Feat: Support for Persimmon Model (#7983)
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Praneth Paruchuri
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GitHub
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2db6719cc5
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83c104b188
@@ -110,6 +110,17 @@ class NewGELU(CustomOp):
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return self.forward_native(x)
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class ReLU2(nn.Module):
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"""
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Applies the squared Rectified Linear Unit function.
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y = max(0, x)^2
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"""
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = F.relu(x)
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return x * x
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class QuickGELU(CustomOp):
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def forward_native(self, x: torch.Tensor) -> torch.Tensor:
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return x * torch.sigmoid(1.702 * x)
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@@ -165,6 +176,7 @@ _ACTIVATION_REGISTRY = {
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"gelu": nn.GELU(),
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"gelu_pytorch_tanh": nn.GELU(approximate="tanh"),
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"gelu_new": NewGELU(),
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"relu2": ReLU2(),
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}
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330
python/sglang/srt/models/persimmon.py
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330
python/sglang/srt/models/persimmon.py
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@@ -0,0 +1,330 @@
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from collections.abc import Iterable
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from typing import Optional
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import torch
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from torch import nn
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from transformers import PersimmonConfig
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from sglang.srt.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from sglang.srt.layers.activation import get_act_fn
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from sglang.srt.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 sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
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from sglang.srt.layers.quantization import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.utils import PPMissingLayer
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from sglang.srt.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.utils import add_prefix, make_layers
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class PersimmonMLP(nn.Module):
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def __init__(
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self, config: PersimmonConfig, quant_config: Optional[QuantizationConfig] = None
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):
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super().__init__()
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self.dense_h_to_4h = ColumnParallelLinear(
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config.hidden_size, config.intermediate_size, quant_config=quant_config
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)
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self.dense_4h_to_h = RowParallelLinear(
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config.intermediate_size, config.hidden_size, quant_config=quant_config
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)
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self.act = get_act_fn(config.hidden_act)
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def forward(self, hidden_states) -> torch.Tensor:
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hidden_states, _ = self.dense_h_to_4h(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.dense_4h_to_h(hidden_states)
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return hidden_states
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class PersimmonAttention(nn.Module):
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def __init__(
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self,
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config: PersimmonConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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layer_id: int = 0,
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):
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super().__init__()
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self.config = config
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tensor_parallel_world_size = get_tensor_model_parallel_world_size()
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self.hidden_size = config.hidden_size
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self.total_num_heads = config.num_attention_heads
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self.num_heads = self.total_num_heads // tensor_parallel_world_size
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self.head_dim = self.hidden_size // self.total_num_heads
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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.partial_rotary_factor = config.partial_rotary_factor
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self.is_causal = True
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assert (self.head_dim * self.total_num_heads) == self.hidden_size
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assert self.total_num_heads % tensor_parallel_world_size == 0
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self.query_key_value = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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self.total_num_heads,
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bias=True,
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quant_config=quant_config,
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)
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self.dense = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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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.is_qk_layernorm = config.qk_layernorm
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if self.is_qk_layernorm:
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self.q_layernorm = nn.LayerNorm(self.head_dim)
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self.k_layernorm = nn.LayerNorm(self.head_dim)
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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=self.rope_theta,
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partial_rotary_factor=self.partial_rotary_factor,
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)
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self.scaling = self.head_dim**-0.5
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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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self.scaling,
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num_kv_heads=self.num_heads,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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)
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def _split_heads(self, x: torch.Tensor) -> torch.Tensor:
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seq_length = x.shape[0]
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return x.view(seq_length, self.num_heads, self.head_dim)
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def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
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seq_length = x.shape[0]
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return x.view(seq_length, self.num_heads * self.head_dim)
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def forward(
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self,
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position_ids: torch.Tensor,
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forward_batch: ForwardBatch,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.query_key_value(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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if self.is_qk_layernorm:
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q = self._split_heads(q)
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k = self._split_heads(k)
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q = self.q_layernorm(q)
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k = self.k_layernorm(k)
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q = self._merge_heads(q)
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k = self._merge_heads(k)
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q, k = self.rotary_emb(position_ids, q, k)
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attn_output = self.attn(q, k, v, forward_batch=forward_batch)
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output, _ = self.dense(attn_output)
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return output
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class PersimmonDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PersimmonConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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idx: int = 0,
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):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = PersimmonAttention(
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config=config,
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quant_config=quant_config,
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prefix=add_prefix("self_attn", prefix),
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layer_id=idx,
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)
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self.mlp = PersimmonMLP(config, quant_config=quant_config)
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self.input_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_eps
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)
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self.post_attention_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_eps
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)
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def forward(
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self,
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position_ids: torch.Tensor,
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forward_batch: ForwardBatch,
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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.input_layernorm(hidden_states)
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hidden_states = self.self_attn(
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position_ids=position_ids,
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hidden_states=hidden_states,
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forward_batch=forward_batch,
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)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = hidden_states + residual
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outputs = hidden_states
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return outputs
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class PersimmonModel(nn.Module):
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def __init__(
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self,
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config: PersimmonConfig,
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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.config = config
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self.pp_group = get_pp_group()
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if self.pp_group.is_first_rank:
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size, config.hidden_size
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)
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else:
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self.embed_tokens = PPMissingLayer()
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self.layers, self.start_layer, self.end_layer = make_layers(
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config.num_hidden_layers,
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lambda idx, prefix: PersimmonDecoderLayer(
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config, quant_config=quant_config, prefix=prefix, idx=idx
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),
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prefix="model.layers",
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pp_rank=self.pp_group.rank_in_group,
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pp_size=self.pp_group.world_size,
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)
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if self.pp_group.is_last_rank:
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self.final_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_eps
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)
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else:
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self.final_layernorm = PPMissingLayer()
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(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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forward_batch: ForwardBatch,
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positions: torch.Tensor,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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if self.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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hidden_states = forward_batch.pp_input_hidden
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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(
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position_ids=positions,
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forward_batch=forward_batch,
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hidden_states=hidden_states,
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)
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return self.final_layernorm(hidden_states)
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class PersimmonForCausalLM(nn.Module):
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def __init__(
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self,
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config: PersimmonConfig,
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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.config = config
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self.quant_config = quant_config
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self.model = PersimmonModel(
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config=config, quant_config=quant_config, prefix=add_prefix("model", prefix)
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)
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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bias=False,
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quant_config=quant_config,
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)
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self.logits_processor = LogitsProcessor(config)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.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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forward_batch: ForwardBatch,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> LogitsProcessorOutput:
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hidden_states = self.model(
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input_ids=input_ids,
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forward_batch=forward_batch,
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positions=positions,
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inputs_embeds=inputs_embeds,
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)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head, forward_batch
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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())
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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if name not in params_dict:
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if name == "lm_head.weight":
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continue
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print(f"Warning: weight {name} not found in model.")
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continue
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param = params_dict[name]
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if "query_key_value" in name:
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output_dim = getattr(param, "output_dim", None)
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if output_dim is not None:
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loaded_weight_shape = loaded_weight.shape
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num_heads = self.config.num_attention_heads
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loaded_weight = loaded_weight.view(
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loaded_weight_shape[:output_dim]
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+ (num_heads, 3, -1)
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+ loaded_weight_shape[output_dim + 1 :]
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)
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loaded_weight = loaded_weight.transpose(output_dim, output_dim + 1)
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loaded_weight = loaded_weight.reshape(loaded_weight_shape)
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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EntryClass = PersimmonForCausalLM
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