utils import is_hip
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from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP
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from vllm.model_executor.models.utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers)
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from .configuration_hairuo import HairuoConfig
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class HairuoMLP(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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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 HairuoAttention(nn.Module):
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def __init__(
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self,
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config: HairuoConfig,
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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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cache_config: Optional[CacheConfig] = 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 = 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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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,
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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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if quant_config is not None and quant_config.get_name() == "gguf":
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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.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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is_neox_style=is_neox_style,
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)
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self.rotary_emb.cos_sin_cache = self.rotary_emb._compute_cos_sin_cache()
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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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)
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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: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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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# orig_dtype = q.dtype
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# q, k = q.float(), k.float()
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q, k = self.rotary_emb(positions, q, k)
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# q, k = q.to(orig_dtype), k.to(orig_dtype)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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output, _ = self.o_proj(attn_output)
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return output
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class HairuoDecoderLayer(nn.Module):
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def __init__(
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self,
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config: HairuoConfig,
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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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self.mup_scale_hidden_states = config.mup_scale_depth / math.sqrt(config.num_hidden_layers)
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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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attention_bias = getattr(config, "attention_bias", False) or getattr(
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config, "bias", False)
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self.self_attn = HairuoAttention(
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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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cache_config=cache_config,
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prefix=f"{prefix}.self_attn",
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)
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self.mlp = HairuoMLP(
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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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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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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kv_cache=kv_cache,
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attn_metadata=attn_metadata)
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hidden_states = residual + hidden_states * self.mup_scale_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 * self.mup_scale_hidden_states
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return hidden_states, residual
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class HairuoModel(nn.Module):
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def __init__(
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self,
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config: HairuoConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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lora_config: Optional[LoRAConfig] = None,
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prefix: str = "",
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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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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: HairuoDecoderLayer(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) * self.config.mup_scale_emb
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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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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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 i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states, residual = layer(positions, hidden_states,
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kv_caches[i - self.start_layer],
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attn_metadata, 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, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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(".gate_up_proj", ".gate_proj", 0),
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(".gate_up_proj", ".up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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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 scale_name := get_compressed_tensors_cache_scale(name):
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# Loading kv cache scales for compressed-tensors quantization
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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[0]
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weight_loader(param, loaded_weight)
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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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# 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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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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# If this function is called, it should always initialize KV cache scale
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# factors (or else raise an exception). Thus, handled exceptions should
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# make sure to leave KV cache scale factors in a known good (dummy) state
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def load_kv_cache_scales(self, quantization_param_path: str) -> None:
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tp_size = get_tensor_model_parallel_world_size()
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tp_rank = get_tensor_model_parallel_rank()
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for layer_idx, scaling_factor in kv_cache_scales_loader(
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quantization_param_path, tp_rank, tp_size,
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self.config.num_hidden_layers,
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self.config.__class__.model_type):
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if not isinstance(self.layers[layer_idx], nn.Identity):
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layer_self_attn = self.layers[layer_idx].self_attn
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if is_hip():
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# The scaling factor convention we are assuming is
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# quantized_value * scaling_factor ~= true_value
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# which is consistent with the practice of setting
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# scaling_factor = tensor_amax / FPtype_max
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scaling_factor *= 2
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if hasattr(layer_self_attn, "kv_scale"):
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layer_self_attn.attn._kv_scale = scaling_factor
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else:
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raise RuntimeError("Self attention has no KV cache scaling "
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"factor attribute!")
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class HairuoForCausalLM(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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supported_lora_modules = [
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"qkv_proj", "o_proj", "gate_up_proj", "down_proj", "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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# BitandBytes specific attributes
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default_bitsandbytes_target_modules = [
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".gate_proj.",
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".down_proj.",
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".up_proj.",
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".q_proj.",
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".k_proj.",
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".v_proj.",
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".o_proj.",
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]
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# in TP, these weights are partitioned along the column dimension (dim=-1)
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column_parallel_weights_modules = [".down_proj.", ".o_proj."]
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bitsandbytes_stacked_params_mapping = {
|
|
# shard_name, weight_name, index
|
|
"q_proj": ("qkv_proj", 0),
|
|
"k_proj": ("qkv_proj", 1),
|
|
"v_proj": ("qkv_proj", 2),
|
|
"gate_proj": ("gate_up_proj", 0),
|
|
"up_proj": ("gate_up_proj", 1),
|
|
}
|
|
|
|
mistral_mapping = {
|
|
"layers": "model.layers",
|
|
"attention": "self_attn",
|
|
"wq": "q_proj",
|
|
"wk": "k_proj",
|
|
"wv": "v_proj",
|
|
"wo": "o_proj",
|
|
"attention_norm": "input_layernorm",
|
|
"feed_forward": "mlp",
|
|
"w1": "gate_proj",
|
|
"w2": "down_proj",
|
|
"w3": "up_proj",
|
|
"ffn_norm": "post_attention_layernorm",
|
|
"tok_embeddings": "model.embed_tokens",
|
|
"output": "lm_head",
|
|
"norm": "model.norm"
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
config: HairuoConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
lora_config: Optional[LoRAConfig] = None
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
self.lora_config = lora_config
|
|
|
|
self.model = HairuoModel(config,
|
|
cache_config,
|
|
quant_config,
|
|
lora_config = lora_config,
|
|
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,
|
|
)
|
|
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)
|
|
self.sampler = Sampler()
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
model_output = self.model(input_ids, positions, kv_caches,
|
|
attn_metadata, intermediate_tensors)
|
|
return model_output
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
hidden_states = hidden_states / self.config.mup_scale_width
|
|
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]]):
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=(["lm_head."]
|
|
if self.config.tie_word_embeddings else None),
|
|
)
|
|
loader.load_weights(
|
|
self.maybe_remap_mistral(name, loaded_weight)
|
|
for name, loaded_weight in weights)
|
|
|
|
|
|
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:
|
|
loaded_weight = permute(loaded_weight,
|
|
self.config.num_key_value_heads)
|
|
elif "wq" in modules:
|
|
loaded_weight = permute(loaded_weight,
|
|
self.config.num_attention_heads)
|
|
|
|
for item in modules:
|
|
if item in mapping and mapping[item] not in name:
|
|
name = name.replace(item, mapping[item])
|
|
|
|
return name, loaded_weight
|
|
|