563 lines
24 KiB
Python
563 lines
24 KiB
Python
"""Inference-only Snowflake Arctic model."""
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from typing import Iterable, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig
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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.logger import init_logger
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import fused_experts, fused_topk
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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.quantization.deepspeedfp import (
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DeepSpeedFPConfig, DeepSpeedFPParameter)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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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.sequence import IntermediateTensors
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from vllm.transformers_utils.configs.arctic import ArcticConfig
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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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logger = init_logger(__name__)
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class ArcticMLP(nn.Module):
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def __init__(self,
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config: ArcticConfig,
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layer_id: int,
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expert_id: int = -1,
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is_residual_mlp: bool = False,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: bool = True):
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super(ArcticMLP, self).__init__()
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self.hidden_size = config.hidden_size
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self.expert_id = expert_id
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self.layer_id = layer_id
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self.ffn_dim = config.intermediate_size if not is_residual_mlp \
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else self.hidden_size
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self.w13 = MergedColumnParallelLinear(self.hidden_size,
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[self.ffn_dim] * 2,
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bias=False,
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quant_config=quant_config)
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self.w2 = RowParallelLinear(self.ffn_dim,
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self.hidden_size,
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bias=False,
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reduce_results=reduce_results,
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quant_config=quant_config)
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if config.hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now.")
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self.act_fn = SiluAndMul()
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def forward(self, hidden_states):
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gate_up, _ = self.w13(hidden_states)
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hidden_states = self.act_fn(gate_up)
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hidden_states, _ = self.w2(hidden_states)
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return hidden_states
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class ArcticMoE(nn.Module):
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"""
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Model-parallel implementation of Arctic MoE Layer.
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"""
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def __init__(self,
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config: ArcticConfig,
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layer_id: int,
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tp_size: Optional[int] = None,
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params_dtype: Optional[torch.dtype] = None,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: bool = True):
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super(ArcticMoE, self).__init__()
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self.tp_size = tp_size or get_tensor_model_parallel_world_size()
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self.hidden_size = config.hidden_size
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self.num_experts = config.num_local_experts
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self.layer_id = layer_id
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self.top_k = config.num_experts_per_tok
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self.intermediate_size = config.intermediate_size // self.tp_size
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self.is_moe_layer = (layer_id + 1) % config.moe_layer_frequency == 0
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self.is_quant = isinstance(quant_config, DeepSpeedFPConfig)
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self.reduce_results = reduce_results
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# Some other parameters
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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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if not self.is_moe_layer:
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self.mlp = ArcticMLP(config,
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layer_id=layer_id,
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quant_config=quant_config,
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reduce_results=reduce_results)
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else:
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self.gate = ReplicatedLinear(self.hidden_size,
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self.num_experts,
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bias=False,
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params_dtype=self.params_dtype,
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quant_config=quant_config)
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if self.is_quant:
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self.ws = DeepSpeedFPParameter(
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torch.Size((self.num_experts, 2 * self.intermediate_size,
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self.hidden_size)),
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params_dtype=params_dtype,
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quant_config=quant_config,
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)
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self.w2s = DeepSpeedFPParameter(
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torch.Size((self.num_experts, self.hidden_size,
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self.intermediate_size)),
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params_dtype=params_dtype,
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quant_config=quant_config,
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)
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else:
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self.ws = nn.Parameter(
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torch.empty(self.num_experts,
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2 * self.intermediate_size,
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self.hidden_size,
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device="cuda",
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dtype=self.params_dtype))
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self.w2s = nn.Parameter(
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torch.empty(self.num_experts,
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self.hidden_size,
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self.intermediate_size,
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device="cuda",
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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.ds_dequantize() if self.is_quant else 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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if self.is_quant:
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param.ds_quantize_(param_data)
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def local_moe_fused(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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do_normalize = self.top_k > 1
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topk_weights, topk_ids = fused_topk(hidden_states,
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router_logits,
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self.top_k,
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renormalize=do_normalize)
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# topk_ids: (num_tokens, k)
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if self.is_quant:
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if 2 * num_tokens <= self.num_experts:
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# If much fewer tokens than experts, use selective dequantize.
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ws_dequantized = self.ws.ds_selective_dequantize(
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topk_ids.flatten())
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w2s_dequantized = self.w2s.ds_selective_dequantize(
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topk_ids.flatten())
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# We gathered the experts to the tokens so update the mapping.
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topk_ids = torch.arange(
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0,
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topk_ids.numel(),
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device=topk_ids.device,
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).reshape(topk_ids.shape)
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else:
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ws_dequantized = self.ws.ds_dequantize()
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w2s_dequantized = self.w2s.ds_dequantize()
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final_hidden_states = fused_experts(
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hidden_states,
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ws_dequantized if self.is_quant else self.ws,
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w2s_dequantized if self.is_quant else self.w2s,
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topk_weights,
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topk_ids,
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inplace=True)
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if self.reduce_results and 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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def forward(self, hidden_states: torch.Tensor):
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if self.is_moe_layer:
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final_hidden_states = self.local_moe_fused(hidden_states)
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else:
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final_hidden_states = self.mlp(hidden_states)
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return final_hidden_states
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class ArcticAttention(nn.Module):
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def __init__(
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self,
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config: ArcticConfig,
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layer_idx: Optional[int] = None,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_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 = config.num_key_value_heads
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if self.total_num_kv_heads >= tp_size:
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assert self.total_num_kv_heads % tp_size == 0
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else:
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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 = 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.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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self.scaling = self.head_dim**-0.5
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self.qkv_proj = QKVParallelLinear(self.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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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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self.hidden_size,
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bias=False,
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reduce_results=True,
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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_embeddings,
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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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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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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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class ArcticDecoderLayer(nn.Module):
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def __init__(
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self,
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config: ArcticConfig,
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layer_idx: int,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.layer_idx = layer_idx
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self.hidden_size = config.hidden_size
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is_moe_layer = (layer_idx + 1) % config.moe_layer_frequency == 0
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self.use_residual = config.use_residual and is_moe_layer
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self.self_attn = ArcticAttention(config,
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layer_idx,
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cache_config,
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quant_config=quant_config)
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self.block_sparse_moe = ArcticMoE(
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config,
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layer_id=layer_idx,
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quant_config=quant_config,
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reduce_results=(not self.use_residual))
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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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if self.use_residual:
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self.residual_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.residual_mlp = ArcticMLP(config,
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layer_id=layer_idx,
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is_residual_mlp=True,
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reduce_results=False)
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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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residual_input = 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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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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hidden_states = residual_input + hidden_states
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residual_attn = hidden_states
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if self.use_residual:
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hidden_states = self.residual_layernorm(hidden_states)
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hidden_states = self.residual_mlp(hidden_states)
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residual_mlp = hidden_states
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hidden_states = self.post_attention_layernorm(residual_input)
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hidden_states = self.block_sparse_moe(hidden_states)
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hidden_states = residual_mlp + hidden_states
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hidden_states = tensor_model_parallel_all_reduce(hidden_states)
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hidden_states = residual_attn + hidden_states
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else:
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.block_sparse_moe(hidden_states)
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hidden_states = residual_attn + hidden_states
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return hidden_states
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class ArcticModel(nn.Module):
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def __init__(
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self,
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config: ArcticConfig,
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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.padding_idx = config.pad_token_id
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self.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=self.vocab_size)
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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: ArcticDecoderLayer(config, int(
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prefix.split(".")[-1]), cache_config, quant_config),
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prefix=f"{prefix}.layers")
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self._attn_implementation = config._attn_implementation
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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(["hidden_states"],
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config.hidden_size))
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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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors],
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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hidden_states = self.embed_tokens(input_ids)
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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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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states = layer(positions, hidden_states,
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kv_caches[i - self.start_layer],
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attn_metadata)
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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(hidden_states)
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return hidden_states
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class ArcticForCausalLM(nn.Module, SupportsPP):
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def __init__(self,
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config: ArcticConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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**kwargs) -> None:
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super().__init__()
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self.config = config
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self.model = ArcticModel(config, cache_config, quant_config)
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self.vocab_size = config.vocab_size
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self.lm_head = ParallelLMHead(
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self.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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)
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if self.config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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self.num_experts = config.num_local_experts
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self.num_experts_per_tok = config.num_experts_per_tok
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self.unpadded_vocab_size = config.vocab_size
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size)
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self.sampler = Sampler()
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def 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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(input_ids, positions, kv_caches,
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attn_metadata, intermediate_tensors)
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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 sample(
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self,
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logits: Optional[torch.Tensor],
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
]
|
|
|
|
mlp_params_mapping: List[Tuple[str, str, int]] = []
|
|
expert_params_mapping: List[Tuple[str, str, int]] = []
|
|
num_layers = self.config.num_hidden_layers
|
|
|
|
for layer in range(num_layers):
|
|
mlp_params_mapping.append(
|
|
(f"layers.{layer}.residual_mlp.w13.weight",
|
|
f"layers.{layer}.residual_mlp.w1.weight", 0))
|
|
mlp_params_mapping.append(
|
|
(f"layers.{layer}.residual_mlp.w13.weight",
|
|
f"layers.{layer}.residual_mlp.w3.weight", 1))
|
|
if layer % 2 == 0:
|
|
# MLP layers
|
|
mlp_params_mapping.append(
|
|
(f"layers.{layer}.block_sparse_moe.mlp.w13.weight",
|
|
f"layers.{layer}.block_sparse_moe.mlp.w1.weight", 0))
|
|
mlp_params_mapping.append(
|
|
(f"layers.{layer}.block_sparse_moe.mlp.w13.weight",
|
|
f"layers.{layer}.block_sparse_moe.mlp.w3.weight", 1))
|
|
else:
|
|
# MoE layers
|
|
for expert_id in range(self.config.num_local_experts):
|
|
expert_params_mapping.append(
|
|
("ws", f"experts.{expert_id}.w1.weight", expert_id))
|
|
expert_params_mapping.append(
|
|
("w2s", f"experts.{expert_id}.w2.weight", expert_id))
|
|
expert_params_mapping.append(
|
|
("ws", f"experts.{expert_id}.w3.weight", expert_id))
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
|
|
logger.info(
|
|
"It will take ~10 minutes loading from the 16-bit weights. "
|
|
"Alternatively, use the prequantized 8-bit weights of arctic "
|
|
"and set load-format to `sharded_state` will accelerate loading.")
|
|
for name, loaded_weight in weights:
|
|
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:
|
|
for param_name, weight_name, shard_id in mlp_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, shard_id)
|
|
break
|
|
else:
|
|
for param_name, weight_name, shard_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=shard_id)
|
|
break
|
|
else:
|
|
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)
|