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+# coding=utf-8
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+# Adapted from
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+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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+# Copyright 2023 The PygmalionAI team.
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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 Solar model compatible with HuggingFace weights."""
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+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
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+
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+import torch
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+from torch import nn
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+
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+from aphrodite.attention import Attention, AttentionMetadata
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+from aphrodite.common.config import CacheConfig, LoRAConfig
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+from aphrodite.common.sequence import IntermediateTensors, SamplerOutput
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+from aphrodite.common.utils import is_hip, progress_bar
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+from aphrodite.distributed import (get_pp_group,
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+ get_tensor_model_parallel_rank,
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+ get_tensor_model_parallel_world_size)
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+from aphrodite.modeling.layers.activation import SiluAndMul
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+from aphrodite.modeling.layers.layernorm import RMSNorm
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+from aphrodite.modeling.layers.linear import (MergedColumnParallelLinear,
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+ QKVParallelLinear,
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+ RowParallelLinear)
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+from aphrodite.modeling.layers.logits_processor import LogitsProcessor
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+from aphrodite.modeling.layers.rotary_embedding import get_rope
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+from aphrodite.modeling.layers.sampler import Sampler
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+from aphrodite.modeling.layers.vocab_parallel_embedding import (
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+ DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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+from aphrodite.modeling.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 aphrodite.modeling.models.interfaces import SupportsLoRA
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+from aphrodite.modeling.models.utils import (PPMissingLayer,
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+ is_pp_missing_parameter,
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+ make_layers)
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+from aphrodite.modeling.sampling_metadata import SamplingMetadata
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+from aphrodite.quantization.base_config import QuantizationConfig
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+from aphrodite.quantization.compressed_tensors.utils import (
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+ get_compressed_tensors_cache_scale)
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+
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+
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+class SolarMLP(nn.Module):
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+
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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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+ self.down_proj = RowParallelLinear(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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+ 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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+
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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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+
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+
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+class SolarAttention(nn.Module):
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+
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+ def __init__(
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+ self,
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+ config,
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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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+
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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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+
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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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+ )
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+ self.attn = Attention(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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+ q, k = self.rotary_emb(positions, q, k)
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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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+
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+
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+class SolarDecoderLayer(nn.Module):
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+
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+ def __init__(
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+ self,
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+ config,
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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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+ self.self_attn = SolarAttention(
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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 = SolarMLP(
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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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+
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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(
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+ positions=positions,
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+ hidden_states=hidden_states,
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+ kv_cache=kv_cache,
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+ attn_metadata=attn_metadata,
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+ )
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+
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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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+
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+
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+class SolarModel(nn.Module):
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+
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+ def __init__(
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+ self,
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+ config,
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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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+ )
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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: SolarDecoderLayer(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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+ 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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+
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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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+
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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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+
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+ bskcn_h_1 = None
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+ bskcn_h_2 = None
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+ bskcn_r_1 = None
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+ bskcn_r_2 = None
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+ bskcn_tv = (self.config.bskcn_tv[0] \
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+ if self.training else self.config.bskcn_tv[1])
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+
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+ for i in range(self.start_layer, self.end_layer):
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+ if i in self.config.bskcn_1:
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+ bskcn_h_1 = hidden_states.clone()
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+ bskcn_r_1 = residual.clone()
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+ if i in self.config.bskcn_2:
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+ bskcn_h_2 = hidden_states.clone()
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+ bskcn_r_2 = residual.clone()
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+ if i in self.config.bskcn_3:
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+ hidden_states = bskcn_h_1*bskcn_tv + hidden_states*(1-bskcn_tv)
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+ residual = bskcn_r_1*bskcn_tv + residual*(1-bskcn_tv)
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+ if i in self.config.bskcn_4:
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+ hidden_states = bskcn_h_2*bskcn_tv + hidden_states*(1-bskcn_tv)
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+ residual = bskcn_r_2*bskcn_tv + residual*(1-bskcn_tv)
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+ layer = self.layers[i]
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+ hidden_states, residual = layer(
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+ positions,
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+ hidden_states,
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+ kv_caches[i - self.start_layer],
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+ attn_metadata,
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+ residual,
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+ )
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+
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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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+
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+ hidden_states, _ = self.norm(hidden_states, residual)
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+ return hidden_states
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+
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+
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+class SolarForCausalLM(nn.Module, SupportsLoRA):
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+ packed_modules_mapping = {
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+ "qkv_proj": [
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+ "q_proj",
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+ "k_proj",
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+ "v_proj",
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+ ],
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+ "gate_up_proj": [
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+ "gate_proj",
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+ "up_proj",
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+ ],
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+ }
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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",
|
|
|
+ "lm_head"
|
|
|
+ ]
|
|
|
+ embedding_modules = {
|
|
|
+ "embed_tokens": "input_embeddings",
|
|
|
+ "lm_head": "output_embeddings",
|
|
|
+ }
|
|
|
+ embedding_padding_modules = ["lm_head"]
|
|
|
+ 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),
|
|
|
+ }
|
|
|
+
|
|
|
+ def __init__(
|
|
|
+ self,
|
|
|
+ config,
|
|
|
+ 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 = SolarModel(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.weight = self.model.embed_tokens.weight
|
|
|
+
|
|
|
+ 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()
|
|
|
+
|
|
|
+ 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) -> 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 make_empty_intermediate_tensors(
|
|
|
+ self, batch_size: int, dtype: torch.dtype,
|
|
|
+ device: torch.device) -> IntermediateTensors:
|
|
|
+ return IntermediateTensors({
|
|
|
+ "hidden_states":
|
|
|
+ torch.zeros((batch_size, self.config.hidden_size),
|
|
|
+ dtype=dtype,
|
|
|
+ device=device),
|
|
|
+ "residual":
|
|
|
+ torch.zeros((batch_size, self.config.hidden_size),
|
|
|
+ dtype=dtype,
|
|
|
+ device=device),
|
|
|
+ })
|
|
|
+
|
|
|
+ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
|
+ stacked_params_mapping = [
|
|
|
+ # (param_name, shard_name, shard_id)
|
|
|
+ (".qkv_proj", ".q_proj", "q"),
|
|
|
+ (".qkv_proj", ".k_proj", "k"),
|
|
|
+ (".qkv_proj", ".v_proj", "v"),
|
|
|
+ (".gate_up_proj", ".gate_proj", 0),
|
|
|
+ (".gate_up_proj", ".up_proj", 1),
|
|
|
+ ]
|
|
|
+ params_dict = dict(self.named_parameters())
|
|
|
+ weights_list = list(weights)
|
|
|
+ for name, loaded_weight in progress_bar(weights_list,
|
|
|
+ desc="Loading modules..."):
|
|
|
+ if "rotary_emb.inv_freq" in name:
|
|
|
+ continue
|
|
|
+ if ("rotary_emb.cos_cached" in name
|
|
|
+ or "rotary_emb.sin_cached" in name):
|
|
|
+ # Models trained using ColossalAI may include these tensors in
|
|
|
+ # the checkpoint. Skip them.
|
|
|
+ continue
|
|
|
+ if scale_name := get_compressed_tensors_cache_scale(name):
|
|
|
+ # Loading kv cache scales for compressed-tensors quantization
|
|
|
+ param = params_dict[scale_name]
|
|
|
+ weight_loader = getattr(param, "weight_loader",
|
|
|
+ default_weight_loader)
|
|
|
+ loaded_weight = loaded_weight[0]
|
|
|
+ weight_loader(param, loaded_weight)
|
|
|
+ continue
|
|
|
+ for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
|
|
+ if weight_name not in name:
|
|
|
+ continue
|
|
|
+ name = name.replace(weight_name, param_name)
|
|
|
+ # Skip loading extra bias for GPTQ models.
|
|
|
+ if name.endswith(".bias") and name not in params_dict:
|
|
|
+ continue
|
|
|
+
|
|
|
+ if is_pp_missing_parameter(name, self):
|
|
|
+ continue
|
|
|
+
|
|
|
+ param = params_dict[name]
|
|
|
+ weight_loader = param.weight_loader
|
|
|
+ weight_loader(param, loaded_weight, shard_id)
|
|
|
+
|
|
|
+ break
|
|
|
+ else:
|
|
|
+ # Skip loading extra bias for GPTQ models.
|
|
|
+ if name.endswith(".bias") and name not in params_dict:
|
|
|
+ continue
|
|
|
+ # Remapping the name of FP8 kv-scale.
|
|
|
+ name = maybe_remap_kv_scale_name(name, params_dict)
|
|
|
+ if name is None:
|
|
|
+ continue
|
|
|
+
|
|
|
+ if is_pp_missing_parameter(name, self):
|
|
|
+ continue
|
|
|
+
|
|
|
+ param = params_dict[name]
|
|
|
+ weight_loader = getattr(param, "weight_loader",
|
|
|
+ default_weight_loader)
|
|
|
+ weight_loader(param, loaded_weight)
|
|
|
+
|
|
|
+ # If this function is called, it should always initialize KV cache scale
|
|
|
+ # factors (or else raise an exception). Thus, handled exceptions should
|
|
|
+ # make sure to leave KV cache scale factors in a known good (dummy) state
|
|
|
+ def load_kv_cache_scales(self, quantization_param_path: str) -> None:
|
|
|
+ tp_size = get_tensor_model_parallel_world_size()
|
|
|
+ tp_rank = get_tensor_model_parallel_rank()
|
|
|
+ for layer_idx, scaling_factor in kv_cache_scales_loader(
|
|
|
+ quantization_param_path, tp_rank, tp_size,
|
|
|
+ self.config.num_hidden_layers,
|
|
|
+ self.config.__class__.model_type):
|
|
|
+ if not isinstance(self.model.layers[layer_idx], nn.Identity):
|
|
|
+ layer_self_attn = self.model.layers[layer_idx].self_attn
|
|
|
+
|
|
|
+ if is_hip():
|
|
|
+ # The scaling factor convention we are assuming is
|
|
|
+ # quantized_value * scaling_factor ~= true_value
|
|
|
+ # which is consistent with the practice of setting
|
|
|
+ # scaling_factor = tensor_amax / FPtype_max
|
|
|
+ scaling_factor *= 2
|
|
|
+ if hasattr(layer_self_attn, "kv_scale"):
|
|
|
+ layer_self_attn.attn._kv_scale = scaling_factor
|
|
|
+ else:
|
|
|
+ raise RuntimeError("Self attention has no KV cache scaling "
|
|
|
+ "factor attribute!")
|
AlpinDale