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Add Gemma model (#2964)
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Xiang Xu
GitHub
parent
017d9f1515
commit
5253edaacb
@ -20,6 +20,7 @@ _MODELS = {
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"DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"),
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"DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"),
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"FalconForCausalLM": ("falcon", "FalconForCausalLM"),
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"GemmaForCausalLM": ("gemma", "GemmaForCausalLM"),
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"GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"),
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"GPTBigCodeForCausalLM": ("gpt_bigcode", "GPTBigCodeForCausalLM"),
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"GPTJForCausalLM": ("gpt_j", "GPTJForCausalLM"),
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333
vllm/model_executor/models/gemma.py
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333
vllm/model_executor/models/gemma.py
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@ -0,0 +1,333 @@
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# coding=utf-8
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# Copyright 2023 The vLLM team.
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# Copyright (c) Google Inc.
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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 Gemma model compatible with HuggingFace weights."""
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from typing import List, Optional, Tuple
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import torch
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from torch import nn
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from transformers import GemmaConfig
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from vllm.model_executor.input_metadata import InputMetadata
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from vllm.model_executor.layers.attention import PagedAttention
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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LinearMethodBase,
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QKVParallelLinear,
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RowParallelLinear)
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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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VocabParallelEmbedding)
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from vllm.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_world_size)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.model_executor.weight_utils import (default_weight_loader,
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hf_model_weights_iterator)
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from vllm.sequence import SamplerOutput
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class GemmaRMSNorm(nn.Module):
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.zeros(dim))
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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output = self._norm(x.float()).type_as(x)
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return output * (1 + self.weight)
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class GemmaMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.gate_proj = ColumnParallelLinear(hidden_size,
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intermediate_size,
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bias=False,
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linear_method=linear_method)
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self.up_proj = ColumnParallelLinear(hidden_size,
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intermediate_size,
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bias=False,
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linear_method=linear_method)
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self.down_proj = RowParallelLinear(intermediate_size,
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hidden_size,
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bias=False,
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linear_method=linear_method)
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self.act_fn = nn.GELU()
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def forward(self, x):
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gate, _ = self.gate_proj(x)
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gate = self.act_fn(gate)
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up, _ = self.up_proj(x)
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fuse = gate * up
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outputs, _ = self.down_proj(fuse)
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return outputs
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class GemmaAttention(nn.Module):
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def __init__(self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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head_dim: int,
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max_position_embeddings: int = 8192,
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rope_theta: float = 10000,
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linear_method: Optional[LinearMethodBase] = None) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = head_dim
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=False,
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linear_method=linear_method,
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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linear_method=linear_method,
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)
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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=max_position_embeddings,
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base=self.rope_theta,
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is_neox_style=True,
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)
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self.attn = PagedAttention(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_kv_heads)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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k_cache, v_cache = kv_cache
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attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
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output, _ = self.o_proj(attn_output)
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return output
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class GemmaDecoderLayer(nn.Module):
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def __init__(
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self,
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config: GemmaConfig,
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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = GemmaAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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head_dim=config.head_dim,
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max_position_embeddings=config.max_position_embeddings,
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rope_theta=config.rope_theta,
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linear_method=linear_method,
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)
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self.mlp = GemmaMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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linear_method=linear_method,
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)
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self.input_layernorm = GemmaRMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = GemmaRMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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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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positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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input_metadata=input_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 = residual + hidden_states
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return hidden_states
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class GemmaModel(nn.Module):
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def __init__(
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self,
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config: GemmaConfig,
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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.config = config
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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)
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self.layers = nn.ModuleList([
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GemmaDecoderLayer(config, linear_method)
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for _ in range(config.num_hidden_layers)
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])
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self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_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[KVCache],
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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hidden_states = self.embed_tokens(input_ids)
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# Normalize the embedding by sqrt(hidden_size)
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hidden_states = hidden_states * (self.config.hidden_size**0.5)
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for i in range(len(self.layers)):
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layer = self.layers[i]
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hidden_states = layer(
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positions,
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hidden_states,
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kv_caches[i],
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input_metadata,
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)
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hidden_states = self.norm(hidden_states)
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return hidden_states
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class GemmaForCausalLM(nn.Module):
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def __init__(
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self,
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config: GemmaConfig,
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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.config = config
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self.linear_method = linear_method
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self.model = GemmaModel(config, linear_method)
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self.sampler = Sampler(config.vocab_size)
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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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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, kv_caches,
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input_metadata)
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return hidden_states
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def sample(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(self.model.embed_tokens.weight,
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hidden_states, sampling_metadata)
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return next_tokens
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def load_weights(self,
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model_name_or_path: str,
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cache_dir: Optional[str] = None,
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load_format: str = "auto",
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revision: Optional[str] = None):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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]
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params_dict = dict(self.named_parameters())
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loaded_params = set()
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, load_format, revision):
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for (param_name, shard_name, shard_id) in stacked_params_mapping:
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if shard_name not in name:
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continue
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name = name.replace(shard_name, param_name)
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra layer for lora models.
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if "lm_head" in name:
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continue
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param = params_dict[name]
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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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loaded_params.add(name)
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unloaded_params = params_dict.keys() - loaded_params
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if unloaded_params:
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raise RuntimeError(
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f"Some weights are not initialized from checkpoints: {unloaded_params}"
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)
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