[Model] Initialize Fuyu-8B support (#3924)
Co-authored-by: Roger Wang <ywang@roblox.com>
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Isotr0py
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vllm/model_executor/models/persimmon.py
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vllm/model_executor/models/persimmon.py
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# coding=utf-8
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# adapted from https://github.com/huggingface/transformers/blob/v4.39.3/src/transformers/models/persimmon/modeling_persimmon.py
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# Copyright 2023 The vLLM team.
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# Copyright 2023 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only persimmon model compatible with HuggingFace weights."""
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from typing import Iterable, List, Optional, Tuple
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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 transformers.activations import ReLUSquaredActivation
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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_tensor_model_parallel_world_size
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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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.base_config import (
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QuantizationConfig)
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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
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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.sequence import IntermediateTensors, SamplerOutput
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class PersimmonMLP(nn.Module):
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def __init__(self,
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config: PersimmonConfig,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__()
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self.dense_h_to_4h = ColumnParallelLinear(config.hidden_size,
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config.intermediate_size,
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quant_config=quant_config)
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self.dense_4h_to_h = RowParallelLinear(config.intermediate_size,
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config.hidden_size,
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quant_config=quant_config)
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self.act = ReLUSquaredActivation()
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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__(self,
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config: PersimmonConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None):
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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.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=int(self.partial_rotary_factor * 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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)
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self.scaling = self.head_dim**-0.5
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self.attn = Attention(self.num_heads,
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self.head_dim,
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scale=self.scaling,
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cache_config=cache_config,
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quant_config=quant_config)
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def _split_heads(self, x: torch.Tensor) -> torch.Tensor:
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# [seq_length, hidden_size] -> [seq_length, num_heads, head_dim]
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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, num_heads, head_dim] -> [seq_length, hidden_size]
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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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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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# [seq_length, 3 x hidden_size]
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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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# [seq_length, num_heads, head_dim]
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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, kv_cache, attn_metadata)
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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__(self,
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config: PersimmonConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = PersimmonAttention(config=config,
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cache_config=cache_config,
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quant_config=quant_config)
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self.mlp = PersimmonMLP(config, quant_config=quant_config)
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self.input_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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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# Self Attention
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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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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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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__(self,
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config: PersimmonConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__()
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self.vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
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config.hidden_size)
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self.layers = nn.ModuleList([
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PersimmonDecoderLayer(config,
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cache_config=cache_config,
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quant_config=quant_config)
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for _ in range(config.num_hidden_layers)
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])
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self.final_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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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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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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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.embed_tokens(input_ids)
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for i in range(len(self.layers)):
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hidden_states = self.layers[i](
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positions,
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hidden_states,
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kv_caches[i],
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attn_metadata,
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)
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states
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class PersimmonForCausalLM(nn.Module):
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def __init__(self,
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config,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__()
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self.config = config
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self.vocab_size = config.vocab_size
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self.model = PersimmonModel(config,
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cache_config=cache_config,
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quant_config=quant_config)
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self.lm_head = ParallelLMHead(config.vocab_size,
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config.hidden_size,
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bias=False)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.sampler = Sampler()
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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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inputs_embeds: Optional[torch.Tensor] = None,
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):
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hidden_states = self.model(
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input_ids=input_ids,
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positions=positions,
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kv_caches=kv_caches,
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attn_metadata=attn_metadata,
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inputs_embeds=inputs_embeds,
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)
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return hidden_states
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def compute_logits(self, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> 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: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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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 "rotary_emb.inv_freq" in name:
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continue
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if ("rotary_emb.cos_cached" in name
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or "rotary_emb.sin_cached" in name):
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# Models trained using ColossalAI may include these tensors in
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# the checkpoint. Skip them.
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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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# copy from vllm/model_executor/models/bloom.py
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# NOTE: Persimmon's fused QKV's output_dim has the shape of
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# (num_heads * 3 * head_size), while the
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# required shape is (3 * num_heads * head_size).
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# Thus, we need weight conversion.
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output_dim = getattr(param, "output_dim", None)
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num_heads = self.config.num_attention_heads
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if output_dim is not None:
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loaded_weight_shape = loaded_weight.shape
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loaded_weight = loaded_weight.view(
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loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
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loaded_weight_shape[output_dim + 1:])
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loaded_weight = loaded_weight.transpose(
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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",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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