Support gpt-bigcode model class (#681)
This commit is contained in:
Liangsheng Yin
282
python/sglang/srt/models/gpt_bigcode.py
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282
python/sglang/srt/models/gpt_bigcode.py
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# Adapted from:
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# https://github.com/vllm-project/vllm/blob/07eb6f19f3b0ee9f7adf6eb689607028aa40bfd5/vllm/model_executor/models/gpt_bigcode.py
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"""Inference-only GPTBigCode model compatible with HuggingFace weights."""
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from typing import Iterable, Optional, Tuple
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import torch
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from torch import nn
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from transformers import GPTBigCodeConfig
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from vllm.config import CacheConfig, LoRAConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.managers.controller.infer_batch import InputMetadata
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class GPTBigCodeAttention(nn.Module):
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def __init__(
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self,
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layer_id: int,
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config: GPTBigCodeConfig,
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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.hidden_size = config.hidden_size
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total_num_heads = config.num_attention_heads
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self.tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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assert total_num_heads % self.tensor_model_parallel_world_size == 0
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self.num_heads = total_num_heads // self.tensor_model_parallel_world_size
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self.head_dim = self.hidden_size // total_num_heads
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self.scale = self.head_dim**-0.5
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self.multi_query = config.multi_query
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if self.multi_query:
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total_num_kv_heads = 1
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self.num_kv_heads = 1
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else:
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total_num_kv_heads = total_num_heads
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self.num_kv_heads = self.num_heads
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self.kv_dim = self.head_dim * self.num_kv_heads
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self.c_attn = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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total_num_heads,
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total_num_kv_heads,
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bias=True,
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quant_config=quant_config,
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)
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self.c_proj = RowParallelLinear(
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self.hidden_size,
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self.hidden_size,
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bias=True,
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quant_config=quant_config,
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)
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self.attn = RadixAttention(
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self.num_heads,
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self.head_dim,
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scaling=self.scale,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_id,
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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qkv, _ = self.c_attn(hidden_states)
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q, k, v = qkv.split(
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[
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self.hidden_size // self.tensor_model_parallel_world_size,
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self.kv_dim,
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self.kv_dim,
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],
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dim=-1,
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)
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attn_output = self.attn(q, k, v, input_metadata)
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attn_output, _ = self.c_proj(attn_output)
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return attn_output
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class GPTBigMLP(nn.Module):
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def __init__(
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self,
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intermediate_size: int,
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config: GPTBigCodeConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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hidden_size = config.hidden_size
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self.c_fc = ColumnParallelLinear(
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hidden_size,
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intermediate_size,
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bias=True,
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quant_config=quant_config,
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)
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self.c_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=True,
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quant_config=quant_config,
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)
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self.act = get_act_fn(
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config.activation_function, quant_config, intermediate_size
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.c_fc(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.c_proj(hidden_states)
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return hidden_states
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class GPTBigCodeBlock(nn.Module):
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def __init__(
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self,
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layer_id: int,
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config: GPTBigCodeConfig,
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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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hidden_size = config.hidden_size
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inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
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self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.attn = GPTBigCodeAttention(layer_id, config, cache_config, quant_config)
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self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.mlp = GPTBigMLP(inner_dim, config, quant_config)
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def forward(
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self,
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hidden_states: torch.Tensor,
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.ln_1(hidden_states)
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attn_output = self.attn(
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hidden_states=hidden_states, input_metadata=input_metadata
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)
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# residual connection
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hidden_states = attn_output + residual
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residual = hidden_states
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hidden_states = self.ln_2(hidden_states)
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feed_forward_hidden_states = self.mlp(hidden_states)
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# residual connection
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hidden_states = residual + feed_forward_hidden_states
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return hidden_states
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class GPTBigCodeModel(nn.Module):
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def __init__(
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self,
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config: GPTBigCodeConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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lora_config: Optional[LoRAConfig] = None,
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):
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super().__init__()
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self.config = config
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assert not config.add_cross_attention
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self.embed_dim = config.hidden_size
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lora_vocab = (
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(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
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if lora_config
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else 0
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)
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self.vocab_size = config.vocab_size + lora_vocab
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self.wte = VocabParallelEmbedding(
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self.vocab_size, self.embed_dim, org_num_embeddings=config.vocab_size
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)
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self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
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self.h = nn.ModuleList(
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[
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GPTBigCodeBlock(i, config, cache_config, quant_config)
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for i in range(config.num_hidden_layers)
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]
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)
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self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
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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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input_metadata: InputMetadata,
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) -> torch.Tensor:
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inputs_embeds = self.wte(input_ids)
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position_embeds = self.wpe(position_ids)
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hidden_states = inputs_embeds + position_embeds
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for i in range(len(self.h)):
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layer = self.h[i]
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hidden_states = layer(hidden_states, input_metadata)
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hidden_states = self.ln_f(hidden_states)
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return hidden_states
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class GPTBigCodeForCausalLM(nn.Module):
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packed_modules_mapping = {"c_attn": ["c_attn"]}
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supported_lora_modules = ["c_fc", "c_proj", "wte", "c_attn"]
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embedding_modules = {
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"wte": "input_embeddings",
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"lm_head": "output_embeddings",
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}
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embedding_padding_modules = []
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def __init__(
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self,
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config: GPTBigCodeConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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lora_config: Optional[LoRAConfig] = None,
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):
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super().__init__()
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self.config = config
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self.lora_config = lora_config
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self.quant_config = quant_config
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self.transformer = GPTBigCodeModel(
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config, cache_config, quant_config, lora_config
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)
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self.lm_head = self.transformer.wte
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self.unpadded_vocab_size = config.vocab_size
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if lora_config:
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self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
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self.logits_processor = LogitsProcessor(config)
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@torch.no_grad()
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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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input_metadata: InputMetadata,
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) -> torch.Tensor:
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hidden_states = self.transformer(input_ids, positions, input_metadata)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head.weight, input_metadata
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)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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for name, loaded_weight in weights:
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if "lm_head.weight" in name:
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continue
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if ".attn.bias" in name:
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# Skip attention mask.
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# NOTE: "c_attn.bias" should not be skipped.
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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# TODO (@robertgshaw2-neuralmagic): move to fp8 linear method
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if "c_attn.input_scale" in name or "c_attn.weight_scale" in name:
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weight_loader(param, loaded_weight, "q")
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weight_loader(param, loaded_weight, "k")
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weight_loader(param, loaded_weight, "v")
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else:
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weight_loader(param, loaded_weight)
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EntryClass = GPTBigCodeForCausalLM
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