Add StableLM model.

python/sglang/srt/models/stablelm.py

@@ -0,0 +1,293 @@
+# This code is based on:
+# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/stablelm.py
+"""Inference-only StabeLM-2 (https://huggingface.co/stabilityai/stablelm-2-1_6b)
+model compatible with HuggingFace weights."""
+from typing import Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.router.model_runner import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.linear import (
+ LinearMethodBase,
+ MergedColumnParallelLinear,
+ QKVParallelLinear,
+ RowParallelLinear,
+)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+ VocabParallelEmbedding,
+ ParallelLMHead,
+)
+from vllm.model_executor.parallel_utils.parallel_state import (
+ get_tensor_model_parallel_world_size,
+)
+from vllm.model_executor.weight_utils import (
+ default_weight_loader,
+ hf_model_weights_iterator,
+)
+
+
+class StablelmMLP(nn.Module):
+ def __init__(
+ self, config: PretrainedConfig, linear_method: Optional[LinearMethodBase] = None
+ ) -> None:
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_up_proj = MergedColumnParallelLinear(
+ config.hidden_size,
+ [config.intermediate_size] * 2,
+ bias=False,
+ linear_method=linear_method,
+ )
+ self.down_proj = RowParallelLinear(
+ config.intermediate_size, config.hidden_size, bias=False
+ )
+ self.act_fn = SiluAndMul()
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ gate_up, _ = self.gate_up_proj(x)
+ x = self.act_fn(gate_up)
+ x, _ = self.down_proj(x)
+ return x
+
+
+class StablelmAttention(nn.Module):
+ def __init__(
+ self,
+ config: PretrainedConfig,
+ layer_id: int = 0,
+ linear_method: Optional[LinearMethodBase] = None,
+ ) -> None:
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ tp_size = get_tensor_model_parallel_world_size()
+ self.total_num_heads = config.num_attention_heads
+ self.num_heads = self.total_num_heads // tp_size
+
+ self.total_num_key_value_heads = config.num_key_value_heads
+ if self.total_num_key_value_heads >= tp_size:
+ # Number of KV heads is greater than TP size, so we partition
+ # the KV heads across multiple tensor parallel GPUs.
+ assert self.total_num_key_value_heads % tp_size == 0
+ else:
+ # Number of KV heads is less than TP size, so we replicate
+ # the KV heads across multiple tensor parallel GPUs.
+ assert tp_size % self.total_num_key_value_heads == 0
+ self.num_key_value_heads = max(1, self.total_num_key_value_heads // tp_size)
+ self.head_dim = self.hidden_size // self.total_num_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ rope_pct = getattr(
+ config, "rope_pct", getattr(config, "partial_rotary_factor", 1)
+ )
+ self.rotary_ndims = int(self.head_dim * rope_pct)
+ self.scaling = self.head_dim**-0.5
+ self.q_size = self.num_heads * self.head_dim
+ self.kv_size = self.num_key_value_heads * self.head_dim
+ self.qkv_bias = getattr(config, "use_qkv_bias", False)
+ if (self.head_dim * self.num_heads * tp_size) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads "
+ f"(got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ self.qkv_proj = QKVParallelLinear(
+ self.hidden_size,
+ self.head_dim,
+ self.total_num_heads,
+ self.total_num_key_value_heads,
+ self.qkv_bias,
+ linear_method=linear_method,
+ )
+ self.o_proj = RowParallelLinear(
+ self.total_num_heads * self.head_dim,
+ self.hidden_size,
+ bias=False,
+ linear_method=linear_method,
+ )
+ self.rotary_emb = get_rope(
+ self.head_dim,
+ rotary_dim=self.rotary_ndims,
+ max_position=self.config.max_position_embeddings,
+ base=self.config.rope_theta,
+ )
+ self.attn = RadixAttention(
+ self.num_heads,
+ self.head_dim,
+ self.scaling,
+ num_kv_heads=self.num_key_value_heads,
+ layer_id=layer_id,
+ )
+
+ def forward(
+ self,
+ positions: torch.Tensor,
+ hidden_states: torch.Tensor,
+ input_metadata: InputMetadata,
+ ) -> torch.Tensor:
+ qkv, _ = self.qkv_proj(hidden_states)
+ q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+ q, k = self.rotary_emb(positions, q, k)
+ attn_output = self.attn(q, k, v, input_metadata)
+ output, _ = self.o_proj(attn_output)
+ return output
+
+
+class StablelmDecoderLayer(nn.Module):
+ def __init__(
+ self,
+ config: PretrainedConfig,
+ layer_id: int = 0,
+ linear_method: Optional[LinearMethodBase] = None,
+ ) -> None:
+ super().__init__()
+ self.self_attn = StablelmAttention(config, layer_id=layer_id)
+ self.mlp = StablelmMLP(config, linear_method)
+ norm_eps = getattr(config, "norm_eps", getattr(config, "layer_norm_eps", 1e-05))
+ self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
+ self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
+
+ def forward(
+ self,
+ positions: torch.Tensor,
+ hidden_states: torch.Tensor,
+ input_metadata: InputMetadata,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ # Self Attention
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+ hidden_states = self.self_attn(
+ positions=positions,
+ hidden_states=hidden_states,
+ input_metadata=input_metadata,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ return hidden_states, residual
+
+
+class StableLMEpochModel(nn.Module):
+ def __init__(
+ self, config: PretrainedConfig, linear_method: Optional[LinearMethodBase] = None
+ ) -> None:
+ super().__init__()
+ self.embed_tokens = VocabParallelEmbedding(
+ config.vocab_size,
+ config.hidden_size,
+ )
+ self.layers = nn.ModuleList(
+ [
+ StablelmDecoderLayer(config, i, linear_method)
+ for i in range(config.num_hidden_layers)
+ ]
+ )
+ norm_eps = getattr(config, "norm_eps", getattr(config, "layer_norm_eps", 1e-05))
+ self.norm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
+
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ positions: torch.Tensor,
+ input_metadata: InputMetadata,
+ input_embeds: torch.Tensor = None,
+ ) -> torch.Tensor:
+ if input_embeds is None:
+ hidden_states = self.embed_tokens(input_ids)
+ else:
+ hidden_states = input_embeds
+ for i in range(len(self.layers)):
+ layer = self.layers[i]
+ hidden_states, residual = layer(
+ positions,
+ hidden_states,
+ input_metadata,
+ )
+ hidden_states = self.norm(hidden_states)
+ return hidden_states
+
+
+class StableLmForCausalLM(nn.Module):
+ def __init__(
+ self,
+ config: PretrainedConfig,
+ linear_method: Optional[LinearMethodBase] = None,
+ ) -> None:
+ super().__init__()
+ self.config = config
+ self.linear_method = linear_method
+ self.model = StableLMEpochModel(config, linear_method)
+ self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+ self.logits_processor = LogitsProcessor(config)
+
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ positions: torch.Tensor,
+ input_metadata: InputMetadata,
+ input_embeds: torch.Tensor = None,
+ ) -> torch.Tensor:
+ hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
+ return self.logits_processor(
+ input_ids, hidden_states, self.lm_head.weight, input_metadata
+ )
+
+ def load_weights(
+ self,
+ model_name_or_path: str,
+ cache_dir: Optional[str] = None,
+ load_format: str = "auto",
+ revision: Optional[str] = None,
+ ):
+ stacked_params_mapping = [
+ # (param_name, shard_name, shard_id)
+ ("qkv_proj", "q_proj", "q"),
+ ("qkv_proj", "k_proj", "k"),
+ ("qkv_proj", "v_proj", "v"),
+ ("gate_up_proj", "gate_proj", 0),
+ ("gate_up_proj", "up_proj", 1),
+ ]
+ params_dict = dict(self.named_parameters())
+ for name, loaded_weight in hf_model_weights_iterator(
+ model_name_or_path, cache_dir, load_format, revision
+ ):
+ if "rotary_emb.inv_freq" in name:
+ continue
+ if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+ # Models trained using ColossalAI may include these tensors in
+ # the checkpoint. Skip them.
+ continue
+ 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
+ param = params_dict[name]
+ weight_loader = param.weight_loader
+ weight_loader(param, loaded_weight, shard_id)
+ break
+ else:
+ # Skip loading extra bias for GPTQ models.
+ if name.endswith(".bias") and name not in params_dict:
+ continue
+ param = params_dict[name]
+ weight_loader = getattr(param, "weight_loader", default_weight_loader)
+ weight_loader(param, loaded_weight)
+
+
+EntryClass = StableLmForCausalLM