592 lines
24 KiB
Python
592 lines
24 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only FM9G model compatible with HuggingFace weights."""
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import math
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from typing import Any, Dict, Iterable, Optional, Set, Tuple, Union, List
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import torch
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from torch import nn
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from vllm.transformers_utils.configs import FM9GConfig
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from vllm.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.model_executor.layers.activation import FatreluAndMul, SiluAndMul
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from vllm.model_executor.layers.fused_moe import fused_moe
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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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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.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 default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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class FM9GMoE(nn.Module):
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"""A tensor-parallel MoE implementation that shards each expert
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across all ranks.
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Each expert's weights are sharded across all ranks and a fused MoE
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kernel is used for the forward pass, and finally we reduce the outputs
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across ranks.
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"""
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def __init__(
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self,
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num_experts: int,
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top_k: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: Optional[torch.dtype] = None,
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tp_size: Optional[int] = None,
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):
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super().__init__()
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self.tp_size = tp_size or get_tensor_model_parallel_world_size()
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self.num_total_experts = num_experts
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self.top_k = top_k
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size // self.tp_size
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if params_dtype is None:
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params_dtype = torch.get_default_dtype()
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self.params_dtype = params_dtype
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self.gate = ReplicatedLinear(self.hidden_size,
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self.num_total_experts,
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bias=False,
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params_dtype=self.params_dtype,
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quant_config=None)
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self.ws = nn.Parameter(
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torch.empty(self.num_total_experts,
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2 * self.intermediate_size,
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self.hidden_size,
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device=current_platform.device_type,
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dtype=self.params_dtype))
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self.w2s = nn.Parameter(
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torch.empty(self.num_total_experts,
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self.hidden_size,
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self.intermediate_size,
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device=current_platform.device_type,
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dtype=self.params_dtype))
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set_weight_attrs(self.ws, {
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"weight_loader": self.weight_loader,
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})
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set_weight_attrs(self.w2s, {
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"weight_loader": self.weight_loader,
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})
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def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor,
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weight_name: str, expert_id: int):
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tp_rank = get_tensor_model_parallel_rank()
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param_data = param.data
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shard_size = self.intermediate_size
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shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
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if weight_name.endswith("w1.weight"):
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param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :]
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if weight_name.endswith("w3.weight"):
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param_data[expert_id,
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shard_size:2 * shard_size, :] = loaded_weight[shard, :]
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if weight_name.endswith("w2.weight"):
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param_data[expert_id, :, :] = loaded_weight[:, shard]
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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num_tokens, hidden_size = hidden_states.shape
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hidden_states = hidden_states.view(-1, self.hidden_size)
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = fused_moe(hidden_states,
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self.ws,
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self.w2s,
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router_logits,
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self.top_k,
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renormalize=True,
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inplace=True)
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if self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(
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final_hidden_states)
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return final_hidden_states.view(num_tokens, hidden_size)
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class FM9GMLP(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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hidden_act_param: float,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size, [intermediate_size] * 2,
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bias=False,
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quant_config=quant_config)
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self.down_proj = RowParallelLinear(intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config)
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if hidden_act == "silu":
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self.act_fn = SiluAndMul()
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elif hidden_act == "fatrelu":
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self.act_fn = FatreluAndMul(threshold=hidden_act_param)
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else:
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu and fatrelu are supported for now.")
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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 FM9GAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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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 = 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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self.head_dim = hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=False,
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quant_config=quant_config,
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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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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# set rope as fp32 instead of bf16
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self.rotary_emb.cos_sin_cache = self.rotary_emb._compute_cos_sin_cache(
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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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prefix=f"{prefix}.attn")
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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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)
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output, _ = self.o_proj(attn_output)
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return output
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class FM9GDecoderLayer(nn.Module):
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def __init__(
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self,
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config: FM9GConfig,
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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.config = config
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self.cache_config = cache_config
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self.quant_config = quant_config
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self.hidden_size = config.hidden_size
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self.rope_theta = getattr(config, "rope_theta", 10000)
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self.rope_scaling = getattr(config, "rope_scaling", None)
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self.max_position_embeddings = getattr(config,
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"max_position_embeddings", 8192)
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self.prefix = prefix
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self._init_attn_block()
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self._init_ffn_block()
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def _init_attn_block(self):
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self.input_layernorm = RMSNorm(self.config.hidden_size,
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eps=self.config.rms_norm_eps)
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self.self_attn = FM9GAttention(
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hidden_size=self.hidden_size,
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num_heads=self.config.num_attention_heads,
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num_kv_heads=self.config.num_key_value_heads,
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rope_theta=self.rope_theta,
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rope_scaling=self.rope_scaling,
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max_position_embeddings=self.max_position_embeddings,
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cache_config=self.cache_config,
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quant_config=self.quant_config,
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prefix=f"{self.prefix}.self_attn",
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)
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def _init_ffn_block(self):
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self.post_attention_layernorm = RMSNorm(self.config.hidden_size,
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eps=self.config.rms_norm_eps)
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self.num_experts = getattr(self.config, "num_experts", 0)
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if self.num_experts == 0:
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self.mlp = FM9GMLP(
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hidden_size=self.hidden_size,
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intermediate_size=self.config.intermediate_size,
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hidden_act=self.config.hidden_act,
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hidden_act_param=getattr(self.config, "hidden_act_param", 0.),
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quant_config=self.quant_config,
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)
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else:
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self.mlp = FM9GMoE(
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num_experts=self.config.num_experts,
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top_k=self.config.num_experts_per_tok,
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hidden_size=self.config.hidden_size,
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intermediate_size=self.config.intermediate_size)
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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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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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hidden_states = residual + hidden_states * \
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(self.config.scale_depth / math.sqrt(self.config.num_hidden_layers))
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# Fully Connected
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states * \
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(self.config.scale_depth / math.sqrt(self.config.num_hidden_layers))
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return hidden_states, None
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@support_torch_compile
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class FM9GModel(nn.Module):
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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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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.cache_config = cache_config
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self.quant_config = quant_config
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lora_vocab = (lora_config.lora_extra_vocab_size *
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(lora_config.max_loras or 1)) if lora_config else 0
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self.vocab_size = config.vocab_size + lora_vocab
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self.org_vocab_size = config.vocab_size
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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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self.num_experts = getattr(self.config, "num_experts", 0)
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self._init_layers(prefix, config, cache_config, quant_config)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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"], self.config.hidden_size))
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def _init_layers(
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self,
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prefix: str,
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config: FM9GConfig,
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cache_config: Optional[CacheConfig],
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quant_config: Optional[QuantizationConfig],
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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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lambda prefix: FM9GDecoderLayer(
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config, cache_config, quant_config, prefix=prefix),
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prefix=f"{prefix}.layers")
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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embedding = self.embed_tokens(input_ids)
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return embedding * self.config.scale_emb
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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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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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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for layer in self.layers[self.start_layer:self.end_layer]:
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hidden_states, residual = layer(
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positions,
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hidden_states,
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residual,
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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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hidden_states = self.norm(hidden_states)
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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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expert_params_mapping = [
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# (param_name, weight_name, expert_id)
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("ws" if weight_name in ["w1", "w3"] else "w2s",
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f"experts.{expert_id}.{weight_name}.weight", expert_id)
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for expert_id in range(self.num_experts)
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for weight_name in ["w1", "w2", "w3"]
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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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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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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:
|
|
for param_name, weight_name, expert_id in expert_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param,
|
|
loaded_weight,
|
|
weight_name,
|
|
expert_id=expert_id)
|
|
break
|
|
else:
|
|
# 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 = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
class FM9GForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
"gate_up_proj": [
|
|
"gate_proj",
|
|
"up_proj",
|
|
],
|
|
}
|
|
|
|
# LoRA specific attributes
|
|
embedding_modules = {
|
|
"embed_tokens": "input_embeddings",
|
|
"lm_head": "output_embeddings",
|
|
}
|
|
embedding_padding_modules = ["lm_head"]
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
cache_config = vllm_config.cache_config
|
|
quant_config = vllm_config.quant_config
|
|
lora_config = vllm_config.lora_config
|
|
|
|
self.prefix = prefix
|
|
self.vllm_config = vllm_config
|
|
self.config = config
|
|
self.lora_config = lora_config
|
|
self.cache_config = cache_config
|
|
self.quant_config = quant_config
|
|
|
|
self.model = self._init_model(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
|
|
unpadded_vocab_size = config.vocab_size
|
|
if lora_config:
|
|
unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
|
self.lm_head = ParallelLMHead(
|
|
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)
|
|
self.scale_width = self.config.hidden_size / self.config.dim_model_base
|
|
|
|
self.logits_processor = LogitsProcessor(unpadded_vocab_size,
|
|
config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
def _init_model(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
return FM9GModel(vllm_config=vllm_config, prefix=prefix)
|
|
|
|
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.get_input_embeddings(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
hidden_states = self.model(input_ids, positions, intermediate_tensors,
|
|
inputs_embeds)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
hidden_states = hidden_states / self.scale_width
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=(["lm_head."]
|
|
if self.config.tie_word_embeddings else None),
|
|
)
|
|
return loader.load_weights(weights) |