stablelm.py

# coding=utf-8
# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team.
# All rights reserved.
#
# 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.
#
# This code is based off the following work:
# https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/modeling_stablelm_epoch.py
# https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/config.json
"""Inference-only StabeLM (https://github.com/Stability-AI/StableLM)
model compatible with HuggingFace weights."""
from typing import List, Optional, Tuple

import torch
from torch import nn
from transformers import PretrainedConfig

from vllm.model_executor.input_metadata import InputMetadata
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.linear import (LinearMethodBase,
                                               MergedColumnParallelLinear,
                                               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, ParallelLMHead)
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 StablelmMLP(nn.Module):

    def __init__(self,
                 config: PretrainedConfig,
                 linear_method: Optional[LinearMethodBase] = None) -> None:
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_up_proj = MergedColumnParallelLinear(
            config.hidden_size, [config.intermediate_size] * 2,
            bias=False,
            linear_method=linear_method)
        self.down_proj = RowParallelLinear(config.intermediate_size,
                                           config.hidden_size,
                                           bias=False)
        self.act_fn = SiluAndMul()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class StablelmAttention(nn.Module):

    def __init__(self,
                 config: PretrainedConfig,
                 linear_method: Optional[LinearMethodBase] = None) -> None:
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = config.num_attention_heads
        self.num_heads = self.total_num_heads // tp_size

        self.total_num_key_value_heads = config.num_key_value_heads
        if self.total_num_key_value_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_key_value_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_key_value_heads == 0
        self.num_key_value_heads = max(
            1, self.total_num_key_value_heads // tp_size)
        self.head_dim = self.hidden_size // self.total_num_heads
        self.max_position_embeddings = config.max_position_embeddings
        rope_pct = getattr(config, "rope_pct",
                           getattr(config, "partial_rotary_factor", 1))
        self.rotary_ndims = int(self.head_dim * rope_pct)
        self.scaling = self.head_dim**-0.5
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_key_value_heads * self.head_dim
        self.qkv_bias = getattr(config, "use_qkv_bias", False)
        if (self.head_dim * self.num_heads * tp_size) != self.hidden_size:
            raise ValueError(f"hidden_size must be divisible by num_heads "
                             f"(got `hidden_size`: {self.hidden_size}"
                             f" and `num_heads`: {self.num_heads}).")

        self.qkv_proj = QKVParallelLinear(self.hidden_size,
                                          self.head_dim,
                                          self.total_num_heads,
                                          self.total_num_key_value_heads,
                                          self.qkv_bias,
                                          linear_method=linear_method)
        self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
                                        self.hidden_size,
                                        bias=False,
                                        linear_method=linear_method)
        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.rotary_ndims,
            max_position=self.config.max_position_embeddings,
            base=self.config.rope_theta,
        )
        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
                              num_kv_heads=self.num_key_value_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 StablelmDecoderLayer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ) -> None:
        super().__init__()
        self.self_attn = StablelmAttention(config)
        self.mlp = StablelmMLP(config, linear_method)
        norm_eps = getattr(config, "norm_eps",
                           getattr(config, "layer_norm_eps", 1e-05))
        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
                                                     eps=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, residual


class StableLMEpochModel(nn.Module):

    def __init__(self,
                 config: PretrainedConfig,
                 linear_method: Optional[LinearMethodBase] = None) -> None:
        super().__init__()
        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )
        self.layers = nn.ModuleList([
            StablelmDecoderLayer(config, linear_method)
            for _ in range(config.num_hidden_layers)
        ])
        norm_eps = getattr(config, "norm_eps",
                           getattr(config, "layer_norm_eps", 1e-05))
        self.norm = nn.LayerNorm(config.hidden_size, eps=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)
        for i in range(len(self.layers)):
            layer = self.layers[i]
            hidden_states, residual = layer(
                positions,
                hidden_states,
                kv_caches[i],
                input_metadata,
            )
        hidden_states = self.norm(hidden_states)
        return hidden_states


class StablelmForCausalLM(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ) -> None:
        super().__init__()
        self.config = config
        self.linear_method = linear_method
        self.model = StableLMEpochModel(config, linear_method)
        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
        self.sampler = Sampler(config.vocab_size)

    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.lm_head.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"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
        for name, loaded_weight in hf_model_weights_iterator(
                model_name_or_path, cache_dir, load_format, revision):
            if "rotary_emb.inv_freq" in name:
                continue
            if ("rotary_emb.cos_cached" in name
                    or "rotary_emb.sin_cached" in name):
                # Models trained using ColossalAI may include these tensors in
                # the checkpoint. Skip them.
                continue
            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)