mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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470 lines
18 KiB
Python
470 lines
18 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Iterable, Optional, Set, Tuple, Union
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import torch
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import torch.nn as nn
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from vllm.attention import Attention
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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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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.linear import (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 (
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default_weight_loader, maybe_remap_kv_scale_name)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs.dbrx import DbrxConfig
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from .interfaces import SupportsPP
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from .utils import (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 DbrxRouter(nn.Module):
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"""A Router implementation for DBRX that returns logits for each expert
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per token.
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"""
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def __init__(
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self,
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config: DbrxConfig,
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params_dtype: Optional[torch.dtype] = None,
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.num_total_experts = config.ffn_config.moe_num_experts
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self.d_model = config.d_model
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self.layer = ReplicatedLinear(
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self.d_model,
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self.num_total_experts,
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bias=False,
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params_dtype=params_dtype,
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quant_config=None,
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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router_logits, _ = self.layer(hidden_states)
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return router_logits
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class DbrxExperts(FusedMoE):
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def __init__(
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self,
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config: DbrxConfig,
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quant_config: Optional[QuantizationConfig] = None,
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params_dtype: Optional[torch.dtype] = None,
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prefix: str = "",
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):
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super().__init__(
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num_experts=config.ffn_config.moe_num_experts,
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top_k=config.ffn_config.moe_top_k,
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hidden_size=config.d_model,
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intermediate_size=config.ffn_config.ffn_hidden_size,
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params_dtype=params_dtype,
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reduce_results=True,
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renormalize=True,
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quant_config=quant_config,
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tp_size=get_tensor_model_parallel_world_size(),
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prefix=prefix,
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)
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self.config = config
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self.tp_size = get_tensor_model_parallel_world_size()
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self.d_model = config.d_model
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self.intermediate_size = (self.config.ffn_config.ffn_hidden_size //
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self.tp_size)
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# Define custom weight loader for dbrx model
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def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor,
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weight_name: str, param_name: str):
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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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# DBRX uses GLU for each experts.
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# GLU has 3 linear layers: w1, v1 and w2.
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if weight_name.endswith("w1"):
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if param_name.endswith("weight"):
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loaded_weight = torch.reshape(
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loaded_weight,
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[-1, self.intermediate_size * self.tp_size, self.d_model],
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)
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param_data[:, 0:shard_size, :] = loaded_weight[:, shard, :]
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elif param_name.endswith("weight_scale"):
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param_data[:, 0] = loaded_weight
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else:
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param_data = loaded_weight
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if weight_name.endswith("v1"):
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if param_name.endswith("weight"):
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loaded_weight = torch.reshape(
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loaded_weight,
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[-1, self.intermediate_size * self.tp_size, self.d_model],
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)
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param_data[:, shard_size:2 *
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shard_size, :] = loaded_weight[:, shard, :]
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elif param_name.endswith("weight_scale"):
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param_data[:, 1] = loaded_weight
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else:
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param_data[:] = loaded_weight
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if weight_name.endswith("w2"):
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if param_name.endswith("weight"):
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loaded_weight = torch.reshape(
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loaded_weight,
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[-1, self.intermediate_size * self.tp_size, self.d_model],
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).transpose(1, 2)
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param_data[:] = loaded_weight[:, :, shard]
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else:
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param_data[:] = loaded_weight
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class DbrxMoE(nn.Module):
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"""A tensor-parallel MoE implementation for DBRX.
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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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config: DbrxConfig,
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quant_config: Optional[QuantizationConfig] = None,
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params_dtype: Optional[torch.dtype] = None,
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prefix: str = "",
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):
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super().__init__()
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self.d_model = config.d_model
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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.router = DbrxRouter(config, self.params_dtype)
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self.experts = DbrxExperts(config=config,
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quant_config=quant_config,
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params_dtype=self.params_dtype,
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prefix=f"{prefix}.experts")
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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orig_shape = hidden_states.shape
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hidden_states = hidden_states.view(-1, self.d_model)
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# router_logits: (num_tokens, n_experts)
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router_logits = self.router(hidden_states)
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final_hidden_states = self.experts(hidden_states, router_logits)
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return final_hidden_states.view(orig_shape)
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class DbrxAttention(nn.Module):
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def __init__(
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self,
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config: DbrxConfig,
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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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):
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super().__init__()
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self.d_model = config.d_model
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self.total_num_heads = config.n_heads
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self.head_dim = self.d_model // self.total_num_heads
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self.total_num_kv_heads = config.attn_config.kv_n_heads
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self.clip_qkv = config.attn_config.clip_qkv
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self.rope_theta = config.attn_config.rope_theta
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self.max_position = config.max_seq_len
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# pylint: disable=invalid-name
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self.Wqkv = QKVParallelLinear(
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self.d_model,
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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.out_proj = RowParallelLinear(
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self.d_model,
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self.d_model,
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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=self.max_position,
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base=int(self.rope_theta),
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is_neox_style=True,
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)
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tp_world_size = get_tensor_model_parallel_world_size()
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self.tp_size = tp_world_size
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assert self.total_num_heads % tp_world_size == 0
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self.num_heads = self.total_num_heads // tp_world_size
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if self.total_num_kv_heads >= tp_world_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_world_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_world_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_world_size)
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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.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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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.Wqkv(hidden_states)
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if self.clip_qkv is not None:
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qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
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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(position_ids, q, k)
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attn_output = self.attn(q, k, v)
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hidden_states, _ = self.out_proj(attn_output)
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return hidden_states
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class DbrxFusedNormAttention(nn.Module):
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def __init__(
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self,
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config: DbrxConfig,
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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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):
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super().__init__()
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self.d_model = config.d_model
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self.attn = DbrxAttention(config,
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cache_config,
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quant_config,
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prefix=f"{prefix}.attn")
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self.norm_1 = nn.LayerNorm(self.d_model)
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self.norm_2 = nn.LayerNorm(self.d_model)
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.norm_1(hidden_states)
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x = self.attn(
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position_ids=position_ids,
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hidden_states=hidden_states,
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)
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hidden_states = residual + x
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residual = hidden_states
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hidden_states = self.norm_2(hidden_states)
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return hidden_states, residual
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class DbrxBlock(nn.Module):
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def __init__(
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self,
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config: DbrxConfig,
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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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):
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super().__init__()
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self.norm_attn_norm = DbrxFusedNormAttention(
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config,
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cache_config,
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quant_config,
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prefix=f"{prefix}.norm_attn_norm")
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self.ffn = DbrxMoE(config, quant_config, prefix=f"{prefix}.ffn")
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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hidden_states, residual = self.norm_attn_norm(
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position_ids=position_ids,
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hidden_states=hidden_states,
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)
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hidden_states = self.ffn(hidden_states)
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hidden_states = hidden_states + residual
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return hidden_states
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class DbrxModel(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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self.wte = VocabParallelEmbedding(
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config.vocab_size,
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config.d_model,
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)
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self.start_layer, self.end_layer, self.blocks = make_layers(
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config.n_layers,
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lambda prefix: DbrxBlock(
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config, cache_config, quant_config, prefix=prefix),
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prefix=f"{prefix}.blocks",
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)
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self.norm_f = nn.LayerNorm(config.d_model, eps=1e-5)
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for module in self.modules():
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if hasattr(module, "bias") and isinstance(module.bias,
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nn.Parameter):
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# Remove the bias term in Linear and LayerNorm.
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module.register_parameter("bias", None)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(["hidden_states"],
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config.d_model))
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.wte(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors],
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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else:
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assert intermediate_tensors
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hidden_states = intermediate_tensors["hidden_states"]
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for block in self.blocks[self.start_layer:self.end_layer]:
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hidden_states = block(position_ids, hidden_states)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({"hidden_states": hidden_states})
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hidden_states = self.norm_f(hidden_states)
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return hidden_states
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class DbrxForCausalLM(nn.Module, SupportsPP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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if config.tie_word_embeddings:
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raise ValueError(
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"tie_word_embeddings is not supported for Dbrx models.")
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self.quant_config = quant_config
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self.unpadded_vocab_size = config.vocab_size
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self.transformer = DbrxModel(vllm_config=vllm_config,
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prefix=maybe_prefix(
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prefix, "transformer"))
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.d_model,
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org_num_embeddings=config.vocab_size,
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padding_size=DEFAULT_VOCAB_PADDING_SIZE,
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quant_config=quant_config,
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)
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size)
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self.make_empty_intermediate_tensors = (
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self.transformer.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.transformer.get_input_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.transformer(input_ids, positions,
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intermediate_tensors, inputs_embeds)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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expert_params_mapping = [(
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"w13" if weight_name in ["w1", "v1"] else "w2",
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f"mlp.{weight_name}",
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) for weight_name in ["w1", "v1", "w2"]]
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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loaded_params: Set[str] = set()
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for name, loaded_weight in weights:
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if (self.quant_config is not None and
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(scale_name := self.quant_config.get_cache_scale(name))):
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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
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loaded_weight[0])
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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if name.endswith(("w1", "w2", "v1")):
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name = name + "_weight"
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for param_name, weight_name in expert_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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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, weight_name, name)
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break
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else:
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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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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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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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