From 5253edaacb3d023fad83d0549d525dd404ff1a26 Mon Sep 17 00:00:00 2001 From: Xiang Xu <117880274+xiangxu-google@users.noreply.github.com> Date: Wed, 21 Feb 2024 09:34:30 -0800 Subject: [PATCH] Add Gemma model (#2964) --- vllm/model_executor/models/__init__.py | 1 + vllm/model_executor/models/gemma.py | 333 +++++++++++++++++++++++++ 2 files changed, 334 insertions(+) create mode 100644 vllm/model_executor/models/gemma.py diff --git a/vllm/model_executor/models/__init__.py b/vllm/model_executor/models/__init__.py index 0f6a4bd9a4ad..17d8d69ba867 100644 --- a/vllm/model_executor/models/__init__.py +++ b/vllm/model_executor/models/__init__.py @@ -20,6 +20,7 @@ _MODELS = { "DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"), "DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"), "FalconForCausalLM": ("falcon", "FalconForCausalLM"), + "GemmaForCausalLM": ("gemma", "GemmaForCausalLM"), "GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"), "GPTBigCodeForCausalLM": ("gpt_bigcode", "GPTBigCodeForCausalLM"), "GPTJForCausalLM": ("gpt_j", "GPTJForCausalLM"), diff --git a/vllm/model_executor/models/gemma.py b/vllm/model_executor/models/gemma.py new file mode 100644 index 000000000000..affe54c448a2 --- /dev/null +++ b/vllm/model_executor/models/gemma.py @@ -0,0 +1,333 @@ +# coding=utf-8 +# Copyright 2023 The vLLM team. +# Copyright (c) Google Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Inference-only Gemma model compatible with HuggingFace weights.""" +from typing import List, Optional, Tuple + +import torch +from torch import nn +from transformers import GemmaConfig + +from vllm.model_executor.input_metadata import InputMetadata +from vllm.model_executor.layers.attention import PagedAttention +from vllm.model_executor.layers.linear import (ColumnParallelLinear, + LinearMethodBase, + QKVParallelLinear, + RowParallelLinear) +from vllm.model_executor.layers.rotary_embedding import get_rope +from vllm.model_executor.layers.sampler import Sampler +from vllm.model_executor.layers.vocab_parallel_embedding import ( + VocabParallelEmbedding) +from vllm.model_executor.parallel_utils.parallel_state import ( + get_tensor_model_parallel_world_size) +from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.model_executor.weight_utils import (default_weight_loader, + hf_model_weights_iterator) +from vllm.sequence import SamplerOutput + +KVCache = Tuple[torch.Tensor, torch.Tensor] + + +class GemmaRMSNorm(nn.Module): + + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.zeros(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + output = self._norm(x.float()).type_as(x) + return output * (1 + self.weight) + + +class GemmaMLP(nn.Module): + + def __init__( + self, + hidden_size: int, + intermediate_size: int, + linear_method: Optional[LinearMethodBase] = None, + ) -> None: + super().__init__() + self.gate_proj = ColumnParallelLinear(hidden_size, + intermediate_size, + bias=False, + linear_method=linear_method) + self.up_proj = ColumnParallelLinear(hidden_size, + intermediate_size, + bias=False, + linear_method=linear_method) + self.down_proj = RowParallelLinear(intermediate_size, + hidden_size, + bias=False, + linear_method=linear_method) + self.act_fn = nn.GELU() + + def forward(self, x): + gate, _ = self.gate_proj(x) + gate = self.act_fn(gate) + up, _ = self.up_proj(x) + fuse = gate * up + outputs, _ = self.down_proj(fuse) + return outputs + + +class GemmaAttention(nn.Module): + + def __init__(self, + hidden_size: int, + num_heads: int, + num_kv_heads: int, + head_dim: int, + max_position_embeddings: int = 8192, + rope_theta: float = 10000, + linear_method: Optional[LinearMethodBase] = None) -> None: + super().__init__() + self.hidden_size = hidden_size + tp_size = get_tensor_model_parallel_world_size() + self.total_num_heads = num_heads + assert self.total_num_heads % tp_size == 0 + self.num_heads = self.total_num_heads // tp_size + self.total_num_kv_heads = num_kv_heads + if self.total_num_kv_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_kv_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_kv_heads == 0 + self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) + self.head_dim = head_dim + self.q_size = self.num_heads * self.head_dim + self.kv_size = self.num_kv_heads * self.head_dim + self.scaling = self.head_dim**-0.5 + self.rope_theta = rope_theta + + self.qkv_proj = QKVParallelLinear( + hidden_size, + self.head_dim, + self.total_num_heads, + self.total_num_kv_heads, + bias=False, + linear_method=linear_method, + ) + self.o_proj = RowParallelLinear( + self.total_num_heads * self.head_dim, + hidden_size, + bias=False, + linear_method=linear_method, + ) + + self.rotary_emb = get_rope( + self.head_dim, + rotary_dim=self.head_dim, + max_position=max_position_embeddings, + base=self.rope_theta, + is_neox_style=True, + ) + self.attn = PagedAttention(self.num_heads, + self.head_dim, + self.scaling, + num_kv_heads=self.num_kv_heads) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + kv_cache: KVCache, + 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) + k_cache, v_cache = kv_cache + attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata) + output, _ = self.o_proj(attn_output) + return output + + +class GemmaDecoderLayer(nn.Module): + + def __init__( + self, + config: GemmaConfig, + linear_method: Optional[LinearMethodBase] = None, + ) -> None: + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = GemmaAttention( + hidden_size=self.hidden_size, + num_heads=config.num_attention_heads, + num_kv_heads=config.num_key_value_heads, + head_dim=config.head_dim, + max_position_embeddings=config.max_position_embeddings, + rope_theta=config.rope_theta, + linear_method=linear_method, + ) + self.mlp = GemmaMLP( + hidden_size=self.hidden_size, + intermediate_size=config.intermediate_size, + linear_method=linear_method, + ) + self.input_layernorm = GemmaRMSNorm(config.hidden_size, + eps=config.rms_norm_eps) + self.post_attention_layernorm = GemmaRMSNorm(config.hidden_size, + eps=config.rms_norm_eps) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + kv_cache: KVCache, + 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, + kv_cache=kv_cache, + 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 + + +class GemmaModel(nn.Module): + + def __init__( + self, + config: GemmaConfig, + linear_method: Optional[LinearMethodBase] = None, + ) -> None: + super().__init__() + self.config = config + + self.embed_tokens = VocabParallelEmbedding( + config.vocab_size, + config.hidden_size, + ) + self.layers = nn.ModuleList([ + GemmaDecoderLayer(config, linear_method) + for _ in range(config.num_hidden_layers) + ]) + self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + kv_caches: List[KVCache], + input_metadata: InputMetadata, + ) -> torch.Tensor: + hidden_states = self.embed_tokens(input_ids) + # Normalize the embedding by sqrt(hidden_size) + hidden_states = hidden_states * (self.config.hidden_size**0.5) + + for i in range(len(self.layers)): + layer = self.layers[i] + hidden_states = layer( + positions, + hidden_states, + kv_caches[i], + input_metadata, + ) + hidden_states = self.norm(hidden_states) + return hidden_states + + +class GemmaForCausalLM(nn.Module): + + def __init__( + self, + config: GemmaConfig, + linear_method: Optional[LinearMethodBase] = None, + ) -> None: + super().__init__() + self.config = config + self.linear_method = linear_method + self.model = GemmaModel(config, linear_method) + self.sampler = Sampler(config.vocab_size) + + @torch.no_grad() + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + kv_caches: List[KVCache], + input_metadata: InputMetadata, + ) -> torch.Tensor: + hidden_states = self.model(input_ids, positions, kv_caches, + input_metadata) + return hidden_states + + def sample( + self, + hidden_states: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> Optional[SamplerOutput]: + next_tokens = self.sampler(self.model.embed_tokens.weight, + hidden_states, sampling_metadata) + return next_tokens + + 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"), + ] + params_dict = dict(self.named_parameters()) + loaded_params = set() + for name, loaded_weight in hf_model_weights_iterator( + model_name_or_path, cache_dir, load_format, revision): + for (param_name, shard_name, shard_id) in stacked_params_mapping: + if shard_name not in name: + continue + name = name.replace(shard_name, param_name) + param = params_dict[name] + weight_loader = param.weight_loader + weight_loader(param, loaded_weight, shard_id) + break + else: + # Skip loading extra layer for lora models. + if "lm_head" in name: + continue + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, loaded_weight) + loaded_params.add(name) + unloaded_params = params_dict.keys() - loaded_params + if unloaded_params: + raise RuntimeError( + f"Some weights are not initialized from checkpoints: {unloaded_params}" + )